From c111c34876878c389ffb229adb411946297baa13 Mon Sep 17 00:00:00 2001
From: fbarez <fazlbarez93@gmail.com>
Date: Fri, 6 Nov 2026 21:16:36 +0000
Subject: [PATCH 1/2] Show the arXiv version (2611.08438v1) on every tab;
 public-ready copy
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Ingest the arXiv LaTeX source as final: 15 chapters, acknowledgements, 3 appendices, 517 references; no preview banners or withheld chapter. Page numbers link to the arXiv PDF.
- Ingest fixes: \ref uses LaTeX numbers (figure numbering was off by one), unnumbered chapters keep their title, .bib optional with a .bbl, main.tex labels can point at site pages.
- Chapter 10 now at handbook/learning-dynamics/; 318 anchor and 3 method-page redirects keep every published link working.
- Methods, evaluation and safety quotes re-selected from the arXiv text, verbatim; context-dependent quotes extended to their antecedent; no duplicated sentences on a page.
- Orientations from §12; safety page links the six orientations; risks card quotes §13.7 mitigation strategies.
- Site copy rewritten: plain, short, no self-description; empty fields hidden; preprints by colour only; quotes as plain paragraphs.
- New scripts: build_orientations_arxiv.py, reanchor_excerpts.py, extend_context.py, check_verbatim.py, urls/suggest_anchor_redirects.py.
---
 CHANGELOG.md                                  |    8 +
 docs/DECISIONS.md                             |    2 +
 public/figures/11_modeldiffing.svg            | 1772 ++---
 public/figures/11_steering.svg                |  823 ++-
 public/figures/1_general_summary.svg          | 6516 +++++++++++++++++
 .../5_superposition_and_polysemanticity.svg   |  537 ++
 public/figures/6_clt.svg                      | 1453 ++--
 public/figures/6_crosscoders.svg              |  866 +--
 public/figures/6_sae.svg                      |  839 ++-
 public/figures/8_latent_qa.svg                | 1845 +++++
 .../clt_size_vs_depth_e32_clean_v3.svg        |  321 +
 public/figures/jumprelu.svg                   |  113 +
 .../research_directions_compressed.png        |  Bin 0 -> 7578954 bytes
 .../reanchor_excerpts.cpython-39.pyc          |  Bin 0 -> 7753 bytes
 scripts/data/build_orientations_arxiv.py      |    6 +-
 scripts/data/check_verbatim.py                |   45 +
 scripts/data/extend_context.py                |   66 +
 scripts/data/reanchor_excerpts.py             |  168 +
 scripts/ingest/ingest.py                      |   17 +-
 scripts/urls/suggest_anchor_redirects.py      |  117 +
 src/components/DemoFrame.astro                |    3 +-
 src/components/Excerpt.astro                  |    2 +-
 src/components/ManuscriptNote.astro           |    6 +-
 src/components/MethodsExplorer.tsx            |    6 +-
 src/components/demos/PatchingDemo.tsx         |    2 +-
 src/data/chapter-ids.json                     |   18 +-
 src/data/framework.json                       | 1401 +---
 src/data/links.json                           |  138 +-
 src/data/methods.json                         | 1125 +--
 src/data/orientations-arxiv.json              |    6 +-
 src/data/published-urls.json                  |  539 +-
 src/data/redirects.json                       |  386 +-
 src/generated/chapters/acknowledgements.html  |    3 +
 src/generated/chapters/circuits.html          |  697 +-
 src/generated/chapters/colour-key.html        |   45 +-
 src/generated/chapters/conclusion.html        |   36 +-
 src/generated/chapters/core-hypotheses.html   |  210 +-
 .../developmental-interpretability.html       |   51 -
 .../chapters/feature-extraction.html          |  872 +--
 .../chapters/foundational-concepts.html       |  140 +-
 .../intermediate-representations.html         |  279 +-
 .../chapters/interpretability-landscape.html  |   90 +-
 src/generated/chapters/introduction.html      |  120 +-
 src/generated/chapters/learning-dynamics.html |  193 +
 .../limits-of-behavioural-evaluation.html     |   53 +-
 .../chapters/mathematical-foundations.html    |  144 +-
 src/generated/chapters/notation.html          |   93 +-
 src/generated/chapters/open-problems.html     |   99 +-
 .../chapters/representation-analysis.html     |  119 +-
 .../chapters/research-orientations.html       |  443 +-
 .../chapters/sae-architecture-details.html    |   86 -
 .../chapters/safety-applications.html         |  436 +-
 .../chapters/training-data-attribution.html   |  342 +-
 src/generated/manifest.json                   | 4407 +++++++----
 src/generated/references.html                 |  555 +-
 src/generated/withheld-fingerprints.json      | 1729 +----
 src/lib/data.ts                               |    5 +-
 src/lib/manuscript.ts                         |    8 +-
 src/pages/demos/index.astro                   |    4 +-
 src/pages/demos/patching.astro                |    2 +-
 src/pages/demos/sae.astro                     |    2 +-
 src/pages/evaluation.astro                    |   16 +-
 src/pages/handbook/[slug].astro               |    2 +-
 src/pages/handbook/index.astro                |   13 +-
 src/pages/index.astro                         |   22 +-
 src/pages/methods/[id].astro                  |    9 +-
 src/pages/methods/index.astro                 |   18 +-
 src/pages/notebooks.astro                     |   16 +-
 src/pages/orientations.astro                  |    3 +-
 src/pages/safety.astro                        |   37 +-
 src/pages/search.astro                        |    2 +-
 src/styles/global.css                         |    5 +-
 72 files changed, 20586 insertions(+), 9966 deletions(-)
 create mode 100644 public/figures/1_general_summary.svg
 create mode 100644 public/figures/5_superposition_and_polysemanticity.svg
 create mode 100644 public/figures/8_latent_qa.svg
 create mode 100644 public/figures/clt_size_vs_depth_e32_clean_v3.svg
 create mode 100644 public/figures/jumprelu.svg
 create mode 100644 public/figures/research_directions_compressed.png
 create mode 100644 scripts/data/__pycache__/reanchor_excerpts.cpython-39.pyc
 create mode 100644 scripts/data/check_verbatim.py
 create mode 100644 scripts/data/extend_context.py
 create mode 100644 scripts/data/reanchor_excerpts.py
 create mode 100644 scripts/urls/suggest_anchor_redirects.py
 create mode 100644 src/generated/chapters/acknowledgements.html
 delete mode 100644 src/generated/chapters/developmental-interpretability.html
 create mode 100644 src/generated/chapters/learning-dynamics.html
 delete mode 100644 src/generated/chapters/sae-architecture-details.html

diff --git a/CHANGELOG.md b/CHANGELOG.md
index f92d39d913..6b5d54ad05 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,13 @@
 # Changelog
 
+## Handbook now shows the arXiv version (2611.08438v1)
+- Every tab is built from the arXiv LaTeX source, ingested with `--status final`: 15 chapters, Acknowledgements, 3 appendices, 517 references. Preview banners, the withheld chapter and draft `noindex` are gone.
+- Chapter 10 is now *Learning Dynamics and Mechanistic Interpretability* at `/handbook/learning-dynamics/`. `/handbook/developmental-interpretability/` and the removed *SAE Architecture Details* appendix redirect.
+- Every previously published section anchor still resolves: 318 anchor redirects (`scripts/urls/suggest_anchor_redirects.py`, same-chapter matches only, else the chapter top), and 47 references the arXiv version no longer cites point to the reference list.
+- Methods, Evaluation and Safety & open problems quotes re-selected from the arXiv text, verbatim (`scripts/data/check_verbatim.py`).
+- Page numbers link to that page of the arXiv PDF.
+- Ingest fixes: `\ref` always prints LaTeX's number (pandoc's own figure numbering was off by one because the taxonomy figure sits in main.tex); unnumbered chapters keep their title; a `.bib` is optional when a `.bbl` is present.
+
 ## Paper on arXiv
 - The paper is out as [arXiv:2611.08438](https://arxiv.org/abs/2611.08438). The homepage links the abstract page and PDF, and the citation section shows the arXiv BibTeX with a copy button. README gains a Citation section.
 
diff --git a/docs/DECISIONS.md b/docs/DECISIONS.md
index caacdaf3ff..dc9ab9e23a 100644
--- a/docs/DECISIONS.md
+++ b/docs/DECISIONS.md
@@ -44,6 +44,8 @@ The 5 Nov PDF has six orientations, §12.1 to §12.6: Pragmatic, Ambitious, Deve
 The 5 Nov PDF defines one framework in §7.5 with five dimensions: attribution faithfulness, completeness, stability, causal validity and human interpretability. §14.2 refers back to §7.5. A text search found no second framework. The 13 Jul draft also had a four-dimension framework in §15.2.1. **Decide:** confirm that §7.5 is the only framework in the approved source.
 
 ### D4. Publication status of draft text
+**Resolved 6 Nov 2026:** the paper is public (arXiv:2611.08438v1), so the site now shows the arXiv version, ingested with `--status final` from the arXiv LaTeX source. Preview banners, the withheld chapter and draft `noindex` no longer apply.
+
 Now: the live site publishes the 13 Jul draft text, with "Preview build" banners, for every chapter except chapter 11.
 
 **This PR.** Pages built from draft text now carry `<meta name="robots" content="noindex">`. They stay reachable by link and in the site's own search. The switch is `indexDraftPages` in `src/data/publication.json`. `noindex` is not access control: anything deployed is public.
diff --git a/public/figures/11_modeldiffing.svg b/public/figures/11_modeldiffing.svg
index 9053ffaf58..3aa09f6d73 100644
--- a/public/figures/11_modeldiffing.svg
+++ b/public/figures/11_modeldiffing.svg
@@ -20,57 +20,57 @@
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 <g filter="url(#filter-remove-color)">
-<use xlink:href="#source-84" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
+<use xlink:href="#source-9" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
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@@ -247,10 +247,10 @@
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+<use xlink:href="#source-16" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
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-<use xlink:href="#source-98" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
+<use xlink:href="#source-23" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
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@@ -271,10 +271,10 @@
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 <g filter="url(#filter-remove-color)">
-<use xlink:href="#source-105" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
+<use xlink:href="#source-30" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
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@@ -283,10 +283,10 @@
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+<use xlink:href="#source-37" transform="matrix(1, 0, 0, 1, 0, 0.000000000000227374)"/>
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"/>
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475.402344 153.015625 475.40625 153.015625 475.410156 153.015625 C 475.417969 153.015625 475.421875 153.015625 475.425781 153.015625 C 475.433594 153.015625 475.4375 153.015625 475.441406 153.015625 C 475.449219 153.015625 475.453125 153.015625 475.457031 153.015625 C 475.460938 153.015625 475.46875 153.015625 475.472656 153.015625 C 475.476562 153.015625 475.484375 153.015625 475.488281 153.015625 C 475.492188 153.015625 475.5 153.015625 475.503906 153.015625 C 475.507812 153.015625 475.515625 153.015625 475.519531 153.015625 C 475.523438 153.015625 475.53125 153.015625 475.535156 153.015625 C 475.539062 153.015625 475.546875 153.015625 475.550781 153.015625 C 475.554688 153.015625 475.558594 153.015625 475.566406 153.015625 C 475.570312 153.015625 475.574219 153.015625 475.582031 153.015625 C 475.585938 153.015625 475.589844 153.015625 475.597656 153.015625 C 475.601562 153.015625 475.605469 153.015625 475.613281 153.015625 C 475.617188 153.015625 475.621094 153.015625 475.628906 153.015625 C 475.632812 153.015625 475.636719 153.015625 475.644531 153.015625 C 475.648438 153.015625 475.652344 153.015625 475.660156 153.015625 C 475.664062 153.015625 475.667969 153.015625 475.675781 153.015625 C 475.679688 153.015625 475.683594 153.015625 475.691406 153.015625 C 475.695312 153.015625 475.699219 153.015625 475.703125 153.015625 C 475.710938 153.015625 475.714844 153.015625 475.71875 153.015625 C 475.726562 153.015625 475.730469 153.015625 475.734375 153.015625 C 475.742188 153.015625 475.746094 153.015625 475.75 153.015625 C 475.757812 153.015625 475.761719 153.015625 475.765625 153.015625 C 475.773438 153.015625 475.777344 153.015625 475.78125 153.015625 C 475.789062 153.015625 475.792969 153.015625 475.796875 153.015625 C 475.804688 153.015625 475.808594 153.015625 475.8125 153.015625 C 475.820312 153.015625 475.824219 153.015625 475.828125 153.015625 C 475.835938 153.015625 475.839844 153.015625 475.84375 153.015625 C 475.851562 153.015625 475.855469 153.015625 475.859375 153.015625 C 475.867188 153.015625 475.871094 153.015625 475.875 153.015625 C 475.882812 153.015625 475.886719 153.015625 475.890625 153.015625 C 475.898438 153.015625 475.902344 153.015625 475.90625 153.015625 C 475.910156 153.015625 475.917969 153.015625 475.921875 153.015625 C 475.925781 153.015625 475.933594 153.015625 475.9375 153.015625 C 475.941406 153.015625 475.949219 153.015625 475.953125 153.015625 C 475.957031 153.015625 475.964844 153.015625 475.96875 153.015625 C 475.972656 153.015625 475.980469 153.015625 475.984375 153.015625 C 475.988281 153.015625 475.996094 153.015625 476 153.015625 C 476.003906 153.015625 476.011719 153.015625 476.015625 153.015625 C 476.019531 153.015625 476.027344 153.015625 476.03125 153.015625 C 476.035156 153.015625 476.042969 153.015625 476.046875 153.015625 C 476.050781 153.015625 476.058594 153.015625 476.0625 153.015625 C 476.066406 153.015625 476.074219 153.015625 476.078125 153.015625 C 476.082031 153.015625 476.089844 153.015625 476.09375 153.015625 C 476.097656 153.015625 476.105469 153.015625 476.109375 153.015625 C 476.113281 153.015625 476.121094 153.015625 476.125 153.015625 C 476.128906 153.015625 476.136719 153.015625 476.140625 153.015625 C 476.144531 153.015625 476.152344 153.015625 476.15625 153.015625 C 476.160156 153.015625 476.167969 153.015625 476.171875 153.015625 C 476.175781 153.015625 476.183594 153.015625 476.1875 153.015625 C 476.191406 153.015625 476.199219 153.015625 476.203125 153.015625 C 476.207031 153.015625 476.214844 153.015625 476.21875 153.015625 C 476.222656 153.015625 476.230469 153.015625 476.234375 153.015625 C 476.238281 153.015625 476.246094 153.015625 476.25 153.015625 C 476.253906 153.015625 476.261719 153.015625 476.265625 153.015625 C 476.269531 153.015625 476.277344 153.015625 476.28125 153.015625 C 476.285156 153.015625 476.292969 153.015625 476.296875 153.015625 C 476.300781 153.015625 476.308594 153.015625 476.3125 153.015625 C 476.316406 153.015625 476.324219 153.015625 476.328125 153.015625 C 476.332031 153.015625 476.339844 153.015625 476.34375 153.015625 C 476.347656 153.015625 476.355469 153.015625 476.359375 153.015625 C 476.363281 153.015625 476.371094 153.015625 476.375 153.015625 C 476.378906 153.015625 476.386719 153.015625 476.390625 153.015625 C 476.394531 153.015625 476.402344 153.015625 476.40625 153.015625 C 476.410156 153.015625 476.417969 153.015625 476.421875 153.015625 C 476.425781 153.015625 476.433594 153.015625 476.4375 153.015625 C 476.441406 153.015625 476.449219 153.015625 476.453125 153.015625 C 476.457031 153.015625 476.464844 153.015625 476.46875 153.015625 C 476.476562 153.015625 476.480469 153.015625 476.484375 153.015625 C 476.492188 153.015625 476.496094 153.015625 476.5 153.015625 C 476.507812 153.015625 476.511719 153.015625 476.515625 153.015625 C 476.523438 153.015625 476.527344 153.015625 476.53125 153.015625 C 476.539062 153.015625 476.542969 153.015625 476.546875 153.015625 C 476.554688 153.015625 476.558594 153.015625 476.5625 153.015625 C 476.570312 153.015625 476.574219 153.015625 476.578125 153.015625 C 476.585938 153.015625 476.589844 153.015625 476.59375 153.015625 C 476.601562 153.015625 476.605469 153.015625 476.609375 153.015625 C 476.617188 153.015625 476.621094 153.015625 476.625 153.015625 C 476.632812 153.015625 476.636719 153.015625 476.640625 153.015625 C 476.648438 153.015625 476.652344 153.015625 476.65625 153.015625 C 476.664062 153.015625 476.667969 153.015625 476.671875 153.015625 C 476.679688 153.015625 476.683594 153.015625 476.6875 153.015625 C 476.695312 153.015625 476.699219 153.015625 476.703125 153.015625 C 476.710938 153.015625 476.714844 153.015625 476.71875 153.015625 C 476.726562 153.015625 476.730469 153.015625 476.738281 153.015625 C 476.742188 153.015625 476.746094 153.015625 476.753906 153.015625 C 476.757812 153.015625 476.761719 153.015625 476.769531 153.015625 C 476.773438 153.015625 476.777344 153.015625 476.785156 153.015625 C 476.789062 153.015625 476.792969 153.015625 476.800781 153.015625 C 476.804688 153.015625 476.808594 153.015625 476.816406 153.015625 C 476.820312 153.015625 476.824219 153.015625 476.832031 153.015625 C 476.835938 153.015625 476.839844 153.015625 476.847656 153.015625 C 476.851562 153.015625 476.855469 153.015625 476.863281 153.015625 C 476.867188 153.015625 476.871094 153.015625 476.878906 153.015625 C 476.882812 153.015625 476.886719 153.015625 476.894531 153.015625 C 476.898438 153.015625 476.90625 153.015625 476.910156 153.015625 C 476.914062 153.015625 476.921875 153.015625 476.925781 153.015625 C 476.929688 153.015625 476.9375 153.015625 476.941406 153.015625 C 476.945312 153.015625 476.953125 153.015625 476.957031 153.015625 C 476.960938 153.015625 476.96875 153.015625 476.972656 153.015625 C 476.976562 153.015625 476.984375 153.015625 476.988281 153.015625 C 476.992188 153.015625 477 153.015625 477.003906 153.015625 C 477.007812 153.015625 477.015625 153.015625 477.019531 153.015625 C 477.023438 153.015625 477.03125 153.015625 477.035156 153.015625 C 477.039062 153.015625 477.046875 153.015625 477.050781 153.015625 C 477.054688 153.015625 477.0625 153.015625 477.066406 153.015625 C 477.074219 153.015625 477.078125 153.015625 477.082031 153.015625 C 477.089844 153.015625 477.09375 153.015625 477.097656 153.015625 C 477.105469 153.015625 477.109375 153.015625 477.113281 153.015625 C 477.121094 153.015625 477.125 153.015625 477.128906 153.015625 C 477.136719 153.015625 477.140625 153.015625 477.144531 153.015625 C 477.152344 153.015625 477.15625 153.015625 477.160156 153.015625 C 477.167969 153.015625 477.171875 153.015625 477.175781 153.015625 C 477.183594 153.015625 477.1875 153.015625 477.191406 153.015625 C 477.199219 153.015625 477.203125 153.015625 477.210938 153.015625 C 477.214844 153.015625 477.21875 153.015625 477.226562 153.015625 C 477.230469 153.015625 477.234375 153.015625 477.242188 153.015625 C 477.246094 153.015625 477.25 153.015625 477.257812 153.015625 C 477.261719 153.015625 477.265625 153.015625 477.273438 153.015625 C 477.277344 153.015625 477.28125 153.015625 477.289062 153.015625 C 477.292969 153.015625 477.296875 153.015625 477.304688 153.015625 C 477.308594 153.015625 477.3125 153.015625 477.320312 153.015625 C 477.324219 153.015625 477.332031 153.015625 477.335938 153.015625 C 477.339844 153.015625 477.347656 153.015625 477.351562 153.015625 C 477.355469 153.015625 477.363281 153.015625 477.367188 153.015625 C 477.371094 153.015625 477.378906 153.015625 477.382812 153.015625 C 477.386719 153.015625 477.394531 153.015625 477.398438 153.015625 C 477.402344 153.015625 477.410156 153.015625 477.414062 153.015625 C 477.417969 153.015625 477.425781 153.015625 477.429688 153.015625 C 477.433594 153.015625 477.441406 153.015625 477.445312 153.015625 C 477.449219 153.015625 477.457031 153.015625 477.460938 153.015625 C 477.46875 153.015625 477.472656 153.015625 477.476562 153.015625 C 477.484375 153.015625 477.488281 153.015625 477.492188 153.015625 C 477.5 153.015625 477.503906 153.015625 477.507812 153.015625 C 477.515625 153.015625 477.519531 153.015625 477.523438 153.015625 C 477.53125 153.015625 477.535156 153.015625 477.539062 153.015625 C 477.546875 153.015625 477.550781 153.015625 477.554688 153.015625 C 477.5625 153.015625 477.566406 153.015625 477.570312 153.015625 C 477.578125 153.015625 477.582031 153.015625 477.589844 153.015625 C 477.59375 153.015625 477.597656 153.015625 477.605469 153.015625 C 477.609375 153.015625 477.613281 153.015625 477.621094 153.015625 C 477.625 153.015625 477.628906 153.015625 477.636719 153.015625 C 477.640625 153.015625 477.644531 153.015625 477.652344 153.015625 C 477.65625 153.015625 477.660156 153.015625 477.667969 153.015625 C 477.671875 153.015625 477.675781 153.015625 477.683594 153.015625 C 477.6875 153.015625 477.691406 153.015625 477.699219 153.015625 C 477.703125 153.015625 477.710938 153.015625 477.714844 153.015625 C 477.71875 153.015625 477.726562 153.015625 477.730469 153.015625 C 477.734375 153.015625 477.742188 153.015625 477.746094 153.015625 C 477.75 153.015625 477.757812 153.015625 477.761719 153.015625 C 477.765625 153.015625 477.773438 153.015625 477.777344 153.015625 C 477.78125 153.015625 477.789062 153.015625 477.792969 153.015625 C 477.796875 153.015625 477.804688 153.015625 477.808594 153.015625 C 477.8125 153.015625 477.820312 153.015625 477.824219 153.015625 C 477.832031 153.015625 477.835938 153.015625 477.839844 153.015625 C 477.847656 153.015625 477.851562 153.015625 477.855469 153.015625 C 477.863281 153.015625 477.867188 153.015625 477.871094 153.015625 C 477.878906 153.015625 477.882812 153.015625 477.886719 153.015625 C 477.894531 153.015625 477.898438 153.015625 477.902344 153.015625 C 477.910156 153.015625 477.914062 153.015625 477.917969 153.015625 C 477.925781 153.015625 477.929688 153.015625 477.933594 153.015625 C 477.941406 153.015625 477.945312 153.015625 477.953125 153.015625 C 477.957031 153.015625 477.960938 153.015625 477.96875 153.015625 C 477.972656 153.015625 477.976562 153.015625 477.984375 153.015625 C 477.988281 153.015625 477.992188 153.015625 478 153.015625 C 478.003906 153.015625 478.007812 153.015625 478.015625 153.015625 C 478.019531 153.015625 478.023438 153.015625 478.03125 153.015625 C 478.035156 153.015625 478.039062 153.015625 478.046875 153.015625 C 478.050781 153.015625 478.054688 153.015625 478.0625 153.015625 C 478.066406 153.015625 478.074219 153.015625 478.078125 153.015625 C 478.082031 153.015625 478.089844 153.015625 478.09375 153.015625 C 478.097656 153.015625 478.105469 153.015625 478.109375 153.015625 C 478.113281 153.015625 478.121094 153.015625 478.125 153.015625 C 478.128906 153.015625 478.136719 153.015625 478.140625 153.015625 C 478.144531 153.015625 478.152344 153.015625 478.15625 153.015625 C 478.160156 153.015625 478.167969 153.015625 478.171875 153.015625 C 478.175781 153.015625 478.183594 153.015625 478.1875 153.015625 C 478.191406 153.015625 478.199219 153.015625 478.203125 153.015625 C 478.210938 153.015625 478.214844 153.015625 478.21875 153.015625 C 478.226562 153.015625 478.230469 153.015625 478.234375 153.015625 C 478.242188 153.015625 478.246094 153.015625 478.25 153.015625 C 478.257812 153.015625 478.261719 153.015625 478.265625 153.015625 C 478.273438 153.015625 478.277344 153.015625 478.28125 153.015625 C 478.289062 153.015625 478.292969 153.015625 478.296875 153.015625 C 478.304688 153.015625 478.308594 153.015625 478.3125 153.015625 C 478.320312 153.015625 478.324219 153.015625 478.328125 153.015625 C 478.335938 153.015625 478.339844 153.015625 478.347656 153.015625 C 478.351562 153.015625 478.355469 153.015625 478.363281 153.015625 C 478.367188 153.015625 478.371094 153.015625 478.378906 153.015625 C 478.382812 153.015625 478.386719 153.015625 478.394531 153.015625 C 478.398438 153.015625 478.402344 153.015625 478.410156 153.015625 C 478.414062 153.015625 478.417969 153.015625 478.425781 153.015625 C 478.429688 153.015625 478.433594 153.015625 478.441406 153.015625 C 478.445312 153.015625 478.449219 153.015625 478.457031 153.015625 C 478.460938 153.015625 478.464844 153.015625 478.472656 153.015625 C 478.476562 153.015625 478.480469 153.015625 478.488281 153.015625 C 478.492188 153.015625 478.496094 153.015625 478.503906 153.015625 C 478.507812 153.015625 478.515625 153.015625 478.519531 153.015625 C 478.523438 153.015625 478.53125 153.015625 478.535156 153.015625 C 478.539062 153.015625 478.546875 153.015625 478.550781 153.015625 C 478.554688 153.015625 478.5625 153.015625 478.566406 153.015625 C 478.570312 153.015625 478.578125 153.015625 478.582031 153.015625 C 478.585938 153.015625 478.59375 153.015625 478.597656 153.015625 C 478.601562 153.015625 478.609375 153.015625 478.613281 153.015625 C 478.617188 153.015625 478.625 153.015625 478.628906 153.015625 C 478.632812 153.015625 478.640625 153.015625 478.644531 153.015625 C 478.648438 153.015625 478.65625 153.015625 478.660156 153.015625 C 478.664062 153.015625 478.671875 153.015625 478.675781 153.015625 C 478.679688 153.015625 478.6875 153.015625 478.691406 153.015625 C 478.699219 153.015625 478.703125 153.015625 478.707031 153.015625 C 478.714844 153.015625 478.71875 153.015625 478.722656 153.015625 C 478.730469 153.015625 478.734375 153.015625 478.738281 153.015625 C 478.746094 153.015625 478.75 153.015625 478.753906 153.015625 C 478.761719 153.015625 478.765625 153.015625 478.769531 153.015625 C 478.777344 153.015625 478.78125 153.015625 478.785156 153.015625 C 478.792969 153.015625 478.796875 153.015625 478.800781 153.015625 C 478.808594 153.015625 478.8125 153.015625 478.816406 153.015625 C 478.824219 153.015625 478.828125 153.015625 478.832031 153.015625 C 478.839844 153.015625 478.84375 153.015625 478.847656 153.015625 C 478.855469 153.015625 478.859375 153.015625 478.863281 153.015625 C 478.871094 153.015625 478.875 153.015625 478.878906 153.015625 C 478.886719 153.015625 478.890625 153.015625 478.894531 153.015625 C 478.902344 153.015625 478.90625 153.015625 478.910156 153.015625 C 478.917969 153.015625 478.921875 153.015625 478.925781 153.015625 C 478.933594 153.015625 478.9375 153.015625 478.941406 153.015625 C 478.949219 153.015625 478.953125 153.015625 478.957031 153.015625 C 478.964844 153.015625 478.96875 153.015625 478.972656 153.015625 C 478.980469 153.015625 478.984375 153.015625 478.992188 153.015625 C 478.996094 153.015625 479 153.015625 479.007812 153.015625 C 479.011719 153.015625 479.015625 153.015625 479.023438 153.015625 C 479.027344 153.015625 479.03125 153.015625 479.039062 153.015625 C 479.042969 153.015625 479.046875 153.015625 479.054688 153.015625 C 479.058594 153.015625 479.0625 153.015625 479.070312 153.015625 C 479.074219 153.015625 479.078125 153.015625 479.085938 153.015625 C 479.089844 153.015625 479.09375 153.015625 479.101562 153.015625 C 479.105469 153.015625 479.109375 153.015625 479.117188 153.015625 C 479.121094 153.015625 479.125 153.015625 479.132812 153.015625 C 479.136719 153.015625 479.140625 153.015625 479.148438 153.015625 C 479.152344 153.015625 479.15625 153.015625 479.164062 153.015625 C 479.167969 153.015625 479.171875 153.015625 479.179688 153.015625 C 479.183594 153.015625 479.1875 153.015625 479.195312 153.015625 C 479.199219 153.015625 479.203125 153.015625 479.210938 153.015625 C 479.214844 153.015625 479.21875 153.015625 479.226562 153.015625 C 479.230469 153.015625 479.234375 153.015625 479.242188 153.015625 C 479.246094 153.015625 479.25 153.015625 479.257812 153.015625 C 479.261719 153.015625 479.265625 153.015625 479.273438 153.015625 C 479.277344 153.015625 479.28125 153.015625 479.289062 153.015625 C 479.292969 153.015625 479.296875 153.015625 479.304688 153.015625 C 479.308594 153.015625 479.3125 153.015625 479.320312 153.015625 C 479.324219 153.015625 479.328125 153.015625 479.335938 153.015625 C 479.339844 153.015625 479.34375 153.015625 479.351562 153.015625 C 479.355469 153.015625 479.359375 153.015625 479.367188 153.015625 C 479.371094 153.015625 479.375 153.015625 479.382812 153.015625 C 479.386719 153.015625 479.390625 153.015625 479.394531 153.015625 C 479.402344 153.015625 479.40625 153.015625 479.410156 153.015625 C 479.417969 153.015625 479.421875 153.015625 479.425781 153.015625 C 479.433594 153.015625 479.4375 153.015625 479.441406 153.015625 C 479.449219 153.015625 479.453125 153.015625 479.457031 153.015625 C 479.464844 153.015625 479.46875 153.015625 479.472656 153.015625 C 479.480469 153.015625 479.484375 153.015625 479.488281 153.015625 C 479.496094 153.015625 479.5 153.015625 479.503906 153.015625 C 479.511719 153.015625 479.515625 153.015625 479.519531 153.015625 C 479.527344 153.015625 479.53125 153.015625 479.535156 153.015625 C 479.542969 153.015625 479.546875 153.015625 479.550781 153.015625 C 479.558594 153.015625 479.5625 153.015625 479.566406 153.015625 C 479.574219 153.015625 479.578125 153.015625 479.582031 153.015625 C 479.589844 153.015625 479.59375 153.015625 479.597656 153.015625 C 479.605469 153.015625 479.609375 153.015625 479.613281 153.015625 C 479.621094 153.015625 479.625 153.015625 479.628906 153.015625 C 479.636719 153.015625 479.640625 153.015625 479.644531 153.015625 C 479.648438 153.015625 479.65625 153.015625 479.660156 153.015625 C 479.664062 153.015625 479.671875 153.015625 479.675781 153.015625 C 479.679688 153.015625 479.6875 153.015625 479.691406 153.015625 C 479.695312 153.015625 479.703125 153.015625 479.707031 153.015625 C 479.710938 153.015625 479.71875 153.015625 479.722656 153.015625 C 479.726562 153.015625 479.734375 153.015625 479.738281 153.015625 C 479.742188 153.015625 479.75 153.015625 479.753906 153.015625 C 479.757812 153.015625 479.765625 153.015625 479.769531 153.015625 C 479.773438 153.015625 479.78125 153.015625 479.785156 153.015625 C 479.789062 153.015625 479.792969 153.015625 479.800781 153.015625 C 479.804688 153.015625 479.808594 153.015625 479.816406 153.015625 C 479.820312 153.015625 479.824219 153.015625 479.832031 153.015625 C 479.835938 153.015625 479.839844 153.015625 479.847656 153.015625 C 479.851562 153.015625 479.855469 153.015625 479.863281 153.015625 C 479.867188 153.015625 479.871094 153.015625 479.878906 153.015625 C 479.882812 153.015625 479.886719 153.015625 479.894531 153.015625 C 479.898438 153.015625 479.902344 153.015625 479.90625 153.015625 C 479.914062 153.015625 479.917969 153.015625 479.921875 153.015625 C 479.929688 153.015625 479.933594 153.015625 479.9375 153.015625 C 479.945312 153.015625 479.949219 153.015625 479.953125 153.015625 C 479.960938 153.015625 479.964844 153.015625 479.96875 153.015625 C 479.976562 153.015625 479.980469 153.015625 479.984375 153.015625 C 479.988281 153.015625 479.996094 153.015625 480 153.015625 C 480.003906 153.015625 480.011719 153.015625 480.015625 153.015625 C 480.019531 153.015625 480.027344 153.015625 480.03125 153.015625 C 480.035156 153.015625 480.042969 153.015625 480.046875 153.015625 C 480.050781 153.015625 480.058594 153.015625 480.0625 153.015625 C 480.066406 153.015625 480.070312 153.015625 480.078125 153.015625 C 480.082031 153.015625 480.085938 153.015625 480.09375 153.015625 C 480.097656 153.015625 480.101562 153.015625 480.109375 153.015625 C 480.113281 153.015625 480.117188 153.015625 480.125 153.015625 C 480.128906 153.015625 480.132812 153.015625 480.140625 153.015625 C 480.144531 153.015625 480.148438 153.015625 480.152344 153.015625 C 480.160156 153.015625 480.164062 153.015625 480.167969 153.015625 C 480.175781 153.015625 480.179688 153.015625 480.183594 153.015625 C 480.191406 153.015625 480.195312 153.015625 480.199219 153.015625 C 480.207031 153.015625 480.210938 153.015625 480.214844 153.015625 C 480.21875 153.015625 480.226562 153.015625 480.230469 153.015625 C 480.234375 153.015625 480.242188 153.015625 480.246094 153.015625 C 480.25 153.015625 480.257812 153.015625 480.261719 153.015625 C 480.265625 153.015625 480.269531 153.015625 480.277344 153.015625 C 480.28125 153.015625 480.285156 153.015625 480.292969 153.015625 C 480.296875 153.015625 480.300781 153.015625 480.308594 153.015625 C 480.3125 153.015625 480.316406 153.015625 480.324219 153.015625 C 480.328125 153.015625 480.332031 153.015625 480.335938 153.015625 C 480.34375 153.015625 480.347656 153.015625 480.351562 153.015625 C 480.359375 153.015625 480.363281 153.015625 480.367188 153.015625 C 480.375 153.015625 480.378906 153.015625 480.382812 153.015625 C 480.386719 153.015625 480.394531 153.015625 480.398438 153.015625 C 480.402344 153.015625 480.410156 153.015625 480.414062 153.015625 C 480.417969 153.015625 480.425781 153.015625 480.429688 153.015625 C 480.433594 153.015625 480.4375 153.015625 480.445312 153.015625 C 480.449219 153.015625 480.453125 153.015625 480.460938 153.015625 C 480.464844 153.015625 480.46875 153.015625 480.476562 153.015625 C 480.480469 153.015625 480.484375 153.015625 480.488281 153.015625 C 480.496094 153.015625 480.5 153.015625 480.503906 153.015625 C 480.511719 153.015625 480.515625 153.015625 480.519531 153.015625 C 480.523438 153.015625 480.53125 153.015625 480.535156 153.015625 C 480.539062 153.015625 480.546875 153.015625 480.550781 153.015625 C 480.554688 153.015625 480.5625 153.015625 480.566406 153.015625 C 480.570312 153.015625 480.574219 153.015625 480.582031 153.015625 C 480.585938 153.015625 480.589844 153.015625 480.597656 153.015625 C 480.601562 153.015625 480.605469 153.015625 480.609375 153.015625 C 480.617188 153.015625 480.621094 153.015625 480.625 153.015625 C 480.632812 153.015625 480.636719 153.015625 480.640625 153.015625 C 480.648438 153.015625 480.652344 153.015625 480.65625 153.015625 C 480.660156 153.015625 480.667969 153.015625 480.671875 153.015625 C 480.675781 153.015625 480.683594 153.015625 480.6875 153.015625 C 480.691406 153.015625 480.695312 153.015625 480.703125 153.015625 C 480.707031 153.015625 480.710938 153.015625 480.71875 153.015625 C 480.722656 153.015625 480.726562 153.015625 480.730469 153.015625 C 480.738281 153.015625 480.742188 153.015625 480.746094 153.015625 C 480.753906 153.015625 480.757812 153.015625 480.761719 153.015625 C 480.765625 153.015625 480.773438 153.015625 480.777344 153.015625 C 480.78125 153.015625 480.789062 153.015625 480.792969 153.015625 C 480.796875 153.015625 480.800781 153.015625 480.808594 153.015625 C 480.8125 153.015625 480.816406 153.015625 480.824219 153.015625 C 480.828125 153.015625 480.832031 153.015625 480.835938 153.015625 C 480.84375 153.015625 480.847656 153.015625 480.851562 153.015625 C 480.859375 153.015625 480.863281 153.015625 480.867188 153.015625 C 480.871094 153.015625 480.878906 153.015625 480.882812 153.015625 C 480.886719 153.015625 480.890625 153.015625 480.898438 153.015625 C 480.902344 153.015625 480.90625 153.015625 480.914062 153.015625 C 480.917969 153.015625 480.921875 153.015625 480.925781 153.015625 C 480.933594 153.015625 480.9375 153.015625 480.941406 153.015625 C 480.949219 153.015625 480.953125 153.015625 480.957031 153.015625 C 480.960938 153.015625 480.96875 153.015625 480.972656 153.015625 C 480.976562 153.015625 480.984375 153.015625 480.988281 153.015625 C 480.992188 153.015625 480.996094 153.015625 481.003906 153.015625 C 481.007812 153.015625 481.011719 153.015625 481.015625 153.015625 C 481.023438 153.015625 481.027344 153.015625 481.03125 153.015625 C 481.039062 153.015625 481.042969 153.015625 481.046875 153.015625 C 481.050781 153.015625 481.058594 153.015625 481.0625 153.015625 C 481.066406 153.015625 481.070312 153.015625 481.078125 153.015625 C 481.082031 153.015625 481.085938 153.015625 481.09375 153.015625 C 481.097656 153.015625 481.101562 153.015625 481.105469 153.015625 C 481.113281 153.015625 481.117188 153.015625 481.121094 153.015625 C 481.125 153.015625 481.132812 153.015625 481.136719 153.015625 C 481.140625 153.015625 481.144531 153.015625 481.152344 153.015625 C 481.15625 153.015625 481.160156 153.015625 481.167969 153.015625 C 481.171875 153.015625 481.175781 153.015625 481.179688 153.015625 C 481.1875 153.015625 481.191406 153.015625 481.195312 153.015625 C 481.199219 153.015625 481.207031 153.015625 481.210938 153.015625 C 481.214844 153.015625 481.222656 153.015625 481.226562 153.015625 C 481.230469 153.015625 481.234375 153.015625 481.242188 153.015625 C 481.246094 153.015625 481.25 153.015625 481.253906 153.015625 C 481.261719 153.015625 481.265625 153.015625 481.269531 153.015625 C 481.273438 153.015625 481.28125 153.015625 481.285156 153.015625 C 481.289062 153.015625 481.292969 153.015625 481.300781 153.015625 C 481.304688 153.015625 481.308594 153.015625 481.3125 153.015625 C 481.320312 153.015625 481.324219 153.015625 481.328125 153.015625 C 481.335938 153.015625 481.339844 153.015625 481.34375 153.015625 C 481.347656 153.015625 481.355469 153.015625 481.359375 153.015625 C 481.363281 153.015625 481.367188 153.015625 481.375 153.015625 C 481.378906 153.015625 481.382812 153.015625 481.386719 153.015625 C 481.394531 153.015625 481.398438 153.015625 481.402344 153.015625 C 481.40625 153.015625 481.414062 153.015625 481.417969 153.015625 C 481.421875 153.015625 481.425781 153.015625 481.433594 153.015625 C 481.4375 153.015625 481.441406 153.015625 481.445312 153.015625 C 481.453125 153.015625 481.457031 153.015625 481.460938 153.015625 C 481.464844 153.015625 481.472656 153.015625 481.476562 153.015625 C 481.480469 153.015625 481.484375 153.015625 481.492188 153.015625 C 481.496094 153.015625 481.5 153.015625 481.503906 153.015625 C 481.511719 153.015625 481.515625 153.015625 481.519531 153.015625 C 481.523438 153.015625 481.53125 153.015625 481.535156 153.015625 C 481.539062 153.015625 481.542969 153.015625 481.550781 153.015625 C 481.554688 153.015625 481.558594 153.015625 481.5625 153.015625 C 481.570312 153.015625 481.574219 153.015625 481.578125 153.015625 C 481.582031 153.015625 481.589844 153.015625 481.59375 153.015625 C 481.597656 153.015625 481.601562 153.015625 481.609375 153.015625 C 481.613281 153.015625 481.617188 153.015625 481.621094 153.015625 L 481.621094 150.109375 C 481.617188 150.109375 481.613281 150.109375 481.609375 150.109375 C 481.601562 150.109375 481.597656 150.109375 481.59375 150.109375 C 481.589844 150.109375 481.582031 150.109375 481.578125 150.109375 C 481.574219 150.109375 481.570312 150.109375 481.5625 150.109375 C 481.558594 150.109375 481.554688 150.109375 481.550781 150.109375 C 481.542969 150.109375 481.539062 150.109375 481.535156 150.109375 C 481.53125 150.109375 481.523438 150.109375 481.519531 150.109375 C 481.515625 150.109375 481.511719 150.109375 481.503906 150.109375 C 481.5 150.109375 481.496094 150.109375 481.492188 150.109375 C 481.484375 150.109375 481.480469 150.109375 481.476562 150.109375 C 481.472656 150.109375 481.464844 150.109375 481.460938 150.109375 C 481.457031 150.109375 481.453125 150.109375 481.445312 150.109375 C 481.441406 150.109375 481.4375 150.109375 481.433594 150.109375 C 481.425781 150.109375 481.421875 150.109375 481.417969 150.109375 C 481.414062 150.109375 481.40625 150.109375 481.402344 150.109375 C 481.398438 150.109375 481.394531 150.109375 481.386719 150.109375 C 481.382812 150.109375 481.378906 150.109375 481.375 150.109375 C 481.367188 150.109375 481.363281 150.109375 481.359375 150.109375 C 481.355469 150.109375 481.347656 150.109375 481.34375 150.109375 C 481.339844 150.109375 481.335938 150.109375 481.328125 150.109375 C 481.324219 150.109375 481.320312 150.109375 481.3125 150.109375 C 481.308594 150.109375 481.304688 150.109375 481.300781 150.109375 C 481.292969 150.109375 481.289062 150.109375 481.285156 150.109375 C 481.28125 150.109375 481.273438 150.109375 481.269531 150.109375 C 481.265625 150.109375 481.261719 150.109375 481.253906 150.109375 C 481.25 150.109375 481.246094 150.109375 481.242188 150.109375 C 481.234375 150.109375 481.230469 150.109375 481.226562 150.109375 C 481.222656 150.109375 481.214844 150.109375 481.210938 150.109375 C 481.207031 150.109375 481.199219 150.109375 481.195312 150.109375 C 481.191406 150.109375 481.1875 150.109375 481.179688 150.109375 C 481.175781 150.109375 481.171875 150.109375 481.167969 150.109375 C 481.160156 150.109375 481.15625 150.109375 481.152344 150.109375 C 481.144531 150.109375 481.140625 150.109375 481.136719 150.109375 C 481.132812 150.109375 481.125 150.109375 481.121094 150.109375 C 481.117188 150.109375 481.113281 150.109375 481.105469 150.109375 C 481.101562 150.109375 481.097656 150.109375 481.09375 150.109375 C 481.085938 150.109375 481.082031 150.109375 481.078125 150.109375 C 481.070312 150.109375 481.066406 150.109375 481.0625 150.109375 C 481.058594 150.109375 481.050781 150.109375 481.046875 150.109375 C 481.042969 150.109375 481.039062 150.109375 481.03125 150.109375 C 481.027344 150.109375 481.023438 150.109375 481.015625 150.109375 C 481.011719 150.109375 481.007812 150.109375 481.003906 150.109375 C 480.996094 150.109375 480.992188 150.109375 480.988281 150.109375 C 480.984375 150.109375 480.976562 150.109375 480.972656 150.109375 C 480.96875 150.109375 480.960938 150.109375 480.957031 150.109375 C 480.953125 150.109375 480.949219 150.109375 480.941406 150.109375 C 480.9375 150.109375 480.933594 150.109375 480.925781 150.109375 C 480.921875 150.109375 480.917969 150.109375 480.914062 150.109375 C 480.90625 150.109375 480.902344 150.109375 480.898438 150.109375 C 480.890625 150.109375 480.886719 150.109375 480.882812 150.109375 C 480.878906 150.109375 480.871094 150.109375 480.867188 150.109375 C 480.863281 150.109375 480.859375 150.109375 480.851562 150.109375 C 480.847656 150.109375 480.84375 150.109375 480.835938 150.109375 C 480.832031 150.109375 480.828125 150.109375 480.824219 150.109375 C 480.816406 150.109375 480.8125 150.109375 480.808594 150.109375 C 480.800781 150.109375 480.796875 150.109375 480.792969 150.109375 C 480.789062 150.109375 480.78125 150.109375 480.777344 150.109375 C 480.773438 150.109375 480.765625 150.109375 480.761719 150.109375 C 480.757812 150.109375 480.753906 150.109375 480.746094 150.109375 C 480.742188 150.109375 480.738281 150.109375 480.730469 150.109375 C 480.726562 150.109375 480.722656 150.109375 480.71875 150.109375 C 480.710938 150.109375 480.707031 150.109375 480.703125 150.109375 C 480.695312 150.109375 480.691406 150.109375 480.6875 150.109375 C 480.683594 150.109375 480.675781 150.109375 480.671875 150.109375 C 480.667969 150.109375 480.660156 150.109375 480.65625 150.109375 C 480.652344 150.109375 480.648438 150.109375 480.640625 150.109375 C 480.636719 150.109375 480.632812 150.109375 480.625 150.109375 C 480.621094 150.109375 480.617188 150.109375 480.609375 150.109375 C 480.605469 150.109375 480.601562 150.109375 480.597656 150.109375 C 480.589844 150.109375 480.585938 150.109375 480.582031 150.109375 C 480.574219 150.109375 480.570312 150.109375 480.566406 150.109375 C 480.5625 150.109375 480.554688 150.109375 480.550781 150.109375 C 480.546875 150.109375 480.539062 150.109375 480.535156 150.109375 C 480.53125 150.109375 480.523438 150.109375 480.519531 150.109375 C 480.515625 150.109375 480.511719 150.109375 480.503906 150.109375 C 480.5 150.109375 480.496094 150.109375 480.488281 150.109375 C 480.484375 150.109375 480.480469 150.109375 480.476562 150.109375 C 480.46875 150.109375 480.464844 150.109375 480.460938 150.109375 C 480.453125 150.109375 480.449219 150.109375 480.445312 150.109375 C 480.4375 150.109375 480.433594 150.109375 480.429688 150.109375 C 480.425781 150.109375 480.417969 150.109375 480.414062 150.109375 C 480.410156 150.109375 480.402344 150.109375 480.398438 150.109375 C 480.394531 150.109375 480.386719 150.109375 480.382812 150.109375 C 480.378906 150.109375 480.375 150.109375 480.367188 150.109375 C 480.363281 150.109375 480.359375 150.109375 480.351562 150.109375 C 480.347656 150.109375 480.34375 150.109375 480.335938 150.109375 C 480.332031 150.109375 480.328125 150.109375 480.324219 150.109375 C 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478.9375 150.109375 478.933594 150.109375 478.925781 150.109375 C 478.921875 150.109375 478.917969 150.109375 478.910156 150.109375 C 478.90625 150.109375 478.902344 150.109375 478.894531 150.109375 C 478.890625 150.109375 478.886719 150.109375 478.878906 150.109375 C 478.875 150.109375 478.871094 150.109375 478.863281 150.109375 C 478.859375 150.109375 478.855469 150.109375 478.847656 150.109375 C 478.84375 150.109375 478.839844 150.109375 478.832031 150.109375 C 478.828125 150.109375 478.824219 150.109375 478.816406 150.109375 C 478.8125 150.109375 478.808594 150.109375 478.800781 150.109375 C 478.796875 150.109375 478.792969 150.109375 478.785156 150.109375 C 478.78125 150.109375 478.777344 150.109375 478.769531 150.109375 C 478.765625 150.109375 478.761719 150.109375 478.753906 150.109375 C 478.75 150.109375 478.746094 150.109375 478.738281 150.109375 C 478.734375 150.109375 478.730469 150.109375 478.722656 150.109375 C 478.71875 150.109375 478.714844 150.109375 478.707031 150.109375 C 478.703125 150.109375 478.699219 150.109375 478.691406 150.109375 C 478.6875 150.109375 478.679688 150.109375 478.675781 150.109375 C 478.671875 150.109375 478.664062 150.109375 478.660156 150.109375 C 478.65625 150.109375 478.648438 150.109375 478.644531 150.109375 C 478.640625 150.109375 478.632812 150.109375 478.628906 150.109375 C 478.625 150.109375 478.617188 150.109375 478.613281 150.109375 C 478.609375 150.109375 478.601562 150.109375 478.597656 150.109375 C 478.59375 150.109375 478.585938 150.109375 478.582031 150.109375 C 478.578125 150.109375 478.570312 150.109375 478.566406 150.109375 C 478.5625 150.109375 478.554688 150.109375 478.550781 150.109375 C 478.546875 150.109375 478.539062 150.109375 478.535156 150.109375 C 478.53125 150.109375 478.523438 150.109375 478.519531 150.109375 C 478.515625 150.109375 478.507812 150.109375 478.503906 150.109375 C 478.496094 150.109375 478.492188 150.109375 478.488281 150.109375 C 478.480469 150.109375 478.476562 150.109375 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551.917969 224.0625 C 551.917969 224.070312 551.917969 224.074219 551.917969 224.078125 C 551.917969 224.082031 551.917969 224.089844 551.917969 224.09375 C 551.917969 224.097656 551.917969 224.105469 551.917969 224.109375 C 551.917969 224.113281 551.917969 224.117188 551.917969 224.125 C 551.917969 224.128906 551.917969 224.132812 551.917969 224.140625 C 551.917969 224.144531 551.917969 224.148438 551.917969 224.152344 C 551.917969 224.160156 551.917969 224.164062 551.917969 224.167969 C 551.917969 224.175781 551.917969 224.179688 551.917969 224.183594 C 551.917969 224.1875 551.917969 224.195312 551.917969 224.199219 C 551.917969 224.203125 551.917969 224.210938 551.917969 224.214844 C 551.917969 224.21875 551.917969 224.222656 551.917969 224.230469 C 551.917969 224.234375 551.917969 224.238281 551.917969 224.246094 C 551.917969 224.25 551.917969 224.253906 551.917969 224.257812 C 551.917969 224.265625 551.917969 224.269531 551.917969 224.273438 C 551.917969 224.28125 551.917969 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551.917969 224.511719 551.917969 224.519531 C 551.917969 224.523438 551.917969 224.527344 551.917969 224.53125 C 551.917969 224.539062 551.917969 224.542969 551.917969 224.546875 C 551.917969 224.554688 551.917969 224.558594 551.917969 224.5625 C 551.917969 224.570312 551.917969 224.574219 551.917969 224.578125 C 551.917969 224.582031 551.917969 224.589844 551.917969 224.59375 C 551.917969 224.597656 551.917969 224.605469 551.917969 224.609375 C 551.917969 224.613281 551.917969 224.621094 551.917969 224.625 C 551.917969 224.628906 551.917969 224.632812 551.917969 224.640625 C 551.917969 224.644531 551.917969 224.648438 551.917969 224.65625 C 551.917969 224.660156 551.917969 224.664062 551.917969 224.671875 C 551.917969 224.675781 551.917969 224.679688 551.917969 224.6875 C 551.917969 224.691406 551.917969 224.695312 551.917969 224.699219 C 551.917969 224.707031 551.917969 224.710938 551.917969 224.714844 C 551.917969 224.722656 551.917969 224.726562 551.917969 224.730469 C 551.917969 224.738281 551.917969 224.742188 551.917969 224.746094 C 551.917969 224.75 551.917969 224.757812 551.917969 224.761719 C 551.917969 224.765625 551.917969 224.773438 551.917969 224.777344 C 551.917969 224.78125 551.917969 224.789062 551.917969 224.792969 C 551.917969 224.796875 551.917969 224.804688 551.917969 224.808594 C 551.917969 224.8125 551.917969 224.816406 551.917969 224.824219 C 551.917969 224.828125 551.917969 224.832031 551.917969 224.839844 C 551.917969 224.84375 551.917969 224.847656 551.917969 224.855469 C 551.917969 224.859375 551.917969 224.863281 551.917969 224.871094 C 551.917969 224.875 551.917969 224.878906 551.917969 224.886719 C 551.917969 224.890625 551.917969 224.894531 551.917969 224.898438 C 551.917969 224.90625 551.917969 224.910156 551.917969 224.914062 C 551.917969 224.921875 551.917969 224.925781 551.917969 224.929688 C 551.917969 224.9375 551.917969 224.941406 551.917969 224.945312 C 551.917969 224.953125 551.917969 224.957031 551.917969 224.960938 C 551.917969 224.96875 551.917969 224.972656 551.917969 224.976562 C 551.917969 224.980469 551.917969 224.988281 551.917969 224.992188 C 551.917969 224.996094 551.917969 225.003906 551.917969 225.007812 C 551.917969 225.011719 551.917969 225.019531 551.917969 225.023438 C 551.917969 225.027344 551.917969 225.035156 551.917969 225.039062 C 551.917969 225.042969 551.917969 225.050781 551.917969 225.054688 C 551.917969 225.058594 551.917969 225.066406 551.917969 225.070312 C 551.917969 225.074219 551.917969 225.078125 551.917969 225.085938 C 551.917969 225.089844 551.917969 225.09375 551.917969 225.101562 C 551.917969 225.105469 551.917969 225.109375 551.917969 225.117188 C 551.917969 225.121094 551.917969 225.125 551.917969 225.132812 C 551.917969 225.136719 551.917969 225.140625 551.917969 225.148438 C 551.917969 225.152344 551.917969 225.15625 551.917969 225.164062 C 551.917969 225.167969 551.917969 225.171875 551.917969 225.179688 C 551.917969 225.183594 551.917969 225.1875 551.917969 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551.917969 225.425781 C 551.917969 225.429688 551.917969 225.4375 551.917969 225.441406 C 551.917969 225.445312 551.917969 225.453125 551.917969 225.457031 C 551.917969 225.460938 551.917969 225.46875 551.917969 225.472656 C 551.917969 225.476562 551.917969 225.484375 551.917969 225.488281 C 551.917969 225.492188 551.917969 225.5 551.917969 225.503906 C 551.917969 225.507812 551.917969 225.515625 551.917969 225.519531 C 551.917969 225.523438 551.917969 225.53125 551.917969 225.535156 C 551.917969 225.539062 551.917969 225.546875 551.917969 225.550781 C 551.917969 225.554688 551.917969 225.5625 551.917969 225.566406 C 551.917969 225.570312 551.917969 225.578125 551.917969 225.582031 C 551.917969 225.585938 551.917969 225.59375 551.917969 225.597656 C 551.917969 225.601562 551.917969 225.609375 551.917969 225.613281 C 551.917969 225.617188 551.917969 225.625 551.917969 225.628906 C 551.917969 225.632812 551.917969 225.640625 551.917969 225.644531 C 551.917969 225.648438 551.917969 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551.917969 225.890625 551.917969 225.894531 C 551.917969 225.898438 551.917969 225.90625 551.917969 225.910156 C 551.917969 225.914062 551.917969 225.921875 551.917969 225.925781 C 551.917969 225.929688 551.917969 225.9375 551.917969 225.941406 C 551.917969 225.945312 551.917969 225.953125 551.917969 225.957031 C 551.917969 225.960938 551.917969 225.96875 551.917969 225.972656 C 551.917969 225.976562 551.917969 225.984375 551.917969 225.988281 C 551.917969 225.992188 551.917969 226 551.917969 226.003906 C 551.917969 226.007812 551.917969 226.015625 551.917969 226.019531 C 551.917969 226.023438 551.917969 226.03125 551.917969 226.035156 C 551.917969 226.039062 551.917969 226.046875 551.917969 226.050781 C 551.917969 226.054688 551.917969 226.0625 551.917969 226.066406 C 551.917969 226.070312 551.917969 226.078125 551.917969 226.082031 C 551.917969 226.085938 551.917969 226.09375 551.917969 226.097656 C 551.917969 226.101562 551.917969 226.109375 551.917969 226.113281 C 551.917969 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551.917969 226.355469 551.917969 226.359375 551.917969 226.363281 C 551.917969 226.371094 551.917969 226.375 551.917969 226.382812 C 551.917969 226.386719 551.917969 226.390625 551.917969 226.398438 C 551.917969 226.402344 551.917969 226.40625 551.917969 226.414062 C 551.917969 226.417969 551.917969 226.421875 551.917969 226.429688 C 551.917969 226.433594 551.917969 226.4375 551.917969 226.445312 C 551.917969 226.449219 551.917969 226.453125 551.917969 226.460938 C 551.917969 226.464844 551.917969 226.46875 551.917969 226.476562 C 551.917969 226.480469 551.917969 226.484375 551.917969 226.492188 C 551.917969 226.496094 551.917969 226.5 551.917969 226.507812 C 551.917969 226.511719 551.917969 226.515625 551.917969 226.523438 C 551.917969 226.527344 551.917969 226.53125 551.917969 226.539062 C 551.917969 226.542969 551.917969 226.550781 551.917969 226.554688 C 551.917969 226.558594 551.917969 226.566406 551.917969 226.570312 C 551.917969 226.574219 551.917969 226.582031 551.917969 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227.054688 551.917969 227.058594 C 551.917969 227.0625 551.917969 227.070312 551.917969 227.074219 C 551.917969 227.082031 551.917969 227.085938 551.917969 227.089844 C 551.917969 227.097656 551.917969 227.101562 551.917969 227.105469 C 551.917969 227.113281 551.917969 227.117188 551.917969 227.121094 C 551.917969 227.128906 551.917969 227.132812 551.917969 227.136719 C 551.917969 227.144531 551.917969 227.148438 551.917969 227.152344 C 551.917969 227.160156 551.917969 227.164062 551.917969 227.167969 C 551.917969 227.175781 551.917969 227.179688 551.917969 227.183594 C 551.917969 227.191406 551.917969 227.195312 551.917969 227.203125 C 551.917969 227.207031 551.917969 227.210938 551.917969 227.21875 C 551.917969 227.222656 551.917969 227.226562 551.917969 227.234375 C 551.917969 227.238281 551.917969 227.242188 551.917969 227.25 C 551.917969 227.253906 551.917969 227.257812 551.917969 227.265625 C 551.917969 227.269531 551.917969 227.273438 551.917969 227.28125 C 551.917969 227.285156 551.917969 227.289062 551.917969 227.296875 C 551.917969 227.300781 551.917969 227.304688 551.917969 227.3125 C 551.917969 227.316406 551.917969 227.324219 551.917969 227.328125 C 551.917969 227.332031 551.917969 227.339844 551.917969 227.34375 C 551.917969 227.347656 551.917969 227.355469 551.917969 227.359375 C 551.917969 227.363281 551.917969 227.371094 551.917969 227.375 C 551.917969 227.378906 551.917969 227.386719 551.917969 227.390625 C 551.917969 227.394531 551.917969 227.402344 551.917969 227.40625 C 551.917969 227.410156 551.917969 227.417969 551.917969 227.421875 C 551.917969 227.425781 551.917969 227.433594 551.917969 227.4375 C 551.917969 227.445312 551.917969 227.449219 551.917969 227.453125 C 551.917969 227.460938 551.917969 227.464844 551.917969 227.46875 C 551.917969 227.476562 551.917969 227.480469 551.917969 227.484375 C 551.917969 227.492188 551.917969 227.496094 551.917969 227.5 C 551.917969 227.507812 551.917969 227.511719 551.917969 227.515625 C 551.917969 227.523438 551.917969 227.527344 551.917969 227.53125 C 551.917969 227.539062 551.917969 227.542969 551.917969 227.546875 C 551.917969 227.554688 551.917969 227.558594 551.917969 227.566406 C 551.917969 227.570312 551.917969 227.574219 551.917969 227.582031 C 551.917969 227.585938 551.917969 227.589844 551.917969 227.597656 C 551.917969 227.601562 551.917969 227.605469 551.917969 227.613281 C 551.917969 227.617188 551.917969 227.621094 551.917969 227.628906 C 551.917969 227.632812 551.917969 227.636719 551.917969 227.644531 C 551.917969 227.648438 551.917969 227.652344 551.917969 227.660156 C 551.917969 227.664062 551.917969 227.667969 551.917969 227.675781 C 551.917969 227.679688 551.917969 227.683594 551.917969 227.691406 C 551.917969 227.695312 551.917969 227.703125 551.917969 227.707031 C 551.917969 227.710938 551.917969 227.71875 551.917969 227.722656 C 551.917969 227.726562 551.917969 227.734375 551.917969 227.738281 C 551.917969 227.742188 551.917969 227.75 551.917969 227.753906 C 551.917969 227.757812 551.917969 227.765625 551.917969 227.769531 C 551.917969 227.773438 551.917969 227.78125 551.917969 227.785156 C 551.917969 227.789062 551.917969 227.796875 551.917969 227.800781 C 551.917969 227.804688 551.917969 227.8125 551.917969 227.816406 C 551.917969 227.820312 551.917969 227.828125 551.917969 227.832031 C 551.917969 227.839844 551.917969 227.84375 551.917969 227.847656 C 551.917969 227.855469 551.917969 227.859375 551.917969 227.863281 C 551.917969 227.871094 551.917969 227.875 551.917969 227.878906 C 551.917969 227.886719 551.917969 227.890625 551.917969 227.894531 C 551.917969 227.902344 551.917969 227.90625 551.917969 227.910156 C 551.917969 227.917969 551.917969 227.921875 551.917969 227.925781 C 551.917969 227.933594 551.917969 227.9375 551.917969 227.941406 C 551.917969 227.949219 551.917969 227.953125 551.917969 227.957031 C 551.917969 227.964844 551.917969 227.96875 551.917969 227.976562 C 551.917969 227.980469 551.917969 227.984375 551.917969 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551.917969 228.226562 C 551.917969 228.230469 551.917969 228.238281 551.917969 228.242188 C 551.917969 228.246094 551.917969 228.253906 551.917969 228.257812 C 551.917969 228.261719 551.917969 228.269531 551.917969 228.273438 C 551.917969 228.277344 551.917969 228.285156 551.917969 228.289062 C 551.917969 228.292969 551.917969 228.300781 551.917969 228.304688 C 551.917969 228.308594 551.917969 228.316406 551.917969 228.320312 C 551.917969 228.324219 551.917969 228.332031 551.917969 228.335938 C 551.917969 228.339844 551.917969 228.347656 551.917969 228.351562 C 551.917969 228.355469 551.917969 228.363281 551.917969 228.367188 C 551.917969 228.375 551.917969 228.378906 551.917969 228.382812 C 551.917969 228.390625 551.917969 228.394531 551.917969 228.398438 C 551.917969 228.40625 551.917969 228.410156 551.917969 228.414062 C 551.917969 228.421875 551.917969 228.425781 551.917969 228.429688 C 551.917969 228.4375 551.917969 228.441406 551.917969 228.445312 C 551.917969 228.453125 551.917969 228.457031 551.917969 228.460938 C 551.917969 228.46875 551.917969 228.472656 551.917969 228.476562 C 551.917969 228.484375 551.917969 228.488281 551.917969 228.492188 C 551.917969 228.5 551.917969 228.503906 551.917969 228.507812 C 551.917969 228.515625 551.917969 228.519531 551.917969 228.523438 C 551.917969 228.53125 551.917969 228.535156 551.917969 228.539062 C 551.917969 228.546875 551.917969 228.550781 551.917969 228.554688 C 551.917969 228.5625 551.917969 228.566406 551.917969 228.570312 C 551.917969 228.578125 551.917969 228.582031 551.917969 228.585938 C 551.917969 228.59375 551.917969 228.597656 551.917969 228.601562 C 551.917969 228.609375 551.917969 228.613281 551.917969 228.617188 C 551.917969 228.625 551.917969 228.628906 551.917969 228.632812 C 551.917969 228.640625 551.917969 228.644531 551.917969 228.648438 C 551.917969 228.65625 551.917969 228.660156 551.917969 228.664062 C 551.917969 228.671875 551.917969 228.675781 551.917969 228.679688 C 551.917969 228.6875 551.917969 228.691406 551.917969 228.695312 C 551.917969 228.703125 551.917969 228.707031 551.917969 228.710938 C 551.917969 228.71875 551.917969 228.722656 551.917969 228.726562 C 551.917969 228.734375 551.917969 228.738281 551.917969 228.742188 C 551.917969 228.75 551.917969 228.753906 551.917969 228.757812 C 551.917969 228.765625 551.917969 228.769531 551.917969 228.773438 C 551.917969 228.78125 551.917969 228.785156 551.917969 228.789062 C 551.917969 228.796875 551.917969 228.800781 551.917969 228.804688 C 551.917969 228.8125 551.917969 228.816406 551.917969 228.820312 C 551.917969 228.828125 551.917969 228.832031 551.917969 228.835938 C 551.917969 228.84375 551.917969 228.847656 551.917969 228.851562 C 551.917969 228.859375 551.917969 228.863281 551.917969 228.867188 C 551.917969 228.875 551.917969 228.878906 551.917969 228.882812 C 551.917969 228.890625 551.917969 228.894531 551.917969 228.898438 C 551.917969 228.90625 551.917969 228.910156 551.917969 228.914062 C 551.917969 228.921875 551.917969 228.925781 551.917969 228.929688 C 551.917969 228.9375 551.917969 228.941406 551.917969 228.945312 C 551.917969 228.953125 551.917969 228.957031 551.917969 228.960938 C 551.917969 228.96875 551.917969 228.972656 551.917969 228.976562 C 551.917969 228.984375 551.917969 228.988281 551.917969 228.992188 C 551.917969 229 551.917969 229.003906 551.917969 229.007812 C 551.917969 229.015625 551.917969 229.019531 551.917969 229.023438 C 551.917969 229.03125 551.917969 229.035156 551.917969 229.039062 C 551.917969 229.046875 551.917969 229.050781 551.917969 229.054688 C 551.917969 229.0625 551.917969 229.066406 551.917969 229.070312 C 551.917969 229.078125 551.917969 229.082031 551.917969 229.085938 C 551.917969 229.09375 551.917969 229.097656 551.917969 229.101562 C 551.917969 229.109375 551.917969 229.113281 551.917969 229.117188 C 551.917969 229.121094 551.917969 229.128906 551.917969 229.132812 C 551.917969 229.136719 551.917969 229.144531 551.917969 229.148438 C 551.917969 229.152344 551.917969 229.160156 551.917969 229.164062 C 551.917969 229.167969 551.917969 229.175781 551.917969 229.179688 C 551.917969 229.183594 551.917969 229.191406 551.917969 229.195312 C 551.917969 229.199219 551.917969 229.207031 551.917969 229.210938 C 551.917969 229.214844 551.917969 229.222656 551.917969 229.226562 C 551.917969 229.230469 551.917969 229.238281 551.917969 229.242188 C 551.917969 229.246094 551.917969 229.253906 551.917969 229.257812 C 551.917969 229.261719 551.917969 229.269531 551.917969 229.273438 C 551.917969 229.277344 551.917969 229.285156 551.917969 229.289062 C 551.917969 229.292969 551.917969 229.296875 551.917969 229.304688 C 551.917969 229.308594 551.917969 229.3125 551.917969 229.320312 C 551.917969 229.324219 551.917969 229.328125 551.917969 229.335938 C 551.917969 229.339844 551.917969 229.34375 551.917969 229.351562 C 551.917969 229.355469 551.917969 229.359375 551.917969 229.367188 C 551.917969 229.371094 551.917969 229.375 551.917969 229.382812 C 551.917969 229.386719 551.917969 229.390625 551.917969 229.398438 C 551.917969 229.402344 551.917969 229.40625 551.917969 229.410156 C 551.917969 229.417969 551.917969 229.421875 551.917969 229.425781 C 551.917969 229.433594 551.917969 229.4375 551.917969 229.441406 C 551.917969 229.449219 551.917969 229.453125 551.917969 229.457031 C 551.917969 229.464844 551.917969 229.46875 551.917969 229.472656 C 551.917969 229.480469 551.917969 229.484375 551.917969 229.488281 C 551.917969 229.496094 551.917969 229.5 551.917969 229.503906 C 551.917969 229.511719 551.917969 229.515625 551.917969 229.519531 C 551.917969 229.523438 551.917969 229.53125 551.917969 229.535156 C 551.917969 229.539062 551.917969 229.546875 551.917969 229.550781 C 551.917969 229.554688 551.917969 229.5625 551.917969 229.566406 C 551.917969 229.570312 551.917969 229.578125 551.917969 229.582031 C 551.917969 229.585938 551.917969 229.59375 551.917969 229.597656 C 551.917969 229.601562 551.917969 229.605469 551.917969 229.613281 C 551.917969 229.617188 551.917969 229.621094 551.917969 229.628906 C 551.917969 229.632812 551.917969 229.636719 551.917969 229.644531 C 551.917969 229.648438 551.917969 229.652344 551.917969 229.660156 C 551.917969 229.664062 551.917969 229.667969 551.917969 229.675781 C 551.917969 229.679688 551.917969 229.683594 551.917969 229.6875 C 551.917969 229.695312 551.917969 229.699219 551.917969 229.703125 C 551.917969 229.710938 551.917969 229.714844 551.917969 229.71875 C 551.917969 229.726562 551.917969 229.730469 551.917969 229.734375 C 551.917969 229.742188 551.917969 229.746094 551.917969 229.75 C 551.917969 229.753906 551.917969 229.761719 551.917969 229.765625 C 551.917969 229.769531 551.917969 229.777344 551.917969 229.78125 C 551.917969 229.785156 551.917969 229.792969 551.917969 229.796875 C 551.917969 229.800781 551.917969 229.808594 551.917969 229.8125 C 551.917969 229.816406 551.917969 229.820312 551.917969 229.828125 C 551.917969 229.832031 551.917969 229.835938 551.917969 229.84375 C 551.917969 229.847656 551.917969 229.851562 551.917969 229.859375 C 551.917969 229.863281 551.917969 229.867188 551.917969 229.871094 C 551.917969 229.878906 551.917969 229.882812 551.917969 229.886719 C 551.917969 229.894531 551.917969 229.898438 551.917969 229.902344 C 551.917969 229.910156 551.917969 229.914062 551.917969 229.917969 C 551.917969 229.921875 551.917969 229.929688 551.917969 229.933594 C 551.917969 229.9375 551.917969 229.945312 551.917969 229.949219 C 551.917969 229.953125 551.917969 229.960938 551.917969 229.964844 C 551.917969 229.96875 551.917969 229.972656 551.917969 229.980469 C 551.917969 229.984375 551.917969 229.988281 551.917969 229.996094 C 551.917969 230 551.917969 230.003906 551.917969 230.011719 C 551.917969 230.015625 551.917969 230.019531 551.917969 230.023438 C 551.917969 230.03125 551.917969 230.035156 551.917969 230.039062 C 551.917969 230.046875 551.917969 230.050781 551.917969 230.054688 C 551.917969 230.0625 551.917969 230.066406 551.917969 230.070312 C 551.917969 230.074219 551.917969 230.082031 551.917969 230.085938 C 551.917969 230.089844 551.917969 230.097656 551.917969 230.101562 C 551.917969 230.105469 551.917969 230.113281 551.917969 230.117188 C 551.917969 230.121094 551.917969 230.125 551.917969 230.132812 C 551.917969 230.136719 551.917969 230.140625 551.917969 230.148438 C 551.917969 230.152344 551.917969 230.15625 551.917969 230.160156 C 551.917969 230.167969 551.917969 230.171875 551.917969 230.175781 C 551.917969 230.183594 551.917969 230.1875 551.917969 230.191406 C 551.917969 230.195312 551.917969 230.203125 551.917969 230.207031 C 551.917969 230.210938 551.917969 230.21875 551.917969 230.222656 C 551.917969 230.226562 551.917969 230.234375 551.917969 230.238281 C 551.917969 230.242188 551.917969 230.246094 551.917969 230.253906 C 551.917969 230.257812 551.917969 230.261719 551.917969 230.269531 C 551.917969 230.273438 551.917969 230.277344 551.917969 230.28125 C 551.917969 230.289062 551.917969 230.292969 551.917969 230.296875 C 551.917969 230.304688 551.917969 230.308594 551.917969 230.3125 C 551.917969 230.316406 551.917969 230.324219 551.917969 230.328125 C 551.917969 230.332031 551.917969 230.339844 551.917969 230.34375 C 551.917969 230.347656 551.917969 230.351562 551.917969 230.359375 C 551.917969 230.363281 551.917969 230.367188 551.917969 230.375 C 551.917969 230.378906 551.917969 230.382812 551.917969 230.386719 C 551.917969 230.394531 551.917969 230.398438 551.917969 230.402344 C 551.917969 230.410156 551.917969 230.414062 551.917969 230.417969 C 551.917969 230.421875 551.917969 230.429688 551.917969 230.433594 C 551.917969 230.4375 551.917969 230.445312 551.917969 230.449219 C 551.917969 230.453125 551.917969 230.457031 551.917969 230.464844 C 551.917969 230.46875 551.917969 230.472656 551.917969 230.476562 C 551.917969 230.484375 551.917969 230.488281 551.917969 230.492188 C 551.917969 230.5 551.917969 230.503906 551.917969 230.507812 C 551.917969 230.511719 551.917969 230.519531 551.917969 230.523438 C 551.917969 230.527344 551.917969 230.535156 551.917969 230.539062 C 551.917969 230.542969 551.917969 230.546875 551.917969 230.554688 C 551.917969 230.558594 551.917969 230.5625 551.917969 230.566406 C 551.917969 230.574219 551.917969 230.578125 551.917969 230.582031 C 551.917969 230.589844 551.917969 230.59375 551.917969 230.597656 C 551.917969 230.601562 551.917969 230.609375 551.917969 230.613281 C 551.917969 230.617188 551.917969 230.621094 551.917969 230.628906 C 551.917969 230.632812 551.917969 230.636719 551.917969 230.644531 C 551.917969 230.648438 551.917969 230.652344 551.917969 230.65625 C 551.917969 230.664062 551.917969 230.667969 551.917969 230.671875 C 551.917969 230.675781 551.917969 230.683594 551.917969 230.6875 C 551.917969 230.691406 551.917969 230.699219 551.917969 230.703125 C 551.917969 230.707031 551.917969 230.710938 551.917969 230.71875 C 551.917969 230.722656 551.917969 230.726562 551.917969 230.730469 C 551.917969 230.738281 551.917969 230.742188 551.917969 230.746094 C 551.917969 230.75 551.917969 230.757812 551.917969 230.761719 C 551.917969 230.765625 551.917969 230.773438 551.917969 230.777344 C 551.917969 230.78125 551.917969 230.785156 551.917969 230.792969 C 551.917969 230.796875 551.917969 230.800781 551.917969 230.804688 C 551.917969 230.8125 551.917969 230.816406 551.917969 230.820312 C 551.917969 230.824219 551.917969 230.832031 551.917969 230.835938 C 551.917969 230.839844 551.917969 230.847656 551.917969 230.851562 C 551.917969 230.855469 551.917969 230.859375 551.917969 230.867188 C 551.917969 230.871094 551.917969 230.875 551.917969 230.878906 C 551.917969 230.886719 551.917969 230.890625 551.917969 230.894531 C 551.917969 230.898438 551.917969 230.90625 551.917969 230.910156 C 551.917969 230.914062 551.917969 230.917969 551.917969 230.925781 C 551.917969 230.929688 551.917969 230.933594 551.917969 230.9375 C 551.917969 230.945312 551.917969 230.949219 551.917969 230.953125 C 551.917969 230.957031 551.917969 230.964844 551.917969 230.96875 C 551.917969 230.972656 551.917969 230.980469 551.917969 230.984375 C 551.917969 230.988281 551.917969 230.992188 551.917969 231 C 551.917969 231.003906 551.917969 231.007812 551.917969 231.011719 C 551.917969 231.019531 551.917969 231.023438 551.917969 231.027344 C 551.917969 231.03125 551.917969 231.039062 551.917969 231.042969 C 551.917969 231.046875 551.917969 231.050781 551.917969 231.058594 C 551.917969 231.0625 551.917969 231.066406 551.917969 231.070312 C 551.917969 231.078125 551.917969 231.082031 551.917969 231.085938 C 551.917969 231.089844 551.917969 231.097656 551.917969 231.101562 C 551.917969 231.105469 551.917969 231.109375 551.917969 231.117188 C 551.917969 231.121094 551.917969 231.125 551.917969 231.128906 C 551.917969 231.136719 551.917969 231.140625 551.917969 231.144531 C 551.917969 231.148438 551.917969 231.15625 551.917969 231.160156 C 551.917969 231.164062 551.917969 231.167969 551.917969 231.175781 C 551.917969 231.179688 551.917969 231.183594 551.917969 231.1875 L 554.820312 231.1875 C 554.820312 231.183594 554.820312 231.179688 554.820312 231.175781 C 554.820312 231.167969 554.820312 231.164062 554.820312 231.160156 C 554.820312 231.15625 554.820312 231.148438 554.820312 231.144531 C 554.820312 231.140625 554.820312 231.136719 554.820312 231.128906 C 554.820312 231.125 554.820312 231.121094 554.820312 231.117188 C 554.820312 231.109375 554.820312 231.105469 554.820312 231.101562 C 554.820312 231.097656 554.820312 231.089844 554.820312 231.085938 C 554.820312 231.082031 554.820312 231.078125 554.820312 231.070312 C 554.820312 231.066406 554.820312 231.0625 554.820312 231.058594 C 554.820312 231.050781 554.820312 231.046875 554.820312 231.042969 C 554.820312 231.039062 554.820312 231.03125 554.820312 231.027344 C 554.820312 231.023438 554.820312 231.019531 554.820312 231.011719 C 554.820312 231.007812 554.820312 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554.820312 230.78125 554.820312 230.777344 C 554.820312 230.773438 554.820312 230.765625 554.820312 230.761719 C 554.820312 230.757812 554.820312 230.75 554.820312 230.746094 C 554.820312 230.742188 554.820312 230.738281 554.820312 230.730469 C 554.820312 230.726562 554.820312 230.722656 554.820312 230.71875 C 554.820312 230.710938 554.820312 230.707031 554.820312 230.703125 C 554.820312 230.699219 554.820312 230.691406 554.820312 230.6875 C 554.820312 230.683594 554.820312 230.675781 554.820312 230.671875 C 554.820312 230.667969 554.820312 230.664062 554.820312 230.65625 C 554.820312 230.652344 554.820312 230.648438 554.820312 230.644531 C 554.820312 230.636719 554.820312 230.632812 554.820312 230.628906 C 554.820312 230.621094 554.820312 230.617188 554.820312 230.613281 C 554.820312 230.609375 554.820312 230.601562 554.820312 230.597656 C 554.820312 230.59375 554.820312 230.589844 554.820312 230.582031 C 554.820312 230.578125 554.820312 230.574219 554.820312 230.566406 C 554.820312 230.5625 554.820312 230.558594 554.820312 230.554688 C 554.820312 230.546875 554.820312 230.542969 554.820312 230.539062 C 554.820312 230.535156 554.820312 230.527344 554.820312 230.523438 C 554.820312 230.519531 554.820312 230.511719 554.820312 230.507812 C 554.820312 230.503906 554.820312 230.5 554.820312 230.492188 C 554.820312 230.488281 554.820312 230.484375 554.820312 230.476562 C 554.820312 230.472656 554.820312 230.46875 554.820312 230.464844 C 554.820312 230.457031 554.820312 230.453125 554.820312 230.449219 C 554.820312 230.445312 554.820312 230.4375 554.820312 230.433594 C 554.820312 230.429688 554.820312 230.421875 554.820312 230.417969 C 554.820312 230.414062 554.820312 230.410156 554.820312 230.402344 C 554.820312 230.398438 554.820312 230.394531 554.820312 230.386719 C 554.820312 230.382812 554.820312 230.378906 554.820312 230.375 C 554.820312 230.367188 554.820312 230.363281 554.820312 230.359375 C 554.820312 230.351562 554.820312 230.347656 554.820312 230.34375 C 554.820312 230.339844 554.820312 230.332031 554.820312 230.328125 C 554.820312 230.324219 554.820312 230.316406 554.820312 230.3125 C 554.820312 230.308594 554.820312 230.304688 554.820312 230.296875 C 554.820312 230.292969 554.820312 230.289062 554.820312 230.28125 C 554.820312 230.277344 554.820312 230.273438 554.820312 230.269531 C 554.820312 230.261719 554.820312 230.257812 554.820312 230.253906 C 554.820312 230.246094 554.820312 230.242188 554.820312 230.238281 C 554.820312 230.234375 554.820312 230.226562 554.820312 230.222656 C 554.820312 230.21875 554.820312 230.210938 554.820312 230.207031 C 554.820312 230.203125 554.820312 230.195312 554.820312 230.191406 C 554.820312 230.1875 554.820312 230.183594 554.820312 230.175781 C 554.820312 230.171875 554.820312 230.167969 554.820312 230.160156 C 554.820312 230.15625 554.820312 230.152344 554.820312 230.148438 C 554.820312 230.140625 554.820312 230.136719 554.820312 230.132812 C 554.820312 230.125 554.820312 230.121094 554.820312 230.117188 C 554.820312 230.113281 554.820312 230.105469 554.820312 230.101562 C 554.820312 230.097656 554.820312 230.089844 554.820312 230.085938 C 554.820312 230.082031 554.820312 230.074219 554.820312 230.070312 C 554.820312 230.066406 554.820312 230.0625 554.820312 230.054688 C 554.820312 230.050781 554.820312 230.046875 554.820312 230.039062 C 554.820312 230.035156 554.820312 230.03125 554.820312 230.023438 C 554.820312 230.019531 554.820312 230.015625 554.820312 230.011719 C 554.820312 230.003906 554.820312 230 554.820312 229.996094 C 554.820312 229.988281 554.820312 229.984375 554.820312 229.980469 C 554.820312 229.972656 554.820312 229.96875 554.820312 229.964844 C 554.820312 229.960938 554.820312 229.953125 554.820312 229.949219 C 554.820312 229.945312 554.820312 229.9375 554.820312 229.933594 C 554.820312 229.929688 554.820312 229.921875 554.820312 229.917969 C 554.820312 229.914062 554.820312 229.910156 554.820312 229.902344 C 554.820312 229.898438 554.820312 229.894531 554.820312 229.886719 C 554.820312 229.882812 554.820312 229.878906 554.820312 229.871094 C 554.820312 229.867188 554.820312 229.863281 554.820312 229.859375 C 554.820312 229.851562 554.820312 229.847656 554.820312 229.84375 C 554.820312 229.835938 554.820312 229.832031 554.820312 229.828125 C 554.820312 229.820312 554.820312 229.816406 554.820312 229.8125 C 554.820312 229.808594 554.820312 229.800781 554.820312 229.796875 C 554.820312 229.792969 554.820312 229.785156 554.820312 229.78125 C 554.820312 229.777344 554.820312 229.769531 554.820312 229.765625 C 554.820312 229.761719 554.820312 229.753906 554.820312 229.75 C 554.820312 229.746094 554.820312 229.742188 554.820312 229.734375 C 554.820312 229.730469 554.820312 229.726562 554.820312 229.71875 C 554.820312 229.714844 554.820312 229.710938 554.820312 229.703125 C 554.820312 229.699219 554.820312 229.695312 554.820312 229.6875 C 554.820312 229.683594 554.820312 229.679688 554.820312 229.675781 C 554.820312 229.667969 554.820312 229.664062 554.820312 229.660156 C 554.820312 229.652344 554.820312 229.648438 554.820312 229.644531 C 554.820312 229.636719 554.820312 229.632812 554.820312 229.628906 C 554.820312 229.621094 554.820312 229.617188 554.820312 229.613281 C 554.820312 229.605469 554.820312 229.601562 554.820312 229.597656 C 554.820312 229.59375 554.820312 229.585938 554.820312 229.582031 C 554.820312 229.578125 554.820312 229.570312 554.820312 229.566406 C 554.820312 229.5625 554.820312 229.554688 554.820312 229.550781 C 554.820312 229.546875 554.820312 229.539062 554.820312 229.535156 C 554.820312 229.53125 554.820312 229.523438 554.820312 229.519531 C 554.820312 229.515625 554.820312 229.511719 554.820312 229.503906 C 554.820312 229.5 554.820312 229.496094 554.820312 229.488281 C 554.820312 229.484375 554.820312 229.480469 554.820312 229.472656 C 554.820312 229.46875 554.820312 229.464844 554.820312 229.457031 C 554.820312 229.453125 554.820312 229.449219 554.820312 229.441406 C 554.820312 229.4375 554.820312 229.433594 554.820312 229.425781 C 554.820312 229.421875 554.820312 229.417969 554.820312 229.410156 C 554.820312 229.40625 554.820312 229.402344 554.820312 229.398438 C 554.820312 229.390625 554.820312 229.386719 554.820312 229.382812 C 554.820312 229.375 554.820312 229.371094 554.820312 229.367188 C 554.820312 229.359375 554.820312 229.355469 554.820312 229.351562 C 554.820312 229.34375 554.820312 229.339844 554.820312 229.335938 C 554.820312 229.328125 554.820312 229.324219 554.820312 229.320312 C 554.820312 229.3125 554.820312 229.308594 554.820312 229.304688 C 554.820312 229.296875 554.820312 229.292969 554.820312 229.289062 C 554.820312 229.285156 554.820312 229.277344 554.820312 229.273438 C 554.820312 229.269531 554.820312 229.261719 554.820312 229.257812 C 554.820312 229.253906 554.820312 229.246094 554.820312 229.242188 C 554.820312 229.238281 554.820312 229.230469 554.820312 229.226562 C 554.820312 229.222656 554.820312 229.214844 554.820312 229.210938 C 554.820312 229.207031 554.820312 229.199219 554.820312 229.195312 C 554.820312 229.191406 554.820312 229.183594 554.820312 229.179688 C 554.820312 229.175781 554.820312 229.167969 554.820312 229.164062 C 554.820312 229.160156 554.820312 229.152344 554.820312 229.148438 C 554.820312 229.144531 554.820312 229.136719 554.820312 229.132812 C 554.820312 229.128906 554.820312 229.121094 554.820312 229.117188 C 554.820312 229.113281 554.820312 229.109375 554.820312 229.101562 C 554.820312 229.097656 554.820312 229.09375 554.820312 229.085938 C 554.820312 229.082031 554.820312 229.078125 554.820312 229.070312 C 554.820312 229.066406 554.820312 229.0625 554.820312 229.054688 C 554.820312 229.050781 554.820312 229.046875 554.820312 229.039062 C 554.820312 229.035156 554.820312 229.03125 554.820312 229.023438 C 554.820312 229.019531 554.820312 229.015625 554.820312 229.007812 C 554.820312 229.003906 554.820312 229 554.820312 228.992188 C 554.820312 228.988281 554.820312 228.984375 554.820312 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554.820312 226.644531 554.820312 226.636719 554.820312 226.632812 C 554.820312 226.628906 554.820312 226.621094 554.820312 226.617188 C 554.820312 226.613281 554.820312 226.605469 554.820312 226.601562 C 554.820312 226.597656 554.820312 226.589844 554.820312 226.585938 C 554.820312 226.582031 554.820312 226.574219 554.820312 226.570312 C 554.820312 226.566406 554.820312 226.558594 554.820312 226.554688 C 554.820312 226.550781 554.820312 226.542969 554.820312 226.539062 C 554.820312 226.53125 554.820312 226.527344 554.820312 226.523438 C 554.820312 226.515625 554.820312 226.511719 554.820312 226.507812 C 554.820312 226.5 554.820312 226.496094 554.820312 226.492188 C 554.820312 226.484375 554.820312 226.480469 554.820312 226.476562 C 554.820312 226.46875 554.820312 226.464844 554.820312 226.460938 C 554.820312 226.453125 554.820312 226.449219 554.820312 226.445312 C 554.820312 226.4375 554.820312 226.433594 554.820312 226.429688 C 554.820312 226.421875 554.820312 226.417969 554.820312 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554.820312 226.175781 C 554.820312 226.171875 554.820312 226.167969 554.820312 226.160156 C 554.820312 226.15625 554.820312 226.148438 554.820312 226.144531 C 554.820312 226.140625 554.820312 226.132812 554.820312 226.128906 C 554.820312 226.125 554.820312 226.117188 554.820312 226.113281 C 554.820312 226.109375 554.820312 226.101562 554.820312 226.097656 C 554.820312 226.09375 554.820312 226.085938 554.820312 226.082031 C 554.820312 226.078125 554.820312 226.070312 554.820312 226.066406 C 554.820312 226.0625 554.820312 226.054688 554.820312 226.050781 C 554.820312 226.046875 554.820312 226.039062 554.820312 226.035156 C 554.820312 226.03125 554.820312 226.023438 554.820312 226.019531 C 554.820312 226.015625 554.820312 226.007812 554.820312 226.003906 C 554.820312 226 554.820312 225.992188 554.820312 225.988281 C 554.820312 225.984375 554.820312 225.976562 554.820312 225.972656 C 554.820312 225.96875 554.820312 225.960938 554.820312 225.957031 C 554.820312 225.953125 554.820312 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554.820312 225.25 554.820312 225.246094 554.820312 225.238281 C 554.820312 225.234375 554.820312 225.230469 554.820312 225.222656 C 554.820312 225.21875 554.820312 225.214844 554.820312 225.207031 C 554.820312 225.203125 554.820312 225.199219 554.820312 225.195312 C 554.820312 225.1875 554.820312 225.183594 554.820312 225.179688 C 554.820312 225.171875 554.820312 225.167969 554.820312 225.164062 C 554.820312 225.15625 554.820312 225.152344 554.820312 225.148438 C 554.820312 225.140625 554.820312 225.136719 554.820312 225.132812 C 554.820312 225.125 554.820312 225.121094 554.820312 225.117188 C 554.820312 225.109375 554.820312 225.105469 554.820312 225.101562 C 554.820312 225.09375 554.820312 225.089844 554.820312 225.085938 C 554.820312 225.078125 554.820312 225.074219 554.820312 225.070312 C 554.820312 225.066406 554.820312 225.058594 554.820312 225.054688 C 554.820312 225.050781 554.820312 225.042969 554.820312 225.039062 C 554.820312 225.035156 554.820312 225.027344 554.820312 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609.484375 551.917969 609.488281 C 551.917969 609.492188 551.917969 609.5 551.917969 609.503906 C 551.917969 609.507812 551.917969 609.515625 551.917969 609.519531 C 551.917969 609.523438 551.917969 609.53125 551.917969 609.535156 C 551.917969 609.539062 551.917969 609.546875 551.917969 609.550781 C 551.917969 609.554688 551.917969 609.5625 551.917969 609.566406 C 551.917969 609.570312 551.917969 609.578125 551.917969 609.582031 C 551.917969 609.585938 551.917969 609.59375 551.917969 609.597656 C 551.917969 609.601562 551.917969 609.609375 551.917969 609.613281 C 551.917969 609.617188 551.917969 609.625 551.917969 609.628906 C 551.917969 609.632812 551.917969 609.640625 551.917969 609.644531 C 551.917969 609.648438 551.917969 609.65625 551.917969 609.660156 C 551.917969 609.667969 551.917969 609.671875 551.917969 609.675781 C 551.917969 609.683594 551.917969 609.6875 551.917969 609.691406 C 551.917969 609.699219 551.917969 609.703125 551.917969 609.707031 C 551.917969 609.714844 551.917969 609.71875 551.917969 609.722656 C 551.917969 609.730469 551.917969 609.734375 551.917969 609.738281 C 551.917969 609.746094 551.917969 609.75 551.917969 609.753906 C 551.917969 609.761719 551.917969 609.765625 551.917969 609.769531 C 551.917969 609.777344 551.917969 609.78125 551.917969 609.785156 C 551.917969 609.792969 551.917969 609.796875 551.917969 609.800781 C 551.917969 609.808594 551.917969 609.8125 551.917969 609.820312 C 551.917969 609.824219 551.917969 609.828125 551.917969 609.835938 C 551.917969 609.839844 551.917969 609.84375 551.917969 609.851562 C 551.917969 609.855469 551.917969 609.859375 551.917969 609.867188 C 551.917969 609.871094 551.917969 609.875 551.917969 609.882812 C 551.917969 609.886719 551.917969 609.890625 551.917969 609.898438 C 551.917969 609.902344 551.917969 609.90625 551.917969 609.914062 C 551.917969 609.917969 551.917969 609.921875 551.917969 609.929688 C 551.917969 609.933594 551.917969 609.9375 551.917969 609.945312 C 551.917969 609.949219 551.917969 609.953125 551.917969 609.960938 C 551.917969 609.964844 551.917969 609.972656 551.917969 609.976562 C 551.917969 609.980469 551.917969 609.988281 551.917969 609.992188 C 551.917969 609.996094 551.917969 610.003906 551.917969 610.007812 C 551.917969 610.011719 551.917969 610.019531 551.917969 610.023438 C 551.917969 610.027344 551.917969 610.035156 551.917969 610.039062 C 551.917969 610.042969 551.917969 610.050781 551.917969 610.054688 C 551.917969 610.058594 551.917969 610.066406 551.917969 610.070312 C 551.917969 610.074219 551.917969 610.082031 551.917969 610.085938 C 551.917969 610.09375 551.917969 610.097656 551.917969 610.101562 C 551.917969 610.109375 551.917969 610.113281 551.917969 610.117188 C 551.917969 610.125 551.917969 610.128906 551.917969 610.132812 C 551.917969 610.140625 551.917969 610.144531 551.917969 610.148438 C 551.917969 610.15625 551.917969 610.160156 551.917969 610.164062 C 551.917969 610.171875 551.917969 610.175781 551.917969 610.179688 C 551.917969 610.1875 551.917969 610.191406 551.917969 610.195312 C 551.917969 610.203125 551.917969 610.207031 551.917969 610.214844 C 551.917969 610.21875 551.917969 610.222656 551.917969 610.230469 C 551.917969 610.234375 551.917969 610.238281 551.917969 610.246094 C 551.917969 610.25 551.917969 610.253906 551.917969 610.261719 C 551.917969 610.265625 551.917969 610.269531 551.917969 610.277344 C 551.917969 610.28125 551.917969 610.285156 551.917969 610.292969 C 551.917969 610.296875 551.917969 610.300781 551.917969 610.308594 C 551.917969 610.3125 551.917969 610.316406 551.917969 610.324219 C 551.917969 610.328125 551.917969 610.332031 551.917969 610.339844 C 551.917969 610.34375 551.917969 610.351562 551.917969 610.355469 C 551.917969 610.359375 551.917969 610.367188 551.917969 610.371094 C 551.917969 610.375 551.917969 610.382812 551.917969 610.386719 C 551.917969 610.390625 551.917969 610.398438 551.917969 610.402344 C 551.917969 610.40625 551.917969 610.414062 551.917969 610.417969 C 551.917969 610.421875 551.917969 610.429688 551.917969 610.433594 C 551.917969 610.4375 551.917969 610.445312 551.917969 610.449219 C 551.917969 610.453125 551.917969 610.460938 551.917969 610.464844 C 551.917969 610.472656 551.917969 610.476562 551.917969 610.480469 C 551.917969 610.488281 551.917969 610.492188 551.917969 610.496094 C 551.917969 610.503906 551.917969 610.507812 551.917969 610.511719 C 551.917969 610.519531 551.917969 610.523438 551.917969 610.527344 C 551.917969 610.535156 551.917969 610.539062 551.917969 610.542969 C 551.917969 610.550781 551.917969 610.554688 551.917969 610.558594 C 551.917969 610.566406 551.917969 610.570312 551.917969 610.574219 C 551.917969 610.582031 551.917969 610.585938 551.917969 610.59375 C 551.917969 610.597656 551.917969 610.601562 551.917969 610.609375 C 551.917969 610.613281 551.917969 610.617188 551.917969 610.625 C 551.917969 610.628906 551.917969 610.632812 551.917969 610.640625 C 551.917969 610.644531 551.917969 610.648438 551.917969 610.65625 C 551.917969 610.660156 551.917969 610.664062 551.917969 610.671875 C 551.917969 610.675781 551.917969 610.679688 551.917969 610.6875 C 551.917969 610.691406 551.917969 610.695312 551.917969 610.703125 C 551.917969 610.707031 551.917969 610.714844 551.917969 610.71875 C 551.917969 610.722656 551.917969 610.730469 551.917969 610.734375 C 551.917969 610.738281 551.917969 610.746094 551.917969 610.75 C 551.917969 610.753906 551.917969 610.761719 551.917969 610.765625 C 551.917969 610.769531 551.917969 610.777344 551.917969 610.78125 C 551.917969 610.785156 551.917969 610.792969 551.917969 610.796875 C 551.917969 610.800781 551.917969 610.808594 551.917969 610.8125 C 551.917969 610.816406 551.917969 610.824219 551.917969 610.828125 C 551.917969 610.835938 551.917969 610.839844 551.917969 610.84375 C 551.917969 610.851562 551.917969 610.855469 551.917969 610.859375 C 551.917969 610.867188 551.917969 610.871094 551.917969 610.875 C 551.917969 610.882812 551.917969 610.886719 551.917969 610.890625 C 551.917969 610.898438 551.917969 610.902344 551.917969 610.90625 C 551.917969 610.914062 551.917969 610.917969 551.917969 610.921875 C 551.917969 610.929688 551.917969 610.933594 551.917969 610.9375 C 551.917969 610.945312 551.917969 610.949219 551.917969 610.953125 C 551.917969 610.960938 551.917969 610.964844 551.917969 610.972656 C 551.917969 610.976562 551.917969 610.980469 551.917969 610.988281 C 551.917969 610.992188 551.917969 610.996094 551.917969 611.003906 C 551.917969 611.007812 551.917969 611.011719 551.917969 611.019531 C 551.917969 611.023438 551.917969 611.027344 551.917969 611.035156 C 551.917969 611.039062 551.917969 611.042969 551.917969 611.050781 C 551.917969 611.054688 551.917969 611.058594 551.917969 611.066406 C 551.917969 611.070312 551.917969 611.074219 551.917969 611.082031 C 551.917969 611.085938 551.917969 611.089844 551.917969 611.097656 C 551.917969 611.101562 551.917969 611.109375 551.917969 611.113281 C 551.917969 611.117188 551.917969 611.125 551.917969 611.128906 C 551.917969 611.132812 551.917969 611.140625 551.917969 611.144531 C 551.917969 611.148438 551.917969 611.15625 551.917969 611.160156 C 551.917969 611.164062 551.917969 611.171875 551.917969 611.175781 C 551.917969 611.179688 551.917969 611.1875 551.917969 611.191406 C 551.917969 611.195312 551.917969 611.203125 551.917969 611.207031 C 551.917969 611.210938 551.917969 611.21875 551.917969 611.222656 C 551.917969 611.226562 551.917969 611.234375 551.917969 611.238281 C 551.917969 611.242188 551.917969 611.25 551.917969 611.253906 C 551.917969 611.257812 551.917969 611.265625 551.917969 611.269531 C 551.917969 611.277344 551.917969 611.28125 551.917969 611.285156 C 551.917969 611.292969 551.917969 611.296875 551.917969 611.300781 C 551.917969 611.308594 551.917969 611.3125 551.917969 611.316406 C 551.917969 611.324219 551.917969 611.328125 551.917969 611.332031 C 551.917969 611.339844 551.917969 611.34375 551.917969 611.347656 C 551.917969 611.355469 551.917969 611.359375 551.917969 611.363281 C 551.917969 611.371094 551.917969 611.375 551.917969 611.378906 C 551.917969 611.386719 551.917969 611.390625 551.917969 611.394531 C 551.917969 611.402344 551.917969 611.40625 551.917969 611.410156 C 551.917969 611.417969 551.917969 611.421875 551.917969 611.425781 C 551.917969 611.433594 551.917969 611.4375 551.917969 611.441406 C 551.917969 611.449219 551.917969 611.453125 551.917969 611.460938 C 551.917969 611.464844 551.917969 611.46875 551.917969 611.476562 C 551.917969 611.480469 551.917969 611.484375 551.917969 611.492188 C 551.917969 611.496094 551.917969 611.5 551.917969 611.507812 C 551.917969 611.511719 551.917969 611.515625 551.917969 611.523438 C 551.917969 611.527344 551.917969 611.53125 551.917969 611.539062 C 551.917969 611.542969 551.917969 611.546875 551.917969 611.554688 C 551.917969 611.558594 551.917969 611.5625 551.917969 611.570312 C 551.917969 611.574219 551.917969 611.578125 551.917969 611.585938 C 551.917969 611.589844 551.917969 611.59375 551.917969 611.601562 C 551.917969 611.605469 551.917969 611.609375 551.917969 611.617188 C 551.917969 611.621094 551.917969 611.625 551.917969 611.632812 C 551.917969 611.636719 551.917969 611.640625 551.917969 611.648438 C 551.917969 611.652344 551.917969 611.65625 551.917969 611.664062 C 551.917969 611.667969 551.917969 611.671875 551.917969 611.679688 C 551.917969 611.683594 551.917969 611.6875 551.917969 611.695312 C 551.917969 611.699219 551.917969 611.703125 551.917969 611.710938 C 551.917969 611.714844 551.917969 611.71875 551.917969 611.726562 C 551.917969 611.730469 551.917969 611.738281 551.917969 611.742188 C 551.917969 611.746094 551.917969 611.753906 551.917969 611.757812 C 551.917969 611.761719 551.917969 611.769531 551.917969 611.773438 C 551.917969 611.777344 551.917969 611.785156 551.917969 611.789062 C 551.917969 611.792969 551.917969 611.800781 551.917969 611.804688 C 551.917969 611.808594 551.917969 611.816406 551.917969 611.820312 C 551.917969 611.824219 551.917969 611.832031 551.917969 611.835938 C 551.917969 611.839844 551.917969 611.847656 551.917969 611.851562 C 551.917969 611.855469 551.917969 611.863281 551.917969 611.867188 C 551.917969 611.871094 551.917969 611.878906 551.917969 611.882812 C 551.917969 611.886719 551.917969 611.894531 551.917969 611.898438 C 551.917969 611.902344 551.917969 611.910156 551.917969 611.914062 C 551.917969 611.917969 551.917969 611.925781 551.917969 611.929688 C 551.917969 611.933594 551.917969 611.941406 551.917969 611.945312 C 551.917969 611.949219 551.917969 611.957031 551.917969 611.960938 C 551.917969 611.964844 551.917969 611.972656 551.917969 611.976562 C 551.917969 611.980469 551.917969 611.988281 551.917969 611.992188 C 551.917969 611.996094 551.917969 612.003906 551.917969 612.007812 C 551.917969 612.011719 551.917969 612.019531 551.917969 612.023438 C 551.917969 612.027344 551.917969 612.035156 551.917969 612.039062 C 551.917969 612.042969 551.917969 612.050781 551.917969 612.054688 C 551.917969 612.058594 551.917969 612.066406 551.917969 612.070312 C 551.917969 612.074219 551.917969 612.082031 551.917969 612.085938 C 551.917969 612.089844 551.917969 612.097656 551.917969 612.101562 C 551.917969 612.105469 551.917969 612.113281 551.917969 612.117188 C 551.917969 612.121094 551.917969 612.128906 551.917969 612.132812 C 551.917969 612.136719 551.917969 612.144531 551.917969 612.148438 C 551.917969 612.152344 551.917969 612.160156 551.917969 612.164062 C 551.917969 612.167969 551.917969 612.171875 551.917969 612.179688 C 551.917969 612.183594 551.917969 612.1875 551.917969 612.195312 C 551.917969 612.199219 551.917969 612.203125 551.917969 612.210938 C 551.917969 612.214844 551.917969 612.21875 551.917969 612.226562 C 551.917969 612.230469 551.917969 612.234375 551.917969 612.242188 C 551.917969 612.246094 551.917969 612.25 551.917969 612.257812 C 551.917969 612.261719 551.917969 612.265625 551.917969 612.273438 C 551.917969 612.277344 551.917969 612.28125 551.917969 612.289062 C 551.917969 612.292969 551.917969 612.296875 551.917969 612.304688 C 551.917969 612.308594 551.917969 612.3125 551.917969 612.320312 C 551.917969 612.324219 551.917969 612.328125 551.917969 612.335938 C 551.917969 612.339844 551.917969 612.34375 551.917969 612.351562 C 551.917969 612.355469 551.917969 612.359375 551.917969 612.367188 C 551.917969 612.371094 551.917969 612.375 551.917969 612.382812 C 551.917969 612.386719 551.917969 612.390625 551.917969 612.398438 C 551.917969 612.402344 551.917969 612.40625 551.917969 612.410156 C 551.917969 612.417969 551.917969 612.421875 551.917969 612.425781 C 551.917969 612.433594 551.917969 612.4375 551.917969 612.441406 C 551.917969 612.449219 551.917969 612.453125 551.917969 612.457031 C 551.917969 612.464844 551.917969 612.46875 551.917969 612.472656 C 551.917969 612.480469 551.917969 612.484375 551.917969 612.488281 C 551.917969 612.496094 551.917969 612.5 551.917969 612.503906 C 551.917969 612.511719 551.917969 612.515625 551.917969 612.519531 C 551.917969 612.527344 551.917969 612.53125 551.917969 612.535156 C 551.917969 612.542969 551.917969 612.546875 551.917969 612.550781 C 551.917969 612.554688 551.917969 612.5625 551.917969 612.566406 C 551.917969 612.570312 551.917969 612.578125 551.917969 612.582031 C 551.917969 612.585938 551.917969 612.59375 551.917969 612.597656 C 551.917969 612.601562 551.917969 612.609375 551.917969 612.613281 C 551.917969 612.617188 551.917969 612.625 551.917969 612.628906 C 551.917969 612.632812 551.917969 612.640625 551.917969 612.644531 C 551.917969 612.648438 551.917969 612.65625 551.917969 612.660156 C 551.917969 612.664062 551.917969 612.667969 551.917969 612.675781 C 551.917969 612.679688 551.917969 612.683594 551.917969 612.691406 C 551.917969 612.695312 551.917969 612.699219 551.917969 612.707031 C 551.917969 612.710938 551.917969 612.714844 551.917969 612.722656 C 551.917969 612.726562 551.917969 612.730469 551.917969 612.738281 C 551.917969 612.742188 551.917969 612.746094 551.917969 612.753906 C 551.917969 612.757812 551.917969 612.761719 551.917969 612.765625 C 551.917969 612.773438 551.917969 612.777344 551.917969 612.78125 C 551.917969 612.789062 551.917969 612.792969 551.917969 612.796875 C 551.917969 612.804688 551.917969 612.808594 551.917969 612.8125 C 551.917969 612.820312 551.917969 612.824219 551.917969 612.828125 C 551.917969 612.835938 551.917969 612.839844 551.917969 612.84375 C 551.917969 612.847656 551.917969 612.855469 551.917969 612.859375 C 551.917969 612.863281 551.917969 612.871094 551.917969 612.875 C 551.917969 612.878906 551.917969 612.886719 551.917969 612.890625 C 551.917969 612.894531 551.917969 612.902344 551.917969 612.90625 C 551.917969 612.910156 551.917969 612.914062 551.917969 612.921875 C 551.917969 612.925781 551.917969 612.929688 551.917969 612.9375 C 551.917969 612.941406 551.917969 612.945312 551.917969 612.953125 C 551.917969 612.957031 551.917969 612.960938 551.917969 612.96875 C 551.917969 612.972656 551.917969 612.976562 551.917969 612.980469 C 551.917969 612.988281 551.917969 612.992188 551.917969 612.996094 C 551.917969 613.003906 551.917969 613.007812 551.917969 613.011719 C 551.917969 613.019531 551.917969 613.023438 551.917969 613.027344 C 551.917969 613.035156 551.917969 613.039062 551.917969 613.042969 C 551.917969 613.046875 551.917969 613.054688 551.917969 613.058594 C 551.917969 613.0625 551.917969 613.070312 551.917969 613.074219 C 551.917969 613.078125 551.917969 613.085938 551.917969 613.089844 C 551.917969 613.09375 551.917969 613.097656 551.917969 613.105469 C 551.917969 613.109375 551.917969 613.113281 551.917969 613.121094 C 551.917969 613.125 551.917969 613.128906 551.917969 613.136719 C 551.917969 613.140625 551.917969 613.144531 551.917969 613.148438 C 551.917969 613.15625 551.917969 613.160156 551.917969 613.164062 C 551.917969 613.171875 551.917969 613.175781 551.917969 613.179688 C 551.917969 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551.917969 613.414062 551.917969 613.417969 551.917969 613.421875 C 551.917969 613.429688 551.917969 613.433594 551.917969 613.4375 C 551.917969 613.445312 551.917969 613.449219 551.917969 613.453125 C 551.917969 613.457031 551.917969 613.464844 551.917969 613.46875 C 551.917969 613.472656 551.917969 613.480469 551.917969 613.484375 C 551.917969 613.488281 551.917969 613.492188 551.917969 613.5 C 551.917969 613.503906 551.917969 613.507812 551.917969 613.515625 C 551.917969 613.519531 551.917969 613.523438 551.917969 613.527344 C 551.917969 613.535156 551.917969 613.539062 551.917969 613.542969 C 551.917969 613.550781 551.917969 613.554688 551.917969 613.558594 C 551.917969 613.5625 551.917969 613.570312 551.917969 613.574219 C 551.917969 613.578125 551.917969 613.585938 551.917969 613.589844 C 551.917969 613.59375 551.917969 613.597656 551.917969 613.605469 C 551.917969 613.609375 551.917969 613.613281 551.917969 613.621094 C 551.917969 613.625 551.917969 613.628906 551.917969 613.632812 C 551.917969 613.640625 551.917969 613.644531 551.917969 613.648438 C 551.917969 613.65625 551.917969 613.660156 551.917969 613.664062 C 551.917969 613.667969 551.917969 613.675781 551.917969 613.679688 C 551.917969 613.683594 551.917969 613.691406 551.917969 613.695312 C 551.917969 613.699219 551.917969 613.703125 551.917969 613.710938 C 551.917969 613.714844 551.917969 613.71875 551.917969 613.722656 C 551.917969 613.730469 551.917969 613.734375 551.917969 613.738281 C 551.917969 613.746094 551.917969 613.75 551.917969 613.753906 C 551.917969 613.757812 551.917969 613.765625 551.917969 613.769531 C 551.917969 613.773438 551.917969 613.78125 551.917969 613.785156 C 551.917969 613.789062 551.917969 613.792969 551.917969 613.800781 C 551.917969 613.804688 551.917969 613.808594 551.917969 613.8125 C 551.917969 613.820312 551.917969 613.824219 551.917969 613.828125 C 551.917969 613.835938 551.917969 613.839844 551.917969 613.84375 C 551.917969 613.847656 551.917969 613.855469 551.917969 613.859375 C 551.917969 613.863281 551.917969 613.867188 551.917969 613.875 C 551.917969 613.878906 551.917969 613.882812 551.917969 613.890625 C 551.917969 613.894531 551.917969 613.898438 551.917969 613.902344 C 551.917969 613.910156 551.917969 613.914062 551.917969 613.917969 C 551.917969 613.921875 551.917969 613.929688 551.917969 613.933594 C 551.917969 613.9375 551.917969 613.941406 551.917969 613.949219 C 551.917969 613.953125 551.917969 613.957031 551.917969 613.964844 C 551.917969 613.96875 551.917969 613.972656 551.917969 613.976562 C 551.917969 613.984375 551.917969 613.988281 551.917969 613.992188 C 551.917969 613.996094 551.917969 614.003906 551.917969 614.007812 C 551.917969 614.011719 551.917969 614.015625 551.917969 614.023438 C 551.917969 614.027344 551.917969 614.03125 551.917969 614.035156 C 551.917969 614.042969 551.917969 614.046875 551.917969 614.050781 C 551.917969 614.058594 551.917969 614.0625 551.917969 614.066406 C 551.917969 614.070312 551.917969 614.078125 551.917969 614.082031 C 551.917969 614.085938 551.917969 614.089844 551.917969 614.097656 C 551.917969 614.101562 551.917969 614.105469 551.917969 614.109375 C 551.917969 614.117188 551.917969 614.121094 551.917969 614.125 C 551.917969 614.128906 551.917969 614.136719 551.917969 614.140625 C 551.917969 614.144531 551.917969 614.148438 551.917969 614.15625 C 551.917969 614.160156 551.917969 614.164062 551.917969 614.167969 C 551.917969 614.175781 551.917969 614.179688 551.917969 614.183594 C 551.917969 614.191406 551.917969 614.195312 551.917969 614.199219 C 551.917969 614.203125 551.917969 614.210938 551.917969 614.214844 C 551.917969 614.21875 551.917969 614.222656 551.917969 614.230469 C 551.917969 614.234375 551.917969 614.238281 551.917969 614.242188 C 551.917969 614.25 551.917969 614.253906 551.917969 614.257812 C 551.917969 614.261719 551.917969 614.269531 551.917969 614.273438 C 551.917969 614.277344 551.917969 614.28125 551.917969 614.289062 C 551.917969 614.292969 551.917969 614.296875 551.917969 614.300781 C 551.917969 614.308594 551.917969 614.3125 551.917969 614.316406 C 551.917969 614.320312 551.917969 614.328125 551.917969 614.332031 C 551.917969 614.335938 551.917969 614.339844 551.917969 614.347656 C 551.917969 614.351562 551.917969 614.355469 551.917969 614.359375 C 551.917969 614.367188 551.917969 614.371094 551.917969 614.375 C 551.917969 614.378906 551.917969 614.386719 551.917969 614.390625 L 554.820312 614.390625 C 554.820312 614.386719 554.820312 614.378906 554.820312 614.375 C 554.820312 614.371094 554.820312 614.367188 554.820312 614.359375 C 554.820312 614.355469 554.820312 614.351562 554.820312 614.347656 C 554.820312 614.339844 554.820312 614.335938 554.820312 614.332031 C 554.820312 614.328125 554.820312 614.320312 554.820312 614.316406 C 554.820312 614.3125 554.820312 614.308594 554.820312 614.300781 C 554.820312 614.296875 554.820312 614.292969 554.820312 614.289062 C 554.820312 614.28125 554.820312 614.277344 554.820312 614.273438 C 554.820312 614.269531 554.820312 614.261719 554.820312 614.257812 C 554.820312 614.253906 554.820312 614.25 554.820312 614.242188 C 554.820312 614.238281 554.820312 614.234375 554.820312 614.230469 C 554.820312 614.222656 554.820312 614.21875 554.820312 614.214844 C 554.820312 614.210938 554.820312 614.203125 554.820312 614.199219 C 554.820312 614.195312 554.820312 614.191406 554.820312 614.183594 C 554.820312 614.179688 554.820312 614.175781 554.820312 614.167969 C 554.820312 614.164062 554.820312 614.160156 554.820312 614.15625 C 554.820312 614.148438 554.820312 614.144531 554.820312 614.140625 C 554.820312 614.136719 554.820312 614.128906 554.820312 614.125 C 554.820312 614.121094 554.820312 614.117188 554.820312 614.109375 C 554.820312 614.105469 554.820312 614.101562 554.820312 614.097656 C 554.820312 614.089844 554.820312 614.085938 554.820312 614.082031 C 554.820312 614.078125 554.820312 614.070312 554.820312 614.066406 C 554.820312 614.0625 554.820312 614.058594 554.820312 614.050781 C 554.820312 614.046875 554.820312 614.042969 554.820312 614.035156 C 554.820312 614.03125 554.820312 614.027344 554.820312 614.023438 C 554.820312 614.015625 554.820312 614.011719 554.820312 614.007812 C 554.820312 614.003906 554.820312 613.996094 554.820312 613.992188 C 554.820312 613.988281 554.820312 613.984375 554.820312 613.976562 C 554.820312 613.972656 554.820312 613.96875 554.820312 613.964844 C 554.820312 613.957031 554.820312 613.953125 554.820312 613.949219 C 554.820312 613.941406 554.820312 613.9375 554.820312 613.933594 C 554.820312 613.929688 554.820312 613.921875 554.820312 613.917969 C 554.820312 613.914062 554.820312 613.910156 554.820312 613.902344 C 554.820312 613.898438 554.820312 613.894531 554.820312 613.890625 C 554.820312 613.882812 554.820312 613.878906 554.820312 613.875 C 554.820312 613.867188 554.820312 613.863281 554.820312 613.859375 C 554.820312 613.855469 554.820312 613.847656 554.820312 613.84375 C 554.820312 613.839844 554.820312 613.835938 554.820312 613.828125 C 554.820312 613.824219 554.820312 613.820312 554.820312 613.8125 C 554.820312 613.808594 554.820312 613.804688 554.820312 613.800781 C 554.820312 613.792969 554.820312 613.789062 554.820312 613.785156 C 554.820312 613.78125 554.820312 613.773438 554.820312 613.769531 C 554.820312 613.765625 554.820312 613.757812 554.820312 613.753906 C 554.820312 613.75 554.820312 613.746094 554.820312 613.738281 C 554.820312 613.734375 554.820312 613.730469 554.820312 613.722656 C 554.820312 613.71875 554.820312 613.714844 554.820312 613.710938 C 554.820312 613.703125 554.820312 613.699219 554.820312 613.695312 C 554.820312 613.691406 554.820312 613.683594 554.820312 613.679688 C 554.820312 613.675781 554.820312 613.667969 554.820312 613.664062 C 554.820312 613.660156 554.820312 613.65625 554.820312 613.648438 C 554.820312 613.644531 554.820312 613.640625 554.820312 613.632812 C 554.820312 613.628906 554.820312 613.625 554.820312 613.621094 C 554.820312 613.613281 554.820312 613.609375 554.820312 613.605469 C 554.820312 613.597656 554.820312 613.59375 554.820312 613.589844 C 554.820312 613.585938 554.820312 613.578125 554.820312 613.574219 C 554.820312 613.570312 554.820312 613.5625 554.820312 613.558594 C 554.820312 613.554688 554.820312 613.550781 554.820312 613.542969 C 554.820312 613.539062 554.820312 613.535156 554.820312 613.527344 C 554.820312 613.523438 554.820312 613.519531 554.820312 613.515625 C 554.820312 613.507812 554.820312 613.503906 554.820312 613.5 C 554.820312 613.492188 554.820312 613.488281 554.820312 613.484375 C 554.820312 613.480469 554.820312 613.472656 554.820312 613.46875 C 554.820312 613.464844 554.820312 613.457031 554.820312 613.453125 C 554.820312 613.449219 554.820312 613.445312 554.820312 613.4375 C 554.820312 613.433594 554.820312 613.429688 554.820312 613.421875 C 554.820312 613.417969 554.820312 613.414062 554.820312 613.410156 C 554.820312 613.402344 554.820312 613.398438 554.820312 613.394531 C 554.820312 613.386719 554.820312 613.382812 554.820312 613.378906 C 554.820312 613.375 554.820312 613.367188 554.820312 613.363281 C 554.820312 613.359375 554.820312 613.351562 554.820312 613.347656 C 554.820312 613.34375 554.820312 613.335938 554.820312 613.332031 C 554.820312 613.328125 554.820312 613.324219 554.820312 613.316406 C 554.820312 613.3125 554.820312 613.308594 554.820312 613.300781 C 554.820312 613.296875 554.820312 613.292969 554.820312 613.289062 C 554.820312 613.28125 554.820312 613.277344 554.820312 613.273438 C 554.820312 613.265625 554.820312 613.261719 554.820312 613.257812 C 554.820312 613.25 554.820312 613.246094 554.820312 613.242188 C 554.820312 613.238281 554.820312 613.230469 554.820312 613.226562 C 554.820312 613.222656 554.820312 613.214844 554.820312 613.210938 C 554.820312 613.207031 554.820312 613.199219 554.820312 613.195312 C 554.820312 613.191406 554.820312 613.1875 554.820312 613.179688 C 554.820312 613.175781 554.820312 613.171875 554.820312 613.164062 C 554.820312 613.160156 554.820312 613.15625 554.820312 613.148438 C 554.820312 613.144531 554.820312 613.140625 554.820312 613.136719 C 554.820312 613.128906 554.820312 613.125 554.820312 613.121094 C 554.820312 613.113281 554.820312 613.109375 554.820312 613.105469 C 554.820312 613.097656 554.820312 613.09375 554.820312 613.089844 C 554.820312 613.085938 554.820312 613.078125 554.820312 613.074219 C 554.820312 613.070312 554.820312 613.0625 554.820312 613.058594 C 554.820312 613.054688 554.820312 613.046875 554.820312 613.042969 C 554.820312 613.039062 554.820312 613.035156 554.820312 613.027344 C 554.820312 613.023438 554.820312 613.019531 554.820312 613.011719 C 554.820312 613.007812 554.820312 613.003906 554.820312 612.996094 C 554.820312 612.992188 554.820312 612.988281 554.820312 612.980469 C 554.820312 612.976562 554.820312 612.972656 554.820312 612.96875 C 554.820312 612.960938 554.820312 612.957031 554.820312 612.953125 C 554.820312 612.945312 554.820312 612.941406 554.820312 612.9375 C 554.820312 612.929688 554.820312 612.925781 554.820312 612.921875 C 554.820312 612.914062 554.820312 612.910156 554.820312 612.90625 C 554.820312 612.902344 554.820312 612.894531 554.820312 612.890625 C 554.820312 612.886719 554.820312 612.878906 554.820312 612.875 C 554.820312 612.871094 554.820312 612.863281 554.820312 612.859375 C 554.820312 612.855469 554.820312 612.847656 554.820312 612.84375 C 554.820312 612.839844 554.820312 612.835938 554.820312 612.828125 C 554.820312 612.824219 554.820312 612.820312 554.820312 612.8125 C 554.820312 612.808594 554.820312 612.804688 554.820312 612.796875 C 554.820312 612.792969 554.820312 612.789062 554.820312 612.78125 C 554.820312 612.777344 554.820312 612.773438 554.820312 612.765625 C 554.820312 612.761719 554.820312 612.757812 554.820312 612.753906 C 554.820312 612.746094 554.820312 612.742188 554.820312 612.738281 C 554.820312 612.730469 554.820312 612.726562 554.820312 612.722656 C 554.820312 612.714844 554.820312 612.710938 554.820312 612.707031 C 554.820312 612.699219 554.820312 612.695312 554.820312 612.691406 C 554.820312 612.683594 554.820312 612.679688 554.820312 612.675781 C 554.820312 612.667969 554.820312 612.664062 554.820312 612.660156 C 554.820312 612.65625 554.820312 612.648438 554.820312 612.644531 C 554.820312 612.640625 554.820312 612.632812 554.820312 612.628906 C 554.820312 612.625 554.820312 612.617188 554.820312 612.613281 C 554.820312 612.609375 554.820312 612.601562 554.820312 612.597656 C 554.820312 612.59375 554.820312 612.585938 554.820312 612.582031 C 554.820312 612.578125 554.820312 612.570312 554.820312 612.566406 C 554.820312 612.5625 554.820312 612.554688 554.820312 612.550781 C 554.820312 612.546875 554.820312 612.542969 554.820312 612.535156 C 554.820312 612.53125 554.820312 612.527344 554.820312 612.519531 C 554.820312 612.515625 554.820312 612.511719 554.820312 612.503906 C 554.820312 612.5 554.820312 612.496094 554.820312 612.488281 C 554.820312 612.484375 554.820312 612.480469 554.820312 612.472656 C 554.820312 612.46875 554.820312 612.464844 554.820312 612.457031 C 554.820312 612.453125 554.820312 612.449219 554.820312 612.441406 C 554.820312 612.4375 554.820312 612.433594 554.820312 612.425781 C 554.820312 612.421875 554.820312 612.417969 554.820312 612.410156 C 554.820312 612.40625 554.820312 612.402344 554.820312 612.398438 C 554.820312 612.390625 554.820312 612.386719 554.820312 612.382812 C 554.820312 612.375 554.820312 612.371094 554.820312 612.367188 C 554.820312 612.359375 554.820312 612.355469 554.820312 612.351562 C 554.820312 612.34375 554.820312 612.339844 554.820312 612.335938 C 554.820312 612.328125 554.820312 612.324219 554.820312 612.320312 C 554.820312 612.3125 554.820312 612.308594 554.820312 612.304688 C 554.820312 612.296875 554.820312 612.292969 554.820312 612.289062 C 554.820312 612.28125 554.820312 612.277344 554.820312 612.273438 C 554.820312 612.265625 554.820312 612.261719 554.820312 612.257812 C 554.820312 612.25 554.820312 612.246094 554.820312 612.242188 C 554.820312 612.234375 554.820312 612.230469 554.820312 612.226562 C 554.820312 612.21875 554.820312 612.214844 554.820312 612.210938 C 554.820312 612.203125 554.820312 612.199219 554.820312 612.195312 C 554.820312 612.1875 554.820312 612.183594 554.820312 612.179688 C 554.820312 612.171875 554.820312 612.167969 554.820312 612.164062 C 554.820312 612.160156 554.820312 612.152344 554.820312 612.148438 C 554.820312 612.144531 554.820312 612.136719 554.820312 612.132812 C 554.820312 612.128906 554.820312 612.121094 554.820312 612.117188 C 554.820312 612.113281 554.820312 612.105469 554.820312 612.101562 C 554.820312 612.097656 554.820312 612.089844 554.820312 612.085938 C 554.820312 612.082031 554.820312 612.074219 554.820312 612.070312 C 554.820312 612.066406 554.820312 612.058594 554.820312 612.054688 C 554.820312 612.050781 554.820312 612.042969 554.820312 612.039062 C 554.820312 612.035156 554.820312 612.027344 554.820312 612.023438 C 554.820312 612.019531 554.820312 612.011719 554.820312 612.007812 C 554.820312 612.003906 554.820312 611.996094 554.820312 611.992188 C 554.820312 611.988281 554.820312 611.980469 554.820312 611.976562 C 554.820312 611.972656 554.820312 611.964844 554.820312 611.960938 C 554.820312 611.957031 554.820312 611.949219 554.820312 611.945312 C 554.820312 611.941406 554.820312 611.933594 554.820312 611.929688 C 554.820312 611.925781 554.820312 611.917969 554.820312 611.914062 C 554.820312 611.910156 554.820312 611.902344 554.820312 611.898438 C 554.820312 611.894531 554.820312 611.886719 554.820312 611.882812 C 554.820312 611.878906 554.820312 611.871094 554.820312 611.867188 C 554.820312 611.863281 554.820312 611.855469 554.820312 611.851562 C 554.820312 611.847656 554.820312 611.839844 554.820312 611.835938 C 554.820312 611.832031 554.820312 611.824219 554.820312 611.820312 C 554.820312 611.816406 554.820312 611.808594 554.820312 611.804688 C 554.820312 611.800781 554.820312 611.792969 554.820312 611.789062 C 554.820312 611.785156 554.820312 611.777344 554.820312 611.773438 C 554.820312 611.769531 554.820312 611.761719 554.820312 611.757812 C 554.820312 611.753906 554.820312 611.746094 554.820312 611.742188 C 554.820312 611.738281 554.820312 611.730469 554.820312 611.726562 C 554.820312 611.71875 554.820312 611.714844 554.820312 611.710938 C 554.820312 611.703125 554.820312 611.699219 554.820312 611.695312 C 554.820312 611.6875 554.820312 611.683594 554.820312 611.679688 C 554.820312 611.671875 554.820312 611.667969 554.820312 611.664062 C 554.820312 611.65625 554.820312 611.652344 554.820312 611.648438 C 554.820312 611.640625 554.820312 611.636719 554.820312 611.632812 C 554.820312 611.625 554.820312 611.621094 554.820312 611.617188 C 554.820312 611.609375 554.820312 611.605469 554.820312 611.601562 C 554.820312 611.59375 554.820312 611.589844 554.820312 611.585938 C 554.820312 611.578125 554.820312 611.574219 554.820312 611.570312 C 554.820312 611.5625 554.820312 611.558594 554.820312 611.554688 C 554.820312 611.546875 554.820312 611.542969 554.820312 611.539062 C 554.820312 611.53125 554.820312 611.527344 554.820312 611.523438 C 554.820312 611.515625 554.820312 611.511719 554.820312 611.507812 C 554.820312 611.5 554.820312 611.496094 554.820312 611.492188 C 554.820312 611.484375 554.820312 611.480469 554.820312 611.476562 C 554.820312 611.46875 554.820312 611.464844 554.820312 611.460938 C 554.820312 611.453125 554.820312 611.449219 554.820312 611.441406 C 554.820312 611.4375 554.820312 611.433594 554.820312 611.425781 C 554.820312 611.421875 554.820312 611.417969 554.820312 611.410156 C 554.820312 611.40625 554.820312 611.402344 554.820312 611.394531 C 554.820312 611.390625 554.820312 611.386719 554.820312 611.378906 C 554.820312 611.375 554.820312 611.371094 554.820312 611.363281 C 554.820312 611.359375 554.820312 611.355469 554.820312 611.347656 C 554.820312 611.34375 554.820312 611.339844 554.820312 611.332031 C 554.820312 611.328125 554.820312 611.324219 554.820312 611.316406 C 554.820312 611.3125 554.820312 611.308594 554.820312 611.300781 C 554.820312 611.296875 554.820312 611.292969 554.820312 611.285156 C 554.820312 611.28125 554.820312 611.277344 554.820312 611.269531 C 554.820312 611.265625 554.820312 611.257812 554.820312 611.253906 C 554.820312 611.25 554.820312 611.242188 554.820312 611.238281 C 554.820312 611.234375 554.820312 611.226562 554.820312 611.222656 C 554.820312 611.21875 554.820312 611.210938 554.820312 611.207031 C 554.820312 611.203125 554.820312 611.195312 554.820312 611.191406 C 554.820312 611.1875 554.820312 611.179688 554.820312 611.175781 C 554.820312 611.171875 554.820312 611.164062 554.820312 611.160156 C 554.820312 611.15625 554.820312 611.148438 554.820312 611.144531 C 554.820312 611.140625 554.820312 611.132812 554.820312 611.128906 C 554.820312 611.125 554.820312 611.117188 554.820312 611.113281 C 554.820312 611.109375 554.820312 611.101562 554.820312 611.097656 C 554.820312 611.089844 554.820312 611.085938 554.820312 611.082031 C 554.820312 611.074219 554.820312 611.070312 554.820312 611.066406 C 554.820312 611.058594 554.820312 611.054688 554.820312 611.050781 C 554.820312 611.042969 554.820312 611.039062 554.820312 611.035156 C 554.820312 611.027344 554.820312 611.023438 554.820312 611.019531 C 554.820312 611.011719 554.820312 611.007812 554.820312 611.003906 C 554.820312 610.996094 554.820312 610.992188 554.820312 610.988281 C 554.820312 610.980469 554.820312 610.976562 554.820312 610.972656 C 554.820312 610.964844 554.820312 610.960938 554.820312 610.953125 C 554.820312 610.949219 554.820312 610.945312 554.820312 610.9375 C 554.820312 610.933594 554.820312 610.929688 554.820312 610.921875 C 554.820312 610.917969 554.820312 610.914062 554.820312 610.90625 C 554.820312 610.902344 554.820312 610.898438 554.820312 610.890625 C 554.820312 610.886719 554.820312 610.882812 554.820312 610.875 C 554.820312 610.871094 554.820312 610.867188 554.820312 610.859375 C 554.820312 610.855469 554.820312 610.851562 554.820312 610.84375 C 554.820312 610.839844 554.820312 610.835938 554.820312 610.828125 C 554.820312 610.824219 554.820312 610.816406 554.820312 610.8125 C 554.820312 610.808594 554.820312 610.800781 554.820312 610.796875 C 554.820312 610.792969 554.820312 610.785156 554.820312 610.78125 C 554.820312 610.777344 554.820312 610.769531 554.820312 610.765625 C 554.820312 610.761719 554.820312 610.753906 554.820312 610.75 C 554.820312 610.746094 554.820312 610.738281 554.820312 610.734375 C 554.820312 610.730469 554.820312 610.722656 554.820312 610.71875 C 554.820312 610.714844 554.820312 610.707031 554.820312 610.703125 C 554.820312 610.695312 554.820312 610.691406 554.820312 610.6875 C 554.820312 610.679688 554.820312 610.675781 554.820312 610.671875 C 554.820312 610.664062 554.820312 610.660156 554.820312 610.65625 C 554.820312 610.648438 554.820312 610.644531 554.820312 610.640625 C 554.820312 610.632812 554.820312 610.628906 554.820312 610.625 C 554.820312 610.617188 554.820312 610.613281 554.820312 610.609375 C 554.820312 610.601562 554.820312 610.597656 554.820312 610.59375 C 554.820312 610.585938 554.820312 610.582031 554.820312 610.574219 C 554.820312 610.570312 554.820312 610.566406 554.820312 610.558594 C 554.820312 610.554688 554.820312 610.550781 554.820312 610.542969 C 554.820312 610.539062 554.820312 610.535156 554.820312 610.527344 C 554.820312 610.523438 554.820312 610.519531 554.820312 610.511719 C 554.820312 610.507812 554.820312 610.503906 554.820312 610.496094 C 554.820312 610.492188 554.820312 610.488281 554.820312 610.480469 C 554.820312 610.476562 554.820312 610.472656 554.820312 610.464844 C 554.820312 610.460938 554.820312 610.453125 554.820312 610.449219 C 554.820312 610.445312 554.820312 610.4375 554.820312 610.433594 C 554.820312 610.429688 554.820312 610.421875 554.820312 610.417969 C 554.820312 610.414062 554.820312 610.40625 554.820312 610.402344 C 554.820312 610.398438 554.820312 610.390625 554.820312 610.386719 C 554.820312 610.382812 554.820312 610.375 554.820312 610.371094 C 554.820312 610.367188 554.820312 610.359375 554.820312 610.355469 C 554.820312 610.351562 554.820312 610.34375 554.820312 610.339844 C 554.820312 610.332031 554.820312 610.328125 554.820312 610.324219 C 554.820312 610.316406 554.820312 610.3125 554.820312 610.308594 C 554.820312 610.300781 554.820312 610.296875 554.820312 610.292969 C 554.820312 610.285156 554.820312 610.28125 554.820312 610.277344 C 554.820312 610.269531 554.820312 610.265625 554.820312 610.261719 C 554.820312 610.253906 554.820312 610.25 554.820312 610.246094 C 554.820312 610.238281 554.820312 610.234375 554.820312 610.230469 C 554.820312 610.222656 554.820312 610.21875 554.820312 610.214844 C 554.820312 610.207031 554.820312 610.203125 554.820312 610.195312 C 554.820312 610.191406 554.820312 610.1875 554.820312 610.179688 C 554.820312 610.175781 554.820312 610.171875 554.820312 610.164062 C 554.820312 610.160156 554.820312 610.15625 554.820312 610.148438 C 554.820312 610.144531 554.820312 610.140625 554.820312 610.132812 C 554.820312 610.128906 554.820312 610.125 554.820312 610.117188 C 554.820312 610.113281 554.820312 610.109375 554.820312 610.101562 C 554.820312 610.097656 554.820312 610.09375 554.820312 610.085938 C 554.820312 610.082031 554.820312 610.074219 554.820312 610.070312 C 554.820312 610.066406 554.820312 610.058594 554.820312 610.054688 C 554.820312 610.050781 554.820312 610.042969 554.820312 610.039062 C 554.820312 610.035156 554.820312 610.027344 554.820312 610.023438 C 554.820312 610.019531 554.820312 610.011719 554.820312 610.007812 C 554.820312 610.003906 554.820312 609.996094 554.820312 609.992188 C 554.820312 609.988281 554.820312 609.980469 554.820312 609.976562 C 554.820312 609.972656 554.820312 609.964844 554.820312 609.960938 C 554.820312 609.953125 554.820312 609.949219 554.820312 609.945312 C 554.820312 609.9375 554.820312 609.933594 554.820312 609.929688 C 554.820312 609.921875 554.820312 609.917969 554.820312 609.914062 C 554.820312 609.90625 554.820312 609.902344 554.820312 609.898438 C 554.820312 609.890625 554.820312 609.886719 554.820312 609.882812 C 554.820312 609.875 554.820312 609.871094 554.820312 609.867188 C 554.820312 609.859375 554.820312 609.855469 554.820312 609.851562 C 554.820312 609.84375 554.820312 609.839844 554.820312 609.835938 C 554.820312 609.828125 554.820312 609.824219 554.820312 609.820312 C 554.820312 609.8125 554.820312 609.808594 554.820312 609.800781 C 554.820312 609.796875 554.820312 609.792969 554.820312 609.785156 C 554.820312 609.78125 554.820312 609.777344 554.820312 609.769531 C 554.820312 609.765625 554.820312 609.761719 554.820312 609.753906 C 554.820312 609.75 554.820312 609.746094 554.820312 609.738281 C 554.820312 609.734375 554.820312 609.730469 554.820312 609.722656 C 554.820312 609.71875 554.820312 609.714844 554.820312 609.707031 C 554.820312 609.703125 554.820312 609.699219 554.820312 609.691406 C 554.820312 609.6875 554.820312 609.683594 554.820312 609.675781 C 554.820312 609.671875 554.820312 609.667969 554.820312 609.660156 C 554.820312 609.65625 554.820312 609.648438 554.820312 609.644531 C 554.820312 609.640625 554.820312 609.632812 554.820312 609.628906 C 554.820312 609.625 554.820312 609.617188 554.820312 609.613281 C 554.820312 609.609375 554.820312 609.601562 554.820312 609.597656 C 554.820312 609.59375 554.820312 609.585938 554.820312 609.582031 C 554.820312 609.578125 554.820312 609.570312 554.820312 609.566406 C 554.820312 609.5625 554.820312 609.554688 554.820312 609.550781 C 554.820312 609.546875 554.820312 609.539062 554.820312 609.535156 C 554.820312 609.53125 554.820312 609.523438 554.820312 609.519531 C 554.820312 609.515625 554.820312 609.507812 554.820312 609.503906 C 554.820312 609.5 554.820312 609.492188 554.820312 609.488281 C 554.820312 609.484375 554.820312 609.476562 554.820312 609.472656 C 554.820312 609.464844 554.820312 609.460938 554.820312 609.457031 C 554.820312 609.449219 554.820312 609.445312 554.820312 609.441406 C 554.820312 609.433594 554.820312 609.429688 554.820312 609.425781 C 554.820312 609.417969 554.820312 609.414062 554.820312 609.410156 C 554.820312 609.402344 554.820312 609.398438 554.820312 609.394531 C 554.820312 609.386719 554.820312 609.382812 554.820312 609.378906 C 554.820312 609.371094 554.820312 609.367188 554.820312 609.363281 C 554.820312 609.355469 554.820312 609.351562 554.820312 609.347656 C 554.820312 609.339844 554.820312 609.335938 554.820312 609.332031 C 554.820312 609.324219 554.820312 609.320312 554.820312 609.316406 C 554.820312 609.308594 554.820312 609.304688 554.820312 609.300781 C 554.820312 609.292969 554.820312 609.289062 554.820312 609.285156 C 554.820312 609.277344 554.820312 609.273438 554.820312 609.269531 C 554.820312 609.261719 554.820312 609.257812 554.820312 609.253906 C 554.820312 609.246094 554.820312 609.242188 554.820312 609.238281 C 554.820312 609.230469 554.820312 609.226562 554.820312 609.21875 C 554.820312 609.214844 554.820312 609.210938 554.820312 609.203125 C 554.820312 609.199219 554.820312 609.195312 554.820312 609.1875 C 554.820312 609.183594 554.820312 609.179688 554.820312 609.171875 C 554.820312 609.167969 554.820312 609.164062 554.820312 609.15625 C 554.820312 609.152344 554.820312 609.148438 554.820312 609.140625 C 554.820312 609.136719 554.820312 609.132812 554.820312 609.125 C 554.820312 609.121094 554.820312 609.117188 554.820312 609.109375 C 554.820312 609.105469 554.820312 609.101562 554.820312 609.09375 C 554.820312 609.089844 554.820312 609.085938 554.820312 609.078125 C 554.820312 609.074219 554.820312 609.070312 554.820312 609.0625 C 554.820312 609.058594 554.820312 609.054688 554.820312 609.046875 C 554.820312 609.042969 554.820312 609.039062 554.820312 609.03125 C 554.820312 609.027344 554.820312 609.023438 554.820312 609.015625 C 554.820312 609.011719 554.820312 609.007812 554.820312 609 C 554.820312 608.996094 554.820312 608.992188 554.820312 608.984375 C 554.820312 608.980469 554.820312 608.976562 554.820312 608.96875 C 554.820312 608.964844 554.820312 608.960938 554.820312 608.953125 C 554.820312 608.949219 554.820312 608.945312 554.820312 608.9375 C 554.820312 608.933594 554.820312 608.929688 554.820312 608.921875 C 554.820312 608.917969 554.820312 608.914062 554.820312 608.90625 C 554.820312 608.902344 554.820312 608.898438 554.820312 608.890625 C 554.820312 608.886719 554.820312 608.882812 554.820312 608.875 C 554.820312 608.871094 554.820312 608.867188 554.820312 608.859375 C 554.820312 608.855469 554.820312 608.851562 554.820312 608.84375 C 554.820312 608.839844 554.820312 608.835938 554.820312 608.828125 C 554.820312 608.824219 554.820312 608.820312 554.820312 608.8125 C 554.820312 608.808594 554.820312 608.804688 554.820312 608.796875 C 554.820312 608.792969 554.820312 608.789062 554.820312 608.78125 C 554.820312 608.777344 554.820312 608.773438 554.820312 608.765625 C 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684.683594 480.972656 684.683594 C 480.96875 684.683594 480.960938 684.683594 480.957031 684.683594 C 480.953125 684.683594 480.949219 684.683594 480.941406 684.683594 C 480.9375 684.683594 480.933594 684.683594 480.925781 684.683594 C 480.921875 684.683594 480.917969 684.683594 480.914062 684.683594 C 480.90625 684.683594 480.902344 684.683594 480.898438 684.683594 C 480.890625 684.683594 480.886719 684.683594 480.882812 684.683594 C 480.878906 684.683594 480.871094 684.683594 480.867188 684.683594 C 480.863281 684.683594 480.859375 684.683594 480.851562 684.683594 C 480.847656 684.683594 480.84375 684.683594 480.835938 684.683594 C 480.832031 684.683594 480.828125 684.683594 480.824219 684.683594 C 480.816406 684.683594 480.8125 684.683594 480.808594 684.683594 C 480.800781 684.683594 480.796875 684.683594 480.792969 684.683594 C 480.789062 684.683594 480.78125 684.683594 480.777344 684.683594 C 480.773438 684.683594 480.765625 684.683594 480.761719 684.683594 C 480.757812 684.683594 480.753906 684.683594 480.746094 684.683594 C 480.742188 684.683594 480.738281 684.683594 480.730469 684.683594 C 480.726562 684.683594 480.722656 684.683594 480.71875 684.683594 C 480.710938 684.683594 480.707031 684.683594 480.703125 684.683594 C 480.695312 684.683594 480.691406 684.683594 480.6875 684.683594 C 480.683594 684.683594 480.675781 684.683594 480.671875 684.683594 C 480.667969 684.683594 480.660156 684.683594 480.65625 684.683594 C 480.652344 684.683594 480.648438 684.683594 480.640625 684.683594 C 480.636719 684.683594 480.632812 684.683594 480.625 684.683594 C 480.621094 684.683594 480.617188 684.683594 480.609375 684.683594 C 480.605469 684.683594 480.601562 684.683594 480.597656 684.683594 C 480.589844 684.683594 480.585938 684.683594 480.582031 684.683594 C 480.574219 684.683594 480.570312 684.683594 480.566406 684.683594 C 480.5625 684.683594 480.554688 684.683594 480.550781 684.683594 C 480.546875 684.683594 480.539062 684.683594 480.535156 684.683594 C 480.53125 684.683594 480.523438 684.683594 480.519531 684.683594 C 480.515625 684.683594 480.511719 684.683594 480.503906 684.683594 C 480.5 684.683594 480.496094 684.683594 480.488281 684.683594 C 480.484375 684.683594 480.480469 684.683594 480.476562 684.683594 C 480.46875 684.683594 480.464844 684.683594 480.460938 684.683594 C 480.453125 684.683594 480.449219 684.683594 480.445312 684.683594 C 480.4375 684.683594 480.433594 684.683594 480.429688 684.683594 C 480.425781 684.683594 480.417969 684.683594 480.414062 684.683594 C 480.410156 684.683594 480.402344 684.683594 480.398438 684.683594 C 480.394531 684.683594 480.386719 684.683594 480.382812 684.683594 C 480.378906 684.683594 480.375 684.683594 480.367188 684.683594 C 480.363281 684.683594 480.359375 684.683594 480.351562 684.683594 C 480.347656 684.683594 480.34375 684.683594 480.335938 684.683594 C 480.332031 684.683594 480.328125 684.683594 480.324219 684.683594 C 480.316406 684.683594 480.3125 684.683594 480.308594 684.683594 C 480.300781 684.683594 480.296875 684.683594 480.292969 684.683594 C 480.285156 684.683594 480.28125 684.683594 480.277344 684.683594 C 480.269531 684.683594 480.265625 684.683594 480.261719 684.683594 C 480.257812 684.683594 480.25 684.683594 480.246094 684.683594 C 480.242188 684.683594 480.234375 684.683594 480.230469 684.683594 C 480.226562 684.683594 480.21875 684.683594 480.214844 684.683594 C 480.210938 684.683594 480.207031 684.683594 480.199219 684.683594 C 480.195312 684.683594 480.191406 684.683594 480.183594 684.683594 C 480.179688 684.683594 480.175781 684.683594 480.167969 684.683594 C 480.164062 684.683594 480.160156 684.683594 480.152344 684.683594 C 480.148438 684.683594 480.144531 684.683594 480.140625 684.683594 C 480.132812 684.683594 480.128906 684.683594 480.125 684.683594 C 480.117188 684.683594 480.113281 684.683594 480.109375 684.683594 C 480.101562 684.683594 480.097656 684.683594 480.09375 684.683594 C 480.085938 684.683594 480.082031 684.683594 480.078125 684.683594 C 480.070312 684.683594 480.066406 684.683594 480.0625 684.683594 C 480.058594 684.683594 480.050781 684.683594 480.046875 684.683594 C 480.042969 684.683594 480.035156 684.683594 480.03125 684.683594 C 480.027344 684.683594 480.019531 684.683594 480.015625 684.683594 C 480.011719 684.683594 480.003906 684.683594 480 684.683594 C 479.996094 684.683594 479.988281 684.683594 479.984375 684.683594 C 479.980469 684.683594 479.976562 684.683594 479.96875 684.683594 C 479.964844 684.683594 479.960938 684.683594 479.953125 684.683594 C 479.949219 684.683594 479.945312 684.683594 479.9375 684.683594 C 479.933594 684.683594 479.929688 684.683594 479.921875 684.683594 C 479.917969 684.683594 479.914062 684.683594 479.90625 684.683594 C 479.902344 684.683594 479.898438 684.683594 479.894531 684.683594 C 479.886719 684.683594 479.882812 684.683594 479.878906 684.683594 C 479.871094 684.683594 479.867188 684.683594 479.863281 684.683594 C 479.855469 684.683594 479.851562 684.683594 479.847656 684.683594 C 479.839844 684.683594 479.835938 684.683594 479.832031 684.683594 C 479.824219 684.683594 479.820312 684.683594 479.816406 684.683594 C 479.808594 684.683594 479.804688 684.683594 479.800781 684.683594 C 479.792969 684.683594 479.789062 684.683594 479.785156 684.683594 C 479.78125 684.683594 479.773438 684.683594 479.769531 684.683594 C 479.765625 684.683594 479.757812 684.683594 479.753906 684.683594 C 479.75 684.683594 479.742188 684.683594 479.738281 684.683594 C 479.734375 684.683594 479.726562 684.683594 479.722656 684.683594 C 479.71875 684.683594 479.710938 684.683594 479.707031 684.683594 C 479.703125 684.683594 479.695312 684.683594 479.691406 684.683594 C 479.6875 684.683594 479.679688 684.683594 479.675781 684.683594 C 479.671875 684.683594 479.664062 684.683594 479.660156 684.683594 C 479.65625 684.683594 479.648438 684.683594 479.644531 684.683594 C 479.640625 684.683594 479.636719 684.683594 479.628906 684.683594 C 479.625 684.683594 479.621094 684.683594 479.613281 684.683594 C 479.609375 684.683594 479.605469 684.683594 479.597656 684.683594 C 479.59375 684.683594 479.589844 684.683594 479.582031 684.683594 C 479.578125 684.683594 479.574219 684.683594 479.566406 684.683594 C 479.5625 684.683594 479.558594 684.683594 479.550781 684.683594 C 479.546875 684.683594 479.542969 684.683594 479.535156 684.683594 C 479.53125 684.683594 479.527344 684.683594 479.519531 684.683594 C 479.515625 684.683594 479.511719 684.683594 479.503906 684.683594 C 479.5 684.683594 479.496094 684.683594 479.488281 684.683594 C 479.484375 684.683594 479.480469 684.683594 479.472656 684.683594 C 479.46875 684.683594 479.464844 684.683594 479.457031 684.683594 C 479.453125 684.683594 479.449219 684.683594 479.441406 684.683594 C 479.4375 684.683594 479.433594 684.683594 479.425781 684.683594 C 479.421875 684.683594 479.417969 684.683594 479.410156 684.683594 C 479.40625 684.683594 479.402344 684.683594 479.394531 684.683594 C 479.390625 684.683594 479.386719 684.683594 479.382812 684.683594 C 479.375 684.683594 479.371094 684.683594 479.367188 684.683594 C 479.359375 684.683594 479.355469 684.683594 479.351562 684.683594 C 479.34375 684.683594 479.339844 684.683594 479.335938 684.683594 C 479.328125 684.683594 479.324219 684.683594 479.320312 684.683594 C 479.3125 684.683594 479.308594 684.683594 479.304688 684.683594 C 479.296875 684.683594 479.292969 684.683594 479.289062 684.683594 C 479.28125 684.683594 479.277344 684.683594 479.273438 684.683594 C 479.265625 684.683594 479.261719 684.683594 479.257812 684.683594 C 479.25 684.683594 479.246094 684.683594 479.242188 684.683594 C 479.234375 684.683594 479.230469 684.683594 479.226562 684.683594 C 479.21875 684.683594 479.214844 684.683594 479.210938 684.683594 C 479.203125 684.683594 479.199219 684.683594 479.195312 684.683594 C 479.1875 684.683594 479.183594 684.683594 479.179688 684.683594 C 479.171875 684.683594 479.167969 684.683594 479.164062 684.683594 C 479.15625 684.683594 479.152344 684.683594 479.148438 684.683594 C 479.140625 684.683594 479.136719 684.683594 479.132812 684.683594 C 479.125 684.683594 479.121094 684.683594 479.117188 684.683594 C 479.109375 684.683594 479.105469 684.683594 479.101562 684.683594 C 479.09375 684.683594 479.089844 684.683594 479.085938 684.683594 C 479.078125 684.683594 479.074219 684.683594 479.070312 684.683594 C 479.0625 684.683594 479.058594 684.683594 479.054688 684.683594 C 479.046875 684.683594 479.042969 684.683594 479.039062 684.683594 C 479.03125 684.683594 479.027344 684.683594 479.023438 684.683594 C 479.015625 684.683594 479.011719 684.683594 479.007812 684.683594 C 479 684.683594 478.996094 684.683594 478.992188 684.683594 C 478.984375 684.683594 478.980469 684.683594 478.972656 684.683594 C 478.96875 684.683594 478.964844 684.683594 478.957031 684.683594 C 478.953125 684.683594 478.949219 684.683594 478.941406 684.683594 C 478.9375 684.683594 478.933594 684.683594 478.925781 684.683594 C 478.921875 684.683594 478.917969 684.683594 478.910156 684.683594 C 478.90625 684.683594 478.902344 684.683594 478.894531 684.683594 C 478.890625 684.683594 478.886719 684.683594 478.878906 684.683594 C 478.875 684.683594 478.871094 684.683594 478.863281 684.683594 C 478.859375 684.683594 478.855469 684.683594 478.847656 684.683594 C 478.84375 684.683594 478.839844 684.683594 478.832031 684.683594 C 478.828125 684.683594 478.824219 684.683594 478.816406 684.683594 C 478.8125 684.683594 478.808594 684.683594 478.800781 684.683594 C 478.796875 684.683594 478.792969 684.683594 478.785156 684.683594 C 478.78125 684.683594 478.777344 684.683594 478.769531 684.683594 C 478.765625 684.683594 478.761719 684.683594 478.753906 684.683594 C 478.75 684.683594 478.746094 684.683594 478.738281 684.683594 C 478.734375 684.683594 478.730469 684.683594 478.722656 684.683594 C 478.71875 684.683594 478.714844 684.683594 478.707031 684.683594 C 478.703125 684.683594 478.699219 684.683594 478.691406 684.683594 C 478.6875 684.683594 478.679688 684.683594 478.675781 684.683594 C 478.671875 684.683594 478.664062 684.683594 478.660156 684.683594 C 478.65625 684.683594 478.648438 684.683594 478.644531 684.683594 C 478.640625 684.683594 478.632812 684.683594 478.628906 684.683594 C 478.625 684.683594 478.617188 684.683594 478.613281 684.683594 C 478.609375 684.683594 478.601562 684.683594 478.597656 684.683594 C 478.59375 684.683594 478.585938 684.683594 478.582031 684.683594 C 478.578125 684.683594 478.570312 684.683594 478.566406 684.683594 C 478.5625 684.683594 478.554688 684.683594 478.550781 684.683594 C 478.546875 684.683594 478.539062 684.683594 478.535156 684.683594 C 478.53125 684.683594 478.523438 684.683594 478.519531 684.683594 C 478.515625 684.683594 478.507812 684.683594 478.503906 684.683594 C 478.496094 684.683594 478.492188 684.683594 478.488281 684.683594 C 478.480469 684.683594 478.476562 684.683594 478.472656 684.683594 C 478.464844 684.683594 478.460938 684.683594 478.457031 684.683594 C 478.449219 684.683594 478.445312 684.683594 478.441406 684.683594 C 478.433594 684.683594 478.429688 684.683594 478.425781 684.683594 C 478.417969 684.683594 478.414062 684.683594 478.410156 684.683594 C 478.402344 684.683594 478.398438 684.683594 478.394531 684.683594 C 478.386719 684.683594 478.382812 684.683594 478.378906 684.683594 C 478.371094 684.683594 478.367188 684.683594 478.363281 684.683594 C 478.355469 684.683594 478.351562 684.683594 478.347656 684.683594 C 478.339844 684.683594 478.335938 684.683594 478.328125 684.683594 C 478.324219 684.683594 478.320312 684.683594 478.3125 684.683594 C 478.308594 684.683594 478.304688 684.683594 478.296875 684.683594 C 478.292969 684.683594 478.289062 684.683594 478.28125 684.683594 C 478.277344 684.683594 478.273438 684.683594 478.265625 684.683594 C 478.261719 684.683594 478.257812 684.683594 478.25 684.683594 C 478.246094 684.683594 478.242188 684.683594 478.234375 684.683594 C 478.230469 684.683594 478.226562 684.683594 478.21875 684.683594 C 478.214844 684.683594 478.210938 684.683594 478.203125 684.683594 C 478.199219 684.683594 478.191406 684.683594 478.1875 684.683594 C 478.183594 684.683594 478.175781 684.683594 478.171875 684.683594 C 478.167969 684.683594 478.160156 684.683594 478.15625 684.683594 C 478.152344 684.683594 478.144531 684.683594 478.140625 684.683594 C 478.136719 684.683594 478.128906 684.683594 478.125 684.683594 C 478.121094 684.683594 478.113281 684.683594 478.109375 684.683594 C 478.105469 684.683594 478.097656 684.683594 478.09375 684.683594 C 478.089844 684.683594 478.082031 684.683594 478.078125 684.683594 C 478.074219 684.683594 478.066406 684.683594 478.0625 684.683594 C 478.054688 684.683594 478.050781 684.683594 478.046875 684.683594 C 478.039062 684.683594 478.035156 684.683594 478.03125 684.683594 C 478.023438 684.683594 478.019531 684.683594 478.015625 684.683594 C 478.007812 684.683594 478.003906 684.683594 478 684.683594 C 477.992188 684.683594 477.988281 684.683594 477.984375 684.683594 C 477.976562 684.683594 477.972656 684.683594 477.96875 684.683594 C 477.960938 684.683594 477.957031 684.683594 477.953125 684.683594 C 477.945312 684.683594 477.941406 684.683594 477.933594 684.683594 C 477.929688 684.683594 477.925781 684.683594 477.917969 684.683594 C 477.914062 684.683594 477.910156 684.683594 477.902344 684.683594 C 477.898438 684.683594 477.894531 684.683594 477.886719 684.683594 C 477.882812 684.683594 477.878906 684.683594 477.871094 684.683594 C 477.867188 684.683594 477.863281 684.683594 477.855469 684.683594 C 477.851562 684.683594 477.847656 684.683594 477.839844 684.683594 C 477.835938 684.683594 477.832031 684.683594 477.824219 684.683594 C 477.820312 684.683594 477.8125 684.683594 477.808594 684.683594 C 477.804688 684.683594 477.796875 684.683594 477.792969 684.683594 C 477.789062 684.683594 477.78125 684.683594 477.777344 684.683594 C 477.773438 684.683594 477.765625 684.683594 477.761719 684.683594 C 477.757812 684.683594 477.75 684.683594 477.746094 684.683594 C 477.742188 684.683594 477.734375 684.683594 477.730469 684.683594 C 477.726562 684.683594 477.71875 684.683594 477.714844 684.683594 C 477.710938 684.683594 477.703125 684.683594 477.699219 684.683594 C 477.691406 684.683594 477.6875 684.683594 477.683594 684.683594 C 477.675781 684.683594 477.671875 684.683594 477.667969 684.683594 C 477.660156 684.683594 477.65625 684.683594 477.652344 684.683594 C 477.644531 684.683594 477.640625 684.683594 477.636719 684.683594 C 477.628906 684.683594 477.625 684.683594 477.621094 684.683594 C 477.613281 684.683594 477.609375 684.683594 477.605469 684.683594 C 477.597656 684.683594 477.59375 684.683594 477.589844 684.683594 C 477.582031 684.683594 477.578125 684.683594 477.570312 684.683594 C 477.566406 684.683594 477.5625 684.683594 477.554688 684.683594 C 477.550781 684.683594 477.546875 684.683594 477.539062 684.683594 C 477.535156 684.683594 477.53125 684.683594 477.523438 684.683594 C 477.519531 684.683594 477.515625 684.683594 477.507812 684.683594 C 477.503906 684.683594 477.5 684.683594 477.492188 684.683594 C 477.488281 684.683594 477.484375 684.683594 477.476562 684.683594 C 477.472656 684.683594 477.46875 684.683594 477.460938 684.683594 C 477.457031 684.683594 477.449219 684.683594 477.445312 684.683594 C 477.441406 684.683594 477.433594 684.683594 477.429688 684.683594 C 477.425781 684.683594 477.417969 684.683594 477.414062 684.683594 C 477.410156 684.683594 477.402344 684.683594 477.398438 684.683594 C 477.394531 684.683594 477.386719 684.683594 477.382812 684.683594 C 477.378906 684.683594 477.371094 684.683594 477.367188 684.683594 C 477.363281 684.683594 477.355469 684.683594 477.351562 684.683594 C 477.347656 684.683594 477.339844 684.683594 477.335938 684.683594 C 477.332031 684.683594 477.324219 684.683594 477.320312 684.683594 C 477.3125 684.683594 477.308594 684.683594 477.304688 684.683594 C 477.296875 684.683594 477.292969 684.683594 477.289062 684.683594 C 477.28125 684.683594 477.277344 684.683594 477.273438 684.683594 C 477.265625 684.683594 477.261719 684.683594 477.257812 684.683594 C 477.25 684.683594 477.246094 684.683594 477.242188 684.683594 C 477.234375 684.683594 477.230469 684.683594 477.226562 684.683594 C 477.21875 684.683594 477.214844 684.683594 477.210938 684.683594 C 477.203125 684.683594 477.199219 684.683594 477.191406 684.683594 C 477.1875 684.683594 477.183594 684.683594 477.175781 684.683594 C 477.171875 684.683594 477.167969 684.683594 477.160156 684.683594 C 477.15625 684.683594 477.152344 684.683594 477.144531 684.683594 C 477.140625 684.683594 477.136719 684.683594 477.128906 684.683594 C 477.125 684.683594 477.121094 684.683594 477.113281 684.683594 C 477.109375 684.683594 477.105469 684.683594 477.097656 684.683594 C 477.09375 684.683594 477.089844 684.683594 477.082031 684.683594 C 477.078125 684.683594 477.074219 684.683594 477.066406 684.683594 C 477.0625 684.683594 477.054688 684.683594 477.050781 684.683594 C 477.046875 684.683594 477.039062 684.683594 477.035156 684.683594 C 477.03125 684.683594 477.023438 684.683594 477.019531 684.683594 C 477.015625 684.683594 477.007812 684.683594 477.003906 684.683594 C 477 684.683594 476.992188 684.683594 476.988281 684.683594 C 476.984375 684.683594 476.976562 684.683594 476.972656 684.683594 C 476.96875 684.683594 476.960938 684.683594 476.957031 684.683594 C 476.953125 684.683594 476.945312 684.683594 476.941406 684.683594 C 476.9375 684.683594 476.929688 684.683594 476.925781 684.683594 C 476.921875 684.683594 476.914062 684.683594 476.910156 684.683594 C 476.90625 684.683594 476.898438 684.683594 476.894531 684.683594 C 476.886719 684.683594 476.882812 684.683594 476.878906 684.683594 C 476.871094 684.683594 476.867188 684.683594 476.863281 684.683594 C 476.855469 684.683594 476.851562 684.683594 476.847656 684.683594 C 476.839844 684.683594 476.835938 684.683594 476.832031 684.683594 C 476.824219 684.683594 476.820312 684.683594 476.816406 684.683594 C 476.808594 684.683594 476.804688 684.683594 476.800781 684.683594 C 476.792969 684.683594 476.789062 684.683594 476.785156 684.683594 C 476.777344 684.683594 476.773438 684.683594 476.769531 684.683594 C 476.761719 684.683594 476.757812 684.683594 476.753906 684.683594 C 476.746094 684.683594 476.742188 684.683594 476.738281 684.683594 C 476.730469 684.683594 476.726562 684.683594 476.71875 684.683594 C 476.714844 684.683594 476.710938 684.683594 476.703125 684.683594 C 476.699219 684.683594 476.695312 684.683594 476.6875 684.683594 C 476.683594 684.683594 476.679688 684.683594 476.671875 684.683594 C 476.667969 684.683594 476.664062 684.683594 476.65625 684.683594 C 476.652344 684.683594 476.648438 684.683594 476.640625 684.683594 C 476.636719 684.683594 476.632812 684.683594 476.625 684.683594 C 476.621094 684.683594 476.617188 684.683594 476.609375 684.683594 C 476.605469 684.683594 476.601562 684.683594 476.59375 684.683594 C 476.589844 684.683594 476.585938 684.683594 476.578125 684.683594 C 476.574219 684.683594 476.570312 684.683594 476.5625 684.683594 C 476.558594 684.683594 476.554688 684.683594 476.546875 684.683594 C 476.542969 684.683594 476.539062 684.683594 476.53125 684.683594 C 476.527344 684.683594 476.523438 684.683594 476.515625 684.683594 C 476.511719 684.683594 476.507812 684.683594 476.5 684.683594 C 476.496094 684.683594 476.492188 684.683594 476.484375 684.683594 C 476.480469 684.683594 476.476562 684.683594 476.46875 684.683594 C 476.464844 684.683594 476.457031 684.683594 476.453125 684.683594 C 476.449219 684.683594 476.441406 684.683594 476.4375 684.683594 C 476.433594 684.683594 476.425781 684.683594 476.421875 684.683594 C 476.417969 684.683594 476.410156 684.683594 476.40625 684.683594 C 476.402344 684.683594 476.394531 684.683594 476.390625 684.683594 C 476.386719 684.683594 476.378906 684.683594 476.375 684.683594 C 476.371094 684.683594 476.363281 684.683594 476.359375 684.683594 C 476.355469 684.683594 476.347656 684.683594 476.34375 684.683594 C 476.339844 684.683594 476.332031 684.683594 476.328125 684.683594 C 476.324219 684.683594 476.316406 684.683594 476.3125 684.683594 C 476.308594 684.683594 476.300781 684.683594 476.296875 684.683594 C 476.292969 684.683594 476.285156 684.683594 476.28125 684.683594 C 476.277344 684.683594 476.269531 684.683594 476.265625 684.683594 C 476.261719 684.683594 476.253906 684.683594 476.25 684.683594 C 476.246094 684.683594 476.238281 684.683594 476.234375 684.683594 C 476.230469 684.683594 476.222656 684.683594 476.21875 684.683594 C 476.214844 684.683594 476.207031 684.683594 476.203125 684.683594 C 476.199219 684.683594 476.191406 684.683594 476.1875 684.683594 C 476.183594 684.683594 476.175781 684.683594 476.171875 684.683594 C 476.167969 684.683594 476.160156 684.683594 476.15625 684.683594 C 476.152344 684.683594 476.144531 684.683594 476.140625 684.683594 C 476.136719 684.683594 476.128906 684.683594 476.125 684.683594 C 476.121094 684.683594 476.113281 684.683594 476.109375 684.683594 C 476.105469 684.683594 476.097656 684.683594 476.09375 684.683594 C 476.089844 684.683594 476.082031 684.683594 476.078125 684.683594 C 476.074219 684.683594 476.066406 684.683594 476.0625 684.683594 C 476.058594 684.683594 476.050781 684.683594 476.046875 684.683594 C 476.042969 684.683594 476.035156 684.683594 476.03125 684.683594 C 476.027344 684.683594 476.019531 684.683594 476.015625 684.683594 C 476.011719 684.683594 476.003906 684.683594 476 684.683594 C 475.996094 684.683594 475.988281 684.683594 475.984375 684.683594 C 475.980469 684.683594 475.972656 684.683594 475.96875 684.683594 C 475.964844 684.683594 475.957031 684.683594 475.953125 684.683594 C 475.949219 684.683594 475.941406 684.683594 475.9375 684.683594 C 475.933594 684.683594 475.925781 684.683594 475.921875 684.683594 C 475.917969 684.683594 475.910156 684.683594 475.90625 684.683594 C 475.902344 684.683594 475.898438 684.683594 475.890625 684.683594 C 475.886719 684.683594 475.882812 684.683594 475.875 684.683594 C 475.871094 684.683594 475.867188 684.683594 475.859375 684.683594 C 475.855469 684.683594 475.851562 684.683594 475.84375 684.683594 C 475.839844 684.683594 475.835938 684.683594 475.828125 684.683594 C 475.824219 684.683594 475.820312 684.683594 475.8125 684.683594 C 475.808594 684.683594 475.804688 684.683594 475.796875 684.683594 C 475.792969 684.683594 475.789062 684.683594 475.78125 684.683594 C 475.777344 684.683594 475.773438 684.683594 475.765625 684.683594 C 475.761719 684.683594 475.757812 684.683594 475.75 684.683594 C 475.746094 684.683594 475.742188 684.683594 475.734375 684.683594 C 475.730469 684.683594 475.726562 684.683594 475.71875 684.683594 C 475.714844 684.683594 475.710938 684.683594 475.703125 684.683594 C 475.699219 684.683594 475.695312 684.683594 475.691406 684.683594 C 475.683594 684.683594 475.679688 684.683594 475.675781 684.683594 C 475.667969 684.683594 475.664062 684.683594 475.660156 684.683594 C 475.652344 684.683594 475.648438 684.683594 475.644531 684.683594 C 475.636719 684.683594 475.632812 684.683594 475.628906 684.683594 C 475.621094 684.683594 475.617188 684.683594 475.613281 684.683594 C 475.605469 684.683594 475.601562 684.683594 475.597656 684.683594 C 475.589844 684.683594 475.585938 684.683594 475.582031 684.683594 C 475.574219 684.683594 475.570312 684.683594 475.566406 684.683594 C 475.558594 684.683594 475.554688 684.683594 475.550781 684.683594 C 475.546875 684.683594 475.539062 684.683594 475.535156 684.683594 C 475.53125 684.683594 475.523438 684.683594 475.519531 684.683594 C 475.515625 684.683594 475.507812 684.683594 475.503906 684.683594 C 475.5 684.683594 475.492188 684.683594 475.488281 684.683594 C 475.484375 684.683594 475.476562 684.683594 475.472656 684.683594 C 475.46875 684.683594 475.460938 684.683594 475.457031 684.683594 C 475.453125 684.683594 475.449219 684.683594 475.441406 684.683594 C 475.4375 684.683594 475.433594 684.683594 475.425781 684.683594 C 475.421875 684.683594 475.417969 684.683594 475.410156 684.683594 C 475.40625 684.683594 475.402344 684.683594 475.394531 684.683594 C 475.390625 684.683594 475.386719 684.683594 475.378906 684.683594 C 475.375 684.683594 475.371094 684.683594 475.363281 684.683594 C 475.359375 684.683594 475.355469 684.683594 475.351562 684.683594 C 475.34375 684.683594 475.339844 684.683594 475.335938 684.683594 C 475.328125 684.683594 475.324219 684.683594 475.320312 684.683594 C 475.3125 684.683594 475.308594 684.683594 475.304688 684.683594 C 475.296875 684.683594 475.292969 684.683594 475.289062 684.683594 C 475.28125 684.683594 475.277344 684.683594 475.273438 684.683594 C 475.269531 684.683594 475.261719 684.683594 475.257812 684.683594 C 475.253906 684.683594 475.246094 684.683594 475.242188 684.683594 C 475.238281 684.683594 475.230469 684.683594 475.226562 684.683594 C 475.222656 684.683594 475.214844 684.683594 475.210938 684.683594 C 475.207031 684.683594 475.203125 684.683594 475.195312 684.683594 C 475.191406 684.683594 475.1875 684.683594 475.179688 684.683594 C 475.175781 684.683594 475.171875 684.683594 475.164062 684.683594 C 475.160156 684.683594 475.15625 684.683594 475.148438 684.683594 C 475.144531 684.683594 475.140625 684.683594 475.136719 684.683594 C 475.128906 684.683594 475.125 684.683594 475.121094 684.683594 C 475.113281 684.683594 475.109375 684.683594 475.105469 684.683594 C 475.097656 684.683594 475.09375 684.683594 475.089844 684.683594 C 475.085938 684.683594 475.078125 684.683594 475.074219 684.683594 C 475.070312 684.683594 475.0625 684.683594 475.058594 684.683594 C 475.054688 684.683594 475.046875 684.683594 475.042969 684.683594 C 475.039062 684.683594 475.03125 684.683594 475.027344 684.683594 C 475.023438 684.683594 475.019531 684.683594 475.011719 684.683594 C 475.007812 684.683594 475.003906 684.683594 474.996094 684.683594 C 474.992188 684.683594 474.988281 684.683594 474.980469 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687.585938 474.9375 687.585938 C 474.941406 687.585938 474.945312 687.585938 474.953125 687.585938 C 474.957031 687.585938 474.960938 687.585938 474.96875 687.585938 C 474.972656 687.585938 474.976562 687.585938 474.980469 687.585938 C 474.988281 687.585938 474.992188 687.585938 474.996094 687.585938 C 475.003906 687.585938 475.007812 687.585938 475.011719 687.585938 C 475.019531 687.585938 475.023438 687.585938 475.027344 687.585938 C 475.03125 687.585938 475.039062 687.585938 475.042969 687.585938 C 475.046875 687.585938 475.054688 687.585938 475.058594 687.585938 C 475.0625 687.585938 475.070312 687.585938 475.074219 687.585938 C 475.078125 687.585938 475.085938 687.585938 475.089844 687.585938 C 475.09375 687.585938 475.097656 687.585938 475.105469 687.585938 C 475.109375 687.585938 475.113281 687.585938 475.121094 687.585938 C 475.125 687.585938 475.128906 687.585938 475.136719 687.585938 C 475.140625 687.585938 475.144531 687.585938 475.148438 687.585938 C 475.15625 687.585938 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687.585938 478.183594 687.585938 478.1875 687.585938 C 478.191406 687.585938 478.199219 687.585938 478.203125 687.585938 C 478.210938 687.585938 478.214844 687.585938 478.21875 687.585938 C 478.226562 687.585938 478.230469 687.585938 478.234375 687.585938 C 478.242188 687.585938 478.246094 687.585938 478.25 687.585938 C 478.257812 687.585938 478.261719 687.585938 478.265625 687.585938 C 478.273438 687.585938 478.277344 687.585938 478.28125 687.585938 C 478.289062 687.585938 478.292969 687.585938 478.296875 687.585938 C 478.304688 687.585938 478.308594 687.585938 478.3125 687.585938 C 478.320312 687.585938 478.324219 687.585938 478.328125 687.585938 C 478.335938 687.585938 478.339844 687.585938 478.347656 687.585938 C 478.351562 687.585938 478.355469 687.585938 478.363281 687.585938 C 478.367188 687.585938 478.371094 687.585938 478.378906 687.585938 C 478.382812 687.585938 478.386719 687.585938 478.394531 687.585938 C 478.398438 687.585938 478.402344 687.585938 478.410156 687.585938 C 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479.804688 687.585938 479.808594 687.585938 479.816406 687.585938 C 479.820312 687.585938 479.824219 687.585938 479.832031 687.585938 C 479.835938 687.585938 479.839844 687.585938 479.847656 687.585938 C 479.851562 687.585938 479.855469 687.585938 479.863281 687.585938 C 479.867188 687.585938 479.871094 687.585938 479.878906 687.585938 C 479.882812 687.585938 479.886719 687.585938 479.894531 687.585938 C 479.898438 687.585938 479.902344 687.585938 479.90625 687.585938 C 479.914062 687.585938 479.917969 687.585938 479.921875 687.585938 C 479.929688 687.585938 479.933594 687.585938 479.9375 687.585938 C 479.945312 687.585938 479.949219 687.585938 479.953125 687.585938 C 479.960938 687.585938 479.964844 687.585938 479.96875 687.585938 C 479.976562 687.585938 479.980469 687.585938 479.984375 687.585938 C 479.988281 687.585938 479.996094 687.585938 480 687.585938 C 480.003906 687.585938 480.011719 687.585938 480.015625 687.585938 C 480.019531 687.585938 480.027344 687.585938 480.03125 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480.933594 687.585938 480.9375 687.585938 480.941406 687.585938 C 480.949219 687.585938 480.953125 687.585938 480.957031 687.585938 C 480.960938 687.585938 480.96875 687.585938 480.972656 687.585938 C 480.976562 687.585938 480.984375 687.585938 480.988281 687.585938 C 480.992188 687.585938 480.996094 687.585938 481.003906 687.585938 C 481.007812 687.585938 481.011719 687.585938 481.015625 687.585938 C 481.023438 687.585938 481.027344 687.585938 481.03125 687.585938 C 481.039062 687.585938 481.042969 687.585938 481.046875 687.585938 C 481.050781 687.585938 481.058594 687.585938 481.0625 687.585938 C 481.066406 687.585938 481.070312 687.585938 481.078125 687.585938 C 481.082031 687.585938 481.085938 687.585938 481.09375 687.585938 C 481.097656 687.585938 481.101562 687.585938 481.105469 687.585938 C 481.113281 687.585938 481.117188 687.585938 481.121094 687.585938 C 481.125 687.585938 481.132812 687.585938 481.136719 687.585938 C 481.140625 687.585938 481.144531 687.585938 481.152344 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98.039062 684.683594 C 98.035156 684.683594 98.03125 684.683594 98.023438 684.683594 C 98.019531 684.683594 98.015625 684.683594 98.007812 684.683594 C 98.003906 684.683594 98 684.683594 97.996094 684.683594 C 97.988281 684.683594 97.984375 684.683594 97.980469 684.683594 C 97.976562 684.683594 97.96875 684.683594 97.964844 684.683594 C 97.960938 684.683594 97.957031 684.683594 97.949219 684.683594 C 97.945312 684.683594 97.941406 684.683594 97.933594 684.683594 C 97.929688 684.683594 97.925781 684.683594 97.921875 684.683594 C 97.914062 684.683594 97.910156 684.683594 97.90625 684.683594 C 97.902344 684.683594 97.894531 684.683594 97.890625 684.683594 C 97.886719 684.683594 97.878906 684.683594 97.875 684.683594 C 97.871094 684.683594 97.867188 684.683594 97.859375 684.683594 C 97.855469 684.683594 97.851562 684.683594 97.847656 684.683594 C 97.839844 684.683594 97.835938 684.683594 97.832031 684.683594 C 97.824219 684.683594 97.820312 684.683594 97.816406 684.683594 C 97.8125 684.683594 97.804688 684.683594 97.800781 684.683594 C 97.796875 684.683594 97.792969 684.683594 97.785156 684.683594 C 97.78125 684.683594 97.777344 684.683594 97.769531 684.683594 C 97.765625 684.683594 97.761719 684.683594 97.757812 684.683594 C 97.75 684.683594 97.746094 684.683594 97.742188 684.683594 C 97.738281 684.683594 97.730469 684.683594 97.726562 684.683594 C 97.722656 684.683594 97.714844 684.683594 97.710938 684.683594 C 97.707031 684.683594 97.703125 684.683594 97.695312 684.683594 C 97.691406 684.683594 97.6875 684.683594 97.679688 684.683594 C 97.675781 684.683594 97.671875 684.683594 97.667969 684.683594 C 97.660156 684.683594 97.65625 684.683594 97.652344 684.683594 C 97.644531 684.683594 97.640625 684.683594 97.636719 684.683594 C 97.632812 684.683594 97.625 684.683594 97.621094 684.683594 C 97.617188 684.683594 97.609375 684.683594 97.605469 684.683594 C 97.601562 684.683594 97.597656 684.683594 97.589844 684.683594 C 97.585938 684.683594 97.582031 684.683594 97.574219 684.683594 C 97.570312 684.683594 97.566406 684.683594 97.5625 684.683594 C 97.554688 684.683594 97.550781 684.683594 97.546875 684.683594 C 97.539062 684.683594 97.535156 684.683594 97.53125 684.683594 C 97.527344 684.683594 97.519531 684.683594 97.515625 684.683594 C 97.511719 684.683594 97.503906 684.683594 97.5 684.683594 C 97.496094 684.683594 97.492188 684.683594 97.484375 684.683594 C 97.480469 684.683594 97.476562 684.683594 97.46875 684.683594 C 97.464844 684.683594 97.460938 684.683594 97.457031 684.683594 C 97.449219 684.683594 97.445312 684.683594 97.441406 684.683594 C 97.433594 684.683594 97.429688 684.683594 97.425781 684.683594 C 97.421875 684.683594 97.414062 684.683594 97.410156 684.683594 C 97.40625 684.683594 97.398438 684.683594 97.394531 684.683594 C 97.390625 684.683594 97.382812 684.683594 97.378906 684.683594 C 97.375 684.683594 97.371094 684.683594 97.363281 684.683594 C 97.359375 684.683594 97.355469 684.683594 97.347656 684.683594 C 97.34375 684.683594 97.339844 684.683594 97.335938 684.683594 C 97.328125 684.683594 97.324219 684.683594 97.320312 684.683594 C 97.3125 684.683594 97.308594 684.683594 97.304688 684.683594 C 97.296875 684.683594 97.292969 684.683594 97.289062 684.683594 C 97.285156 684.683594 97.277344 684.683594 97.273438 684.683594 C 97.269531 684.683594 97.261719 684.683594 97.257812 684.683594 C 97.253906 684.683594 97.25 684.683594 97.242188 684.683594 C 97.238281 684.683594 97.234375 684.683594 97.226562 684.683594 C 97.222656 684.683594 97.21875 684.683594 97.210938 684.683594 C 97.207031 684.683594 97.203125 684.683594 97.199219 684.683594 C 97.191406 684.683594 97.1875 684.683594 97.183594 684.683594 C 97.175781 684.683594 97.171875 684.683594 97.167969 684.683594 C 97.160156 684.683594 97.15625 684.683594 97.152344 684.683594 C 97.148438 684.683594 97.140625 684.683594 97.136719 684.683594 C 97.132812 684.683594 97.125 684.683594 97.121094 684.683594 C 97.117188 684.683594 97.109375 684.683594 97.105469 684.683594 C 97.101562 684.683594 97.09375 684.683594 97.089844 684.683594 C 97.085938 684.683594 97.082031 684.683594 97.074219 684.683594 C 97.070312 684.683594 97.066406 684.683594 97.058594 684.683594 C 97.054688 684.683594 97.050781 684.683594 97.042969 684.683594 C 97.039062 684.683594 97.035156 684.683594 97.03125 684.683594 C 97.023438 684.683594 97.019531 684.683594 97.015625 684.683594 C 97.007812 684.683594 97.003906 684.683594 97 684.683594 C 96.992188 684.683594 96.988281 684.683594 96.984375 684.683594 C 96.976562 684.683594 96.972656 684.683594 96.96875 684.683594 C 96.964844 684.683594 96.957031 684.683594 96.953125 684.683594 C 96.949219 684.683594 96.941406 684.683594 96.9375 684.683594 C 96.933594 684.683594 96.925781 684.683594 96.921875 684.683594 C 96.917969 684.683594 96.910156 684.683594 96.90625 684.683594 C 96.902344 684.683594 96.898438 684.683594 96.890625 684.683594 C 96.886719 684.683594 96.882812 684.683594 96.875 684.683594 C 96.871094 684.683594 96.867188 684.683594 96.859375 684.683594 C 96.855469 684.683594 96.851562 684.683594 96.84375 684.683594 C 96.839844 684.683594 96.835938 684.683594 96.828125 684.683594 C 96.824219 684.683594 96.820312 684.683594 96.816406 684.683594 C 96.808594 684.683594 96.804688 684.683594 96.800781 684.683594 C 96.792969 684.683594 96.789062 684.683594 96.785156 684.683594 C 96.777344 684.683594 96.773438 684.683594 96.769531 684.683594 C 96.761719 684.683594 96.757812 684.683594 96.753906 684.683594 C 96.746094 684.683594 96.742188 684.683594 96.738281 684.683594 C 96.734375 684.683594 96.726562 684.683594 96.722656 684.683594 C 96.71875 684.683594 96.710938 684.683594 96.707031 684.683594 C 96.703125 684.683594 96.695312 684.683594 96.691406 684.683594 C 96.6875 684.683594 96.679688 684.683594 96.675781 684.683594 C 96.671875 684.683594 96.664062 684.683594 96.660156 684.683594 C 96.65625 684.683594 96.648438 684.683594 96.644531 684.683594 C 96.640625 684.683594 96.636719 684.683594 96.628906 684.683594 C 96.625 684.683594 96.621094 684.683594 96.613281 684.683594 C 96.609375 684.683594 96.605469 684.683594 96.597656 684.683594 C 96.59375 684.683594 96.589844 684.683594 96.582031 684.683594 C 96.578125 684.683594 96.574219 684.683594 96.566406 684.683594 C 96.5625 684.683594 96.558594 684.683594 96.550781 684.683594 C 96.546875 684.683594 96.542969 684.683594 96.535156 684.683594 C 96.53125 684.683594 96.527344 684.683594 96.519531 684.683594 C 96.515625 684.683594 96.511719 684.683594 96.507812 684.683594 C 96.5 684.683594 96.496094 684.683594 96.492188 684.683594 C 96.484375 684.683594 96.480469 684.683594 96.476562 684.683594 C 96.46875 684.683594 96.464844 684.683594 96.460938 684.683594 C 96.453125 684.683594 96.449219 684.683594 96.445312 684.683594 C 96.4375 684.683594 96.433594 684.683594 96.429688 684.683594 C 96.421875 684.683594 96.417969 684.683594 96.414062 684.683594 C 96.40625 684.683594 96.402344 684.683594 96.398438 684.683594 C 96.390625 684.683594 96.386719 684.683594 96.382812 684.683594 C 96.375 684.683594 96.371094 684.683594 96.367188 684.683594 C 96.359375 684.683594 96.355469 684.683594 96.351562 684.683594 C 96.34375 684.683594 96.339844 684.683594 96.335938 684.683594 C 96.332031 684.683594 96.324219 684.683594 96.320312 684.683594 C 96.316406 684.683594 96.308594 684.683594 96.304688 684.683594 C 96.300781 684.683594 96.292969 684.683594 96.289062 684.683594 C 96.285156 684.683594 96.277344 684.683594 96.273438 684.683594 C 96.269531 684.683594 96.261719 684.683594 96.257812 684.683594 C 96.253906 684.683594 96.246094 684.683594 96.242188 684.683594 C 96.238281 684.683594 96.230469 684.683594 96.226562 684.683594 C 96.222656 684.683594 96.214844 684.683594 96.210938 684.683594 C 96.207031 684.683594 96.199219 684.683594 96.195312 684.683594 C 96.191406 684.683594 96.183594 684.683594 96.179688 684.683594 C 96.175781 684.683594 96.167969 684.683594 96.164062 684.683594 C 96.160156 684.683594 96.152344 684.683594 96.148438 684.683594 C 96.144531 684.683594 96.136719 684.683594 96.132812 684.683594 C 96.128906 684.683594 96.121094 684.683594 96.117188 684.683594 C 96.113281 684.683594 96.105469 684.683594 96.101562 684.683594 C 96.097656 684.683594 96.089844 684.683594 96.085938 684.683594 C 96.082031 684.683594 96.074219 684.683594 96.070312 684.683594 C 96.066406 684.683594 96.058594 684.683594 96.054688 684.683594 C 96.050781 684.683594 96.042969 684.683594 96.039062 684.683594 C 96.035156 684.683594 96.027344 684.683594 96.023438 684.683594 C 96.019531 684.683594 96.011719 684.683594 96.007812 684.683594 C 96.003906 684.683594 95.996094 684.683594 95.992188 684.683594 C 95.988281 684.683594 95.980469 684.683594 95.976562 684.683594 C 95.972656 684.683594 95.964844 684.683594 95.960938 684.683594 C 95.957031 684.683594 95.949219 684.683594 95.945312 684.683594 C 95.941406 684.683594 95.933594 684.683594 95.929688 684.683594 C 95.925781 684.683594 95.917969 684.683594 95.914062 684.683594 C 95.910156 684.683594 95.902344 684.683594 95.898438 684.683594 C 95.894531 684.683594 95.886719 684.683594 95.882812 684.683594 C 95.878906 684.683594 95.871094 684.683594 95.867188 684.683594 C 95.863281 684.683594 95.855469 684.683594 95.851562 684.683594 C 95.847656 684.683594 95.839844 684.683594 95.835938 684.683594 C 95.832031 684.683594 95.824219 684.683594 95.820312 684.683594 C 95.816406 684.683594 95.808594 684.683594 95.804688 684.683594 C 95.800781 684.683594 95.792969 684.683594 95.789062 684.683594 C 95.785156 684.683594 95.777344 684.683594 95.773438 684.683594 C 95.769531 684.683594 95.761719 684.683594 95.757812 684.683594 C 95.753906 684.683594 95.746094 684.683594 95.742188 684.683594 C 95.738281 684.683594 95.730469 684.683594 95.726562 684.683594 C 95.722656 684.683594 95.714844 684.683594 95.710938 684.683594 C 95.707031 684.683594 95.699219 684.683594 95.695312 684.683594 C 95.691406 684.683594 95.683594 684.683594 95.679688 684.683594 C 95.675781 684.683594 95.667969 684.683594 95.664062 684.683594 C 95.660156 684.683594 95.652344 684.683594 95.648438 684.683594 C 95.644531 684.683594 95.636719 684.683594 95.632812 684.683594 C 95.628906 684.683594 95.621094 684.683594 95.617188 684.683594 C 95.609375 684.683594 95.605469 684.683594 95.601562 684.683594 C 95.59375 684.683594 95.589844 684.683594 95.585938 684.683594 C 95.578125 684.683594 95.574219 684.683594 95.570312 684.683594 C 95.5625 684.683594 95.558594 684.683594 95.554688 684.683594 C 95.546875 684.683594 95.542969 684.683594 95.539062 684.683594 C 95.53125 684.683594 95.527344 684.683594 95.523438 684.683594 C 95.515625 684.683594 95.511719 684.683594 95.507812 684.683594 C 95.5 684.683594 95.496094 684.683594 95.492188 684.683594 C 95.484375 684.683594 95.480469 684.683594 95.476562 684.683594 C 95.46875 684.683594 95.464844 684.683594 95.460938 684.683594 C 95.453125 684.683594 95.449219 684.683594 95.445312 684.683594 C 95.4375 684.683594 95.433594 684.683594 95.429688 684.683594 C 95.421875 684.683594 95.417969 684.683594 95.414062 684.683594 C 95.40625 684.683594 95.402344 684.683594 95.398438 684.683594 C 95.390625 684.683594 95.386719 684.683594 95.378906 684.683594 C 95.375 684.683594 95.371094 684.683594 95.363281 684.683594 C 95.359375 684.683594 95.355469 684.683594 95.347656 684.683594 C 95.34375 684.683594 95.339844 684.683594 95.332031 684.683594 C 95.328125 684.683594 95.324219 684.683594 95.316406 684.683594 C 95.3125 684.683594 95.308594 684.683594 95.300781 684.683594 C 95.296875 684.683594 95.292969 684.683594 95.285156 684.683594 C 95.28125 684.683594 95.277344 684.683594 95.269531 684.683594 C 95.265625 684.683594 95.261719 684.683594 95.253906 684.683594 C 95.25 684.683594 95.246094 684.683594 95.238281 684.683594 C 95.234375 684.683594 95.230469 684.683594 95.222656 684.683594 C 95.21875 684.683594 95.210938 684.683594 95.207031 684.683594 C 95.203125 684.683594 95.195312 684.683594 95.191406 684.683594 C 95.1875 684.683594 95.179688 684.683594 95.175781 684.683594 C 95.171875 684.683594 95.164062 684.683594 95.160156 684.683594 C 95.15625 684.683594 95.148438 684.683594 95.144531 684.683594 C 95.140625 684.683594 95.132812 684.683594 95.128906 684.683594 C 95.125 684.683594 95.117188 684.683594 95.113281 684.683594 C 95.109375 684.683594 95.101562 684.683594 95.097656 684.683594 C 95.09375 684.683594 95.085938 684.683594 95.082031 684.683594 C 95.078125 684.683594 95.070312 684.683594 95.066406 684.683594 C 95.058594 684.683594 95.054688 684.683594 95.050781 684.683594 C 95.042969 684.683594 95.039062 684.683594 95.035156 684.683594 C 95.027344 684.683594 95.023438 684.683594 95.019531 684.683594 C 95.011719 684.683594 95.007812 684.683594 95.003906 684.683594 C 94.996094 684.683594 94.992188 684.683594 94.988281 684.683594 C 94.980469 684.683594 94.976562 684.683594 94.972656 684.683594 C 94.964844 684.683594 94.960938 684.683594 94.957031 684.683594 C 94.949219 684.683594 94.945312 684.683594 94.941406 684.683594 C 94.933594 684.683594 94.929688 684.683594 94.921875 684.683594 C 94.917969 684.683594 94.914062 684.683594 94.90625 684.683594 C 94.902344 684.683594 94.898438 684.683594 94.890625 684.683594 C 94.886719 684.683594 94.882812 684.683594 94.875 684.683594 C 94.871094 684.683594 94.867188 684.683594 94.859375 684.683594 C 94.855469 684.683594 94.851562 684.683594 94.84375 684.683594 C 94.839844 684.683594 94.835938 684.683594 94.828125 684.683594 C 94.824219 684.683594 94.820312 684.683594 94.8125 684.683594 C 94.808594 684.683594 94.800781 684.683594 94.796875 684.683594 C 94.792969 684.683594 94.785156 684.683594 94.78125 684.683594 C 94.777344 684.683594 94.769531 684.683594 94.765625 684.683594 C 94.761719 684.683594 94.753906 684.683594 94.75 684.683594 C 94.746094 684.683594 94.738281 684.683594 94.734375 684.683594 C 94.730469 684.683594 94.722656 684.683594 94.71875 684.683594 C 94.714844 684.683594 94.707031 684.683594 94.703125 684.683594 C 94.699219 684.683594 94.691406 684.683594 94.6875 684.683594 C 94.683594 684.683594 94.675781 684.683594 94.671875 684.683594 C 94.664062 684.683594 94.660156 684.683594 94.65625 684.683594 C 94.648438 684.683594 94.644531 684.683594 94.640625 684.683594 C 94.632812 684.683594 94.628906 684.683594 94.625 684.683594 C 94.617188 684.683594 94.613281 684.683594 94.609375 684.683594 C 94.601562 684.683594 94.597656 684.683594 94.59375 684.683594 C 94.585938 684.683594 94.582031 684.683594 94.578125 684.683594 C 94.570312 684.683594 94.566406 684.683594 94.5625 684.683594 C 94.554688 684.683594 94.550781 684.683594 94.542969 684.683594 C 94.539062 684.683594 94.535156 684.683594 94.527344 684.683594 C 94.523438 684.683594 94.519531 684.683594 94.511719 684.683594 C 94.507812 684.683594 94.503906 684.683594 94.496094 684.683594 C 94.492188 684.683594 94.488281 684.683594 94.480469 684.683594 C 94.476562 684.683594 94.472656 684.683594 94.464844 684.683594 C 94.460938 684.683594 94.457031 684.683594 94.449219 684.683594 C 94.445312 684.683594 94.441406 684.683594 94.433594 684.683594 C 94.429688 684.683594 94.421875 684.683594 94.417969 684.683594 C 94.414062 684.683594 94.40625 684.683594 94.402344 684.683594 C 94.398438 684.683594 94.390625 684.683594 94.386719 684.683594 C 94.382812 684.683594 94.375 684.683594 94.371094 684.683594 C 94.367188 684.683594 94.359375 684.683594 94.355469 684.683594 C 94.351562 684.683594 94.34375 684.683594 94.339844 684.683594 C 94.335938 684.683594 94.328125 684.683594 94.324219 684.683594 C 94.320312 684.683594 94.3125 684.683594 94.308594 684.683594 C 94.300781 684.683594 94.296875 684.683594 94.292969 684.683594 C 94.285156 684.683594 94.28125 684.683594 94.277344 684.683594 C 94.269531 684.683594 94.265625 684.683594 94.261719 684.683594 C 94.253906 684.683594 94.25 684.683594 94.246094 684.683594 C 94.238281 684.683594 94.234375 684.683594 94.230469 684.683594 C 94.222656 684.683594 94.21875 684.683594 94.214844 684.683594 C 94.207031 684.683594 94.203125 684.683594 94.199219 684.683594 C 94.191406 684.683594 94.1875 684.683594 94.179688 684.683594 C 94.175781 684.683594 94.171875 684.683594 94.164062 684.683594 C 94.160156 684.683594 94.15625 684.683594 94.148438 684.683594 C 94.144531 684.683594 94.140625 684.683594 94.132812 684.683594 C 94.128906 684.683594 94.125 684.683594 94.117188 684.683594 C 94.113281 684.683594 94.109375 684.683594 94.101562 684.683594 C 94.097656 684.683594 94.09375 684.683594 94.085938 684.683594 C 94.082031 684.683594 94.078125 684.683594 94.070312 684.683594 C 94.066406 684.683594 94.058594 684.683594 94.054688 684.683594 C 94.050781 684.683594 94.042969 684.683594 94.039062 684.683594 C 94.035156 684.683594 94.027344 684.683594 94.023438 684.683594 C 94.019531 684.683594 94.011719 684.683594 94.007812 684.683594 C 94.003906 684.683594 93.996094 684.683594 93.992188 684.683594 C 93.988281 684.683594 93.980469 684.683594 93.976562 684.683594 C 93.972656 684.683594 93.964844 684.683594 93.960938 684.683594 C 93.957031 684.683594 93.949219 684.683594 93.945312 684.683594 C 93.941406 684.683594 93.933594 684.683594 93.929688 684.683594 C 93.921875 684.683594 93.917969 684.683594 93.914062 684.683594 C 93.90625 684.683594 93.902344 684.683594 93.898438 684.683594 C 93.890625 684.683594 93.886719 684.683594 93.882812 684.683594 C 93.875 684.683594 93.871094 684.683594 93.867188 684.683594 C 93.859375 684.683594 93.855469 684.683594 93.851562 684.683594 C 93.84375 684.683594 93.839844 684.683594 93.835938 684.683594 C 93.828125 684.683594 93.824219 684.683594 93.820312 684.683594 C 93.8125 684.683594 93.808594 684.683594 93.804688 684.683594 C 93.796875 684.683594 93.792969 684.683594 93.789062 684.683594 C 93.78125 684.683594 93.777344 684.683594 93.769531 684.683594 C 93.765625 684.683594 93.761719 684.683594 93.753906 684.683594 C 93.75 684.683594 93.746094 684.683594 93.738281 684.683594 C 93.734375 684.683594 93.730469 684.683594 93.722656 684.683594 C 93.71875 684.683594 93.714844 684.683594 93.707031 684.683594 C 93.703125 684.683594 93.699219 684.683594 93.691406 684.683594 C 93.6875 684.683594 93.683594 684.683594 93.675781 684.683594 C 93.671875 684.683594 93.667969 684.683594 93.660156 684.683594 C 93.65625 684.683594 93.652344 684.683594 93.644531 684.683594 C 93.640625 684.683594 93.636719 684.683594 93.628906 684.683594 C 93.625 684.683594 93.621094 684.683594 93.613281 684.683594 C 93.609375 684.683594 93.601562 684.683594 93.597656 684.683594 C 93.59375 684.683594 93.585938 684.683594 93.582031 684.683594 C 93.578125 684.683594 93.570312 684.683594 93.566406 684.683594 C 93.5625 684.683594 93.554688 684.683594 93.550781 684.683594 C 93.546875 684.683594 93.539062 684.683594 93.535156 684.683594 C 93.53125 684.683594 93.523438 684.683594 93.519531 684.683594 C 93.515625 684.683594 93.507812 684.683594 93.503906 684.683594 C 93.5 684.683594 93.492188 684.683594 93.488281 684.683594 C 93.484375 684.683594 93.476562 684.683594 93.472656 684.683594 C 93.46875 684.683594 93.460938 684.683594 93.457031 684.683594 C 93.453125 684.683594 93.445312 684.683594 93.441406 684.683594 C 93.4375 684.683594 93.429688 684.683594 93.425781 684.683594 C 93.421875 684.683594 93.414062 684.683594 93.410156 684.683594 C 93.402344 684.683594 93.398438 684.683594 93.394531 684.683594 C 93.386719 684.683594 93.382812 684.683594 93.378906 684.683594 C 93.371094 684.683594 93.367188 684.683594 93.363281 684.683594 C 93.355469 684.683594 93.351562 684.683594 93.347656 684.683594 C 93.339844 684.683594 93.335938 684.683594 93.332031 684.683594 C 93.324219 684.683594 93.320312 684.683594 93.316406 684.683594 C 93.308594 684.683594 93.304688 684.683594 93.300781 684.683594 C 93.292969 684.683594 93.289062 684.683594 93.285156 684.683594 C 93.277344 684.683594 93.273438 684.683594 93.269531 684.683594 C 93.261719 684.683594 93.257812 684.683594 93.253906 684.683594 C 93.246094 684.683594 93.242188 684.683594 93.238281 684.683594 C 93.230469 684.683594 93.226562 684.683594 93.222656 684.683594 C 93.214844 684.683594 93.210938 684.683594 93.207031 684.683594 C 93.199219 684.683594 93.195312 684.683594 93.191406 684.683594 C 93.183594 684.683594 93.179688 684.683594 93.175781 684.683594 C 93.167969 684.683594 93.164062 684.683594 93.160156 684.683594 C 93.152344 684.683594 93.148438 684.683594 93.144531 684.683594 C 93.136719 684.683594 93.132812 684.683594 93.128906 684.683594 C 93.121094 684.683594 93.117188 684.683594 93.113281 684.683594 C 93.105469 684.683594 93.101562 684.683594 93.097656 684.683594 C 93.089844 684.683594 93.085938 684.683594 93.082031 684.683594 C 93.074219 684.683594 93.070312 684.683594 93.066406 684.683594 C 93.058594 684.683594 93.054688 684.683594 93.050781 684.683594 C 93.042969 684.683594 93.039062 684.683594 93.035156 684.683594 C 93.027344 684.683594 93.023438 684.683594 93.019531 684.683594 C 93.011719 684.683594 93.007812 684.683594 93.003906 684.683594 C 92.996094 684.683594 92.992188 684.683594 92.988281 684.683594 C 92.980469 684.683594 92.976562 684.683594 92.972656 684.683594 C 92.964844 684.683594 92.960938 684.683594 92.957031 684.683594 C 92.949219 684.683594 92.945312 684.683594 92.941406 684.683594 C 92.933594 684.683594 92.929688 684.683594 92.925781 684.683594 C 92.917969 684.683594 92.914062 684.683594 92.910156 684.683594 C 92.902344 684.683594 92.898438 684.683594 92.894531 684.683594 C 92.886719 684.683594 92.882812 684.683594 92.878906 684.683594 C 92.871094 684.683594 92.867188 684.683594 92.863281 684.683594 C 92.855469 684.683594 92.851562 684.683594 92.847656 684.683594 C 92.839844 684.683594 92.835938 684.683594 92.832031 684.683594 C 92.824219 684.683594 92.820312 684.683594 92.816406 684.683594 C 92.808594 684.683594 92.804688 684.683594 92.800781 684.683594 C 92.792969 684.683594 92.789062 684.683594 92.785156 684.683594 C 92.777344 684.683594 92.773438 684.683594 92.769531 684.683594 C 92.761719 684.683594 92.757812 684.683594 92.753906 684.683594 C 92.746094 684.683594 92.742188 684.683594 92.738281 684.683594 C 92.730469 684.683594 92.726562 684.683594 92.722656 684.683594 C 92.714844 684.683594 92.710938 684.683594 92.707031 684.683594 C 92.699219 684.683594 92.695312 684.683594 92.691406 684.683594 C 92.683594 684.683594 92.679688 684.683594 92.675781 684.683594 C 92.667969 684.683594 92.664062 684.683594 92.660156 684.683594 C 92.652344 684.683594 92.648438 684.683594 92.644531 684.683594 C 92.636719 684.683594 92.632812 684.683594 92.628906 684.683594 C 92.621094 684.683594 92.617188 684.683594 92.613281 684.683594 C 92.605469 684.683594 92.601562 684.683594 92.597656 684.683594 C 92.589844 684.683594 92.585938 684.683594 92.582031 684.683594 C 92.578125 684.683594 92.570312 684.683594 92.566406 684.683594 C 92.5625 684.683594 92.554688 684.683594 92.550781 684.683594 C 92.546875 684.683594 92.539062 684.683594 92.535156 684.683594 C 92.53125 684.683594 92.523438 684.683594 92.519531 684.683594 C 92.515625 684.683594 92.507812 684.683594 92.503906 684.683594 C 92.5 684.683594 92.492188 684.683594 92.488281 684.683594 C 92.484375 684.683594 92.476562 684.683594 92.472656 684.683594 C 92.46875 684.683594 92.460938 684.683594 92.457031 684.683594 C 92.453125 684.683594 92.445312 684.683594 92.441406 684.683594 C 92.4375 684.683594 92.429688 684.683594 92.425781 684.683594 C 92.421875 684.683594 92.417969 684.683594 92.410156 684.683594 C 92.40625 684.683594 92.402344 684.683594 92.394531 684.683594 C 92.390625 684.683594 92.386719 684.683594 92.378906 684.683594 C 92.375 684.683594 92.371094 684.683594 92.363281 684.683594 C 92.359375 684.683594 92.355469 684.683594 92.347656 684.683594 C 92.34375 684.683594 92.339844 684.683594 92.332031 684.683594 C 92.328125 684.683594 92.324219 684.683594 92.316406 684.683594 C 92.3125 684.683594 92.308594 684.683594 92.300781 684.683594 C 92.296875 684.683594 92.292969 684.683594 92.289062 684.683594 C 92.28125 684.683594 92.277344 684.683594 92.273438 684.683594 C 92.265625 684.683594 92.261719 684.683594 92.257812 684.683594 C 92.25 684.683594 92.246094 684.683594 92.242188 684.683594 C 92.234375 684.683594 92.230469 684.683594 92.226562 684.683594 C 92.21875 684.683594 92.214844 684.683594 92.210938 684.683594 C 92.203125 684.683594 92.199219 684.683594 92.195312 684.683594 C 92.191406 684.683594 92.183594 684.683594 92.179688 684.683594 C 92.175781 684.683594 92.167969 684.683594 92.164062 684.683594 C 92.160156 684.683594 92.152344 684.683594 92.148438 684.683594 C 92.144531 684.683594 92.136719 684.683594 92.132812 684.683594 C 92.128906 684.683594 92.121094 684.683594 92.117188 684.683594 C 92.113281 684.683594 92.109375 684.683594 92.101562 684.683594 C 92.097656 684.683594 92.09375 684.683594 92.085938 684.683594 C 92.082031 684.683594 92.078125 684.683594 92.070312 684.683594 C 92.066406 684.683594 92.0625 684.683594 92.054688 684.683594 C 92.050781 684.683594 92.046875 684.683594 92.042969 684.683594 C 92.035156 684.683594 92.03125 684.683594 92.027344 684.683594 C 92.019531 684.683594 92.015625 684.683594 92.011719 684.683594 C 92.003906 684.683594 92 684.683594 91.996094 684.683594 C 91.988281 684.683594 91.984375 684.683594 91.980469 684.683594 C 91.976562 684.683594 91.96875 684.683594 91.964844 684.683594 C 91.960938 684.683594 91.953125 684.683594 91.949219 684.683594 C 91.945312 684.683594 91.9375 684.683594 91.933594 684.683594 C 91.929688 684.683594 91.921875 684.683594 91.917969 684.683594 C 91.914062 684.683594 91.910156 684.683594 91.902344 684.683594 C 91.898438 684.683594 91.894531 684.683594 91.886719 684.683594 C 91.882812 684.683594 91.878906 684.683594 91.871094 684.683594 C 91.867188 684.683594 91.863281 684.683594 91.855469 684.683594 C 91.851562 684.683594 91.847656 684.683594 91.84375 684.683594 C 91.835938 684.683594 91.832031 684.683594 91.828125 684.683594 C 91.820312 684.683594 91.816406 684.683594 91.8125 684.683594 C 91.804688 684.683594 91.800781 684.683594 91.796875 684.683594 C 91.792969 684.683594 91.785156 684.683594 91.78125 684.683594 C 91.777344 684.683594 91.769531 684.683594 91.765625 684.683594 C 91.761719 684.683594 91.753906 684.683594 91.75 684.683594 C 91.746094 684.683594 91.742188 684.683594 91.734375 684.683594 C 91.730469 684.683594 91.726562 684.683594 91.71875 684.683594 C 91.714844 684.683594 91.710938 684.683594 91.703125 684.683594 C 91.699219 684.683594 91.695312 684.683594 91.691406 684.683594 C 91.683594 684.683594 91.679688 684.683594 91.675781 684.683594 C 91.667969 684.683594 91.664062 684.683594 91.660156 684.683594 C 91.652344 684.683594 91.648438 684.683594 91.644531 684.683594 C 91.640625 684.683594 91.632812 684.683594 91.628906 684.683594 C 91.625 684.683594 91.617188 684.683594 91.613281 684.683594 C 91.609375 684.683594 91.605469 684.683594 91.597656 684.683594 C 91.59375 684.683594 91.589844 684.683594 91.582031 684.683594 C 91.578125 684.683594 91.574219 684.683594 91.566406 684.683594 C 91.5625 684.683594 91.558594 684.683594 91.554688 684.683594 C 91.546875 684.683594 91.542969 684.683594 91.539062 684.683594 C 91.53125 684.683594 91.527344 684.683594 91.523438 684.683594 C 91.519531 684.683594 91.511719 684.683594 91.507812 684.683594 C 91.503906 684.683594 91.496094 684.683594 91.492188 684.683594 C 91.488281 684.683594 91.484375 684.683594 91.476562 684.683594 C 91.472656 684.683594 91.46875 684.683594 91.460938 684.683594 C 91.457031 684.683594 91.453125 684.683594 91.449219 684.683594 C 91.441406 684.683594 91.4375 684.683594 91.433594 684.683594 C 91.425781 684.683594 91.421875 684.683594 91.417969 684.683594 C 91.414062 684.683594 91.40625 684.683594 91.402344 684.683594 C 91.398438 684.683594 91.390625 684.683594 91.386719 684.683594 C 91.382812 684.683594 91.378906 684.683594 91.371094 684.683594 C 91.367188 684.683594 91.363281 684.683594 91.355469 684.683594 C 91.351562 684.683594 91.347656 684.683594 91.34375 684.683594 C 91.335938 684.683594 91.332031 684.683594 91.328125 684.683594 C 91.320312 684.683594 91.316406 684.683594 91.3125 684.683594 C 91.308594 684.683594 91.300781 684.683594 91.296875 684.683594 C 91.292969 684.683594 91.285156 684.683594 91.28125 684.683594 C 91.277344 684.683594 91.273438 684.683594 91.265625 684.683594 C 91.261719 684.683594 91.257812 684.683594 91.25 684.683594 C 91.246094 684.683594 91.242188 684.683594 91.238281 684.683594 C 91.230469 684.683594 91.226562 684.683594 91.222656 684.683594 C 91.21875 684.683594 91.210938 684.683594 91.207031 684.683594 C 91.203125 684.683594 91.195312 684.683594 91.191406 684.683594 C 91.1875 684.683594 91.183594 684.683594 91.175781 684.683594 C 91.171875 684.683594 91.167969 684.683594 91.160156 684.683594 C 91.15625 684.683594 91.152344 684.683594 91.148438 684.683594 C 91.140625 684.683594 91.136719 684.683594 91.132812 684.683594 C 91.128906 684.683594 91.121094 684.683594 91.117188 684.683594 C 91.113281 684.683594 91.105469 684.683594 91.101562 684.683594 C 91.097656 684.683594 91.09375 684.683594 91.085938 684.683594 C 91.082031 684.683594 91.078125 684.683594 91.074219 684.683594 C 91.066406 684.683594 91.0625 684.683594 91.058594 684.683594 C 91.050781 684.683594 91.046875 684.683594 91.042969 684.683594 C 91.039062 684.683594 91.03125 684.683594 91.027344 684.683594 C 91.023438 684.683594 91.019531 684.683594 91.011719 684.683594 C 91.007812 684.683594 91.003906 684.683594 91 684.683594 C 90.992188 684.683594 90.988281 684.683594 90.984375 684.683594 C 90.976562 684.683594 90.972656 684.683594 90.96875 684.683594 C 90.964844 684.683594 90.957031 684.683594 90.953125 684.683594 C 90.949219 684.683594 90.945312 684.683594 90.9375 684.683594 C 90.933594 684.683594 90.929688 684.683594 90.925781 684.683594 C 90.917969 684.683594 90.914062 684.683594 90.910156 684.683594 C 90.902344 684.683594 90.898438 684.683594 90.894531 684.683594 C 90.890625 684.683594 90.882812 684.683594 90.878906 684.683594 C 90.875 684.683594 90.871094 684.683594 90.863281 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90.632812 684.683594 90.628906 684.683594 C 90.625 684.683594 90.621094 684.683594 90.613281 684.683594 C 90.609375 684.683594 90.605469 684.683594 90.601562 684.683594 C 90.59375 684.683594 90.589844 684.683594 90.585938 684.683594 C 90.582031 684.683594 90.574219 684.683594 90.570312 684.683594 C 90.566406 684.683594 90.5625 684.683594 90.554688 684.683594 C 90.550781 684.683594 90.546875 684.683594 90.542969 684.683594 L 90.542969 687.585938 C 90.546875 687.585938 90.550781 687.585938 90.554688 687.585938 C 90.5625 687.585938 90.566406 687.585938 90.570312 687.585938 C 90.574219 687.585938 90.582031 687.585938 90.585938 687.585938 C 90.589844 687.585938 90.59375 687.585938 90.601562 687.585938 C 90.605469 687.585938 90.609375 687.585938 90.613281 687.585938 C 90.621094 687.585938 90.625 687.585938 90.628906 687.585938 C 90.632812 687.585938 90.640625 687.585938 90.644531 687.585938 C 90.648438 687.585938 90.652344 687.585938 90.660156 687.585938 C 90.664062 687.585938 90.667969 687.585938 90.671875 687.585938 C 90.679688 687.585938 90.683594 687.585938 90.6875 687.585938 C 90.691406 687.585938 90.699219 687.585938 90.703125 687.585938 C 90.707031 687.585938 90.710938 687.585938 90.71875 687.585938 C 90.722656 687.585938 90.726562 687.585938 90.730469 687.585938 C 90.738281 687.585938 90.742188 687.585938 90.746094 687.585938 C 90.75 687.585938 90.757812 687.585938 90.761719 687.585938 C 90.765625 687.585938 90.769531 687.585938 90.777344 687.585938 C 90.78125 687.585938 90.785156 687.585938 90.789062 687.585938 C 90.796875 687.585938 90.800781 687.585938 90.804688 687.585938 C 90.8125 687.585938 90.816406 687.585938 90.820312 687.585938 C 90.824219 687.585938 90.832031 687.585938 90.835938 687.585938 C 90.839844 687.585938 90.84375 687.585938 90.851562 687.585938 C 90.855469 687.585938 90.859375 687.585938 90.863281 687.585938 C 90.871094 687.585938 90.875 687.585938 90.878906 687.585938 C 90.882812 687.585938 90.890625 687.585938 90.894531 687.585938 C 90.898438 687.585938 90.902344 687.585938 90.910156 687.585938 C 90.914062 687.585938 90.917969 687.585938 90.925781 687.585938 C 90.929688 687.585938 90.933594 687.585938 90.9375 687.585938 C 90.945312 687.585938 90.949219 687.585938 90.953125 687.585938 C 90.957031 687.585938 90.964844 687.585938 90.96875 687.585938 C 90.972656 687.585938 90.976562 687.585938 90.984375 687.585938 C 90.988281 687.585938 90.992188 687.585938 91 687.585938 C 91.003906 687.585938 91.007812 687.585938 91.011719 687.585938 C 91.019531 687.585938 91.023438 687.585938 91.027344 687.585938 C 91.03125 687.585938 91.039062 687.585938 91.042969 687.585938 C 91.046875 687.585938 91.050781 687.585938 91.058594 687.585938 C 91.0625 687.585938 91.066406 687.585938 91.074219 687.585938 C 91.078125 687.585938 91.082031 687.585938 91.085938 687.585938 C 91.09375 687.585938 91.097656 687.585938 91.101562 687.585938 C 91.105469 687.585938 91.113281 687.585938 91.117188 687.585938 C 91.121094 687.585938 91.128906 687.585938 91.132812 687.585938 C 91.136719 687.585938 91.140625 687.585938 91.148438 687.585938 C 91.152344 687.585938 91.15625 687.585938 91.160156 687.585938 C 91.167969 687.585938 91.171875 687.585938 91.175781 687.585938 C 91.183594 687.585938 91.1875 687.585938 91.191406 687.585938 C 91.195312 687.585938 91.203125 687.585938 91.207031 687.585938 C 91.210938 687.585938 91.21875 687.585938 91.222656 687.585938 C 91.226562 687.585938 91.230469 687.585938 91.238281 687.585938 C 91.242188 687.585938 91.246094 687.585938 91.25 687.585938 C 91.257812 687.585938 91.261719 687.585938 91.265625 687.585938 C 91.273438 687.585938 91.277344 687.585938 91.28125 687.585938 C 91.285156 687.585938 91.292969 687.585938 91.296875 687.585938 C 91.300781 687.585938 91.308594 687.585938 91.3125 687.585938 C 91.316406 687.585938 91.320312 687.585938 91.328125 687.585938 C 91.332031 687.585938 91.335938 687.585938 91.34375 687.585938 C 91.347656 687.585938 91.351562 687.585938 91.355469 687.585938 C 91.363281 687.585938 91.367188 687.585938 91.371094 687.585938 C 91.378906 687.585938 91.382812 687.585938 91.386719 687.585938 C 91.390625 687.585938 91.398438 687.585938 91.402344 687.585938 C 91.40625 687.585938 91.414062 687.585938 91.417969 687.585938 C 91.421875 687.585938 91.425781 687.585938 91.433594 687.585938 C 91.4375 687.585938 91.441406 687.585938 91.449219 687.585938 C 91.453125 687.585938 91.457031 687.585938 91.460938 687.585938 C 91.46875 687.585938 91.472656 687.585938 91.476562 687.585938 C 91.484375 687.585938 91.488281 687.585938 91.492188 687.585938 C 91.496094 687.585938 91.503906 687.585938 91.507812 687.585938 C 91.511719 687.585938 91.519531 687.585938 91.523438 687.585938 C 91.527344 687.585938 91.53125 687.585938 91.539062 687.585938 C 91.542969 687.585938 91.546875 687.585938 91.554688 687.585938 C 91.558594 687.585938 91.5625 687.585938 91.566406 687.585938 C 91.574219 687.585938 91.578125 687.585938 91.582031 687.585938 C 91.589844 687.585938 91.59375 687.585938 91.597656 687.585938 C 91.605469 687.585938 91.609375 687.585938 91.613281 687.585938 C 91.617188 687.585938 91.625 687.585938 91.628906 687.585938 C 91.632812 687.585938 91.640625 687.585938 91.644531 687.585938 C 91.648438 687.585938 91.652344 687.585938 91.660156 687.585938 C 91.664062 687.585938 91.667969 687.585938 91.675781 687.585938 C 91.679688 687.585938 91.683594 687.585938 91.691406 687.585938 C 91.695312 687.585938 91.699219 687.585938 91.703125 687.585938 C 91.710938 687.585938 91.714844 687.585938 91.71875 687.585938 C 91.726562 687.585938 91.730469 687.585938 91.734375 687.585938 C 91.742188 687.585938 91.746094 687.585938 91.75 687.585938 C 91.753906 687.585938 91.761719 687.585938 91.765625 687.585938 C 91.769531 687.585938 91.777344 687.585938 91.78125 687.585938 C 91.785156 687.585938 91.792969 687.585938 91.796875 687.585938 C 91.800781 687.585938 91.804688 687.585938 91.8125 687.585938 C 91.816406 687.585938 91.820312 687.585938 91.828125 687.585938 C 91.832031 687.585938 91.835938 687.585938 91.84375 687.585938 C 91.847656 687.585938 91.851562 687.585938 91.855469 687.585938 C 91.863281 687.585938 91.867188 687.585938 91.871094 687.585938 C 91.878906 687.585938 91.882812 687.585938 91.886719 687.585938 C 91.894531 687.585938 91.898438 687.585938 91.902344 687.585938 C 91.910156 687.585938 91.914062 687.585938 91.917969 687.585938 C 91.921875 687.585938 91.929688 687.585938 91.933594 687.585938 C 91.9375 687.585938 91.945312 687.585938 91.949219 687.585938 C 91.953125 687.585938 91.960938 687.585938 91.964844 687.585938 C 91.96875 687.585938 91.976562 687.585938 91.980469 687.585938 C 91.984375 687.585938 91.988281 687.585938 91.996094 687.585938 C 92 687.585938 92.003906 687.585938 92.011719 687.585938 C 92.015625 687.585938 92.019531 687.585938 92.027344 687.585938 C 92.03125 687.585938 92.035156 687.585938 92.042969 687.585938 C 92.046875 687.585938 92.050781 687.585938 92.054688 687.585938 C 92.0625 687.585938 92.066406 687.585938 92.070312 687.585938 C 92.078125 687.585938 92.082031 687.585938 92.085938 687.585938 C 92.09375 687.585938 92.097656 687.585938 92.101562 687.585938 C 92.109375 687.585938 92.113281 687.585938 92.117188 687.585938 C 92.121094 687.585938 92.128906 687.585938 92.132812 687.585938 C 92.136719 687.585938 92.144531 687.585938 92.148438 687.585938 C 92.152344 687.585938 92.160156 687.585938 92.164062 687.585938 C 92.167969 687.585938 92.175781 687.585938 92.179688 687.585938 C 92.183594 687.585938 92.191406 687.585938 92.195312 687.585938 C 92.199219 687.585938 92.203125 687.585938 92.210938 687.585938 C 92.214844 687.585938 92.21875 687.585938 92.226562 687.585938 C 92.230469 687.585938 92.234375 687.585938 92.242188 687.585938 C 92.246094 687.585938 92.25 687.585938 92.257812 687.585938 C 92.261719 687.585938 92.265625 687.585938 92.273438 687.585938 C 92.277344 687.585938 92.28125 687.585938 92.289062 687.585938 C 92.292969 687.585938 92.296875 687.585938 92.300781 687.585938 C 92.308594 687.585938 92.3125 687.585938 92.316406 687.585938 C 92.324219 687.585938 92.328125 687.585938 92.332031 687.585938 C 92.339844 687.585938 92.34375 687.585938 92.347656 687.585938 C 92.355469 687.585938 92.359375 687.585938 92.363281 687.585938 C 92.371094 687.585938 92.375 687.585938 92.378906 687.585938 C 92.386719 687.585938 92.390625 687.585938 92.394531 687.585938 C 92.402344 687.585938 92.40625 687.585938 92.410156 687.585938 C 92.417969 687.585938 92.421875 687.585938 92.425781 687.585938 C 92.429688 687.585938 92.4375 687.585938 92.441406 687.585938 C 92.445312 687.585938 92.453125 687.585938 92.457031 687.585938 C 92.460938 687.585938 92.46875 687.585938 92.472656 687.585938 C 92.476562 687.585938 92.484375 687.585938 92.488281 687.585938 C 92.492188 687.585938 92.5 687.585938 92.503906 687.585938 C 92.507812 687.585938 92.515625 687.585938 92.519531 687.585938 C 92.523438 687.585938 92.53125 687.585938 92.535156 687.585938 C 92.539062 687.585938 92.546875 687.585938 92.550781 687.585938 C 92.554688 687.585938 92.5625 687.585938 92.566406 687.585938 C 92.570312 687.585938 92.578125 687.585938 92.582031 687.585938 C 92.585938 687.585938 92.589844 687.585938 92.597656 687.585938 C 92.601562 687.585938 92.605469 687.585938 92.613281 687.585938 C 92.617188 687.585938 92.621094 687.585938 92.628906 687.585938 C 92.632812 687.585938 92.636719 687.585938 92.644531 687.585938 C 92.648438 687.585938 92.652344 687.585938 92.660156 687.585938 C 92.664062 687.585938 92.667969 687.585938 92.675781 687.585938 C 92.679688 687.585938 92.683594 687.585938 92.691406 687.585938 C 92.695312 687.585938 92.699219 687.585938 92.707031 687.585938 C 92.710938 687.585938 92.714844 687.585938 92.722656 687.585938 C 92.726562 687.585938 92.730469 687.585938 92.738281 687.585938 C 92.742188 687.585938 92.746094 687.585938 92.753906 687.585938 C 92.757812 687.585938 92.761719 687.585938 92.769531 687.585938 C 92.773438 687.585938 92.777344 687.585938 92.785156 687.585938 C 92.789062 687.585938 92.792969 687.585938 92.800781 687.585938 C 92.804688 687.585938 92.808594 687.585938 92.816406 687.585938 C 92.820312 687.585938 92.824219 687.585938 92.832031 687.585938 C 92.835938 687.585938 92.839844 687.585938 92.847656 687.585938 C 92.851562 687.585938 92.855469 687.585938 92.863281 687.585938 C 92.867188 687.585938 92.871094 687.585938 92.878906 687.585938 C 92.882812 687.585938 92.886719 687.585938 92.894531 687.585938 C 92.898438 687.585938 92.902344 687.585938 92.910156 687.585938 C 92.914062 687.585938 92.917969 687.585938 92.925781 687.585938 C 92.929688 687.585938 92.933594 687.585938 92.941406 687.585938 C 92.945312 687.585938 92.949219 687.585938 92.957031 687.585938 C 92.960938 687.585938 92.964844 687.585938 92.972656 687.585938 C 92.976562 687.585938 92.980469 687.585938 92.988281 687.585938 C 92.992188 687.585938 92.996094 687.585938 93.003906 687.585938 C 93.007812 687.585938 93.011719 687.585938 93.019531 687.585938 C 93.023438 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C 20.246094 610.917969 20.246094 610.914062 20.246094 610.90625 C 20.246094 610.902344 20.246094 610.898438 20.246094 610.890625 C 20.246094 610.886719 20.246094 610.882812 20.246094 610.875 C 20.246094 610.871094 20.246094 610.867188 20.246094 610.859375 C 20.246094 610.855469 20.246094 610.851562 20.246094 610.84375 C 20.246094 610.839844 20.246094 610.835938 20.246094 610.828125 C 20.246094 610.824219 20.246094 610.816406 20.246094 610.8125 C 20.246094 610.808594 20.246094 610.800781 20.246094 610.796875 C 20.246094 610.792969 20.246094 610.785156 20.246094 610.78125 C 20.246094 610.777344 20.246094 610.769531 20.246094 610.765625 C 20.246094 610.761719 20.246094 610.753906 20.246094 610.75 C 20.246094 610.746094 20.246094 610.738281 20.246094 610.734375 C 20.246094 610.730469 20.246094 610.722656 20.246094 610.71875 C 20.246094 610.714844 20.246094 610.707031 20.246094 610.703125 C 20.246094 610.695312 20.246094 610.691406 20.246094 610.6875 C 20.246094 610.679688 20.246094 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C 20.246094 610.429688 20.246094 610.421875 20.246094 610.417969 C 20.246094 610.414062 20.246094 610.40625 20.246094 610.402344 C 20.246094 610.398438 20.246094 610.390625 20.246094 610.386719 C 20.246094 610.382812 20.246094 610.375 20.246094 610.371094 C 20.246094 610.367188 20.246094 610.359375 20.246094 610.355469 C 20.246094 610.351562 20.246094 610.34375 20.246094 610.339844 C 20.246094 610.332031 20.246094 610.328125 20.246094 610.324219 C 20.246094 610.316406 20.246094 610.3125 20.246094 610.308594 C 20.246094 610.300781 20.246094 610.296875 20.246094 610.292969 C 20.246094 610.285156 20.246094 610.28125 20.246094 610.277344 C 20.246094 610.269531 20.246094 610.265625 20.246094 610.261719 C 20.246094 610.253906 20.246094 610.25 20.246094 610.246094 C 20.246094 610.238281 20.246094 610.234375 20.246094 610.230469 C 20.246094 610.222656 20.246094 610.21875 20.246094 610.214844 C 20.246094 610.207031 20.246094 610.203125 20.246094 610.195312 C 20.246094 610.191406 20.246094 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20.246094 225.9375 20.246094 225.929688 20.246094 225.925781 C 20.246094 225.921875 20.246094 225.914062 20.246094 225.910156 C 20.246094 225.90625 20.246094 225.898438 20.246094 225.894531 C 20.246094 225.890625 20.246094 225.882812 20.246094 225.878906 C 20.246094 225.875 20.246094 225.867188 20.246094 225.863281 C 20.246094 225.859375 20.246094 225.851562 20.246094 225.847656 C 20.246094 225.84375 20.246094 225.835938 20.246094 225.832031 C 20.246094 225.828125 20.246094 225.820312 20.246094 225.816406 C 20.246094 225.8125 20.246094 225.804688 20.246094 225.800781 C 20.246094 225.796875 20.246094 225.789062 20.246094 225.785156 C 20.246094 225.78125 20.246094 225.773438 20.246094 225.769531 C 20.246094 225.765625 20.246094 225.757812 20.246094 225.753906 C 20.246094 225.75 20.246094 225.742188 20.246094 225.738281 C 20.246094 225.734375 20.246094 225.726562 20.246094 225.722656 C 20.246094 225.71875 20.246094 225.710938 20.246094 225.707031 C 20.246094 225.703125 20.246094 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20.246094 225.453125 20.246094 225.445312 20.246094 225.441406 C 20.246094 225.4375 20.246094 225.429688 20.246094 225.425781 C 20.246094 225.421875 20.246094 225.414062 20.246094 225.410156 C 20.246094 225.40625 20.246094 225.398438 20.246094 225.394531 C 20.246094 225.390625 20.246094 225.382812 20.246094 225.378906 C 20.246094 225.375 20.246094 225.367188 20.246094 225.363281 C 20.246094 225.359375 20.246094 225.355469 20.246094 225.347656 C 20.246094 225.34375 20.246094 225.339844 20.246094 225.332031 C 20.246094 225.328125 20.246094 225.324219 20.246094 225.316406 C 20.246094 225.3125 20.246094 225.308594 20.246094 225.300781 C 20.246094 225.296875 20.246094 225.292969 20.246094 225.285156 C 20.246094 225.28125 20.246094 225.277344 20.246094 225.269531 C 20.246094 225.265625 20.246094 225.261719 20.246094 225.253906 C 20.246094 225.25 20.246094 225.246094 20.246094 225.238281 C 20.246094 225.234375 20.246094 225.230469 20.246094 225.222656 C 20.246094 225.21875 20.246094 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476.078125 783.695312 C 476.082031 783.695312 476.089844 783.695312 476.09375 783.695312 C 476.097656 783.695312 476.105469 783.695312 476.109375 783.695312 C 476.113281 783.695312 476.121094 783.695312 476.125 783.695312 C 476.128906 783.695312 476.136719 783.695312 476.140625 783.695312 C 476.144531 783.695312 476.152344 783.695312 476.15625 783.695312 C 476.160156 783.695312 476.167969 783.695312 476.171875 783.695312 C 476.175781 783.695312 476.183594 783.695312 476.1875 783.695312 C 476.191406 783.695312 476.199219 783.695312 476.203125 783.695312 C 476.207031 783.695312 476.214844 783.695312 476.21875 783.695312 C 476.222656 783.695312 476.230469 783.695312 476.234375 783.695312 C 476.238281 783.695312 476.246094 783.695312 476.25 783.695312 C 476.253906 783.695312 476.261719 783.695312 476.265625 783.695312 C 476.269531 783.695312 476.277344 783.695312 476.28125 783.695312 C 476.285156 783.695312 476.292969 783.695312 476.296875 783.695312 C 476.300781 783.695312 476.308594 783.695312 476.3125 783.695312 C 476.316406 783.695312 476.324219 783.695312 476.328125 783.695312 C 476.332031 783.695312 476.339844 783.695312 476.34375 783.695312 C 476.347656 783.695312 476.355469 783.695312 476.359375 783.695312 C 476.363281 783.695312 476.371094 783.695312 476.375 783.695312 C 476.378906 783.695312 476.386719 783.695312 476.390625 783.695312 C 476.394531 783.695312 476.402344 783.695312 476.40625 783.695312 C 476.410156 783.695312 476.417969 783.695312 476.421875 783.695312 C 476.425781 783.695312 476.433594 783.695312 476.4375 783.695312 C 476.441406 783.695312 476.449219 783.695312 476.453125 783.695312 C 476.457031 783.695312 476.464844 783.695312 476.46875 783.695312 C 476.476562 783.695312 476.480469 783.695312 476.484375 783.695312 C 476.492188 783.695312 476.496094 783.695312 476.5 783.695312 C 476.507812 783.695312 476.511719 783.695312 476.515625 783.695312 C 476.523438 783.695312 476.527344 783.695312 476.53125 783.695312 C 476.539062 783.695312 476.542969 783.695312 476.546875 783.695312 C 476.554688 783.695312 476.558594 783.695312 476.5625 783.695312 C 476.570312 783.695312 476.574219 783.695312 476.578125 783.695312 C 476.585938 783.695312 476.589844 783.695312 476.59375 783.695312 C 476.601562 783.695312 476.605469 783.695312 476.609375 783.695312 C 476.617188 783.695312 476.621094 783.695312 476.625 783.695312 C 476.632812 783.695312 476.636719 783.695312 476.640625 783.695312 C 476.648438 783.695312 476.652344 783.695312 476.65625 783.695312 C 476.664062 783.695312 476.667969 783.695312 476.671875 783.695312 C 476.679688 783.695312 476.683594 783.695312 476.6875 783.695312 C 476.695312 783.695312 476.699219 783.695312 476.703125 783.695312 C 476.710938 783.695312 476.714844 783.695312 476.71875 783.695312 C 476.726562 783.695312 476.730469 783.695312 476.738281 783.695312 C 476.742188 783.695312 476.746094 783.695312 476.753906 783.695312 C 476.757812 783.695312 476.761719 783.695312 476.769531 783.695312 C 476.773438 783.695312 476.777344 783.695312 476.785156 783.695312 C 476.789062 783.695312 476.792969 783.695312 476.800781 783.695312 C 476.804688 783.695312 476.808594 783.695312 476.816406 783.695312 C 476.820312 783.695312 476.824219 783.695312 476.832031 783.695312 C 476.835938 783.695312 476.839844 783.695312 476.847656 783.695312 C 476.851562 783.695312 476.855469 783.695312 476.863281 783.695312 C 476.867188 783.695312 476.871094 783.695312 476.878906 783.695312 C 476.882812 783.695312 476.886719 783.695312 476.894531 783.695312 C 476.898438 783.695312 476.90625 783.695312 476.910156 783.695312 C 476.914062 783.695312 476.921875 783.695312 476.925781 783.695312 C 476.929688 783.695312 476.9375 783.695312 476.941406 783.695312 C 476.945312 783.695312 476.953125 783.695312 476.957031 783.695312 C 476.960938 783.695312 476.96875 783.695312 476.972656 783.695312 C 476.976562 783.695312 476.984375 783.695312 476.988281 783.695312 C 476.992188 783.695312 477 783.695312 477.003906 783.695312 C 477.007812 783.695312 477.015625 783.695312 477.019531 783.695312 C 477.023438 783.695312 477.03125 783.695312 477.035156 783.695312 C 477.039062 783.695312 477.046875 783.695312 477.050781 783.695312 C 477.054688 783.695312 477.0625 783.695312 477.066406 783.695312 C 477.074219 783.695312 477.078125 783.695312 477.082031 783.695312 C 477.089844 783.695312 477.09375 783.695312 477.097656 783.695312 C 477.105469 783.695312 477.109375 783.695312 477.113281 783.695312 C 477.121094 783.695312 477.125 783.695312 477.128906 783.695312 C 477.136719 783.695312 477.140625 783.695312 477.144531 783.695312 C 477.152344 783.695312 477.15625 783.695312 477.160156 783.695312 C 477.167969 783.695312 477.171875 783.695312 477.175781 783.695312 C 477.183594 783.695312 477.1875 783.695312 477.191406 783.695312 C 477.199219 783.695312 477.203125 783.695312 477.210938 783.695312 C 477.214844 783.695312 477.21875 783.695312 477.226562 783.695312 C 477.230469 783.695312 477.234375 783.695312 477.242188 783.695312 C 477.246094 783.695312 477.25 783.695312 477.257812 783.695312 C 477.261719 783.695312 477.265625 783.695312 477.273438 783.695312 C 477.277344 783.695312 477.28125 783.695312 477.289062 783.695312 C 477.292969 783.695312 477.296875 783.695312 477.304688 783.695312 C 477.308594 783.695312 477.3125 783.695312 477.320312 783.695312 C 477.324219 783.695312 477.332031 783.695312 477.335938 783.695312 C 477.339844 783.695312 477.347656 783.695312 477.351562 783.695312 C 477.355469 783.695312 477.363281 783.695312 477.367188 783.695312 C 477.371094 783.695312 477.378906 783.695312 477.382812 783.695312 C 477.386719 783.695312 477.394531 783.695312 477.398438 783.695312 C 477.402344 783.695312 477.410156 783.695312 477.414062 783.695312 C 477.417969 783.695312 477.425781 783.695312 477.429688 783.695312 C 477.433594 783.695312 477.441406 783.695312 477.445312 783.695312 C 477.449219 783.695312 477.457031 783.695312 477.460938 783.695312 C 477.46875 783.695312 477.472656 783.695312 477.476562 783.695312 C 477.484375 783.695312 477.488281 783.695312 477.492188 783.695312 C 477.5 783.695312 477.503906 783.695312 477.507812 783.695312 C 477.515625 783.695312 477.519531 783.695312 477.523438 783.695312 C 477.53125 783.695312 477.535156 783.695312 477.539062 783.695312 C 477.546875 783.695312 477.550781 783.695312 477.554688 783.695312 C 477.5625 783.695312 477.566406 783.695312 477.570312 783.695312 C 477.578125 783.695312 477.582031 783.695312 477.589844 783.695312 C 477.59375 783.695312 477.597656 783.695312 477.605469 783.695312 C 477.609375 783.695312 477.613281 783.695312 477.621094 783.695312 C 477.625 783.695312 477.628906 783.695312 477.636719 783.695312 C 477.640625 783.695312 477.644531 783.695312 477.652344 783.695312 C 477.65625 783.695312 477.660156 783.695312 477.667969 783.695312 C 477.671875 783.695312 477.675781 783.695312 477.683594 783.695312 C 477.6875 783.695312 477.691406 783.695312 477.699219 783.695312 C 477.703125 783.695312 477.710938 783.695312 477.714844 783.695312 C 477.71875 783.695312 477.726562 783.695312 477.730469 783.695312 C 477.734375 783.695312 477.742188 783.695312 477.746094 783.695312 C 477.75 783.695312 477.757812 783.695312 477.761719 783.695312 C 477.765625 783.695312 477.773438 783.695312 477.777344 783.695312 C 477.78125 783.695312 477.789062 783.695312 477.792969 783.695312 C 477.796875 783.695312 477.804688 783.695312 477.808594 783.695312 C 477.8125 783.695312 477.820312 783.695312 477.824219 783.695312 C 477.832031 783.695312 477.835938 783.695312 477.839844 783.695312 C 477.847656 783.695312 477.851562 783.695312 477.855469 783.695312 C 477.863281 783.695312 477.867188 783.695312 477.871094 783.695312 C 477.878906 783.695312 477.882812 783.695312 477.886719 783.695312 C 477.894531 783.695312 477.898438 783.695312 477.902344 783.695312 C 477.910156 783.695312 477.914062 783.695312 477.917969 783.695312 C 477.925781 783.695312 477.929688 783.695312 477.933594 783.695312 C 477.941406 783.695312 477.945312 783.695312 477.953125 783.695312 C 477.957031 783.695312 477.960938 783.695312 477.96875 783.695312 C 477.972656 783.695312 477.976562 783.695312 477.984375 783.695312 C 477.988281 783.695312 477.992188 783.695312 478 783.695312 C 478.003906 783.695312 478.007812 783.695312 478.015625 783.695312 C 478.019531 783.695312 478.023438 783.695312 478.03125 783.695312 C 478.035156 783.695312 478.039062 783.695312 478.046875 783.695312 C 478.050781 783.695312 478.054688 783.695312 478.0625 783.695312 C 478.066406 783.695312 478.074219 783.695312 478.078125 783.695312 C 478.082031 783.695312 478.089844 783.695312 478.09375 783.695312 C 478.097656 783.695312 478.105469 783.695312 478.109375 783.695312 C 478.113281 783.695312 478.121094 783.695312 478.125 783.695312 C 478.128906 783.695312 478.136719 783.695312 478.140625 783.695312 C 478.144531 783.695312 478.152344 783.695312 478.15625 783.695312 C 478.160156 783.695312 478.167969 783.695312 478.171875 783.695312 C 478.175781 783.695312 478.183594 783.695312 478.1875 783.695312 C 478.191406 783.695312 478.199219 783.695312 478.203125 783.695312 C 478.210938 783.695312 478.214844 783.695312 478.21875 783.695312 C 478.226562 783.695312 478.230469 783.695312 478.234375 783.695312 C 478.242188 783.695312 478.246094 783.695312 478.25 783.695312 C 478.257812 783.695312 478.261719 783.695312 478.265625 783.695312 C 478.273438 783.695312 478.277344 783.695312 478.28125 783.695312 C 478.289062 783.695312 478.292969 783.695312 478.296875 783.695312 C 478.304688 783.695312 478.308594 783.695312 478.3125 783.695312 C 478.320312 783.695312 478.324219 783.695312 478.328125 783.695312 C 478.335938 783.695312 478.339844 783.695312 478.347656 783.695312 C 478.351562 783.695312 478.355469 783.695312 478.363281 783.695312 C 478.367188 783.695312 478.371094 783.695312 478.378906 783.695312 C 478.382812 783.695312 478.386719 783.695312 478.394531 783.695312 C 478.398438 783.695312 478.402344 783.695312 478.410156 783.695312 C 478.414062 783.695312 478.417969 783.695312 478.425781 783.695312 C 478.429688 783.695312 478.433594 783.695312 478.441406 783.695312 C 478.445312 783.695312 478.449219 783.695312 478.457031 783.695312 C 478.460938 783.695312 478.464844 783.695312 478.472656 783.695312 C 478.476562 783.695312 478.480469 783.695312 478.488281 783.695312 C 478.492188 783.695312 478.496094 783.695312 478.503906 783.695312 C 478.507812 783.695312 478.515625 783.695312 478.519531 783.695312 C 478.523438 783.695312 478.53125 783.695312 478.535156 783.695312 C 478.539062 783.695312 478.546875 783.695312 478.550781 783.695312 C 478.554688 783.695312 478.5625 783.695312 478.566406 783.695312 C 478.570312 783.695312 478.578125 783.695312 478.582031 783.695312 C 478.585938 783.695312 478.59375 783.695312 478.597656 783.695312 C 478.601562 783.695312 478.609375 783.695312 478.613281 783.695312 C 478.617188 783.695312 478.625 783.695312 478.628906 783.695312 C 478.632812 783.695312 478.640625 783.695312 478.644531 783.695312 C 478.648438 783.695312 478.65625 783.695312 478.660156 783.695312 C 478.664062 783.695312 478.671875 783.695312 478.675781 783.695312 C 478.679688 783.695312 478.6875 783.695312 478.691406 783.695312 C 478.699219 783.695312 478.703125 783.695312 478.707031 783.695312 C 478.714844 783.695312 478.71875 783.695312 478.722656 783.695312 C 478.730469 783.695312 478.734375 783.695312 478.738281 783.695312 C 478.746094 783.695312 478.75 783.695312 478.753906 783.695312 C 478.761719 783.695312 478.765625 783.695312 478.769531 783.695312 C 478.777344 783.695312 478.78125 783.695312 478.785156 783.695312 C 478.792969 783.695312 478.796875 783.695312 478.800781 783.695312 C 478.808594 783.695312 478.8125 783.695312 478.816406 783.695312 C 478.824219 783.695312 478.828125 783.695312 478.832031 783.695312 C 478.839844 783.695312 478.84375 783.695312 478.847656 783.695312 C 478.855469 783.695312 478.859375 783.695312 478.863281 783.695312 C 478.871094 783.695312 478.875 783.695312 478.878906 783.695312 C 478.886719 783.695312 478.890625 783.695312 478.894531 783.695312 C 478.902344 783.695312 478.90625 783.695312 478.910156 783.695312 C 478.917969 783.695312 478.921875 783.695312 478.925781 783.695312 C 478.933594 783.695312 478.9375 783.695312 478.941406 783.695312 C 478.949219 783.695312 478.953125 783.695312 478.957031 783.695312 C 478.964844 783.695312 478.96875 783.695312 478.972656 783.695312 C 478.980469 783.695312 478.984375 783.695312 478.992188 783.695312 C 478.996094 783.695312 479 783.695312 479.007812 783.695312 C 479.011719 783.695312 479.015625 783.695312 479.023438 783.695312 C 479.027344 783.695312 479.03125 783.695312 479.039062 783.695312 C 479.042969 783.695312 479.046875 783.695312 479.054688 783.695312 C 479.058594 783.695312 479.0625 783.695312 479.070312 783.695312 C 479.074219 783.695312 479.078125 783.695312 479.085938 783.695312 C 479.089844 783.695312 479.09375 783.695312 479.101562 783.695312 C 479.105469 783.695312 479.109375 783.695312 479.117188 783.695312 C 479.121094 783.695312 479.125 783.695312 479.132812 783.695312 C 479.136719 783.695312 479.140625 783.695312 479.148438 783.695312 C 479.152344 783.695312 479.15625 783.695312 479.164062 783.695312 C 479.167969 783.695312 479.171875 783.695312 479.179688 783.695312 C 479.183594 783.695312 479.1875 783.695312 479.195312 783.695312 C 479.199219 783.695312 479.203125 783.695312 479.210938 783.695312 C 479.214844 783.695312 479.21875 783.695312 479.226562 783.695312 C 479.230469 783.695312 479.234375 783.695312 479.242188 783.695312 C 479.246094 783.695312 479.25 783.695312 479.257812 783.695312 C 479.261719 783.695312 479.265625 783.695312 479.273438 783.695312 C 479.277344 783.695312 479.28125 783.695312 479.289062 783.695312 C 479.292969 783.695312 479.296875 783.695312 479.304688 783.695312 C 479.308594 783.695312 479.3125 783.695312 479.320312 783.695312 C 479.324219 783.695312 479.328125 783.695312 479.335938 783.695312 C 479.339844 783.695312 479.34375 783.695312 479.351562 783.695312 C 479.355469 783.695312 479.359375 783.695312 479.367188 783.695312 C 479.371094 783.695312 479.375 783.695312 479.382812 783.695312 C 479.386719 783.695312 479.390625 783.695312 479.394531 783.695312 C 479.402344 783.695312 479.40625 783.695312 479.410156 783.695312 C 479.417969 783.695312 479.421875 783.695312 479.425781 783.695312 C 479.433594 783.695312 479.4375 783.695312 479.441406 783.695312 C 479.449219 783.695312 479.453125 783.695312 479.457031 783.695312 C 479.464844 783.695312 479.46875 783.695312 479.472656 783.695312 C 479.480469 783.695312 479.484375 783.695312 479.488281 783.695312 C 479.496094 783.695312 479.5 783.695312 479.503906 783.695312 C 479.511719 783.695312 479.515625 783.695312 479.519531 783.695312 C 479.527344 783.695312 479.53125 783.695312 479.535156 783.695312 C 479.542969 783.695312 479.546875 783.695312 479.550781 783.695312 C 479.558594 783.695312 479.5625 783.695312 479.566406 783.695312 C 479.574219 783.695312 479.578125 783.695312 479.582031 783.695312 C 479.589844 783.695312 479.59375 783.695312 479.597656 783.695312 C 479.605469 783.695312 479.609375 783.695312 479.613281 783.695312 C 479.621094 783.695312 479.625 783.695312 479.628906 783.695312 C 479.636719 783.695312 479.640625 783.695312 479.644531 783.695312 C 479.648438 783.695312 479.65625 783.695312 479.660156 783.695312 C 479.664062 783.695312 479.671875 783.695312 479.675781 783.695312 C 479.679688 783.695312 479.6875 783.695312 479.691406 783.695312 C 479.695312 783.695312 479.703125 783.695312 479.707031 783.695312 C 479.710938 783.695312 479.71875 783.695312 479.722656 783.695312 C 479.726562 783.695312 479.734375 783.695312 479.738281 783.695312 C 479.742188 783.695312 479.75 783.695312 479.753906 783.695312 C 479.757812 783.695312 479.765625 783.695312 479.769531 783.695312 C 479.773438 783.695312 479.78125 783.695312 479.785156 783.695312 C 479.789062 783.695312 479.792969 783.695312 479.800781 783.695312 C 479.804688 783.695312 479.808594 783.695312 479.816406 783.695312 C 479.820312 783.695312 479.824219 783.695312 479.832031 783.695312 C 479.835938 783.695312 479.839844 783.695312 479.847656 783.695312 C 479.851562 783.695312 479.855469 783.695312 479.863281 783.695312 C 479.867188 783.695312 479.871094 783.695312 479.878906 783.695312 C 479.882812 783.695312 479.886719 783.695312 479.894531 783.695312 C 479.898438 783.695312 479.902344 783.695312 479.90625 783.695312 C 479.914062 783.695312 479.917969 783.695312 479.921875 783.695312 C 479.929688 783.695312 479.933594 783.695312 479.9375 783.695312 C 479.945312 783.695312 479.949219 783.695312 479.953125 783.695312 C 479.960938 783.695312 479.964844 783.695312 479.96875 783.695312 C 479.976562 783.695312 479.980469 783.695312 479.984375 783.695312 C 479.988281 783.695312 479.996094 783.695312 480 783.695312 C 480.003906 783.695312 480.011719 783.695312 480.015625 783.695312 C 480.019531 783.695312 480.027344 783.695312 480.03125 783.695312 C 480.035156 783.695312 480.042969 783.695312 480.046875 783.695312 C 480.050781 783.695312 480.058594 783.695312 480.0625 783.695312 C 480.066406 783.695312 480.070312 783.695312 480.078125 783.695312 C 480.082031 783.695312 480.085938 783.695312 480.09375 783.695312 C 480.097656 783.695312 480.101562 783.695312 480.109375 783.695312 C 480.113281 783.695312 480.117188 783.695312 480.125 783.695312 C 480.128906 783.695312 480.132812 783.695312 480.140625 783.695312 C 480.144531 783.695312 480.148438 783.695312 480.152344 783.695312 C 480.160156 783.695312 480.164062 783.695312 480.167969 783.695312 C 480.175781 783.695312 480.179688 783.695312 480.183594 783.695312 C 480.191406 783.695312 480.195312 783.695312 480.199219 783.695312 C 480.207031 783.695312 480.210938 783.695312 480.214844 783.695312 C 480.21875 783.695312 480.226562 783.695312 480.230469 783.695312 C 480.234375 783.695312 480.242188 783.695312 480.246094 783.695312 C 480.25 783.695312 480.257812 783.695312 480.261719 783.695312 C 480.265625 783.695312 480.269531 783.695312 480.277344 783.695312 C 480.28125 783.695312 480.285156 783.695312 480.292969 783.695312 C 480.296875 783.695312 480.300781 783.695312 480.308594 783.695312 C 480.3125 783.695312 480.316406 783.695312 480.324219 783.695312 C 480.328125 783.695312 480.332031 783.695312 480.335938 783.695312 C 480.34375 783.695312 480.347656 783.695312 480.351562 783.695312 C 480.359375 783.695312 480.363281 783.695312 480.367188 783.695312 C 480.375 783.695312 480.378906 783.695312 480.382812 783.695312 C 480.386719 783.695312 480.394531 783.695312 480.398438 783.695312 C 480.402344 783.695312 480.410156 783.695312 480.414062 783.695312 C 480.417969 783.695312 480.425781 783.695312 480.429688 783.695312 C 480.433594 783.695312 480.4375 783.695312 480.445312 783.695312 C 480.449219 783.695312 480.453125 783.695312 480.460938 783.695312 C 480.464844 783.695312 480.46875 783.695312 480.476562 783.695312 C 480.480469 783.695312 480.484375 783.695312 480.488281 783.695312 C 480.496094 783.695312 480.5 783.695312 480.503906 783.695312 C 480.511719 783.695312 480.515625 783.695312 480.519531 783.695312 C 480.523438 783.695312 480.53125 783.695312 480.535156 783.695312 C 480.539062 783.695312 480.546875 783.695312 480.550781 783.695312 C 480.554688 783.695312 480.5625 783.695312 480.566406 783.695312 C 480.570312 783.695312 480.574219 783.695312 480.582031 783.695312 C 480.585938 783.695312 480.589844 783.695312 480.597656 783.695312 C 480.601562 783.695312 480.605469 783.695312 480.609375 783.695312 C 480.617188 783.695312 480.621094 783.695312 480.625 783.695312 C 480.632812 783.695312 480.636719 783.695312 480.640625 783.695312 C 480.648438 783.695312 480.652344 783.695312 480.65625 783.695312 C 480.660156 783.695312 480.667969 783.695312 480.671875 783.695312 C 480.675781 783.695312 480.683594 783.695312 480.6875 783.695312 C 480.691406 783.695312 480.695312 783.695312 480.703125 783.695312 C 480.707031 783.695312 480.710938 783.695312 480.71875 783.695312 C 480.722656 783.695312 480.726562 783.695312 480.730469 783.695312 C 480.738281 783.695312 480.742188 783.695312 480.746094 783.695312 C 480.753906 783.695312 480.757812 783.695312 480.761719 783.695312 C 480.765625 783.695312 480.773438 783.695312 480.777344 783.695312 C 480.78125 783.695312 480.789062 783.695312 480.792969 783.695312 C 480.796875 783.695312 480.800781 783.695312 480.808594 783.695312 C 480.8125 783.695312 480.816406 783.695312 480.824219 783.695312 C 480.828125 783.695312 480.832031 783.695312 480.835938 783.695312 C 480.84375 783.695312 480.847656 783.695312 480.851562 783.695312 C 480.859375 783.695312 480.863281 783.695312 480.867188 783.695312 C 480.871094 783.695312 480.878906 783.695312 480.882812 783.695312 C 480.886719 783.695312 480.890625 783.695312 480.898438 783.695312 C 480.902344 783.695312 480.90625 783.695312 480.914062 783.695312 C 480.917969 783.695312 480.921875 783.695312 480.925781 783.695312 C 480.933594 783.695312 480.9375 783.695312 480.941406 783.695312 C 480.949219 783.695312 480.953125 783.695312 480.957031 783.695312 C 480.960938 783.695312 480.96875 783.695312 480.972656 783.695312 C 480.976562 783.695312 480.984375 783.695312 480.988281 783.695312 C 480.992188 783.695312 480.996094 783.695312 481.003906 783.695312 C 481.007812 783.695312 481.011719 783.695312 481.015625 783.695312 C 481.023438 783.695312 481.027344 783.695312 481.03125 783.695312 C 481.039062 783.695312 481.042969 783.695312 481.046875 783.695312 C 481.050781 783.695312 481.058594 783.695312 481.0625 783.695312 C 481.066406 783.695312 481.070312 783.695312 481.078125 783.695312 C 481.082031 783.695312 481.085938 783.695312 481.09375 783.695312 C 481.097656 783.695312 481.101562 783.695312 481.105469 783.695312 C 481.113281 783.695312 481.117188 783.695312 481.121094 783.695312 C 481.125 783.695312 481.132812 783.695312 481.136719 783.695312 C 481.140625 783.695312 481.144531 783.695312 481.152344 783.695312 C 481.15625 783.695312 481.160156 783.695312 481.167969 783.695312 C 481.171875 783.695312 481.175781 783.695312 481.179688 783.695312 C 481.1875 783.695312 481.191406 783.695312 481.195312 783.695312 C 481.199219 783.695312 481.207031 783.695312 481.210938 783.695312 C 481.214844 783.695312 481.222656 783.695312 481.226562 783.695312 C 481.230469 783.695312 481.234375 783.695312 481.242188 783.695312 C 481.246094 783.695312 481.25 783.695312 481.253906 783.695312 C 481.261719 783.695312 481.265625 783.695312 481.269531 783.695312 C 481.273438 783.695312 481.28125 783.695312 481.285156 783.695312 C 481.289062 783.695312 481.292969 783.695312 481.300781 783.695312 C 481.304688 783.695312 481.308594 783.695312 481.3125 783.695312 C 481.320312 783.695312 481.324219 783.695312 481.328125 783.695312 C 481.335938 783.695312 481.339844 783.695312 481.34375 783.695312 C 481.347656 783.695312 481.355469 783.695312 481.359375 783.695312 C 481.363281 783.695312 481.367188 783.695312 481.375 783.695312 C 481.378906 783.695312 481.382812 783.695312 481.386719 783.695312 C 481.394531 783.695312 481.398438 783.695312 481.402344 783.695312 C 481.40625 783.695312 481.414062 783.695312 481.417969 783.695312 C 481.421875 783.695312 481.425781 783.695312 481.433594 783.695312 C 481.4375 783.695312 481.441406 783.695312 481.445312 783.695312 C 481.453125 783.695312 481.457031 783.695312 481.460938 783.695312 C 481.464844 783.695312 481.472656 783.695312 481.476562 783.695312 C 481.480469 783.695312 481.484375 783.695312 481.492188 783.695312 C 481.496094 783.695312 481.5 783.695312 481.503906 783.695312 C 481.511719 783.695312 481.515625 783.695312 481.519531 783.695312 C 481.523438 783.695312 481.53125 783.695312 481.535156 783.695312 C 481.539062 783.695312 481.542969 783.695312 481.550781 783.695312 C 481.554688 783.695312 481.558594 783.695312 481.5625 783.695312 C 481.570312 783.695312 481.574219 783.695312 481.578125 783.695312 C 481.582031 783.695312 481.589844 783.695312 481.59375 783.695312 C 481.597656 783.695312 481.601562 783.695312 481.609375 783.695312 C 481.613281 783.695312 481.617188 783.695312 481.621094 783.695312 L 481.621094 780.792969 C 481.617188 780.792969 481.613281 780.792969 481.609375 780.792969 C 481.601562 780.792969 481.597656 780.792969 481.59375 780.792969 C 481.589844 780.792969 481.582031 780.792969 481.578125 780.792969 C 481.574219 780.792969 481.570312 780.792969 481.5625 780.792969 C 481.558594 780.792969 481.554688 780.792969 481.550781 780.792969 C 481.542969 780.792969 481.539062 780.792969 481.535156 780.792969 C 481.53125 780.792969 481.523438 780.792969 481.519531 780.792969 C 481.515625 780.792969 481.511719 780.792969 481.503906 780.792969 C 481.5 780.792969 481.496094 780.792969 481.492188 780.792969 C 481.484375 780.792969 481.480469 780.792969 481.476562 780.792969 C 481.472656 780.792969 481.464844 780.792969 481.460938 780.792969 C 481.457031 780.792969 481.453125 780.792969 481.445312 780.792969 C 481.441406 780.792969 481.4375 780.792969 481.433594 780.792969 C 481.425781 780.792969 481.421875 780.792969 481.417969 780.792969 C 481.414062 780.792969 481.40625 780.792969 481.402344 780.792969 C 481.398438 780.792969 481.394531 780.792969 481.386719 780.792969 C 481.382812 780.792969 481.378906 780.792969 481.375 780.792969 C 481.367188 780.792969 481.363281 780.792969 481.359375 780.792969 C 481.355469 780.792969 481.347656 780.792969 481.34375 780.792969 C 481.339844 780.792969 481.335938 780.792969 481.328125 780.792969 C 481.324219 780.792969 481.320312 780.792969 481.3125 780.792969 C 481.308594 780.792969 481.304688 780.792969 481.300781 780.792969 C 481.292969 780.792969 481.289062 780.792969 481.285156 780.792969 C 481.28125 780.792969 481.273438 780.792969 481.269531 780.792969 C 481.265625 780.792969 481.261719 780.792969 481.253906 780.792969 C 481.25 780.792969 481.246094 780.792969 481.242188 780.792969 C 481.234375 780.792969 481.230469 780.792969 481.226562 780.792969 C 481.222656 780.792969 481.214844 780.792969 481.210938 780.792969 C 481.207031 780.792969 481.199219 780.792969 481.195312 780.792969 C 481.191406 780.792969 481.1875 780.792969 481.179688 780.792969 C 481.175781 780.792969 481.171875 780.792969 481.167969 780.792969 C 481.160156 780.792969 481.15625 780.792969 481.152344 780.792969 C 481.144531 780.792969 481.140625 780.792969 481.136719 780.792969 C 481.132812 780.792969 481.125 780.792969 481.121094 780.792969 C 481.117188 780.792969 481.113281 780.792969 481.105469 780.792969 C 481.101562 780.792969 481.097656 780.792969 481.09375 780.792969 C 481.085938 780.792969 481.082031 780.792969 481.078125 780.792969 C 481.070312 780.792969 481.066406 780.792969 481.0625 780.792969 C 481.058594 780.792969 481.050781 780.792969 481.046875 780.792969 C 481.042969 780.792969 481.039062 780.792969 481.03125 780.792969 C 481.027344 780.792969 481.023438 780.792969 481.015625 780.792969 C 481.011719 780.792969 481.007812 780.792969 481.003906 780.792969 C 480.996094 780.792969 480.992188 780.792969 480.988281 780.792969 C 480.984375 780.792969 480.976562 780.792969 480.972656 780.792969 C 480.96875 780.792969 480.960938 780.792969 480.957031 780.792969 C 480.953125 780.792969 480.949219 780.792969 480.941406 780.792969 C 480.9375 780.792969 480.933594 780.792969 480.925781 780.792969 C 480.921875 780.792969 480.917969 780.792969 480.914062 780.792969 C 480.90625 780.792969 480.902344 780.792969 480.898438 780.792969 C 480.890625 780.792969 480.886719 780.792969 480.882812 780.792969 C 480.878906 780.792969 480.871094 780.792969 480.867188 780.792969 C 480.863281 780.792969 480.859375 780.792969 480.851562 780.792969 C 480.847656 780.792969 480.84375 780.792969 480.835938 780.792969 C 480.832031 780.792969 480.828125 780.792969 480.824219 780.792969 C 480.816406 780.792969 480.8125 780.792969 480.808594 780.792969 C 480.800781 780.792969 480.796875 780.792969 480.792969 780.792969 C 480.789062 780.792969 480.78125 780.792969 480.777344 780.792969 C 480.773438 780.792969 480.765625 780.792969 480.761719 780.792969 C 480.757812 780.792969 480.753906 780.792969 480.746094 780.792969 C 480.742188 780.792969 480.738281 780.792969 480.730469 780.792969 C 480.726562 780.792969 480.722656 780.792969 480.71875 780.792969 C 480.710938 780.792969 480.707031 780.792969 480.703125 780.792969 C 480.695312 780.792969 480.691406 780.792969 480.6875 780.792969 C 480.683594 780.792969 480.675781 780.792969 480.671875 780.792969 C 480.667969 780.792969 480.660156 780.792969 480.65625 780.792969 C 480.652344 780.792969 480.648438 780.792969 480.640625 780.792969 C 480.636719 780.792969 480.632812 780.792969 480.625 780.792969 C 480.621094 780.792969 480.617188 780.792969 480.609375 780.792969 C 480.605469 780.792969 480.601562 780.792969 480.597656 780.792969 C 480.589844 780.792969 480.585938 780.792969 480.582031 780.792969 C 480.574219 780.792969 480.570312 780.792969 480.566406 780.792969 C 480.5625 780.792969 480.554688 780.792969 480.550781 780.792969 C 480.546875 780.792969 480.539062 780.792969 480.535156 780.792969 C 480.53125 780.792969 480.523438 780.792969 480.519531 780.792969 C 480.515625 780.792969 480.511719 780.792969 480.503906 780.792969 C 480.5 780.792969 480.496094 780.792969 480.488281 780.792969 C 480.484375 780.792969 480.480469 780.792969 480.476562 780.792969 C 480.46875 780.792969 480.464844 780.792969 480.460938 780.792969 C 480.453125 780.792969 480.449219 780.792969 480.445312 780.792969 C 480.4375 780.792969 480.433594 780.792969 480.429688 780.792969 C 480.425781 780.792969 480.417969 780.792969 480.414062 780.792969 C 480.410156 780.792969 480.402344 780.792969 480.398438 780.792969 C 480.394531 780.792969 480.386719 780.792969 480.382812 780.792969 C 480.378906 780.792969 480.375 780.792969 480.367188 780.792969 C 480.363281 780.792969 480.359375 780.792969 480.351562 780.792969 C 480.347656 780.792969 480.34375 780.792969 480.335938 780.792969 C 480.332031 780.792969 480.328125 780.792969 480.324219 780.792969 C 480.316406 780.792969 480.3125 780.792969 480.308594 780.792969 C 480.300781 780.792969 480.296875 780.792969 480.292969 780.792969 C 480.285156 780.792969 480.28125 780.792969 480.277344 780.792969 C 480.269531 780.792969 480.265625 780.792969 480.261719 780.792969 C 480.257812 780.792969 480.25 780.792969 480.246094 780.792969 C 480.242188 780.792969 480.234375 780.792969 480.230469 780.792969 C 480.226562 780.792969 480.21875 780.792969 480.214844 780.792969 C 480.210938 780.792969 480.207031 780.792969 480.199219 780.792969 C 480.195312 780.792969 480.191406 780.792969 480.183594 780.792969 C 480.179688 780.792969 480.175781 780.792969 480.167969 780.792969 C 480.164062 780.792969 480.160156 780.792969 480.152344 780.792969 C 480.148438 780.792969 480.144531 780.792969 480.140625 780.792969 C 480.132812 780.792969 480.128906 780.792969 480.125 780.792969 C 480.117188 780.792969 480.113281 780.792969 480.109375 780.792969 C 480.101562 780.792969 480.097656 780.792969 480.09375 780.792969 C 480.085938 780.792969 480.082031 780.792969 480.078125 780.792969 C 480.070312 780.792969 480.066406 780.792969 480.0625 780.792969 C 480.058594 780.792969 480.050781 780.792969 480.046875 780.792969 C 480.042969 780.792969 480.035156 780.792969 480.03125 780.792969 C 480.027344 780.792969 480.019531 780.792969 480.015625 780.792969 C 480.011719 780.792969 480.003906 780.792969 480 780.792969 C 479.996094 780.792969 479.988281 780.792969 479.984375 780.792969 C 479.980469 780.792969 479.976562 780.792969 479.96875 780.792969 C 479.964844 780.792969 479.960938 780.792969 479.953125 780.792969 C 479.949219 780.792969 479.945312 780.792969 479.9375 780.792969 C 479.933594 780.792969 479.929688 780.792969 479.921875 780.792969 C 479.917969 780.792969 479.914062 780.792969 479.90625 780.792969 C 479.902344 780.792969 479.898438 780.792969 479.894531 780.792969 C 479.886719 780.792969 479.882812 780.792969 479.878906 780.792969 C 479.871094 780.792969 479.867188 780.792969 479.863281 780.792969 C 479.855469 780.792969 479.851562 780.792969 479.847656 780.792969 C 479.839844 780.792969 479.835938 780.792969 479.832031 780.792969 C 479.824219 780.792969 479.820312 780.792969 479.816406 780.792969 C 479.808594 780.792969 479.804688 780.792969 479.800781 780.792969 C 479.792969 780.792969 479.789062 780.792969 479.785156 780.792969 C 479.78125 780.792969 479.773438 780.792969 479.769531 780.792969 C 479.765625 780.792969 479.757812 780.792969 479.753906 780.792969 C 479.75 780.792969 479.742188 780.792969 479.738281 780.792969 C 479.734375 780.792969 479.726562 780.792969 479.722656 780.792969 C 479.71875 780.792969 479.710938 780.792969 479.707031 780.792969 C 479.703125 780.792969 479.695312 780.792969 479.691406 780.792969 C 479.6875 780.792969 479.679688 780.792969 479.675781 780.792969 C 479.671875 780.792969 479.664062 780.792969 479.660156 780.792969 C 479.65625 780.792969 479.648438 780.792969 479.644531 780.792969 C 479.640625 780.792969 479.636719 780.792969 479.628906 780.792969 C 479.625 780.792969 479.621094 780.792969 479.613281 780.792969 C 479.609375 780.792969 479.605469 780.792969 479.597656 780.792969 C 479.59375 780.792969 479.589844 780.792969 479.582031 780.792969 C 479.578125 780.792969 479.574219 780.792969 479.566406 780.792969 C 479.5625 780.792969 479.558594 780.792969 479.550781 780.792969 C 479.546875 780.792969 479.542969 780.792969 479.535156 780.792969 C 479.53125 780.792969 479.527344 780.792969 479.519531 780.792969 C 479.515625 780.792969 479.511719 780.792969 479.503906 780.792969 C 479.5 780.792969 479.496094 780.792969 479.488281 780.792969 C 479.484375 780.792969 479.480469 780.792969 479.472656 780.792969 C 479.46875 780.792969 479.464844 780.792969 479.457031 780.792969 C 479.453125 780.792969 479.449219 780.792969 479.441406 780.792969 C 479.4375 780.792969 479.433594 780.792969 479.425781 780.792969 C 479.421875 780.792969 479.417969 780.792969 479.410156 780.792969 C 479.40625 780.792969 479.402344 780.792969 479.394531 780.792969 C 479.390625 780.792969 479.386719 780.792969 479.382812 780.792969 C 479.375 780.792969 479.371094 780.792969 479.367188 780.792969 C 479.359375 780.792969 479.355469 780.792969 479.351562 780.792969 C 479.34375 780.792969 479.339844 780.792969 479.335938 780.792969 C 479.328125 780.792969 479.324219 780.792969 479.320312 780.792969 C 479.3125 780.792969 479.308594 780.792969 479.304688 780.792969 C 479.296875 780.792969 479.292969 780.792969 479.289062 780.792969 C 479.28125 780.792969 479.277344 780.792969 479.273438 780.792969 C 479.265625 780.792969 479.261719 780.792969 479.257812 780.792969 C 479.25 780.792969 479.246094 780.792969 479.242188 780.792969 C 479.234375 780.792969 479.230469 780.792969 479.226562 780.792969 C 479.21875 780.792969 479.214844 780.792969 479.210938 780.792969 C 479.203125 780.792969 479.199219 780.792969 479.195312 780.792969 C 479.1875 780.792969 479.183594 780.792969 479.179688 780.792969 C 479.171875 780.792969 479.167969 780.792969 479.164062 780.792969 C 479.15625 780.792969 479.152344 780.792969 479.148438 780.792969 C 479.140625 780.792969 479.136719 780.792969 479.132812 780.792969 C 479.125 780.792969 479.121094 780.792969 479.117188 780.792969 C 479.109375 780.792969 479.105469 780.792969 479.101562 780.792969 C 479.09375 780.792969 479.089844 780.792969 479.085938 780.792969 C 479.078125 780.792969 479.074219 780.792969 479.070312 780.792969 C 479.0625 780.792969 479.058594 780.792969 479.054688 780.792969 C 479.046875 780.792969 479.042969 780.792969 479.039062 780.792969 C 479.03125 780.792969 479.027344 780.792969 479.023438 780.792969 C 479.015625 780.792969 479.011719 780.792969 479.007812 780.792969 C 479 780.792969 478.996094 780.792969 478.992188 780.792969 C 478.984375 780.792969 478.980469 780.792969 478.972656 780.792969 C 478.96875 780.792969 478.964844 780.792969 478.957031 780.792969 C 478.953125 780.792969 478.949219 780.792969 478.941406 780.792969 C 478.9375 780.792969 478.933594 780.792969 478.925781 780.792969 C 478.921875 780.792969 478.917969 780.792969 478.910156 780.792969 C 478.90625 780.792969 478.902344 780.792969 478.894531 780.792969 C 478.890625 780.792969 478.886719 780.792969 478.878906 780.792969 C 478.875 780.792969 478.871094 780.792969 478.863281 780.792969 C 478.859375 780.792969 478.855469 780.792969 478.847656 780.792969 C 478.84375 780.792969 478.839844 780.792969 478.832031 780.792969 C 478.828125 780.792969 478.824219 780.792969 478.816406 780.792969 C 478.8125 780.792969 478.808594 780.792969 478.800781 780.792969 C 478.796875 780.792969 478.792969 780.792969 478.785156 780.792969 C 478.78125 780.792969 478.777344 780.792969 478.769531 780.792969 C 478.765625 780.792969 478.761719 780.792969 478.753906 780.792969 C 478.75 780.792969 478.746094 780.792969 478.738281 780.792969 C 478.734375 780.792969 478.730469 780.792969 478.722656 780.792969 C 478.71875 780.792969 478.714844 780.792969 478.707031 780.792969 C 478.703125 780.792969 478.699219 780.792969 478.691406 780.792969 C 478.6875 780.792969 478.679688 780.792969 478.675781 780.792969 C 478.671875 780.792969 478.664062 780.792969 478.660156 780.792969 C 478.65625 780.792969 478.648438 780.792969 478.644531 780.792969 C 478.640625 780.792969 478.632812 780.792969 478.628906 780.792969 C 478.625 780.792969 478.617188 780.792969 478.613281 780.792969 C 478.609375 780.792969 478.601562 780.792969 478.597656 780.792969 C 478.59375 780.792969 478.585938 780.792969 478.582031 780.792969 C 478.578125 780.792969 478.570312 780.792969 478.566406 780.792969 C 478.5625 780.792969 478.554688 780.792969 478.550781 780.792969 C 478.546875 780.792969 478.539062 780.792969 478.535156 780.792969 C 478.53125 780.792969 478.523438 780.792969 478.519531 780.792969 C 478.515625 780.792969 478.507812 780.792969 478.503906 780.792969 C 478.496094 780.792969 478.492188 780.792969 478.488281 780.792969 C 478.480469 780.792969 478.476562 780.792969 478.472656 780.792969 C 478.464844 780.792969 478.460938 780.792969 478.457031 780.792969 C 478.449219 780.792969 478.445312 780.792969 478.441406 780.792969 C 478.433594 780.792969 478.429688 780.792969 478.425781 780.792969 C 478.417969 780.792969 478.414062 780.792969 478.410156 780.792969 C 478.402344 780.792969 478.398438 780.792969 478.394531 780.792969 C 478.386719 780.792969 478.382812 780.792969 478.378906 780.792969 C 478.371094 780.792969 478.367188 780.792969 478.363281 780.792969 C 478.355469 780.792969 478.351562 780.792969 478.347656 780.792969 C 478.339844 780.792969 478.335938 780.792969 478.328125 780.792969 C 478.324219 780.792969 478.320312 780.792969 478.3125 780.792969 C 478.308594 780.792969 478.304688 780.792969 478.296875 780.792969 C 478.292969 780.792969 478.289062 780.792969 478.28125 780.792969 C 478.277344 780.792969 478.273438 780.792969 478.265625 780.792969 C 478.261719 780.792969 478.257812 780.792969 478.25 780.792969 C 478.246094 780.792969 478.242188 780.792969 478.234375 780.792969 C 478.230469 780.792969 478.226562 780.792969 478.21875 780.792969 C 478.214844 780.792969 478.210938 780.792969 478.203125 780.792969 C 478.199219 780.792969 478.191406 780.792969 478.1875 780.792969 C 478.183594 780.792969 478.175781 780.792969 478.171875 780.792969 C 478.167969 780.792969 478.160156 780.792969 478.15625 780.792969 C 478.152344 780.792969 478.144531 780.792969 478.140625 780.792969 C 478.136719 780.792969 478.128906 780.792969 478.125 780.792969 C 478.121094 780.792969 478.113281 780.792969 478.109375 780.792969 C 478.105469 780.792969 478.097656 780.792969 478.09375 780.792969 C 478.089844 780.792969 478.082031 780.792969 478.078125 780.792969 C 478.074219 780.792969 478.066406 780.792969 478.0625 780.792969 C 478.054688 780.792969 478.050781 780.792969 478.046875 780.792969 C 478.039062 780.792969 478.035156 780.792969 478.03125 780.792969 C 478.023438 780.792969 478.019531 780.792969 478.015625 780.792969 C 478.007812 780.792969 478.003906 780.792969 478 780.792969 C 477.992188 780.792969 477.988281 780.792969 477.984375 780.792969 C 477.976562 780.792969 477.972656 780.792969 477.96875 780.792969 C 477.960938 780.792969 477.957031 780.792969 477.953125 780.792969 C 477.945312 780.792969 477.941406 780.792969 477.933594 780.792969 C 477.929688 780.792969 477.925781 780.792969 477.917969 780.792969 C 477.914062 780.792969 477.910156 780.792969 477.902344 780.792969 C 477.898438 780.792969 477.894531 780.792969 477.886719 780.792969 C 477.882812 780.792969 477.878906 780.792969 477.871094 780.792969 C 477.867188 780.792969 477.863281 780.792969 477.855469 780.792969 C 477.851562 780.792969 477.847656 780.792969 477.839844 780.792969 C 477.835938 780.792969 477.832031 780.792969 477.824219 780.792969 C 477.820312 780.792969 477.8125 780.792969 477.808594 780.792969 C 477.804688 780.792969 477.796875 780.792969 477.792969 780.792969 C 477.789062 780.792969 477.78125 780.792969 477.777344 780.792969 C 477.773438 780.792969 477.765625 780.792969 477.761719 780.792969 C 477.757812 780.792969 477.75 780.792969 477.746094 780.792969 C 477.742188 780.792969 477.734375 780.792969 477.730469 780.792969 C 477.726562 780.792969 477.71875 780.792969 477.714844 780.792969 C 477.710938 780.792969 477.703125 780.792969 477.699219 780.792969 C 477.691406 780.792969 477.6875 780.792969 477.683594 780.792969 C 477.675781 780.792969 477.671875 780.792969 477.667969 780.792969 C 477.660156 780.792969 477.65625 780.792969 477.652344 780.792969 C 477.644531 780.792969 477.640625 780.792969 477.636719 780.792969 C 477.628906 780.792969 477.625 780.792969 477.621094 780.792969 C 477.613281 780.792969 477.609375 780.792969 477.605469 780.792969 C 477.597656 780.792969 477.59375 780.792969 477.589844 780.792969 C 477.582031 780.792969 477.578125 780.792969 477.570312 780.792969 C 477.566406 780.792969 477.5625 780.792969 477.554688 780.792969 C 477.550781 780.792969 477.546875 780.792969 477.539062 780.792969 C 477.535156 780.792969 477.53125 780.792969 477.523438 780.792969 C 477.519531 780.792969 477.515625 780.792969 477.507812 780.792969 C 477.503906 780.792969 477.5 780.792969 477.492188 780.792969 C 477.488281 780.792969 477.484375 780.792969 477.476562 780.792969 C 477.472656 780.792969 477.46875 780.792969 477.460938 780.792969 C 477.457031 780.792969 477.449219 780.792969 477.445312 780.792969 C 477.441406 780.792969 477.433594 780.792969 477.429688 780.792969 C 477.425781 780.792969 477.417969 780.792969 477.414062 780.792969 C 477.410156 780.792969 477.402344 780.792969 477.398438 780.792969 C 477.394531 780.792969 477.386719 780.792969 477.382812 780.792969 C 477.378906 780.792969 477.371094 780.792969 477.367188 780.792969 C 477.363281 780.792969 477.355469 780.792969 477.351562 780.792969 C 477.347656 780.792969 477.339844 780.792969 477.335938 780.792969 C 477.332031 780.792969 477.324219 780.792969 477.320312 780.792969 C 477.3125 780.792969 477.308594 780.792969 477.304688 780.792969 C 477.296875 780.792969 477.292969 780.792969 477.289062 780.792969 C 477.28125 780.792969 477.277344 780.792969 477.273438 780.792969 C 477.265625 780.792969 477.261719 780.792969 477.257812 780.792969 C 477.25 780.792969 477.246094 780.792969 477.242188 780.792969 C 477.234375 780.792969 477.230469 780.792969 477.226562 780.792969 C 477.21875 780.792969 477.214844 780.792969 477.210938 780.792969 C 477.203125 780.792969 477.199219 780.792969 477.191406 780.792969 C 477.1875 780.792969 477.183594 780.792969 477.175781 780.792969 C 477.171875 780.792969 477.167969 780.792969 477.160156 780.792969 C 477.15625 780.792969 477.152344 780.792969 477.144531 780.792969 C 477.140625 780.792969 477.136719 780.792969 477.128906 780.792969 C 477.125 780.792969 477.121094 780.792969 477.113281 780.792969 C 477.109375 780.792969 477.105469 780.792969 477.097656 780.792969 C 477.09375 780.792969 477.089844 780.792969 477.082031 780.792969 C 477.078125 780.792969 477.074219 780.792969 477.066406 780.792969 C 477.0625 780.792969 477.054688 780.792969 477.050781 780.792969 C 477.046875 780.792969 477.039062 780.792969 477.035156 780.792969 C 477.03125 780.792969 477.023438 780.792969 477.019531 780.792969 C 477.015625 780.792969 477.007812 780.792969 477.003906 780.792969 C 477 780.792969 476.992188 780.792969 476.988281 780.792969 C 476.984375 780.792969 476.976562 780.792969 476.972656 780.792969 C 476.96875 780.792969 476.960938 780.792969 476.957031 780.792969 C 476.953125 780.792969 476.945312 780.792969 476.941406 780.792969 C 476.9375 780.792969 476.929688 780.792969 476.925781 780.792969 C 476.921875 780.792969 476.914062 780.792969 476.910156 780.792969 C 476.90625 780.792969 476.898438 780.792969 476.894531 780.792969 C 476.886719 780.792969 476.882812 780.792969 476.878906 780.792969 C 476.871094 780.792969 476.867188 780.792969 476.863281 780.792969 C 476.855469 780.792969 476.851562 780.792969 476.847656 780.792969 C 476.839844 780.792969 476.835938 780.792969 476.832031 780.792969 C 476.824219 780.792969 476.820312 780.792969 476.816406 780.792969 C 476.808594 780.792969 476.804688 780.792969 476.800781 780.792969 C 476.792969 780.792969 476.789062 780.792969 476.785156 780.792969 C 476.777344 780.792969 476.773438 780.792969 476.769531 780.792969 C 476.761719 780.792969 476.757812 780.792969 476.753906 780.792969 C 476.746094 780.792969 476.742188 780.792969 476.738281 780.792969 C 476.730469 780.792969 476.726562 780.792969 476.71875 780.792969 C 476.714844 780.792969 476.710938 780.792969 476.703125 780.792969 C 476.699219 780.792969 476.695312 780.792969 476.6875 780.792969 C 476.683594 780.792969 476.679688 780.792969 476.671875 780.792969 C 476.667969 780.792969 476.664062 780.792969 476.65625 780.792969 C 476.652344 780.792969 476.648438 780.792969 476.640625 780.792969 C 476.636719 780.792969 476.632812 780.792969 476.625 780.792969 C 476.621094 780.792969 476.617188 780.792969 476.609375 780.792969 C 476.605469 780.792969 476.601562 780.792969 476.59375 780.792969 C 476.589844 780.792969 476.585938 780.792969 476.578125 780.792969 C 476.574219 780.792969 476.570312 780.792969 476.5625 780.792969 C 476.558594 780.792969 476.554688 780.792969 476.546875 780.792969 C 476.542969 780.792969 476.539062 780.792969 476.53125 780.792969 C 476.527344 780.792969 476.523438 780.792969 476.515625 780.792969 C 476.511719 780.792969 476.507812 780.792969 476.5 780.792969 C 476.496094 780.792969 476.492188 780.792969 476.484375 780.792969 C 476.480469 780.792969 476.476562 780.792969 476.46875 780.792969 C 476.464844 780.792969 476.457031 780.792969 476.453125 780.792969 C 476.449219 780.792969 476.441406 780.792969 476.4375 780.792969 C 476.433594 780.792969 476.425781 780.792969 476.421875 780.792969 C 476.417969 780.792969 476.410156 780.792969 476.40625 780.792969 C 476.402344 780.792969 476.394531 780.792969 476.390625 780.792969 C 476.386719 780.792969 476.378906 780.792969 476.375 780.792969 C 476.371094 780.792969 476.363281 780.792969 476.359375 780.792969 C 476.355469 780.792969 476.347656 780.792969 476.34375 780.792969 C 476.339844 780.792969 476.332031 780.792969 476.328125 780.792969 C 476.324219 780.792969 476.316406 780.792969 476.3125 780.792969 C 476.308594 780.792969 476.300781 780.792969 476.296875 780.792969 C 476.292969 780.792969 476.285156 780.792969 476.28125 780.792969 C 476.277344 780.792969 476.269531 780.792969 476.265625 780.792969 C 476.261719 780.792969 476.253906 780.792969 476.25 780.792969 C 476.246094 780.792969 476.238281 780.792969 476.234375 780.792969 C 476.230469 780.792969 476.222656 780.792969 476.21875 780.792969 C 476.214844 780.792969 476.207031 780.792969 476.203125 780.792969 C 476.199219 780.792969 476.191406 780.792969 476.1875 780.792969 C 476.183594 780.792969 476.175781 780.792969 476.171875 780.792969 C 476.167969 780.792969 476.160156 780.792969 476.15625 780.792969 C 476.152344 780.792969 476.144531 780.792969 476.140625 780.792969 C 476.136719 780.792969 476.128906 780.792969 476.125 780.792969 C 476.121094 780.792969 476.113281 780.792969 476.109375 780.792969 C 476.105469 780.792969 476.097656 780.792969 476.09375 780.792969 C 476.089844 780.792969 476.082031 780.792969 476.078125 780.792969 C 476.074219 780.792969 476.066406 780.792969 476.0625 780.792969 C 476.058594 780.792969 476.050781 780.792969 476.046875 780.792969 C 476.042969 780.792969 476.035156 780.792969 476.03125 780.792969 C 476.027344 780.792969 476.019531 780.792969 476.015625 780.792969 C 476.011719 780.792969 476.003906 780.792969 476 780.792969 C 475.996094 780.792969 475.988281 780.792969 475.984375 780.792969 C 475.980469 780.792969 475.972656 780.792969 475.96875 780.792969 C 475.964844 780.792969 475.957031 780.792969 475.953125 780.792969 C 475.949219 780.792969 475.941406 780.792969 475.9375 780.792969 C 475.933594 780.792969 475.925781 780.792969 475.921875 780.792969 C 475.917969 780.792969 475.910156 780.792969 475.90625 780.792969 C 475.902344 780.792969 475.898438 780.792969 475.890625 780.792969 C 475.886719 780.792969 475.882812 780.792969 475.875 780.792969 C 475.871094 780.792969 475.867188 780.792969 475.859375 780.792969 C 475.855469 780.792969 475.851562 780.792969 475.84375 780.792969 C 475.839844 780.792969 475.835938 780.792969 475.828125 780.792969 C 475.824219 780.792969 475.820312 780.792969 475.8125 780.792969 C 475.808594 780.792969 475.804688 780.792969 475.796875 780.792969 C 475.792969 780.792969 475.789062 780.792969 475.78125 780.792969 C 475.777344 780.792969 475.773438 780.792969 475.765625 780.792969 C 475.761719 780.792969 475.757812 780.792969 475.75 780.792969 C 475.746094 780.792969 475.742188 780.792969 475.734375 780.792969 C 475.730469 780.792969 475.726562 780.792969 475.71875 780.792969 C 475.714844 780.792969 475.710938 780.792969 475.703125 780.792969 C 475.699219 780.792969 475.695312 780.792969 475.691406 780.792969 C 475.683594 780.792969 475.679688 780.792969 475.675781 780.792969 C 475.667969 780.792969 475.664062 780.792969 475.660156 780.792969 C 475.652344 780.792969 475.648438 780.792969 475.644531 780.792969 C 475.636719 780.792969 475.632812 780.792969 475.628906 780.792969 C 475.621094 780.792969 475.617188 780.792969 475.613281 780.792969 C 475.605469 780.792969 475.601562 780.792969 475.597656 780.792969 C 475.589844 780.792969 475.585938 780.792969 475.582031 780.792969 C 475.574219 780.792969 475.570312 780.792969 475.566406 780.792969 C 475.558594 780.792969 475.554688 780.792969 475.550781 780.792969 C 475.546875 780.792969 475.539062 780.792969 475.535156 780.792969 C 475.53125 780.792969 475.523438 780.792969 475.519531 780.792969 C 475.515625 780.792969 475.507812 780.792969 475.503906 780.792969 C 475.5 780.792969 475.492188 780.792969 475.488281 780.792969 C 475.484375 780.792969 475.476562 780.792969 475.472656 780.792969 C 475.46875 780.792969 475.460938 780.792969 475.457031 780.792969 C 475.453125 780.792969 475.449219 780.792969 475.441406 780.792969 C 475.4375 780.792969 475.433594 780.792969 475.425781 780.792969 C 475.421875 780.792969 475.417969 780.792969 475.410156 780.792969 C 475.40625 780.792969 475.402344 780.792969 475.394531 780.792969 C 475.390625 780.792969 475.386719 780.792969 475.378906 780.792969 C 475.375 780.792969 475.371094 780.792969 475.363281 780.792969 C 475.359375 780.792969 475.355469 780.792969 475.351562 780.792969 C 475.34375 780.792969 475.339844 780.792969 475.335938 780.792969 C 475.328125 780.792969 475.324219 780.792969 475.320312 780.792969 C 475.3125 780.792969 475.308594 780.792969 475.304688 780.792969 C 475.296875 780.792969 475.292969 780.792969 475.289062 780.792969 C 475.28125 780.792969 475.277344 780.792969 475.273438 780.792969 C 475.269531 780.792969 475.261719 780.792969 475.257812 780.792969 C 475.253906 780.792969 475.246094 780.792969 475.242188 780.792969 C 475.238281 780.792969 475.230469 780.792969 475.226562 780.792969 C 475.222656 780.792969 475.214844 780.792969 475.210938 780.792969 C 475.207031 780.792969 475.203125 780.792969 475.195312 780.792969 C 475.191406 780.792969 475.1875 780.792969 475.179688 780.792969 C 475.175781 780.792969 475.171875 780.792969 475.164062 780.792969 C 475.160156 780.792969 475.15625 780.792969 475.148438 780.792969 C 475.144531 780.792969 475.140625 780.792969 475.136719 780.792969 C 475.128906 780.792969 475.125 780.792969 475.121094 780.792969 C 475.113281 780.792969 475.109375 780.792969 475.105469 780.792969 C 475.097656 780.792969 475.09375 780.792969 475.089844 780.792969 C 475.085938 780.792969 475.078125 780.792969 475.074219 780.792969 C 475.070312 780.792969 475.0625 780.792969 475.058594 780.792969 C 475.054688 780.792969 475.046875 780.792969 475.042969 780.792969 C 475.039062 780.792969 475.03125 780.792969 475.027344 780.792969 C 475.023438 780.792969 475.019531 780.792969 475.011719 780.792969 C 475.007812 780.792969 475.003906 780.792969 474.996094 780.792969 C 474.992188 780.792969 474.988281 780.792969 474.980469 780.792969 C 474.976562 780.792969 474.972656 780.792969 474.96875 780.792969 C 474.960938 780.792969 474.957031 780.792969 474.953125 780.792969 C 474.945312 780.792969 474.941406 780.792969 474.9375 780.792969 C 474.929688 780.792969 474.925781 780.792969 474.921875 780.792969 C 474.917969 780.792969 474.910156 780.792969 474.90625 780.792969 C 474.902344 780.792969 474.894531 780.792969 474.890625 780.792969 C 474.886719 780.792969 474.878906 780.792969 474.875 780.792969 C 474.871094 780.792969 474.867188 780.792969 474.859375 780.792969 C 474.855469 780.792969 474.851562 780.792969 474.84375 780.792969 C 474.839844 780.792969 474.835938 780.792969 474.832031 780.792969 C 474.824219 780.792969 474.820312 780.792969 474.816406 780.792969 C 474.808594 780.792969 474.804688 780.792969 474.800781 780.792969 C 474.792969 780.792969 474.789062 780.792969 474.785156 780.792969 C 474.78125 780.792969 474.773438 780.792969 474.769531 780.792969 C 474.765625 780.792969 474.757812 780.792969 474.753906 780.792969 C 474.75 780.792969 474.746094 780.792969 474.738281 780.792969 C 474.734375 780.792969 474.730469 780.792969 474.722656 780.792969 C 474.71875 780.792969 474.714844 780.792969 474.710938 780.792969 C 474.703125 780.792969 474.699219 780.792969 474.695312 780.792969 C 474.6875 780.792969 474.683594 780.792969 474.679688 780.792969 C 474.671875 780.792969 474.667969 780.792969 474.664062 780.792969 C 474.660156 780.792969 474.652344 780.792969 474.648438 780.792969 C 474.644531 780.792969 474.636719 780.792969 474.632812 780.792969 C 474.628906 780.792969 474.625 780.792969 474.617188 780.792969 C 474.613281 780.792969 474.609375 780.792969 474.601562 780.792969 C 474.597656 780.792969 474.59375 780.792969 474.589844 780.792969 C 474.582031 780.792969 474.578125 780.792969 474.574219 780.792969 C 474.566406 780.792969 474.5625 780.792969 474.558594 780.792969 C 474.554688 780.792969 474.546875 780.792969 474.542969 780.792969 C 474.539062 780.792969 474.53125 780.792969 474.527344 780.792969 C 474.523438 780.792969 474.519531 780.792969 474.511719 780.792969 C 474.507812 780.792969 474.503906 780.792969 474.5 780.792969 C 474.492188 780.792969 474.488281 780.792969 474.484375 780.792969 C 474.476562 780.792969 474.472656 780.792969 474.46875 780.792969 C 474.464844 780.792969 474.457031 780.792969 474.453125 780.792969 C 474.449219 780.792969 474.441406 780.792969 474.4375 780.792969 C 474.433594 780.792969 474.429688 780.792969 474.421875 780.792969 C 474.417969 780.792969 474.414062 780.792969 474.40625 780.792969 C 474.402344 780.792969 474.398438 780.792969 474.394531 780.792969 C 474.386719 780.792969 474.382812 780.792969 474.378906 780.792969 C 474.375 780.792969 474.367188 780.792969 474.363281 780.792969 C 474.359375 780.792969 474.351562 780.792969 474.347656 780.792969 C 474.34375 780.792969 474.339844 780.792969 474.332031 780.792969 C 474.328125 780.792969 474.324219 780.792969 474.320312 780.792969 C 474.3125 780.792969 474.308594 780.792969 474.304688 780.792969 C 474.296875 780.792969 474.292969 780.792969 474.289062 780.792969 C 474.285156 780.792969 474.277344 780.792969 474.273438 780.792969 C 474.269531 780.792969 474.265625 780.792969 474.257812 780.792969 C 474.253906 780.792969 474.25 780.792969 474.242188 780.792969 C 474.238281 780.792969 474.234375 780.792969 474.230469 780.792969 C 474.222656 780.792969 474.21875 780.792969 474.214844 780.792969 C 474.210938 780.792969 474.203125 780.792969 474.199219 780.792969 C 474.195312 780.792969 474.1875 780.792969 474.183594 780.792969 C 474.179688 780.792969 474.175781 780.792969 474.167969 780.792969 C 474.164062 780.792969 474.160156 780.792969 474.15625 780.792969 C 474.148438 780.792969 474.144531 780.792969 474.140625 780.792969 C 474.136719 780.792969 474.128906 780.792969 474.125 780.792969 C 474.121094 780.792969 474.117188 780.792969 474.109375 780.792969 C 474.105469 780.792969 474.101562 780.792969 474.09375 780.792969 C 474.089844 780.792969 474.085938 780.792969 474.082031 780.792969 C 474.074219 780.792969 474.070312 780.792969 474.066406 780.792969 C 474.0625 780.792969 474.054688 780.792969 474.050781 780.792969 C 474.046875 780.792969 474.042969 780.792969 474.035156 780.792969 C 474.03125 780.792969 474.027344 780.792969 474.023438 780.792969 C 474.015625 780.792969 474.011719 780.792969 474.007812 780.792969 C 474.003906 780.792969 473.996094 780.792969 473.992188 780.792969 C 473.988281 780.792969 473.980469 780.792969 473.976562 780.792969 C 473.972656 780.792969 473.96875 780.792969 473.960938 780.792969 C 473.957031 780.792969 473.953125 780.792969 473.949219 780.792969 C 473.941406 780.792969 473.9375 780.792969 473.933594 780.792969 C 473.929688 780.792969 473.921875 780.792969 473.917969 780.792969 C 473.914062 780.792969 473.910156 780.792969 473.902344 780.792969 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551.917969 856.546875 C 551.917969 856.550781 551.917969 856.554688 551.917969 856.5625 C 551.917969 856.566406 551.917969 856.570312 551.917969 856.578125 C 551.917969 856.582031 551.917969 856.585938 551.917969 856.59375 C 551.917969 856.597656 551.917969 856.601562 551.917969 856.609375 C 551.917969 856.613281 551.917969 856.617188 551.917969 856.625 C 551.917969 856.628906 551.917969 856.632812 551.917969 856.640625 C 551.917969 856.644531 551.917969 856.648438 551.917969 856.65625 C 551.917969 856.660156 551.917969 856.664062 551.917969 856.671875 C 551.917969 856.675781 551.917969 856.679688 551.917969 856.6875 C 551.917969 856.691406 551.917969 856.695312 551.917969 856.703125 C 551.917969 856.707031 551.917969 856.710938 551.917969 856.71875 C 551.917969 856.722656 551.917969 856.726562 551.917969 856.734375 C 551.917969 856.738281 551.917969 856.742188 551.917969 856.75 C 551.917969 856.753906 551.917969 856.757812 551.917969 856.765625 C 551.917969 856.769531 551.917969 856.773438 551.917969 856.78125 C 551.917969 856.785156 551.917969 856.789062 551.917969 856.796875 C 551.917969 856.800781 551.917969 856.804688 551.917969 856.8125 C 551.917969 856.816406 551.917969 856.820312 551.917969 856.828125 C 551.917969 856.832031 551.917969 856.835938 551.917969 856.84375 C 551.917969 856.847656 551.917969 856.855469 551.917969 856.859375 C 551.917969 856.863281 551.917969 856.871094 551.917969 856.875 C 551.917969 856.878906 551.917969 856.886719 551.917969 856.890625 C 551.917969 856.894531 551.917969 856.902344 551.917969 856.90625 C 551.917969 856.910156 551.917969 856.917969 551.917969 856.921875 C 551.917969 856.925781 551.917969 856.933594 551.917969 856.9375 C 551.917969 856.941406 551.917969 856.949219 551.917969 856.953125 C 551.917969 856.957031 551.917969 856.964844 551.917969 856.96875 C 551.917969 856.972656 551.917969 856.980469 551.917969 856.984375 C 551.917969 856.988281 551.917969 856.996094 551.917969 857 C 551.917969 857.003906 551.917969 857.011719 551.917969 857.015625 C 551.917969 857.019531 551.917969 857.027344 551.917969 857.03125 C 551.917969 857.035156 551.917969 857.042969 551.917969 857.046875 C 551.917969 857.050781 551.917969 857.058594 551.917969 857.0625 C 551.917969 857.070312 551.917969 857.074219 551.917969 857.078125 C 551.917969 857.085938 551.917969 857.089844 551.917969 857.09375 C 551.917969 857.101562 551.917969 857.105469 551.917969 857.109375 C 551.917969 857.117188 551.917969 857.121094 551.917969 857.125 C 551.917969 857.132812 551.917969 857.136719 551.917969 857.140625 C 551.917969 857.148438 551.917969 857.152344 551.917969 857.15625 C 551.917969 857.164062 551.917969 857.167969 551.917969 857.171875 C 551.917969 857.179688 551.917969 857.183594 551.917969 857.1875 C 551.917969 857.195312 551.917969 857.199219 551.917969 857.203125 C 551.917969 857.210938 551.917969 857.214844 551.917969 857.21875 C 551.917969 857.226562 551.917969 857.230469 551.917969 857.238281 C 551.917969 857.242188 551.917969 857.246094 551.917969 857.253906 C 551.917969 857.257812 551.917969 857.261719 551.917969 857.269531 C 551.917969 857.273438 551.917969 857.277344 551.917969 857.285156 C 551.917969 857.289062 551.917969 857.292969 551.917969 857.300781 C 551.917969 857.304688 551.917969 857.308594 551.917969 857.316406 C 551.917969 857.320312 551.917969 857.324219 551.917969 857.332031 C 551.917969 857.335938 551.917969 857.339844 551.917969 857.347656 C 551.917969 857.351562 551.917969 857.355469 551.917969 857.363281 C 551.917969 857.367188 551.917969 857.375 551.917969 857.378906 C 551.917969 857.382812 551.917969 857.390625 551.917969 857.394531 C 551.917969 857.398438 551.917969 857.40625 551.917969 857.410156 C 551.917969 857.414062 551.917969 857.421875 551.917969 857.425781 C 551.917969 857.429688 551.917969 857.4375 551.917969 857.441406 C 551.917969 857.445312 551.917969 857.453125 551.917969 857.457031 C 551.917969 857.460938 551.917969 857.46875 551.917969 857.472656 C 551.917969 857.476562 551.917969 857.484375 551.917969 857.488281 C 551.917969 857.492188 551.917969 857.5 551.917969 857.503906 C 551.917969 857.511719 551.917969 857.515625 551.917969 857.519531 C 551.917969 857.527344 551.917969 857.53125 551.917969 857.535156 C 551.917969 857.542969 551.917969 857.546875 551.917969 857.550781 C 551.917969 857.558594 551.917969 857.5625 551.917969 857.566406 C 551.917969 857.574219 551.917969 857.578125 551.917969 857.582031 C 551.917969 857.589844 551.917969 857.59375 551.917969 857.597656 C 551.917969 857.605469 551.917969 857.609375 551.917969 857.613281 C 551.917969 857.621094 551.917969 857.625 551.917969 857.628906 C 551.917969 857.636719 551.917969 857.640625 551.917969 857.648438 C 551.917969 857.652344 551.917969 857.65625 551.917969 857.664062 C 551.917969 857.667969 551.917969 857.671875 551.917969 857.679688 C 551.917969 857.683594 551.917969 857.6875 551.917969 857.695312 C 551.917969 857.699219 551.917969 857.703125 551.917969 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551.917969 857.945312 C 551.917969 857.953125 551.917969 857.957031 551.917969 857.960938 C 551.917969 857.96875 551.917969 857.972656 551.917969 857.976562 C 551.917969 857.984375 551.917969 857.988281 551.917969 857.992188 C 551.917969 858 551.917969 858.003906 551.917969 858.011719 C 551.917969 858.015625 551.917969 858.019531 551.917969 858.027344 C 551.917969 858.03125 551.917969 858.035156 551.917969 858.042969 C 551.917969 858.046875 551.917969 858.050781 551.917969 858.058594 C 551.917969 858.0625 551.917969 858.066406 551.917969 858.074219 C 551.917969 858.078125 551.917969 858.082031 551.917969 858.089844 C 551.917969 858.09375 551.917969 858.097656 551.917969 858.105469 C 551.917969 858.109375 551.917969 858.113281 551.917969 858.121094 C 551.917969 858.125 551.917969 858.132812 551.917969 858.136719 C 551.917969 858.140625 551.917969 858.148438 551.917969 858.152344 C 551.917969 858.15625 551.917969 858.164062 551.917969 858.167969 C 551.917969 858.171875 551.917969 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858.410156 551.917969 858.414062 551.917969 858.421875 C 551.917969 858.425781 551.917969 858.429688 551.917969 858.4375 C 551.917969 858.441406 551.917969 858.445312 551.917969 858.453125 C 551.917969 858.457031 551.917969 858.460938 551.917969 858.46875 C 551.917969 858.472656 551.917969 858.476562 551.917969 858.484375 C 551.917969 858.488281 551.917969 858.492188 551.917969 858.5 C 551.917969 858.503906 551.917969 858.507812 551.917969 858.515625 C 551.917969 858.519531 551.917969 858.527344 551.917969 858.53125 C 551.917969 858.535156 551.917969 858.542969 551.917969 858.546875 C 551.917969 858.550781 551.917969 858.558594 551.917969 858.5625 C 551.917969 858.566406 551.917969 858.574219 551.917969 858.578125 C 551.917969 858.582031 551.917969 858.589844 551.917969 858.59375 C 551.917969 858.597656 551.917969 858.605469 551.917969 858.609375 C 551.917969 858.613281 551.917969 858.621094 551.917969 858.625 C 551.917969 858.628906 551.917969 858.636719 551.917969 858.640625 C 551.917969 858.644531 551.917969 858.652344 551.917969 858.65625 C 551.917969 858.664062 551.917969 858.667969 551.917969 858.671875 C 551.917969 858.679688 551.917969 858.683594 551.917969 858.6875 C 551.917969 858.695312 551.917969 858.699219 551.917969 858.703125 C 551.917969 858.710938 551.917969 858.714844 551.917969 858.71875 C 551.917969 858.726562 551.917969 858.730469 551.917969 858.734375 C 551.917969 858.742188 551.917969 858.746094 551.917969 858.75 C 551.917969 858.757812 551.917969 858.761719 551.917969 858.765625 C 551.917969 858.773438 551.917969 858.777344 551.917969 858.78125 C 551.917969 858.789062 551.917969 858.792969 551.917969 858.796875 C 551.917969 858.804688 551.917969 858.808594 551.917969 858.8125 C 551.917969 858.820312 551.917969 858.824219 551.917969 858.828125 C 551.917969 858.835938 551.917969 858.839844 551.917969 858.847656 C 551.917969 858.851562 551.917969 858.855469 551.917969 858.863281 C 551.917969 858.867188 551.917969 858.871094 551.917969 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551.917969 859.34375 551.917969 859.347656 C 551.917969 859.351562 551.917969 859.359375 551.917969 859.363281 C 551.917969 859.367188 551.917969 859.375 551.917969 859.378906 C 551.917969 859.382812 551.917969 859.390625 551.917969 859.394531 C 551.917969 859.398438 551.917969 859.40625 551.917969 859.410156 C 551.917969 859.414062 551.917969 859.421875 551.917969 859.425781 C 551.917969 859.429688 551.917969 859.4375 551.917969 859.441406 C 551.917969 859.445312 551.917969 859.453125 551.917969 859.457031 C 551.917969 859.460938 551.917969 859.46875 551.917969 859.472656 C 551.917969 859.476562 551.917969 859.484375 551.917969 859.488281 C 551.917969 859.492188 551.917969 859.5 551.917969 859.503906 C 551.917969 859.507812 551.917969 859.515625 551.917969 859.519531 C 551.917969 859.523438 551.917969 859.53125 551.917969 859.535156 C 551.917969 859.539062 551.917969 859.546875 551.917969 859.550781 C 551.917969 859.554688 551.917969 859.5625 551.917969 859.566406 C 551.917969 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551.917969 859.804688 551.917969 859.808594 551.917969 859.816406 C 551.917969 859.820312 551.917969 859.824219 551.917969 859.832031 C 551.917969 859.835938 551.917969 859.839844 551.917969 859.847656 C 551.917969 859.851562 551.917969 859.855469 551.917969 859.863281 C 551.917969 859.867188 551.917969 859.871094 551.917969 859.878906 C 551.917969 859.882812 551.917969 859.886719 551.917969 859.894531 C 551.917969 859.898438 551.917969 859.902344 551.917969 859.910156 C 551.917969 859.914062 551.917969 859.917969 551.917969 859.925781 C 551.917969 859.929688 551.917969 859.933594 551.917969 859.941406 C 551.917969 859.945312 551.917969 859.949219 551.917969 859.957031 C 551.917969 859.960938 551.917969 859.964844 551.917969 859.96875 C 551.917969 859.976562 551.917969 859.980469 551.917969 859.984375 C 551.917969 859.992188 551.917969 859.996094 551.917969 860 C 551.917969 860.007812 551.917969 860.011719 551.917969 860.015625 C 551.917969 860.023438 551.917969 860.027344 551.917969 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551.917969 860.488281 551.917969 860.492188 C 551.917969 860.5 551.917969 860.503906 551.917969 860.507812 C 551.917969 860.515625 551.917969 860.519531 551.917969 860.523438 C 551.917969 860.53125 551.917969 860.535156 551.917969 860.539062 C 551.917969 860.546875 551.917969 860.550781 551.917969 860.554688 C 551.917969 860.558594 551.917969 860.566406 551.917969 860.570312 C 551.917969 860.574219 551.917969 860.582031 551.917969 860.585938 C 551.917969 860.589844 551.917969 860.597656 551.917969 860.601562 C 551.917969 860.605469 551.917969 860.609375 551.917969 860.617188 C 551.917969 860.621094 551.917969 860.625 551.917969 860.632812 C 551.917969 860.636719 551.917969 860.640625 551.917969 860.648438 C 551.917969 860.652344 551.917969 860.65625 551.917969 860.660156 C 551.917969 860.667969 551.917969 860.671875 551.917969 860.675781 C 551.917969 860.683594 551.917969 860.6875 551.917969 860.691406 C 551.917969 860.699219 551.917969 860.703125 551.917969 860.707031 C 551.917969 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551.917969 860.941406 551.917969 860.945312 551.917969 860.949219 C 551.917969 860.953125 551.917969 860.960938 551.917969 860.964844 C 551.917969 860.96875 551.917969 860.976562 551.917969 860.980469 C 551.917969 860.984375 551.917969 860.988281 551.917969 860.996094 C 551.917969 861 551.917969 861.003906 551.917969 861.011719 C 551.917969 861.015625 551.917969 861.019531 551.917969 861.027344 C 551.917969 861.03125 551.917969 861.035156 551.917969 861.039062 C 551.917969 861.046875 551.917969 861.050781 551.917969 861.054688 C 551.917969 861.058594 551.917969 861.066406 551.917969 861.070312 C 551.917969 861.074219 551.917969 861.082031 551.917969 861.085938 C 551.917969 861.089844 551.917969 861.09375 551.917969 861.101562 C 551.917969 861.105469 551.917969 861.109375 551.917969 861.117188 C 551.917969 861.121094 551.917969 861.125 551.917969 861.128906 C 551.917969 861.136719 551.917969 861.140625 551.917969 861.144531 C 551.917969 861.152344 551.917969 861.15625 551.917969 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551.917969 861.382812 C 551.917969 861.390625 551.917969 861.394531 551.917969 861.398438 C 551.917969 861.40625 551.917969 861.410156 551.917969 861.414062 C 551.917969 861.417969 551.917969 861.425781 551.917969 861.429688 C 551.917969 861.433594 551.917969 861.4375 551.917969 861.445312 C 551.917969 861.449219 551.917969 861.453125 551.917969 861.457031 C 551.917969 861.464844 551.917969 861.46875 551.917969 861.472656 C 551.917969 861.480469 551.917969 861.484375 551.917969 861.488281 C 551.917969 861.492188 551.917969 861.5 551.917969 861.503906 C 551.917969 861.507812 551.917969 861.511719 551.917969 861.519531 C 551.917969 861.523438 551.917969 861.527344 551.917969 861.53125 C 551.917969 861.539062 551.917969 861.542969 551.917969 861.546875 C 551.917969 861.550781 551.917969 861.558594 551.917969 861.5625 C 551.917969 861.566406 551.917969 861.574219 551.917969 861.578125 C 551.917969 861.582031 551.917969 861.585938 551.917969 861.59375 C 551.917969 861.597656 551.917969 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551.917969 861.820312 551.917969 861.828125 C 551.917969 861.832031 551.917969 861.835938 551.917969 861.839844 C 551.917969 861.847656 551.917969 861.851562 551.917969 861.855469 C 551.917969 861.859375 551.917969 861.867188 551.917969 861.871094 L 554.820312 861.871094 C 554.820312 861.867188 554.820312 861.859375 554.820312 861.855469 C 554.820312 861.851562 554.820312 861.847656 554.820312 861.839844 C 554.820312 861.835938 554.820312 861.832031 554.820312 861.828125 C 554.820312 861.820312 554.820312 861.816406 554.820312 861.8125 C 554.820312 861.808594 554.820312 861.800781 554.820312 861.796875 C 554.820312 861.792969 554.820312 861.789062 554.820312 861.78125 C 554.820312 861.777344 554.820312 861.773438 554.820312 861.769531 C 554.820312 861.761719 554.820312 861.757812 554.820312 861.753906 C 554.820312 861.75 554.820312 861.742188 554.820312 861.738281 C 554.820312 861.734375 554.820312 861.730469 554.820312 861.722656 C 554.820312 861.71875 554.820312 861.714844 554.820312 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1240.222656 551.917969 1240.226562 551.917969 1240.234375 C 551.917969 1240.238281 551.917969 1240.242188 551.917969 1240.25 C 551.917969 1240.253906 551.917969 1240.257812 551.917969 1240.265625 C 551.917969 1240.269531 551.917969 1240.273438 551.917969 1240.28125 C 551.917969 1240.285156 551.917969 1240.289062 551.917969 1240.296875 C 551.917969 1240.300781 551.917969 1240.304688 551.917969 1240.3125 C 551.917969 1240.316406 551.917969 1240.320312 551.917969 1240.328125 C 551.917969 1240.332031 551.917969 1240.335938 551.917969 1240.34375 C 551.917969 1240.347656 551.917969 1240.355469 551.917969 1240.359375 C 551.917969 1240.363281 551.917969 1240.371094 551.917969 1240.375 C 551.917969 1240.378906 551.917969 1240.386719 551.917969 1240.390625 C 551.917969 1240.394531 551.917969 1240.402344 551.917969 1240.40625 C 551.917969 1240.410156 551.917969 1240.417969 551.917969 1240.421875 C 551.917969 1240.425781 551.917969 1240.433594 551.917969 1240.4375 C 551.917969 1240.441406 551.917969 1240.449219 551.917969 1240.453125 C 551.917969 1240.457031 551.917969 1240.464844 551.917969 1240.46875 C 551.917969 1240.472656 551.917969 1240.480469 551.917969 1240.484375 C 551.917969 1240.488281 551.917969 1240.496094 551.917969 1240.5 C 551.917969 1240.507812 551.917969 1240.511719 551.917969 1240.515625 C 551.917969 1240.523438 551.917969 1240.527344 551.917969 1240.53125 C 551.917969 1240.539062 551.917969 1240.542969 551.917969 1240.546875 C 551.917969 1240.554688 551.917969 1240.558594 551.917969 1240.5625 C 551.917969 1240.570312 551.917969 1240.574219 551.917969 1240.578125 C 551.917969 1240.585938 551.917969 1240.589844 551.917969 1240.59375 C 551.917969 1240.601562 551.917969 1240.605469 551.917969 1240.609375 C 551.917969 1240.617188 551.917969 1240.621094 551.917969 1240.625 C 551.917969 1240.632812 551.917969 1240.636719 551.917969 1240.640625 C 551.917969 1240.648438 551.917969 1240.652344 551.917969 1240.660156 C 551.917969 1240.664062 551.917969 1240.667969 551.917969 1240.675781 C 551.917969 1240.679688 551.917969 1240.683594 551.917969 1240.691406 C 551.917969 1240.695312 551.917969 1240.699219 551.917969 1240.707031 C 551.917969 1240.710938 551.917969 1240.714844 551.917969 1240.722656 C 551.917969 1240.726562 551.917969 1240.730469 551.917969 1240.738281 C 551.917969 1240.742188 551.917969 1240.746094 551.917969 1240.753906 C 551.917969 1240.757812 551.917969 1240.761719 551.917969 1240.769531 C 551.917969 1240.773438 551.917969 1240.78125 551.917969 1240.785156 C 551.917969 1240.789062 551.917969 1240.796875 551.917969 1240.800781 C 551.917969 1240.804688 551.917969 1240.8125 551.917969 1240.816406 C 551.917969 1240.820312 551.917969 1240.828125 551.917969 1240.832031 C 551.917969 1240.835938 551.917969 1240.84375 551.917969 1240.847656 C 551.917969 1240.851562 551.917969 1240.859375 551.917969 1240.863281 C 551.917969 1240.867188 551.917969 1240.875 551.917969 1240.878906 C 551.917969 1240.882812 551.917969 1240.890625 551.917969 1240.894531 C 551.917969 1240.898438 551.917969 1240.90625 551.917969 1240.910156 C 551.917969 1240.917969 551.917969 1240.921875 551.917969 1240.925781 C 551.917969 1240.933594 551.917969 1240.9375 551.917969 1240.941406 C 551.917969 1240.949219 551.917969 1240.953125 551.917969 1240.957031 C 551.917969 1240.964844 551.917969 1240.96875 551.917969 1240.972656 C 551.917969 1240.980469 551.917969 1240.984375 551.917969 1240.988281 C 551.917969 1240.996094 551.917969 1241 551.917969 1241.003906 C 551.917969 1241.011719 551.917969 1241.015625 551.917969 1241.019531 C 551.917969 1241.027344 551.917969 1241.03125 551.917969 1241.039062 C 551.917969 1241.042969 551.917969 1241.046875 551.917969 1241.054688 C 551.917969 1241.058594 551.917969 1241.0625 551.917969 1241.070312 C 551.917969 1241.074219 551.917969 1241.078125 551.917969 1241.085938 C 551.917969 1241.089844 551.917969 1241.09375 551.917969 1241.101562 C 551.917969 1241.105469 551.917969 1241.109375 551.917969 1241.117188 C 551.917969 1241.121094 551.917969 1241.125 551.917969 1241.132812 C 551.917969 1241.136719 551.917969 1241.140625 551.917969 1241.148438 C 551.917969 1241.152344 551.917969 1241.160156 551.917969 1241.164062 C 551.917969 1241.167969 551.917969 1241.175781 551.917969 1241.179688 C 551.917969 1241.183594 551.917969 1241.191406 551.917969 1241.195312 C 551.917969 1241.199219 551.917969 1241.207031 551.917969 1241.210938 C 551.917969 1241.214844 551.917969 1241.222656 551.917969 1241.226562 C 551.917969 1241.230469 551.917969 1241.238281 551.917969 1241.242188 C 551.917969 1241.246094 551.917969 1241.253906 551.917969 1241.257812 C 551.917969 1241.261719 551.917969 1241.269531 551.917969 1241.273438 C 551.917969 1241.28125 551.917969 1241.285156 551.917969 1241.289062 C 551.917969 1241.296875 551.917969 1241.300781 551.917969 1241.304688 C 551.917969 1241.3125 551.917969 1241.316406 551.917969 1241.320312 C 551.917969 1241.328125 551.917969 1241.332031 551.917969 1241.335938 C 551.917969 1241.34375 551.917969 1241.347656 551.917969 1241.351562 C 551.917969 1241.359375 551.917969 1241.363281 551.917969 1241.367188 C 551.917969 1241.375 551.917969 1241.378906 551.917969 1241.382812 C 551.917969 1241.390625 551.917969 1241.394531 551.917969 1241.402344 C 551.917969 1241.40625 551.917969 1241.410156 551.917969 1241.417969 C 551.917969 1241.421875 551.917969 1241.425781 551.917969 1241.433594 C 551.917969 1241.4375 551.917969 1241.441406 551.917969 1241.449219 C 551.917969 1241.453125 551.917969 1241.457031 551.917969 1241.464844 C 551.917969 1241.46875 551.917969 1241.472656 551.917969 1241.480469 C 551.917969 1241.484375 551.917969 1241.488281 551.917969 1241.496094 C 551.917969 1241.5 551.917969 1241.503906 551.917969 1241.511719 C 551.917969 1241.515625 551.917969 1241.523438 551.917969 1241.527344 C 551.917969 1241.53125 551.917969 1241.539062 551.917969 1241.542969 C 551.917969 1241.546875 551.917969 1241.554688 551.917969 1241.558594 C 551.917969 1241.5625 551.917969 1241.570312 551.917969 1241.574219 C 551.917969 1241.578125 551.917969 1241.585938 551.917969 1241.589844 C 551.917969 1241.59375 551.917969 1241.601562 551.917969 1241.605469 C 551.917969 1241.609375 551.917969 1241.617188 551.917969 1241.621094 C 551.917969 1241.625 551.917969 1241.632812 551.917969 1241.636719 C 551.917969 1241.640625 551.917969 1241.648438 551.917969 1241.652344 C 551.917969 1241.660156 551.917969 1241.664062 551.917969 1241.667969 C 551.917969 1241.675781 551.917969 1241.679688 551.917969 1241.683594 C 551.917969 1241.691406 551.917969 1241.695312 551.917969 1241.699219 C 551.917969 1241.707031 551.917969 1241.710938 551.917969 1241.714844 C 551.917969 1241.722656 551.917969 1241.726562 551.917969 1241.730469 C 551.917969 1241.738281 551.917969 1241.742188 551.917969 1241.746094 C 551.917969 1241.753906 551.917969 1241.757812 551.917969 1241.761719 C 551.917969 1241.769531 551.917969 1241.773438 551.917969 1241.777344 C 551.917969 1241.785156 551.917969 1241.789062 551.917969 1241.796875 C 551.917969 1241.800781 551.917969 1241.804688 551.917969 1241.8125 C 551.917969 1241.816406 551.917969 1241.820312 551.917969 1241.828125 C 551.917969 1241.832031 551.917969 1241.835938 551.917969 1241.84375 C 551.917969 1241.847656 551.917969 1241.851562 551.917969 1241.859375 C 551.917969 1241.863281 551.917969 1241.867188 551.917969 1241.875 C 551.917969 1241.878906 551.917969 1241.882812 551.917969 1241.890625 C 551.917969 1241.894531 551.917969 1241.898438 551.917969 1241.90625 C 551.917969 1241.910156 551.917969 1241.914062 551.917969 1241.921875 C 551.917969 1241.925781 551.917969 1241.929688 551.917969 1241.9375 C 551.917969 1241.941406 551.917969 1241.945312 551.917969 1241.953125 C 551.917969 1241.957031 551.917969 1241.964844 551.917969 1241.96875 C 551.917969 1241.972656 551.917969 1241.980469 551.917969 1241.984375 C 551.917969 1241.988281 551.917969 1241.996094 551.917969 1242 C 551.917969 1242.003906 551.917969 1242.011719 551.917969 1242.015625 C 551.917969 1242.019531 551.917969 1242.027344 551.917969 1242.03125 C 551.917969 1242.035156 551.917969 1242.042969 551.917969 1242.046875 C 551.917969 1242.050781 551.917969 1242.058594 551.917969 1242.0625 C 551.917969 1242.066406 551.917969 1242.074219 551.917969 1242.078125 C 551.917969 1242.082031 551.917969 1242.089844 551.917969 1242.09375 C 551.917969 1242.097656 551.917969 1242.105469 551.917969 1242.109375 C 551.917969 1242.113281 551.917969 1242.121094 551.917969 1242.125 C 551.917969 1242.128906 551.917969 1242.136719 551.917969 1242.140625 C 551.917969 1242.148438 551.917969 1242.152344 551.917969 1242.15625 C 551.917969 1242.164062 551.917969 1242.167969 551.917969 1242.171875 C 551.917969 1242.179688 551.917969 1242.183594 551.917969 1242.1875 C 551.917969 1242.195312 551.917969 1242.199219 551.917969 1242.203125 C 551.917969 1242.210938 551.917969 1242.214844 551.917969 1242.21875 C 551.917969 1242.226562 551.917969 1242.230469 551.917969 1242.234375 C 551.917969 1242.242188 551.917969 1242.246094 551.917969 1242.25 C 551.917969 1242.257812 551.917969 1242.261719 551.917969 1242.265625 C 551.917969 1242.273438 551.917969 1242.277344 551.917969 1242.28125 C 551.917969 1242.289062 551.917969 1242.292969 551.917969 1242.296875 C 551.917969 1242.304688 551.917969 1242.308594 551.917969 1242.3125 C 551.917969 1242.320312 551.917969 1242.324219 551.917969 1242.328125 C 551.917969 1242.335938 551.917969 1242.339844 551.917969 1242.34375 C 551.917969 1242.351562 551.917969 1242.355469 551.917969 1242.359375 C 551.917969 1242.367188 551.917969 1242.371094 551.917969 1242.375 C 551.917969 1242.382812 551.917969 1242.386719 551.917969 1242.390625 C 551.917969 1242.398438 551.917969 1242.402344 551.917969 1242.40625 C 551.917969 1242.414062 551.917969 1242.417969 551.917969 1242.421875 C 551.917969 1242.429688 551.917969 1242.433594 551.917969 1242.441406 C 551.917969 1242.445312 551.917969 1242.449219 551.917969 1242.457031 C 551.917969 1242.460938 551.917969 1242.464844 551.917969 1242.472656 C 551.917969 1242.476562 551.917969 1242.480469 551.917969 1242.488281 C 551.917969 1242.492188 551.917969 1242.496094 551.917969 1242.503906 C 551.917969 1242.507812 551.917969 1242.511719 551.917969 1242.519531 C 551.917969 1242.523438 551.917969 1242.527344 551.917969 1242.535156 C 551.917969 1242.539062 551.917969 1242.542969 551.917969 1242.550781 C 551.917969 1242.554688 551.917969 1242.558594 551.917969 1242.566406 C 551.917969 1242.570312 551.917969 1242.574219 551.917969 1242.582031 C 551.917969 1242.585938 551.917969 1242.589844 551.917969 1242.597656 C 551.917969 1242.601562 551.917969 1242.605469 551.917969 1242.613281 C 551.917969 1242.617188 551.917969 1242.621094 551.917969 1242.628906 C 551.917969 1242.632812 551.917969 1242.636719 551.917969 1242.644531 C 551.917969 1242.648438 551.917969 1242.652344 551.917969 1242.660156 C 551.917969 1242.664062 551.917969 1242.667969 551.917969 1242.675781 C 551.917969 1242.679688 551.917969 1242.683594 551.917969 1242.691406 C 551.917969 1242.695312 551.917969 1242.699219 551.917969 1242.707031 C 551.917969 1242.710938 551.917969 1242.714844 551.917969 1242.722656 C 551.917969 1242.726562 551.917969 1242.730469 551.917969 1242.738281 C 551.917969 1242.742188 551.917969 1242.746094 551.917969 1242.753906 C 551.917969 1242.757812 551.917969 1242.761719 551.917969 1242.769531 C 551.917969 1242.773438 551.917969 1242.777344 551.917969 1242.785156 C 551.917969 1242.789062 551.917969 1242.792969 551.917969 1242.800781 C 551.917969 1242.804688 551.917969 1242.808594 551.917969 1242.816406 C 551.917969 1242.820312 551.917969 1242.824219 551.917969 1242.832031 C 551.917969 1242.835938 551.917969 1242.839844 551.917969 1242.84375 C 551.917969 1242.851562 551.917969 1242.855469 551.917969 1242.859375 C 551.917969 1242.867188 551.917969 1242.871094 551.917969 1242.875 C 551.917969 1242.882812 551.917969 1242.886719 551.917969 1242.890625 C 551.917969 1242.898438 551.917969 1242.902344 551.917969 1242.90625 C 551.917969 1242.914062 551.917969 1242.917969 551.917969 1242.921875 C 551.917969 1242.929688 551.917969 1242.933594 551.917969 1242.9375 C 551.917969 1242.945312 551.917969 1242.949219 551.917969 1242.953125 C 551.917969 1242.960938 551.917969 1242.964844 551.917969 1242.96875 C 551.917969 1242.976562 551.917969 1242.980469 551.917969 1242.984375 C 551.917969 1242.992188 551.917969 1242.996094 551.917969 1243 C 551.917969 1243.007812 551.917969 1243.011719 551.917969 1243.015625 C 551.917969 1243.023438 551.917969 1243.027344 551.917969 1243.03125 C 551.917969 1243.039062 551.917969 1243.042969 551.917969 1243.046875 C 551.917969 1243.054688 551.917969 1243.058594 551.917969 1243.0625 C 551.917969 1243.070312 551.917969 1243.074219 551.917969 1243.078125 C 551.917969 1243.085938 551.917969 1243.089844 551.917969 1243.09375 C 551.917969 1243.097656 551.917969 1243.105469 551.917969 1243.109375 C 551.917969 1243.113281 551.917969 1243.121094 551.917969 1243.125 C 551.917969 1243.128906 551.917969 1243.136719 551.917969 1243.140625 C 551.917969 1243.144531 551.917969 1243.152344 551.917969 1243.15625 C 551.917969 1243.160156 551.917969 1243.167969 551.917969 1243.171875 C 551.917969 1243.175781 551.917969 1243.183594 551.917969 1243.1875 C 551.917969 1243.191406 551.917969 1243.199219 551.917969 1243.203125 C 551.917969 1243.207031 551.917969 1243.214844 551.917969 1243.21875 C 551.917969 1243.222656 551.917969 1243.230469 551.917969 1243.234375 C 551.917969 1243.238281 551.917969 1243.242188 551.917969 1243.25 C 551.917969 1243.253906 551.917969 1243.257812 551.917969 1243.265625 C 551.917969 1243.269531 551.917969 1243.273438 551.917969 1243.28125 C 551.917969 1243.285156 551.917969 1243.289062 551.917969 1243.296875 C 551.917969 1243.300781 551.917969 1243.304688 551.917969 1243.3125 C 551.917969 1243.316406 551.917969 1243.320312 551.917969 1243.328125 C 551.917969 1243.332031 551.917969 1243.335938 551.917969 1243.34375 C 551.917969 1243.347656 551.917969 1243.351562 551.917969 1243.355469 C 551.917969 1243.363281 551.917969 1243.367188 551.917969 1243.371094 C 551.917969 1243.378906 551.917969 1243.382812 551.917969 1243.386719 C 551.917969 1243.394531 551.917969 1243.398438 551.917969 1243.402344 C 551.917969 1243.410156 551.917969 1243.414062 551.917969 1243.417969 C 551.917969 1243.425781 551.917969 1243.429688 551.917969 1243.433594 C 551.917969 1243.4375 551.917969 1243.445312 551.917969 1243.449219 C 551.917969 1243.453125 551.917969 1243.460938 551.917969 1243.464844 C 551.917969 1243.46875 551.917969 1243.476562 551.917969 1243.480469 C 551.917969 1243.484375 551.917969 1243.492188 551.917969 1243.496094 C 551.917969 1243.5 551.917969 1243.507812 551.917969 1243.511719 C 551.917969 1243.515625 551.917969 1243.519531 551.917969 1243.527344 C 551.917969 1243.53125 551.917969 1243.535156 551.917969 1243.542969 C 551.917969 1243.546875 551.917969 1243.550781 551.917969 1243.558594 C 551.917969 1243.5625 551.917969 1243.566406 551.917969 1243.574219 C 551.917969 1243.578125 551.917969 1243.582031 551.917969 1243.589844 C 551.917969 1243.59375 551.917969 1243.597656 551.917969 1243.601562 C 551.917969 1243.609375 551.917969 1243.613281 551.917969 1243.617188 C 551.917969 1243.625 551.917969 1243.628906 551.917969 1243.632812 C 551.917969 1243.640625 551.917969 1243.644531 551.917969 1243.648438 C 551.917969 1243.65625 551.917969 1243.660156 551.917969 1243.664062 C 551.917969 1243.667969 551.917969 1243.675781 551.917969 1243.679688 C 551.917969 1243.683594 551.917969 1243.691406 551.917969 1243.695312 C 551.917969 1243.699219 551.917969 1243.707031 551.917969 1243.710938 C 551.917969 1243.714844 551.917969 1243.71875 551.917969 1243.726562 C 551.917969 1243.730469 551.917969 1243.734375 551.917969 1243.742188 C 551.917969 1243.746094 551.917969 1243.75 551.917969 1243.757812 C 551.917969 1243.761719 551.917969 1243.765625 551.917969 1243.773438 C 551.917969 1243.777344 551.917969 1243.78125 551.917969 1243.785156 C 551.917969 1243.792969 551.917969 1243.796875 551.917969 1243.800781 C 551.917969 1243.808594 551.917969 1243.8125 551.917969 1243.816406 C 551.917969 1243.824219 551.917969 1243.828125 551.917969 1243.832031 C 551.917969 1243.835938 551.917969 1243.84375 551.917969 1243.847656 C 551.917969 1243.851562 551.917969 1243.859375 551.917969 1243.863281 C 551.917969 1243.867188 551.917969 1243.875 551.917969 1243.878906 C 551.917969 1243.882812 551.917969 1243.886719 551.917969 1243.894531 C 551.917969 1243.898438 551.917969 1243.902344 551.917969 1243.910156 C 551.917969 1243.914062 551.917969 1243.917969 551.917969 1243.925781 C 551.917969 1243.929688 551.917969 1243.933594 551.917969 1243.9375 C 551.917969 1243.945312 551.917969 1243.949219 551.917969 1243.953125 C 551.917969 1243.960938 551.917969 1243.964844 551.917969 1243.96875 C 551.917969 1243.972656 551.917969 1243.980469 551.917969 1243.984375 C 551.917969 1243.988281 551.917969 1243.996094 551.917969 1244 C 551.917969 1244.003906 551.917969 1244.011719 551.917969 1244.015625 C 551.917969 1244.019531 551.917969 1244.023438 551.917969 1244.03125 C 551.917969 1244.035156 551.917969 1244.039062 551.917969 1244.046875 C 551.917969 1244.050781 551.917969 1244.054688 551.917969 1244.058594 C 551.917969 1244.066406 551.917969 1244.070312 551.917969 1244.074219 C 551.917969 1244.082031 551.917969 1244.085938 551.917969 1244.089844 C 551.917969 1244.097656 551.917969 1244.101562 551.917969 1244.105469 C 551.917969 1244.109375 551.917969 1244.117188 551.917969 1244.121094 C 551.917969 1244.125 551.917969 1244.132812 551.917969 1244.136719 C 551.917969 1244.140625 551.917969 1244.144531 551.917969 1244.152344 C 551.917969 1244.15625 551.917969 1244.160156 551.917969 1244.167969 C 551.917969 1244.171875 551.917969 1244.175781 551.917969 1244.179688 C 551.917969 1244.1875 551.917969 1244.191406 551.917969 1244.195312 C 551.917969 1244.203125 551.917969 1244.207031 551.917969 1244.210938 C 551.917969 1244.214844 551.917969 1244.222656 551.917969 1244.226562 C 551.917969 1244.230469 551.917969 1244.238281 551.917969 1244.242188 C 551.917969 1244.246094 551.917969 1244.25 551.917969 1244.257812 C 551.917969 1244.261719 551.917969 1244.265625 551.917969 1244.273438 C 551.917969 1244.277344 551.917969 1244.28125 551.917969 1244.285156 C 551.917969 1244.292969 551.917969 1244.296875 551.917969 1244.300781 C 551.917969 1244.308594 551.917969 1244.3125 551.917969 1244.316406 C 551.917969 1244.320312 551.917969 1244.328125 551.917969 1244.332031 C 551.917969 1244.335938 551.917969 1244.339844 551.917969 1244.347656 C 551.917969 1244.351562 551.917969 1244.355469 551.917969 1244.363281 C 551.917969 1244.367188 551.917969 1244.371094 551.917969 1244.375 C 551.917969 1244.382812 551.917969 1244.386719 551.917969 1244.390625 C 551.917969 1244.398438 551.917969 1244.402344 551.917969 1244.40625 C 551.917969 1244.410156 551.917969 1244.417969 551.917969 1244.421875 C 551.917969 1244.425781 551.917969 1244.433594 551.917969 1244.4375 C 551.917969 1244.441406 551.917969 1244.445312 551.917969 1244.453125 C 551.917969 1244.457031 551.917969 1244.460938 551.917969 1244.464844 C 551.917969 1244.472656 551.917969 1244.476562 551.917969 1244.480469 C 551.917969 1244.488281 551.917969 1244.492188 551.917969 1244.496094 C 551.917969 1244.5 551.917969 1244.507812 551.917969 1244.511719 C 551.917969 1244.515625 551.917969 1244.519531 551.917969 1244.527344 C 551.917969 1244.53125 551.917969 1244.535156 551.917969 1244.542969 C 551.917969 1244.546875 551.917969 1244.550781 551.917969 1244.554688 C 551.917969 1244.5625 551.917969 1244.566406 551.917969 1244.570312 C 551.917969 1244.574219 551.917969 1244.582031 551.917969 1244.585938 C 551.917969 1244.589844 551.917969 1244.597656 551.917969 1244.601562 C 551.917969 1244.605469 551.917969 1244.609375 551.917969 1244.617188 C 551.917969 1244.621094 551.917969 1244.625 551.917969 1244.628906 C 551.917969 1244.636719 551.917969 1244.640625 551.917969 1244.644531 C 551.917969 1244.648438 551.917969 1244.65625 551.917969 1244.660156 C 551.917969 1244.664062 551.917969 1244.671875 551.917969 1244.675781 C 551.917969 1244.679688 551.917969 1244.683594 551.917969 1244.691406 C 551.917969 1244.695312 551.917969 1244.699219 551.917969 1244.703125 C 551.917969 1244.710938 551.917969 1244.714844 551.917969 1244.71875 C 551.917969 1244.722656 551.917969 1244.730469 551.917969 1244.734375 C 551.917969 1244.738281 551.917969 1244.742188 551.917969 1244.75 C 551.917969 1244.753906 551.917969 1244.757812 551.917969 1244.761719 C 551.917969 1244.769531 551.917969 1244.773438 551.917969 1244.777344 C 551.917969 1244.785156 551.917969 1244.789062 551.917969 1244.792969 C 551.917969 1244.796875 551.917969 1244.804688 551.917969 1244.808594 C 551.917969 1244.8125 551.917969 1244.816406 551.917969 1244.824219 C 551.917969 1244.828125 551.917969 1244.832031 551.917969 1244.835938 C 551.917969 1244.84375 551.917969 1244.847656 551.917969 1244.851562 C 551.917969 1244.855469 551.917969 1244.863281 551.917969 1244.867188 C 551.917969 1244.871094 551.917969 1244.875 551.917969 1244.882812 C 551.917969 1244.886719 551.917969 1244.890625 551.917969 1244.894531 C 551.917969 1244.902344 551.917969 1244.90625 551.917969 1244.910156 C 551.917969 1244.914062 551.917969 1244.921875 551.917969 1244.925781 C 551.917969 1244.929688 551.917969 1244.933594 551.917969 1244.941406 C 551.917969 1244.945312 551.917969 1244.949219 551.917969 1244.953125 C 551.917969 1244.960938 551.917969 1244.964844 551.917969 1244.96875 C 551.917969 1244.972656 551.917969 1244.980469 551.917969 1244.984375 C 551.917969 1244.988281 551.917969 1244.992188 551.917969 1245 C 551.917969 1245.003906 551.917969 1245.007812 551.917969 1245.011719 C 551.917969 1245.019531 551.917969 1245.023438 551.917969 1245.027344 C 551.917969 1245.03125 551.917969 1245.039062 551.917969 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554.820312 1244.894531 C 554.820312 1244.890625 554.820312 1244.886719 554.820312 1244.882812 C 554.820312 1244.875 554.820312 1244.871094 554.820312 1244.867188 C 554.820312 1244.863281 554.820312 1244.855469 554.820312 1244.851562 C 554.820312 1244.847656 554.820312 1244.84375 554.820312 1244.835938 C 554.820312 1244.832031 554.820312 1244.828125 554.820312 1244.824219 C 554.820312 1244.816406 554.820312 1244.8125 554.820312 1244.808594 C 554.820312 1244.804688 554.820312 1244.796875 554.820312 1244.792969 C 554.820312 1244.789062 554.820312 1244.785156 554.820312 1244.777344 C 554.820312 1244.773438 554.820312 1244.769531 554.820312 1244.761719 C 554.820312 1244.757812 554.820312 1244.753906 554.820312 1244.75 C 554.820312 1244.742188 554.820312 1244.738281 554.820312 1244.734375 C 554.820312 1244.730469 554.820312 1244.722656 554.820312 1244.71875 C 554.820312 1244.714844 554.820312 1244.710938 554.820312 1244.703125 C 554.820312 1244.699219 554.820312 1244.695312 554.820312 1244.691406 C 554.820312 1244.683594 554.820312 1244.679688 554.820312 1244.675781 C 554.820312 1244.671875 554.820312 1244.664062 554.820312 1244.660156 C 554.820312 1244.65625 554.820312 1244.648438 554.820312 1244.644531 C 554.820312 1244.640625 554.820312 1244.636719 554.820312 1244.628906 C 554.820312 1244.625 554.820312 1244.621094 554.820312 1244.617188 C 554.820312 1244.609375 554.820312 1244.605469 554.820312 1244.601562 C 554.820312 1244.597656 554.820312 1244.589844 554.820312 1244.585938 C 554.820312 1244.582031 554.820312 1244.574219 554.820312 1244.570312 C 554.820312 1244.566406 554.820312 1244.5625 554.820312 1244.554688 C 554.820312 1244.550781 554.820312 1244.546875 554.820312 1244.542969 C 554.820312 1244.535156 554.820312 1244.53125 554.820312 1244.527344 C 554.820312 1244.519531 554.820312 1244.515625 554.820312 1244.511719 C 554.820312 1244.507812 554.820312 1244.5 554.820312 1244.496094 C 554.820312 1244.492188 554.820312 1244.488281 554.820312 1244.480469 C 554.820312 1244.476562 554.820312 1244.472656 554.820312 1244.464844 C 554.820312 1244.460938 554.820312 1244.457031 554.820312 1244.453125 C 554.820312 1244.445312 554.820312 1244.441406 554.820312 1244.4375 C 554.820312 1244.433594 554.820312 1244.425781 554.820312 1244.421875 C 554.820312 1244.417969 554.820312 1244.410156 554.820312 1244.40625 C 554.820312 1244.402344 554.820312 1244.398438 554.820312 1244.390625 C 554.820312 1244.386719 554.820312 1244.382812 554.820312 1244.375 C 554.820312 1244.371094 554.820312 1244.367188 554.820312 1244.363281 C 554.820312 1244.355469 554.820312 1244.351562 554.820312 1244.347656 C 554.820312 1244.339844 554.820312 1244.335938 554.820312 1244.332031 C 554.820312 1244.328125 554.820312 1244.320312 554.820312 1244.316406 C 554.820312 1244.3125 554.820312 1244.308594 554.820312 1244.300781 C 554.820312 1244.296875 554.820312 1244.292969 554.820312 1244.285156 C 554.820312 1244.28125 554.820312 1244.277344 554.820312 1244.273438 C 554.820312 1244.265625 554.820312 1244.261719 554.820312 1244.257812 C 554.820312 1244.25 554.820312 1244.246094 554.820312 1244.242188 C 554.820312 1244.238281 554.820312 1244.230469 554.820312 1244.226562 C 554.820312 1244.222656 554.820312 1244.214844 554.820312 1244.210938 C 554.820312 1244.207031 554.820312 1244.203125 554.820312 1244.195312 C 554.820312 1244.191406 554.820312 1244.1875 554.820312 1244.179688 C 554.820312 1244.175781 554.820312 1244.171875 554.820312 1244.167969 C 554.820312 1244.160156 554.820312 1244.15625 554.820312 1244.152344 C 554.820312 1244.144531 554.820312 1244.140625 554.820312 1244.136719 C 554.820312 1244.132812 554.820312 1244.125 554.820312 1244.121094 C 554.820312 1244.117188 554.820312 1244.109375 554.820312 1244.105469 C 554.820312 1244.101562 554.820312 1244.097656 554.820312 1244.089844 C 554.820312 1244.085938 554.820312 1244.082031 554.820312 1244.074219 C 554.820312 1244.070312 554.820312 1244.066406 554.820312 1244.058594 C 554.820312 1244.054688 554.820312 1244.050781 554.820312 1244.046875 C 554.820312 1244.039062 554.820312 1244.035156 554.820312 1244.03125 C 554.820312 1244.023438 554.820312 1244.019531 554.820312 1244.015625 C 554.820312 1244.011719 554.820312 1244.003906 554.820312 1244 C 554.820312 1243.996094 554.820312 1243.988281 554.820312 1243.984375 C 554.820312 1243.980469 554.820312 1243.972656 554.820312 1243.96875 C 554.820312 1243.964844 554.820312 1243.960938 554.820312 1243.953125 C 554.820312 1243.949219 554.820312 1243.945312 554.820312 1243.9375 C 554.820312 1243.933594 554.820312 1243.929688 554.820312 1243.925781 C 554.820312 1243.917969 554.820312 1243.914062 554.820312 1243.910156 C 554.820312 1243.902344 554.820312 1243.898438 554.820312 1243.894531 C 554.820312 1243.886719 554.820312 1243.882812 554.820312 1243.878906 C 554.820312 1243.875 554.820312 1243.867188 554.820312 1243.863281 C 554.820312 1243.859375 554.820312 1243.851562 554.820312 1243.847656 C 554.820312 1243.84375 554.820312 1243.835938 554.820312 1243.832031 C 554.820312 1243.828125 554.820312 1243.824219 554.820312 1243.816406 C 554.820312 1243.8125 554.820312 1243.808594 554.820312 1243.800781 C 554.820312 1243.796875 554.820312 1243.792969 554.820312 1243.785156 C 554.820312 1243.78125 554.820312 1243.777344 554.820312 1243.773438 C 554.820312 1243.765625 554.820312 1243.761719 554.820312 1243.757812 C 554.820312 1243.75 554.820312 1243.746094 554.820312 1243.742188 C 554.820312 1243.734375 554.820312 1243.730469 554.820312 1243.726562 C 554.820312 1243.71875 554.820312 1243.714844 554.820312 1243.710938 C 554.820312 1243.707031 554.820312 1243.699219 554.820312 1243.695312 C 554.820312 1243.691406 554.820312 1243.683594 554.820312 1243.679688 C 554.820312 1243.675781 554.820312 1243.667969 554.820312 1243.664062 C 554.820312 1243.660156 554.820312 1243.65625 554.820312 1243.648438 C 554.820312 1243.644531 554.820312 1243.640625 554.820312 1243.632812 C 554.820312 1243.628906 554.820312 1243.625 554.820312 1243.617188 C 554.820312 1243.613281 554.820312 1243.609375 554.820312 1243.601562 C 554.820312 1243.597656 554.820312 1243.59375 554.820312 1243.589844 C 554.820312 1243.582031 554.820312 1243.578125 554.820312 1243.574219 C 554.820312 1243.566406 554.820312 1243.5625 554.820312 1243.558594 C 554.820312 1243.550781 554.820312 1243.546875 554.820312 1243.542969 C 554.820312 1243.535156 554.820312 1243.53125 554.820312 1243.527344 C 554.820312 1243.519531 554.820312 1243.515625 554.820312 1243.511719 C 554.820312 1243.507812 554.820312 1243.5 554.820312 1243.496094 C 554.820312 1243.492188 554.820312 1243.484375 554.820312 1243.480469 C 554.820312 1243.476562 554.820312 1243.46875 554.820312 1243.464844 C 554.820312 1243.460938 554.820312 1243.453125 554.820312 1243.449219 C 554.820312 1243.445312 554.820312 1243.4375 554.820312 1243.433594 C 554.820312 1243.429688 554.820312 1243.425781 554.820312 1243.417969 C 554.820312 1243.414062 554.820312 1243.410156 554.820312 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554.820312 1241.851562 554.820312 1241.847656 554.820312 1241.84375 C 554.820312 1241.835938 554.820312 1241.832031 554.820312 1241.828125 C 554.820312 1241.820312 554.820312 1241.816406 554.820312 1241.8125 C 554.820312 1241.804688 554.820312 1241.800781 554.820312 1241.796875 C 554.820312 1241.789062 554.820312 1241.785156 554.820312 1241.777344 C 554.820312 1241.773438 554.820312 1241.769531 554.820312 1241.761719 C 554.820312 1241.757812 554.820312 1241.753906 554.820312 1241.746094 C 554.820312 1241.742188 554.820312 1241.738281 554.820312 1241.730469 C 554.820312 1241.726562 554.820312 1241.722656 554.820312 1241.714844 C 554.820312 1241.710938 554.820312 1241.707031 554.820312 1241.699219 C 554.820312 1241.695312 554.820312 1241.691406 554.820312 1241.683594 C 554.820312 1241.679688 554.820312 1241.675781 554.820312 1241.667969 C 554.820312 1241.664062 554.820312 1241.660156 554.820312 1241.652344 C 554.820312 1241.648438 554.820312 1241.640625 554.820312 1241.636719 C 554.820312 1241.632812 554.820312 1241.625 554.820312 1241.621094 C 554.820312 1241.617188 554.820312 1241.609375 554.820312 1241.605469 C 554.820312 1241.601562 554.820312 1241.59375 554.820312 1241.589844 C 554.820312 1241.585938 554.820312 1241.578125 554.820312 1241.574219 C 554.820312 1241.570312 554.820312 1241.5625 554.820312 1241.558594 C 554.820312 1241.554688 554.820312 1241.546875 554.820312 1241.542969 C 554.820312 1241.539062 554.820312 1241.53125 554.820312 1241.527344 C 554.820312 1241.523438 554.820312 1241.515625 554.820312 1241.511719 C 554.820312 1241.503906 554.820312 1241.5 554.820312 1241.496094 C 554.820312 1241.488281 554.820312 1241.484375 554.820312 1241.480469 C 554.820312 1241.472656 554.820312 1241.46875 554.820312 1241.464844 C 554.820312 1241.457031 554.820312 1241.453125 554.820312 1241.449219 C 554.820312 1241.441406 554.820312 1241.4375 554.820312 1241.433594 C 554.820312 1241.425781 554.820312 1241.421875 554.820312 1241.417969 C 554.820312 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1315.367188 481.496094 1315.367188 481.492188 1315.367188 C 481.484375 1315.367188 481.480469 1315.367188 481.476562 1315.367188 C 481.472656 1315.367188 481.464844 1315.367188 481.460938 1315.367188 C 481.457031 1315.367188 481.453125 1315.367188 481.445312 1315.367188 C 481.441406 1315.367188 481.4375 1315.367188 481.433594 1315.367188 C 481.425781 1315.367188 481.421875 1315.367188 481.417969 1315.367188 C 481.414062 1315.367188 481.40625 1315.367188 481.402344 1315.367188 C 481.398438 1315.367188 481.394531 1315.367188 481.386719 1315.367188 C 481.382812 1315.367188 481.378906 1315.367188 481.375 1315.367188 C 481.367188 1315.367188 481.363281 1315.367188 481.359375 1315.367188 C 481.355469 1315.367188 481.347656 1315.367188 481.34375 1315.367188 C 481.339844 1315.367188 481.335938 1315.367188 481.328125 1315.367188 C 481.324219 1315.367188 481.320312 1315.367188 481.3125 1315.367188 C 481.308594 1315.367188 481.304688 1315.367188 481.300781 1315.367188 C 481.292969 1315.367188 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1315.367188 481.078125 1315.367188 C 481.070312 1315.367188 481.066406 1315.367188 481.0625 1315.367188 C 481.058594 1315.367188 481.050781 1315.367188 481.046875 1315.367188 C 481.042969 1315.367188 481.039062 1315.367188 481.03125 1315.367188 C 481.027344 1315.367188 481.023438 1315.367188 481.015625 1315.367188 C 481.011719 1315.367188 481.007812 1315.367188 481.003906 1315.367188 C 480.996094 1315.367188 480.992188 1315.367188 480.988281 1315.367188 C 480.984375 1315.367188 480.976562 1315.367188 480.972656 1315.367188 C 480.96875 1315.367188 480.960938 1315.367188 480.957031 1315.367188 C 480.953125 1315.367188 480.949219 1315.367188 480.941406 1315.367188 C 480.9375 1315.367188 480.933594 1315.367188 480.925781 1315.367188 C 480.921875 1315.367188 480.917969 1315.367188 480.914062 1315.367188 C 480.90625 1315.367188 480.902344 1315.367188 480.898438 1315.367188 C 480.890625 1315.367188 480.886719 1315.367188 480.882812 1315.367188 C 480.878906 1315.367188 480.871094 1315.367188 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1315.367188 C 480.652344 1315.367188 480.648438 1315.367188 480.640625 1315.367188 C 480.636719 1315.367188 480.632812 1315.367188 480.625 1315.367188 C 480.621094 1315.367188 480.617188 1315.367188 480.609375 1315.367188 C 480.605469 1315.367188 480.601562 1315.367188 480.597656 1315.367188 C 480.589844 1315.367188 480.585938 1315.367188 480.582031 1315.367188 C 480.574219 1315.367188 480.570312 1315.367188 480.566406 1315.367188 C 480.5625 1315.367188 480.554688 1315.367188 480.550781 1315.367188 C 480.546875 1315.367188 480.539062 1315.367188 480.535156 1315.367188 C 480.53125 1315.367188 480.523438 1315.367188 480.519531 1315.367188 C 480.515625 1315.367188 480.511719 1315.367188 480.503906 1315.367188 C 480.5 1315.367188 480.496094 1315.367188 480.488281 1315.367188 C 480.484375 1315.367188 480.480469 1315.367188 480.476562 1315.367188 C 480.46875 1315.367188 480.464844 1315.367188 480.460938 1315.367188 C 480.453125 1315.367188 480.449219 1315.367188 480.445312 1315.367188 C 480.4375 1315.367188 480.433594 1315.367188 480.429688 1315.367188 C 480.425781 1315.367188 480.417969 1315.367188 480.414062 1315.367188 C 480.410156 1315.367188 480.402344 1315.367188 480.398438 1315.367188 C 480.394531 1315.367188 480.386719 1315.367188 480.382812 1315.367188 C 480.378906 1315.367188 480.375 1315.367188 480.367188 1315.367188 C 480.363281 1315.367188 480.359375 1315.367188 480.351562 1315.367188 C 480.347656 1315.367188 480.34375 1315.367188 480.335938 1315.367188 C 480.332031 1315.367188 480.328125 1315.367188 480.324219 1315.367188 C 480.316406 1315.367188 480.3125 1315.367188 480.308594 1315.367188 C 480.300781 1315.367188 480.296875 1315.367188 480.292969 1315.367188 C 480.285156 1315.367188 480.28125 1315.367188 480.277344 1315.367188 C 480.269531 1315.367188 480.265625 1315.367188 480.261719 1315.367188 C 480.257812 1315.367188 480.25 1315.367188 480.246094 1315.367188 C 480.242188 1315.367188 480.234375 1315.367188 480.230469 1315.367188 C 480.226562 1315.367188 480.21875 1315.367188 480.214844 1315.367188 C 480.210938 1315.367188 480.207031 1315.367188 480.199219 1315.367188 C 480.195312 1315.367188 480.191406 1315.367188 480.183594 1315.367188 C 480.179688 1315.367188 480.175781 1315.367188 480.167969 1315.367188 C 480.164062 1315.367188 480.160156 1315.367188 480.152344 1315.367188 C 480.148438 1315.367188 480.144531 1315.367188 480.140625 1315.367188 C 480.132812 1315.367188 480.128906 1315.367188 480.125 1315.367188 C 480.117188 1315.367188 480.113281 1315.367188 480.109375 1315.367188 C 480.101562 1315.367188 480.097656 1315.367188 480.09375 1315.367188 C 480.085938 1315.367188 480.082031 1315.367188 480.078125 1315.367188 C 480.070312 1315.367188 480.066406 1315.367188 480.0625 1315.367188 C 480.058594 1315.367188 480.050781 1315.367188 480.046875 1315.367188 C 480.042969 1315.367188 480.035156 1315.367188 480.03125 1315.367188 C 480.027344 1315.367188 480.019531 1315.367188 480.015625 1315.367188 C 480.011719 1315.367188 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1315.367188 479.785156 1315.367188 C 479.78125 1315.367188 479.773438 1315.367188 479.769531 1315.367188 C 479.765625 1315.367188 479.757812 1315.367188 479.753906 1315.367188 C 479.75 1315.367188 479.742188 1315.367188 479.738281 1315.367188 C 479.734375 1315.367188 479.726562 1315.367188 479.722656 1315.367188 C 479.71875 1315.367188 479.710938 1315.367188 479.707031 1315.367188 C 479.703125 1315.367188 479.695312 1315.367188 479.691406 1315.367188 C 479.6875 1315.367188 479.679688 1315.367188 479.675781 1315.367188 C 479.671875 1315.367188 479.664062 1315.367188 479.660156 1315.367188 C 479.65625 1315.367188 479.648438 1315.367188 479.644531 1315.367188 C 479.640625 1315.367188 479.636719 1315.367188 479.628906 1315.367188 C 479.625 1315.367188 479.621094 1315.367188 479.613281 1315.367188 C 479.609375 1315.367188 479.605469 1315.367188 479.597656 1315.367188 C 479.59375 1315.367188 479.589844 1315.367188 479.582031 1315.367188 C 479.578125 1315.367188 479.574219 1315.367188 479.566406 1315.367188 C 479.5625 1315.367188 479.558594 1315.367188 479.550781 1315.367188 C 479.546875 1315.367188 479.542969 1315.367188 479.535156 1315.367188 C 479.53125 1315.367188 479.527344 1315.367188 479.519531 1315.367188 C 479.515625 1315.367188 479.511719 1315.367188 479.503906 1315.367188 C 479.5 1315.367188 479.496094 1315.367188 479.488281 1315.367188 C 479.484375 1315.367188 479.480469 1315.367188 479.472656 1315.367188 C 479.46875 1315.367188 479.464844 1315.367188 479.457031 1315.367188 C 479.453125 1315.367188 479.449219 1315.367188 479.441406 1315.367188 C 479.4375 1315.367188 479.433594 1315.367188 479.425781 1315.367188 C 479.421875 1315.367188 479.417969 1315.367188 479.410156 1315.367188 C 479.40625 1315.367188 479.402344 1315.367188 479.394531 1315.367188 C 479.390625 1315.367188 479.386719 1315.367188 479.382812 1315.367188 C 479.375 1315.367188 479.371094 1315.367188 479.367188 1315.367188 C 479.359375 1315.367188 479.355469 1315.367188 479.351562 1315.367188 C 479.34375 1315.367188 479.339844 1315.367188 479.335938 1315.367188 C 479.328125 1315.367188 479.324219 1315.367188 479.320312 1315.367188 C 479.3125 1315.367188 479.308594 1315.367188 479.304688 1315.367188 C 479.296875 1315.367188 479.292969 1315.367188 479.289062 1315.367188 C 479.28125 1315.367188 479.277344 1315.367188 479.273438 1315.367188 C 479.265625 1315.367188 479.261719 1315.367188 479.257812 1315.367188 C 479.25 1315.367188 479.246094 1315.367188 479.242188 1315.367188 C 479.234375 1315.367188 479.230469 1315.367188 479.226562 1315.367188 C 479.21875 1315.367188 479.214844 1315.367188 479.210938 1315.367188 C 479.203125 1315.367188 479.199219 1315.367188 479.195312 1315.367188 C 479.1875 1315.367188 479.183594 1315.367188 479.179688 1315.367188 C 479.171875 1315.367188 479.167969 1315.367188 479.164062 1315.367188 C 479.15625 1315.367188 479.152344 1315.367188 479.148438 1315.367188 C 479.140625 1315.367188 479.136719 1315.367188 479.132812 1315.367188 C 479.125 1315.367188 479.121094 1315.367188 479.117188 1315.367188 C 479.109375 1315.367188 479.105469 1315.367188 479.101562 1315.367188 C 479.09375 1315.367188 479.089844 1315.367188 479.085938 1315.367188 C 479.078125 1315.367188 479.074219 1315.367188 479.070312 1315.367188 C 479.0625 1315.367188 479.058594 1315.367188 479.054688 1315.367188 C 479.046875 1315.367188 479.042969 1315.367188 479.039062 1315.367188 C 479.03125 1315.367188 479.027344 1315.367188 479.023438 1315.367188 C 479.015625 1315.367188 479.011719 1315.367188 479.007812 1315.367188 C 479 1315.367188 478.996094 1315.367188 478.992188 1315.367188 C 478.984375 1315.367188 478.980469 1315.367188 478.972656 1315.367188 C 478.96875 1315.367188 478.964844 1315.367188 478.957031 1315.367188 C 478.953125 1315.367188 478.949219 1315.367188 478.941406 1315.367188 C 478.9375 1315.367188 478.933594 1315.367188 478.925781 1315.367188 C 478.921875 1315.367188 478.917969 1315.367188 478.910156 1315.367188 C 478.90625 1315.367188 478.902344 1315.367188 478.894531 1315.367188 C 478.890625 1315.367188 478.886719 1315.367188 478.878906 1315.367188 C 478.875 1315.367188 478.871094 1315.367188 478.863281 1315.367188 C 478.859375 1315.367188 478.855469 1315.367188 478.847656 1315.367188 C 478.84375 1315.367188 478.839844 1315.367188 478.832031 1315.367188 C 478.828125 1315.367188 478.824219 1315.367188 478.816406 1315.367188 C 478.8125 1315.367188 478.808594 1315.367188 478.800781 1315.367188 C 478.796875 1315.367188 478.792969 1315.367188 478.785156 1315.367188 C 478.78125 1315.367188 478.777344 1315.367188 478.769531 1315.367188 C 478.765625 1315.367188 478.761719 1315.367188 478.753906 1315.367188 C 478.75 1315.367188 478.746094 1315.367188 478.738281 1315.367188 C 478.734375 1315.367188 478.730469 1315.367188 478.722656 1315.367188 C 478.71875 1315.367188 478.714844 1315.367188 478.707031 1315.367188 C 478.703125 1315.367188 478.699219 1315.367188 478.691406 1315.367188 C 478.6875 1315.367188 478.679688 1315.367188 478.675781 1315.367188 C 478.671875 1315.367188 478.664062 1315.367188 478.660156 1315.367188 C 478.65625 1315.367188 478.648438 1315.367188 478.644531 1315.367188 C 478.640625 1315.367188 478.632812 1315.367188 478.628906 1315.367188 C 478.625 1315.367188 478.617188 1315.367188 478.613281 1315.367188 C 478.609375 1315.367188 478.601562 1315.367188 478.597656 1315.367188 C 478.59375 1315.367188 478.585938 1315.367188 478.582031 1315.367188 C 478.578125 1315.367188 478.570312 1315.367188 478.566406 1315.367188 C 478.5625 1315.367188 478.554688 1315.367188 478.550781 1315.367188 C 478.546875 1315.367188 478.539062 1315.367188 478.535156 1315.367188 C 478.53125 1315.367188 478.523438 1315.367188 478.519531 1315.367188 C 478.515625 1315.367188 478.507812 1315.367188 478.503906 1315.367188 C 478.496094 1315.367188 478.492188 1315.367188 478.488281 1315.367188 C 478.480469 1315.367188 478.476562 1315.367188 478.472656 1315.367188 C 478.464844 1315.367188 478.460938 1315.367188 478.457031 1315.367188 C 478.449219 1315.367188 478.445312 1315.367188 478.441406 1315.367188 C 478.433594 1315.367188 478.429688 1315.367188 478.425781 1315.367188 C 478.417969 1315.367188 478.414062 1315.367188 478.410156 1315.367188 C 478.402344 1315.367188 478.398438 1315.367188 478.394531 1315.367188 C 478.386719 1315.367188 478.382812 1315.367188 478.378906 1315.367188 C 478.371094 1315.367188 478.367188 1315.367188 478.363281 1315.367188 C 478.355469 1315.367188 478.351562 1315.367188 478.347656 1315.367188 C 478.339844 1315.367188 478.335938 1315.367188 478.328125 1315.367188 C 478.324219 1315.367188 478.320312 1315.367188 478.3125 1315.367188 C 478.308594 1315.367188 478.304688 1315.367188 478.296875 1315.367188 C 478.292969 1315.367188 478.289062 1315.367188 478.28125 1315.367188 C 478.277344 1315.367188 478.273438 1315.367188 478.265625 1315.367188 C 478.261719 1315.367188 478.257812 1315.367188 478.25 1315.367188 C 478.246094 1315.367188 478.242188 1315.367188 478.234375 1315.367188 C 478.230469 1315.367188 478.226562 1315.367188 478.21875 1315.367188 C 478.214844 1315.367188 478.210938 1315.367188 478.203125 1315.367188 C 478.199219 1315.367188 478.191406 1315.367188 478.1875 1315.367188 C 478.183594 1315.367188 478.175781 1315.367188 478.171875 1315.367188 C 478.167969 1315.367188 478.160156 1315.367188 478.15625 1315.367188 C 478.152344 1315.367188 478.144531 1315.367188 478.140625 1315.367188 C 478.136719 1315.367188 478.128906 1315.367188 478.125 1315.367188 C 478.121094 1315.367188 478.113281 1315.367188 478.109375 1315.367188 C 478.105469 1315.367188 478.097656 1315.367188 478.09375 1315.367188 C 478.089844 1315.367188 478.082031 1315.367188 478.078125 1315.367188 C 478.074219 1315.367188 478.066406 1315.367188 478.0625 1315.367188 C 478.054688 1315.367188 478.050781 1315.367188 478.046875 1315.367188 C 478.039062 1315.367188 478.035156 1315.367188 478.03125 1315.367188 C 478.023438 1315.367188 478.019531 1315.367188 478.015625 1315.367188 C 478.007812 1315.367188 478.003906 1315.367188 478 1315.367188 C 477.992188 1315.367188 477.988281 1315.367188 477.984375 1315.367188 C 477.976562 1315.367188 477.972656 1315.367188 477.96875 1315.367188 C 477.960938 1315.367188 477.957031 1315.367188 477.953125 1315.367188 C 477.945312 1315.367188 477.941406 1315.367188 477.933594 1315.367188 C 477.929688 1315.367188 477.925781 1315.367188 477.917969 1315.367188 C 477.914062 1315.367188 477.910156 1315.367188 477.902344 1315.367188 C 477.898438 1315.367188 477.894531 1315.367188 477.886719 1315.367188 C 477.882812 1315.367188 477.878906 1315.367188 477.871094 1315.367188 C 477.867188 1315.367188 477.863281 1315.367188 477.855469 1315.367188 C 477.851562 1315.367188 477.847656 1315.367188 477.839844 1315.367188 C 477.835938 1315.367188 477.832031 1315.367188 477.824219 1315.367188 C 477.820312 1315.367188 477.8125 1315.367188 477.808594 1315.367188 C 477.804688 1315.367188 477.796875 1315.367188 477.792969 1315.367188 C 477.789062 1315.367188 477.78125 1315.367188 477.777344 1315.367188 C 477.773438 1315.367188 477.765625 1315.367188 477.761719 1315.367188 C 477.757812 1315.367188 477.75 1315.367188 477.746094 1315.367188 C 477.742188 1315.367188 477.734375 1315.367188 477.730469 1315.367188 C 477.726562 1315.367188 477.71875 1315.367188 477.714844 1315.367188 C 477.710938 1315.367188 477.703125 1315.367188 477.699219 1315.367188 C 477.691406 1315.367188 477.6875 1315.367188 477.683594 1315.367188 C 477.675781 1315.367188 477.671875 1315.367188 477.667969 1315.367188 C 477.660156 1315.367188 477.65625 1315.367188 477.652344 1315.367188 C 477.644531 1315.367188 477.640625 1315.367188 477.636719 1315.367188 C 477.628906 1315.367188 477.625 1315.367188 477.621094 1315.367188 C 477.613281 1315.367188 477.609375 1315.367188 477.605469 1315.367188 C 477.597656 1315.367188 477.59375 1315.367188 477.589844 1315.367188 C 477.582031 1315.367188 477.578125 1315.367188 477.570312 1315.367188 C 477.566406 1315.367188 477.5625 1315.367188 477.554688 1315.367188 C 477.550781 1315.367188 477.546875 1315.367188 477.539062 1315.367188 C 477.535156 1315.367188 477.53125 1315.367188 477.523438 1315.367188 C 477.519531 1315.367188 477.515625 1315.367188 477.507812 1315.367188 C 477.503906 1315.367188 477.5 1315.367188 477.492188 1315.367188 C 477.488281 1315.367188 477.484375 1315.367188 477.476562 1315.367188 C 477.472656 1315.367188 477.46875 1315.367188 477.460938 1315.367188 C 477.457031 1315.367188 477.449219 1315.367188 477.445312 1315.367188 C 477.441406 1315.367188 477.433594 1315.367188 477.429688 1315.367188 C 477.425781 1315.367188 477.417969 1315.367188 477.414062 1315.367188 C 477.410156 1315.367188 477.402344 1315.367188 477.398438 1315.367188 C 477.394531 1315.367188 477.386719 1315.367188 477.382812 1315.367188 C 477.378906 1315.367188 477.371094 1315.367188 477.367188 1315.367188 C 477.363281 1315.367188 477.355469 1315.367188 477.351562 1315.367188 C 477.347656 1315.367188 477.339844 1315.367188 477.335938 1315.367188 C 477.332031 1315.367188 477.324219 1315.367188 477.320312 1315.367188 C 477.3125 1315.367188 477.308594 1315.367188 477.304688 1315.367188 C 477.296875 1315.367188 477.292969 1315.367188 477.289062 1315.367188 C 477.28125 1315.367188 477.277344 1315.367188 477.273438 1315.367188 C 477.265625 1315.367188 477.261719 1315.367188 477.257812 1315.367188 C 477.25 1315.367188 477.246094 1315.367188 477.242188 1315.367188 C 477.234375 1315.367188 477.230469 1315.367188 477.226562 1315.367188 C 477.21875 1315.367188 477.214844 1315.367188 477.210938 1315.367188 C 477.203125 1315.367188 477.199219 1315.367188 477.191406 1315.367188 C 477.1875 1315.367188 477.183594 1315.367188 477.175781 1315.367188 C 477.171875 1315.367188 477.167969 1315.367188 477.160156 1315.367188 C 477.15625 1315.367188 477.152344 1315.367188 477.144531 1315.367188 C 477.140625 1315.367188 477.136719 1315.367188 477.128906 1315.367188 C 477.125 1315.367188 477.121094 1315.367188 477.113281 1315.367188 C 477.109375 1315.367188 477.105469 1315.367188 477.097656 1315.367188 C 477.09375 1315.367188 477.089844 1315.367188 477.082031 1315.367188 C 477.078125 1315.367188 477.074219 1315.367188 477.066406 1315.367188 C 477.0625 1315.367188 477.054688 1315.367188 477.050781 1315.367188 C 477.046875 1315.367188 477.039062 1315.367188 477.035156 1315.367188 C 477.03125 1315.367188 477.023438 1315.367188 477.019531 1315.367188 C 477.015625 1315.367188 477.007812 1315.367188 477.003906 1315.367188 C 477 1315.367188 476.992188 1315.367188 476.988281 1315.367188 C 476.984375 1315.367188 476.976562 1315.367188 476.972656 1315.367188 C 476.96875 1315.367188 476.960938 1315.367188 476.957031 1315.367188 C 476.953125 1315.367188 476.945312 1315.367188 476.941406 1315.367188 C 476.9375 1315.367188 476.929688 1315.367188 476.925781 1315.367188 C 476.921875 1315.367188 476.914062 1315.367188 476.910156 1315.367188 C 476.90625 1315.367188 476.898438 1315.367188 476.894531 1315.367188 C 476.886719 1315.367188 476.882812 1315.367188 476.878906 1315.367188 C 476.871094 1315.367188 476.867188 1315.367188 476.863281 1315.367188 C 476.855469 1315.367188 476.851562 1315.367188 476.847656 1315.367188 C 476.839844 1315.367188 476.835938 1315.367188 476.832031 1315.367188 C 476.824219 1315.367188 476.820312 1315.367188 476.816406 1315.367188 C 476.808594 1315.367188 476.804688 1315.367188 476.800781 1315.367188 C 476.792969 1315.367188 476.789062 1315.367188 476.785156 1315.367188 C 476.777344 1315.367188 476.773438 1315.367188 476.769531 1315.367188 C 476.761719 1315.367188 476.757812 1315.367188 476.753906 1315.367188 C 476.746094 1315.367188 476.742188 1315.367188 476.738281 1315.367188 C 476.730469 1315.367188 476.726562 1315.367188 476.71875 1315.367188 C 476.714844 1315.367188 476.710938 1315.367188 476.703125 1315.367188 C 476.699219 1315.367188 476.695312 1315.367188 476.6875 1315.367188 C 476.683594 1315.367188 476.679688 1315.367188 476.671875 1315.367188 C 476.667969 1315.367188 476.664062 1315.367188 476.65625 1315.367188 C 476.652344 1315.367188 476.648438 1315.367188 476.640625 1315.367188 C 476.636719 1315.367188 476.632812 1315.367188 476.625 1315.367188 C 476.621094 1315.367188 476.617188 1315.367188 476.609375 1315.367188 C 476.605469 1315.367188 476.601562 1315.367188 476.59375 1315.367188 C 476.589844 1315.367188 476.585938 1315.367188 476.578125 1315.367188 C 476.574219 1315.367188 476.570312 1315.367188 476.5625 1315.367188 C 476.558594 1315.367188 476.554688 1315.367188 476.546875 1315.367188 C 476.542969 1315.367188 476.539062 1315.367188 476.53125 1315.367188 C 476.527344 1315.367188 476.523438 1315.367188 476.515625 1315.367188 C 476.511719 1315.367188 476.507812 1315.367188 476.5 1315.367188 C 476.496094 1315.367188 476.492188 1315.367188 476.484375 1315.367188 C 476.480469 1315.367188 476.476562 1315.367188 476.46875 1315.367188 C 476.464844 1315.367188 476.457031 1315.367188 476.453125 1315.367188 C 476.449219 1315.367188 476.441406 1315.367188 476.4375 1315.367188 C 476.433594 1315.367188 476.425781 1315.367188 476.421875 1315.367188 C 476.417969 1315.367188 476.410156 1315.367188 476.40625 1315.367188 C 476.402344 1315.367188 476.394531 1315.367188 476.390625 1315.367188 C 476.386719 1315.367188 476.378906 1315.367188 476.375 1315.367188 C 476.371094 1315.367188 476.363281 1315.367188 476.359375 1315.367188 C 476.355469 1315.367188 476.347656 1315.367188 476.34375 1315.367188 C 476.339844 1315.367188 476.332031 1315.367188 476.328125 1315.367188 C 476.324219 1315.367188 476.316406 1315.367188 476.3125 1315.367188 C 476.308594 1315.367188 476.300781 1315.367188 476.296875 1315.367188 C 476.292969 1315.367188 476.285156 1315.367188 476.28125 1315.367188 C 476.277344 1315.367188 476.269531 1315.367188 476.265625 1315.367188 C 476.261719 1315.367188 476.253906 1315.367188 476.25 1315.367188 C 476.246094 1315.367188 476.238281 1315.367188 476.234375 1315.367188 C 476.230469 1315.367188 476.222656 1315.367188 476.21875 1315.367188 C 476.214844 1315.367188 476.207031 1315.367188 476.203125 1315.367188 C 476.199219 1315.367188 476.191406 1315.367188 476.1875 1315.367188 C 476.183594 1315.367188 476.175781 1315.367188 476.171875 1315.367188 C 476.167969 1315.367188 476.160156 1315.367188 476.15625 1315.367188 C 476.152344 1315.367188 476.144531 1315.367188 476.140625 1315.367188 C 476.136719 1315.367188 476.128906 1315.367188 476.125 1315.367188 C 476.121094 1315.367188 476.113281 1315.367188 476.109375 1315.367188 C 476.105469 1315.367188 476.097656 1315.367188 476.09375 1315.367188 C 476.089844 1315.367188 476.082031 1315.367188 476.078125 1315.367188 C 476.074219 1315.367188 476.066406 1315.367188 476.0625 1315.367188 C 476.058594 1315.367188 476.050781 1315.367188 476.046875 1315.367188 C 476.042969 1315.367188 476.035156 1315.367188 476.03125 1315.367188 C 476.027344 1315.367188 476.019531 1315.367188 476.015625 1315.367188 C 476.011719 1315.367188 476.003906 1315.367188 476 1315.367188 C 475.996094 1315.367188 475.988281 1315.367188 475.984375 1315.367188 C 475.980469 1315.367188 475.972656 1315.367188 475.96875 1315.367188 C 475.964844 1315.367188 475.957031 1315.367188 475.953125 1315.367188 C 475.949219 1315.367188 475.941406 1315.367188 475.9375 1315.367188 C 475.933594 1315.367188 475.925781 1315.367188 475.921875 1315.367188 C 475.917969 1315.367188 475.910156 1315.367188 475.90625 1315.367188 C 475.902344 1315.367188 475.898438 1315.367188 475.890625 1315.367188 C 475.886719 1315.367188 475.882812 1315.367188 475.875 1315.367188 C 475.871094 1315.367188 475.867188 1315.367188 475.859375 1315.367188 C 475.855469 1315.367188 475.851562 1315.367188 475.84375 1315.367188 C 475.839844 1315.367188 475.835938 1315.367188 475.828125 1315.367188 C 475.824219 1315.367188 475.820312 1315.367188 475.8125 1315.367188 C 475.808594 1315.367188 475.804688 1315.367188 475.796875 1315.367188 C 475.792969 1315.367188 475.789062 1315.367188 475.78125 1315.367188 C 475.777344 1315.367188 475.773438 1315.367188 475.765625 1315.367188 C 475.761719 1315.367188 475.757812 1315.367188 475.75 1315.367188 C 475.746094 1315.367188 475.742188 1315.367188 475.734375 1315.367188 C 475.730469 1315.367188 475.726562 1315.367188 475.71875 1315.367188 C 475.714844 1315.367188 475.710938 1315.367188 475.703125 1315.367188 C 475.699219 1315.367188 475.695312 1315.367188 475.691406 1315.367188 C 475.683594 1315.367188 475.679688 1315.367188 475.675781 1315.367188 C 475.667969 1315.367188 475.664062 1315.367188 475.660156 1315.367188 C 475.652344 1315.367188 475.648438 1315.367188 475.644531 1315.367188 C 475.636719 1315.367188 475.632812 1315.367188 475.628906 1315.367188 C 475.621094 1315.367188 475.617188 1315.367188 475.613281 1315.367188 C 475.605469 1315.367188 475.601562 1315.367188 475.597656 1315.367188 C 475.589844 1315.367188 475.585938 1315.367188 475.582031 1315.367188 C 475.574219 1315.367188 475.570312 1315.367188 475.566406 1315.367188 C 475.558594 1315.367188 475.554688 1315.367188 475.550781 1315.367188 C 475.546875 1315.367188 475.539062 1315.367188 475.535156 1315.367188 C 475.53125 1315.367188 475.523438 1315.367188 475.519531 1315.367188 C 475.515625 1315.367188 475.507812 1315.367188 475.503906 1315.367188 C 475.5 1315.367188 475.492188 1315.367188 475.488281 1315.367188 C 475.484375 1315.367188 475.476562 1315.367188 475.472656 1315.367188 C 475.46875 1315.367188 475.460938 1315.367188 475.457031 1315.367188 C 475.453125 1315.367188 475.449219 1315.367188 475.441406 1315.367188 C 475.4375 1315.367188 475.433594 1315.367188 475.425781 1315.367188 C 475.421875 1315.367188 475.417969 1315.367188 475.410156 1315.367188 C 475.40625 1315.367188 475.402344 1315.367188 475.394531 1315.367188 C 475.390625 1315.367188 475.386719 1315.367188 475.378906 1315.367188 C 475.375 1315.367188 475.371094 1315.367188 475.363281 1315.367188 C 475.359375 1315.367188 475.355469 1315.367188 475.351562 1315.367188 C 475.34375 1315.367188 475.339844 1315.367188 475.335938 1315.367188 C 475.328125 1315.367188 475.324219 1315.367188 475.320312 1315.367188 C 475.3125 1315.367188 475.308594 1315.367188 475.304688 1315.367188 C 475.296875 1315.367188 475.292969 1315.367188 475.289062 1315.367188 C 475.28125 1315.367188 475.277344 1315.367188 475.273438 1315.367188 C 475.269531 1315.367188 475.261719 1315.367188 475.257812 1315.367188 C 475.253906 1315.367188 475.246094 1315.367188 475.242188 1315.367188 C 475.238281 1315.367188 475.230469 1315.367188 475.226562 1315.367188 C 475.222656 1315.367188 475.214844 1315.367188 475.210938 1315.367188 C 475.207031 1315.367188 475.203125 1315.367188 475.195312 1315.367188 C 475.191406 1315.367188 475.1875 1315.367188 475.179688 1315.367188 C 475.175781 1315.367188 475.171875 1315.367188 475.164062 1315.367188 C 475.160156 1315.367188 475.15625 1315.367188 475.148438 1315.367188 C 475.144531 1315.367188 475.140625 1315.367188 475.136719 1315.367188 C 475.128906 1315.367188 475.125 1315.367188 475.121094 1315.367188 C 475.113281 1315.367188 475.109375 1315.367188 475.105469 1315.367188 C 475.097656 1315.367188 475.09375 1315.367188 475.089844 1315.367188 C 475.085938 1315.367188 475.078125 1315.367188 475.074219 1315.367188 C 475.070312 1315.367188 475.0625 1315.367188 475.058594 1315.367188 C 475.054688 1315.367188 475.046875 1315.367188 475.042969 1315.367188 C 475.039062 1315.367188 475.03125 1315.367188 475.027344 1315.367188 C 475.023438 1315.367188 475.019531 1315.367188 475.011719 1315.367188 C 475.007812 1315.367188 475.003906 1315.367188 474.996094 1315.367188 C 474.992188 1315.367188 474.988281 1315.367188 474.980469 1315.367188 C 474.976562 1315.367188 474.972656 1315.367188 474.96875 1315.367188 C 474.960938 1315.367188 474.957031 1315.367188 474.953125 1315.367188 C 474.945312 1315.367188 474.941406 1315.367188 474.9375 1315.367188 C 474.929688 1315.367188 474.925781 1315.367188 474.921875 1315.367188 C 474.917969 1315.367188 474.910156 1315.367188 474.90625 1315.367188 C 474.902344 1315.367188 474.894531 1315.367188 474.890625 1315.367188 C 474.886719 1315.367188 474.878906 1315.367188 474.875 1315.367188 C 474.871094 1315.367188 474.867188 1315.367188 474.859375 1315.367188 C 474.855469 1315.367188 474.851562 1315.367188 474.84375 1315.367188 C 474.839844 1315.367188 474.835938 1315.367188 474.832031 1315.367188 C 474.824219 1315.367188 474.820312 1315.367188 474.816406 1315.367188 C 474.808594 1315.367188 474.804688 1315.367188 474.800781 1315.367188 C 474.792969 1315.367188 474.789062 1315.367188 474.785156 1315.367188 C 474.78125 1315.367188 474.773438 1315.367188 474.769531 1315.367188 C 474.765625 1315.367188 474.757812 1315.367188 474.753906 1315.367188 C 474.75 1315.367188 474.746094 1315.367188 474.738281 1315.367188 C 474.734375 1315.367188 474.730469 1315.367188 474.722656 1315.367188 C 474.71875 1315.367188 474.714844 1315.367188 474.710938 1315.367188 C 474.703125 1315.367188 474.699219 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474.0625 1315.367188 474.054688 1315.367188 474.050781 1315.367188 C 474.046875 1315.367188 474.042969 1315.367188 474.035156 1315.367188 C 474.03125 1315.367188 474.027344 1315.367188 474.023438 1315.367188 C 474.015625 1315.367188 474.011719 1315.367188 474.007812 1315.367188 C 474.003906 1315.367188 473.996094 1315.367188 473.992188 1315.367188 C 473.988281 1315.367188 473.980469 1315.367188 473.976562 1315.367188 C 473.972656 1315.367188 473.96875 1315.367188 473.960938 1315.367188 C 473.957031 1315.367188 473.953125 1315.367188 473.949219 1315.367188 C 473.941406 1315.367188 473.9375 1315.367188 473.933594 1315.367188 C 473.929688 1315.367188 473.921875 1315.367188 473.917969 1315.367188 C 473.914062 1315.367188 473.910156 1315.367188 473.902344 1315.367188 C 473.898438 1315.367188 473.894531 1315.367188 473.890625 1315.367188 C 473.882812 1315.367188 473.878906 1315.367188 473.875 1315.367188 C 473.871094 1315.367188 473.863281 1315.367188 473.859375 1315.367188 C 473.855469 1315.367188 473.851562 1315.367188 473.84375 1315.367188 C 473.839844 1315.367188 473.835938 1315.367188 473.832031 1315.367188 C 473.824219 1315.367188 473.820312 1315.367188 473.816406 1315.367188 C 473.8125 1315.367188 473.804688 1315.367188 473.800781 1315.367188 C 473.796875 1315.367188 473.792969 1315.367188 473.785156 1315.367188 C 473.78125 1315.367188 473.777344 1315.367188 473.773438 1315.367188 C 473.765625 1315.367188 473.761719 1315.367188 473.757812 1315.367188 C 473.753906 1315.367188 473.746094 1315.367188 473.742188 1315.367188 L 473.742188 1318.269531 C 473.746094 1318.269531 473.753906 1318.269531 473.757812 1318.269531 C 473.761719 1318.269531 473.765625 1318.269531 473.773438 1318.269531 C 473.777344 1318.269531 473.78125 1318.269531 473.785156 1318.269531 C 473.792969 1318.269531 473.796875 1318.269531 473.800781 1318.269531 C 473.804688 1318.269531 473.8125 1318.269531 473.816406 1318.269531 C 473.820312 1318.269531 473.824219 1318.269531 473.832031 1318.269531 C 473.835938 1318.269531 473.839844 1318.269531 473.84375 1318.269531 C 473.851562 1318.269531 473.855469 1318.269531 473.859375 1318.269531 C 473.863281 1318.269531 473.871094 1318.269531 473.875 1318.269531 C 473.878906 1318.269531 473.882812 1318.269531 473.890625 1318.269531 C 473.894531 1318.269531 473.898438 1318.269531 473.902344 1318.269531 C 473.910156 1318.269531 473.914062 1318.269531 473.917969 1318.269531 C 473.921875 1318.269531 473.929688 1318.269531 473.933594 1318.269531 C 473.9375 1318.269531 473.941406 1318.269531 473.949219 1318.269531 C 473.953125 1318.269531 473.957031 1318.269531 473.960938 1318.269531 C 473.96875 1318.269531 473.972656 1318.269531 473.976562 1318.269531 C 473.980469 1318.269531 473.988281 1318.269531 473.992188 1318.269531 C 473.996094 1318.269531 474.003906 1318.269531 474.007812 1318.269531 C 474.011719 1318.269531 474.015625 1318.269531 474.023438 1318.269531 C 474.027344 1318.269531 474.03125 1318.269531 474.035156 1318.269531 C 474.042969 1318.269531 474.046875 1318.269531 474.050781 1318.269531 C 474.054688 1318.269531 474.0625 1318.269531 474.066406 1318.269531 C 474.070312 1318.269531 474.074219 1318.269531 474.082031 1318.269531 C 474.085938 1318.269531 474.089844 1318.269531 474.09375 1318.269531 C 474.101562 1318.269531 474.105469 1318.269531 474.109375 1318.269531 C 474.117188 1318.269531 474.121094 1318.269531 474.125 1318.269531 C 474.128906 1318.269531 474.136719 1318.269531 474.140625 1318.269531 C 474.144531 1318.269531 474.148438 1318.269531 474.15625 1318.269531 C 474.160156 1318.269531 474.164062 1318.269531 474.167969 1318.269531 C 474.175781 1318.269531 474.179688 1318.269531 474.183594 1318.269531 C 474.1875 1318.269531 474.195312 1318.269531 474.199219 1318.269531 C 474.203125 1318.269531 474.210938 1318.269531 474.214844 1318.269531 C 474.21875 1318.269531 474.222656 1318.269531 474.230469 1318.269531 C 474.234375 1318.269531 474.238281 1318.269531 474.242188 1318.269531 C 474.25 1318.269531 474.253906 1318.269531 474.257812 1318.269531 C 474.265625 1318.269531 474.269531 1318.269531 474.273438 1318.269531 C 474.277344 1318.269531 474.285156 1318.269531 474.289062 1318.269531 C 474.292969 1318.269531 474.296875 1318.269531 474.304688 1318.269531 C 474.308594 1318.269531 474.3125 1318.269531 474.320312 1318.269531 C 474.324219 1318.269531 474.328125 1318.269531 474.332031 1318.269531 C 474.339844 1318.269531 474.34375 1318.269531 474.347656 1318.269531 C 474.351562 1318.269531 474.359375 1318.269531 474.363281 1318.269531 C 474.367188 1318.269531 474.375 1318.269531 474.378906 1318.269531 C 474.382812 1318.269531 474.386719 1318.269531 474.394531 1318.269531 C 474.398438 1318.269531 474.402344 1318.269531 474.40625 1318.269531 C 474.414062 1318.269531 474.417969 1318.269531 474.421875 1318.269531 C 474.429688 1318.269531 474.433594 1318.269531 474.4375 1318.269531 C 474.441406 1318.269531 474.449219 1318.269531 474.453125 1318.269531 C 474.457031 1318.269531 474.464844 1318.269531 474.46875 1318.269531 C 474.472656 1318.269531 474.476562 1318.269531 474.484375 1318.269531 C 474.488281 1318.269531 474.492188 1318.269531 474.5 1318.269531 C 474.503906 1318.269531 474.507812 1318.269531 474.511719 1318.269531 C 474.519531 1318.269531 474.523438 1318.269531 474.527344 1318.269531 C 474.53125 1318.269531 474.539062 1318.269531 474.542969 1318.269531 C 474.546875 1318.269531 474.554688 1318.269531 474.558594 1318.269531 C 474.5625 1318.269531 474.566406 1318.269531 474.574219 1318.269531 C 474.578125 1318.269531 474.582031 1318.269531 474.589844 1318.269531 C 474.59375 1318.269531 474.597656 1318.269531 474.601562 1318.269531 C 474.609375 1318.269531 474.613281 1318.269531 474.617188 1318.269531 C 474.625 1318.269531 474.628906 1318.269531 474.632812 1318.269531 C 474.636719 1318.269531 474.644531 1318.269531 474.648438 1318.269531 C 474.652344 1318.269531 474.660156 1318.269531 474.664062 1318.269531 C 474.667969 1318.269531 474.671875 1318.269531 474.679688 1318.269531 C 474.683594 1318.269531 474.6875 1318.269531 474.695312 1318.269531 C 474.699219 1318.269531 474.703125 1318.269531 474.710938 1318.269531 C 474.714844 1318.269531 474.71875 1318.269531 474.722656 1318.269531 C 474.730469 1318.269531 474.734375 1318.269531 474.738281 1318.269531 C 474.746094 1318.269531 474.75 1318.269531 474.753906 1318.269531 C 474.757812 1318.269531 474.765625 1318.269531 474.769531 1318.269531 C 474.773438 1318.269531 474.78125 1318.269531 474.785156 1318.269531 C 474.789062 1318.269531 474.792969 1318.269531 474.800781 1318.269531 C 474.804688 1318.269531 474.808594 1318.269531 474.816406 1318.269531 C 474.820312 1318.269531 474.824219 1318.269531 474.832031 1318.269531 C 474.835938 1318.269531 474.839844 1318.269531 474.84375 1318.269531 C 474.851562 1318.269531 474.855469 1318.269531 474.859375 1318.269531 C 474.867188 1318.269531 474.871094 1318.269531 474.875 1318.269531 C 474.878906 1318.269531 474.886719 1318.269531 474.890625 1318.269531 C 474.894531 1318.269531 474.902344 1318.269531 474.90625 1318.269531 C 474.910156 1318.269531 474.917969 1318.269531 474.921875 1318.269531 C 474.925781 1318.269531 474.929688 1318.269531 474.9375 1318.269531 C 474.941406 1318.269531 474.945312 1318.269531 474.953125 1318.269531 C 474.957031 1318.269531 474.960938 1318.269531 474.96875 1318.269531 C 474.972656 1318.269531 474.976562 1318.269531 474.980469 1318.269531 C 474.988281 1318.269531 474.992188 1318.269531 474.996094 1318.269531 C 475.003906 1318.269531 475.007812 1318.269531 475.011719 1318.269531 C 475.019531 1318.269531 475.023438 1318.269531 475.027344 1318.269531 C 475.03125 1318.269531 475.039062 1318.269531 475.042969 1318.269531 C 475.046875 1318.269531 475.054688 1318.269531 475.058594 1318.269531 C 475.0625 1318.269531 475.070312 1318.269531 475.074219 1318.269531 C 475.078125 1318.269531 475.085938 1318.269531 475.089844 1318.269531 C 475.09375 1318.269531 475.097656 1318.269531 475.105469 1318.269531 C 475.109375 1318.269531 475.113281 1318.269531 475.121094 1318.269531 C 475.125 1318.269531 475.128906 1318.269531 475.136719 1318.269531 C 475.140625 1318.269531 475.144531 1318.269531 475.148438 1318.269531 C 475.15625 1318.269531 475.160156 1318.269531 475.164062 1318.269531 C 475.171875 1318.269531 475.175781 1318.269531 475.179688 1318.269531 C 475.1875 1318.269531 475.191406 1318.269531 475.195312 1318.269531 C 475.203125 1318.269531 475.207031 1318.269531 475.210938 1318.269531 C 475.214844 1318.269531 475.222656 1318.269531 475.226562 1318.269531 C 475.230469 1318.269531 475.238281 1318.269531 475.242188 1318.269531 C 475.246094 1318.269531 475.253906 1318.269531 475.257812 1318.269531 C 475.261719 1318.269531 475.269531 1318.269531 475.273438 1318.269531 C 475.277344 1318.269531 475.28125 1318.269531 475.289062 1318.269531 C 475.292969 1318.269531 475.296875 1318.269531 475.304688 1318.269531 C 475.308594 1318.269531 475.3125 1318.269531 475.320312 1318.269531 C 475.324219 1318.269531 475.328125 1318.269531 475.335938 1318.269531 C 475.339844 1318.269531 475.34375 1318.269531 475.351562 1318.269531 C 475.355469 1318.269531 475.359375 1318.269531 475.363281 1318.269531 C 475.371094 1318.269531 475.375 1318.269531 475.378906 1318.269531 C 475.386719 1318.269531 475.390625 1318.269531 475.394531 1318.269531 C 475.402344 1318.269531 475.40625 1318.269531 475.410156 1318.269531 C 475.417969 1318.269531 475.421875 1318.269531 475.425781 1318.269531 C 475.433594 1318.269531 475.4375 1318.269531 475.441406 1318.269531 C 475.449219 1318.269531 475.453125 1318.269531 475.457031 1318.269531 C 475.460938 1318.269531 475.46875 1318.269531 475.472656 1318.269531 C 475.476562 1318.269531 475.484375 1318.269531 475.488281 1318.269531 C 475.492188 1318.269531 475.5 1318.269531 475.503906 1318.269531 C 475.507812 1318.269531 475.515625 1318.269531 475.519531 1318.269531 C 475.523438 1318.269531 475.53125 1318.269531 475.535156 1318.269531 C 475.539062 1318.269531 475.546875 1318.269531 475.550781 1318.269531 C 475.554688 1318.269531 475.558594 1318.269531 475.566406 1318.269531 C 475.570312 1318.269531 475.574219 1318.269531 475.582031 1318.269531 C 475.585938 1318.269531 475.589844 1318.269531 475.597656 1318.269531 C 475.601562 1318.269531 475.605469 1318.269531 475.613281 1318.269531 C 475.617188 1318.269531 475.621094 1318.269531 475.628906 1318.269531 C 475.632812 1318.269531 475.636719 1318.269531 475.644531 1318.269531 C 475.648438 1318.269531 475.652344 1318.269531 475.660156 1318.269531 C 475.664062 1318.269531 475.667969 1318.269531 475.675781 1318.269531 C 475.679688 1318.269531 475.683594 1318.269531 475.691406 1318.269531 C 475.695312 1318.269531 475.699219 1318.269531 475.703125 1318.269531 C 475.710938 1318.269531 475.714844 1318.269531 475.71875 1318.269531 C 475.726562 1318.269531 475.730469 1318.269531 475.734375 1318.269531 C 475.742188 1318.269531 475.746094 1318.269531 475.75 1318.269531 C 475.757812 1318.269531 475.761719 1318.269531 475.765625 1318.269531 C 475.773438 1318.269531 475.777344 1318.269531 475.78125 1318.269531 C 475.789062 1318.269531 475.792969 1318.269531 475.796875 1318.269531 C 475.804688 1318.269531 475.808594 1318.269531 475.8125 1318.269531 C 475.820312 1318.269531 475.824219 1318.269531 475.828125 1318.269531 C 475.835938 1318.269531 475.839844 1318.269531 475.84375 1318.269531 C 475.851562 1318.269531 475.855469 1318.269531 475.859375 1318.269531 C 475.867188 1318.269531 475.871094 1318.269531 475.875 1318.269531 C 475.882812 1318.269531 475.886719 1318.269531 475.890625 1318.269531 C 475.898438 1318.269531 475.902344 1318.269531 475.90625 1318.269531 C 475.910156 1318.269531 475.917969 1318.269531 475.921875 1318.269531 C 475.925781 1318.269531 475.933594 1318.269531 475.9375 1318.269531 C 475.941406 1318.269531 475.949219 1318.269531 475.953125 1318.269531 C 475.957031 1318.269531 475.964844 1318.269531 475.96875 1318.269531 C 475.972656 1318.269531 475.980469 1318.269531 475.984375 1318.269531 C 475.988281 1318.269531 475.996094 1318.269531 476 1318.269531 C 476.003906 1318.269531 476.011719 1318.269531 476.015625 1318.269531 C 476.019531 1318.269531 476.027344 1318.269531 476.03125 1318.269531 C 476.035156 1318.269531 476.042969 1318.269531 476.046875 1318.269531 C 476.050781 1318.269531 476.058594 1318.269531 476.0625 1318.269531 C 476.066406 1318.269531 476.074219 1318.269531 476.078125 1318.269531 C 476.082031 1318.269531 476.089844 1318.269531 476.09375 1318.269531 C 476.097656 1318.269531 476.105469 1318.269531 476.109375 1318.269531 C 476.113281 1318.269531 476.121094 1318.269531 476.125 1318.269531 C 476.128906 1318.269531 476.136719 1318.269531 476.140625 1318.269531 C 476.144531 1318.269531 476.152344 1318.269531 476.15625 1318.269531 C 476.160156 1318.269531 476.167969 1318.269531 476.171875 1318.269531 C 476.175781 1318.269531 476.183594 1318.269531 476.1875 1318.269531 C 476.191406 1318.269531 476.199219 1318.269531 476.203125 1318.269531 C 476.207031 1318.269531 476.214844 1318.269531 476.21875 1318.269531 C 476.222656 1318.269531 476.230469 1318.269531 476.234375 1318.269531 C 476.238281 1318.269531 476.246094 1318.269531 476.25 1318.269531 C 476.253906 1318.269531 476.261719 1318.269531 476.265625 1318.269531 C 476.269531 1318.269531 476.277344 1318.269531 476.28125 1318.269531 C 476.285156 1318.269531 476.292969 1318.269531 476.296875 1318.269531 C 476.300781 1318.269531 476.308594 1318.269531 476.3125 1318.269531 C 476.316406 1318.269531 476.324219 1318.269531 476.328125 1318.269531 C 476.332031 1318.269531 476.339844 1318.269531 476.34375 1318.269531 C 476.347656 1318.269531 476.355469 1318.269531 476.359375 1318.269531 C 476.363281 1318.269531 476.371094 1318.269531 476.375 1318.269531 C 476.378906 1318.269531 476.386719 1318.269531 476.390625 1318.269531 C 476.394531 1318.269531 476.402344 1318.269531 476.40625 1318.269531 C 476.410156 1318.269531 476.417969 1318.269531 476.421875 1318.269531 C 476.425781 1318.269531 476.433594 1318.269531 476.4375 1318.269531 C 476.441406 1318.269531 476.449219 1318.269531 476.453125 1318.269531 C 476.457031 1318.269531 476.464844 1318.269531 476.46875 1318.269531 C 476.476562 1318.269531 476.480469 1318.269531 476.484375 1318.269531 C 476.492188 1318.269531 476.496094 1318.269531 476.5 1318.269531 C 476.507812 1318.269531 476.511719 1318.269531 476.515625 1318.269531 C 476.523438 1318.269531 476.527344 1318.269531 476.53125 1318.269531 C 476.539062 1318.269531 476.542969 1318.269531 476.546875 1318.269531 C 476.554688 1318.269531 476.558594 1318.269531 476.5625 1318.269531 C 476.570312 1318.269531 476.574219 1318.269531 476.578125 1318.269531 C 476.585938 1318.269531 476.589844 1318.269531 476.59375 1318.269531 C 476.601562 1318.269531 476.605469 1318.269531 476.609375 1318.269531 C 476.617188 1318.269531 476.621094 1318.269531 476.625 1318.269531 C 476.632812 1318.269531 476.636719 1318.269531 476.640625 1318.269531 C 476.648438 1318.269531 476.652344 1318.269531 476.65625 1318.269531 C 476.664062 1318.269531 476.667969 1318.269531 476.671875 1318.269531 C 476.679688 1318.269531 476.683594 1318.269531 476.6875 1318.269531 C 476.695312 1318.269531 476.699219 1318.269531 476.703125 1318.269531 C 476.710938 1318.269531 476.714844 1318.269531 476.71875 1318.269531 C 476.726562 1318.269531 476.730469 1318.269531 476.738281 1318.269531 C 476.742188 1318.269531 476.746094 1318.269531 476.753906 1318.269531 C 476.757812 1318.269531 476.761719 1318.269531 476.769531 1318.269531 C 476.773438 1318.269531 476.777344 1318.269531 476.785156 1318.269531 C 476.789062 1318.269531 476.792969 1318.269531 476.800781 1318.269531 C 476.804688 1318.269531 476.808594 1318.269531 476.816406 1318.269531 C 476.820312 1318.269531 476.824219 1318.269531 476.832031 1318.269531 C 476.835938 1318.269531 476.839844 1318.269531 476.847656 1318.269531 C 476.851562 1318.269531 476.855469 1318.269531 476.863281 1318.269531 C 476.867188 1318.269531 476.871094 1318.269531 476.878906 1318.269531 C 476.882812 1318.269531 476.886719 1318.269531 476.894531 1318.269531 C 476.898438 1318.269531 476.90625 1318.269531 476.910156 1318.269531 C 476.914062 1318.269531 476.921875 1318.269531 476.925781 1318.269531 C 476.929688 1318.269531 476.9375 1318.269531 476.941406 1318.269531 C 476.945312 1318.269531 476.953125 1318.269531 476.957031 1318.269531 C 476.960938 1318.269531 476.96875 1318.269531 476.972656 1318.269531 C 476.976562 1318.269531 476.984375 1318.269531 476.988281 1318.269531 C 476.992188 1318.269531 477 1318.269531 477.003906 1318.269531 C 477.007812 1318.269531 477.015625 1318.269531 477.019531 1318.269531 C 477.023438 1318.269531 477.03125 1318.269531 477.035156 1318.269531 C 477.039062 1318.269531 477.046875 1318.269531 477.050781 1318.269531 C 477.054688 1318.269531 477.0625 1318.269531 477.066406 1318.269531 C 477.074219 1318.269531 477.078125 1318.269531 477.082031 1318.269531 C 477.089844 1318.269531 477.09375 1318.269531 477.097656 1318.269531 C 477.105469 1318.269531 477.109375 1318.269531 477.113281 1318.269531 C 477.121094 1318.269531 477.125 1318.269531 477.128906 1318.269531 C 477.136719 1318.269531 477.140625 1318.269531 477.144531 1318.269531 C 477.152344 1318.269531 477.15625 1318.269531 477.160156 1318.269531 C 477.167969 1318.269531 477.171875 1318.269531 477.175781 1318.269531 C 477.183594 1318.269531 477.1875 1318.269531 477.191406 1318.269531 C 477.199219 1318.269531 477.203125 1318.269531 477.210938 1318.269531 C 477.214844 1318.269531 477.21875 1318.269531 477.226562 1318.269531 C 477.230469 1318.269531 477.234375 1318.269531 477.242188 1318.269531 C 477.246094 1318.269531 477.25 1318.269531 477.257812 1318.269531 C 477.261719 1318.269531 477.265625 1318.269531 477.273438 1318.269531 C 477.277344 1318.269531 477.28125 1318.269531 477.289062 1318.269531 C 477.292969 1318.269531 477.296875 1318.269531 477.304688 1318.269531 C 477.308594 1318.269531 477.3125 1318.269531 477.320312 1318.269531 C 477.324219 1318.269531 477.332031 1318.269531 477.335938 1318.269531 C 477.339844 1318.269531 477.347656 1318.269531 477.351562 1318.269531 C 477.355469 1318.269531 477.363281 1318.269531 477.367188 1318.269531 C 477.371094 1318.269531 477.378906 1318.269531 477.382812 1318.269531 C 477.386719 1318.269531 477.394531 1318.269531 477.398438 1318.269531 C 477.402344 1318.269531 477.410156 1318.269531 477.414062 1318.269531 C 477.417969 1318.269531 477.425781 1318.269531 477.429688 1318.269531 C 477.433594 1318.269531 477.441406 1318.269531 477.445312 1318.269531 C 477.449219 1318.269531 477.457031 1318.269531 477.460938 1318.269531 C 477.46875 1318.269531 477.472656 1318.269531 477.476562 1318.269531 C 477.484375 1318.269531 477.488281 1318.269531 477.492188 1318.269531 C 477.5 1318.269531 477.503906 1318.269531 477.507812 1318.269531 C 477.515625 1318.269531 477.519531 1318.269531 477.523438 1318.269531 C 477.53125 1318.269531 477.535156 1318.269531 477.539062 1318.269531 C 477.546875 1318.269531 477.550781 1318.269531 477.554688 1318.269531 C 477.5625 1318.269531 477.566406 1318.269531 477.570312 1318.269531 C 477.578125 1318.269531 477.582031 1318.269531 477.589844 1318.269531 C 477.59375 1318.269531 477.597656 1318.269531 477.605469 1318.269531 C 477.609375 1318.269531 477.613281 1318.269531 477.621094 1318.269531 C 477.625 1318.269531 477.628906 1318.269531 477.636719 1318.269531 C 477.640625 1318.269531 477.644531 1318.269531 477.652344 1318.269531 C 477.65625 1318.269531 477.660156 1318.269531 477.667969 1318.269531 C 477.671875 1318.269531 477.675781 1318.269531 477.683594 1318.269531 C 477.6875 1318.269531 477.691406 1318.269531 477.699219 1318.269531 C 477.703125 1318.269531 477.710938 1318.269531 477.714844 1318.269531 C 477.71875 1318.269531 477.726562 1318.269531 477.730469 1318.269531 C 477.734375 1318.269531 477.742188 1318.269531 477.746094 1318.269531 C 477.75 1318.269531 477.757812 1318.269531 477.761719 1318.269531 C 477.765625 1318.269531 477.773438 1318.269531 477.777344 1318.269531 C 477.78125 1318.269531 477.789062 1318.269531 477.792969 1318.269531 C 477.796875 1318.269531 477.804688 1318.269531 477.808594 1318.269531 C 477.8125 1318.269531 477.820312 1318.269531 477.824219 1318.269531 C 477.832031 1318.269531 477.835938 1318.269531 477.839844 1318.269531 C 477.847656 1318.269531 477.851562 1318.269531 477.855469 1318.269531 C 477.863281 1318.269531 477.867188 1318.269531 477.871094 1318.269531 C 477.878906 1318.269531 477.882812 1318.269531 477.886719 1318.269531 C 477.894531 1318.269531 477.898438 1318.269531 477.902344 1318.269531 C 477.910156 1318.269531 477.914062 1318.269531 477.917969 1318.269531 C 477.925781 1318.269531 477.929688 1318.269531 477.933594 1318.269531 C 477.941406 1318.269531 477.945312 1318.269531 477.953125 1318.269531 C 477.957031 1318.269531 477.960938 1318.269531 477.96875 1318.269531 C 477.972656 1318.269531 477.976562 1318.269531 477.984375 1318.269531 C 477.988281 1318.269531 477.992188 1318.269531 478 1318.269531 C 478.003906 1318.269531 478.007812 1318.269531 478.015625 1318.269531 C 478.019531 1318.269531 478.023438 1318.269531 478.03125 1318.269531 C 478.035156 1318.269531 478.039062 1318.269531 478.046875 1318.269531 C 478.050781 1318.269531 478.054688 1318.269531 478.0625 1318.269531 C 478.066406 1318.269531 478.074219 1318.269531 478.078125 1318.269531 C 478.082031 1318.269531 478.089844 1318.269531 478.09375 1318.269531 C 478.097656 1318.269531 478.105469 1318.269531 478.109375 1318.269531 C 478.113281 1318.269531 478.121094 1318.269531 478.125 1318.269531 C 478.128906 1318.269531 478.136719 1318.269531 478.140625 1318.269531 C 478.144531 1318.269531 478.152344 1318.269531 478.15625 1318.269531 C 478.160156 1318.269531 478.167969 1318.269531 478.171875 1318.269531 C 478.175781 1318.269531 478.183594 1318.269531 478.1875 1318.269531 C 478.191406 1318.269531 478.199219 1318.269531 478.203125 1318.269531 C 478.210938 1318.269531 478.214844 1318.269531 478.21875 1318.269531 C 478.226562 1318.269531 478.230469 1318.269531 478.234375 1318.269531 C 478.242188 1318.269531 478.246094 1318.269531 478.25 1318.269531 C 478.257812 1318.269531 478.261719 1318.269531 478.265625 1318.269531 C 478.273438 1318.269531 478.277344 1318.269531 478.28125 1318.269531 C 478.289062 1318.269531 478.292969 1318.269531 478.296875 1318.269531 C 478.304688 1318.269531 478.308594 1318.269531 478.3125 1318.269531 C 478.320312 1318.269531 478.324219 1318.269531 478.328125 1318.269531 C 478.335938 1318.269531 478.339844 1318.269531 478.347656 1318.269531 C 478.351562 1318.269531 478.355469 1318.269531 478.363281 1318.269531 C 478.367188 1318.269531 478.371094 1318.269531 478.378906 1318.269531 C 478.382812 1318.269531 478.386719 1318.269531 478.394531 1318.269531 C 478.398438 1318.269531 478.402344 1318.269531 478.410156 1318.269531 C 478.414062 1318.269531 478.417969 1318.269531 478.425781 1318.269531 C 478.429688 1318.269531 478.433594 1318.269531 478.441406 1318.269531 C 478.445312 1318.269531 478.449219 1318.269531 478.457031 1318.269531 C 478.460938 1318.269531 478.464844 1318.269531 478.472656 1318.269531 C 478.476562 1318.269531 478.480469 1318.269531 478.488281 1318.269531 C 478.492188 1318.269531 478.496094 1318.269531 478.503906 1318.269531 C 478.507812 1318.269531 478.515625 1318.269531 478.519531 1318.269531 C 478.523438 1318.269531 478.53125 1318.269531 478.535156 1318.269531 C 478.539062 1318.269531 478.546875 1318.269531 478.550781 1318.269531 C 478.554688 1318.269531 478.5625 1318.269531 478.566406 1318.269531 C 478.570312 1318.269531 478.578125 1318.269531 478.582031 1318.269531 C 478.585938 1318.269531 478.59375 1318.269531 478.597656 1318.269531 C 478.601562 1318.269531 478.609375 1318.269531 478.613281 1318.269531 C 478.617188 1318.269531 478.625 1318.269531 478.628906 1318.269531 C 478.632812 1318.269531 478.640625 1318.269531 478.644531 1318.269531 C 478.648438 1318.269531 478.65625 1318.269531 478.660156 1318.269531 C 478.664062 1318.269531 478.671875 1318.269531 478.675781 1318.269531 C 478.679688 1318.269531 478.6875 1318.269531 478.691406 1318.269531 C 478.699219 1318.269531 478.703125 1318.269531 478.707031 1318.269531 C 478.714844 1318.269531 478.71875 1318.269531 478.722656 1318.269531 C 478.730469 1318.269531 478.734375 1318.269531 478.738281 1318.269531 C 478.746094 1318.269531 478.75 1318.269531 478.753906 1318.269531 C 478.761719 1318.269531 478.765625 1318.269531 478.769531 1318.269531 C 478.777344 1318.269531 478.78125 1318.269531 478.785156 1318.269531 C 478.792969 1318.269531 478.796875 1318.269531 478.800781 1318.269531 C 478.808594 1318.269531 478.8125 1318.269531 478.816406 1318.269531 C 478.824219 1318.269531 478.828125 1318.269531 478.832031 1318.269531 C 478.839844 1318.269531 478.84375 1318.269531 478.847656 1318.269531 C 478.855469 1318.269531 478.859375 1318.269531 478.863281 1318.269531 C 478.871094 1318.269531 478.875 1318.269531 478.878906 1318.269531 C 478.886719 1318.269531 478.890625 1318.269531 478.894531 1318.269531 C 478.902344 1318.269531 478.90625 1318.269531 478.910156 1318.269531 C 478.917969 1318.269531 478.921875 1318.269531 478.925781 1318.269531 C 478.933594 1318.269531 478.9375 1318.269531 478.941406 1318.269531 C 478.949219 1318.269531 478.953125 1318.269531 478.957031 1318.269531 C 478.964844 1318.269531 478.96875 1318.269531 478.972656 1318.269531 C 478.980469 1318.269531 478.984375 1318.269531 478.992188 1318.269531 C 478.996094 1318.269531 479 1318.269531 479.007812 1318.269531 C 479.011719 1318.269531 479.015625 1318.269531 479.023438 1318.269531 C 479.027344 1318.269531 479.03125 1318.269531 479.039062 1318.269531 C 479.042969 1318.269531 479.046875 1318.269531 479.054688 1318.269531 C 479.058594 1318.269531 479.0625 1318.269531 479.070312 1318.269531 C 479.074219 1318.269531 479.078125 1318.269531 479.085938 1318.269531 C 479.089844 1318.269531 479.09375 1318.269531 479.101562 1318.269531 C 479.105469 1318.269531 479.109375 1318.269531 479.117188 1318.269531 C 479.121094 1318.269531 479.125 1318.269531 479.132812 1318.269531 C 479.136719 1318.269531 479.140625 1318.269531 479.148438 1318.269531 C 479.152344 1318.269531 479.15625 1318.269531 479.164062 1318.269531 C 479.167969 1318.269531 479.171875 1318.269531 479.179688 1318.269531 C 479.183594 1318.269531 479.1875 1318.269531 479.195312 1318.269531 C 479.199219 1318.269531 479.203125 1318.269531 479.210938 1318.269531 C 479.214844 1318.269531 479.21875 1318.269531 479.226562 1318.269531 C 479.230469 1318.269531 479.234375 1318.269531 479.242188 1318.269531 C 479.246094 1318.269531 479.25 1318.269531 479.257812 1318.269531 C 479.261719 1318.269531 479.265625 1318.269531 479.273438 1318.269531 C 479.277344 1318.269531 479.28125 1318.269531 479.289062 1318.269531 C 479.292969 1318.269531 479.296875 1318.269531 479.304688 1318.269531 C 479.308594 1318.269531 479.3125 1318.269531 479.320312 1318.269531 C 479.324219 1318.269531 479.328125 1318.269531 479.335938 1318.269531 C 479.339844 1318.269531 479.34375 1318.269531 479.351562 1318.269531 C 479.355469 1318.269531 479.359375 1318.269531 479.367188 1318.269531 C 479.371094 1318.269531 479.375 1318.269531 479.382812 1318.269531 C 479.386719 1318.269531 479.390625 1318.269531 479.394531 1318.269531 C 479.402344 1318.269531 479.40625 1318.269531 479.410156 1318.269531 C 479.417969 1318.269531 479.421875 1318.269531 479.425781 1318.269531 C 479.433594 1318.269531 479.4375 1318.269531 479.441406 1318.269531 C 479.449219 1318.269531 479.453125 1318.269531 479.457031 1318.269531 C 479.464844 1318.269531 479.46875 1318.269531 479.472656 1318.269531 C 479.480469 1318.269531 479.484375 1318.269531 479.488281 1318.269531 C 479.496094 1318.269531 479.5 1318.269531 479.503906 1318.269531 C 479.511719 1318.269531 479.515625 1318.269531 479.519531 1318.269531 C 479.527344 1318.269531 479.53125 1318.269531 479.535156 1318.269531 C 479.542969 1318.269531 479.546875 1318.269531 479.550781 1318.269531 C 479.558594 1318.269531 479.5625 1318.269531 479.566406 1318.269531 C 479.574219 1318.269531 479.578125 1318.269531 479.582031 1318.269531 C 479.589844 1318.269531 479.59375 1318.269531 479.597656 1318.269531 C 479.605469 1318.269531 479.609375 1318.269531 479.613281 1318.269531 C 479.621094 1318.269531 479.625 1318.269531 479.628906 1318.269531 C 479.636719 1318.269531 479.640625 1318.269531 479.644531 1318.269531 C 479.648438 1318.269531 479.65625 1318.269531 479.660156 1318.269531 C 479.664062 1318.269531 479.671875 1318.269531 479.675781 1318.269531 C 479.679688 1318.269531 479.6875 1318.269531 479.691406 1318.269531 C 479.695312 1318.269531 479.703125 1318.269531 479.707031 1318.269531 C 479.710938 1318.269531 479.71875 1318.269531 479.722656 1318.269531 C 479.726562 1318.269531 479.734375 1318.269531 479.738281 1318.269531 C 479.742188 1318.269531 479.75 1318.269531 479.753906 1318.269531 C 479.757812 1318.269531 479.765625 1318.269531 479.769531 1318.269531 C 479.773438 1318.269531 479.78125 1318.269531 479.785156 1318.269531 C 479.789062 1318.269531 479.792969 1318.269531 479.800781 1318.269531 C 479.804688 1318.269531 479.808594 1318.269531 479.816406 1318.269531 C 479.820312 1318.269531 479.824219 1318.269531 479.832031 1318.269531 C 479.835938 1318.269531 479.839844 1318.269531 479.847656 1318.269531 C 479.851562 1318.269531 479.855469 1318.269531 479.863281 1318.269531 C 479.867188 1318.269531 479.871094 1318.269531 479.878906 1318.269531 C 479.882812 1318.269531 479.886719 1318.269531 479.894531 1318.269531 C 479.898438 1318.269531 479.902344 1318.269531 479.90625 1318.269531 C 479.914062 1318.269531 479.917969 1318.269531 479.921875 1318.269531 C 479.929688 1318.269531 479.933594 1318.269531 479.9375 1318.269531 C 479.945312 1318.269531 479.949219 1318.269531 479.953125 1318.269531 C 479.960938 1318.269531 479.964844 1318.269531 479.96875 1318.269531 C 479.976562 1318.269531 479.980469 1318.269531 479.984375 1318.269531 C 479.988281 1318.269531 479.996094 1318.269531 480 1318.269531 C 480.003906 1318.269531 480.011719 1318.269531 480.015625 1318.269531 C 480.019531 1318.269531 480.027344 1318.269531 480.03125 1318.269531 C 480.035156 1318.269531 480.042969 1318.269531 480.046875 1318.269531 C 480.050781 1318.269531 480.058594 1318.269531 480.0625 1318.269531 C 480.066406 1318.269531 480.070312 1318.269531 480.078125 1318.269531 C 480.082031 1318.269531 480.085938 1318.269531 480.09375 1318.269531 C 480.097656 1318.269531 480.101562 1318.269531 480.109375 1318.269531 C 480.113281 1318.269531 480.117188 1318.269531 480.125 1318.269531 C 480.128906 1318.269531 480.132812 1318.269531 480.140625 1318.269531 C 480.144531 1318.269531 480.148438 1318.269531 480.152344 1318.269531 C 480.160156 1318.269531 480.164062 1318.269531 480.167969 1318.269531 C 480.175781 1318.269531 480.179688 1318.269531 480.183594 1318.269531 C 480.191406 1318.269531 480.195312 1318.269531 480.199219 1318.269531 C 480.207031 1318.269531 480.210938 1318.269531 480.214844 1318.269531 C 480.21875 1318.269531 480.226562 1318.269531 480.230469 1318.269531 C 480.234375 1318.269531 480.242188 1318.269531 480.246094 1318.269531 C 480.25 1318.269531 480.257812 1318.269531 480.261719 1318.269531 C 480.265625 1318.269531 480.269531 1318.269531 480.277344 1318.269531 C 480.28125 1318.269531 480.285156 1318.269531 480.292969 1318.269531 C 480.296875 1318.269531 480.300781 1318.269531 480.308594 1318.269531 C 480.3125 1318.269531 480.316406 1318.269531 480.324219 1318.269531 C 480.328125 1318.269531 480.332031 1318.269531 480.335938 1318.269531 C 480.34375 1318.269531 480.347656 1318.269531 480.351562 1318.269531 C 480.359375 1318.269531 480.363281 1318.269531 480.367188 1318.269531 C 480.375 1318.269531 480.378906 1318.269531 480.382812 1318.269531 C 480.386719 1318.269531 480.394531 1318.269531 480.398438 1318.269531 C 480.402344 1318.269531 480.410156 1318.269531 480.414062 1318.269531 C 480.417969 1318.269531 480.425781 1318.269531 480.429688 1318.269531 C 480.433594 1318.269531 480.4375 1318.269531 480.445312 1318.269531 C 480.449219 1318.269531 480.453125 1318.269531 480.460938 1318.269531 C 480.464844 1318.269531 480.46875 1318.269531 480.476562 1318.269531 C 480.480469 1318.269531 480.484375 1318.269531 480.488281 1318.269531 C 480.496094 1318.269531 480.5 1318.269531 480.503906 1318.269531 C 480.511719 1318.269531 480.515625 1318.269531 480.519531 1318.269531 C 480.523438 1318.269531 480.53125 1318.269531 480.535156 1318.269531 C 480.539062 1318.269531 480.546875 1318.269531 480.550781 1318.269531 C 480.554688 1318.269531 480.5625 1318.269531 480.566406 1318.269531 C 480.570312 1318.269531 480.574219 1318.269531 480.582031 1318.269531 C 480.585938 1318.269531 480.589844 1318.269531 480.597656 1318.269531 C 480.601562 1318.269531 480.605469 1318.269531 480.609375 1318.269531 C 480.617188 1318.269531 480.621094 1318.269531 480.625 1318.269531 C 480.632812 1318.269531 480.636719 1318.269531 480.640625 1318.269531 C 480.648438 1318.269531 480.652344 1318.269531 480.65625 1318.269531 C 480.660156 1318.269531 480.667969 1318.269531 480.671875 1318.269531 C 480.675781 1318.269531 480.683594 1318.269531 480.6875 1318.269531 C 480.691406 1318.269531 480.695312 1318.269531 480.703125 1318.269531 C 480.707031 1318.269531 480.710938 1318.269531 480.71875 1318.269531 C 480.722656 1318.269531 480.726562 1318.269531 480.730469 1318.269531 C 480.738281 1318.269531 480.742188 1318.269531 480.746094 1318.269531 C 480.753906 1318.269531 480.757812 1318.269531 480.761719 1318.269531 C 480.765625 1318.269531 480.773438 1318.269531 480.777344 1318.269531 C 480.78125 1318.269531 480.789062 1318.269531 480.792969 1318.269531 C 480.796875 1318.269531 480.800781 1318.269531 480.808594 1318.269531 C 480.8125 1318.269531 480.816406 1318.269531 480.824219 1318.269531 C 480.828125 1318.269531 480.832031 1318.269531 480.835938 1318.269531 C 480.84375 1318.269531 480.847656 1318.269531 480.851562 1318.269531 C 480.859375 1318.269531 480.863281 1318.269531 480.867188 1318.269531 C 480.871094 1318.269531 480.878906 1318.269531 480.882812 1318.269531 C 480.886719 1318.269531 480.890625 1318.269531 480.898438 1318.269531 C 480.902344 1318.269531 480.90625 1318.269531 480.914062 1318.269531 C 480.917969 1318.269531 480.921875 1318.269531 480.925781 1318.269531 C 480.933594 1318.269531 480.9375 1318.269531 480.941406 1318.269531 C 480.949219 1318.269531 480.953125 1318.269531 480.957031 1318.269531 C 480.960938 1318.269531 480.96875 1318.269531 480.972656 1318.269531 C 480.976562 1318.269531 480.984375 1318.269531 480.988281 1318.269531 C 480.992188 1318.269531 480.996094 1318.269531 481.003906 1318.269531 C 481.007812 1318.269531 481.011719 1318.269531 481.015625 1318.269531 C 481.023438 1318.269531 481.027344 1318.269531 481.03125 1318.269531 C 481.039062 1318.269531 481.042969 1318.269531 481.046875 1318.269531 C 481.050781 1318.269531 481.058594 1318.269531 481.0625 1318.269531 C 481.066406 1318.269531 481.070312 1318.269531 481.078125 1318.269531 C 481.082031 1318.269531 481.085938 1318.269531 481.09375 1318.269531 C 481.097656 1318.269531 481.101562 1318.269531 481.105469 1318.269531 C 481.113281 1318.269531 481.117188 1318.269531 481.121094 1318.269531 C 481.125 1318.269531 481.132812 1318.269531 481.136719 1318.269531 C 481.140625 1318.269531 481.144531 1318.269531 481.152344 1318.269531 C 481.15625 1318.269531 481.160156 1318.269531 481.167969 1318.269531 C 481.171875 1318.269531 481.175781 1318.269531 481.179688 1318.269531 C 481.1875 1318.269531 481.191406 1318.269531 481.195312 1318.269531 C 481.199219 1318.269531 481.207031 1318.269531 481.210938 1318.269531 C 481.214844 1318.269531 481.222656 1318.269531 481.226562 1318.269531 C 481.230469 1318.269531 481.234375 1318.269531 481.242188 1318.269531 C 481.246094 1318.269531 481.25 1318.269531 481.253906 1318.269531 C 481.261719 1318.269531 481.265625 1318.269531 481.269531 1318.269531 C 481.273438 1318.269531 481.28125 1318.269531 481.285156 1318.269531 C 481.289062 1318.269531 481.292969 1318.269531 481.300781 1318.269531 C 481.304688 1318.269531 481.308594 1318.269531 481.3125 1318.269531 C 481.320312 1318.269531 481.324219 1318.269531 481.328125 1318.269531 C 481.335938 1318.269531 481.339844 1318.269531 481.34375 1318.269531 C 481.347656 1318.269531 481.355469 1318.269531 481.359375 1318.269531 C 481.363281 1318.269531 481.367188 1318.269531 481.375 1318.269531 C 481.378906 1318.269531 481.382812 1318.269531 481.386719 1318.269531 C 481.394531 1318.269531 481.398438 1318.269531 481.402344 1318.269531 C 481.40625 1318.269531 481.414062 1318.269531 481.417969 1318.269531 C 481.421875 1318.269531 481.425781 1318.269531 481.433594 1318.269531 C 481.4375 1318.269531 481.441406 1318.269531 481.445312 1318.269531 C 481.453125 1318.269531 481.457031 1318.269531 481.460938 1318.269531 C 481.464844 1318.269531 481.472656 1318.269531 481.476562 1318.269531 C 481.480469 1318.269531 481.484375 1318.269531 481.492188 1318.269531 C 481.496094 1318.269531 481.5 1318.269531 481.503906 1318.269531 C 481.511719 1318.269531 481.515625 1318.269531 481.519531 1318.269531 C 481.523438 1318.269531 481.53125 1318.269531 481.535156 1318.269531 C 481.539062 1318.269531 481.542969 1318.269531 481.550781 1318.269531 C 481.554688 1318.269531 481.558594 1318.269531 481.5625 1318.269531 C 481.570312 1318.269531 481.574219 1318.269531 481.578125 1318.269531 C 481.582031 1318.269531 481.589844 1318.269531 481.59375 1318.269531 C 481.597656 1318.269531 481.601562 1318.269531 481.609375 1318.269531 C 481.613281 1318.269531 481.617188 1318.269531 481.621094 1318.269531 Z M 473.742188 1316.820312 L 473.742188 1315.367188 L 98.421875 1315.367188 L 98.421875 1318.269531 L 473.742188 1318.269531 Z M 98.421875 1316.820312 L 98.421875 1315.367188 C 98.417969 1315.367188 98.410156 1315.367188 98.40625 1315.367188 C 98.402344 1315.367188 98.398438 1315.367188 98.390625 1315.367188 C 98.386719 1315.367188 98.382812 1315.367188 98.378906 1315.367188 C 98.371094 1315.367188 98.367188 1315.367188 98.363281 1315.367188 C 98.359375 1315.367188 98.351562 1315.367188 98.347656 1315.367188 C 98.34375 1315.367188 98.339844 1315.367188 98.332031 1315.367188 C 98.328125 1315.367188 98.324219 1315.367188 98.320312 1315.367188 C 98.3125 1315.367188 98.308594 1315.367188 98.304688 1315.367188 C 98.300781 1315.367188 98.292969 1315.367188 98.289062 1315.367188 C 98.285156 1315.367188 98.28125 1315.367188 98.273438 1315.367188 C 98.269531 1315.367188 98.265625 1315.367188 98.261719 1315.367188 C 98.253906 1315.367188 98.25 1315.367188 98.246094 1315.367188 C 98.242188 1315.367188 98.234375 1315.367188 98.230469 1315.367188 C 98.226562 1315.367188 98.222656 1315.367188 98.214844 1315.367188 C 98.210938 1315.367188 98.207031 1315.367188 98.203125 1315.367188 C 98.195312 1315.367188 98.191406 1315.367188 98.1875 1315.367188 C 98.183594 1315.367188 98.175781 1315.367188 98.171875 1315.367188 C 98.167969 1315.367188 98.164062 1315.367188 98.15625 1315.367188 C 98.152344 1315.367188 98.148438 1315.367188 98.144531 1315.367188 C 98.136719 1315.367188 98.132812 1315.367188 98.128906 1315.367188 C 98.125 1315.367188 98.117188 1315.367188 98.113281 1315.367188 C 98.109375 1315.367188 98.101562 1315.367188 98.097656 1315.367188 C 98.09375 1315.367188 98.089844 1315.367188 98.082031 1315.367188 C 98.078125 1315.367188 98.074219 1315.367188 98.070312 1315.367188 C 98.0625 1315.367188 98.058594 1315.367188 98.054688 1315.367188 C 98.050781 1315.367188 98.042969 1315.367188 98.039062 1315.367188 C 98.035156 1315.367188 98.03125 1315.367188 98.023438 1315.367188 C 98.019531 1315.367188 98.015625 1315.367188 98.007812 1315.367188 C 98.003906 1315.367188 98 1315.367188 97.996094 1315.367188 C 97.988281 1315.367188 97.984375 1315.367188 97.980469 1315.367188 C 97.976562 1315.367188 97.96875 1315.367188 97.964844 1315.367188 C 97.960938 1315.367188 97.957031 1315.367188 97.949219 1315.367188 C 97.945312 1315.367188 97.941406 1315.367188 97.933594 1315.367188 C 97.929688 1315.367188 97.925781 1315.367188 97.921875 1315.367188 C 97.914062 1315.367188 97.910156 1315.367188 97.90625 1315.367188 C 97.902344 1315.367188 97.894531 1315.367188 97.890625 1315.367188 C 97.886719 1315.367188 97.878906 1315.367188 97.875 1315.367188 C 97.871094 1315.367188 97.867188 1315.367188 97.859375 1315.367188 C 97.855469 1315.367188 97.851562 1315.367188 97.847656 1315.367188 C 97.839844 1315.367188 97.835938 1315.367188 97.832031 1315.367188 C 97.824219 1315.367188 97.820312 1315.367188 97.816406 1315.367188 C 97.8125 1315.367188 97.804688 1315.367188 97.800781 1315.367188 C 97.796875 1315.367188 97.792969 1315.367188 97.785156 1315.367188 C 97.78125 1315.367188 97.777344 1315.367188 97.769531 1315.367188 C 97.765625 1315.367188 97.761719 1315.367188 97.757812 1315.367188 C 97.75 1315.367188 97.746094 1315.367188 97.742188 1315.367188 C 97.738281 1315.367188 97.730469 1315.367188 97.726562 1315.367188 C 97.722656 1315.367188 97.714844 1315.367188 97.710938 1315.367188 C 97.707031 1315.367188 97.703125 1315.367188 97.695312 1315.367188 C 97.691406 1315.367188 97.6875 1315.367188 97.679688 1315.367188 C 97.675781 1315.367188 97.671875 1315.367188 97.667969 1315.367188 C 97.660156 1315.367188 97.65625 1315.367188 97.652344 1315.367188 C 97.644531 1315.367188 97.640625 1315.367188 97.636719 1315.367188 C 97.632812 1315.367188 97.625 1315.367188 97.621094 1315.367188 C 97.617188 1315.367188 97.609375 1315.367188 97.605469 1315.367188 C 97.601562 1315.367188 97.597656 1315.367188 97.589844 1315.367188 C 97.585938 1315.367188 97.582031 1315.367188 97.574219 1315.367188 C 97.570312 1315.367188 97.566406 1315.367188 97.5625 1315.367188 C 97.554688 1315.367188 97.550781 1315.367188 97.546875 1315.367188 C 97.539062 1315.367188 97.535156 1315.367188 97.53125 1315.367188 C 97.527344 1315.367188 97.519531 1315.367188 97.515625 1315.367188 C 97.511719 1315.367188 97.503906 1315.367188 97.5 1315.367188 C 97.496094 1315.367188 97.492188 1315.367188 97.484375 1315.367188 C 97.480469 1315.367188 97.476562 1315.367188 97.46875 1315.367188 C 97.464844 1315.367188 97.460938 1315.367188 97.457031 1315.367188 C 97.449219 1315.367188 97.445312 1315.367188 97.441406 1315.367188 C 97.433594 1315.367188 97.429688 1315.367188 97.425781 1315.367188 C 97.421875 1315.367188 97.414062 1315.367188 97.410156 1315.367188 C 97.40625 1315.367188 97.398438 1315.367188 97.394531 1315.367188 C 97.390625 1315.367188 97.382812 1315.367188 97.378906 1315.367188 C 97.375 1315.367188 97.371094 1315.367188 97.363281 1315.367188 C 97.359375 1315.367188 97.355469 1315.367188 97.347656 1315.367188 C 97.34375 1315.367188 97.339844 1315.367188 97.335938 1315.367188 C 97.328125 1315.367188 97.324219 1315.367188 97.320312 1315.367188 C 97.3125 1315.367188 97.308594 1315.367188 97.304688 1315.367188 C 97.296875 1315.367188 97.292969 1315.367188 97.289062 1315.367188 C 97.285156 1315.367188 97.277344 1315.367188 97.273438 1315.367188 C 97.269531 1315.367188 97.261719 1315.367188 97.257812 1315.367188 C 97.253906 1315.367188 97.25 1315.367188 97.242188 1315.367188 C 97.238281 1315.367188 97.234375 1315.367188 97.226562 1315.367188 C 97.222656 1315.367188 97.21875 1315.367188 97.210938 1315.367188 C 97.207031 1315.367188 97.203125 1315.367188 97.199219 1315.367188 C 97.191406 1315.367188 97.1875 1315.367188 97.183594 1315.367188 C 97.175781 1315.367188 97.171875 1315.367188 97.167969 1315.367188 C 97.160156 1315.367188 97.15625 1315.367188 97.152344 1315.367188 C 97.148438 1315.367188 97.140625 1315.367188 97.136719 1315.367188 C 97.132812 1315.367188 97.125 1315.367188 97.121094 1315.367188 C 97.117188 1315.367188 97.109375 1315.367188 97.105469 1315.367188 C 97.101562 1315.367188 97.09375 1315.367188 97.089844 1315.367188 C 97.085938 1315.367188 97.082031 1315.367188 97.074219 1315.367188 C 97.070312 1315.367188 97.066406 1315.367188 97.058594 1315.367188 C 97.054688 1315.367188 97.050781 1315.367188 97.042969 1315.367188 C 97.039062 1315.367188 97.035156 1315.367188 97.03125 1315.367188 C 97.023438 1315.367188 97.019531 1315.367188 97.015625 1315.367188 C 97.007812 1315.367188 97.003906 1315.367188 97 1315.367188 C 96.992188 1315.367188 96.988281 1315.367188 96.984375 1315.367188 C 96.976562 1315.367188 96.972656 1315.367188 96.96875 1315.367188 C 96.964844 1315.367188 96.957031 1315.367188 96.953125 1315.367188 C 96.949219 1315.367188 96.941406 1315.367188 96.9375 1315.367188 C 96.933594 1315.367188 96.925781 1315.367188 96.921875 1315.367188 C 96.917969 1315.367188 96.910156 1315.367188 96.90625 1315.367188 C 96.902344 1315.367188 96.898438 1315.367188 96.890625 1315.367188 C 96.886719 1315.367188 96.882812 1315.367188 96.875 1315.367188 C 96.871094 1315.367188 96.867188 1315.367188 96.859375 1315.367188 C 96.855469 1315.367188 96.851562 1315.367188 96.84375 1315.367188 C 96.839844 1315.367188 96.835938 1315.367188 96.828125 1315.367188 C 96.824219 1315.367188 96.820312 1315.367188 96.816406 1315.367188 C 96.808594 1315.367188 96.804688 1315.367188 96.800781 1315.367188 C 96.792969 1315.367188 96.789062 1315.367188 96.785156 1315.367188 C 96.777344 1315.367188 96.773438 1315.367188 96.769531 1315.367188 C 96.761719 1315.367188 96.757812 1315.367188 96.753906 1315.367188 C 96.746094 1315.367188 96.742188 1315.367188 96.738281 1315.367188 C 96.734375 1315.367188 96.726562 1315.367188 96.722656 1315.367188 C 96.71875 1315.367188 96.710938 1315.367188 96.707031 1315.367188 C 96.703125 1315.367188 96.695312 1315.367188 96.691406 1315.367188 C 96.6875 1315.367188 96.679688 1315.367188 96.675781 1315.367188 C 96.671875 1315.367188 96.664062 1315.367188 96.660156 1315.367188 C 96.65625 1315.367188 96.648438 1315.367188 96.644531 1315.367188 C 96.640625 1315.367188 96.636719 1315.367188 96.628906 1315.367188 C 96.625 1315.367188 96.621094 1315.367188 96.613281 1315.367188 C 96.609375 1315.367188 96.605469 1315.367188 96.597656 1315.367188 C 96.59375 1315.367188 96.589844 1315.367188 96.582031 1315.367188 C 96.578125 1315.367188 96.574219 1315.367188 96.566406 1315.367188 C 96.5625 1315.367188 96.558594 1315.367188 96.550781 1315.367188 C 96.546875 1315.367188 96.542969 1315.367188 96.535156 1315.367188 C 96.53125 1315.367188 96.527344 1315.367188 96.519531 1315.367188 C 96.515625 1315.367188 96.511719 1315.367188 96.507812 1315.367188 C 96.5 1315.367188 96.496094 1315.367188 96.492188 1315.367188 C 96.484375 1315.367188 96.480469 1315.367188 96.476562 1315.367188 C 96.46875 1315.367188 96.464844 1315.367188 96.460938 1315.367188 C 96.453125 1315.367188 96.449219 1315.367188 96.445312 1315.367188 C 96.4375 1315.367188 96.433594 1315.367188 96.429688 1315.367188 C 96.421875 1315.367188 96.417969 1315.367188 96.414062 1315.367188 C 96.40625 1315.367188 96.402344 1315.367188 96.398438 1315.367188 C 96.390625 1315.367188 96.386719 1315.367188 96.382812 1315.367188 C 96.375 1315.367188 96.371094 1315.367188 96.367188 1315.367188 C 96.359375 1315.367188 96.355469 1315.367188 96.351562 1315.367188 C 96.34375 1315.367188 96.339844 1315.367188 96.335938 1315.367188 C 96.332031 1315.367188 96.324219 1315.367188 96.320312 1315.367188 C 96.316406 1315.367188 96.308594 1315.367188 96.304688 1315.367188 C 96.300781 1315.367188 96.292969 1315.367188 96.289062 1315.367188 C 96.285156 1315.367188 96.277344 1315.367188 96.273438 1315.367188 C 96.269531 1315.367188 96.261719 1315.367188 96.257812 1315.367188 C 96.253906 1315.367188 96.246094 1315.367188 96.242188 1315.367188 C 96.238281 1315.367188 96.230469 1315.367188 96.226562 1315.367188 C 96.222656 1315.367188 96.214844 1315.367188 96.210938 1315.367188 C 96.207031 1315.367188 96.199219 1315.367188 96.195312 1315.367188 C 96.191406 1315.367188 96.183594 1315.367188 96.179688 1315.367188 C 96.175781 1315.367188 96.167969 1315.367188 96.164062 1315.367188 C 96.160156 1315.367188 96.152344 1315.367188 96.148438 1315.367188 C 96.144531 1315.367188 96.136719 1315.367188 96.132812 1315.367188 C 96.128906 1315.367188 96.121094 1315.367188 96.117188 1315.367188 C 96.113281 1315.367188 96.105469 1315.367188 96.101562 1315.367188 C 96.097656 1315.367188 96.089844 1315.367188 96.085938 1315.367188 C 96.082031 1315.367188 96.074219 1315.367188 96.070312 1315.367188 C 96.066406 1315.367188 96.058594 1315.367188 96.054688 1315.367188 C 96.050781 1315.367188 96.042969 1315.367188 96.039062 1315.367188 C 96.035156 1315.367188 96.027344 1315.367188 96.023438 1315.367188 C 96.019531 1315.367188 96.011719 1315.367188 96.007812 1315.367188 C 96.003906 1315.367188 95.996094 1315.367188 95.992188 1315.367188 C 95.988281 1315.367188 95.980469 1315.367188 95.976562 1315.367188 C 95.972656 1315.367188 95.964844 1315.367188 95.960938 1315.367188 C 95.957031 1315.367188 95.949219 1315.367188 95.945312 1315.367188 C 95.941406 1315.367188 95.933594 1315.367188 95.929688 1315.367188 C 95.925781 1315.367188 95.917969 1315.367188 95.914062 1315.367188 C 95.910156 1315.367188 95.902344 1315.367188 95.898438 1315.367188 C 95.894531 1315.367188 95.886719 1315.367188 95.882812 1315.367188 C 95.878906 1315.367188 95.871094 1315.367188 95.867188 1315.367188 C 95.863281 1315.367188 95.855469 1315.367188 95.851562 1315.367188 C 95.847656 1315.367188 95.839844 1315.367188 95.835938 1315.367188 C 95.832031 1315.367188 95.824219 1315.367188 95.820312 1315.367188 C 95.816406 1315.367188 95.808594 1315.367188 95.804688 1315.367188 C 95.800781 1315.367188 95.792969 1315.367188 95.789062 1315.367188 C 95.785156 1315.367188 95.777344 1315.367188 95.773438 1315.367188 C 95.769531 1315.367188 95.761719 1315.367188 95.757812 1315.367188 C 95.753906 1315.367188 95.746094 1315.367188 95.742188 1315.367188 C 95.738281 1315.367188 95.730469 1315.367188 95.726562 1315.367188 C 95.722656 1315.367188 95.714844 1315.367188 95.710938 1315.367188 C 95.707031 1315.367188 95.699219 1315.367188 95.695312 1315.367188 C 95.691406 1315.367188 95.683594 1315.367188 95.679688 1315.367188 C 95.675781 1315.367188 95.667969 1315.367188 95.664062 1315.367188 C 95.660156 1315.367188 95.652344 1315.367188 95.648438 1315.367188 C 95.644531 1315.367188 95.636719 1315.367188 95.632812 1315.367188 C 95.628906 1315.367188 95.621094 1315.367188 95.617188 1315.367188 C 95.609375 1315.367188 95.605469 1315.367188 95.601562 1315.367188 C 95.59375 1315.367188 95.589844 1315.367188 95.585938 1315.367188 C 95.578125 1315.367188 95.574219 1315.367188 95.570312 1315.367188 C 95.5625 1315.367188 95.558594 1315.367188 95.554688 1315.367188 C 95.546875 1315.367188 95.542969 1315.367188 95.539062 1315.367188 C 95.53125 1315.367188 95.527344 1315.367188 95.523438 1315.367188 C 95.515625 1315.367188 95.511719 1315.367188 95.507812 1315.367188 C 95.5 1315.367188 95.496094 1315.367188 95.492188 1315.367188 C 95.484375 1315.367188 95.480469 1315.367188 95.476562 1315.367188 C 95.46875 1315.367188 95.464844 1315.367188 95.460938 1315.367188 C 95.453125 1315.367188 95.449219 1315.367188 95.445312 1315.367188 C 95.4375 1315.367188 95.433594 1315.367188 95.429688 1315.367188 C 95.421875 1315.367188 95.417969 1315.367188 95.414062 1315.367188 C 95.40625 1315.367188 95.402344 1315.367188 95.398438 1315.367188 C 95.390625 1315.367188 95.386719 1315.367188 95.378906 1315.367188 C 95.375 1315.367188 95.371094 1315.367188 95.363281 1315.367188 C 95.359375 1315.367188 95.355469 1315.367188 95.347656 1315.367188 C 95.34375 1315.367188 95.339844 1315.367188 95.332031 1315.367188 C 95.328125 1315.367188 95.324219 1315.367188 95.316406 1315.367188 C 95.3125 1315.367188 95.308594 1315.367188 95.300781 1315.367188 C 95.296875 1315.367188 95.292969 1315.367188 95.285156 1315.367188 C 95.28125 1315.367188 95.277344 1315.367188 95.269531 1315.367188 C 95.265625 1315.367188 95.261719 1315.367188 95.253906 1315.367188 C 95.25 1315.367188 95.246094 1315.367188 95.238281 1315.367188 C 95.234375 1315.367188 95.230469 1315.367188 95.222656 1315.367188 C 95.21875 1315.367188 95.210938 1315.367188 95.207031 1315.367188 C 95.203125 1315.367188 95.195312 1315.367188 95.191406 1315.367188 C 95.1875 1315.367188 95.179688 1315.367188 95.175781 1315.367188 C 95.171875 1315.367188 95.164062 1315.367188 95.160156 1315.367188 C 95.15625 1315.367188 95.148438 1315.367188 95.144531 1315.367188 C 95.140625 1315.367188 95.132812 1315.367188 95.128906 1315.367188 C 95.125 1315.367188 95.117188 1315.367188 95.113281 1315.367188 C 95.109375 1315.367188 95.101562 1315.367188 95.097656 1315.367188 C 95.09375 1315.367188 95.085938 1315.367188 95.082031 1315.367188 C 95.078125 1315.367188 95.070312 1315.367188 95.066406 1315.367188 C 95.058594 1315.367188 95.054688 1315.367188 95.050781 1315.367188 C 95.042969 1315.367188 95.039062 1315.367188 95.035156 1315.367188 C 95.027344 1315.367188 95.023438 1315.367188 95.019531 1315.367188 C 95.011719 1315.367188 95.007812 1315.367188 95.003906 1315.367188 C 94.996094 1315.367188 94.992188 1315.367188 94.988281 1315.367188 C 94.980469 1315.367188 94.976562 1315.367188 94.972656 1315.367188 C 94.964844 1315.367188 94.960938 1315.367188 94.957031 1315.367188 C 94.949219 1315.367188 94.945312 1315.367188 94.941406 1315.367188 C 94.933594 1315.367188 94.929688 1315.367188 94.921875 1315.367188 C 94.917969 1315.367188 94.914062 1315.367188 94.90625 1315.367188 C 94.902344 1315.367188 94.898438 1315.367188 94.890625 1315.367188 C 94.886719 1315.367188 94.882812 1315.367188 94.875 1315.367188 C 94.871094 1315.367188 94.867188 1315.367188 94.859375 1315.367188 C 94.855469 1315.367188 94.851562 1315.367188 94.84375 1315.367188 C 94.839844 1315.367188 94.835938 1315.367188 94.828125 1315.367188 C 94.824219 1315.367188 94.820312 1315.367188 94.8125 1315.367188 C 94.808594 1315.367188 94.800781 1315.367188 94.796875 1315.367188 C 94.792969 1315.367188 94.785156 1315.367188 94.78125 1315.367188 C 94.777344 1315.367188 94.769531 1315.367188 94.765625 1315.367188 C 94.761719 1315.367188 94.753906 1315.367188 94.75 1315.367188 C 94.746094 1315.367188 94.738281 1315.367188 94.734375 1315.367188 C 94.730469 1315.367188 94.722656 1315.367188 94.71875 1315.367188 C 94.714844 1315.367188 94.707031 1315.367188 94.703125 1315.367188 C 94.699219 1315.367188 94.691406 1315.367188 94.6875 1315.367188 C 94.683594 1315.367188 94.675781 1315.367188 94.671875 1315.367188 C 94.664062 1315.367188 94.660156 1315.367188 94.65625 1315.367188 C 94.648438 1315.367188 94.644531 1315.367188 94.640625 1315.367188 C 94.632812 1315.367188 94.628906 1315.367188 94.625 1315.367188 C 94.617188 1315.367188 94.613281 1315.367188 94.609375 1315.367188 C 94.601562 1315.367188 94.597656 1315.367188 94.59375 1315.367188 C 94.585938 1315.367188 94.582031 1315.367188 94.578125 1315.367188 C 94.570312 1315.367188 94.566406 1315.367188 94.5625 1315.367188 C 94.554688 1315.367188 94.550781 1315.367188 94.542969 1315.367188 C 94.539062 1315.367188 94.535156 1315.367188 94.527344 1315.367188 C 94.523438 1315.367188 94.519531 1315.367188 94.511719 1315.367188 C 94.507812 1315.367188 94.503906 1315.367188 94.496094 1315.367188 C 94.492188 1315.367188 94.488281 1315.367188 94.480469 1315.367188 C 94.476562 1315.367188 94.472656 1315.367188 94.464844 1315.367188 C 94.460938 1315.367188 94.457031 1315.367188 94.449219 1315.367188 C 94.445312 1315.367188 94.441406 1315.367188 94.433594 1315.367188 C 94.429688 1315.367188 94.421875 1315.367188 94.417969 1315.367188 C 94.414062 1315.367188 94.40625 1315.367188 94.402344 1315.367188 C 94.398438 1315.367188 94.390625 1315.367188 94.386719 1315.367188 C 94.382812 1315.367188 94.375 1315.367188 94.371094 1315.367188 C 94.367188 1315.367188 94.359375 1315.367188 94.355469 1315.367188 C 94.351562 1315.367188 94.34375 1315.367188 94.339844 1315.367188 C 94.335938 1315.367188 94.328125 1315.367188 94.324219 1315.367188 C 94.320312 1315.367188 94.3125 1315.367188 94.308594 1315.367188 C 94.300781 1315.367188 94.296875 1315.367188 94.292969 1315.367188 C 94.285156 1315.367188 94.28125 1315.367188 94.277344 1315.367188 C 94.269531 1315.367188 94.265625 1315.367188 94.261719 1315.367188 C 94.253906 1315.367188 94.25 1315.367188 94.246094 1315.367188 C 94.238281 1315.367188 94.234375 1315.367188 94.230469 1315.367188 C 94.222656 1315.367188 94.21875 1315.367188 94.214844 1315.367188 C 94.207031 1315.367188 94.203125 1315.367188 94.199219 1315.367188 C 94.191406 1315.367188 94.1875 1315.367188 94.179688 1315.367188 C 94.175781 1315.367188 94.171875 1315.367188 94.164062 1315.367188 C 94.160156 1315.367188 94.15625 1315.367188 94.148438 1315.367188 C 94.144531 1315.367188 94.140625 1315.367188 94.132812 1315.367188 C 94.128906 1315.367188 94.125 1315.367188 94.117188 1315.367188 C 94.113281 1315.367188 94.109375 1315.367188 94.101562 1315.367188 C 94.097656 1315.367188 94.09375 1315.367188 94.085938 1315.367188 C 94.082031 1315.367188 94.078125 1315.367188 94.070312 1315.367188 C 94.066406 1315.367188 94.058594 1315.367188 94.054688 1315.367188 C 94.050781 1315.367188 94.042969 1315.367188 94.039062 1315.367188 C 94.035156 1315.367188 94.027344 1315.367188 94.023438 1315.367188 C 94.019531 1315.367188 94.011719 1315.367188 94.007812 1315.367188 C 94.003906 1315.367188 93.996094 1315.367188 93.992188 1315.367188 C 93.988281 1315.367188 93.980469 1315.367188 93.976562 1315.367188 C 93.972656 1315.367188 93.964844 1315.367188 93.960938 1315.367188 C 93.957031 1315.367188 93.949219 1315.367188 93.945312 1315.367188 C 93.941406 1315.367188 93.933594 1315.367188 93.929688 1315.367188 C 93.921875 1315.367188 93.917969 1315.367188 93.914062 1315.367188 C 93.90625 1315.367188 93.902344 1315.367188 93.898438 1315.367188 C 93.890625 1315.367188 93.886719 1315.367188 93.882812 1315.367188 C 93.875 1315.367188 93.871094 1315.367188 93.867188 1315.367188 C 93.859375 1315.367188 93.855469 1315.367188 93.851562 1315.367188 C 93.84375 1315.367188 93.839844 1315.367188 93.835938 1315.367188 C 93.828125 1315.367188 93.824219 1315.367188 93.820312 1315.367188 C 93.8125 1315.367188 93.808594 1315.367188 93.804688 1315.367188 C 93.796875 1315.367188 93.792969 1315.367188 93.789062 1315.367188 C 93.78125 1315.367188 93.777344 1315.367188 93.769531 1315.367188 C 93.765625 1315.367188 93.761719 1315.367188 93.753906 1315.367188 C 93.75 1315.367188 93.746094 1315.367188 93.738281 1315.367188 C 93.734375 1315.367188 93.730469 1315.367188 93.722656 1315.367188 C 93.71875 1315.367188 93.714844 1315.367188 93.707031 1315.367188 C 93.703125 1315.367188 93.699219 1315.367188 93.691406 1315.367188 C 93.6875 1315.367188 93.683594 1315.367188 93.675781 1315.367188 C 93.671875 1315.367188 93.667969 1315.367188 93.660156 1315.367188 C 93.65625 1315.367188 93.652344 1315.367188 93.644531 1315.367188 C 93.640625 1315.367188 93.636719 1315.367188 93.628906 1315.367188 C 93.625 1315.367188 93.621094 1315.367188 93.613281 1315.367188 C 93.609375 1315.367188 93.601562 1315.367188 93.597656 1315.367188 C 93.59375 1315.367188 93.585938 1315.367188 93.582031 1315.367188 C 93.578125 1315.367188 93.570312 1315.367188 93.566406 1315.367188 C 93.5625 1315.367188 93.554688 1315.367188 93.550781 1315.367188 C 93.546875 1315.367188 93.539062 1315.367188 93.535156 1315.367188 C 93.53125 1315.367188 93.523438 1315.367188 93.519531 1315.367188 C 93.515625 1315.367188 93.507812 1315.367188 93.503906 1315.367188 C 93.5 1315.367188 93.492188 1315.367188 93.488281 1315.367188 C 93.484375 1315.367188 93.476562 1315.367188 93.472656 1315.367188 C 93.46875 1315.367188 93.460938 1315.367188 93.457031 1315.367188 C 93.453125 1315.367188 93.445312 1315.367188 93.441406 1315.367188 C 93.4375 1315.367188 93.429688 1315.367188 93.425781 1315.367188 C 93.421875 1315.367188 93.414062 1315.367188 93.410156 1315.367188 C 93.402344 1315.367188 93.398438 1315.367188 93.394531 1315.367188 C 93.386719 1315.367188 93.382812 1315.367188 93.378906 1315.367188 C 93.371094 1315.367188 93.367188 1315.367188 93.363281 1315.367188 C 93.355469 1315.367188 93.351562 1315.367188 93.347656 1315.367188 C 93.339844 1315.367188 93.335938 1315.367188 93.332031 1315.367188 C 93.324219 1315.367188 93.320312 1315.367188 93.316406 1315.367188 C 93.308594 1315.367188 93.304688 1315.367188 93.300781 1315.367188 C 93.292969 1315.367188 93.289062 1315.367188 93.285156 1315.367188 C 93.277344 1315.367188 93.273438 1315.367188 93.269531 1315.367188 C 93.261719 1315.367188 93.257812 1315.367188 93.253906 1315.367188 C 93.246094 1315.367188 93.242188 1315.367188 93.238281 1315.367188 C 93.230469 1315.367188 93.226562 1315.367188 93.222656 1315.367188 C 93.214844 1315.367188 93.210938 1315.367188 93.207031 1315.367188 C 93.199219 1315.367188 93.195312 1315.367188 93.191406 1315.367188 C 93.183594 1315.367188 93.179688 1315.367188 93.175781 1315.367188 C 93.167969 1315.367188 93.164062 1315.367188 93.160156 1315.367188 C 93.152344 1315.367188 93.148438 1315.367188 93.144531 1315.367188 C 93.136719 1315.367188 93.132812 1315.367188 93.128906 1315.367188 C 93.121094 1315.367188 93.117188 1315.367188 93.113281 1315.367188 C 93.105469 1315.367188 93.101562 1315.367188 93.097656 1315.367188 C 93.089844 1315.367188 93.085938 1315.367188 93.082031 1315.367188 C 93.074219 1315.367188 93.070312 1315.367188 93.066406 1315.367188 C 93.058594 1315.367188 93.054688 1315.367188 93.050781 1315.367188 C 93.042969 1315.367188 93.039062 1315.367188 93.035156 1315.367188 C 93.027344 1315.367188 93.023438 1315.367188 93.019531 1315.367188 C 93.011719 1315.367188 93.007812 1315.367188 93.003906 1315.367188 C 92.996094 1315.367188 92.992188 1315.367188 92.988281 1315.367188 C 92.980469 1315.367188 92.976562 1315.367188 92.972656 1315.367188 C 92.964844 1315.367188 92.960938 1315.367188 92.957031 1315.367188 C 92.949219 1315.367188 92.945312 1315.367188 92.941406 1315.367188 C 92.933594 1315.367188 92.929688 1315.367188 92.925781 1315.367188 C 92.917969 1315.367188 92.914062 1315.367188 92.910156 1315.367188 C 92.902344 1315.367188 92.898438 1315.367188 92.894531 1315.367188 C 92.886719 1315.367188 92.882812 1315.367188 92.878906 1315.367188 C 92.871094 1315.367188 92.867188 1315.367188 92.863281 1315.367188 C 92.855469 1315.367188 92.851562 1315.367188 92.847656 1315.367188 C 92.839844 1315.367188 92.835938 1315.367188 92.832031 1315.367188 C 92.824219 1315.367188 92.820312 1315.367188 92.816406 1315.367188 C 92.808594 1315.367188 92.804688 1315.367188 92.800781 1315.367188 C 92.792969 1315.367188 92.789062 1315.367188 92.785156 1315.367188 C 92.777344 1315.367188 92.773438 1315.367188 92.769531 1315.367188 C 92.761719 1315.367188 92.757812 1315.367188 92.753906 1315.367188 C 92.746094 1315.367188 92.742188 1315.367188 92.738281 1315.367188 C 92.730469 1315.367188 92.726562 1315.367188 92.722656 1315.367188 C 92.714844 1315.367188 92.710938 1315.367188 92.707031 1315.367188 C 92.699219 1315.367188 92.695312 1315.367188 92.691406 1315.367188 C 92.683594 1315.367188 92.679688 1315.367188 92.675781 1315.367188 C 92.667969 1315.367188 92.664062 1315.367188 92.660156 1315.367188 C 92.652344 1315.367188 92.648438 1315.367188 92.644531 1315.367188 C 92.636719 1315.367188 92.632812 1315.367188 92.628906 1315.367188 C 92.621094 1315.367188 92.617188 1315.367188 92.613281 1315.367188 C 92.605469 1315.367188 92.601562 1315.367188 92.597656 1315.367188 C 92.589844 1315.367188 92.585938 1315.367188 92.582031 1315.367188 C 92.578125 1315.367188 92.570312 1315.367188 92.566406 1315.367188 C 92.5625 1315.367188 92.554688 1315.367188 92.550781 1315.367188 C 92.546875 1315.367188 92.539062 1315.367188 92.535156 1315.367188 C 92.53125 1315.367188 92.523438 1315.367188 92.519531 1315.367188 C 92.515625 1315.367188 92.507812 1315.367188 92.503906 1315.367188 C 92.5 1315.367188 92.492188 1315.367188 92.488281 1315.367188 C 92.484375 1315.367188 92.476562 1315.367188 92.472656 1315.367188 C 92.46875 1315.367188 92.460938 1315.367188 92.457031 1315.367188 C 92.453125 1315.367188 92.445312 1315.367188 92.441406 1315.367188 C 92.4375 1315.367188 92.429688 1315.367188 92.425781 1315.367188 C 92.421875 1315.367188 92.417969 1315.367188 92.410156 1315.367188 C 92.40625 1315.367188 92.402344 1315.367188 92.394531 1315.367188 C 92.390625 1315.367188 92.386719 1315.367188 92.378906 1315.367188 C 92.375 1315.367188 92.371094 1315.367188 92.363281 1315.367188 C 92.359375 1315.367188 92.355469 1315.367188 92.347656 1315.367188 C 92.34375 1315.367188 92.339844 1315.367188 92.332031 1315.367188 C 92.328125 1315.367188 92.324219 1315.367188 92.316406 1315.367188 C 92.3125 1315.367188 92.308594 1315.367188 92.300781 1315.367188 C 92.296875 1315.367188 92.292969 1315.367188 92.289062 1315.367188 C 92.28125 1315.367188 92.277344 1315.367188 92.273438 1315.367188 C 92.265625 1315.367188 92.261719 1315.367188 92.257812 1315.367188 C 92.25 1315.367188 92.246094 1315.367188 92.242188 1315.367188 C 92.234375 1315.367188 92.230469 1315.367188 92.226562 1315.367188 C 92.21875 1315.367188 92.214844 1315.367188 92.210938 1315.367188 C 92.203125 1315.367188 92.199219 1315.367188 92.195312 1315.367188 C 92.191406 1315.367188 92.183594 1315.367188 92.179688 1315.367188 C 92.175781 1315.367188 92.167969 1315.367188 92.164062 1315.367188 C 92.160156 1315.367188 92.152344 1315.367188 92.148438 1315.367188 C 92.144531 1315.367188 92.136719 1315.367188 92.132812 1315.367188 C 92.128906 1315.367188 92.121094 1315.367188 92.117188 1315.367188 C 92.113281 1315.367188 92.109375 1315.367188 92.101562 1315.367188 C 92.097656 1315.367188 92.09375 1315.367188 92.085938 1315.367188 C 92.082031 1315.367188 92.078125 1315.367188 92.070312 1315.367188 C 92.066406 1315.367188 92.0625 1315.367188 92.054688 1315.367188 C 92.050781 1315.367188 92.046875 1315.367188 92.042969 1315.367188 C 92.035156 1315.367188 92.03125 1315.367188 92.027344 1315.367188 C 92.019531 1315.367188 92.015625 1315.367188 92.011719 1315.367188 C 92.003906 1315.367188 92 1315.367188 91.996094 1315.367188 C 91.988281 1315.367188 91.984375 1315.367188 91.980469 1315.367188 C 91.976562 1315.367188 91.96875 1315.367188 91.964844 1315.367188 C 91.960938 1315.367188 91.953125 1315.367188 91.949219 1315.367188 C 91.945312 1315.367188 91.9375 1315.367188 91.933594 1315.367188 C 91.929688 1315.367188 91.921875 1315.367188 91.917969 1315.367188 C 91.914062 1315.367188 91.910156 1315.367188 91.902344 1315.367188 C 91.898438 1315.367188 91.894531 1315.367188 91.886719 1315.367188 C 91.882812 1315.367188 91.878906 1315.367188 91.871094 1315.367188 C 91.867188 1315.367188 91.863281 1315.367188 91.855469 1315.367188 C 91.851562 1315.367188 91.847656 1315.367188 91.84375 1315.367188 C 91.835938 1315.367188 91.832031 1315.367188 91.828125 1315.367188 C 91.820312 1315.367188 91.816406 1315.367188 91.8125 1315.367188 C 91.804688 1315.367188 91.800781 1315.367188 91.796875 1315.367188 C 91.792969 1315.367188 91.785156 1315.367188 91.78125 1315.367188 C 91.777344 1315.367188 91.769531 1315.367188 91.765625 1315.367188 C 91.761719 1315.367188 91.753906 1315.367188 91.75 1315.367188 C 91.746094 1315.367188 91.742188 1315.367188 91.734375 1315.367188 C 91.730469 1315.367188 91.726562 1315.367188 91.71875 1315.367188 C 91.714844 1315.367188 91.710938 1315.367188 91.703125 1315.367188 C 91.699219 1315.367188 91.695312 1315.367188 91.691406 1315.367188 C 91.683594 1315.367188 91.679688 1315.367188 91.675781 1315.367188 C 91.667969 1315.367188 91.664062 1315.367188 91.660156 1315.367188 C 91.652344 1315.367188 91.648438 1315.367188 91.644531 1315.367188 C 91.640625 1315.367188 91.632812 1315.367188 91.628906 1315.367188 C 91.625 1315.367188 91.617188 1315.367188 91.613281 1315.367188 C 91.609375 1315.367188 91.605469 1315.367188 91.597656 1315.367188 C 91.59375 1315.367188 91.589844 1315.367188 91.582031 1315.367188 C 91.578125 1315.367188 91.574219 1315.367188 91.566406 1315.367188 C 91.5625 1315.367188 91.558594 1315.367188 91.554688 1315.367188 C 91.546875 1315.367188 91.542969 1315.367188 91.539062 1315.367188 C 91.53125 1315.367188 91.527344 1315.367188 91.523438 1315.367188 C 91.519531 1315.367188 91.511719 1315.367188 91.507812 1315.367188 C 91.503906 1315.367188 91.496094 1315.367188 91.492188 1315.367188 C 91.488281 1315.367188 91.484375 1315.367188 91.476562 1315.367188 C 91.472656 1315.367188 91.46875 1315.367188 91.460938 1315.367188 C 91.457031 1315.367188 91.453125 1315.367188 91.449219 1315.367188 C 91.441406 1315.367188 91.4375 1315.367188 91.433594 1315.367188 C 91.425781 1315.367188 91.421875 1315.367188 91.417969 1315.367188 C 91.414062 1315.367188 91.40625 1315.367188 91.402344 1315.367188 C 91.398438 1315.367188 91.390625 1315.367188 91.386719 1315.367188 C 91.382812 1315.367188 91.378906 1315.367188 91.371094 1315.367188 C 91.367188 1315.367188 91.363281 1315.367188 91.355469 1315.367188 C 91.351562 1315.367188 91.347656 1315.367188 91.34375 1315.367188 C 91.335938 1315.367188 91.332031 1315.367188 91.328125 1315.367188 C 91.320312 1315.367188 91.316406 1315.367188 91.3125 1315.367188 C 91.308594 1315.367188 91.300781 1315.367188 91.296875 1315.367188 C 91.292969 1315.367188 91.285156 1315.367188 91.28125 1315.367188 C 91.277344 1315.367188 91.273438 1315.367188 91.265625 1315.367188 C 91.261719 1315.367188 91.257812 1315.367188 91.25 1315.367188 C 91.246094 1315.367188 91.242188 1315.367188 91.238281 1315.367188 C 91.230469 1315.367188 91.226562 1315.367188 91.222656 1315.367188 C 91.21875 1315.367188 91.210938 1315.367188 91.207031 1315.367188 C 91.203125 1315.367188 91.195312 1315.367188 91.191406 1315.367188 C 91.1875 1315.367188 91.183594 1315.367188 91.175781 1315.367188 C 91.171875 1315.367188 91.167969 1315.367188 91.160156 1315.367188 C 91.15625 1315.367188 91.152344 1315.367188 91.148438 1315.367188 C 91.140625 1315.367188 91.136719 1315.367188 91.132812 1315.367188 C 91.128906 1315.367188 91.121094 1315.367188 91.117188 1315.367188 C 91.113281 1315.367188 91.105469 1315.367188 91.101562 1315.367188 C 91.097656 1315.367188 91.09375 1315.367188 91.085938 1315.367188 C 91.082031 1315.367188 91.078125 1315.367188 91.074219 1315.367188 C 91.066406 1315.367188 91.0625 1315.367188 91.058594 1315.367188 C 91.050781 1315.367188 91.046875 1315.367188 91.042969 1315.367188 C 91.039062 1315.367188 91.03125 1315.367188 91.027344 1315.367188 C 91.023438 1315.367188 91.019531 1315.367188 91.011719 1315.367188 C 91.007812 1315.367188 91.003906 1315.367188 91 1315.367188 C 90.992188 1315.367188 90.988281 1315.367188 90.984375 1315.367188 C 90.976562 1315.367188 90.972656 1315.367188 90.96875 1315.367188 C 90.964844 1315.367188 90.957031 1315.367188 90.953125 1315.367188 C 90.949219 1315.367188 90.945312 1315.367188 90.9375 1315.367188 C 90.933594 1315.367188 90.929688 1315.367188 90.925781 1315.367188 C 90.917969 1315.367188 90.914062 1315.367188 90.910156 1315.367188 C 90.902344 1315.367188 90.898438 1315.367188 90.894531 1315.367188 C 90.890625 1315.367188 90.882812 1315.367188 90.878906 1315.367188 C 90.875 1315.367188 90.871094 1315.367188 90.863281 1315.367188 C 90.859375 1315.367188 90.855469 1315.367188 90.851562 1315.367188 C 90.84375 1315.367188 90.839844 1315.367188 90.835938 1315.367188 C 90.832031 1315.367188 90.824219 1315.367188 90.820312 1315.367188 C 90.816406 1315.367188 90.8125 1315.367188 90.804688 1315.367188 C 90.800781 1315.367188 90.796875 1315.367188 90.789062 1315.367188 C 90.785156 1315.367188 90.78125 1315.367188 90.777344 1315.367188 C 90.769531 1315.367188 90.765625 1315.367188 90.761719 1315.367188 C 90.757812 1315.367188 90.75 1315.367188 90.746094 1315.367188 C 90.742188 1315.367188 90.738281 1315.367188 90.730469 1315.367188 C 90.726562 1315.367188 90.722656 1315.367188 90.71875 1315.367188 C 90.710938 1315.367188 90.707031 1315.367188 90.703125 1315.367188 C 90.699219 1315.367188 90.691406 1315.367188 90.6875 1315.367188 C 90.683594 1315.367188 90.679688 1315.367188 90.671875 1315.367188 C 90.667969 1315.367188 90.664062 1315.367188 90.660156 1315.367188 C 90.652344 1315.367188 90.648438 1315.367188 90.644531 1315.367188 C 90.640625 1315.367188 90.632812 1315.367188 90.628906 1315.367188 C 90.625 1315.367188 90.621094 1315.367188 90.613281 1315.367188 C 90.609375 1315.367188 90.605469 1315.367188 90.601562 1315.367188 C 90.59375 1315.367188 90.589844 1315.367188 90.585938 1315.367188 C 90.582031 1315.367188 90.574219 1315.367188 90.570312 1315.367188 C 90.566406 1315.367188 90.5625 1315.367188 90.554688 1315.367188 C 90.550781 1315.367188 90.546875 1315.367188 90.542969 1315.367188 L 90.542969 1318.269531 C 90.546875 1318.269531 90.550781 1318.269531 90.554688 1318.269531 C 90.5625 1318.269531 90.566406 1318.269531 90.570312 1318.269531 C 90.574219 1318.269531 90.582031 1318.269531 90.585938 1318.269531 C 90.589844 1318.269531 90.59375 1318.269531 90.601562 1318.269531 C 90.605469 1318.269531 90.609375 1318.269531 90.613281 1318.269531 C 90.621094 1318.269531 90.625 1318.269531 90.628906 1318.269531 C 90.632812 1318.269531 90.640625 1318.269531 90.644531 1318.269531 C 90.648438 1318.269531 90.652344 1318.269531 90.660156 1318.269531 C 90.664062 1318.269531 90.667969 1318.269531 90.671875 1318.269531 C 90.679688 1318.269531 90.683594 1318.269531 90.6875 1318.269531 C 90.691406 1318.269531 90.699219 1318.269531 90.703125 1318.269531 C 90.707031 1318.269531 90.710938 1318.269531 90.71875 1318.269531 C 90.722656 1318.269531 90.726562 1318.269531 90.730469 1318.269531 C 90.738281 1318.269531 90.742188 1318.269531 90.746094 1318.269531 C 90.75 1318.269531 90.757812 1318.269531 90.761719 1318.269531 C 90.765625 1318.269531 90.769531 1318.269531 90.777344 1318.269531 C 90.78125 1318.269531 90.785156 1318.269531 90.789062 1318.269531 C 90.796875 1318.269531 90.800781 1318.269531 90.804688 1318.269531 C 90.8125 1318.269531 90.816406 1318.269531 90.820312 1318.269531 C 90.824219 1318.269531 90.832031 1318.269531 90.835938 1318.269531 C 90.839844 1318.269531 90.84375 1318.269531 90.851562 1318.269531 C 90.855469 1318.269531 90.859375 1318.269531 90.863281 1318.269531 C 90.871094 1318.269531 90.875 1318.269531 90.878906 1318.269531 C 90.882812 1318.269531 90.890625 1318.269531 90.894531 1318.269531 C 90.898438 1318.269531 90.902344 1318.269531 90.910156 1318.269531 C 90.914062 1318.269531 90.917969 1318.269531 90.925781 1318.269531 C 90.929688 1318.269531 90.933594 1318.269531 90.9375 1318.269531 C 90.945312 1318.269531 90.949219 1318.269531 90.953125 1318.269531 C 90.957031 1318.269531 90.964844 1318.269531 90.96875 1318.269531 C 90.972656 1318.269531 90.976562 1318.269531 90.984375 1318.269531 C 90.988281 1318.269531 90.992188 1318.269531 91 1318.269531 C 91.003906 1318.269531 91.007812 1318.269531 91.011719 1318.269531 C 91.019531 1318.269531 91.023438 1318.269531 91.027344 1318.269531 C 91.03125 1318.269531 91.039062 1318.269531 91.042969 1318.269531 C 91.046875 1318.269531 91.050781 1318.269531 91.058594 1318.269531 C 91.0625 1318.269531 91.066406 1318.269531 91.074219 1318.269531 C 91.078125 1318.269531 91.082031 1318.269531 91.085938 1318.269531 C 91.09375 1318.269531 91.097656 1318.269531 91.101562 1318.269531 C 91.105469 1318.269531 91.113281 1318.269531 91.117188 1318.269531 C 91.121094 1318.269531 91.128906 1318.269531 91.132812 1318.269531 C 91.136719 1318.269531 91.140625 1318.269531 91.148438 1318.269531 C 91.152344 1318.269531 91.15625 1318.269531 91.160156 1318.269531 C 91.167969 1318.269531 91.171875 1318.269531 91.175781 1318.269531 C 91.183594 1318.269531 91.1875 1318.269531 91.191406 1318.269531 C 91.195312 1318.269531 91.203125 1318.269531 91.207031 1318.269531 C 91.210938 1318.269531 91.21875 1318.269531 91.222656 1318.269531 C 91.226562 1318.269531 91.230469 1318.269531 91.238281 1318.269531 C 91.242188 1318.269531 91.246094 1318.269531 91.25 1318.269531 C 91.257812 1318.269531 91.261719 1318.269531 91.265625 1318.269531 C 91.273438 1318.269531 91.277344 1318.269531 91.28125 1318.269531 C 91.285156 1318.269531 91.292969 1318.269531 91.296875 1318.269531 C 91.300781 1318.269531 91.308594 1318.269531 91.3125 1318.269531 C 91.316406 1318.269531 91.320312 1318.269531 91.328125 1318.269531 C 91.332031 1318.269531 91.335938 1318.269531 91.34375 1318.269531 C 91.347656 1318.269531 91.351562 1318.269531 91.355469 1318.269531 C 91.363281 1318.269531 91.367188 1318.269531 91.371094 1318.269531 C 91.378906 1318.269531 91.382812 1318.269531 91.386719 1318.269531 C 91.390625 1318.269531 91.398438 1318.269531 91.402344 1318.269531 C 91.40625 1318.269531 91.414062 1318.269531 91.417969 1318.269531 C 91.421875 1318.269531 91.425781 1318.269531 91.433594 1318.269531 C 91.4375 1318.269531 91.441406 1318.269531 91.449219 1318.269531 C 91.453125 1318.269531 91.457031 1318.269531 91.460938 1318.269531 C 91.46875 1318.269531 91.472656 1318.269531 91.476562 1318.269531 C 91.484375 1318.269531 91.488281 1318.269531 91.492188 1318.269531 C 91.496094 1318.269531 91.503906 1318.269531 91.507812 1318.269531 C 91.511719 1318.269531 91.519531 1318.269531 91.523438 1318.269531 C 91.527344 1318.269531 91.53125 1318.269531 91.539062 1318.269531 C 91.542969 1318.269531 91.546875 1318.269531 91.554688 1318.269531 C 91.558594 1318.269531 91.5625 1318.269531 91.566406 1318.269531 C 91.574219 1318.269531 91.578125 1318.269531 91.582031 1318.269531 C 91.589844 1318.269531 91.59375 1318.269531 91.597656 1318.269531 C 91.605469 1318.269531 91.609375 1318.269531 91.613281 1318.269531 C 91.617188 1318.269531 91.625 1318.269531 91.628906 1318.269531 C 91.632812 1318.269531 91.640625 1318.269531 91.644531 1318.269531 C 91.648438 1318.269531 91.652344 1318.269531 91.660156 1318.269531 C 91.664062 1318.269531 91.667969 1318.269531 91.675781 1318.269531 C 91.679688 1318.269531 91.683594 1318.269531 91.691406 1318.269531 C 91.695312 1318.269531 91.699219 1318.269531 91.703125 1318.269531 C 91.710938 1318.269531 91.714844 1318.269531 91.71875 1318.269531 C 91.726562 1318.269531 91.730469 1318.269531 91.734375 1318.269531 C 91.742188 1318.269531 91.746094 1318.269531 91.75 1318.269531 C 91.753906 1318.269531 91.761719 1318.269531 91.765625 1318.269531 C 91.769531 1318.269531 91.777344 1318.269531 91.78125 1318.269531 C 91.785156 1318.269531 91.792969 1318.269531 91.796875 1318.269531 C 91.800781 1318.269531 91.804688 1318.269531 91.8125 1318.269531 C 91.816406 1318.269531 91.820312 1318.269531 91.828125 1318.269531 C 91.832031 1318.269531 91.835938 1318.269531 91.84375 1318.269531 C 91.847656 1318.269531 91.851562 1318.269531 91.855469 1318.269531 C 91.863281 1318.269531 91.867188 1318.269531 91.871094 1318.269531 C 91.878906 1318.269531 91.882812 1318.269531 91.886719 1318.269531 C 91.894531 1318.269531 91.898438 1318.269531 91.902344 1318.269531 C 91.910156 1318.269531 91.914062 1318.269531 91.917969 1318.269531 C 91.921875 1318.269531 91.929688 1318.269531 91.933594 1318.269531 C 91.9375 1318.269531 91.945312 1318.269531 91.949219 1318.269531 C 91.953125 1318.269531 91.960938 1318.269531 91.964844 1318.269531 C 91.96875 1318.269531 91.976562 1318.269531 91.980469 1318.269531 C 91.984375 1318.269531 91.988281 1318.269531 91.996094 1318.269531 C 92 1318.269531 92.003906 1318.269531 92.011719 1318.269531 C 92.015625 1318.269531 92.019531 1318.269531 92.027344 1318.269531 C 92.03125 1318.269531 92.035156 1318.269531 92.042969 1318.269531 C 92.046875 1318.269531 92.050781 1318.269531 92.054688 1318.269531 C 92.0625 1318.269531 92.066406 1318.269531 92.070312 1318.269531 C 92.078125 1318.269531 92.082031 1318.269531 92.085938 1318.269531 C 92.09375 1318.269531 92.097656 1318.269531 92.101562 1318.269531 C 92.109375 1318.269531 92.113281 1318.269531 92.117188 1318.269531 C 92.121094 1318.269531 92.128906 1318.269531 92.132812 1318.269531 C 92.136719 1318.269531 92.144531 1318.269531 92.148438 1318.269531 C 92.152344 1318.269531 92.160156 1318.269531 92.164062 1318.269531 C 92.167969 1318.269531 92.175781 1318.269531 92.179688 1318.269531 C 92.183594 1318.269531 92.191406 1318.269531 92.195312 1318.269531 C 92.199219 1318.269531 92.203125 1318.269531 92.210938 1318.269531 C 92.214844 1318.269531 92.21875 1318.269531 92.226562 1318.269531 C 92.230469 1318.269531 92.234375 1318.269531 92.242188 1318.269531 C 92.246094 1318.269531 92.25 1318.269531 92.257812 1318.269531 C 92.261719 1318.269531 92.265625 1318.269531 92.273438 1318.269531 C 92.277344 1318.269531 92.28125 1318.269531 92.289062 1318.269531 C 92.292969 1318.269531 92.296875 1318.269531 92.300781 1318.269531 C 92.308594 1318.269531 92.3125 1318.269531 92.316406 1318.269531 C 92.324219 1318.269531 92.328125 1318.269531 92.332031 1318.269531 C 92.339844 1318.269531 92.34375 1318.269531 92.347656 1318.269531 C 92.355469 1318.269531 92.359375 1318.269531 92.363281 1318.269531 C 92.371094 1318.269531 92.375 1318.269531 92.378906 1318.269531 C 92.386719 1318.269531 92.390625 1318.269531 92.394531 1318.269531 C 92.402344 1318.269531 92.40625 1318.269531 92.410156 1318.269531 C 92.417969 1318.269531 92.421875 1318.269531 92.425781 1318.269531 C 92.429688 1318.269531 92.4375 1318.269531 92.441406 1318.269531 C 92.445312 1318.269531 92.453125 1318.269531 92.457031 1318.269531 C 92.460938 1318.269531 92.46875 1318.269531 92.472656 1318.269531 C 92.476562 1318.269531 92.484375 1318.269531 92.488281 1318.269531 C 92.492188 1318.269531 92.5 1318.269531 92.503906 1318.269531 C 92.507812 1318.269531 92.515625 1318.269531 92.519531 1318.269531 C 92.523438 1318.269531 92.53125 1318.269531 92.535156 1318.269531 C 92.539062 1318.269531 92.546875 1318.269531 92.550781 1318.269531 C 92.554688 1318.269531 92.5625 1318.269531 92.566406 1318.269531 C 92.570312 1318.269531 92.578125 1318.269531 92.582031 1318.269531 C 92.585938 1318.269531 92.589844 1318.269531 92.597656 1318.269531 C 92.601562 1318.269531 92.605469 1318.269531 92.613281 1318.269531 C 92.617188 1318.269531 92.621094 1318.269531 92.628906 1318.269531 C 92.632812 1318.269531 92.636719 1318.269531 92.644531 1318.269531 C 92.648438 1318.269531 92.652344 1318.269531 92.660156 1318.269531 C 92.664062 1318.269531 92.667969 1318.269531 92.675781 1318.269531 C 92.679688 1318.269531 92.683594 1318.269531 92.691406 1318.269531 C 92.695312 1318.269531 92.699219 1318.269531 92.707031 1318.269531 C 92.710938 1318.269531 92.714844 1318.269531 92.722656 1318.269531 C 92.726562 1318.269531 92.730469 1318.269531 92.738281 1318.269531 C 92.742188 1318.269531 92.746094 1318.269531 92.753906 1318.269531 C 92.757812 1318.269531 92.761719 1318.269531 92.769531 1318.269531 C 92.773438 1318.269531 92.777344 1318.269531 92.785156 1318.269531 C 92.789062 1318.269531 92.792969 1318.269531 92.800781 1318.269531 C 92.804688 1318.269531 92.808594 1318.269531 92.816406 1318.269531 C 92.820312 1318.269531 92.824219 1318.269531 92.832031 1318.269531 C 92.835938 1318.269531 92.839844 1318.269531 92.847656 1318.269531 C 92.851562 1318.269531 92.855469 1318.269531 92.863281 1318.269531 C 92.867188 1318.269531 92.871094 1318.269531 92.878906 1318.269531 C 92.882812 1318.269531 92.886719 1318.269531 92.894531 1318.269531 C 92.898438 1318.269531 92.902344 1318.269531 92.910156 1318.269531 C 92.914062 1318.269531 92.917969 1318.269531 92.925781 1318.269531 C 92.929688 1318.269531 92.933594 1318.269531 92.941406 1318.269531 C 92.945312 1318.269531 92.949219 1318.269531 92.957031 1318.269531 C 92.960938 1318.269531 92.964844 1318.269531 92.972656 1318.269531 C 92.976562 1318.269531 92.980469 1318.269531 92.988281 1318.269531 C 92.992188 1318.269531 92.996094 1318.269531 93.003906 1318.269531 C 93.007812 1318.269531 93.011719 1318.269531 93.019531 1318.269531 C 93.023438 1318.269531 93.027344 1318.269531 93.035156 1318.269531 C 93.039062 1318.269531 93.042969 1318.269531 93.050781 1318.269531 C 93.054688 1318.269531 93.058594 1318.269531 93.066406 1318.269531 C 93.070312 1318.269531 93.074219 1318.269531 93.082031 1318.269531 C 93.085938 1318.269531 93.089844 1318.269531 93.097656 1318.269531 C 93.101562 1318.269531 93.105469 1318.269531 93.113281 1318.269531 C 93.117188 1318.269531 93.121094 1318.269531 93.128906 1318.269531 C 93.132812 1318.269531 93.136719 1318.269531 93.144531 1318.269531 C 93.148438 1318.269531 93.152344 1318.269531 93.160156 1318.269531 C 93.164062 1318.269531 93.167969 1318.269531 93.175781 1318.269531 C 93.179688 1318.269531 93.183594 1318.269531 93.191406 1318.269531 C 93.195312 1318.269531 93.199219 1318.269531 93.207031 1318.269531 C 93.210938 1318.269531 93.214844 1318.269531 93.222656 1318.269531 C 93.226562 1318.269531 93.230469 1318.269531 93.238281 1318.269531 C 93.242188 1318.269531 93.246094 1318.269531 93.253906 1318.269531 C 93.257812 1318.269531 93.261719 1318.269531 93.269531 1318.269531 C 93.273438 1318.269531 93.277344 1318.269531 93.285156 1318.269531 C 93.289062 1318.269531 93.292969 1318.269531 93.300781 1318.269531 C 93.304688 1318.269531 93.308594 1318.269531 93.316406 1318.269531 C 93.320312 1318.269531 93.324219 1318.269531 93.332031 1318.269531 C 93.335938 1318.269531 93.339844 1318.269531 93.347656 1318.269531 C 93.351562 1318.269531 93.355469 1318.269531 93.363281 1318.269531 C 93.367188 1318.269531 93.371094 1318.269531 93.378906 1318.269531 C 93.382812 1318.269531 93.386719 1318.269531 93.394531 1318.269531 C 93.398438 1318.269531 93.402344 1318.269531 93.410156 1318.269531 C 93.414062 1318.269531 93.421875 1318.269531 93.425781 1318.269531 C 93.429688 1318.269531 93.4375 1318.269531 93.441406 1318.269531 C 93.445312 1318.269531 93.453125 1318.269531 93.457031 1318.269531 C 93.460938 1318.269531 93.46875 1318.269531 93.472656 1318.269531 C 93.476562 1318.269531 93.484375 1318.269531 93.488281 1318.269531 C 93.492188 1318.269531 93.5 1318.269531 93.503906 1318.269531 C 93.507812 1318.269531 93.515625 1318.269531 93.519531 1318.269531 C 93.523438 1318.269531 93.53125 1318.269531 93.535156 1318.269531 C 93.539062 1318.269531 93.546875 1318.269531 93.550781 1318.269531 C 93.554688 1318.269531 93.5625 1318.269531 93.566406 1318.269531 C 93.570312 1318.269531 93.578125 1318.269531 93.582031 1318.269531 C 93.585938 1318.269531 93.59375 1318.269531 93.597656 1318.269531 C 93.601562 1318.269531 93.609375 1318.269531 93.613281 1318.269531 C 93.621094 1318.269531 93.625 1318.269531 93.628906 1318.269531 C 93.636719 1318.269531 93.640625 1318.269531 93.644531 1318.269531 C 93.652344 1318.269531 93.65625 1318.269531 93.660156 1318.269531 C 93.667969 1318.269531 93.671875 1318.269531 93.675781 1318.269531 C 93.683594 1318.269531 93.6875 1318.269531 93.691406 1318.269531 C 93.699219 1318.269531 93.703125 1318.269531 93.707031 1318.269531 C 93.714844 1318.269531 93.71875 1318.269531 93.722656 1318.269531 C 93.730469 1318.269531 93.734375 1318.269531 93.738281 1318.269531 C 93.746094 1318.269531 93.75 1318.269531 93.753906 1318.269531 C 93.761719 1318.269531 93.765625 1318.269531 93.769531 1318.269531 C 93.777344 1318.269531 93.78125 1318.269531 93.789062 1318.269531 C 93.792969 1318.269531 93.796875 1318.269531 93.804688 1318.269531 C 93.808594 1318.269531 93.8125 1318.269531 93.820312 1318.269531 C 93.824219 1318.269531 93.828125 1318.269531 93.835938 1318.269531 C 93.839844 1318.269531 93.84375 1318.269531 93.851562 1318.269531 C 93.855469 1318.269531 93.859375 1318.269531 93.867188 1318.269531 C 93.871094 1318.269531 93.875 1318.269531 93.882812 1318.269531 C 93.886719 1318.269531 93.890625 1318.269531 93.898438 1318.269531 C 93.902344 1318.269531 93.90625 1318.269531 93.914062 1318.269531 C 93.917969 1318.269531 93.921875 1318.269531 93.929688 1318.269531 C 93.933594 1318.269531 93.941406 1318.269531 93.945312 1318.269531 C 93.949219 1318.269531 93.957031 1318.269531 93.960938 1318.269531 C 93.964844 1318.269531 93.972656 1318.269531 93.976562 1318.269531 C 93.980469 1318.269531 93.988281 1318.269531 93.992188 1318.269531 C 93.996094 1318.269531 94.003906 1318.269531 94.007812 1318.269531 C 94.011719 1318.269531 94.019531 1318.269531 94.023438 1318.269531 C 94.027344 1318.269531 94.035156 1318.269531 94.039062 1318.269531 C 94.042969 1318.269531 94.050781 1318.269531 94.054688 1318.269531 C 94.058594 1318.269531 94.066406 1318.269531 94.070312 1318.269531 C 94.078125 1318.269531 94.082031 1318.269531 94.085938 1318.269531 C 94.09375 1318.269531 94.097656 1318.269531 94.101562 1318.269531 C 94.109375 1318.269531 94.113281 1318.269531 94.117188 1318.269531 C 94.125 1318.269531 94.128906 1318.269531 94.132812 1318.269531 C 94.140625 1318.269531 94.144531 1318.269531 94.148438 1318.269531 C 94.15625 1318.269531 94.160156 1318.269531 94.164062 1318.269531 C 94.171875 1318.269531 94.175781 1318.269531 94.179688 1318.269531 C 94.1875 1318.269531 94.191406 1318.269531 94.199219 1318.269531 C 94.203125 1318.269531 94.207031 1318.269531 94.214844 1318.269531 C 94.21875 1318.269531 94.222656 1318.269531 94.230469 1318.269531 C 94.234375 1318.269531 94.238281 1318.269531 94.246094 1318.269531 C 94.25 1318.269531 94.253906 1318.269531 94.261719 1318.269531 C 94.265625 1318.269531 94.269531 1318.269531 94.277344 1318.269531 C 94.28125 1318.269531 94.285156 1318.269531 94.292969 1318.269531 C 94.296875 1318.269531 94.300781 1318.269531 94.308594 1318.269531 C 94.3125 1318.269531 94.320312 1318.269531 94.324219 1318.269531 C 94.328125 1318.269531 94.335938 1318.269531 94.339844 1318.269531 C 94.34375 1318.269531 94.351562 1318.269531 94.355469 1318.269531 C 94.359375 1318.269531 94.367188 1318.269531 94.371094 1318.269531 C 94.375 1318.269531 94.382812 1318.269531 94.386719 1318.269531 C 94.390625 1318.269531 94.398438 1318.269531 94.402344 1318.269531 C 94.40625 1318.269531 94.414062 1318.269531 94.417969 1318.269531 C 94.421875 1318.269531 94.429688 1318.269531 94.433594 1318.269531 C 94.441406 1318.269531 94.445312 1318.269531 94.449219 1318.269531 C 94.457031 1318.269531 94.460938 1318.269531 94.464844 1318.269531 C 94.472656 1318.269531 94.476562 1318.269531 94.480469 1318.269531 C 94.488281 1318.269531 94.492188 1318.269531 94.496094 1318.269531 C 94.503906 1318.269531 94.507812 1318.269531 94.511719 1318.269531 C 94.519531 1318.269531 94.523438 1318.269531 94.527344 1318.269531 C 94.535156 1318.269531 94.539062 1318.269531 94.542969 1318.269531 C 94.550781 1318.269531 94.554688 1318.269531 94.5625 1318.269531 C 94.566406 1318.269531 94.570312 1318.269531 94.578125 1318.269531 C 94.582031 1318.269531 94.585938 1318.269531 94.59375 1318.269531 C 94.597656 1318.269531 94.601562 1318.269531 94.609375 1318.269531 C 94.613281 1318.269531 94.617188 1318.269531 94.625 1318.269531 C 94.628906 1318.269531 94.632812 1318.269531 94.640625 1318.269531 C 94.644531 1318.269531 94.648438 1318.269531 94.65625 1318.269531 C 94.660156 1318.269531 94.664062 1318.269531 94.671875 1318.269531 C 94.675781 1318.269531 94.683594 1318.269531 94.6875 1318.269531 C 94.691406 1318.269531 94.699219 1318.269531 94.703125 1318.269531 C 94.707031 1318.269531 94.714844 1318.269531 94.71875 1318.269531 C 94.722656 1318.269531 94.730469 1318.269531 94.734375 1318.269531 C 94.738281 1318.269531 94.746094 1318.269531 94.75 1318.269531 C 94.753906 1318.269531 94.761719 1318.269531 94.765625 1318.269531 C 94.769531 1318.269531 94.777344 1318.269531 94.78125 1318.269531 C 94.785156 1318.269531 94.792969 1318.269531 94.796875 1318.269531 C 94.800781 1318.269531 94.808594 1318.269531 94.8125 1318.269531 C 94.820312 1318.269531 94.824219 1318.269531 94.828125 1318.269531 C 94.835938 1318.269531 94.839844 1318.269531 94.84375 1318.269531 C 94.851562 1318.269531 94.855469 1318.269531 94.859375 1318.269531 C 94.867188 1318.269531 94.871094 1318.269531 94.875 1318.269531 C 94.882812 1318.269531 94.886719 1318.269531 94.890625 1318.269531 C 94.898438 1318.269531 94.902344 1318.269531 94.90625 1318.269531 C 94.914062 1318.269531 94.917969 1318.269531 94.921875 1318.269531 C 94.929688 1318.269531 94.933594 1318.269531 94.941406 1318.269531 C 94.945312 1318.269531 94.949219 1318.269531 94.957031 1318.269531 C 94.960938 1318.269531 94.964844 1318.269531 94.972656 1318.269531 C 94.976562 1318.269531 94.980469 1318.269531 94.988281 1318.269531 C 94.992188 1318.269531 94.996094 1318.269531 95.003906 1318.269531 C 95.007812 1318.269531 95.011719 1318.269531 95.019531 1318.269531 C 95.023438 1318.269531 95.027344 1318.269531 95.035156 1318.269531 C 95.039062 1318.269531 95.042969 1318.269531 95.050781 1318.269531 C 95.054688 1318.269531 95.058594 1318.269531 95.066406 1318.269531 C 95.070312 1318.269531 95.078125 1318.269531 95.082031 1318.269531 C 95.085938 1318.269531 95.09375 1318.269531 95.097656 1318.269531 C 95.101562 1318.269531 95.109375 1318.269531 95.113281 1318.269531 C 95.117188 1318.269531 95.125 1318.269531 95.128906 1318.269531 C 95.132812 1318.269531 95.140625 1318.269531 95.144531 1318.269531 C 95.148438 1318.269531 95.15625 1318.269531 95.160156 1318.269531 C 95.164062 1318.269531 95.171875 1318.269531 95.175781 1318.269531 C 95.179688 1318.269531 95.1875 1318.269531 95.191406 1318.269531 C 95.195312 1318.269531 95.203125 1318.269531 95.207031 1318.269531 C 95.210938 1318.269531 95.21875 1318.269531 95.222656 1318.269531 C 95.230469 1318.269531 95.234375 1318.269531 95.238281 1318.269531 C 95.246094 1318.269531 95.25 1318.269531 95.253906 1318.269531 C 95.261719 1318.269531 95.265625 1318.269531 95.269531 1318.269531 C 95.277344 1318.269531 95.28125 1318.269531 95.285156 1318.269531 C 95.292969 1318.269531 95.296875 1318.269531 95.300781 1318.269531 C 95.308594 1318.269531 95.3125 1318.269531 95.316406 1318.269531 C 95.324219 1318.269531 95.328125 1318.269531 95.332031 1318.269531 C 95.339844 1318.269531 95.34375 1318.269531 95.347656 1318.269531 C 95.355469 1318.269531 95.359375 1318.269531 95.363281 1318.269531 C 95.371094 1318.269531 95.375 1318.269531 95.378906 1318.269531 C 95.386719 1318.269531 95.390625 1318.269531 95.398438 1318.269531 C 95.402344 1318.269531 95.40625 1318.269531 95.414062 1318.269531 C 95.417969 1318.269531 95.421875 1318.269531 95.429688 1318.269531 C 95.433594 1318.269531 95.4375 1318.269531 95.445312 1318.269531 C 95.449219 1318.269531 95.453125 1318.269531 95.460938 1318.269531 C 95.464844 1318.269531 95.46875 1318.269531 95.476562 1318.269531 C 95.480469 1318.269531 95.484375 1318.269531 95.492188 1318.269531 C 95.496094 1318.269531 95.5 1318.269531 95.507812 1318.269531 C 95.511719 1318.269531 95.515625 1318.269531 95.523438 1318.269531 C 95.527344 1318.269531 95.53125 1318.269531 95.539062 1318.269531 C 95.542969 1318.269531 95.546875 1318.269531 95.554688 1318.269531 C 95.558594 1318.269531 95.5625 1318.269531 95.570312 1318.269531 C 95.574219 1318.269531 95.578125 1318.269531 95.585938 1318.269531 C 95.589844 1318.269531 95.59375 1318.269531 95.601562 1318.269531 C 95.605469 1318.269531 95.609375 1318.269531 95.617188 1318.269531 C 95.621094 1318.269531 95.628906 1318.269531 95.632812 1318.269531 C 95.636719 1318.269531 95.644531 1318.269531 95.648438 1318.269531 C 95.652344 1318.269531 95.660156 1318.269531 95.664062 1318.269531 C 95.667969 1318.269531 95.675781 1318.269531 95.679688 1318.269531 C 95.683594 1318.269531 95.691406 1318.269531 95.695312 1318.269531 C 95.699219 1318.269531 95.707031 1318.269531 95.710938 1318.269531 C 95.714844 1318.269531 95.722656 1318.269531 95.726562 1318.269531 C 95.730469 1318.269531 95.738281 1318.269531 95.742188 1318.269531 C 95.746094 1318.269531 95.753906 1318.269531 95.757812 1318.269531 C 95.761719 1318.269531 95.769531 1318.269531 95.773438 1318.269531 C 95.777344 1318.269531 95.785156 1318.269531 95.789062 1318.269531 C 95.792969 1318.269531 95.800781 1318.269531 95.804688 1318.269531 C 95.808594 1318.269531 95.816406 1318.269531 95.820312 1318.269531 C 95.824219 1318.269531 95.832031 1318.269531 95.835938 1318.269531 C 95.839844 1318.269531 95.847656 1318.269531 95.851562 1318.269531 C 95.855469 1318.269531 95.863281 1318.269531 95.867188 1318.269531 C 95.871094 1318.269531 95.878906 1318.269531 95.882812 1318.269531 C 95.886719 1318.269531 95.894531 1318.269531 95.898438 1318.269531 C 95.902344 1318.269531 95.910156 1318.269531 95.914062 1318.269531 C 95.917969 1318.269531 95.925781 1318.269531 95.929688 1318.269531 C 95.933594 1318.269531 95.941406 1318.269531 95.945312 1318.269531 C 95.949219 1318.269531 95.957031 1318.269531 95.960938 1318.269531 C 95.964844 1318.269531 95.972656 1318.269531 95.976562 1318.269531 C 95.980469 1318.269531 95.988281 1318.269531 95.992188 1318.269531 C 95.996094 1318.269531 96.003906 1318.269531 96.007812 1318.269531 C 96.011719 1318.269531 96.019531 1318.269531 96.023438 1318.269531 C 96.027344 1318.269531 96.035156 1318.269531 96.039062 1318.269531 C 96.042969 1318.269531 96.050781 1318.269531 96.054688 1318.269531 C 96.058594 1318.269531 96.066406 1318.269531 96.070312 1318.269531 C 96.074219 1318.269531 96.082031 1318.269531 96.085938 1318.269531 C 96.089844 1318.269531 96.097656 1318.269531 96.101562 1318.269531 C 96.105469 1318.269531 96.113281 1318.269531 96.117188 1318.269531 C 96.121094 1318.269531 96.128906 1318.269531 96.132812 1318.269531 C 96.136719 1318.269531 96.144531 1318.269531 96.148438 1318.269531 C 96.152344 1318.269531 96.160156 1318.269531 96.164062 1318.269531 C 96.167969 1318.269531 96.175781 1318.269531 96.179688 1318.269531 C 96.183594 1318.269531 96.191406 1318.269531 96.195312 1318.269531 C 96.199219 1318.269531 96.207031 1318.269531 96.210938 1318.269531 C 96.214844 1318.269531 96.222656 1318.269531 96.226562 1318.269531 C 96.230469 1318.269531 96.238281 1318.269531 96.242188 1318.269531 C 96.246094 1318.269531 96.253906 1318.269531 96.257812 1318.269531 C 96.261719 1318.269531 96.269531 1318.269531 96.273438 1318.269531 C 96.277344 1318.269531 96.285156 1318.269531 96.289062 1318.269531 C 96.292969 1318.269531 96.300781 1318.269531 96.304688 1318.269531 C 96.308594 1318.269531 96.316406 1318.269531 96.320312 1318.269531 C 96.324219 1318.269531 96.332031 1318.269531 96.335938 1318.269531 C 96.339844 1318.269531 96.34375 1318.269531 96.351562 1318.269531 C 96.355469 1318.269531 96.359375 1318.269531 96.367188 1318.269531 C 96.371094 1318.269531 96.375 1318.269531 96.382812 1318.269531 C 96.386719 1318.269531 96.390625 1318.269531 96.398438 1318.269531 C 96.402344 1318.269531 96.40625 1318.269531 96.414062 1318.269531 C 96.417969 1318.269531 96.421875 1318.269531 96.429688 1318.269531 C 96.433594 1318.269531 96.4375 1318.269531 96.445312 1318.269531 C 96.449219 1318.269531 96.453125 1318.269531 96.460938 1318.269531 C 96.464844 1318.269531 96.46875 1318.269531 96.476562 1318.269531 C 96.480469 1318.269531 96.484375 1318.269531 96.492188 1318.269531 C 96.496094 1318.269531 96.5 1318.269531 96.507812 1318.269531 C 96.511719 1318.269531 96.515625 1318.269531 96.519531 1318.269531 C 96.527344 1318.269531 96.53125 1318.269531 96.535156 1318.269531 C 96.542969 1318.269531 96.546875 1318.269531 96.550781 1318.269531 C 96.558594 1318.269531 96.5625 1318.269531 96.566406 1318.269531 C 96.574219 1318.269531 96.578125 1318.269531 96.582031 1318.269531 C 96.589844 1318.269531 96.59375 1318.269531 96.597656 1318.269531 C 96.605469 1318.269531 96.609375 1318.269531 96.613281 1318.269531 C 96.621094 1318.269531 96.625 1318.269531 96.628906 1318.269531 C 96.636719 1318.269531 96.640625 1318.269531 96.644531 1318.269531 C 96.648438 1318.269531 96.65625 1318.269531 96.660156 1318.269531 C 96.664062 1318.269531 96.671875 1318.269531 96.675781 1318.269531 C 96.679688 1318.269531 96.6875 1318.269531 96.691406 1318.269531 C 96.695312 1318.269531 96.703125 1318.269531 96.707031 1318.269531 C 96.710938 1318.269531 96.71875 1318.269531 96.722656 1318.269531 C 96.726562 1318.269531 96.734375 1318.269531 96.738281 1318.269531 C 96.742188 1318.269531 96.746094 1318.269531 96.753906 1318.269531 C 96.757812 1318.269531 96.761719 1318.269531 96.769531 1318.269531 C 96.773438 1318.269531 96.777344 1318.269531 96.785156 1318.269531 C 96.789062 1318.269531 96.792969 1318.269531 96.800781 1318.269531 C 96.804688 1318.269531 96.808594 1318.269531 96.816406 1318.269531 C 96.820312 1318.269531 96.824219 1318.269531 96.828125 1318.269531 C 96.835938 1318.269531 96.839844 1318.269531 96.84375 1318.269531 C 96.851562 1318.269531 96.855469 1318.269531 96.859375 1318.269531 C 96.867188 1318.269531 96.871094 1318.269531 96.875 1318.269531 C 96.882812 1318.269531 96.886719 1318.269531 96.890625 1318.269531 C 96.898438 1318.269531 96.902344 1318.269531 96.90625 1318.269531 C 96.910156 1318.269531 96.917969 1318.269531 96.921875 1318.269531 C 96.925781 1318.269531 96.933594 1318.269531 96.9375 1318.269531 C 96.941406 1318.269531 96.949219 1318.269531 96.953125 1318.269531 C 96.957031 1318.269531 96.964844 1318.269531 96.96875 1318.269531 C 96.972656 1318.269531 96.976562 1318.269531 96.984375 1318.269531 C 96.988281 1318.269531 96.992188 1318.269531 97 1318.269531 C 97.003906 1318.269531 97.007812 1318.269531 97.015625 1318.269531 C 97.019531 1318.269531 97.023438 1318.269531 97.03125 1318.269531 C 97.035156 1318.269531 97.039062 1318.269531 97.042969 1318.269531 C 97.050781 1318.269531 97.054688 1318.269531 97.058594 1318.269531 C 97.066406 1318.269531 97.070312 1318.269531 97.074219 1318.269531 C 97.082031 1318.269531 97.085938 1318.269531 97.089844 1318.269531 C 97.09375 1318.269531 97.101562 1318.269531 97.105469 1318.269531 C 97.109375 1318.269531 97.117188 1318.269531 97.121094 1318.269531 C 97.125 1318.269531 97.132812 1318.269531 97.136719 1318.269531 C 97.140625 1318.269531 97.148438 1318.269531 97.152344 1318.269531 C 97.15625 1318.269531 97.160156 1318.269531 97.167969 1318.269531 C 97.171875 1318.269531 97.175781 1318.269531 97.183594 1318.269531 C 97.1875 1318.269531 97.191406 1318.269531 97.199219 1318.269531 C 97.203125 1318.269531 97.207031 1318.269531 97.210938 1318.269531 C 97.21875 1318.269531 97.222656 1318.269531 97.226562 1318.269531 C 97.234375 1318.269531 97.238281 1318.269531 97.242188 1318.269531 C 97.25 1318.269531 97.253906 1318.269531 97.257812 1318.269531 C 97.261719 1318.269531 97.269531 1318.269531 97.273438 1318.269531 C 97.277344 1318.269531 97.285156 1318.269531 97.289062 1318.269531 C 97.292969 1318.269531 97.296875 1318.269531 97.304688 1318.269531 C 97.308594 1318.269531 97.3125 1318.269531 97.320312 1318.269531 C 97.324219 1318.269531 97.328125 1318.269531 97.335938 1318.269531 C 97.339844 1318.269531 97.34375 1318.269531 97.347656 1318.269531 C 97.355469 1318.269531 97.359375 1318.269531 97.363281 1318.269531 C 97.371094 1318.269531 97.375 1318.269531 97.378906 1318.269531 C 97.382812 1318.269531 97.390625 1318.269531 97.394531 1318.269531 C 97.398438 1318.269531 97.40625 1318.269531 97.410156 1318.269531 C 97.414062 1318.269531 97.421875 1318.269531 97.425781 1318.269531 C 97.429688 1318.269531 97.433594 1318.269531 97.441406 1318.269531 C 97.445312 1318.269531 97.449219 1318.269531 97.457031 1318.269531 C 97.460938 1318.269531 97.464844 1318.269531 97.46875 1318.269531 C 97.476562 1318.269531 97.480469 1318.269531 97.484375 1318.269531 C 97.492188 1318.269531 97.496094 1318.269531 97.5 1318.269531 C 97.503906 1318.269531 97.511719 1318.269531 97.515625 1318.269531 C 97.519531 1318.269531 97.527344 1318.269531 97.53125 1318.269531 C 97.535156 1318.269531 97.539062 1318.269531 97.546875 1318.269531 C 97.550781 1318.269531 97.554688 1318.269531 97.5625 1318.269531 C 97.566406 1318.269531 97.570312 1318.269531 97.574219 1318.269531 C 97.582031 1318.269531 97.585938 1318.269531 97.589844 1318.269531 C 97.597656 1318.269531 97.601562 1318.269531 97.605469 1318.269531 C 97.609375 1318.269531 97.617188 1318.269531 97.621094 1318.269531 C 97.625 1318.269531 97.632812 1318.269531 97.636719 1318.269531 C 97.640625 1318.269531 97.644531 1318.269531 97.652344 1318.269531 C 97.65625 1318.269531 97.660156 1318.269531 97.667969 1318.269531 C 97.671875 1318.269531 97.675781 1318.269531 97.679688 1318.269531 C 97.6875 1318.269531 97.691406 1318.269531 97.695312 1318.269531 C 97.703125 1318.269531 97.707031 1318.269531 97.710938 1318.269531 C 97.714844 1318.269531 97.722656 1318.269531 97.726562 1318.269531 C 97.730469 1318.269531 97.738281 1318.269531 97.742188 1318.269531 C 97.746094 1318.269531 97.75 1318.269531 97.757812 1318.269531 C 97.761719 1318.269531 97.765625 1318.269531 97.769531 1318.269531 C 97.777344 1318.269531 97.78125 1318.269531 97.785156 1318.269531 C 97.792969 1318.269531 97.796875 1318.269531 97.800781 1318.269531 C 97.804688 1318.269531 97.8125 1318.269531 97.816406 1318.269531 C 97.820312 1318.269531 97.824219 1318.269531 97.832031 1318.269531 C 97.835938 1318.269531 97.839844 1318.269531 97.847656 1318.269531 C 97.851562 1318.269531 97.855469 1318.269531 97.859375 1318.269531 C 97.867188 1318.269531 97.871094 1318.269531 97.875 1318.269531 C 97.878906 1318.269531 97.886719 1318.269531 97.890625 1318.269531 C 97.894531 1318.269531 97.902344 1318.269531 97.90625 1318.269531 C 97.910156 1318.269531 97.914062 1318.269531 97.921875 1318.269531 C 97.925781 1318.269531 97.929688 1318.269531 97.933594 1318.269531 C 97.941406 1318.269531 97.945312 1318.269531 97.949219 1318.269531 C 97.957031 1318.269531 97.960938 1318.269531 97.964844 1318.269531 C 97.96875 1318.269531 97.976562 1318.269531 97.980469 1318.269531 C 97.984375 1318.269531 97.988281 1318.269531 97.996094 1318.269531 C 98 1318.269531 98.003906 1318.269531 98.007812 1318.269531 C 98.015625 1318.269531 98.019531 1318.269531 98.023438 1318.269531 C 98.03125 1318.269531 98.035156 1318.269531 98.039062 1318.269531 C 98.042969 1318.269531 98.050781 1318.269531 98.054688 1318.269531 C 98.058594 1318.269531 98.0625 1318.269531 98.070312 1318.269531 C 98.074219 1318.269531 98.078125 1318.269531 98.082031 1318.269531 C 98.089844 1318.269531 98.09375 1318.269531 98.097656 1318.269531 C 98.101562 1318.269531 98.109375 1318.269531 98.113281 1318.269531 C 98.117188 1318.269531 98.125 1318.269531 98.128906 1318.269531 C 98.132812 1318.269531 98.136719 1318.269531 98.144531 1318.269531 C 98.148438 1318.269531 98.152344 1318.269531 98.15625 1318.269531 C 98.164062 1318.269531 98.167969 1318.269531 98.171875 1318.269531 C 98.175781 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475.84375 759.707031 C 475.851562 759.707031 475.855469 759.707031 475.859375 759.707031 C 475.867188 759.707031 475.871094 759.707031 475.875 759.707031 C 475.882812 759.707031 475.886719 759.707031 475.890625 759.707031 C 475.898438 759.707031 475.902344 759.707031 475.90625 759.707031 C 475.910156 759.707031 475.917969 759.707031 475.921875 759.707031 C 475.925781 759.707031 475.933594 759.707031 475.9375 759.707031 C 475.941406 759.707031 475.949219 759.707031 475.953125 759.707031 C 475.957031 759.707031 475.964844 759.707031 475.96875 759.707031 C 475.972656 759.707031 475.980469 759.707031 475.984375 759.707031 C 475.988281 759.707031 475.996094 759.707031 476 759.707031 C 476.003906 759.707031 476.011719 759.707031 476.015625 759.707031 C 476.019531 759.707031 476.027344 759.707031 476.03125 759.707031 C 476.035156 759.707031 476.042969 759.707031 476.046875 759.707031 C 476.050781 759.707031 476.058594 759.707031 476.0625 759.707031 C 476.066406 759.707031 476.074219 759.707031 476.078125 759.707031 C 476.082031 759.707031 476.089844 759.707031 476.09375 759.707031 C 476.097656 759.707031 476.105469 759.707031 476.109375 759.707031 C 476.113281 759.707031 476.121094 759.707031 476.125 759.707031 C 476.128906 759.707031 476.136719 759.707031 476.140625 759.707031 C 476.144531 759.707031 476.152344 759.707031 476.15625 759.707031 C 476.160156 759.707031 476.167969 759.707031 476.171875 759.707031 C 476.175781 759.707031 476.183594 759.707031 476.1875 759.707031 C 476.191406 759.707031 476.199219 759.707031 476.203125 759.707031 C 476.207031 759.707031 476.214844 759.707031 476.21875 759.707031 C 476.222656 759.707031 476.230469 759.707031 476.234375 759.707031 C 476.238281 759.707031 476.246094 759.707031 476.25 759.707031 C 476.253906 759.707031 476.261719 759.707031 476.265625 759.707031 C 476.269531 759.707031 476.277344 759.707031 476.28125 759.707031 C 476.285156 759.707031 476.292969 759.707031 476.296875 759.707031 C 476.300781 759.707031 476.308594 759.707031 476.3125 759.707031 C 476.316406 759.707031 476.324219 759.707031 476.328125 759.707031 C 476.332031 759.707031 476.339844 759.707031 476.34375 759.707031 C 476.347656 759.707031 476.355469 759.707031 476.359375 759.707031 C 476.363281 759.707031 476.371094 759.707031 476.375 759.707031 C 476.378906 759.707031 476.386719 759.707031 476.390625 759.707031 C 476.394531 759.707031 476.402344 759.707031 476.40625 759.707031 C 476.410156 759.707031 476.417969 759.707031 476.421875 759.707031 C 476.425781 759.707031 476.433594 759.707031 476.4375 759.707031 C 476.441406 759.707031 476.449219 759.707031 476.453125 759.707031 C 476.457031 759.707031 476.464844 759.707031 476.46875 759.707031 C 476.476562 759.707031 476.480469 759.707031 476.484375 759.707031 C 476.492188 759.707031 476.496094 759.707031 476.5 759.707031 C 476.507812 759.707031 476.511719 759.707031 476.515625 759.707031 C 476.523438 759.707031 476.527344 759.707031 476.53125 759.707031 C 476.539062 759.707031 476.542969 759.707031 476.546875 759.707031 C 476.554688 759.707031 476.558594 759.707031 476.5625 759.707031 C 476.570312 759.707031 476.574219 759.707031 476.578125 759.707031 C 476.585938 759.707031 476.589844 759.707031 476.59375 759.707031 C 476.601562 759.707031 476.605469 759.707031 476.609375 759.707031 C 476.617188 759.707031 476.621094 759.707031 476.625 759.707031 C 476.632812 759.707031 476.636719 759.707031 476.640625 759.707031 C 476.648438 759.707031 476.652344 759.707031 476.65625 759.707031 C 476.664062 759.707031 476.667969 759.707031 476.671875 759.707031 C 476.679688 759.707031 476.683594 759.707031 476.6875 759.707031 C 476.695312 759.707031 476.699219 759.707031 476.703125 759.707031 C 476.710938 759.707031 476.714844 759.707031 476.71875 759.707031 C 476.726562 759.707031 476.730469 759.707031 476.738281 759.707031 C 476.742188 759.707031 476.746094 759.707031 476.753906 759.707031 C 476.757812 759.707031 476.761719 759.707031 476.769531 759.707031 C 476.773438 759.707031 476.777344 759.707031 476.785156 759.707031 C 476.789062 759.707031 476.792969 759.707031 476.800781 759.707031 C 476.804688 759.707031 476.808594 759.707031 476.816406 759.707031 C 476.820312 759.707031 476.824219 759.707031 476.832031 759.707031 C 476.835938 759.707031 476.839844 759.707031 476.847656 759.707031 C 476.851562 759.707031 476.855469 759.707031 476.863281 759.707031 C 476.867188 759.707031 476.871094 759.707031 476.878906 759.707031 C 476.882812 759.707031 476.886719 759.707031 476.894531 759.707031 C 476.898438 759.707031 476.90625 759.707031 476.910156 759.707031 C 476.914062 759.707031 476.921875 759.707031 476.925781 759.707031 C 476.929688 759.707031 476.9375 759.707031 476.941406 759.707031 C 476.945312 759.707031 476.953125 759.707031 476.957031 759.707031 C 476.960938 759.707031 476.96875 759.707031 476.972656 759.707031 C 476.976562 759.707031 476.984375 759.707031 476.988281 759.707031 C 476.992188 759.707031 477 759.707031 477.003906 759.707031 C 477.007812 759.707031 477.015625 759.707031 477.019531 759.707031 C 477.023438 759.707031 477.03125 759.707031 477.035156 759.707031 C 477.039062 759.707031 477.046875 759.707031 477.050781 759.707031 C 477.054688 759.707031 477.0625 759.707031 477.066406 759.707031 C 477.074219 759.707031 477.078125 759.707031 477.082031 759.707031 C 477.089844 759.707031 477.09375 759.707031 477.097656 759.707031 C 477.105469 759.707031 477.109375 759.707031 477.113281 759.707031 C 477.121094 759.707031 477.125 759.707031 477.128906 759.707031 C 477.136719 759.707031 477.140625 759.707031 477.144531 759.707031 C 477.152344 759.707031 477.15625 759.707031 477.160156 759.707031 C 477.167969 759.707031 477.171875 759.707031 477.175781 759.707031 C 477.183594 759.707031 477.1875 759.707031 477.191406 759.707031 C 477.199219 759.707031 477.203125 759.707031 477.210938 759.707031 C 477.214844 759.707031 477.21875 759.707031 477.226562 759.707031 C 477.230469 759.707031 477.234375 759.707031 477.242188 759.707031 C 477.246094 759.707031 477.25 759.707031 477.257812 759.707031 C 477.261719 759.707031 477.265625 759.707031 477.273438 759.707031 C 477.277344 759.707031 477.28125 759.707031 477.289062 759.707031 C 477.292969 759.707031 477.296875 759.707031 477.304688 759.707031 C 477.308594 759.707031 477.3125 759.707031 477.320312 759.707031 C 477.324219 759.707031 477.332031 759.707031 477.335938 759.707031 C 477.339844 759.707031 477.347656 759.707031 477.351562 759.707031 C 477.355469 759.707031 477.363281 759.707031 477.367188 759.707031 C 477.371094 759.707031 477.378906 759.707031 477.382812 759.707031 C 477.386719 759.707031 477.394531 759.707031 477.398438 759.707031 C 477.402344 759.707031 477.410156 759.707031 477.414062 759.707031 C 477.417969 759.707031 477.425781 759.707031 477.429688 759.707031 C 477.433594 759.707031 477.441406 759.707031 477.445312 759.707031 C 477.449219 759.707031 477.457031 759.707031 477.460938 759.707031 C 477.46875 759.707031 477.472656 759.707031 477.476562 759.707031 C 477.484375 759.707031 477.488281 759.707031 477.492188 759.707031 C 477.5 759.707031 477.503906 759.707031 477.507812 759.707031 C 477.515625 759.707031 477.519531 759.707031 477.523438 759.707031 C 477.53125 759.707031 477.535156 759.707031 477.539062 759.707031 C 477.546875 759.707031 477.550781 759.707031 477.554688 759.707031 C 477.5625 759.707031 477.566406 759.707031 477.570312 759.707031 C 477.578125 759.707031 477.582031 759.707031 477.589844 759.707031 C 477.59375 759.707031 477.597656 759.707031 477.605469 759.707031 C 477.609375 759.707031 477.613281 759.707031 477.621094 759.707031 C 477.625 759.707031 477.628906 759.707031 477.636719 759.707031 C 477.640625 759.707031 477.644531 759.707031 477.652344 759.707031 C 477.65625 759.707031 477.660156 759.707031 477.667969 759.707031 C 477.671875 759.707031 477.675781 759.707031 477.683594 759.707031 C 477.6875 759.707031 477.691406 759.707031 477.699219 759.707031 C 477.703125 759.707031 477.710938 759.707031 477.714844 759.707031 C 477.71875 759.707031 477.726562 759.707031 477.730469 759.707031 C 477.734375 759.707031 477.742188 759.707031 477.746094 759.707031 C 477.75 759.707031 477.757812 759.707031 477.761719 759.707031 C 477.765625 759.707031 477.773438 759.707031 477.777344 759.707031 C 477.78125 759.707031 477.789062 759.707031 477.792969 759.707031 C 477.796875 759.707031 477.804688 759.707031 477.808594 759.707031 C 477.8125 759.707031 477.820312 759.707031 477.824219 759.707031 C 477.832031 759.707031 477.835938 759.707031 477.839844 759.707031 C 477.847656 759.707031 477.851562 759.707031 477.855469 759.707031 C 477.863281 759.707031 477.867188 759.707031 477.871094 759.707031 C 477.878906 759.707031 477.882812 759.707031 477.886719 759.707031 C 477.894531 759.707031 477.898438 759.707031 477.902344 759.707031 C 477.910156 759.707031 477.914062 759.707031 477.917969 759.707031 C 477.925781 759.707031 477.929688 759.707031 477.933594 759.707031 C 477.941406 759.707031 477.945312 759.707031 477.953125 759.707031 C 477.957031 759.707031 477.960938 759.707031 477.96875 759.707031 C 477.972656 759.707031 477.976562 759.707031 477.984375 759.707031 C 477.988281 759.707031 477.992188 759.707031 478 759.707031 C 478.003906 759.707031 478.007812 759.707031 478.015625 759.707031 C 478.019531 759.707031 478.023438 759.707031 478.03125 759.707031 C 478.035156 759.707031 478.039062 759.707031 478.046875 759.707031 C 478.050781 759.707031 478.054688 759.707031 478.0625 759.707031 C 478.066406 759.707031 478.074219 759.707031 478.078125 759.707031 C 478.082031 759.707031 478.089844 759.707031 478.09375 759.707031 C 478.097656 759.707031 478.105469 759.707031 478.109375 759.707031 C 478.113281 759.707031 478.121094 759.707031 478.125 759.707031 C 478.128906 759.707031 478.136719 759.707031 478.140625 759.707031 C 478.144531 759.707031 478.152344 759.707031 478.15625 759.707031 C 478.160156 759.707031 478.167969 759.707031 478.171875 759.707031 C 478.175781 759.707031 478.183594 759.707031 478.1875 759.707031 C 478.191406 759.707031 478.199219 759.707031 478.203125 759.707031 C 478.210938 759.707031 478.214844 759.707031 478.21875 759.707031 C 478.226562 759.707031 478.230469 759.707031 478.234375 759.707031 C 478.242188 759.707031 478.246094 759.707031 478.25 759.707031 C 478.257812 759.707031 478.261719 759.707031 478.265625 759.707031 C 478.273438 759.707031 478.277344 759.707031 478.28125 759.707031 C 478.289062 759.707031 478.292969 759.707031 478.296875 759.707031 C 478.304688 759.707031 478.308594 759.707031 478.3125 759.707031 C 478.320312 759.707031 478.324219 759.707031 478.328125 759.707031 C 478.335938 759.707031 478.339844 759.707031 478.347656 759.707031 C 478.351562 759.707031 478.355469 759.707031 478.363281 759.707031 C 478.367188 759.707031 478.371094 759.707031 478.378906 759.707031 C 478.382812 759.707031 478.386719 759.707031 478.394531 759.707031 C 478.398438 759.707031 478.402344 759.707031 478.410156 759.707031 C 478.414062 759.707031 478.417969 759.707031 478.425781 759.707031 C 478.429688 759.707031 478.433594 759.707031 478.441406 759.707031 C 478.445312 759.707031 478.449219 759.707031 478.457031 759.707031 C 478.460938 759.707031 478.464844 759.707031 478.472656 759.707031 C 478.476562 759.707031 478.480469 759.707031 478.488281 759.707031 C 478.492188 759.707031 478.496094 759.707031 478.503906 759.707031 C 478.507812 759.707031 478.515625 759.707031 478.519531 759.707031 C 478.523438 759.707031 478.53125 759.707031 478.535156 759.707031 C 478.539062 759.707031 478.546875 759.707031 478.550781 759.707031 C 478.554688 759.707031 478.5625 759.707031 478.566406 759.707031 C 478.570312 759.707031 478.578125 759.707031 478.582031 759.707031 C 478.585938 759.707031 478.59375 759.707031 478.597656 759.707031 C 478.601562 759.707031 478.609375 759.707031 478.613281 759.707031 C 478.617188 759.707031 478.625 759.707031 478.628906 759.707031 C 478.632812 759.707031 478.640625 759.707031 478.644531 759.707031 C 478.648438 759.707031 478.65625 759.707031 478.660156 759.707031 C 478.664062 759.707031 478.671875 759.707031 478.675781 759.707031 C 478.679688 759.707031 478.6875 759.707031 478.691406 759.707031 C 478.699219 759.707031 478.703125 759.707031 478.707031 759.707031 C 478.714844 759.707031 478.71875 759.707031 478.722656 759.707031 C 478.730469 759.707031 478.734375 759.707031 478.738281 759.707031 C 478.746094 759.707031 478.75 759.707031 478.753906 759.707031 C 478.761719 759.707031 478.765625 759.707031 478.769531 759.707031 C 478.777344 759.707031 478.78125 759.707031 478.785156 759.707031 C 478.792969 759.707031 478.796875 759.707031 478.800781 759.707031 C 478.808594 759.707031 478.8125 759.707031 478.816406 759.707031 C 478.824219 759.707031 478.828125 759.707031 478.832031 759.707031 C 478.839844 759.707031 478.84375 759.707031 478.847656 759.707031 C 478.855469 759.707031 478.859375 759.707031 478.863281 759.707031 C 478.871094 759.707031 478.875 759.707031 478.878906 759.707031 C 478.886719 759.707031 478.890625 759.707031 478.894531 759.707031 C 478.902344 759.707031 478.90625 759.707031 478.910156 759.707031 C 478.917969 759.707031 478.921875 759.707031 478.925781 759.707031 C 478.933594 759.707031 478.9375 759.707031 478.941406 759.707031 C 478.949219 759.707031 478.953125 759.707031 478.957031 759.707031 C 478.964844 759.707031 478.96875 759.707031 478.972656 759.707031 C 478.980469 759.707031 478.984375 759.707031 478.992188 759.707031 C 478.996094 759.707031 479 759.707031 479.007812 759.707031 C 479.011719 759.707031 479.015625 759.707031 479.023438 759.707031 C 479.027344 759.707031 479.03125 759.707031 479.039062 759.707031 C 479.042969 759.707031 479.046875 759.707031 479.054688 759.707031 C 479.058594 759.707031 479.0625 759.707031 479.070312 759.707031 C 479.074219 759.707031 479.078125 759.707031 479.085938 759.707031 C 479.089844 759.707031 479.09375 759.707031 479.101562 759.707031 C 479.105469 759.707031 479.109375 759.707031 479.117188 759.707031 C 479.121094 759.707031 479.125 759.707031 479.132812 759.707031 C 479.136719 759.707031 479.140625 759.707031 479.148438 759.707031 C 479.152344 759.707031 479.15625 759.707031 479.164062 759.707031 C 479.167969 759.707031 479.171875 759.707031 479.179688 759.707031 C 479.183594 759.707031 479.1875 759.707031 479.195312 759.707031 C 479.199219 759.707031 479.203125 759.707031 479.210938 759.707031 C 479.214844 759.707031 479.21875 759.707031 479.226562 759.707031 C 479.230469 759.707031 479.234375 759.707031 479.242188 759.707031 C 479.246094 759.707031 479.25 759.707031 479.257812 759.707031 C 479.261719 759.707031 479.265625 759.707031 479.273438 759.707031 C 479.277344 759.707031 479.28125 759.707031 479.289062 759.707031 C 479.292969 759.707031 479.296875 759.707031 479.304688 759.707031 C 479.308594 759.707031 479.3125 759.707031 479.320312 759.707031 C 479.324219 759.707031 479.328125 759.707031 479.335938 759.707031 C 479.339844 759.707031 479.34375 759.707031 479.351562 759.707031 C 479.355469 759.707031 479.359375 759.707031 479.367188 759.707031 C 479.371094 759.707031 479.375 759.707031 479.382812 759.707031 C 479.386719 759.707031 479.390625 759.707031 479.394531 759.707031 C 479.402344 759.707031 479.40625 759.707031 479.410156 759.707031 C 479.417969 759.707031 479.421875 759.707031 479.425781 759.707031 C 479.433594 759.707031 479.4375 759.707031 479.441406 759.707031 C 479.449219 759.707031 479.453125 759.707031 479.457031 759.707031 C 479.464844 759.707031 479.46875 759.707031 479.472656 759.707031 C 479.480469 759.707031 479.484375 759.707031 479.488281 759.707031 C 479.496094 759.707031 479.5 759.707031 479.503906 759.707031 C 479.511719 759.707031 479.515625 759.707031 479.519531 759.707031 C 479.527344 759.707031 479.53125 759.707031 479.535156 759.707031 C 479.542969 759.707031 479.546875 759.707031 479.550781 759.707031 C 479.558594 759.707031 479.5625 759.707031 479.566406 759.707031 C 479.574219 759.707031 479.578125 759.707031 479.582031 759.707031 C 479.589844 759.707031 479.59375 759.707031 479.597656 759.707031 C 479.605469 759.707031 479.609375 759.707031 479.613281 759.707031 C 479.621094 759.707031 479.625 759.707031 479.628906 759.707031 C 479.636719 759.707031 479.640625 759.707031 479.644531 759.707031 C 479.648438 759.707031 479.65625 759.707031 479.660156 759.707031 C 479.664062 759.707031 479.671875 759.707031 479.675781 759.707031 C 479.679688 759.707031 479.6875 759.707031 479.691406 759.707031 C 479.695312 759.707031 479.703125 759.707031 479.707031 759.707031 C 479.710938 759.707031 479.71875 759.707031 479.722656 759.707031 C 479.726562 759.707031 479.734375 759.707031 479.738281 759.707031 C 479.742188 759.707031 479.75 759.707031 479.753906 759.707031 C 479.757812 759.707031 479.765625 759.707031 479.769531 759.707031 C 479.773438 759.707031 479.78125 759.707031 479.785156 759.707031 C 479.789062 759.707031 479.792969 759.707031 479.800781 759.707031 C 479.804688 759.707031 479.808594 759.707031 479.816406 759.707031 C 479.820312 759.707031 479.824219 759.707031 479.832031 759.707031 C 479.835938 759.707031 479.839844 759.707031 479.847656 759.707031 C 479.851562 759.707031 479.855469 759.707031 479.863281 759.707031 C 479.867188 759.707031 479.871094 759.707031 479.878906 759.707031 C 479.882812 759.707031 479.886719 759.707031 479.894531 759.707031 C 479.898438 759.707031 479.902344 759.707031 479.90625 759.707031 C 479.914062 759.707031 479.917969 759.707031 479.921875 759.707031 C 479.929688 759.707031 479.933594 759.707031 479.9375 759.707031 C 479.945312 759.707031 479.949219 759.707031 479.953125 759.707031 C 479.960938 759.707031 479.964844 759.707031 479.96875 759.707031 C 479.976562 759.707031 479.980469 759.707031 479.984375 759.707031 C 479.988281 759.707031 479.996094 759.707031 480 759.707031 C 480.003906 759.707031 480.011719 759.707031 480.015625 759.707031 C 480.019531 759.707031 480.027344 759.707031 480.03125 759.707031 C 480.035156 759.707031 480.042969 759.707031 480.046875 759.707031 C 480.050781 759.707031 480.058594 759.707031 480.0625 759.707031 C 480.066406 759.707031 480.070312 759.707031 480.078125 759.707031 C 480.082031 759.707031 480.085938 759.707031 480.09375 759.707031 C 480.097656 759.707031 480.101562 759.707031 480.109375 759.707031 C 480.113281 759.707031 480.117188 759.707031 480.125 759.707031 C 480.128906 759.707031 480.132812 759.707031 480.140625 759.707031 C 480.144531 759.707031 480.148438 759.707031 480.152344 759.707031 C 480.160156 759.707031 480.164062 759.707031 480.167969 759.707031 C 480.175781 759.707031 480.179688 759.707031 480.183594 759.707031 C 480.191406 759.707031 480.195312 759.707031 480.199219 759.707031 C 480.207031 759.707031 480.210938 759.707031 480.214844 759.707031 C 480.21875 759.707031 480.226562 759.707031 480.230469 759.707031 C 480.234375 759.707031 480.242188 759.707031 480.246094 759.707031 C 480.25 759.707031 480.257812 759.707031 480.261719 759.707031 C 480.265625 759.707031 480.269531 759.707031 480.277344 759.707031 C 480.28125 759.707031 480.285156 759.707031 480.292969 759.707031 C 480.296875 759.707031 480.300781 759.707031 480.308594 759.707031 C 480.3125 759.707031 480.316406 759.707031 480.324219 759.707031 C 480.328125 759.707031 480.332031 759.707031 480.335938 759.707031 C 480.34375 759.707031 480.347656 759.707031 480.351562 759.707031 C 480.359375 759.707031 480.363281 759.707031 480.367188 759.707031 C 480.375 759.707031 480.378906 759.707031 480.382812 759.707031 C 480.386719 759.707031 480.394531 759.707031 480.398438 759.707031 C 480.402344 759.707031 480.410156 759.707031 480.414062 759.707031 C 480.417969 759.707031 480.425781 759.707031 480.429688 759.707031 C 480.433594 759.707031 480.4375 759.707031 480.445312 759.707031 C 480.449219 759.707031 480.453125 759.707031 480.460938 759.707031 C 480.464844 759.707031 480.46875 759.707031 480.476562 759.707031 C 480.480469 759.707031 480.484375 759.707031 480.488281 759.707031 C 480.496094 759.707031 480.5 759.707031 480.503906 759.707031 C 480.511719 759.707031 480.515625 759.707031 480.519531 759.707031 C 480.523438 759.707031 480.53125 759.707031 480.535156 759.707031 C 480.539062 759.707031 480.546875 759.707031 480.550781 759.707031 C 480.554688 759.707031 480.5625 759.707031 480.566406 759.707031 C 480.570312 759.707031 480.574219 759.707031 480.582031 759.707031 C 480.585938 759.707031 480.589844 759.707031 480.597656 759.707031 C 480.601562 759.707031 480.605469 759.707031 480.609375 759.707031 C 480.617188 759.707031 480.621094 759.707031 480.625 759.707031 C 480.632812 759.707031 480.636719 759.707031 480.640625 759.707031 C 480.648438 759.707031 480.652344 759.707031 480.65625 759.707031 C 480.660156 759.707031 480.667969 759.707031 480.671875 759.707031 C 480.675781 759.707031 480.683594 759.707031 480.6875 759.707031 C 480.691406 759.707031 480.695312 759.707031 480.703125 759.707031 C 480.707031 759.707031 480.710938 759.707031 480.71875 759.707031 C 480.722656 759.707031 480.726562 759.707031 480.730469 759.707031 C 480.738281 759.707031 480.742188 759.707031 480.746094 759.707031 C 480.753906 759.707031 480.757812 759.707031 480.761719 759.707031 C 480.765625 759.707031 480.773438 759.707031 480.777344 759.707031 C 480.78125 759.707031 480.789062 759.707031 480.792969 759.707031 C 480.796875 759.707031 480.800781 759.707031 480.808594 759.707031 C 480.8125 759.707031 480.816406 759.707031 480.824219 759.707031 C 480.828125 759.707031 480.832031 759.707031 480.835938 759.707031 C 480.84375 759.707031 480.847656 759.707031 480.851562 759.707031 C 480.859375 759.707031 480.863281 759.707031 480.867188 759.707031 C 480.871094 759.707031 480.878906 759.707031 480.882812 759.707031 C 480.886719 759.707031 480.890625 759.707031 480.898438 759.707031 C 480.902344 759.707031 480.90625 759.707031 480.914062 759.707031 C 480.917969 759.707031 480.921875 759.707031 480.925781 759.707031 C 480.933594 759.707031 480.9375 759.707031 480.941406 759.707031 C 480.949219 759.707031 480.953125 759.707031 480.957031 759.707031 C 480.960938 759.707031 480.96875 759.707031 480.972656 759.707031 C 480.976562 759.707031 480.984375 759.707031 480.988281 759.707031 C 480.992188 759.707031 480.996094 759.707031 481.003906 759.707031 C 481.007812 759.707031 481.011719 759.707031 481.015625 759.707031 C 481.023438 759.707031 481.027344 759.707031 481.03125 759.707031 C 481.039062 759.707031 481.042969 759.707031 481.046875 759.707031 C 481.050781 759.707031 481.058594 759.707031 481.0625 759.707031 C 481.066406 759.707031 481.070312 759.707031 481.078125 759.707031 C 481.082031 759.707031 481.085938 759.707031 481.09375 759.707031 C 481.097656 759.707031 481.101562 759.707031 481.105469 759.707031 C 481.113281 759.707031 481.117188 759.707031 481.121094 759.707031 C 481.125 759.707031 481.132812 759.707031 481.136719 759.707031 C 481.140625 759.707031 481.144531 759.707031 481.152344 759.707031 C 481.15625 759.707031 481.160156 759.707031 481.167969 759.707031 C 481.171875 759.707031 481.175781 759.707031 481.179688 759.707031 C 481.1875 759.707031 481.191406 759.707031 481.195312 759.707031 C 481.199219 759.707031 481.207031 759.707031 481.210938 759.707031 C 481.214844 759.707031 481.222656 759.707031 481.226562 759.707031 C 481.230469 759.707031 481.234375 759.707031 481.242188 759.707031 C 481.246094 759.707031 481.25 759.707031 481.253906 759.707031 C 481.261719 759.707031 481.265625 759.707031 481.269531 759.707031 C 481.273438 759.707031 481.28125 759.707031 481.285156 759.707031 C 481.289062 759.707031 481.292969 759.707031 481.300781 759.707031 C 481.304688 759.707031 481.308594 759.707031 481.3125 759.707031 C 481.320312 759.707031 481.324219 759.707031 481.328125 759.707031 C 481.335938 759.707031 481.339844 759.707031 481.34375 759.707031 C 481.347656 759.707031 481.355469 759.707031 481.359375 759.707031 C 481.363281 759.707031 481.367188 759.707031 481.375 759.707031 C 481.378906 759.707031 481.382812 759.707031 481.386719 759.707031 C 481.394531 759.707031 481.398438 759.707031 481.402344 759.707031 C 481.40625 759.707031 481.414062 759.707031 481.417969 759.707031 C 481.421875 759.707031 481.425781 759.707031 481.433594 759.707031 C 481.4375 759.707031 481.441406 759.707031 481.445312 759.707031 C 481.453125 759.707031 481.457031 759.707031 481.460938 759.707031 C 481.464844 759.707031 481.472656 759.707031 481.476562 759.707031 C 481.480469 759.707031 481.484375 759.707031 481.492188 759.707031 C 481.496094 759.707031 481.5 759.707031 481.503906 759.707031 C 481.511719 759.707031 481.515625 759.707031 481.519531 759.707031 C 481.523438 759.707031 481.53125 759.707031 481.535156 759.707031 C 481.539062 759.707031 481.542969 759.707031 481.550781 759.707031 C 481.554688 759.707031 481.558594 759.707031 481.5625 759.707031 C 481.570312 759.707031 481.574219 759.707031 481.578125 759.707031 C 481.582031 759.707031 481.589844 759.707031 481.59375 759.707031 C 481.597656 759.707031 481.601562 759.707031 481.609375 759.707031 C 481.613281 759.707031 481.617188 759.707031 481.621094 759.707031 L 481.621094 756.804688 C 481.617188 756.804688 481.613281 756.804688 481.609375 756.804688 C 481.601562 756.804688 481.597656 756.804688 481.59375 756.804688 C 481.589844 756.804688 481.582031 756.804688 481.578125 756.804688 C 481.574219 756.804688 481.570312 756.804688 481.5625 756.804688 C 481.558594 756.804688 481.554688 756.804688 481.550781 756.804688 C 481.542969 756.804688 481.539062 756.804688 481.535156 756.804688 C 481.53125 756.804688 481.523438 756.804688 481.519531 756.804688 C 481.515625 756.804688 481.511719 756.804688 481.503906 756.804688 C 481.5 756.804688 481.496094 756.804688 481.492188 756.804688 C 481.484375 756.804688 481.480469 756.804688 481.476562 756.804688 C 481.472656 756.804688 481.464844 756.804688 481.460938 756.804688 C 481.457031 756.804688 481.453125 756.804688 481.445312 756.804688 C 481.441406 756.804688 481.4375 756.804688 481.433594 756.804688 C 481.425781 756.804688 481.421875 756.804688 481.417969 756.804688 C 481.414062 756.804688 481.40625 756.804688 481.402344 756.804688 C 481.398438 756.804688 481.394531 756.804688 481.386719 756.804688 C 481.382812 756.804688 481.378906 756.804688 481.375 756.804688 C 481.367188 756.804688 481.363281 756.804688 481.359375 756.804688 C 481.355469 756.804688 481.347656 756.804688 481.34375 756.804688 C 481.339844 756.804688 481.335938 756.804688 481.328125 756.804688 C 481.324219 756.804688 481.320312 756.804688 481.3125 756.804688 C 481.308594 756.804688 481.304688 756.804688 481.300781 756.804688 C 481.292969 756.804688 481.289062 756.804688 481.285156 756.804688 C 481.28125 756.804688 481.273438 756.804688 481.269531 756.804688 C 481.265625 756.804688 481.261719 756.804688 481.253906 756.804688 C 481.25 756.804688 481.246094 756.804688 481.242188 756.804688 C 481.234375 756.804688 481.230469 756.804688 481.226562 756.804688 C 481.222656 756.804688 481.214844 756.804688 481.210938 756.804688 C 481.207031 756.804688 481.199219 756.804688 481.195312 756.804688 C 481.191406 756.804688 481.1875 756.804688 481.179688 756.804688 C 481.175781 756.804688 481.171875 756.804688 481.167969 756.804688 C 481.160156 756.804688 481.15625 756.804688 481.152344 756.804688 C 481.144531 756.804688 481.140625 756.804688 481.136719 756.804688 C 481.132812 756.804688 481.125 756.804688 481.121094 756.804688 C 481.117188 756.804688 481.113281 756.804688 481.105469 756.804688 C 481.101562 756.804688 481.097656 756.804688 481.09375 756.804688 C 481.085938 756.804688 481.082031 756.804688 481.078125 756.804688 C 481.070312 756.804688 481.066406 756.804688 481.0625 756.804688 C 481.058594 756.804688 481.050781 756.804688 481.046875 756.804688 C 481.042969 756.804688 481.039062 756.804688 481.03125 756.804688 C 481.027344 756.804688 481.023438 756.804688 481.015625 756.804688 C 481.011719 756.804688 481.007812 756.804688 481.003906 756.804688 C 480.996094 756.804688 480.992188 756.804688 480.988281 756.804688 C 480.984375 756.804688 480.976562 756.804688 480.972656 756.804688 C 480.96875 756.804688 480.960938 756.804688 480.957031 756.804688 C 480.953125 756.804688 480.949219 756.804688 480.941406 756.804688 C 480.9375 756.804688 480.933594 756.804688 480.925781 756.804688 C 480.921875 756.804688 480.917969 756.804688 480.914062 756.804688 C 480.90625 756.804688 480.902344 756.804688 480.898438 756.804688 C 480.890625 756.804688 480.886719 756.804688 480.882812 756.804688 C 480.878906 756.804688 480.871094 756.804688 480.867188 756.804688 C 480.863281 756.804688 480.859375 756.804688 480.851562 756.804688 C 480.847656 756.804688 480.84375 756.804688 480.835938 756.804688 C 480.832031 756.804688 480.828125 756.804688 480.824219 756.804688 C 480.816406 756.804688 480.8125 756.804688 480.808594 756.804688 C 480.800781 756.804688 480.796875 756.804688 480.792969 756.804688 C 480.789062 756.804688 480.78125 756.804688 480.777344 756.804688 C 480.773438 756.804688 480.765625 756.804688 480.761719 756.804688 C 480.757812 756.804688 480.753906 756.804688 480.746094 756.804688 C 480.742188 756.804688 480.738281 756.804688 480.730469 756.804688 C 480.726562 756.804688 480.722656 756.804688 480.71875 756.804688 C 480.710938 756.804688 480.707031 756.804688 480.703125 756.804688 C 480.695312 756.804688 480.691406 756.804688 480.6875 756.804688 C 480.683594 756.804688 480.675781 756.804688 480.671875 756.804688 C 480.667969 756.804688 480.660156 756.804688 480.65625 756.804688 C 480.652344 756.804688 480.648438 756.804688 480.640625 756.804688 C 480.636719 756.804688 480.632812 756.804688 480.625 756.804688 C 480.621094 756.804688 480.617188 756.804688 480.609375 756.804688 C 480.605469 756.804688 480.601562 756.804688 480.597656 756.804688 C 480.589844 756.804688 480.585938 756.804688 480.582031 756.804688 C 480.574219 756.804688 480.570312 756.804688 480.566406 756.804688 C 480.5625 756.804688 480.554688 756.804688 480.550781 756.804688 C 480.546875 756.804688 480.539062 756.804688 480.535156 756.804688 C 480.53125 756.804688 480.523438 756.804688 480.519531 756.804688 C 480.515625 756.804688 480.511719 756.804688 480.503906 756.804688 C 480.5 756.804688 480.496094 756.804688 480.488281 756.804688 C 480.484375 756.804688 480.480469 756.804688 480.476562 756.804688 C 480.46875 756.804688 480.464844 756.804688 480.460938 756.804688 C 480.453125 756.804688 480.449219 756.804688 480.445312 756.804688 C 480.4375 756.804688 480.433594 756.804688 480.429688 756.804688 C 480.425781 756.804688 480.417969 756.804688 480.414062 756.804688 C 480.410156 756.804688 480.402344 756.804688 480.398438 756.804688 C 480.394531 756.804688 480.386719 756.804688 480.382812 756.804688 C 480.378906 756.804688 480.375 756.804688 480.367188 756.804688 C 480.363281 756.804688 480.359375 756.804688 480.351562 756.804688 C 480.347656 756.804688 480.34375 756.804688 480.335938 756.804688 C 480.332031 756.804688 480.328125 756.804688 480.324219 756.804688 C 480.316406 756.804688 480.3125 756.804688 480.308594 756.804688 C 480.300781 756.804688 480.296875 756.804688 480.292969 756.804688 C 480.285156 756.804688 480.28125 756.804688 480.277344 756.804688 C 480.269531 756.804688 480.265625 756.804688 480.261719 756.804688 C 480.257812 756.804688 480.25 756.804688 480.246094 756.804688 C 480.242188 756.804688 480.234375 756.804688 480.230469 756.804688 C 480.226562 756.804688 480.21875 756.804688 480.214844 756.804688 C 480.210938 756.804688 480.207031 756.804688 480.199219 756.804688 C 480.195312 756.804688 480.191406 756.804688 480.183594 756.804688 C 480.179688 756.804688 480.175781 756.804688 480.167969 756.804688 C 480.164062 756.804688 480.160156 756.804688 480.152344 756.804688 C 480.148438 756.804688 480.144531 756.804688 480.140625 756.804688 C 480.132812 756.804688 480.128906 756.804688 480.125 756.804688 C 480.117188 756.804688 480.113281 756.804688 480.109375 756.804688 C 480.101562 756.804688 480.097656 756.804688 480.09375 756.804688 C 480.085938 756.804688 480.082031 756.804688 480.078125 756.804688 C 480.070312 756.804688 480.066406 756.804688 480.0625 756.804688 C 480.058594 756.804688 480.050781 756.804688 480.046875 756.804688 C 480.042969 756.804688 480.035156 756.804688 480.03125 756.804688 C 480.027344 756.804688 480.019531 756.804688 480.015625 756.804688 C 480.011719 756.804688 480.003906 756.804688 480 756.804688 C 479.996094 756.804688 479.988281 756.804688 479.984375 756.804688 C 479.980469 756.804688 479.976562 756.804688 479.96875 756.804688 C 479.964844 756.804688 479.960938 756.804688 479.953125 756.804688 C 479.949219 756.804688 479.945312 756.804688 479.9375 756.804688 C 479.933594 756.804688 479.929688 756.804688 479.921875 756.804688 C 479.917969 756.804688 479.914062 756.804688 479.90625 756.804688 C 479.902344 756.804688 479.898438 756.804688 479.894531 756.804688 C 479.886719 756.804688 479.882812 756.804688 479.878906 756.804688 C 479.871094 756.804688 479.867188 756.804688 479.863281 756.804688 C 479.855469 756.804688 479.851562 756.804688 479.847656 756.804688 C 479.839844 756.804688 479.835938 756.804688 479.832031 756.804688 C 479.824219 756.804688 479.820312 756.804688 479.816406 756.804688 C 479.808594 756.804688 479.804688 756.804688 479.800781 756.804688 C 479.792969 756.804688 479.789062 756.804688 479.785156 756.804688 C 479.78125 756.804688 479.773438 756.804688 479.769531 756.804688 C 479.765625 756.804688 479.757812 756.804688 479.753906 756.804688 C 479.75 756.804688 479.742188 756.804688 479.738281 756.804688 C 479.734375 756.804688 479.726562 756.804688 479.722656 756.804688 C 479.71875 756.804688 479.710938 756.804688 479.707031 756.804688 C 479.703125 756.804688 479.695312 756.804688 479.691406 756.804688 C 479.6875 756.804688 479.679688 756.804688 479.675781 756.804688 C 479.671875 756.804688 479.664062 756.804688 479.660156 756.804688 C 479.65625 756.804688 479.648438 756.804688 479.644531 756.804688 C 479.640625 756.804688 479.636719 756.804688 479.628906 756.804688 C 479.625 756.804688 479.621094 756.804688 479.613281 756.804688 C 479.609375 756.804688 479.605469 756.804688 479.597656 756.804688 C 479.59375 756.804688 479.589844 756.804688 479.582031 756.804688 C 479.578125 756.804688 479.574219 756.804688 479.566406 756.804688 C 479.5625 756.804688 479.558594 756.804688 479.550781 756.804688 C 479.546875 756.804688 479.542969 756.804688 479.535156 756.804688 C 479.53125 756.804688 479.527344 756.804688 479.519531 756.804688 C 479.515625 756.804688 479.511719 756.804688 479.503906 756.804688 C 479.5 756.804688 479.496094 756.804688 479.488281 756.804688 C 479.484375 756.804688 479.480469 756.804688 479.472656 756.804688 C 479.46875 756.804688 479.464844 756.804688 479.457031 756.804688 C 479.453125 756.804688 479.449219 756.804688 479.441406 756.804688 C 479.4375 756.804688 479.433594 756.804688 479.425781 756.804688 C 479.421875 756.804688 479.417969 756.804688 479.410156 756.804688 C 479.40625 756.804688 479.402344 756.804688 479.394531 756.804688 C 479.390625 756.804688 479.386719 756.804688 479.382812 756.804688 C 479.375 756.804688 479.371094 756.804688 479.367188 756.804688 C 479.359375 756.804688 479.355469 756.804688 479.351562 756.804688 C 479.34375 756.804688 479.339844 756.804688 479.335938 756.804688 C 479.328125 756.804688 479.324219 756.804688 479.320312 756.804688 C 479.3125 756.804688 479.308594 756.804688 479.304688 756.804688 C 479.296875 756.804688 479.292969 756.804688 479.289062 756.804688 C 479.28125 756.804688 479.277344 756.804688 479.273438 756.804688 C 479.265625 756.804688 479.261719 756.804688 479.257812 756.804688 C 479.25 756.804688 479.246094 756.804688 479.242188 756.804688 C 479.234375 756.804688 479.230469 756.804688 479.226562 756.804688 C 479.21875 756.804688 479.214844 756.804688 479.210938 756.804688 C 479.203125 756.804688 479.199219 756.804688 479.195312 756.804688 C 479.1875 756.804688 479.183594 756.804688 479.179688 756.804688 C 479.171875 756.804688 479.167969 756.804688 479.164062 756.804688 C 479.15625 756.804688 479.152344 756.804688 479.148438 756.804688 C 479.140625 756.804688 479.136719 756.804688 479.132812 756.804688 C 479.125 756.804688 479.121094 756.804688 479.117188 756.804688 C 479.109375 756.804688 479.105469 756.804688 479.101562 756.804688 C 479.09375 756.804688 479.089844 756.804688 479.085938 756.804688 C 479.078125 756.804688 479.074219 756.804688 479.070312 756.804688 C 479.0625 756.804688 479.058594 756.804688 479.054688 756.804688 C 479.046875 756.804688 479.042969 756.804688 479.039062 756.804688 C 479.03125 756.804688 479.027344 756.804688 479.023438 756.804688 C 479.015625 756.804688 479.011719 756.804688 479.007812 756.804688 C 479 756.804688 478.996094 756.804688 478.992188 756.804688 C 478.984375 756.804688 478.980469 756.804688 478.972656 756.804688 C 478.96875 756.804688 478.964844 756.804688 478.957031 756.804688 C 478.953125 756.804688 478.949219 756.804688 478.941406 756.804688 C 478.9375 756.804688 478.933594 756.804688 478.925781 756.804688 C 478.921875 756.804688 478.917969 756.804688 478.910156 756.804688 C 478.90625 756.804688 478.902344 756.804688 478.894531 756.804688 C 478.890625 756.804688 478.886719 756.804688 478.878906 756.804688 C 478.875 756.804688 478.871094 756.804688 478.863281 756.804688 C 478.859375 756.804688 478.855469 756.804688 478.847656 756.804688 C 478.84375 756.804688 478.839844 756.804688 478.832031 756.804688 C 478.828125 756.804688 478.824219 756.804688 478.816406 756.804688 C 478.8125 756.804688 478.808594 756.804688 478.800781 756.804688 C 478.796875 756.804688 478.792969 756.804688 478.785156 756.804688 C 478.78125 756.804688 478.777344 756.804688 478.769531 756.804688 C 478.765625 756.804688 478.761719 756.804688 478.753906 756.804688 C 478.75 756.804688 478.746094 756.804688 478.738281 756.804688 C 478.734375 756.804688 478.730469 756.804688 478.722656 756.804688 C 478.71875 756.804688 478.714844 756.804688 478.707031 756.804688 C 478.703125 756.804688 478.699219 756.804688 478.691406 756.804688 C 478.6875 756.804688 478.679688 756.804688 478.675781 756.804688 C 478.671875 756.804688 478.664062 756.804688 478.660156 756.804688 C 478.65625 756.804688 478.648438 756.804688 478.644531 756.804688 C 478.640625 756.804688 478.632812 756.804688 478.628906 756.804688 C 478.625 756.804688 478.617188 756.804688 478.613281 756.804688 C 478.609375 756.804688 478.601562 756.804688 478.597656 756.804688 C 478.59375 756.804688 478.585938 756.804688 478.582031 756.804688 C 478.578125 756.804688 478.570312 756.804688 478.566406 756.804688 C 478.5625 756.804688 478.554688 756.804688 478.550781 756.804688 C 478.546875 756.804688 478.539062 756.804688 478.535156 756.804688 C 478.53125 756.804688 478.523438 756.804688 478.519531 756.804688 C 478.515625 756.804688 478.507812 756.804688 478.503906 756.804688 C 478.496094 756.804688 478.492188 756.804688 478.488281 756.804688 C 478.480469 756.804688 478.476562 756.804688 478.472656 756.804688 C 478.464844 756.804688 478.460938 756.804688 478.457031 756.804688 C 478.449219 756.804688 478.445312 756.804688 478.441406 756.804688 C 478.433594 756.804688 478.429688 756.804688 478.425781 756.804688 C 478.417969 756.804688 478.414062 756.804688 478.410156 756.804688 C 478.402344 756.804688 478.398438 756.804688 478.394531 756.804688 C 478.386719 756.804688 478.382812 756.804688 478.378906 756.804688 C 478.371094 756.804688 478.367188 756.804688 478.363281 756.804688 C 478.355469 756.804688 478.351562 756.804688 478.347656 756.804688 C 478.339844 756.804688 478.335938 756.804688 478.328125 756.804688 C 478.324219 756.804688 478.320312 756.804688 478.3125 756.804688 C 478.308594 756.804688 478.304688 756.804688 478.296875 756.804688 C 478.292969 756.804688 478.289062 756.804688 478.28125 756.804688 C 478.277344 756.804688 478.273438 756.804688 478.265625 756.804688 C 478.261719 756.804688 478.257812 756.804688 478.25 756.804688 C 478.246094 756.804688 478.242188 756.804688 478.234375 756.804688 C 478.230469 756.804688 478.226562 756.804688 478.21875 756.804688 C 478.214844 756.804688 478.210938 756.804688 478.203125 756.804688 C 478.199219 756.804688 478.191406 756.804688 478.1875 756.804688 C 478.183594 756.804688 478.175781 756.804688 478.171875 756.804688 C 478.167969 756.804688 478.160156 756.804688 478.15625 756.804688 C 478.152344 756.804688 478.144531 756.804688 478.140625 756.804688 C 478.136719 756.804688 478.128906 756.804688 478.125 756.804688 C 478.121094 756.804688 478.113281 756.804688 478.109375 756.804688 C 478.105469 756.804688 478.097656 756.804688 478.09375 756.804688 C 478.089844 756.804688 478.082031 756.804688 478.078125 756.804688 C 478.074219 756.804688 478.066406 756.804688 478.0625 756.804688 C 478.054688 756.804688 478.050781 756.804688 478.046875 756.804688 C 478.039062 756.804688 478.035156 756.804688 478.03125 756.804688 C 478.023438 756.804688 478.019531 756.804688 478.015625 756.804688 C 478.007812 756.804688 478.003906 756.804688 478 756.804688 C 477.992188 756.804688 477.988281 756.804688 477.984375 756.804688 C 477.976562 756.804688 477.972656 756.804688 477.96875 756.804688 C 477.960938 756.804688 477.957031 756.804688 477.953125 756.804688 C 477.945312 756.804688 477.941406 756.804688 477.933594 756.804688 C 477.929688 756.804688 477.925781 756.804688 477.917969 756.804688 C 477.914062 756.804688 477.910156 756.804688 477.902344 756.804688 C 477.898438 756.804688 477.894531 756.804688 477.886719 756.804688 C 477.882812 756.804688 477.878906 756.804688 477.871094 756.804688 C 477.867188 756.804688 477.863281 756.804688 477.855469 756.804688 C 477.851562 756.804688 477.847656 756.804688 477.839844 756.804688 C 477.835938 756.804688 477.832031 756.804688 477.824219 756.804688 C 477.820312 756.804688 477.8125 756.804688 477.808594 756.804688 C 477.804688 756.804688 477.796875 756.804688 477.792969 756.804688 C 477.789062 756.804688 477.78125 756.804688 477.777344 756.804688 C 477.773438 756.804688 477.765625 756.804688 477.761719 756.804688 C 477.757812 756.804688 477.75 756.804688 477.746094 756.804688 C 477.742188 756.804688 477.734375 756.804688 477.730469 756.804688 C 477.726562 756.804688 477.71875 756.804688 477.714844 756.804688 C 477.710938 756.804688 477.703125 756.804688 477.699219 756.804688 C 477.691406 756.804688 477.6875 756.804688 477.683594 756.804688 C 477.675781 756.804688 477.671875 756.804688 477.667969 756.804688 C 477.660156 756.804688 477.65625 756.804688 477.652344 756.804688 C 477.644531 756.804688 477.640625 756.804688 477.636719 756.804688 C 477.628906 756.804688 477.625 756.804688 477.621094 756.804688 C 477.613281 756.804688 477.609375 756.804688 477.605469 756.804688 C 477.597656 756.804688 477.59375 756.804688 477.589844 756.804688 C 477.582031 756.804688 477.578125 756.804688 477.570312 756.804688 C 477.566406 756.804688 477.5625 756.804688 477.554688 756.804688 C 477.550781 756.804688 477.546875 756.804688 477.539062 756.804688 C 477.535156 756.804688 477.53125 756.804688 477.523438 756.804688 C 477.519531 756.804688 477.515625 756.804688 477.507812 756.804688 C 477.503906 756.804688 477.5 756.804688 477.492188 756.804688 C 477.488281 756.804688 477.484375 756.804688 477.476562 756.804688 C 477.472656 756.804688 477.46875 756.804688 477.460938 756.804688 C 477.457031 756.804688 477.449219 756.804688 477.445312 756.804688 C 477.441406 756.804688 477.433594 756.804688 477.429688 756.804688 C 477.425781 756.804688 477.417969 756.804688 477.414062 756.804688 C 477.410156 756.804688 477.402344 756.804688 477.398438 756.804688 C 477.394531 756.804688 477.386719 756.804688 477.382812 756.804688 C 477.378906 756.804688 477.371094 756.804688 477.367188 756.804688 C 477.363281 756.804688 477.355469 756.804688 477.351562 756.804688 C 477.347656 756.804688 477.339844 756.804688 477.335938 756.804688 C 477.332031 756.804688 477.324219 756.804688 477.320312 756.804688 C 477.3125 756.804688 477.308594 756.804688 477.304688 756.804688 C 477.296875 756.804688 477.292969 756.804688 477.289062 756.804688 C 477.28125 756.804688 477.277344 756.804688 477.273438 756.804688 C 477.265625 756.804688 477.261719 756.804688 477.257812 756.804688 C 477.25 756.804688 477.246094 756.804688 477.242188 756.804688 C 477.234375 756.804688 477.230469 756.804688 477.226562 756.804688 C 477.21875 756.804688 477.214844 756.804688 477.210938 756.804688 C 477.203125 756.804688 477.199219 756.804688 477.191406 756.804688 C 477.1875 756.804688 477.183594 756.804688 477.175781 756.804688 C 477.171875 756.804688 477.167969 756.804688 477.160156 756.804688 C 477.15625 756.804688 477.152344 756.804688 477.144531 756.804688 C 477.140625 756.804688 477.136719 756.804688 477.128906 756.804688 C 477.125 756.804688 477.121094 756.804688 477.113281 756.804688 C 477.109375 756.804688 477.105469 756.804688 477.097656 756.804688 C 477.09375 756.804688 477.089844 756.804688 477.082031 756.804688 C 477.078125 756.804688 477.074219 756.804688 477.066406 756.804688 C 477.0625 756.804688 477.054688 756.804688 477.050781 756.804688 C 477.046875 756.804688 477.039062 756.804688 477.035156 756.804688 C 477.03125 756.804688 477.023438 756.804688 477.019531 756.804688 C 477.015625 756.804688 477.007812 756.804688 477.003906 756.804688 C 477 756.804688 476.992188 756.804688 476.988281 756.804688 C 476.984375 756.804688 476.976562 756.804688 476.972656 756.804688 C 476.96875 756.804688 476.960938 756.804688 476.957031 756.804688 C 476.953125 756.804688 476.945312 756.804688 476.941406 756.804688 C 476.9375 756.804688 476.929688 756.804688 476.925781 756.804688 C 476.921875 756.804688 476.914062 756.804688 476.910156 756.804688 C 476.90625 756.804688 476.898438 756.804688 476.894531 756.804688 C 476.886719 756.804688 476.882812 756.804688 476.878906 756.804688 C 476.871094 756.804688 476.867188 756.804688 476.863281 756.804688 C 476.855469 756.804688 476.851562 756.804688 476.847656 756.804688 C 476.839844 756.804688 476.835938 756.804688 476.832031 756.804688 C 476.824219 756.804688 476.820312 756.804688 476.816406 756.804688 C 476.808594 756.804688 476.804688 756.804688 476.800781 756.804688 C 476.792969 756.804688 476.789062 756.804688 476.785156 756.804688 C 476.777344 756.804688 476.773438 756.804688 476.769531 756.804688 C 476.761719 756.804688 476.757812 756.804688 476.753906 756.804688 C 476.746094 756.804688 476.742188 756.804688 476.738281 756.804688 C 476.730469 756.804688 476.726562 756.804688 476.71875 756.804688 C 476.714844 756.804688 476.710938 756.804688 476.703125 756.804688 C 476.699219 756.804688 476.695312 756.804688 476.6875 756.804688 C 476.683594 756.804688 476.679688 756.804688 476.671875 756.804688 C 476.667969 756.804688 476.664062 756.804688 476.65625 756.804688 C 476.652344 756.804688 476.648438 756.804688 476.640625 756.804688 C 476.636719 756.804688 476.632812 756.804688 476.625 756.804688 C 476.621094 756.804688 476.617188 756.804688 476.609375 756.804688 C 476.605469 756.804688 476.601562 756.804688 476.59375 756.804688 C 476.589844 756.804688 476.585938 756.804688 476.578125 756.804688 C 476.574219 756.804688 476.570312 756.804688 476.5625 756.804688 C 476.558594 756.804688 476.554688 756.804688 476.546875 756.804688 C 476.542969 756.804688 476.539062 756.804688 476.53125 756.804688 C 476.527344 756.804688 476.523438 756.804688 476.515625 756.804688 C 476.511719 756.804688 476.507812 756.804688 476.5 756.804688 C 476.496094 756.804688 476.492188 756.804688 476.484375 756.804688 C 476.480469 756.804688 476.476562 756.804688 476.46875 756.804688 C 476.464844 756.804688 476.457031 756.804688 476.453125 756.804688 C 476.449219 756.804688 476.441406 756.804688 476.4375 756.804688 C 476.433594 756.804688 476.425781 756.804688 476.421875 756.804688 C 476.417969 756.804688 476.410156 756.804688 476.40625 756.804688 C 476.402344 756.804688 476.394531 756.804688 476.390625 756.804688 C 476.386719 756.804688 476.378906 756.804688 476.375 756.804688 C 476.371094 756.804688 476.363281 756.804688 476.359375 756.804688 C 476.355469 756.804688 476.347656 756.804688 476.34375 756.804688 C 476.339844 756.804688 476.332031 756.804688 476.328125 756.804688 C 476.324219 756.804688 476.316406 756.804688 476.3125 756.804688 C 476.308594 756.804688 476.300781 756.804688 476.296875 756.804688 C 476.292969 756.804688 476.285156 756.804688 476.28125 756.804688 C 476.277344 756.804688 476.269531 756.804688 476.265625 756.804688 C 476.261719 756.804688 476.253906 756.804688 476.25 756.804688 C 476.246094 756.804688 476.238281 756.804688 476.234375 756.804688 C 476.230469 756.804688 476.222656 756.804688 476.21875 756.804688 C 476.214844 756.804688 476.207031 756.804688 476.203125 756.804688 C 476.199219 756.804688 476.191406 756.804688 476.1875 756.804688 C 476.183594 756.804688 476.175781 756.804688 476.171875 756.804688 C 476.167969 756.804688 476.160156 756.804688 476.15625 756.804688 C 476.152344 756.804688 476.144531 756.804688 476.140625 756.804688 C 476.136719 756.804688 476.128906 756.804688 476.125 756.804688 C 476.121094 756.804688 476.113281 756.804688 476.109375 756.804688 C 476.105469 756.804688 476.097656 756.804688 476.09375 756.804688 C 476.089844 756.804688 476.082031 756.804688 476.078125 756.804688 C 476.074219 756.804688 476.066406 756.804688 476.0625 756.804688 C 476.058594 756.804688 476.050781 756.804688 476.046875 756.804688 C 476.042969 756.804688 476.035156 756.804688 476.03125 756.804688 C 476.027344 756.804688 476.019531 756.804688 476.015625 756.804688 C 476.011719 756.804688 476.003906 756.804688 476 756.804688 C 475.996094 756.804688 475.988281 756.804688 475.984375 756.804688 C 475.980469 756.804688 475.972656 756.804688 475.96875 756.804688 C 475.964844 756.804688 475.957031 756.804688 475.953125 756.804688 C 475.949219 756.804688 475.941406 756.804688 475.9375 756.804688 C 475.933594 756.804688 475.925781 756.804688 475.921875 756.804688 C 475.917969 756.804688 475.910156 756.804688 475.90625 756.804688 C 475.902344 756.804688 475.898438 756.804688 475.890625 756.804688 C 475.886719 756.804688 475.882812 756.804688 475.875 756.804688 C 475.871094 756.804688 475.867188 756.804688 475.859375 756.804688 C 475.855469 756.804688 475.851562 756.804688 475.84375 756.804688 C 475.839844 756.804688 475.835938 756.804688 475.828125 756.804688 C 475.824219 756.804688 475.820312 756.804688 475.8125 756.804688 C 475.808594 756.804688 475.804688 756.804688 475.796875 756.804688 C 475.792969 756.804688 475.789062 756.804688 475.78125 756.804688 C 475.777344 756.804688 475.773438 756.804688 475.765625 756.804688 C 475.761719 756.804688 475.757812 756.804688 475.75 756.804688 C 475.746094 756.804688 475.742188 756.804688 475.734375 756.804688 C 475.730469 756.804688 475.726562 756.804688 475.71875 756.804688 C 475.714844 756.804688 475.710938 756.804688 475.703125 756.804688 C 475.699219 756.804688 475.695312 756.804688 475.691406 756.804688 C 475.683594 756.804688 475.679688 756.804688 475.675781 756.804688 C 475.667969 756.804688 475.664062 756.804688 475.660156 756.804688 C 475.652344 756.804688 475.648438 756.804688 475.644531 756.804688 C 475.636719 756.804688 475.632812 756.804688 475.628906 756.804688 C 475.621094 756.804688 475.617188 756.804688 475.613281 756.804688 C 475.605469 756.804688 475.601562 756.804688 475.597656 756.804688 C 475.589844 756.804688 475.585938 756.804688 475.582031 756.804688 C 475.574219 756.804688 475.570312 756.804688 475.566406 756.804688 C 475.558594 756.804688 475.554688 756.804688 475.550781 756.804688 C 475.546875 756.804688 475.539062 756.804688 475.535156 756.804688 C 475.53125 756.804688 475.523438 756.804688 475.519531 756.804688 C 475.515625 756.804688 475.507812 756.804688 475.503906 756.804688 C 475.5 756.804688 475.492188 756.804688 475.488281 756.804688 C 475.484375 756.804688 475.476562 756.804688 475.472656 756.804688 C 475.46875 756.804688 475.460938 756.804688 475.457031 756.804688 C 475.453125 756.804688 475.449219 756.804688 475.441406 756.804688 C 475.4375 756.804688 475.433594 756.804688 475.425781 756.804688 C 475.421875 756.804688 475.417969 756.804688 475.410156 756.804688 C 475.40625 756.804688 475.402344 756.804688 475.394531 756.804688 C 475.390625 756.804688 475.386719 756.804688 475.378906 756.804688 C 475.375 756.804688 475.371094 756.804688 475.363281 756.804688 C 475.359375 756.804688 475.355469 756.804688 475.351562 756.804688 C 475.34375 756.804688 475.339844 756.804688 475.335938 756.804688 C 475.328125 756.804688 475.324219 756.804688 475.320312 756.804688 C 475.3125 756.804688 475.308594 756.804688 475.304688 756.804688 C 475.296875 756.804688 475.292969 756.804688 475.289062 756.804688 C 475.28125 756.804688 475.277344 756.804688 475.273438 756.804688 C 475.269531 756.804688 475.261719 756.804688 475.257812 756.804688 C 475.253906 756.804688 475.246094 756.804688 475.242188 756.804688 C 475.238281 756.804688 475.230469 756.804688 475.226562 756.804688 C 475.222656 756.804688 475.214844 756.804688 475.210938 756.804688 C 475.207031 756.804688 475.203125 756.804688 475.195312 756.804688 C 475.191406 756.804688 475.1875 756.804688 475.179688 756.804688 C 475.175781 756.804688 475.171875 756.804688 475.164062 756.804688 C 475.160156 756.804688 475.15625 756.804688 475.148438 756.804688 C 475.144531 756.804688 475.140625 756.804688 475.136719 756.804688 C 475.128906 756.804688 475.125 756.804688 475.121094 756.804688 C 475.113281 756.804688 475.109375 756.804688 475.105469 756.804688 C 475.097656 756.804688 475.09375 756.804688 475.089844 756.804688 C 475.085938 756.804688 475.078125 756.804688 475.074219 756.804688 C 475.070312 756.804688 475.0625 756.804688 475.058594 756.804688 C 475.054688 756.804688 475.046875 756.804688 475.042969 756.804688 C 475.039062 756.804688 475.03125 756.804688 475.027344 756.804688 C 475.023438 756.804688 475.019531 756.804688 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756.804688 474.785156 756.804688 C 474.78125 756.804688 474.773438 756.804688 474.769531 756.804688 C 474.765625 756.804688 474.757812 756.804688 474.753906 756.804688 C 474.75 756.804688 474.746094 756.804688 474.738281 756.804688 C 474.734375 756.804688 474.730469 756.804688 474.722656 756.804688 C 474.71875 756.804688 474.714844 756.804688 474.710938 756.804688 C 474.703125 756.804688 474.699219 756.804688 474.695312 756.804688 C 474.6875 756.804688 474.683594 756.804688 474.679688 756.804688 C 474.671875 756.804688 474.667969 756.804688 474.664062 756.804688 C 474.660156 756.804688 474.652344 756.804688 474.648438 756.804688 C 474.644531 756.804688 474.636719 756.804688 474.632812 756.804688 C 474.628906 756.804688 474.625 756.804688 474.617188 756.804688 C 474.613281 756.804688 474.609375 756.804688 474.601562 756.804688 C 474.597656 756.804688 474.59375 756.804688 474.589844 756.804688 C 474.582031 756.804688 474.578125 756.804688 474.574219 756.804688 C 474.566406 756.804688 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551.917969 831.40625 551.917969 831.410156 C 551.917969 831.414062 551.917969 831.421875 551.917969 831.425781 C 551.917969 831.429688 551.917969 831.4375 551.917969 831.441406 C 551.917969 831.445312 551.917969 831.453125 551.917969 831.457031 C 551.917969 831.460938 551.917969 831.464844 551.917969 831.472656 C 551.917969 831.476562 551.917969 831.480469 551.917969 831.488281 C 551.917969 831.492188 551.917969 831.496094 551.917969 831.503906 C 551.917969 831.507812 551.917969 831.511719 551.917969 831.519531 C 551.917969 831.523438 551.917969 831.527344 551.917969 831.53125 C 551.917969 831.539062 551.917969 831.542969 551.917969 831.546875 C 551.917969 831.554688 551.917969 831.558594 551.917969 831.5625 C 551.917969 831.570312 551.917969 831.574219 551.917969 831.578125 C 551.917969 831.585938 551.917969 831.589844 551.917969 831.59375 C 551.917969 831.601562 551.917969 831.605469 551.917969 831.609375 C 551.917969 831.613281 551.917969 831.621094 551.917969 831.625 C 551.917969 831.628906 551.917969 831.636719 551.917969 831.640625 C 551.917969 831.644531 551.917969 831.652344 551.917969 831.65625 C 551.917969 831.660156 551.917969 831.667969 551.917969 831.671875 C 551.917969 831.675781 551.917969 831.683594 551.917969 831.6875 C 551.917969 831.691406 551.917969 831.695312 551.917969 831.703125 C 551.917969 831.707031 551.917969 831.710938 551.917969 831.71875 C 551.917969 831.722656 551.917969 831.726562 551.917969 831.734375 C 551.917969 831.738281 551.917969 831.742188 551.917969 831.75 C 551.917969 831.753906 551.917969 831.757812 551.917969 831.765625 C 551.917969 831.769531 551.917969 831.773438 551.917969 831.78125 C 551.917969 831.785156 551.917969 831.789062 551.917969 831.796875 C 551.917969 831.800781 551.917969 831.804688 551.917969 831.808594 C 551.917969 831.816406 551.917969 831.820312 551.917969 831.824219 C 551.917969 831.832031 551.917969 831.835938 551.917969 831.839844 C 551.917969 831.847656 551.917969 831.851562 551.917969 831.855469 C 551.917969 831.863281 551.917969 831.867188 551.917969 831.871094 C 551.917969 831.878906 551.917969 831.882812 551.917969 831.886719 C 551.917969 831.894531 551.917969 831.898438 551.917969 831.902344 C 551.917969 831.910156 551.917969 831.914062 551.917969 831.917969 C 551.917969 831.925781 551.917969 831.929688 551.917969 831.933594 C 551.917969 831.9375 551.917969 831.945312 551.917969 831.949219 C 551.917969 831.953125 551.917969 831.960938 551.917969 831.964844 C 551.917969 831.96875 551.917969 831.976562 551.917969 831.980469 C 551.917969 831.984375 551.917969 831.992188 551.917969 831.996094 C 551.917969 832 551.917969 832.007812 551.917969 832.011719 C 551.917969 832.015625 551.917969 832.023438 551.917969 832.027344 C 551.917969 832.03125 551.917969 832.039062 551.917969 832.042969 C 551.917969 832.046875 551.917969 832.054688 551.917969 832.058594 C 551.917969 832.0625 551.917969 832.070312 551.917969 832.074219 C 551.917969 832.078125 551.917969 832.085938 551.917969 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551.917969 832.324219 C 551.917969 832.328125 551.917969 832.332031 551.917969 832.339844 C 551.917969 832.34375 551.917969 832.347656 551.917969 832.355469 C 551.917969 832.359375 551.917969 832.363281 551.917969 832.371094 C 551.917969 832.375 551.917969 832.378906 551.917969 832.386719 C 551.917969 832.390625 551.917969 832.394531 551.917969 832.402344 C 551.917969 832.40625 551.917969 832.410156 551.917969 832.417969 C 551.917969 832.421875 551.917969 832.425781 551.917969 832.433594 C 551.917969 832.4375 551.917969 832.441406 551.917969 832.449219 C 551.917969 832.453125 551.917969 832.457031 551.917969 832.464844 C 551.917969 832.46875 551.917969 832.472656 551.917969 832.480469 C 551.917969 832.484375 551.917969 832.488281 551.917969 832.496094 C 551.917969 832.5 551.917969 832.503906 551.917969 832.511719 C 551.917969 832.515625 551.917969 832.519531 551.917969 832.527344 C 551.917969 832.53125 551.917969 832.535156 551.917969 832.542969 C 551.917969 832.546875 551.917969 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551.917969 832.789062 551.917969 832.792969 C 551.917969 832.796875 551.917969 832.804688 551.917969 832.808594 C 551.917969 832.8125 551.917969 832.820312 551.917969 832.824219 C 551.917969 832.828125 551.917969 832.835938 551.917969 832.839844 C 551.917969 832.84375 551.917969 832.851562 551.917969 832.855469 C 551.917969 832.859375 551.917969 832.867188 551.917969 832.871094 C 551.917969 832.875 551.917969 832.882812 551.917969 832.886719 C 551.917969 832.890625 551.917969 832.898438 551.917969 832.902344 C 551.917969 832.90625 551.917969 832.914062 551.917969 832.917969 C 551.917969 832.921875 551.917969 832.929688 551.917969 832.933594 C 551.917969 832.9375 551.917969 832.945312 551.917969 832.949219 C 551.917969 832.953125 551.917969 832.960938 551.917969 832.964844 C 551.917969 832.96875 551.917969 832.976562 551.917969 832.980469 C 551.917969 832.984375 551.917969 832.992188 551.917969 832.996094 C 551.917969 833 551.917969 833.007812 551.917969 833.011719 C 551.917969 833.019531 551.917969 833.023438 551.917969 833.027344 C 551.917969 833.035156 551.917969 833.039062 551.917969 833.042969 C 551.917969 833.050781 551.917969 833.054688 551.917969 833.058594 C 551.917969 833.066406 551.917969 833.070312 551.917969 833.074219 C 551.917969 833.082031 551.917969 833.085938 551.917969 833.089844 C 551.917969 833.097656 551.917969 833.101562 551.917969 833.105469 C 551.917969 833.113281 551.917969 833.117188 551.917969 833.121094 C 551.917969 833.128906 551.917969 833.132812 551.917969 833.136719 C 551.917969 833.144531 551.917969 833.148438 551.917969 833.152344 C 551.917969 833.160156 551.917969 833.164062 551.917969 833.167969 C 551.917969 833.175781 551.917969 833.179688 551.917969 833.183594 C 551.917969 833.191406 551.917969 833.195312 551.917969 833.203125 C 551.917969 833.207031 551.917969 833.210938 551.917969 833.21875 C 551.917969 833.222656 551.917969 833.226562 551.917969 833.234375 C 551.917969 833.238281 551.917969 833.242188 551.917969 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551.917969 833.484375 C 551.917969 833.492188 551.917969 833.496094 551.917969 833.5 C 551.917969 833.507812 551.917969 833.511719 551.917969 833.515625 C 551.917969 833.523438 551.917969 833.527344 551.917969 833.53125 C 551.917969 833.539062 551.917969 833.542969 551.917969 833.546875 C 551.917969 833.554688 551.917969 833.558594 551.917969 833.5625 C 551.917969 833.570312 551.917969 833.574219 551.917969 833.578125 C 551.917969 833.585938 551.917969 833.589844 551.917969 833.59375 C 551.917969 833.601562 551.917969 833.605469 551.917969 833.613281 C 551.917969 833.617188 551.917969 833.621094 551.917969 833.628906 C 551.917969 833.632812 551.917969 833.636719 551.917969 833.644531 C 551.917969 833.648438 551.917969 833.652344 551.917969 833.660156 C 551.917969 833.664062 551.917969 833.667969 551.917969 833.675781 C 551.917969 833.679688 551.917969 833.683594 551.917969 833.691406 C 551.917969 833.695312 551.917969 833.699219 551.917969 833.707031 C 551.917969 833.710938 551.917969 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551.917969 834.421875 551.917969 834.425781 551.917969 834.433594 C 551.917969 834.4375 551.917969 834.441406 551.917969 834.449219 C 551.917969 834.453125 551.917969 834.457031 551.917969 834.464844 C 551.917969 834.46875 551.917969 834.472656 551.917969 834.480469 C 551.917969 834.484375 551.917969 834.492188 551.917969 834.496094 C 551.917969 834.5 551.917969 834.507812 551.917969 834.511719 C 551.917969 834.515625 551.917969 834.523438 551.917969 834.527344 C 551.917969 834.53125 551.917969 834.539062 551.917969 834.542969 C 551.917969 834.546875 551.917969 834.554688 551.917969 834.558594 C 551.917969 834.5625 551.917969 834.570312 551.917969 834.574219 C 551.917969 834.578125 551.917969 834.585938 551.917969 834.589844 C 551.917969 834.59375 551.917969 834.601562 551.917969 834.605469 C 551.917969 834.609375 551.917969 834.617188 551.917969 834.621094 C 551.917969 834.625 551.917969 834.632812 551.917969 834.636719 C 551.917969 834.644531 551.917969 834.648438 551.917969 834.652344 C 551.917969 834.660156 551.917969 834.664062 551.917969 834.667969 C 551.917969 834.675781 551.917969 834.679688 551.917969 834.683594 C 551.917969 834.691406 551.917969 834.695312 551.917969 834.699219 C 551.917969 834.707031 551.917969 834.710938 551.917969 834.714844 C 551.917969 834.722656 551.917969 834.726562 551.917969 834.730469 C 551.917969 834.738281 551.917969 834.742188 551.917969 834.746094 C 551.917969 834.753906 551.917969 834.757812 551.917969 834.761719 C 551.917969 834.769531 551.917969 834.773438 551.917969 834.777344 C 551.917969 834.785156 551.917969 834.789062 551.917969 834.796875 C 551.917969 834.800781 551.917969 834.804688 551.917969 834.8125 C 551.917969 834.816406 551.917969 834.820312 551.917969 834.828125 C 551.917969 834.832031 551.917969 834.835938 551.917969 834.84375 C 551.917969 834.847656 551.917969 834.851562 551.917969 834.859375 C 551.917969 834.863281 551.917969 834.867188 551.917969 834.875 C 551.917969 834.878906 551.917969 834.882812 551.917969 834.890625 C 551.917969 834.894531 551.917969 834.898438 551.917969 834.90625 C 551.917969 834.910156 551.917969 834.914062 551.917969 834.921875 C 551.917969 834.925781 551.917969 834.929688 551.917969 834.9375 C 551.917969 834.941406 551.917969 834.945312 551.917969 834.953125 C 551.917969 834.957031 551.917969 834.960938 551.917969 834.96875 C 551.917969 834.972656 551.917969 834.976562 551.917969 834.984375 C 551.917969 834.988281 551.917969 834.996094 551.917969 835 C 551.917969 835.003906 551.917969 835.011719 551.917969 835.015625 C 551.917969 835.019531 551.917969 835.027344 551.917969 835.03125 C 551.917969 835.035156 551.917969 835.042969 551.917969 835.046875 C 551.917969 835.050781 551.917969 835.058594 551.917969 835.0625 C 551.917969 835.066406 551.917969 835.074219 551.917969 835.078125 C 551.917969 835.082031 551.917969 835.089844 551.917969 835.09375 C 551.917969 835.097656 551.917969 835.105469 551.917969 835.109375 C 551.917969 835.113281 551.917969 835.121094 551.917969 835.125 C 551.917969 835.128906 551.917969 835.136719 551.917969 835.140625 C 551.917969 835.144531 551.917969 835.152344 551.917969 835.15625 C 551.917969 835.160156 551.917969 835.167969 551.917969 835.171875 C 551.917969 835.175781 551.917969 835.183594 551.917969 835.1875 C 551.917969 835.191406 551.917969 835.199219 551.917969 835.203125 C 551.917969 835.207031 551.917969 835.214844 551.917969 835.21875 C 551.917969 835.222656 551.917969 835.230469 551.917969 835.234375 C 551.917969 835.238281 551.917969 835.246094 551.917969 835.25 C 551.917969 835.253906 551.917969 835.261719 551.917969 835.265625 C 551.917969 835.269531 551.917969 835.277344 551.917969 835.28125 C 551.917969 835.285156 551.917969 835.292969 551.917969 835.296875 C 551.917969 835.300781 551.917969 835.308594 551.917969 835.3125 C 551.917969 835.316406 551.917969 835.324219 551.917969 835.328125 C 551.917969 835.332031 551.917969 835.339844 551.917969 835.34375 C 551.917969 835.347656 551.917969 835.355469 551.917969 835.359375 C 551.917969 835.363281 551.917969 835.371094 551.917969 835.375 C 551.917969 835.382812 551.917969 835.386719 551.917969 835.390625 C 551.917969 835.398438 551.917969 835.402344 551.917969 835.40625 C 551.917969 835.414062 551.917969 835.417969 551.917969 835.421875 C 551.917969 835.429688 551.917969 835.433594 551.917969 835.4375 C 551.917969 835.445312 551.917969 835.449219 551.917969 835.453125 C 551.917969 835.460938 551.917969 835.464844 551.917969 835.46875 C 551.917969 835.476562 551.917969 835.480469 551.917969 835.484375 C 551.917969 835.492188 551.917969 835.496094 551.917969 835.5 C 551.917969 835.507812 551.917969 835.511719 551.917969 835.515625 C 551.917969 835.523438 551.917969 835.527344 551.917969 835.53125 C 551.917969 835.539062 551.917969 835.542969 551.917969 835.546875 C 551.917969 835.550781 551.917969 835.558594 551.917969 835.5625 C 551.917969 835.566406 551.917969 835.574219 551.917969 835.578125 C 551.917969 835.582031 551.917969 835.589844 551.917969 835.59375 C 551.917969 835.597656 551.917969 835.605469 551.917969 835.609375 C 551.917969 835.613281 551.917969 835.621094 551.917969 835.625 C 551.917969 835.628906 551.917969 835.636719 551.917969 835.640625 C 551.917969 835.644531 551.917969 835.652344 551.917969 835.65625 C 551.917969 835.660156 551.917969 835.667969 551.917969 835.671875 C 551.917969 835.675781 551.917969 835.683594 551.917969 835.6875 C 551.917969 835.691406 551.917969 835.699219 551.917969 835.703125 C 551.917969 835.707031 551.917969 835.714844 551.917969 835.71875 C 551.917969 835.722656 551.917969 835.730469 551.917969 835.734375 C 551.917969 835.738281 551.917969 835.746094 551.917969 835.75 C 551.917969 835.753906 551.917969 835.761719 551.917969 835.765625 C 551.917969 835.769531 551.917969 835.777344 551.917969 835.78125 C 551.917969 835.785156 551.917969 835.792969 551.917969 835.796875 C 551.917969 835.800781 551.917969 835.808594 551.917969 835.8125 C 551.917969 835.816406 551.917969 835.824219 551.917969 835.828125 C 551.917969 835.832031 551.917969 835.839844 551.917969 835.84375 C 551.917969 835.847656 551.917969 835.855469 551.917969 835.859375 C 551.917969 835.863281 551.917969 835.867188 551.917969 835.875 C 551.917969 835.878906 551.917969 835.882812 551.917969 835.890625 C 551.917969 835.894531 551.917969 835.898438 551.917969 835.90625 C 551.917969 835.910156 551.917969 835.914062 551.917969 835.921875 C 551.917969 835.925781 551.917969 835.929688 551.917969 835.9375 C 551.917969 835.941406 551.917969 835.945312 551.917969 835.953125 C 551.917969 835.957031 551.917969 835.960938 551.917969 835.96875 C 551.917969 835.972656 551.917969 835.976562 551.917969 835.984375 C 551.917969 835.988281 551.917969 835.992188 551.917969 836 C 551.917969 836.003906 551.917969 836.007812 551.917969 836.015625 C 551.917969 836.019531 551.917969 836.023438 551.917969 836.027344 C 551.917969 836.035156 551.917969 836.039062 551.917969 836.042969 C 551.917969 836.050781 551.917969 836.054688 551.917969 836.058594 C 551.917969 836.066406 551.917969 836.070312 551.917969 836.074219 C 551.917969 836.082031 551.917969 836.085938 551.917969 836.089844 C 551.917969 836.097656 551.917969 836.101562 551.917969 836.105469 C 551.917969 836.113281 551.917969 836.117188 551.917969 836.121094 C 551.917969 836.128906 551.917969 836.132812 551.917969 836.136719 C 551.917969 836.140625 551.917969 836.148438 551.917969 836.152344 C 551.917969 836.15625 551.917969 836.164062 551.917969 836.167969 C 551.917969 836.171875 551.917969 836.179688 551.917969 836.183594 C 551.917969 836.1875 551.917969 836.195312 551.917969 836.199219 C 551.917969 836.203125 551.917969 836.210938 551.917969 836.214844 C 551.917969 836.21875 551.917969 836.226562 551.917969 836.230469 C 551.917969 836.234375 551.917969 836.238281 551.917969 836.246094 C 551.917969 836.25 551.917969 836.253906 551.917969 836.261719 C 551.917969 836.265625 551.917969 836.269531 551.917969 836.277344 C 551.917969 836.28125 551.917969 836.285156 551.917969 836.292969 C 551.917969 836.296875 551.917969 836.300781 551.917969 836.308594 C 551.917969 836.3125 551.917969 836.316406 551.917969 836.320312 C 551.917969 836.328125 551.917969 836.332031 551.917969 836.335938 C 551.917969 836.34375 551.917969 836.347656 551.917969 836.351562 C 551.917969 836.359375 551.917969 836.363281 551.917969 836.367188 C 551.917969 836.375 551.917969 836.378906 551.917969 836.382812 C 551.917969 836.386719 551.917969 836.394531 551.917969 836.398438 C 551.917969 836.402344 551.917969 836.410156 551.917969 836.414062 C 551.917969 836.417969 551.917969 836.425781 551.917969 836.429688 C 551.917969 836.433594 551.917969 836.441406 551.917969 836.445312 C 551.917969 836.449219 551.917969 836.453125 551.917969 836.460938 C 551.917969 836.464844 551.917969 836.46875 551.917969 836.476562 C 551.917969 836.480469 551.917969 836.484375 551.917969 836.492188 C 551.917969 836.496094 551.917969 836.5 551.917969 836.507812 C 551.917969 836.511719 551.917969 836.515625 551.917969 836.519531 C 551.917969 836.527344 551.917969 836.53125 551.917969 836.535156 C 551.917969 836.542969 551.917969 836.546875 551.917969 836.550781 C 551.917969 836.558594 551.917969 836.5625 551.917969 836.566406 C 551.917969 836.574219 551.917969 836.578125 551.917969 836.582031 C 551.917969 836.585938 551.917969 836.59375 551.917969 836.597656 C 551.917969 836.601562 551.917969 836.609375 551.917969 836.613281 C 551.917969 836.617188 551.917969 836.625 551.917969 836.628906 C 551.917969 836.632812 551.917969 836.636719 551.917969 836.644531 C 551.917969 836.648438 551.917969 836.652344 551.917969 836.660156 C 551.917969 836.664062 551.917969 836.667969 551.917969 836.675781 C 551.917969 836.679688 551.917969 836.683594 551.917969 836.6875 C 551.917969 836.695312 551.917969 836.699219 551.917969 836.703125 C 551.917969 836.710938 551.917969 836.714844 551.917969 836.71875 C 551.917969 836.726562 551.917969 836.730469 551.917969 836.734375 C 551.917969 836.738281 551.917969 836.746094 551.917969 836.75 C 551.917969 836.753906 551.917969 836.761719 551.917969 836.765625 C 551.917969 836.769531 551.917969 836.773438 551.917969 836.78125 C 551.917969 836.785156 551.917969 836.789062 551.917969 836.796875 C 551.917969 836.800781 551.917969 836.804688 551.917969 836.8125 C 551.917969 836.816406 551.917969 836.820312 551.917969 836.824219 C 551.917969 836.832031 551.917969 836.835938 551.917969 836.839844 C 551.917969 836.847656 551.917969 836.851562 551.917969 836.855469 C 551.917969 836.859375 551.917969 836.867188 551.917969 836.871094 C 551.917969 836.875 551.917969 836.882812 551.917969 836.886719 C 551.917969 836.890625 551.917969 836.898438 551.917969 836.902344 C 551.917969 836.90625 551.917969 836.910156 551.917969 836.917969 C 551.917969 836.921875 551.917969 836.925781 551.917969 836.933594 C 551.917969 836.9375 551.917969 836.941406 551.917969 836.945312 C 551.917969 836.953125 551.917969 836.957031 551.917969 836.960938 C 551.917969 836.96875 551.917969 836.972656 551.917969 836.976562 C 551.917969 836.980469 551.917969 836.988281 551.917969 836.992188 C 551.917969 836.996094 551.917969 837.003906 551.917969 837.007812 C 551.917969 837.011719 551.917969 837.015625 551.917969 837.023438 C 551.917969 837.027344 551.917969 837.03125 551.917969 837.039062 C 551.917969 837.042969 551.917969 837.046875 551.917969 837.050781 C 551.917969 837.058594 551.917969 837.0625 551.917969 837.066406 C 551.917969 837.074219 551.917969 837.078125 551.917969 837.082031 C 551.917969 837.085938 551.917969 837.09375 551.917969 837.097656 C 551.917969 837.101562 551.917969 837.109375 551.917969 837.113281 C 551.917969 837.117188 551.917969 837.121094 551.917969 837.128906 C 551.917969 837.132812 551.917969 837.136719 551.917969 837.144531 C 551.917969 837.148438 551.917969 837.152344 551.917969 837.15625 C 551.917969 837.164062 551.917969 837.167969 551.917969 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551.917969 837.398438 C 551.917969 837.402344 551.917969 837.40625 551.917969 837.410156 C 551.917969 837.417969 551.917969 837.421875 551.917969 837.425781 C 551.917969 837.429688 551.917969 837.4375 551.917969 837.441406 C 551.917969 837.445312 551.917969 837.453125 551.917969 837.457031 C 551.917969 837.460938 551.917969 837.464844 551.917969 837.472656 C 551.917969 837.476562 551.917969 837.480469 551.917969 837.484375 C 551.917969 837.492188 551.917969 837.496094 551.917969 837.5 C 551.917969 837.503906 551.917969 837.511719 551.917969 837.515625 C 551.917969 837.519531 551.917969 837.523438 551.917969 837.53125 C 551.917969 837.535156 551.917969 837.539062 551.917969 837.546875 C 551.917969 837.550781 551.917969 837.554688 551.917969 837.558594 C 551.917969 837.566406 551.917969 837.570312 551.917969 837.574219 C 551.917969 837.578125 551.917969 837.585938 551.917969 837.589844 C 551.917969 837.59375 551.917969 837.597656 551.917969 837.605469 C 551.917969 837.609375 551.917969 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551.917969 837.835938 551.917969 837.839844 C 551.917969 837.84375 551.917969 837.847656 551.917969 837.855469 C 551.917969 837.859375 551.917969 837.863281 551.917969 837.867188 C 551.917969 837.875 551.917969 837.878906 551.917969 837.882812 L 554.820312 837.882812 C 554.820312 837.878906 554.820312 837.875 554.820312 837.867188 C 554.820312 837.863281 554.820312 837.859375 554.820312 837.855469 C 554.820312 837.847656 554.820312 837.84375 554.820312 837.839844 C 554.820312 837.835938 554.820312 837.828125 554.820312 837.824219 C 554.820312 837.820312 554.820312 837.816406 554.820312 837.808594 C 554.820312 837.804688 554.820312 837.800781 554.820312 837.796875 C 554.820312 837.789062 554.820312 837.785156 554.820312 837.78125 C 554.820312 837.777344 554.820312 837.769531 554.820312 837.765625 C 554.820312 837.761719 554.820312 837.757812 554.820312 837.75 C 554.820312 837.746094 554.820312 837.742188 554.820312 837.738281 C 554.820312 837.730469 554.820312 837.726562 554.820312 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554.820312 837.5 C 554.820312 837.496094 554.820312 837.492188 554.820312 837.484375 C 554.820312 837.480469 554.820312 837.476562 554.820312 837.472656 C 554.820312 837.464844 554.820312 837.460938 554.820312 837.457031 C 554.820312 837.453125 554.820312 837.445312 554.820312 837.441406 C 554.820312 837.4375 554.820312 837.429688 554.820312 837.425781 C 554.820312 837.421875 554.820312 837.417969 554.820312 837.410156 C 554.820312 837.40625 554.820312 837.402344 554.820312 837.398438 C 554.820312 837.390625 554.820312 837.386719 554.820312 837.382812 C 554.820312 837.375 554.820312 837.371094 554.820312 837.367188 C 554.820312 837.363281 554.820312 837.355469 554.820312 837.351562 C 554.820312 837.347656 554.820312 837.34375 554.820312 837.335938 C 554.820312 837.332031 554.820312 837.328125 554.820312 837.320312 C 554.820312 837.316406 554.820312 837.3125 554.820312 837.308594 C 554.820312 837.300781 554.820312 837.296875 554.820312 837.292969 C 554.820312 837.289062 554.820312 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554.820312 837.058594 554.820312 837.050781 C 554.820312 837.046875 554.820312 837.042969 554.820312 837.039062 C 554.820312 837.03125 554.820312 837.027344 554.820312 837.023438 C 554.820312 837.015625 554.820312 837.011719 554.820312 837.007812 C 554.820312 837.003906 554.820312 836.996094 554.820312 836.992188 C 554.820312 836.988281 554.820312 836.980469 554.820312 836.976562 C 554.820312 836.972656 554.820312 836.96875 554.820312 836.960938 C 554.820312 836.957031 554.820312 836.953125 554.820312 836.945312 C 554.820312 836.941406 554.820312 836.9375 554.820312 836.933594 C 554.820312 836.925781 554.820312 836.921875 554.820312 836.917969 C 554.820312 836.910156 554.820312 836.90625 554.820312 836.902344 C 554.820312 836.898438 554.820312 836.890625 554.820312 836.886719 C 554.820312 836.882812 554.820312 836.875 554.820312 836.871094 C 554.820312 836.867188 554.820312 836.859375 554.820312 836.855469 C 554.820312 836.851562 554.820312 836.847656 554.820312 836.839844 C 554.820312 836.835938 554.820312 836.832031 554.820312 836.824219 C 554.820312 836.820312 554.820312 836.816406 554.820312 836.8125 C 554.820312 836.804688 554.820312 836.800781 554.820312 836.796875 C 554.820312 836.789062 554.820312 836.785156 554.820312 836.78125 C 554.820312 836.773438 554.820312 836.769531 554.820312 836.765625 C 554.820312 836.761719 554.820312 836.753906 554.820312 836.75 C 554.820312 836.746094 554.820312 836.738281 554.820312 836.734375 C 554.820312 836.730469 554.820312 836.726562 554.820312 836.71875 C 554.820312 836.714844 554.820312 836.710938 554.820312 836.703125 C 554.820312 836.699219 554.820312 836.695312 554.820312 836.6875 C 554.820312 836.683594 554.820312 836.679688 554.820312 836.675781 C 554.820312 836.667969 554.820312 836.664062 554.820312 836.660156 C 554.820312 836.652344 554.820312 836.648438 554.820312 836.644531 C 554.820312 836.636719 554.820312 836.632812 554.820312 836.628906 C 554.820312 836.625 554.820312 836.617188 554.820312 836.613281 C 554.820312 836.609375 554.820312 836.601562 554.820312 836.597656 C 554.820312 836.59375 554.820312 836.585938 554.820312 836.582031 C 554.820312 836.578125 554.820312 836.574219 554.820312 836.566406 C 554.820312 836.5625 554.820312 836.558594 554.820312 836.550781 C 554.820312 836.546875 554.820312 836.542969 554.820312 836.535156 C 554.820312 836.53125 554.820312 836.527344 554.820312 836.519531 C 554.820312 836.515625 554.820312 836.511719 554.820312 836.507812 C 554.820312 836.5 554.820312 836.496094 554.820312 836.492188 C 554.820312 836.484375 554.820312 836.480469 554.820312 836.476562 C 554.820312 836.46875 554.820312 836.464844 554.820312 836.460938 C 554.820312 836.453125 554.820312 836.449219 554.820312 836.445312 C 554.820312 836.441406 554.820312 836.433594 554.820312 836.429688 C 554.820312 836.425781 554.820312 836.417969 554.820312 836.414062 C 554.820312 836.410156 554.820312 836.402344 554.820312 836.398438 C 554.820312 836.394531 554.820312 836.386719 554.820312 836.382812 C 554.820312 836.378906 554.820312 836.375 554.820312 836.367188 C 554.820312 836.363281 554.820312 836.359375 554.820312 836.351562 C 554.820312 836.347656 554.820312 836.34375 554.820312 836.335938 C 554.820312 836.332031 554.820312 836.328125 554.820312 836.320312 C 554.820312 836.316406 554.820312 836.3125 554.820312 836.308594 C 554.820312 836.300781 554.820312 836.296875 554.820312 836.292969 C 554.820312 836.285156 554.820312 836.28125 554.820312 836.277344 C 554.820312 836.269531 554.820312 836.265625 554.820312 836.261719 C 554.820312 836.253906 554.820312 836.25 554.820312 836.246094 C 554.820312 836.238281 554.820312 836.234375 554.820312 836.230469 C 554.820312 836.226562 554.820312 836.21875 554.820312 836.214844 C 554.820312 836.210938 554.820312 836.203125 554.820312 836.199219 C 554.820312 836.195312 554.820312 836.1875 554.820312 836.183594 C 554.820312 836.179688 554.820312 836.171875 554.820312 836.167969 C 554.820312 836.164062 554.820312 836.15625 554.820312 836.152344 C 554.820312 836.148438 554.820312 836.140625 554.820312 836.136719 C 554.820312 836.132812 554.820312 836.128906 554.820312 836.121094 C 554.820312 836.117188 554.820312 836.113281 554.820312 836.105469 C 554.820312 836.101562 554.820312 836.097656 554.820312 836.089844 C 554.820312 836.085938 554.820312 836.082031 554.820312 836.074219 C 554.820312 836.070312 554.820312 836.066406 554.820312 836.058594 C 554.820312 836.054688 554.820312 836.050781 554.820312 836.042969 C 554.820312 836.039062 554.820312 836.035156 554.820312 836.027344 C 554.820312 836.023438 554.820312 836.019531 554.820312 836.015625 C 554.820312 836.007812 554.820312 836.003906 554.820312 836 C 554.820312 835.992188 554.820312 835.988281 554.820312 835.984375 C 554.820312 835.976562 554.820312 835.972656 554.820312 835.96875 C 554.820312 835.960938 554.820312 835.957031 554.820312 835.953125 C 554.820312 835.945312 554.820312 835.941406 554.820312 835.9375 C 554.820312 835.929688 554.820312 835.925781 554.820312 835.921875 C 554.820312 835.914062 554.820312 835.910156 554.820312 835.90625 C 554.820312 835.898438 554.820312 835.894531 554.820312 835.890625 C 554.820312 835.882812 554.820312 835.878906 554.820312 835.875 C 554.820312 835.867188 554.820312 835.863281 554.820312 835.859375 C 554.820312 835.855469 554.820312 835.847656 554.820312 835.84375 C 554.820312 835.839844 554.820312 835.832031 554.820312 835.828125 C 554.820312 835.824219 554.820312 835.816406 554.820312 835.8125 C 554.820312 835.808594 554.820312 835.800781 554.820312 835.796875 C 554.820312 835.792969 554.820312 835.785156 554.820312 835.78125 C 554.820312 835.777344 554.820312 835.769531 554.820312 835.765625 C 554.820312 835.761719 554.820312 835.753906 554.820312 835.75 C 554.820312 835.746094 554.820312 835.738281 554.820312 835.734375 C 554.820312 835.730469 554.820312 835.722656 554.820312 835.71875 C 554.820312 835.714844 554.820312 835.707031 554.820312 835.703125 C 554.820312 835.699219 554.820312 835.691406 554.820312 835.6875 C 554.820312 835.683594 554.820312 835.675781 554.820312 835.671875 C 554.820312 835.667969 554.820312 835.660156 554.820312 835.65625 C 554.820312 835.652344 554.820312 835.644531 554.820312 835.640625 C 554.820312 835.636719 554.820312 835.628906 554.820312 835.625 C 554.820312 835.621094 554.820312 835.613281 554.820312 835.609375 C 554.820312 835.605469 554.820312 835.597656 554.820312 835.59375 C 554.820312 835.589844 554.820312 835.582031 554.820312 835.578125 C 554.820312 835.574219 554.820312 835.566406 554.820312 835.5625 C 554.820312 835.558594 554.820312 835.550781 554.820312 835.546875 C 554.820312 835.542969 554.820312 835.539062 554.820312 835.53125 C 554.820312 835.527344 554.820312 835.523438 554.820312 835.515625 C 554.820312 835.511719 554.820312 835.507812 554.820312 835.5 C 554.820312 835.496094 554.820312 835.492188 554.820312 835.484375 C 554.820312 835.480469 554.820312 835.476562 554.820312 835.46875 C 554.820312 835.464844 554.820312 835.460938 554.820312 835.453125 C 554.820312 835.449219 554.820312 835.445312 554.820312 835.4375 C 554.820312 835.433594 554.820312 835.429688 554.820312 835.421875 C 554.820312 835.417969 554.820312 835.414062 554.820312 835.40625 C 554.820312 835.402344 554.820312 835.398438 554.820312 835.390625 C 554.820312 835.386719 554.820312 835.382812 554.820312 835.375 C 554.820312 835.371094 554.820312 835.363281 554.820312 835.359375 C 554.820312 835.355469 554.820312 835.347656 554.820312 835.34375 C 554.820312 835.339844 554.820312 835.332031 554.820312 835.328125 C 554.820312 835.324219 554.820312 835.316406 554.820312 835.3125 C 554.820312 835.308594 554.820312 835.300781 554.820312 835.296875 C 554.820312 835.292969 554.820312 835.285156 554.820312 835.28125 C 554.820312 835.277344 554.820312 835.269531 554.820312 835.265625 C 554.820312 835.261719 554.820312 835.253906 554.820312 835.25 C 554.820312 835.246094 554.820312 835.238281 554.820312 835.234375 C 554.820312 835.230469 554.820312 835.222656 554.820312 835.21875 C 554.820312 835.214844 554.820312 835.207031 554.820312 835.203125 C 554.820312 835.199219 554.820312 835.191406 554.820312 835.1875 C 554.820312 835.183594 554.820312 835.175781 554.820312 835.171875 C 554.820312 835.167969 554.820312 835.160156 554.820312 835.15625 C 554.820312 835.152344 554.820312 835.144531 554.820312 835.140625 C 554.820312 835.136719 554.820312 835.128906 554.820312 835.125 C 554.820312 835.121094 554.820312 835.113281 554.820312 835.109375 C 554.820312 835.105469 554.820312 835.097656 554.820312 835.09375 C 554.820312 835.089844 554.820312 835.082031 554.820312 835.078125 C 554.820312 835.074219 554.820312 835.066406 554.820312 835.0625 C 554.820312 835.058594 554.820312 835.050781 554.820312 835.046875 C 554.820312 835.042969 554.820312 835.035156 554.820312 835.03125 C 554.820312 835.027344 554.820312 835.019531 554.820312 835.015625 C 554.820312 835.011719 554.820312 835.003906 554.820312 835 C 554.820312 834.996094 554.820312 834.988281 554.820312 834.984375 C 554.820312 834.976562 554.820312 834.972656 554.820312 834.96875 C 554.820312 834.960938 554.820312 834.957031 554.820312 834.953125 C 554.820312 834.945312 554.820312 834.941406 554.820312 834.9375 C 554.820312 834.929688 554.820312 834.925781 554.820312 834.921875 C 554.820312 834.914062 554.820312 834.910156 554.820312 834.90625 C 554.820312 834.898438 554.820312 834.894531 554.820312 834.890625 C 554.820312 834.882812 554.820312 834.878906 554.820312 834.875 C 554.820312 834.867188 554.820312 834.863281 554.820312 834.859375 C 554.820312 834.851562 554.820312 834.847656 554.820312 834.84375 C 554.820312 834.835938 554.820312 834.832031 554.820312 834.828125 C 554.820312 834.820312 554.820312 834.816406 554.820312 834.8125 C 554.820312 834.804688 554.820312 834.800781 554.820312 834.796875 C 554.820312 834.789062 554.820312 834.785156 554.820312 834.777344 C 554.820312 834.773438 554.820312 834.769531 554.820312 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554.820312 834.296875 554.820312 834.289062 C 554.820312 834.285156 554.820312 834.28125 554.820312 834.273438 C 554.820312 834.269531 554.820312 834.265625 554.820312 834.257812 C 554.820312 834.253906 554.820312 834.25 554.820312 834.242188 C 554.820312 834.238281 554.820312 834.234375 554.820312 834.226562 C 554.820312 834.222656 554.820312 834.214844 554.820312 834.210938 C 554.820312 834.207031 554.820312 834.199219 554.820312 834.195312 C 554.820312 834.191406 554.820312 834.183594 554.820312 834.179688 C 554.820312 834.175781 554.820312 834.167969 554.820312 834.164062 C 554.820312 834.160156 554.820312 834.152344 554.820312 834.148438 C 554.820312 834.144531 554.820312 834.136719 554.820312 834.132812 C 554.820312 834.128906 554.820312 834.121094 554.820312 834.117188 C 554.820312 834.113281 554.820312 834.105469 554.820312 834.101562 C 554.820312 834.09375 554.820312 834.089844 554.820312 834.085938 C 554.820312 834.078125 554.820312 834.074219 554.820312 834.070312 C 554.820312 834.0625 554.820312 834.058594 554.820312 834.054688 C 554.820312 834.046875 554.820312 834.042969 554.820312 834.039062 C 554.820312 834.03125 554.820312 834.027344 554.820312 834.023438 C 554.820312 834.015625 554.820312 834.011719 554.820312 834.007812 C 554.820312 834 554.820312 833.996094 554.820312 833.992188 C 554.820312 833.984375 554.820312 833.980469 554.820312 833.972656 C 554.820312 833.96875 554.820312 833.964844 554.820312 833.957031 C 554.820312 833.953125 554.820312 833.949219 554.820312 833.941406 C 554.820312 833.9375 554.820312 833.933594 554.820312 833.925781 C 554.820312 833.921875 554.820312 833.917969 554.820312 833.910156 C 554.820312 833.90625 554.820312 833.902344 554.820312 833.894531 C 554.820312 833.890625 554.820312 833.886719 554.820312 833.878906 C 554.820312 833.875 554.820312 833.871094 554.820312 833.863281 C 554.820312 833.859375 554.820312 833.851562 554.820312 833.847656 C 554.820312 833.84375 554.820312 833.835938 554.820312 833.832031 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554.820312 833.59375 C 554.820312 833.589844 554.820312 833.585938 554.820312 833.578125 C 554.820312 833.574219 554.820312 833.570312 554.820312 833.5625 C 554.820312 833.558594 554.820312 833.554688 554.820312 833.546875 C 554.820312 833.542969 554.820312 833.539062 554.820312 833.53125 C 554.820312 833.527344 554.820312 833.523438 554.820312 833.515625 C 554.820312 833.511719 554.820312 833.507812 554.820312 833.5 C 554.820312 833.496094 554.820312 833.492188 554.820312 833.484375 C 554.820312 833.480469 554.820312 833.472656 554.820312 833.46875 C 554.820312 833.464844 554.820312 833.457031 554.820312 833.453125 C 554.820312 833.449219 554.820312 833.441406 554.820312 833.4375 C 554.820312 833.433594 554.820312 833.425781 554.820312 833.421875 C 554.820312 833.417969 554.820312 833.410156 554.820312 833.40625 C 554.820312 833.402344 554.820312 833.394531 554.820312 833.390625 C 554.820312 833.386719 554.820312 833.378906 554.820312 833.375 C 554.820312 833.371094 554.820312 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554.820312 833.128906 554.820312 833.121094 C 554.820312 833.117188 554.820312 833.113281 554.820312 833.105469 C 554.820312 833.101562 554.820312 833.097656 554.820312 833.089844 C 554.820312 833.085938 554.820312 833.082031 554.820312 833.074219 C 554.820312 833.070312 554.820312 833.066406 554.820312 833.058594 C 554.820312 833.054688 554.820312 833.050781 554.820312 833.042969 C 554.820312 833.039062 554.820312 833.035156 554.820312 833.027344 C 554.820312 833.023438 554.820312 833.019531 554.820312 833.011719 C 554.820312 833.007812 554.820312 833 554.820312 832.996094 C 554.820312 832.992188 554.820312 832.984375 554.820312 832.980469 C 554.820312 832.976562 554.820312 832.96875 554.820312 832.964844 C 554.820312 832.960938 554.820312 832.953125 554.820312 832.949219 C 554.820312 832.945312 554.820312 832.9375 554.820312 832.933594 C 554.820312 832.929688 554.820312 832.921875 554.820312 832.917969 C 554.820312 832.914062 554.820312 832.90625 554.820312 832.902344 C 554.820312 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554.820312 832.660156 554.820312 832.65625 554.820312 832.652344 C 554.820312 832.644531 554.820312 832.640625 554.820312 832.636719 C 554.820312 832.628906 554.820312 832.625 554.820312 832.621094 C 554.820312 832.613281 554.820312 832.609375 554.820312 832.605469 C 554.820312 832.597656 554.820312 832.59375 554.820312 832.589844 C 554.820312 832.582031 554.820312 832.578125 554.820312 832.574219 C 554.820312 832.566406 554.820312 832.5625 554.820312 832.558594 C 554.820312 832.550781 554.820312 832.546875 554.820312 832.542969 C 554.820312 832.535156 554.820312 832.53125 554.820312 832.527344 C 554.820312 832.519531 554.820312 832.515625 554.820312 832.511719 C 554.820312 832.503906 554.820312 832.5 554.820312 832.496094 C 554.820312 832.488281 554.820312 832.484375 554.820312 832.480469 C 554.820312 832.472656 554.820312 832.46875 554.820312 832.464844 C 554.820312 832.457031 554.820312 832.453125 554.820312 832.449219 C 554.820312 832.441406 554.820312 832.4375 554.820312 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551.917969 1214.917969 C 551.917969 1214.925781 551.917969 1214.929688 551.917969 1214.933594 C 551.917969 1214.941406 551.917969 1214.945312 551.917969 1214.949219 C 551.917969 1214.953125 551.917969 1214.960938 551.917969 1214.964844 C 551.917969 1214.96875 551.917969 1214.976562 551.917969 1214.980469 C 551.917969 1214.984375 551.917969 1214.992188 551.917969 1214.996094 C 551.917969 1215 551.917969 1215.007812 551.917969 1215.011719 C 551.917969 1215.015625 551.917969 1215.023438 551.917969 1215.027344 C 551.917969 1215.03125 551.917969 1215.039062 551.917969 1215.042969 C 551.917969 1215.046875 551.917969 1215.054688 551.917969 1215.058594 C 551.917969 1215.0625 551.917969 1215.066406 551.917969 1215.074219 C 551.917969 1215.078125 551.917969 1215.082031 551.917969 1215.089844 C 551.917969 1215.09375 551.917969 1215.097656 551.917969 1215.105469 C 551.917969 1215.109375 551.917969 1215.113281 551.917969 1215.121094 C 551.917969 1215.125 551.917969 1215.128906 551.917969 1215.136719 C 551.917969 1215.140625 551.917969 1215.144531 551.917969 1215.152344 C 551.917969 1215.15625 551.917969 1215.160156 551.917969 1215.167969 C 551.917969 1215.171875 551.917969 1215.175781 551.917969 1215.183594 C 551.917969 1215.1875 551.917969 1215.191406 551.917969 1215.199219 C 551.917969 1215.203125 551.917969 1215.207031 551.917969 1215.210938 C 551.917969 1215.21875 551.917969 1215.222656 551.917969 1215.226562 C 551.917969 1215.234375 551.917969 1215.238281 551.917969 1215.242188 C 551.917969 1215.25 551.917969 1215.253906 551.917969 1215.257812 C 551.917969 1215.265625 551.917969 1215.269531 551.917969 1215.273438 C 551.917969 1215.28125 551.917969 1215.285156 551.917969 1215.289062 C 551.917969 1215.296875 551.917969 1215.300781 551.917969 1215.304688 C 551.917969 1215.3125 551.917969 1215.316406 551.917969 1215.320312 C 551.917969 1215.328125 551.917969 1215.332031 551.917969 1215.335938 C 551.917969 1215.34375 551.917969 1215.347656 551.917969 1215.351562 C 551.917969 1215.359375 551.917969 1215.363281 551.917969 1215.367188 C 551.917969 1215.375 551.917969 1215.378906 551.917969 1215.382812 C 551.917969 1215.390625 551.917969 1215.394531 551.917969 1215.398438 C 551.917969 1215.40625 551.917969 1215.410156 551.917969 1215.414062 C 551.917969 1215.421875 551.917969 1215.425781 551.917969 1215.429688 C 551.917969 1215.4375 551.917969 1215.441406 551.917969 1215.445312 C 551.917969 1215.453125 551.917969 1215.457031 551.917969 1215.460938 C 551.917969 1215.46875 551.917969 1215.472656 551.917969 1215.476562 C 551.917969 1215.480469 551.917969 1215.488281 551.917969 1215.492188 C 551.917969 1215.496094 551.917969 1215.503906 551.917969 1215.507812 C 551.917969 1215.511719 551.917969 1215.519531 551.917969 1215.523438 C 551.917969 1215.527344 551.917969 1215.535156 551.917969 1215.539062 C 551.917969 1215.542969 551.917969 1215.550781 551.917969 1215.554688 C 551.917969 1215.558594 551.917969 1215.566406 551.917969 1215.570312 C 551.917969 1215.574219 551.917969 1215.582031 551.917969 1215.585938 C 551.917969 1215.589844 551.917969 1215.597656 551.917969 1215.601562 C 551.917969 1215.605469 551.917969 1215.613281 551.917969 1215.617188 C 551.917969 1215.621094 551.917969 1215.628906 551.917969 1215.632812 C 551.917969 1215.636719 551.917969 1215.644531 551.917969 1215.648438 C 551.917969 1215.652344 551.917969 1215.660156 551.917969 1215.664062 C 551.917969 1215.667969 551.917969 1215.675781 551.917969 1215.679688 C 551.917969 1215.683594 551.917969 1215.691406 551.917969 1215.695312 C 551.917969 1215.699219 551.917969 1215.707031 551.917969 1215.710938 C 551.917969 1215.714844 551.917969 1215.722656 551.917969 1215.726562 C 551.917969 1215.730469 551.917969 1215.738281 551.917969 1215.742188 C 551.917969 1215.746094 551.917969 1215.753906 551.917969 1215.757812 C 551.917969 1215.761719 551.917969 1215.769531 551.917969 1215.773438 C 551.917969 1215.777344 551.917969 1215.785156 551.917969 1215.789062 C 551.917969 1215.792969 551.917969 1215.800781 551.917969 1215.804688 C 551.917969 1215.808594 551.917969 1215.816406 551.917969 1215.820312 C 551.917969 1215.828125 551.917969 1215.832031 551.917969 1215.835938 C 551.917969 1215.84375 551.917969 1215.847656 551.917969 1215.851562 C 551.917969 1215.859375 551.917969 1215.863281 551.917969 1215.867188 C 551.917969 1215.875 551.917969 1215.878906 551.917969 1215.882812 C 551.917969 1215.890625 551.917969 1215.894531 551.917969 1215.898438 C 551.917969 1215.90625 551.917969 1215.910156 551.917969 1215.914062 C 551.917969 1215.921875 551.917969 1215.925781 551.917969 1215.929688 C 551.917969 1215.9375 551.917969 1215.941406 551.917969 1215.945312 C 551.917969 1215.953125 551.917969 1215.957031 551.917969 1215.960938 C 551.917969 1215.96875 551.917969 1215.972656 551.917969 1215.976562 C 551.917969 1215.984375 551.917969 1215.988281 551.917969 1215.992188 C 551.917969 1216 551.917969 1216.003906 551.917969 1216.007812 C 551.917969 1216.015625 551.917969 1216.019531 551.917969 1216.023438 C 551.917969 1216.03125 551.917969 1216.035156 551.917969 1216.039062 C 551.917969 1216.046875 551.917969 1216.050781 551.917969 1216.054688 C 551.917969 1216.0625 551.917969 1216.066406 551.917969 1216.070312 C 551.917969 1216.078125 551.917969 1216.082031 551.917969 1216.085938 C 551.917969 1216.09375 551.917969 1216.097656 551.917969 1216.101562 C 551.917969 1216.109375 551.917969 1216.113281 551.917969 1216.117188 C 551.917969 1216.125 551.917969 1216.128906 551.917969 1216.136719 C 551.917969 1216.140625 551.917969 1216.144531 551.917969 1216.152344 C 551.917969 1216.15625 551.917969 1216.160156 551.917969 1216.167969 C 551.917969 1216.171875 551.917969 1216.175781 551.917969 1216.183594 C 551.917969 1216.1875 551.917969 1216.191406 551.917969 1216.199219 C 551.917969 1216.203125 551.917969 1216.207031 551.917969 1216.214844 C 551.917969 1216.21875 551.917969 1216.222656 551.917969 1216.230469 C 551.917969 1216.234375 551.917969 1216.238281 551.917969 1216.246094 C 551.917969 1216.25 551.917969 1216.253906 551.917969 1216.261719 C 551.917969 1216.265625 551.917969 1216.269531 551.917969 1216.277344 C 551.917969 1216.28125 551.917969 1216.285156 551.917969 1216.292969 C 551.917969 1216.296875 551.917969 1216.300781 551.917969 1216.308594 C 551.917969 1216.3125 551.917969 1216.320312 551.917969 1216.324219 C 551.917969 1216.328125 551.917969 1216.335938 551.917969 1216.339844 C 551.917969 1216.34375 551.917969 1216.351562 551.917969 1216.355469 C 551.917969 1216.359375 551.917969 1216.367188 551.917969 1216.371094 C 551.917969 1216.375 551.917969 1216.382812 551.917969 1216.386719 C 551.917969 1216.390625 551.917969 1216.398438 551.917969 1216.402344 C 551.917969 1216.40625 551.917969 1216.414062 551.917969 1216.417969 C 551.917969 1216.421875 551.917969 1216.429688 551.917969 1216.433594 C 551.917969 1216.4375 551.917969 1216.445312 551.917969 1216.449219 C 551.917969 1216.453125 551.917969 1216.460938 551.917969 1216.464844 C 551.917969 1216.46875 551.917969 1216.476562 551.917969 1216.480469 C 551.917969 1216.488281 551.917969 1216.492188 551.917969 1216.496094 C 551.917969 1216.503906 551.917969 1216.507812 551.917969 1216.511719 C 551.917969 1216.519531 551.917969 1216.523438 551.917969 1216.527344 C 551.917969 1216.535156 551.917969 1216.539062 551.917969 1216.542969 C 551.917969 1216.550781 551.917969 1216.554688 551.917969 1216.558594 C 551.917969 1216.566406 551.917969 1216.570312 551.917969 1216.574219 C 551.917969 1216.582031 551.917969 1216.585938 551.917969 1216.589844 C 551.917969 1216.597656 551.917969 1216.601562 551.917969 1216.605469 C 551.917969 1216.613281 551.917969 1216.617188 551.917969 1216.625 C 551.917969 1216.628906 551.917969 1216.632812 551.917969 1216.640625 C 551.917969 1216.644531 551.917969 1216.648438 551.917969 1216.65625 C 551.917969 1216.660156 551.917969 1216.664062 551.917969 1216.671875 C 551.917969 1216.675781 551.917969 1216.679688 551.917969 1216.6875 C 551.917969 1216.691406 551.917969 1216.695312 551.917969 1216.703125 C 551.917969 1216.707031 551.917969 1216.710938 551.917969 1216.71875 C 551.917969 1216.722656 551.917969 1216.726562 551.917969 1216.734375 C 551.917969 1216.738281 551.917969 1216.742188 551.917969 1216.75 C 551.917969 1216.753906 551.917969 1216.761719 551.917969 1216.765625 C 551.917969 1216.769531 551.917969 1216.777344 551.917969 1216.78125 C 551.917969 1216.785156 551.917969 1216.792969 551.917969 1216.796875 C 551.917969 1216.800781 551.917969 1216.808594 551.917969 1216.8125 C 551.917969 1216.816406 551.917969 1216.824219 551.917969 1216.828125 C 551.917969 1216.832031 551.917969 1216.839844 551.917969 1216.84375 C 551.917969 1216.847656 551.917969 1216.855469 551.917969 1216.859375 C 551.917969 1216.863281 551.917969 1216.871094 551.917969 1216.875 C 551.917969 1216.882812 551.917969 1216.886719 551.917969 1216.890625 C 551.917969 1216.898438 551.917969 1216.902344 551.917969 1216.90625 C 551.917969 1216.914062 551.917969 1216.917969 551.917969 1216.921875 C 551.917969 1216.929688 551.917969 1216.933594 551.917969 1216.9375 C 551.917969 1216.945312 551.917969 1216.949219 551.917969 1216.953125 C 551.917969 1216.960938 551.917969 1216.964844 551.917969 1216.96875 C 551.917969 1216.976562 551.917969 1216.980469 551.917969 1216.984375 C 551.917969 1216.992188 551.917969 1216.996094 551.917969 1217.003906 C 551.917969 1217.007812 551.917969 1217.011719 551.917969 1217.019531 C 551.917969 1217.023438 551.917969 1217.027344 551.917969 1217.035156 C 551.917969 1217.039062 551.917969 1217.042969 551.917969 1217.050781 C 551.917969 1217.054688 551.917969 1217.058594 551.917969 1217.066406 C 551.917969 1217.070312 551.917969 1217.074219 551.917969 1217.082031 C 551.917969 1217.085938 551.917969 1217.089844 551.917969 1217.097656 C 551.917969 1217.101562 551.917969 1217.105469 551.917969 1217.113281 C 551.917969 1217.117188 551.917969 1217.125 551.917969 1217.128906 C 551.917969 1217.132812 551.917969 1217.140625 551.917969 1217.144531 C 551.917969 1217.148438 551.917969 1217.15625 551.917969 1217.160156 C 551.917969 1217.164062 551.917969 1217.171875 551.917969 1217.175781 C 551.917969 1217.179688 551.917969 1217.1875 551.917969 1217.191406 C 551.917969 1217.195312 551.917969 1217.203125 551.917969 1217.207031 C 551.917969 1217.210938 551.917969 1217.21875 551.917969 1217.222656 C 551.917969 1217.226562 551.917969 1217.234375 551.917969 1217.238281 C 551.917969 1217.246094 551.917969 1217.25 551.917969 1217.253906 C 551.917969 1217.261719 551.917969 1217.265625 551.917969 1217.269531 C 551.917969 1217.277344 551.917969 1217.28125 551.917969 1217.285156 C 551.917969 1217.292969 551.917969 1217.296875 551.917969 1217.300781 C 551.917969 1217.308594 551.917969 1217.3125 551.917969 1217.316406 C 551.917969 1217.324219 551.917969 1217.328125 551.917969 1217.332031 C 551.917969 1217.339844 551.917969 1217.34375 551.917969 1217.347656 C 551.917969 1217.355469 551.917969 1217.359375 551.917969 1217.367188 C 551.917969 1217.371094 551.917969 1217.375 551.917969 1217.382812 C 551.917969 1217.386719 551.917969 1217.390625 551.917969 1217.398438 C 551.917969 1217.402344 551.917969 1217.40625 551.917969 1217.414062 C 551.917969 1217.417969 551.917969 1217.421875 551.917969 1217.429688 C 551.917969 1217.433594 551.917969 1217.4375 551.917969 1217.445312 C 551.917969 1217.449219 551.917969 1217.453125 551.917969 1217.460938 C 551.917969 1217.464844 551.917969 1217.46875 551.917969 1217.476562 C 551.917969 1217.480469 551.917969 1217.484375 551.917969 1217.492188 C 551.917969 1217.496094 551.917969 1217.503906 551.917969 1217.507812 C 551.917969 1217.511719 551.917969 1217.519531 551.917969 1217.523438 C 551.917969 1217.527344 551.917969 1217.535156 551.917969 1217.539062 C 551.917969 1217.542969 551.917969 1217.550781 551.917969 1217.554688 C 551.917969 1217.558594 551.917969 1217.566406 551.917969 1217.570312 C 551.917969 1217.574219 551.917969 1217.582031 551.917969 1217.585938 C 551.917969 1217.589844 551.917969 1217.597656 551.917969 1217.601562 C 551.917969 1217.605469 551.917969 1217.613281 551.917969 1217.617188 C 551.917969 1217.621094 551.917969 1217.628906 551.917969 1217.632812 C 551.917969 1217.640625 551.917969 1217.644531 551.917969 1217.648438 C 551.917969 1217.65625 551.917969 1217.660156 551.917969 1217.664062 C 551.917969 1217.671875 551.917969 1217.675781 551.917969 1217.679688 C 551.917969 1217.6875 551.917969 1217.691406 551.917969 1217.695312 C 551.917969 1217.703125 551.917969 1217.707031 551.917969 1217.710938 C 551.917969 1217.71875 551.917969 1217.722656 551.917969 1217.726562 C 551.917969 1217.734375 551.917969 1217.738281 551.917969 1217.742188 C 551.917969 1217.75 551.917969 1217.753906 551.917969 1217.757812 C 551.917969 1217.765625 551.917969 1217.769531 551.917969 1217.777344 C 551.917969 1217.78125 551.917969 1217.785156 551.917969 1217.792969 C 551.917969 1217.796875 551.917969 1217.800781 551.917969 1217.808594 C 551.917969 1217.8125 551.917969 1217.816406 551.917969 1217.824219 C 551.917969 1217.828125 551.917969 1217.832031 551.917969 1217.839844 C 551.917969 1217.84375 551.917969 1217.847656 551.917969 1217.855469 C 551.917969 1217.859375 551.917969 1217.863281 551.917969 1217.871094 C 551.917969 1217.875 551.917969 1217.878906 551.917969 1217.886719 C 551.917969 1217.890625 551.917969 1217.894531 551.917969 1217.902344 C 551.917969 1217.90625 551.917969 1217.910156 551.917969 1217.917969 C 551.917969 1217.921875 551.917969 1217.929688 551.917969 1217.933594 C 551.917969 1217.9375 551.917969 1217.945312 551.917969 1217.949219 C 551.917969 1217.953125 551.917969 1217.960938 551.917969 1217.964844 C 551.917969 1217.96875 551.917969 1217.976562 551.917969 1217.980469 C 551.917969 1217.984375 551.917969 1217.992188 551.917969 1217.996094 C 551.917969 1218 551.917969 1218.007812 551.917969 1218.011719 C 551.917969 1218.015625 551.917969 1218.023438 551.917969 1218.027344 C 551.917969 1218.03125 551.917969 1218.039062 551.917969 1218.042969 C 551.917969 1218.046875 551.917969 1218.054688 551.917969 1218.058594 C 551.917969 1218.0625 551.917969 1218.070312 551.917969 1218.074219 C 551.917969 1218.078125 551.917969 1218.085938 551.917969 1218.089844 C 551.917969 1218.097656 551.917969 1218.101562 551.917969 1218.105469 C 551.917969 1218.113281 551.917969 1218.117188 551.917969 1218.121094 C 551.917969 1218.128906 551.917969 1218.132812 551.917969 1218.136719 C 551.917969 1218.144531 551.917969 1218.148438 551.917969 1218.152344 C 551.917969 1218.160156 551.917969 1218.164062 551.917969 1218.167969 C 551.917969 1218.175781 551.917969 1218.179688 551.917969 1218.183594 C 551.917969 1218.191406 551.917969 1218.195312 551.917969 1218.199219 C 551.917969 1218.207031 551.917969 1218.210938 551.917969 1218.214844 C 551.917969 1218.222656 551.917969 1218.226562 551.917969 1218.230469 C 551.917969 1218.238281 551.917969 1218.242188 551.917969 1218.246094 C 551.917969 1218.253906 551.917969 1218.257812 551.917969 1218.261719 C 551.917969 1218.269531 551.917969 1218.273438 551.917969 1218.277344 C 551.917969 1218.285156 551.917969 1218.289062 551.917969 1218.292969 C 551.917969 1218.300781 551.917969 1218.304688 551.917969 1218.308594 C 551.917969 1218.316406 551.917969 1218.320312 551.917969 1218.324219 C 551.917969 1218.332031 551.917969 1218.335938 551.917969 1218.34375 C 551.917969 1218.347656 551.917969 1218.351562 551.917969 1218.359375 C 551.917969 1218.363281 551.917969 1218.367188 551.917969 1218.375 C 551.917969 1218.378906 551.917969 1218.382812 551.917969 1218.390625 C 551.917969 1218.394531 551.917969 1218.398438 551.917969 1218.40625 C 551.917969 1218.410156 551.917969 1218.414062 551.917969 1218.421875 C 551.917969 1218.425781 551.917969 1218.429688 551.917969 1218.4375 C 551.917969 1218.441406 551.917969 1218.445312 551.917969 1218.453125 C 551.917969 1218.457031 551.917969 1218.460938 551.917969 1218.46875 C 551.917969 1218.472656 551.917969 1218.476562 551.917969 1218.484375 C 551.917969 1218.488281 551.917969 1218.492188 551.917969 1218.5 C 551.917969 1218.503906 551.917969 1218.507812 551.917969 1218.515625 C 551.917969 1218.519531 551.917969 1218.523438 551.917969 1218.53125 C 551.917969 1218.535156 551.917969 1218.539062 551.917969 1218.546875 C 551.917969 1218.550781 551.917969 1218.554688 551.917969 1218.5625 C 551.917969 1218.566406 551.917969 1218.570312 551.917969 1218.578125 C 551.917969 1218.582031 551.917969 1218.585938 551.917969 1218.59375 C 551.917969 1218.597656 551.917969 1218.601562 551.917969 1218.609375 C 551.917969 1218.613281 551.917969 1218.617188 551.917969 1218.625 C 551.917969 1218.628906 551.917969 1218.632812 551.917969 1218.640625 C 551.917969 1218.644531 551.917969 1218.648438 551.917969 1218.65625 C 551.917969 1218.660156 551.917969 1218.664062 551.917969 1218.671875 C 551.917969 1218.675781 551.917969 1218.679688 551.917969 1218.6875 C 551.917969 1218.691406 551.917969 1218.695312 551.917969 1218.703125 C 551.917969 1218.707031 551.917969 1218.710938 551.917969 1218.71875 C 551.917969 1218.722656 551.917969 1218.726562 551.917969 1218.734375 C 551.917969 1218.738281 551.917969 1218.742188 551.917969 1218.75 C 551.917969 1218.753906 551.917969 1218.757812 551.917969 1218.765625 C 551.917969 1218.769531 551.917969 1218.773438 551.917969 1218.78125 C 551.917969 1218.785156 551.917969 1218.789062 551.917969 1218.796875 C 551.917969 1218.800781 551.917969 1218.804688 551.917969 1218.8125 C 551.917969 1218.816406 551.917969 1218.820312 551.917969 1218.828125 C 551.917969 1218.832031 551.917969 1218.835938 551.917969 1218.84375 C 551.917969 1218.847656 551.917969 1218.851562 551.917969 1218.859375 C 551.917969 1218.863281 551.917969 1218.867188 551.917969 1218.875 C 551.917969 1218.878906 551.917969 1218.882812 551.917969 1218.890625 C 551.917969 1218.894531 551.917969 1218.898438 551.917969 1218.90625 C 551.917969 1218.910156 551.917969 1218.914062 551.917969 1218.921875 C 551.917969 1218.925781 551.917969 1218.929688 551.917969 1218.9375 C 551.917969 1218.941406 551.917969 1218.945312 551.917969 1218.949219 C 551.917969 1218.957031 551.917969 1218.960938 551.917969 1218.964844 C 551.917969 1218.972656 551.917969 1218.976562 551.917969 1218.980469 C 551.917969 1218.988281 551.917969 1218.992188 551.917969 1218.996094 C 551.917969 1219.003906 551.917969 1219.007812 551.917969 1219.011719 C 551.917969 1219.019531 551.917969 1219.023438 551.917969 1219.027344 C 551.917969 1219.035156 551.917969 1219.039062 551.917969 1219.042969 C 551.917969 1219.050781 551.917969 1219.054688 551.917969 1219.058594 C 551.917969 1219.066406 551.917969 1219.070312 551.917969 1219.074219 C 551.917969 1219.082031 551.917969 1219.085938 551.917969 1219.089844 C 551.917969 1219.097656 551.917969 1219.101562 551.917969 1219.105469 C 551.917969 1219.113281 551.917969 1219.117188 551.917969 1219.121094 C 551.917969 1219.128906 551.917969 1219.132812 551.917969 1219.136719 C 551.917969 1219.144531 551.917969 1219.148438 551.917969 1219.152344 C 551.917969 1219.15625 551.917969 1219.164062 551.917969 1219.167969 C 551.917969 1219.171875 551.917969 1219.179688 551.917969 1219.183594 C 551.917969 1219.1875 551.917969 1219.195312 551.917969 1219.199219 C 551.917969 1219.203125 551.917969 1219.210938 551.917969 1219.214844 C 551.917969 1219.21875 551.917969 1219.226562 551.917969 1219.230469 C 551.917969 1219.234375 551.917969 1219.242188 551.917969 1219.246094 C 551.917969 1219.25 551.917969 1219.257812 551.917969 1219.261719 C 551.917969 1219.265625 551.917969 1219.273438 551.917969 1219.277344 C 551.917969 1219.28125 551.917969 1219.285156 551.917969 1219.292969 C 551.917969 1219.296875 551.917969 1219.300781 551.917969 1219.308594 C 551.917969 1219.3125 551.917969 1219.316406 551.917969 1219.324219 C 551.917969 1219.328125 551.917969 1219.332031 551.917969 1219.339844 C 551.917969 1219.34375 551.917969 1219.347656 551.917969 1219.355469 C 551.917969 1219.359375 551.917969 1219.363281 551.917969 1219.371094 C 551.917969 1219.375 551.917969 1219.378906 551.917969 1219.382812 C 551.917969 1219.390625 551.917969 1219.394531 551.917969 1219.398438 C 551.917969 1219.40625 551.917969 1219.410156 551.917969 1219.414062 C 551.917969 1219.421875 551.917969 1219.425781 551.917969 1219.429688 C 551.917969 1219.4375 551.917969 1219.441406 551.917969 1219.445312 C 551.917969 1219.453125 551.917969 1219.457031 551.917969 1219.460938 C 551.917969 1219.46875 551.917969 1219.472656 551.917969 1219.476562 C 551.917969 1219.480469 551.917969 1219.488281 551.917969 1219.492188 C 551.917969 1219.496094 551.917969 1219.503906 551.917969 1219.507812 C 551.917969 1219.511719 551.917969 1219.519531 551.917969 1219.523438 C 551.917969 1219.527344 551.917969 1219.535156 551.917969 1219.539062 C 551.917969 1219.542969 551.917969 1219.550781 551.917969 1219.554688 C 551.917969 1219.558594 551.917969 1219.5625 551.917969 1219.570312 C 551.917969 1219.574219 551.917969 1219.578125 551.917969 1219.585938 C 551.917969 1219.589844 551.917969 1219.59375 551.917969 1219.601562 C 551.917969 1219.605469 551.917969 1219.609375 551.917969 1219.617188 C 551.917969 1219.621094 551.917969 1219.625 551.917969 1219.628906 C 551.917969 1219.636719 551.917969 1219.640625 551.917969 1219.644531 C 551.917969 1219.652344 551.917969 1219.65625 551.917969 1219.660156 C 551.917969 1219.667969 551.917969 1219.671875 551.917969 1219.675781 C 551.917969 1219.683594 551.917969 1219.6875 551.917969 1219.691406 C 551.917969 1219.695312 551.917969 1219.703125 551.917969 1219.707031 C 551.917969 1219.710938 551.917969 1219.71875 551.917969 1219.722656 C 551.917969 1219.726562 551.917969 1219.734375 551.917969 1219.738281 C 551.917969 1219.742188 551.917969 1219.746094 551.917969 1219.753906 C 551.917969 1219.757812 551.917969 1219.761719 551.917969 1219.769531 C 551.917969 1219.773438 551.917969 1219.777344 551.917969 1219.785156 C 551.917969 1219.789062 551.917969 1219.792969 551.917969 1219.800781 C 551.917969 1219.804688 551.917969 1219.808594 551.917969 1219.8125 C 551.917969 1219.820312 551.917969 1219.824219 551.917969 1219.828125 C 551.917969 1219.835938 551.917969 1219.839844 551.917969 1219.84375 C 551.917969 1219.851562 551.917969 1219.855469 551.917969 1219.859375 C 551.917969 1219.863281 551.917969 1219.871094 551.917969 1219.875 C 551.917969 1219.878906 551.917969 1219.886719 551.917969 1219.890625 C 551.917969 1219.894531 551.917969 1219.902344 551.917969 1219.90625 C 551.917969 1219.910156 551.917969 1219.914062 551.917969 1219.921875 C 551.917969 1219.925781 551.917969 1219.929688 551.917969 1219.9375 C 551.917969 1219.941406 551.917969 1219.945312 551.917969 1219.953125 C 551.917969 1219.957031 551.917969 1219.960938 551.917969 1219.964844 C 551.917969 1219.972656 551.917969 1219.976562 551.917969 1219.980469 C 551.917969 1219.988281 551.917969 1219.992188 551.917969 1219.996094 C 551.917969 1220 551.917969 1220.007812 551.917969 1220.011719 C 551.917969 1220.015625 551.917969 1220.023438 551.917969 1220.027344 C 551.917969 1220.03125 551.917969 1220.039062 551.917969 1220.042969 C 551.917969 1220.046875 551.917969 1220.050781 551.917969 1220.058594 C 551.917969 1220.0625 551.917969 1220.066406 551.917969 1220.074219 C 551.917969 1220.078125 551.917969 1220.082031 551.917969 1220.085938 C 551.917969 1220.09375 551.917969 1220.097656 551.917969 1220.101562 C 551.917969 1220.109375 551.917969 1220.113281 551.917969 1220.117188 C 551.917969 1220.121094 551.917969 1220.128906 551.917969 1220.132812 C 551.917969 1220.136719 551.917969 1220.144531 551.917969 1220.148438 C 551.917969 1220.152344 551.917969 1220.160156 551.917969 1220.164062 C 551.917969 1220.167969 551.917969 1220.171875 551.917969 1220.179688 C 551.917969 1220.183594 551.917969 1220.1875 551.917969 1220.195312 C 551.917969 1220.199219 551.917969 1220.203125 551.917969 1220.207031 C 551.917969 1220.214844 551.917969 1220.21875 551.917969 1220.222656 C 551.917969 1220.230469 551.917969 1220.234375 551.917969 1220.238281 C 551.917969 1220.242188 551.917969 1220.25 551.917969 1220.253906 C 551.917969 1220.257812 551.917969 1220.265625 551.917969 1220.269531 C 551.917969 1220.273438 551.917969 1220.277344 551.917969 1220.285156 C 551.917969 1220.289062 551.917969 1220.292969 551.917969 1220.296875 C 551.917969 1220.304688 551.917969 1220.308594 551.917969 1220.3125 C 551.917969 1220.320312 551.917969 1220.324219 551.917969 1220.328125 C 551.917969 1220.332031 551.917969 1220.339844 551.917969 1220.34375 C 551.917969 1220.347656 551.917969 1220.355469 551.917969 1220.359375 C 551.917969 1220.363281 551.917969 1220.367188 551.917969 1220.375 C 551.917969 1220.378906 551.917969 1220.382812 551.917969 1220.390625 C 551.917969 1220.394531 551.917969 1220.398438 551.917969 1220.402344 C 551.917969 1220.410156 551.917969 1220.414062 551.917969 1220.417969 C 551.917969 1220.421875 551.917969 1220.429688 551.917969 1220.433594 C 551.917969 1220.4375 551.917969 1220.445312 551.917969 1220.449219 C 551.917969 1220.453125 551.917969 1220.457031 551.917969 1220.464844 C 551.917969 1220.46875 551.917969 1220.472656 551.917969 1220.480469 C 551.917969 1220.484375 551.917969 1220.488281 551.917969 1220.492188 C 551.917969 1220.5 551.917969 1220.503906 551.917969 1220.507812 C 551.917969 1220.511719 551.917969 1220.519531 551.917969 1220.523438 C 551.917969 1220.527344 551.917969 1220.535156 551.917969 1220.539062 C 551.917969 1220.542969 551.917969 1220.546875 551.917969 1220.554688 C 551.917969 1220.558594 551.917969 1220.5625 551.917969 1220.566406 C 551.917969 1220.574219 551.917969 1220.578125 551.917969 1220.582031 C 551.917969 1220.589844 551.917969 1220.59375 551.917969 1220.597656 C 551.917969 1220.601562 551.917969 1220.609375 551.917969 1220.613281 C 551.917969 1220.617188 551.917969 1220.621094 551.917969 1220.628906 C 551.917969 1220.632812 551.917969 1220.636719 551.917969 1220.640625 C 551.917969 1220.648438 551.917969 1220.652344 551.917969 1220.65625 C 551.917969 1220.664062 551.917969 1220.667969 551.917969 1220.671875 C 551.917969 1220.675781 551.917969 1220.683594 551.917969 1220.6875 C 551.917969 1220.691406 551.917969 1220.695312 551.917969 1220.703125 C 551.917969 1220.707031 551.917969 1220.710938 551.917969 1220.714844 C 551.917969 1220.722656 551.917969 1220.726562 551.917969 1220.730469 C 551.917969 1220.738281 551.917969 1220.742188 551.917969 1220.746094 C 551.917969 1220.75 551.917969 1220.757812 551.917969 1220.761719 C 551.917969 1220.765625 551.917969 1220.769531 551.917969 1220.777344 C 551.917969 1220.78125 551.917969 1220.785156 551.917969 1220.789062 C 551.917969 1220.796875 551.917969 1220.800781 551.917969 1220.804688 C 551.917969 1220.808594 551.917969 1220.816406 551.917969 1220.820312 C 551.917969 1220.824219 551.917969 1220.828125 551.917969 1220.835938 C 551.917969 1220.839844 551.917969 1220.84375 551.917969 1220.847656 C 551.917969 1220.855469 551.917969 1220.859375 551.917969 1220.863281 C 551.917969 1220.867188 551.917969 1220.875 551.917969 1220.878906 C 551.917969 1220.882812 551.917969 1220.890625 551.917969 1220.894531 C 551.917969 1220.898438 551.917969 1220.902344 551.917969 1220.910156 C 551.917969 1220.914062 551.917969 1220.917969 551.917969 1220.921875 C 551.917969 1220.929688 551.917969 1220.933594 551.917969 1220.9375 C 551.917969 1220.941406 551.917969 1220.949219 551.917969 1220.953125 C 551.917969 1220.957031 551.917969 1220.960938 551.917969 1220.96875 C 551.917969 1220.972656 551.917969 1220.976562 551.917969 1220.980469 C 551.917969 1220.988281 551.917969 1220.992188 551.917969 1220.996094 C 551.917969 1221 551.917969 1221.007812 551.917969 1221.011719 C 551.917969 1221.015625 551.917969 1221.019531 551.917969 1221.027344 C 551.917969 1221.03125 551.917969 1221.035156 551.917969 1221.039062 C 551.917969 1221.046875 551.917969 1221.050781 551.917969 1221.054688 C 551.917969 1221.058594 551.917969 1221.066406 551.917969 1221.070312 C 551.917969 1221.074219 551.917969 1221.078125 551.917969 1221.082031 L 554.820312 1221.082031 C 554.820312 1221.078125 554.820312 1221.074219 554.820312 1221.070312 C 554.820312 1221.066406 554.820312 1221.058594 554.820312 1221.054688 C 554.820312 1221.050781 554.820312 1221.046875 554.820312 1221.039062 C 554.820312 1221.035156 554.820312 1221.03125 554.820312 1221.027344 C 554.820312 1221.019531 554.820312 1221.015625 554.820312 1221.011719 C 554.820312 1221.007812 554.820312 1221 554.820312 1220.996094 C 554.820312 1220.992188 554.820312 1220.988281 554.820312 1220.980469 C 554.820312 1220.976562 554.820312 1220.972656 554.820312 1220.96875 C 554.820312 1220.960938 554.820312 1220.957031 554.820312 1220.953125 C 554.820312 1220.949219 554.820312 1220.941406 554.820312 1220.9375 C 554.820312 1220.933594 554.820312 1220.929688 554.820312 1220.921875 C 554.820312 1220.917969 554.820312 1220.914062 554.820312 1220.910156 C 554.820312 1220.902344 554.820312 1220.898438 554.820312 1220.894531 C 554.820312 1220.890625 554.820312 1220.882812 554.820312 1220.878906 C 554.820312 1220.875 554.820312 1220.867188 554.820312 1220.863281 C 554.820312 1220.859375 554.820312 1220.855469 554.820312 1220.847656 C 554.820312 1220.84375 554.820312 1220.839844 554.820312 1220.835938 C 554.820312 1220.828125 554.820312 1220.824219 554.820312 1220.820312 C 554.820312 1220.816406 554.820312 1220.808594 554.820312 1220.804688 C 554.820312 1220.800781 554.820312 1220.796875 554.820312 1220.789062 C 554.820312 1220.785156 554.820312 1220.78125 554.820312 1220.777344 C 554.820312 1220.769531 554.820312 1220.765625 554.820312 1220.761719 C 554.820312 1220.757812 554.820312 1220.75 554.820312 1220.746094 C 554.820312 1220.742188 554.820312 1220.738281 554.820312 1220.730469 C 554.820312 1220.726562 554.820312 1220.722656 554.820312 1220.714844 C 554.820312 1220.710938 554.820312 1220.707031 554.820312 1220.703125 C 554.820312 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554.820312 1219.625 554.820312 1219.621094 554.820312 1219.617188 C 554.820312 1219.609375 554.820312 1219.605469 554.820312 1219.601562 C 554.820312 1219.59375 554.820312 1219.589844 554.820312 1219.585938 C 554.820312 1219.578125 554.820312 1219.574219 554.820312 1219.570312 C 554.820312 1219.5625 554.820312 1219.558594 554.820312 1219.554688 C 554.820312 1219.550781 554.820312 1219.542969 554.820312 1219.539062 C 554.820312 1219.535156 554.820312 1219.527344 554.820312 1219.523438 C 554.820312 1219.519531 554.820312 1219.511719 554.820312 1219.507812 C 554.820312 1219.503906 554.820312 1219.496094 554.820312 1219.492188 C 554.820312 1219.488281 554.820312 1219.480469 554.820312 1219.476562 C 554.820312 1219.472656 554.820312 1219.46875 554.820312 1219.460938 C 554.820312 1219.457031 554.820312 1219.453125 554.820312 1219.445312 C 554.820312 1219.441406 554.820312 1219.4375 554.820312 1219.429688 C 554.820312 1219.425781 554.820312 1219.421875 554.820312 1219.414062 C 554.820312 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554.820312 1216.75 C 554.820312 1216.742188 554.820312 1216.738281 554.820312 1216.734375 C 554.820312 1216.726562 554.820312 1216.722656 554.820312 1216.71875 C 554.820312 1216.710938 554.820312 1216.707031 554.820312 1216.703125 C 554.820312 1216.695312 554.820312 1216.691406 554.820312 1216.6875 C 554.820312 1216.679688 554.820312 1216.675781 554.820312 1216.671875 C 554.820312 1216.664062 554.820312 1216.660156 554.820312 1216.65625 C 554.820312 1216.648438 554.820312 1216.644531 554.820312 1216.640625 C 554.820312 1216.632812 554.820312 1216.628906 554.820312 1216.625 C 554.820312 1216.617188 554.820312 1216.613281 554.820312 1216.605469 C 554.820312 1216.601562 554.820312 1216.597656 554.820312 1216.589844 C 554.820312 1216.585938 554.820312 1216.582031 554.820312 1216.574219 C 554.820312 1216.570312 554.820312 1216.566406 554.820312 1216.558594 C 554.820312 1216.554688 554.820312 1216.550781 554.820312 1216.542969 C 554.820312 1216.539062 554.820312 1216.535156 554.820312 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554.820312 1216.300781 554.820312 1216.296875 554.820312 1216.292969 C 554.820312 1216.285156 554.820312 1216.28125 554.820312 1216.277344 C 554.820312 1216.269531 554.820312 1216.265625 554.820312 1216.261719 C 554.820312 1216.253906 554.820312 1216.25 554.820312 1216.246094 C 554.820312 1216.238281 554.820312 1216.234375 554.820312 1216.230469 C 554.820312 1216.222656 554.820312 1216.21875 554.820312 1216.214844 C 554.820312 1216.207031 554.820312 1216.203125 554.820312 1216.199219 C 554.820312 1216.191406 554.820312 1216.1875 554.820312 1216.183594 C 554.820312 1216.175781 554.820312 1216.171875 554.820312 1216.167969 C 554.820312 1216.160156 554.820312 1216.15625 554.820312 1216.152344 C 554.820312 1216.144531 554.820312 1216.140625 554.820312 1216.136719 C 554.820312 1216.128906 554.820312 1216.125 554.820312 1216.117188 C 554.820312 1216.113281 554.820312 1216.109375 554.820312 1216.101562 C 554.820312 1216.097656 554.820312 1216.09375 554.820312 1216.085938 C 554.820312 1216.082031 554.820312 1216.078125 554.820312 1216.070312 C 554.820312 1216.066406 554.820312 1216.0625 554.820312 1216.054688 C 554.820312 1216.050781 554.820312 1216.046875 554.820312 1216.039062 C 554.820312 1216.035156 554.820312 1216.03125 554.820312 1216.023438 C 554.820312 1216.019531 554.820312 1216.015625 554.820312 1216.007812 C 554.820312 1216.003906 554.820312 1216 554.820312 1215.992188 C 554.820312 1215.988281 554.820312 1215.984375 554.820312 1215.976562 C 554.820312 1215.972656 554.820312 1215.96875 554.820312 1215.960938 C 554.820312 1215.957031 554.820312 1215.953125 554.820312 1215.945312 C 554.820312 1215.941406 554.820312 1215.9375 554.820312 1215.929688 C 554.820312 1215.925781 554.820312 1215.921875 554.820312 1215.914062 C 554.820312 1215.910156 554.820312 1215.90625 554.820312 1215.898438 C 554.820312 1215.894531 554.820312 1215.890625 554.820312 1215.882812 C 554.820312 1215.878906 554.820312 1215.875 554.820312 1215.867188 C 554.820312 1215.863281 554.820312 1215.859375 554.820312 1215.851562 C 554.820312 1215.847656 554.820312 1215.84375 554.820312 1215.835938 C 554.820312 1215.832031 554.820312 1215.828125 554.820312 1215.820312 C 554.820312 1215.816406 554.820312 1215.808594 554.820312 1215.804688 C 554.820312 1215.800781 554.820312 1215.792969 554.820312 1215.789062 C 554.820312 1215.785156 554.820312 1215.777344 554.820312 1215.773438 C 554.820312 1215.769531 554.820312 1215.761719 554.820312 1215.757812 C 554.820312 1215.753906 554.820312 1215.746094 554.820312 1215.742188 C 554.820312 1215.738281 554.820312 1215.730469 554.820312 1215.726562 C 554.820312 1215.722656 554.820312 1215.714844 554.820312 1215.710938 C 554.820312 1215.707031 554.820312 1215.699219 554.820312 1215.695312 C 554.820312 1215.691406 554.820312 1215.683594 554.820312 1215.679688 C 554.820312 1215.675781 554.820312 1215.667969 554.820312 1215.664062 C 554.820312 1215.660156 554.820312 1215.652344 554.820312 1215.648438 C 554.820312 1215.644531 554.820312 1215.636719 554.820312 1215.632812 C 554.820312 1215.628906 554.820312 1215.621094 554.820312 1215.617188 C 554.820312 1215.613281 554.820312 1215.605469 554.820312 1215.601562 C 554.820312 1215.597656 554.820312 1215.589844 554.820312 1215.585938 C 554.820312 1215.582031 554.820312 1215.574219 554.820312 1215.570312 C 554.820312 1215.566406 554.820312 1215.558594 554.820312 1215.554688 C 554.820312 1215.550781 554.820312 1215.542969 554.820312 1215.539062 C 554.820312 1215.535156 554.820312 1215.527344 554.820312 1215.523438 C 554.820312 1215.519531 554.820312 1215.511719 554.820312 1215.507812 C 554.820312 1215.503906 554.820312 1215.496094 554.820312 1215.492188 C 554.820312 1215.488281 554.820312 1215.480469 554.820312 1215.476562 C 554.820312 1215.472656 554.820312 1215.46875 554.820312 1215.460938 C 554.820312 1215.457031 554.820312 1215.453125 554.820312 1215.445312 C 554.820312 1215.441406 554.820312 1215.4375 554.820312 1215.429688 C 554.820312 1215.425781 554.820312 1215.421875 554.820312 1215.414062 C 554.820312 1215.410156 554.820312 1215.40625 554.820312 1215.398438 C 554.820312 1215.394531 554.820312 1215.390625 554.820312 1215.382812 C 554.820312 1215.378906 554.820312 1215.375 554.820312 1215.367188 C 554.820312 1215.363281 554.820312 1215.359375 554.820312 1215.351562 C 554.820312 1215.347656 554.820312 1215.34375 554.820312 1215.335938 C 554.820312 1215.332031 554.820312 1215.328125 554.820312 1215.320312 C 554.820312 1215.316406 554.820312 1215.3125 554.820312 1215.304688 C 554.820312 1215.300781 554.820312 1215.296875 554.820312 1215.289062 C 554.820312 1215.285156 554.820312 1215.28125 554.820312 1215.273438 C 554.820312 1215.269531 554.820312 1215.265625 554.820312 1215.257812 C 554.820312 1215.253906 554.820312 1215.25 554.820312 1215.242188 C 554.820312 1215.238281 554.820312 1215.234375 554.820312 1215.226562 C 554.820312 1215.222656 554.820312 1215.21875 554.820312 1215.210938 C 554.820312 1215.207031 554.820312 1215.203125 554.820312 1215.199219 C 554.820312 1215.191406 554.820312 1215.1875 554.820312 1215.183594 C 554.820312 1215.175781 554.820312 1215.171875 554.820312 1215.167969 C 554.820312 1215.160156 554.820312 1215.15625 554.820312 1215.152344 C 554.820312 1215.144531 554.820312 1215.140625 554.820312 1215.136719 C 554.820312 1215.128906 554.820312 1215.125 554.820312 1215.121094 C 554.820312 1215.113281 554.820312 1215.109375 554.820312 1215.105469 C 554.820312 1215.097656 554.820312 1215.09375 554.820312 1215.089844 C 554.820312 1215.082031 554.820312 1215.078125 554.820312 1215.074219 C 554.820312 1215.066406 554.820312 1215.0625 554.820312 1215.058594 C 554.820312 1215.054688 554.820312 1215.046875 554.820312 1215.042969 C 554.820312 1215.039062 554.820312 1215.03125 554.820312 1215.027344 C 554.820312 1215.023438 554.820312 1215.015625 554.820312 1215.011719 C 554.820312 1215.007812 554.820312 1215 554.820312 1214.996094 C 554.820312 1214.992188 554.820312 1214.984375 554.820312 1214.980469 C 554.820312 1214.976562 554.820312 1214.96875 554.820312 1214.964844 C 554.820312 1214.960938 554.820312 1214.953125 554.820312 1214.949219 C 554.820312 1214.945312 554.820312 1214.941406 554.820312 1214.933594 C 554.820312 1214.929688 554.820312 1214.925781 554.820312 1214.917969 C 554.820312 1214.914062 554.820312 1214.910156 554.820312 1214.902344 C 554.820312 1214.898438 554.820312 1214.894531 554.820312 1214.886719 C 554.820312 1214.882812 554.820312 1214.878906 554.820312 1214.871094 C 554.820312 1214.867188 554.820312 1214.863281 554.820312 1214.855469 C 554.820312 1214.851562 554.820312 1214.847656 554.820312 1214.84375 C 554.820312 1214.835938 554.820312 1214.832031 554.820312 1214.828125 C 554.820312 1214.820312 554.820312 1214.816406 554.820312 1214.8125 C 554.820312 1214.804688 554.820312 1214.800781 554.820312 1214.796875 C 554.820312 1214.789062 554.820312 1214.785156 554.820312 1214.78125 C 554.820312 1214.773438 554.820312 1214.769531 554.820312 1214.765625 C 554.820312 1214.761719 554.820312 1214.753906 554.820312 1214.75 C 554.820312 1214.746094 554.820312 1214.738281 554.820312 1214.734375 C 554.820312 1214.730469 554.820312 1214.722656 554.820312 1214.71875 C 554.820312 1214.714844 554.820312 1214.707031 554.820312 1214.703125 C 554.820312 1214.699219 554.820312 1214.691406 554.820312 1214.6875 C 554.820312 1214.683594 554.820312 1214.679688 554.820312 1214.671875 C 554.820312 1214.667969 554.820312 1214.664062 554.820312 1214.65625 C 554.820312 1214.652344 554.820312 1214.648438 554.820312 1214.640625 C 554.820312 1214.636719 554.820312 1214.632812 554.820312 1214.625 C 554.820312 1214.621094 554.820312 1214.617188 554.820312 1214.613281 C 554.820312 1214.605469 554.820312 1214.601562 554.820312 1214.597656 C 554.820312 1214.589844 554.820312 1214.585938 554.820312 1214.582031 C 554.820312 1214.574219 554.820312 1214.570312 554.820312 1214.566406 C 554.820312 1214.5625 554.820312 1214.554688 554.820312 1214.550781 C 554.820312 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1887.652344 359.238281 Z M 1887.652344 357.519531 C 1888.453125 357.519531 1889.085938 357.28125 1889.550781 356.800781 C 1890.03125 356.308594 1890.273438 355.578125 1890.273438 354.621094 L 1890.273438 353.058594 C 1890.273438 352.101562 1890.03125 351.378906 1889.550781 350.898438 C 1889.085938 350.40625 1888.453125 350.160156 1887.652344 350.160156 C 1886.851562 350.160156 1886.210938 350.40625 1885.730469 350.898438 C 1885.265625 351.378906 1885.03125 352.101562 1885.03125 353.058594 L 1885.03125 354.621094 C 1885.03125 355.578125 1885.265625 356.308594 1885.730469 356.800781 C 1886.210938 357.28125 1886.851562 357.519531 1887.652344 357.519531 Z M 1892.84375 359 Z M 1901.082031 357.238281 L 1900.964844 357.238281 C 1900.65625 357.867188 1900.25 358.359375 1899.742188 358.71875 C 1899.238281 359.066406 1898.605469 359.238281 1897.84375 359.238281 C 1896.628906 359.238281 1895.664062 358.785156 1894.945312 357.878906 C 1894.222656 356.972656 1893.863281 355.625 1893.863281 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1912.644531 349.058594 1913.144531 349.40625 1913.554688 349.859375 C 1913.96875 350.3125 1914.28125 350.851562 1914.496094 351.480469 C 1914.722656 352.105469 1914.835938 352.800781 1914.835938 353.558594 L 1914.835938 354.378906 L 1907.4375 354.378906 L 1907.4375 354.71875 C 1907.4375 355.535156 1907.6875 356.199219 1908.195312 356.71875 C 1908.703125 357.226562 1909.402344 357.480469 1910.296875 357.480469 C 1910.960938 357.480469 1911.527344 357.339844 1911.996094 357.058594 C 1912.476562 356.765625 1912.863281 356.378906 1913.15625 355.898438 L 1914.515625 357.101562 C 1914.167969 357.699219 1913.636719 358.207031 1912.914062 358.621094 C 1912.210938 359.035156 1911.324219 359.238281 1910.257812 359.238281 Z M 1910.097656 350.101562 C 1909.710938 350.101562 1909.347656 350.167969 1909.015625 350.300781 C 1908.695312 350.433594 1908.414062 350.625 1908.175781 350.878906 C 1907.949219 351.121094 1907.769531 351.40625 1907.636719 351.738281 C 1907.503906 352.074219 1907.4375 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1892.453125 377.558594 L 1892.453125 378.378906 L 1885.050781 378.378906 L 1885.050781 378.71875 C 1885.050781 379.535156 1885.304688 380.199219 1885.8125 380.71875 C 1886.316406 381.226562 1887.019531 381.480469 1887.910156 381.480469 C 1888.578125 381.480469 1889.144531 381.339844 1889.613281 381.058594 C 1890.089844 380.765625 1890.476562 380.378906 1890.773438 379.898438 L 1892.132812 381.101562 C 1891.785156 381.699219 1891.25 382.207031 1890.53125 382.621094 C 1889.824219 383.035156 1888.9375 383.238281 1887.871094 383.238281 Z M 1887.710938 374.101562 C 1887.324219 374.101562 1886.964844 374.167969 1886.632812 374.300781 C 1886.3125 374.433594 1886.03125 374.625 1885.792969 374.878906 C 1885.566406 375.121094 1885.386719 375.40625 1885.25 375.738281 C 1885.117188 376.074219 1885.050781 376.441406 1885.050781 376.839844 L 1885.050781 376.980469 L 1890.230469 376.980469 L 1890.230469 376.78125 C 1890.230469 375.964844 1890 375.320312 1889.53125 374.839844 C 1889.078125 374.347656 1888.472656 374.101562 1887.710938 374.101562 Z M 1892.84375 383 Z M 1899.222656 383.238281 C 1898.449219 383.238281 1897.757812 383.113281 1897.144531 382.859375 C 1896.542969 382.605469 1896.03125 382.246094 1895.605469 381.78125 C 1895.191406 381.3125 1894.871094 380.746094 1894.644531 380.078125 C 1894.429688 379.414062 1894.324219 378.667969 1894.324219 377.839844 C 1894.324219 377.011719 1894.429688 376.265625 1894.644531 375.601562 C 1894.871094 374.933594 1895.191406 374.367188 1895.605469 373.898438 C 1896.03125 373.433594 1896.542969 373.074219 1897.144531 372.820312 C 1897.757812 372.566406 1898.445312 372.441406 1899.203125 372.441406 C 1900.269531 372.441406 1901.125 372.667969 1901.765625 373.121094 C 1902.417969 373.574219 1902.902344 374.160156 1903.222656 374.878906 L 1901.523438 375.800781 C 1901.335938 375.320312 1901.050781 374.941406 1900.664062 374.660156 C 1900.289062 374.378906 1899.804688 374.238281 1899.203125 374.238281 C 1898.378906 374.238281 1897.738281 374.492188 1897.285156 375 C 1896.832031 375.492188 1896.605469 376.152344 1896.605469 376.980469 L 1896.605469 378.699219 C 1896.605469 379.511719 1896.832031 380.171875 1897.285156 380.679688 C 1897.738281 381.1875 1898.390625 381.441406 1899.242188 381.441406 C 1899.882812 381.441406 1900.402344 381.292969 1900.804688 381 C 1901.21875 380.707031 1901.542969 380.308594 1901.785156 379.800781 L 1903.382812 380.78125 C 1903.050781 381.5 1902.542969 382.09375 1901.863281 382.558594 C 1901.183594 383.011719 1900.304688 383.238281 1899.222656 383.238281 Z M 1904.035156 383 Z M 1910.65625 383 C 1909.75 383 1909.082031 382.761719 1908.65625 382.28125 C 1908.230469 381.785156 1908.015625 381.152344 1908.015625 380.378906 L 1908.015625 374.441406 L 1904.835938 374.441406 L 1904.835938 372.679688 L 1907.097656 372.679688 C 1907.484375 372.679688 1907.757812 372.605469 1907.914062 372.460938 C 1908.074219 372.300781 1908.15625 372.019531 1908.15625 371.621094 L 1908.15625 369.039062 L 1910.195312 369.039062 L 1910.195312 372.679688 L 1914.597656 372.679688 L 1914.597656 374.441406 L 1910.195312 374.441406 L 1910.195312 381.238281 L 1914.597656 381.238281 L 1914.597656 383 Z M 1915.226562 383 Z M 1921.226562 383.238281 C 1920.46875 383.238281 1919.78125 383.113281 1919.167969 382.859375 C 1918.566406 382.605469 1918.054688 382.246094 1917.628906 381.78125 C 1917.214844 381.3125 1916.894531 380.746094 1916.667969 380.078125 C 1916.441406 379.414062 1916.328125 378.667969 1916.328125 377.839844 C 1916.328125 377.011719 1916.441406 376.265625 1916.667969 375.601562 C 1916.894531 374.933594 1917.214844 374.367188 1917.628906 373.898438 C 1918.054688 373.433594 1918.566406 373.074219 1919.167969 372.820312 C 1919.78125 372.566406 1920.46875 372.441406 1921.226562 372.441406 C 1921.988281 372.441406 1922.667969 372.566406 1923.269531 372.820312 C 1923.882812 373.074219 1924.394531 373.433594 1924.808594 373.898438 C 1925.234375 374.367188 1925.5625 374.933594 1925.789062 375.601562 C 1926.015625 376.265625 1926.128906 377.011719 1926.128906 377.839844 C 1926.128906 378.667969 1926.015625 379.414062 1925.789062 380.078125 C 1925.5625 380.746094 1925.234375 381.3125 1924.808594 381.78125 C 1924.394531 382.246094 1923.882812 382.605469 1923.269531 382.859375 C 1922.667969 383.113281 1921.988281 383.238281 1921.226562 383.238281 Z M 1921.226562 381.519531 C 1922.027344 381.519531 1922.660156 381.28125 1923.128906 380.800781 C 1923.609375 380.308594 1923.847656 379.578125 1923.847656 378.621094 L 1923.847656 377.058594 C 1923.847656 376.101562 1923.609375 375.378906 1923.128906 374.898438 C 1922.660156 374.40625 1922.027344 374.160156 1921.226562 374.160156 C 1920.429688 374.160156 1919.789062 374.40625 1919.308594 374.898438 C 1918.839844 375.378906 1918.609375 376.101562 1918.609375 377.058594 L 1918.609375 378.621094 C 1918.609375 379.578125 1918.839844 380.308594 1919.308594 380.800781 C 1919.789062 381.28125 1920.429688 381.519531 1921.226562 381.519531 Z M 1926.421875 383 Z M 1927.941406 381.238281 L 1930.679688 381.238281 L 1930.679688 374.441406 L 1927.941406 374.441406 L 1927.941406 372.679688 L 1932.859375 372.679688 L 1932.859375 375.421875 L 1932.980469 375.421875 C 1933.074219 375.058594 1933.207031 374.714844 1933.378906 374.378906 C 1933.554688 374.046875 1933.773438 373.761719 1934.039062 373.519531 C 1934.308594 373.265625 1934.632812 373.066406 1935.019531 372.921875 C 1935.40625 372.761719 1935.859375 372.679688 1936.378906 372.679688 L 1937.621094 372.679688 L 1937.621094 374.761719 L 1935.582031 374.761719 C 1934.78125 374.761719 1934.128906 375 1933.621094 375.480469 C 1933.113281 375.945312 1932.859375 376.578125 1932.859375 377.378906 L 1932.859375 381.238281 L 1936.601562 381.238281 L 1936.601562 383 L 1927.941406 383 Z M 1937.613281 383 Z M 1943.632812 383.238281 C 1942.511719 383.238281 1941.546875 383.058594 1940.734375 382.699219 C 1939.933594 382.339844 1939.285156 381.859375 1938.792969 381.261719 L 1940.132812 380.058594 C 1940.585938 380.554688 1941.09375 380.933594 1941.652344 381.199219 C 1942.226562 381.453125 1942.898438 381.578125 1943.671875 381.578125 C 1944.378906 381.578125 1944.945312 381.460938 1945.371094 381.21875 C 1945.8125 380.980469 1946.03125 380.605469 1946.03125 380.101562 C 1946.03125 379.886719 1945.992188 379.707031 1945.914062 379.558594 C 1945.832031 379.414062 1945.71875 379.292969 1945.574219 379.199219 C 1945.425781 379.105469 1945.253906 379.035156 1945.050781 378.980469 C 1944.867188 378.925781 1944.664062 378.886719 1944.453125 378.859375 L 1942.8125 378.601562 C 1942.414062 378.546875 1942.003906 378.460938 1941.59375 378.339844 C 1941.179688 378.21875 1940.804688 378.054688 1940.472656 377.839844 C 1940.152344 377.613281 1939.886719 377.328125 1939.671875 376.980469 C 1939.472656 376.632812 1939.371094 376.191406 1939.371094 375.660156 C 1939.371094 374.605469 1939.773438 373.808594 1940.574219 373.261719 C 1941.386719 372.714844 1942.453125 372.441406 1943.773438 372.441406 C 1944.734375 372.441406 1945.558594 372.585938 1946.253906 372.878906 C 1946.960938 373.160156 1947.546875 373.554688 1948.011719 374.058594 L 1946.792969 375.339844 C 1946.527344 375.035156 1946.144531 374.753906 1945.652344 374.5 C 1945.160156 374.234375 1944.503906 374.101562 1943.691406 374.101562 C 1942.238281 374.101562 1941.511719 374.558594 1941.511719 375.480469 C 1941.511719 375.921875 1941.664062 376.226562 1941.972656 376.398438 C 1942.292969 376.558594 1942.664062 376.667969 1943.09375 376.71875 L 1944.734375 376.980469 C 1945.144531 377.046875 1945.550781 377.132812 1945.953125 377.238281 C 1946.367188 377.347656 1946.734375 377.511719 1947.050781 377.738281 C 1947.386719 377.953125 1947.652344 378.234375 1947.851562 378.578125 C 1948.066406 378.925781 1948.171875 379.367188 1948.171875 379.898438 C 1948.171875 380.941406 1947.765625 381.761719 1946.953125 382.359375 C 1946.152344 382.945312 1945.046875 383.238281 1943.632812 383.238281 Z M 1959.996094 383 Z M 1964.257812 383.238281 C 1963.644531 383.238281 1963.097656 383.140625 1962.617188 382.941406 C 1962.136719 382.726562 1961.730469 382.441406 1961.398438 382.078125 C 1961.078125 381.71875 1960.832031 381.308594 1960.65625 380.839844 C 1960.484375 380.359375 1960.398438 379.847656 1960.398438 379.300781 C 1960.398438 378.539062 1960.582031 377.8125 1960.957031 377.121094 C 1961.332031 376.425781 1961.976562 375.832031 1962.898438 375.339844 C 1962.496094 374.820312 1962.175781 374.3125 1961.9375 373.820312 C 1961.710938 373.328125 1961.597656 372.800781 1961.597656 372.238281 C 1961.597656 371.734375 1961.691406 371.273438 1961.875 370.859375 C 1962.078125 370.433594 1962.34375 370.066406 1962.675781 369.761719 C 1963.011719 369.453125 1963.402344 369.21875 1963.855469 369.058594 C 1964.324219 368.886719 1964.816406 368.800781 1965.335938 368.800781 C 1965.804688 368.800781 1966.230469 368.867188 1966.617188 369 C 1967.003906 369.132812 1967.34375 369.308594 1967.636719 369.519531 C 1967.945312 369.734375 1968.195312 369.980469 1968.398438 370.261719 C 1968.597656 370.527344 1968.75 370.800781 1968.855469 371.078125 L 1967.238281 371.921875 C 1967.078125 371.507812 1966.832031 371.171875 1966.496094 370.921875 C 1966.164062 370.652344 1965.761719 370.519531 1965.296875 370.519531 C 1964.789062 370.519531 1964.375 370.679688 1964.058594 371 C 1963.75 371.308594 1963.597656 371.707031 1963.597656 372.199219 L 1963.597656 372.339844 C 1963.597656 372.539062 1963.625 372.734375 1963.675781 372.921875 C 1963.730469 373.105469 1963.816406 373.308594 1963.9375 373.519531 C 1964.058594 373.734375 1964.210938 373.972656 1964.398438 374.238281 C 1964.597656 374.492188 1964.835938 374.800781 1965.117188 375.160156 L 1966.636719 377.039062 L 1967.898438 378.699219 L 1967.996094 378.699219 C 1968.089844 378.207031 1968.148438 377.640625 1968.175781 377 C 1968.203125 376.359375 1968.222656 375.761719 1968.238281 375.199219 L 1971.375 375.199219 L 1971.375 376.898438 L 1969.917969 376.898438 C 1969.835938 377.460938 1969.742188 377.980469 1969.636719 378.460938 C 1969.542969 378.925781 1969.382812 379.441406 1969.15625 380 L 1971.65625 383 L 1969.238281 383 L 1967.558594 381 L 1967.4375 381 C 1967.289062 381.679688 1966.9375 382.226562 1966.375 382.640625 C 1965.832031 383.039062 1965.125 383.238281 1964.257812 383.238281 Z M 1964.816406 381.460938 C 1965.269531 381.460938 1965.675781 381.375 1966.035156 381.199219 C 1966.410156 381.011719 1966.710938 380.726562 1966.9375 380.339844 L 1963.835938 376.519531 C 1963.332031 376.878906 1962.984375 377.28125 1962.796875 377.71875 C 1962.609375 378.148438 1962.515625 378.605469 1962.515625 379.101562 L 1962.515625 379.28125 C 1962.515625 379.960938 1962.722656 380.492188 1963.136719 380.878906 C 1963.5625 381.265625 1964.125 381.460938 1964.816406 381.460938 Z M 1669 407 Z M 1677.898438 400.320312 L 1677.960938 396.121094 L 1677.800781 396.121094 L 1675 403.300781 L 1672.199219 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1799.292969 416.019531 1799.679688 416.132812 1799.894531 416.359375 C 1800.121094 416.585938 1800.234375 416.867188 1800.234375 417.199219 L 1800.234375 417.621094 C 1800.234375 417.953125 1800.121094 418.234375 1799.894531 418.460938 C 1799.679688 418.6875 1799.300781 418.800781 1798.753906 418.800781 Z M 1794.132812 429.238281 L 1797.652344 429.238281 L 1797.652344 422.441406 L 1794.132812 422.441406 L 1794.132812 420.679688 L 1799.835938 420.679688 L 1799.835938 429.238281 L 1803.132812 429.238281 L 1803.132812 431 L 1794.132812 431 Z M 1803.304688 431 Z M 1804.804688 429.238281 L 1808.226562 429.238281 L 1808.226562 417.960938 L 1804.804688 417.960938 L 1804.804688 416.199219 L 1810.40625 416.199219 L 1810.40625 429.238281 L 1813.828125 429.238281 L 1813.828125 431 L 1804.804688 431 Z M 1814.5 431 Z M 1821.136719 418.800781 C 1820.578125 418.800781 1820.183594 418.6875 1819.957031 418.460938 C 1819.746094 418.234375 1819.636719 417.953125 1819.636719 417.621094 L 1819.636719 417.199219 C 1819.636719 416.867188 1819.746094 416.585938 1819.957031 416.359375 C 1820.183594 416.132812 1820.570312 416.019531 1821.117188 416.019531 C 1821.679688 416.019531 1822.066406 416.132812 1822.277344 416.359375 C 1822.503906 416.585938 1822.617188 416.867188 1822.617188 417.199219 L 1822.617188 417.621094 C 1822.617188 417.953125 1822.503906 418.234375 1822.277344 418.460938 C 1822.066406 418.6875 1821.683594 418.800781 1821.136719 418.800781 Z M 1816.519531 429.238281 L 1820.039062 429.238281 L 1820.039062 422.441406 L 1816.519531 422.441406 L 1816.519531 420.679688 L 1822.21875 420.679688 L 1822.21875 429.238281 L 1825.519531 429.238281 L 1825.519531 431 L 1816.519531 431 Z M 1825.691406 431 Z M 1832.3125 431 C 1831.402344 431 1830.738281 430.761719 1830.3125 430.28125 C 1829.882812 429.785156 1829.671875 429.152344 1829.671875 428.378906 L 1829.671875 422.441406 L 1826.492188 422.441406 L 1826.492188 420.679688 L 1828.75 420.679688 C 1829.136719 420.679688 1829.410156 420.605469 1829.570312 420.460938 C 1829.730469 420.300781 1829.8125 420.019531 1829.8125 419.621094 L 1829.8125 417.039062 L 1831.851562 417.039062 L 1831.851562 420.679688 L 1836.25 420.679688 L 1836.25 422.441406 L 1831.851562 422.441406 L 1831.851562 429.238281 L 1836.25 429.238281 L 1836.25 431 Z M 1836.882812 431 Z M 1845.902344 420.679688 L 1848.101562 420.679688 L 1843.101562 433 C 1842.835938 433.667969 1842.484375 434.167969 1842.042969 434.5 C 1841.617188 434.832031 1840.996094 435 1840.183594 435 L 1838.441406 435 L 1838.441406 433.238281 L 1840.882812 433.238281 L 1841.824219 430.859375 L 1837.664062 420.679688 L 1839.941406 420.679688 L 1841.582031 425 L 1842.863281 428.539062 L 1842.984375 428.539062 L 1844.261719 425 Z M 1859.265625 431 Z M 1868.507812 431 L 1867.445312 427.28125 L 1863.007812 427.28125 L 1861.945312 431 L 1859.667969 431 L 1863.746094 417.039062 L 1866.808594 417.039062 L 1870.886719 431 Z M 1865.308594 419.300781 L 1865.148438 419.300781 L 1863.425781 425.421875 L 1867.027344 425.421875 Z M 1870.460938 431 Z M 1881.859375 432.121094 C 1881.859375 433.1875 1881.414062 433.972656 1880.519531 434.480469 C 1879.625 434.988281 1878.265625 435.238281 1876.4375 435.238281 C 1875.546875 435.238281 1874.792969 435.179688 1874.179688 435.058594 C 1873.578125 434.953125 1873.085938 434.792969 1872.699219 434.578125 C 1872.324219 434.367188 1872.050781 434.105469 1871.878906 433.800781 C 1871.71875 433.492188 1871.640625 433.148438 1871.640625 432.761719 C 1871.640625 432.199219 1871.792969 431.761719 1872.097656 431.441406 C 1872.417969 431.132812 1872.867188 430.90625 1873.4375 430.761719 L 1873.4375 430.558594 C 1873.09375 430.425781 1872.8125 430.238281 1872.597656 430 C 1872.398438 429.761719 1872.300781 429.445312 1872.300781 429.058594 C 1872.300781 428.554688 1872.472656 428.171875 1872.820312 427.921875 C 1873.167969 427.652344 1873.613281 427.453125 1874.160156 427.320312 L 1874.160156 427.21875 C 1873.53125 426.925781 1873.046875 426.511719 1872.699219 425.980469 C 1872.351562 425.445312 1872.179688 424.808594 1872.179688 424.058594 C 1872.179688 423.511719 1872.28125 423.019531 1872.480469 422.578125 C 1872.679688 422.125 1872.960938 421.746094 1873.320312 421.441406 C 1873.691406 421.121094 1874.132812 420.875 1874.640625 420.699219 C 1875.160156 420.527344 1875.738281 420.441406 1876.378906 420.441406 C 1877.097656 420.441406 1877.746094 420.554688 1878.320312 420.78125 L 1878.320312 420.441406 C 1878.320312 420.039062 1878.433594 419.707031 1878.660156 419.441406 C 1878.886719 419.171875 1879.234375 419.039062 1879.699219 419.039062 L 1881.578125 419.039062 L 1881.578125 420.761719 L 1879.179688 420.761719 L 1879.179688 421.238281 C 1879.632812 421.546875 1879.980469 421.941406 1880.21875 422.421875 C 1880.460938 422.886719 1880.578125 423.433594 1880.578125 424.058594 C 1880.578125 424.605469 1880.480469 425.105469 1880.28125 425.558594 C 1880.078125 426 1879.792969 426.378906 1879.417969 426.699219 C 1879.058594 427.007812 1878.621094 427.246094 1878.097656 427.421875 C 1877.59375 427.578125 1877.019531 427.660156 1876.378906 427.660156 C 1875.925781 427.660156 1875.5 427.621094 1875.097656 427.539062 C 1874.859375 427.605469 1874.621094 427.71875 1874.378906 427.878906 C 1874.140625 428.039062 1874.019531 428.261719 1874.019531 428.539062 C 1874.019531 428.859375 1874.171875 429.058594 1874.480469 429.140625 C 1874.785156 429.21875 1875.167969 429.261719 1875.621094 429.261719 L 1877.9375 429.261719 C 1879.324219 429.261719 1880.324219 429.527344 1880.9375 430.058594 C 1881.550781 430.578125 1881.859375 431.265625 1881.859375 432.121094 Z M 1879.820312 432.261719 C 1879.820312 431.914062 1879.679688 431.632812 1879.398438 431.421875 C 1879.121094 431.21875 1878.613281 431.121094 1877.878906 431.121094 L 1874.300781 431.121094 C 1873.792969 431.359375 1873.539062 431.746094 1873.539062 432.28125 C 1873.539062 432.667969 1873.699219 433 1874.019531 433.28125 C 1874.351562 433.558594 1874.898438 433.699219 1875.660156 433.699219 L 1877.28125 433.699219 C 1878.105469 433.699219 1878.734375 433.574219 1879.160156 433.320312 C 1879.597656 433.078125 1879.820312 432.726562 1879.820312 432.261719 Z M 1876.378906 426.160156 C 1877.085938 426.160156 1877.605469 425.992188 1877.9375 425.660156 C 1878.273438 425.328125 1878.4375 424.878906 1878.4375 424.320312 L 1878.4375 423.78125 C 1878.4375 423.21875 1878.273438 422.773438 1877.9375 422.441406 C 1877.605469 422.105469 1877.085938 421.941406 1876.378906 421.941406 C 1875.671875 421.941406 1875.152344 422.105469 1874.820312 422.441406 C 1874.484375 422.773438 1874.320312 423.21875 1874.320312 423.78125 L 1874.320312 424.320312 C 1874.320312 424.878906 1874.484375 425.328125 1874.820312 425.660156 C 1875.152344 425.992188 1875.671875 426.160156 1876.378906 426.160156 Z M 1881.652344 431 Z M 1887.871094 431.238281 C 1887.085938 431.238281 1886.378906 431.113281 1885.75 430.859375 C 1885.125 430.605469 1884.597656 430.246094 1884.171875 429.78125 C 1883.746094 429.3125 1883.417969 428.753906 1883.191406 428.101562 C 1882.964844 427.433594 1882.851562 426.6875 1882.851562 425.859375 C 1882.851562 425.035156 1882.964844 424.285156 1883.191406 423.621094 C 1883.429688 422.953125 1883.757812 422.386719 1884.171875 421.921875 C 1884.597656 421.441406 1885.113281 421.074219 1885.710938 420.820312 C 1886.3125 420.566406 1886.976562 420.441406 1887.710938 420.441406 C 1888.429688 420.441406 1889.085938 420.566406 1889.671875 420.820312 C 1890.257812 421.058594 1890.757812 421.40625 1891.171875 421.859375 C 1891.585938 422.3125 1891.898438 422.851562 1892.113281 423.480469 C 1892.339844 424.105469 1892.453125 424.800781 1892.453125 425.558594 L 1892.453125 426.378906 L 1885.050781 426.378906 L 1885.050781 426.71875 C 1885.050781 427.535156 1885.304688 428.199219 1885.8125 428.71875 C 1886.316406 429.226562 1887.019531 429.480469 1887.910156 429.480469 C 1888.578125 429.480469 1889.144531 429.339844 1889.613281 429.058594 C 1890.089844 428.765625 1890.476562 428.378906 1890.773438 427.898438 L 1892.132812 429.101562 C 1891.785156 429.699219 1891.25 430.207031 1890.53125 430.621094 C 1889.824219 431.035156 1888.9375 431.238281 1887.871094 431.238281 Z M 1887.710938 422.101562 C 1887.324219 422.101562 1886.964844 422.167969 1886.632812 422.300781 C 1886.3125 422.433594 1886.03125 422.625 1885.792969 422.878906 C 1885.566406 423.121094 1885.386719 423.40625 1885.25 423.738281 C 1885.117188 424.074219 1885.050781 424.441406 1885.050781 424.839844 L 1885.050781 424.980469 L 1890.230469 424.980469 L 1890.230469 424.78125 C 1890.230469 423.964844 1890 423.320312 1889.53125 422.839844 C 1889.078125 422.347656 1888.472656 422.101562 1887.710938 422.101562 Z M 1892.84375 431 Z M 1894.523438 431 L 1894.523438 420.679688 L 1896.703125 420.679688 L 1896.703125 422.441406 L 1896.804688 422.441406 C 1896.910156 422.171875 1897.042969 421.921875 1897.203125 421.679688 C 1897.378906 421.425781 1897.582031 421.214844 1897.824219 421.039062 C 1898.0625 420.851562 1898.34375 420.707031 1898.664062 420.601562 C 1898.984375 420.492188 1899.355469 420.441406 1899.785156 420.441406 C 1900.824219 420.441406 1901.664062 420.78125 1902.304688 421.460938 C 1902.945312 422.140625 1903.265625 423.105469 1903.265625 424.359375 L 1903.265625 431 L 1901.082031 431 L 1901.082031 424.679688 C 1901.082031 423.039062 1900.371094 422.21875 1898.945312 422.21875 C 1898.664062 422.21875 1898.390625 422.261719 1898.125 422.339844 C 1897.855469 422.40625 1897.617188 422.511719 1897.402344 422.660156 C 1897.191406 422.808594 1897.015625 422.992188 1896.882812 423.21875 C 1896.765625 423.445312 1896.703125 423.707031 1896.703125 424 L 1896.703125 431 Z M 1904.035156 431 Z M 1910.65625 431 C 1909.75 431 1909.082031 430.761719 1908.65625 430.28125 C 1908.230469 429.785156 1908.015625 429.152344 1908.015625 428.378906 L 1908.015625 422.441406 L 1904.835938 422.441406 L 1904.835938 420.679688 L 1907.097656 420.679688 C 1907.484375 420.679688 1907.757812 420.605469 1907.914062 420.460938 C 1908.074219 420.300781 1908.15625 420.019531 1908.15625 419.621094 L 1908.15625 417.039062 L 1910.195312 417.039062 L 1910.195312 420.679688 L 1914.597656 420.679688 L 1914.597656 422.441406 L 1910.195312 422.441406 L 1910.195312 429.238281 L 1914.597656 429.238281 L 1914.597656 431 Z M 1915.226562 431 Z M 1921.25 431.238281 C 1920.128906 431.238281 1919.160156 431.058594 1918.347656 430.699219 C 1917.546875 430.339844 1916.902344 429.859375 1916.40625 429.261719 L 1917.75 428.058594 C 1918.203125 428.554688 1918.707031 428.933594 1919.269531 429.199219 C 1919.839844 429.453125 1920.515625 429.578125 1921.289062 429.578125 C 1921.996094 429.578125 1922.5625 429.460938 1922.988281 429.21875 C 1923.429688 428.980469 1923.648438 428.605469 1923.648438 428.101562 C 1923.648438 427.886719 1923.609375 427.707031 1923.527344 427.558594 C 1923.449219 427.414062 1923.335938 427.292969 1923.1875 427.199219 C 1923.042969 427.105469 1922.867188 427.035156 1922.667969 426.980469 C 1922.480469 426.925781 1922.28125 426.886719 1922.066406 426.859375 L 1920.429688 426.601562 C 1920.027344 426.546875 1919.621094 426.460938 1919.207031 426.339844 C 1918.792969 426.21875 1918.421875 426.054688 1918.089844 425.839844 C 1917.769531 425.613281 1917.5 425.328125 1917.289062 424.980469 C 1917.089844 424.632812 1916.988281 424.191406 1916.988281 423.660156 C 1916.988281 422.605469 1917.386719 421.808594 1918.1875 421.261719 C 1919 420.714844 1920.066406 420.441406 1921.386719 420.441406 C 1922.347656 420.441406 1923.175781 420.585938 1923.867188 420.878906 C 1924.574219 421.160156 1925.160156 421.554688 1925.628906 422.058594 L 1924.40625 423.339844 C 1924.140625 423.035156 1923.761719 422.753906 1923.269531 422.5 C 1922.773438 422.234375 1922.121094 422.101562 1921.308594 422.101562 C 1919.855469 422.101562 1919.128906 422.558594 1919.128906 423.480469 C 1919.128906 423.921875 1919.28125 424.226562 1919.589844 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1872.21875 568.605469 1872.621094 567.808594 1873.417969 567.261719 C 1874.234375 566.714844 1875.300781 566.441406 1876.621094 566.441406 C 1877.578125 566.441406 1878.40625 566.585938 1879.097656 566.878906 C 1879.804688 567.160156 1880.394531 567.554688 1880.859375 568.058594 L 1879.640625 569.339844 C 1879.371094 569.035156 1878.992188 568.753906 1878.5 568.5 C 1878.007812 568.234375 1877.351562 568.101562 1876.539062 568.101562 C 1875.085938 568.101562 1874.359375 568.558594 1874.359375 569.480469 C 1874.359375 569.921875 1874.511719 570.226562 1874.820312 570.398438 C 1875.140625 570.558594 1875.511719 570.667969 1875.9375 570.71875 L 1877.578125 570.980469 C 1877.992188 571.046875 1878.398438 571.132812 1878.800781 571.238281 C 1879.210938 571.347656 1879.578125 571.511719 1879.898438 571.738281 C 1880.234375 571.953125 1880.5 572.234375 1880.699219 572.578125 C 1880.914062 572.925781 1881.019531 573.367188 1881.019531 573.898438 C 1881.019531 574.941406 1880.613281 575.761719 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1891.425781 571.953125 1891.691406 572.234375 1891.890625 572.578125 C 1892.105469 572.925781 1892.210938 573.367188 1892.210938 573.898438 C 1892.210938 574.941406 1891.804688 575.761719 1890.992188 576.359375 C 1890.191406 576.945312 1889.085938 577.238281 1887.671875 577.238281 Z M 1892.84375 577 Z M 1902.683594 577 C 1902.058594 577 1901.582031 576.839844 1901.265625 576.519531 C 1900.957031 576.199219 1900.769531 575.78125 1900.703125 575.261719 L 1900.605469 575.261719 C 1900.402344 575.886719 1900.035156 576.375 1899.503906 576.71875 C 1898.984375 577.066406 1898.316406 577.238281 1897.503906 577.238281 C 1896.449219 577.238281 1895.609375 576.964844 1894.984375 576.421875 C 1894.355469 575.859375 1894.042969 575.09375 1894.042969 574.121094 C 1894.042969 573.121094 1894.410156 572.359375 1895.144531 571.839844 C 1895.890625 571.308594 1897.03125 571.039062 1898.5625 571.039062 L 1900.542969 571.039062 L 1900.542969 570.238281 C 1900.542969 568.839844 1899.804688 568.140625 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1912.394531 616.761719 1911.742188 617 1911.234375 617.480469 C 1910.730469 617.945312 1910.476562 618.578125 1910.476562 619.378906 L 1910.476562 623.238281 L 1914.214844 623.238281 L 1914.214844 625 L 1905.554688 625 Z M 1915.226562 625 Z M 1921.867188 612.800781 C 1921.308594 612.800781 1920.914062 612.6875 1920.6875 612.460938 C 1920.476562 612.234375 1920.367188 611.953125 1920.367188 611.621094 L 1920.367188 611.199219 C 1920.367188 610.867188 1920.476562 610.585938 1920.6875 610.359375 C 1920.914062 610.132812 1921.300781 610.019531 1921.847656 610.019531 C 1922.40625 610.019531 1922.792969 610.132812 1923.007812 610.359375 C 1923.234375 610.585938 1923.347656 610.867188 1923.347656 611.199219 L 1923.347656 611.621094 C 1923.347656 611.953125 1923.234375 612.234375 1923.007812 612.460938 C 1922.792969 612.6875 1922.414062 612.800781 1921.867188 612.800781 Z M 1917.25 623.238281 L 1920.769531 623.238281 L 1920.769531 616.441406 L 1917.25 616.441406 L 1917.25 614.679688 L 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literal 0
HcmV?d00001

diff --git a/scripts/data/build_orientations_arxiv.py b/scripts/data/build_orientations_arxiv.py
index 38d79f91b1..aed0a2d702 100644
--- a/scripts/data/build_orientations_arxiv.py
+++ b/scripts/data/build_orientations_arxiv.py
@@ -205,9 +205,13 @@ def num(i): return f"{CHAPTER_NUM}.{i + 1}"
         terms = [it["term"] for blk in body if "ul" in blk for it in blk["ul"] if it["term"]]
         lead = next((body[k - 1].get("p", "") for k, blk in enumerate(body) if "ul" in blk and k), "")
         list_label = "Example metrics" if "metrics" in lead else "Methods"
+        definition = " ".join(first[:2])
+        # Drop a leading comparative clause ("In contrast to pragmatic approaches, …") so the card opens on
+        # its own subject; what remains is still a verbatim substring of the paper.
+        definition = re.sub(r"^(In contrast to [^,]+|Complementing [^,]+|Like [^,]+), (?=[A-Z])", "", definition)
         orientations.append({
             "id": oid, "name": name, "subtitle": sub, "section": num(i), "section_title": title,
-            "definition": " ".join(first[:2]), "list_label": list_label, "list_terms": terms, "body": body,
+            "definition": definition, "list_label": list_label, "list_terms": terms, "body": body,
         })
 
     def sec_text(prefix):
diff --git a/scripts/data/check_verbatim.py b/scripts/data/check_verbatim.py
new file mode 100644
index 0000000000..f3dd90cd0b
--- /dev/null
+++ b/scripts/data/check_verbatim.py
@@ -0,0 +1,45 @@
+#!/usr/bin/env python3
+"""Check curated JSON against the ingested manuscript, exactly as tests/data.test.ts does.
+
+  python3 scripts/data/check_verbatim.py FILE.json [FILE.json ...]
+
+Prints every excerpt (string > 25 chars under an excerpt key) that is not verbatim in the
+manuscript plain text, and every section id that does not exist. Exit code 1 if any.
+"""
+import glob, html, json, re, sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent))
+from reanchor_excerpts import NON_EXCERPT, SECTION_KEYS  # same key lists as the test
+
+ROOT = Path(__file__).resolve().parents[2]
+h = "\n".join(open(f).read() for f in sorted(glob.glob(str(ROOT / "src/generated/chapters/*.html"))))
+plain = re.sub(r"\s+", " ", html.unescape(re.sub(r"<[^>]+>", "", re.sub(r"</(p|li|h\d|tr|figcaption|caption)>", "\n", h)))).strip()
+ids = set(re.findall(r'\sid="([^"]+)"', h))
+bad = []
+
+def walk(o, key, path):
+    if key in SECTION_KEYS:
+        for s in (o if isinstance(o, list) else [o]):
+            if isinstance(s, str) and " (" not in s and s not in ids:
+                bad.append(f"{path}: unknown section id {s!r}")
+        return
+    if key in NON_EXCERPT:
+        return
+    if isinstance(o, str):
+        if len(o) > 25 and re.sub(r"\s+", " ", o).strip() not in plain:
+            bad.append(f"{path}: not verbatim: {o[:120]}")
+    elif isinstance(o, list):
+        for i, x in enumerate(o):
+            walk(x, key, f"{path}[{i}]")
+    elif isinstance(o, dict):
+        for k, v in o.items():
+            walk(v, k, f"{path}.{k}")
+
+for f in sys.argv[1:]:
+    d = json.load(open(f))
+    if isinstance(d, dict) and "methods" in d and isinstance(d["methods"], list):
+        walk(d["methods"], "", f"{f}:methods"); walk(d.get("hypotheses", []), "", f"{f}:hypotheses")
+    else:
+        walk(d, "", f)
+print("\n".join(bad) or "OK: all excerpts verbatim, all section ids exist")
+sys.exit(1 if bad else 0)
diff --git a/scripts/data/extend_context.py b/scripts/data/extend_context.py
new file mode 100644
index 0000000000..c8211b669f
--- /dev/null
+++ b/scripts/data/extend_context.py
@@ -0,0 +1,66 @@
+#!/usr/bin/env python3
+"""Give context-dependent quotes their antecedent.
+
+A quote that opens with a pronoun or connective ("This …", "These …", "However, …") reads as a
+fragment when shown alone. For each such quote in the curated data, prepend the preceding
+sentence(s) of the same paragraph from the manuscript, so the quote stays verbatim and contiguous.
+
+  python3 scripts/data/extend_context.py [--write]
+"""
+import glob, html, json, re, sys
+from pathlib import Path
+sys.path.insert(0, str(Path(__file__).parent))
+from reanchor_excerpts import NON_EXCERPT, SECTION_KEYS
+ROOT = Path(__file__).resolve().parents[2]
+START = re.compile(r'^(However|This|These|That|Those|It|Its|They|Their|Such|Both|Here|Moreover|Thus|Therefore|Hence|Specifically|Similarly|Additionally|Furthermore|Instead|Conversely|Nevertheless|Notably|Importantly|Crucially|In contrast|In particular|For example|As a result|Also|But|And)\b')
+SENT = re.compile(r"(?<=[.!?])\s+(?=[A-Z(“\"])")
+
+h = "\n".join(open(f).read() for f in sorted(glob.glob(str(ROOT / "src/generated/chapters/*.html"))))
+blocks = [re.sub(r"\s+", " ", b).strip() for b in html.unescape(re.sub(r"<[^>]+>", "", re.sub(r"</(p|li|h\d|tr|figcaption|caption|td|th)>", "\n", h))).split("\n")]
+plain = " ".join(b for b in blocks if b)
+
+def extend(q):
+    t = q.strip()
+    nonempty = [b for b in blocks if b]
+    for j, b in enumerate(nonempty):
+        i = b.find(t[:80])  # quotes may run on into the next paragraph; anchor on their opening
+        if i < 0:
+            continue
+        # quote opens the paragraph: its antecedent is the end of the previous paragraph (often a run-in heading)
+        prev = b[:i].rstrip() if i > 0 else (nonempty[j - 1] if j else "")
+        if not prev:
+            continue
+        before = SENT.split(prev)
+        add = []
+        while before and (not add or START.match(add[0])) and len(add) < 4:
+            add.insert(0, before.pop())
+        cand = (" ".join(add) + " " + t).strip()
+        if cand in plain and not START.match(cand):
+            return cand
+    return None
+
+def walk(o, key, stats):
+    if key in SECTION_KEYS or key in NON_EXCERPT:
+        return o
+    if isinstance(o, str):
+        if START.match(o.strip()):
+            n = extend(o)
+            if n:
+                stats["extended"] += 1; return n
+            stats["unchanged"].append(o[:100])
+        return o
+    if isinstance(o, list):
+        return [walk(x, key, stats) for x in o]
+    if isinstance(o, dict):
+        return {k: walk(v, k, stats) for k, v in o.items()}
+    return o
+
+stats = {"extended": 0, "unchanged": []}
+for f in ("src/data/methods.json", "src/data/framework.json"):
+    d = json.load(open(ROOT / f))
+    d = {k: (walk(v, "" if k in ("methods", "hypotheses") else k, stats) if k not in ("source", "gaps", "families") else v) for k, v in d.items()}
+    if "--write" in sys.argv:
+        (ROOT / f).write_text(json.dumps(d, indent=1, ensure_ascii=False) + "\n")
+print("extended", stats["extended"], "unchanged", len(stats["unchanged"]))
+for u in stats["unchanged"]:
+    print("  -", u)
diff --git a/scripts/data/reanchor_excerpts.py b/scripts/data/reanchor_excerpts.py
new file mode 100644
index 0000000000..fa8797915e
--- /dev/null
+++ b/scripts/data/reanchor_excerpts.py
@@ -0,0 +1,168 @@
+#!/usr/bin/env python3
+"""Re-anchor curated excerpts to a newly ingested manuscript.
+
+Every quote in src/data/methods.json and src/data/framework.json must appear verbatim in the
+ingested manuscript (tests/data.test.ts). When a new version is ingested, most quotes differ only
+by copy-editing (spelling, citation format, small rewordings). For each quote that is no longer
+verbatim, this finds the passage of the new text with the same number of sentences that is most
+similar, and:
+  - similarity >= ACCEPT: replaces the quote with the new passage, verbatim;
+  - otherwise: drops the quote (the site never shows text the paper does not contain).
+Section ids that no longer exist are remapped through src/data/redirects.json (anchors), else dropped.
+The same replacements are applied to the string literals in build_methods.py / build_framework.py
+where they can be found, so the build scripts stay in step.
+
+  python3 scripts/data/reanchor_excerpts.py [--write] [--report out.tsv]
+"""
+import argparse, difflib, glob, html, json, re
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+ACCEPT = 0.85
+FILES = {"src/data/methods.json": "scripts/data/build_methods.py", "src/data/framework.json": "scripts/data/build_framework.py"}
+NON_EXCERPT = {'id', 'name', 'family', 'title', 'subtitle', 'heading', 'step', 'methods', 'methods_used', 'metrics_named', 'related_methods',
+  'related_orientations', 'orientations', 'related', 'sections', 'mentioned_in', 'chapter_sections', 'formulas_tex', 'public_name', 'draft_name',
+  'draft_sections', 'status', 'source', 'gaps', 'table_id', 'caption', 'columns', 'rows', 'table_row', 'section', 'source_chapter', 'motivation_section',
+  'protocol_section', 'framework_limitations_section', 'decision_framework_section', 'tensions_section', 'comparison_table', 'method_guidance',
+  'risk_taxonomy', 'evidence_key', 'items', 'groups'}
+SECTION_KEYS = {'sections', 'mentioned_in', 'chapter_sections', 'section', 'protocol_section', 'framework_limitations_section',
+                'decision_framework_section', 'tensions_section', 'motivation_section', 'draft_sections'}
+SENT = re.compile(r"(?<=[.!?:])\s+(?=[A-Z(“\"])")
+WORD = re.compile(r"[a-z]{4,}")
+
+
+def plain_blocks():
+    h = "\n".join(open(f).read() for f in sorted(glob.glob(str(ROOT / "src/generated/chapters/*.html"))))
+    h = re.sub(r"</(p|li|h\d|tr|figcaption|caption|td|th)>", "\n", h)
+    t = html.unescape(re.sub(r"<[^>]+>", "", h))
+    return [re.sub(r"\s+", " ", b).strip() for b in t.split("\n") if b.strip()], h
+
+
+def build_index(blocks):
+    sents = []  # (block_no, sentence)
+    for i, b in enumerate(blocks):
+        for s in SENT.split(b):
+            if s.strip():
+                sents.append((i, s.strip()))
+    inv = {}
+    for j, (_, s) in enumerate(sents):
+        for w in set(WORD.findall(s.lower())):
+            inv.setdefault(w, []).append(j)
+    return sents, inv
+
+
+def best_match(text, sents, inv, plain):
+    k = max(1, len([s for s in SENT.split(text) if s.strip()]))
+    words = set(WORD.findall(text.lower()))
+    if not words:
+        return None, 0.0
+    votes = {}
+    for w in words:
+        lst = inv.get(w, [])
+        if len(lst) > 400:  # too common to discriminate
+            continue
+        for j in lst:
+            votes[j] = votes.get(j, 0) + 1
+    starts = set()
+    for j, _ in sorted(votes.items(), key=lambda x: -x[1])[:60]:
+        for d in range(k):
+            starts.add(j - d)
+    best, score = None, 0.0
+    for st in starts:
+        if st < 0 or st + k > len(sents):
+            continue
+        win = sents[st:st + k]
+        if len({b for b, _ in win}) > 1 and k > 1 and win[0][0] != win[-1][0]:
+            pass  # windows may cross paragraph breaks; still verbatim in the joined text check below
+        cand = " ".join(s for _, s in win)
+        sm = difflib.SequenceMatcher(None, text, cand, autojunk=False)
+        if sm.real_quick_ratio() < score or sm.quick_ratio() < score:
+            continue
+        r = sm.ratio()
+        if r > score and cand in plain:
+            best, score = cand, r
+    return best, score
+
+
+def walk(o, key, fn):
+    if key in NON_EXCERPT and key not in SECTION_KEYS:
+        return o
+    if key in SECTION_KEYS:
+        return fn(o, key, section=True)
+    if isinstance(o, str):
+        return fn(o, key, section=False)
+    if isinstance(o, list):
+        return [x for x in (walk(x, key, fn) for x in o) if x is not None]
+    if isinstance(o, dict):
+        return {k: v for k, v in ((k, walk(v, k, fn)) for k, v in o.items()) if v is not None}
+    return o
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--write", action="store_true")
+    ap.add_argument("--report", default=None)
+    a = ap.parse_args()
+    blocks, h = plain_blocks()
+    plain = " ".join(blocks)
+    ids = set(re.findall(r'\sid="([^"]+)"', h))
+    sents, inv = build_index(blocks)
+    red = json.loads((ROOT / "src/data/redirects.json").read_text())["anchors"]
+    id_map = {}
+    for k, v in red.items():
+        if v.startswith("handbook/"):
+            id_map.setdefault(k.split("#", 1)[1], v.split("#", 1)[1])
+    rows = []
+    for jf, pyf in FILES.items():
+        data = json.loads((ROOT / jf).read_text())
+        py = (ROOT / pyf).read_text()
+        repl = {}
+
+        def fn(o, key, section):
+            if section:
+                if isinstance(o, list):
+                    out = []
+                    for x in o:
+                        y = fn(x, key, True)
+                        if y is not None and y not in out:
+                            out.append(y)
+                    return out
+                if not isinstance(o, str) or " (" in o or o in ids:
+                    return o
+                new = id_map.get(o)
+                rows.append((jf, key, "section", "remap" if new in ids else "drop", o, new or ""))
+                return new if new in ids else None
+            if len(o) <= 25 or re.sub(r"\s+", " ", o).strip() in plain:
+                return o
+            best, score = best_match(o, sents, inv, plain)
+            if best and score >= ACCEPT:
+                repl[o] = best
+                rows.append((jf, key, f"{score:.2f}", "replace", o, best))
+                return best
+            rows.append((jf, key, f"{score:.2f}", "drop", o, best or ""))
+            return None
+
+        # top-level "methods"/"hypotheses" are containers here, not the excerpt-free key of the same name
+        new = {k: (v if k in ("source", "gaps") else walk(v, "" if k in ("methods", "hypotheses") else k, fn)) for k, v in data.items()}
+        if a.write:
+            (ROOT / jf).write_text(json.dumps(new, indent=1, ensure_ascii=False) + "\n")
+            hits = 0
+            for o, n in repl.items():
+                for q in (json.dumps(o, ensure_ascii=False), '"' + o.replace('"', '\\"') + '"'):
+                    if q in py:
+                        py = py.replace(q, json.dumps(n, ensure_ascii=False)); hits += 1; break
+            (ROOT / pyf).write_text(py)
+            print(f"{pyf}: {hits}/{len(repl)} replacements applied to source literals")
+    acts = {}
+    for r in rows:
+        acts[(r[0], r[3])] = acts.get((r[0], r[3]), 0) + 1
+    for k, v in sorted(acts.items()):
+        print(*k, v)
+    if a.report:
+        with open(a.report, "w") as f:
+            for r in rows:
+                f.write("\t".join(r) + "\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/scripts/ingest/ingest.py b/scripts/ingest/ingest.py
index c50d605396..d3efde20e0 100644
--- a/scripts/ingest/ingest.py
+++ b/scripts/ingest/ingest.py
@@ -54,6 +54,9 @@
 ]
 ZERO_ARG_DROP = ["fix", "new"]
 
+# Labels defined in main.tex, outside any chapter, mapped to where the site shows them.
+EXTERNAL_TARGETS = {"fig:simplified-taxonomy": "#taxonomy"}  # homepage taxonomy figure
+
 # Citation-colour macros -> (natbib command, evidence tier)
 CITE_MACROS = {
     "citeest": ("citep", "established"), "citeem": ("citep", "emerging"),
@@ -445,11 +448,13 @@ def fig_path(m):
     work = ROOT / ".ingest-tmp"
     work.mkdir(exist_ok=True)
     (work / "combined.tex").write_text(combined)
-    bib = next(iter(ms.glob("*.bib")))
+    bib = next(iter(ms.glob("*.bib")), None)  # only needed without a .bbl (e.g. the arXiv source ships none)
     cmd = ["pandoc", str(work / "combined.tex"), "-f", "latex", "-t", "html5",
            "--katex", "--section-divs", "--wrap=none", "--reference-location=section",
            "-o", str(work / "combined.html")]
     if bbl_items is None:  # no compiled bibliography: fall back to citeproc
+        if bib is None:
+            sys.exit("no .bbl and no .bib in the manuscript directory")
         cmd[5:5] = ["--citeproc", "--bibliography", str(bib), "--metadata", "link-citations=true",
                     "--metadata", "reference-section-title=References"]
         audit["warnings"].append("no .bbl found: citations rendered by citeproc, which may differ from the PDF")
@@ -518,6 +523,8 @@ def build_chapters(doc, labels, toc_nums, tiers, audit, figures, ms, stem, statu
     for m in head_re.finditer(doc):
         sid, lvl, cls = m.group(1), int(m.group(2)), m.group(3)
         if "unnumbered" in cls:
+            if lvl == 1:
+                heading_meta[sid] = {"num": "", "page": None, "level": lvl, "title": strip_tags(m.group(6)).strip()}
             continue
         nxt = next(toc_iter, None)
         if nxt is None or nxt["level"] != lvl:
@@ -544,7 +551,7 @@ def chapter_id(top_id: str, title: str):
         end = tops[i + 1].start() if i + 1 < len(tops) else len(doc)
         frag = doc[m.start():end]
         meta = heading_meta.get(m.group(1), {})
-        num = meta.get("num") or str(i + 1)
+        num = meta["num"] if meta.get("num") is not None else str(i + 1)
         slug = chapter_id(m.group(1), meta.get("title", ""))
         if slug is None:
             unmatched.append(f"  {num}. {meta.get('title', '')}  (label: {m.group(1)})")
@@ -626,10 +633,14 @@ def xref(m):
             lbl = m.group(2)
             num = label_num(lbl)
             text = m.group(3)
-            if num and (text.strip("[]") == lbl or text.strip() in ("", "??")):
+            # \ref always prints LaTeX's number (from main.aux); pandoc's own figure/table
+            # numbering drifts when floats sit outside the chapters (e.g. the taxonomy figure in main.tex).
+            if num and ('data-reference-type="ref"' in m.group(1) or text.strip("[]") == lbl or text.strip() in ("", "??")):
                 text = num
             target = id_to_chapter.get(lbl)
             href = f"{BASE_PLACEHOLDER}handbook/{target}/#{lbl}" if target else f"#{lbl}"
+            if not target and lbl in EXTERNAL_TARGETS:
+                return f'<a href="{BASE_PLACEHOLDER}{EXTERNAL_TARGETS[lbl]}"{m.group(1)}>{text}</a>'
             if target in withheld and target != ch["slug"]:
                 href = f"{BASE_PLACEHOLDER}handbook/{target}/"
             if not target:
diff --git a/scripts/urls/suggest_anchor_redirects.py b/scripts/urls/suggest_anchor_redirects.py
new file mode 100644
index 0000000000..5a726adc5c
--- /dev/null
+++ b/scripts/urls/suggest_anchor_redirects.py
@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Map every published anchor that no longer exists to its closest section in the current manuscript.
+
+Matches stay within the chapter the old page now redirects to; anything below 0.7 lands on the chapter top.
+
+Run after `npm run build`, when a new manuscript version has been ingested:
+
+  python3 scripts/urls/suggest_anchor_redirects.py --old-ref main [--write]
+
+For each anchor in src/data/published-urls.json that is missing from its (redirected) page,
+it looks for, in order: the same id in any chapter; else the section of the new manuscript
+whose title best matches the old section's title (old titles come from the manifest at
+--old-ref); else the top of the chapter the page now redirects to. It prints the proposed
+`anchors` entries for src/data/redirects.json, with the match score, and writes them with --write.
+Review the low-scoring ones by hand.
+"""
+import argparse, difflib, json, re, subprocess
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[2]
+DIST = ROOT / "dist"
+
+
+def norm(s):
+    s = re.sub(r"[^a-z0-9 ]+", " ", s.lower().replace("behavior", "behaviour").replace("ization", "isation").replace("izing", "ising").replace("color", "colour"))
+    return " ".join(w for w in s.split() if w not in {"the", "a", "an", "of", "and", "for", "in", "to", "on", "as", "via", "sec", "subsec", "fig", "tab"})
+
+
+def final_path(path, pages):
+    for _ in range(6):
+        if path in pages:
+            path = pages[path]
+        else:
+            return path
+    return path
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--old-ref", default="main")
+    ap.add_argument("--write", action="store_true")
+    a = ap.parse_args()
+    reg = json.loads((ROOT / "src/data/published-urls.json").read_text())["pages"]
+    red = json.loads((ROOT / "src/data/redirects.json").read_text())
+    new = json.loads((ROOT / "src/generated/manifest.json").read_text())
+    old = json.loads(subprocess.run(["git", "show", f"{a.old_ref}:src/generated/manifest.json"], cwd=ROOT,
+                                    capture_output=True, text=True, check=True).stdout)
+
+    head_re = re.compile(r'<section id="([^"]+)" class="level(\d)[^"]*">\s*<h\d[^>]*>(.*?)</h\d>', re.S)
+    strip = lambda t: re.sub(r"<[^>]+>", "", t)
+
+    def headings(html):
+        return [(m.group(1), int(m.group(2)), strip(m.group(3))) for m in head_re.finditer(html)]
+
+    # every heading (all levels, incl. \paragraph) of the old and new chapter fragments
+    old_titles = {}
+    files = subprocess.run(["git", "ls-tree", "--name-only", f"{a.old_ref}:src/generated/chapters/"], cwd=ROOT,
+                           capture_output=True, text=True, check=True).stdout.split()
+    for f in files:
+        h = subprocess.run(["git", "show", f"{a.old_ref}:src/generated/chapters/{f}"], cwd=ROOT, capture_output=True, text=True).stdout
+        for sid, _, t in headings(h):
+            old_titles.setdefault(sid, t)
+    cands, tops = [], {}  # (slug, id, title)
+    for ch in new["toc"]:
+        h = (ROOT / "src/generated/chapters" / f"{ch['slug']}.html").read_text()
+        hs = headings(h)
+        if hs:
+            tops[ch["slug"]] = hs[0][0]
+        cands += [(ch["slug"], sid, t) for sid, _, t in hs]
+
+    def page_has(path, id_):
+        f = DIST / path / "index.html"
+        return f.exists() and f' id="{id_}"' in f.read_text()
+
+    out, report = {}, []
+    for path, ids in reg.items():
+        if not path.startswith("handbook/"):
+            continue
+        end = final_path(path, red["pages"])
+        for id_ in ids:
+            if page_has(end, id_) or f"{path}#{id_}" in red["anchors"]:
+                continue
+            # 1. same id elsewhere
+            hit = next(((c[0], c[1]) for c in cands if c[1] == id_), None)
+            score = 1.0
+            if not hit:
+                title = norm(old_titles.get(id_, id_.replace("-", " ")))
+                best, score = None, 0.0
+                here = end.split("/")[1]
+                for slug, sid, t in cands:
+                    if slug != here:  # a wrong section elsewhere is worse than the chapter top
+                        continue
+                    r = difflib.SequenceMatcher(None, title, norm(t)).ratio()
+                    if r > score:
+                        best, score = (slug, sid), r
+                hit = best if score >= 0.7 else None
+            if not hit:  # 3. top of the chapter the page now points to
+                slug = end.split("/")[1]
+                first = tops.get(slug)
+                if not first:
+                    report.append((0, f"{path}#{id_}", "?? no target"))
+                    continue
+                hit, score = (slug, first), 0.0
+            target = f"handbook/{hit[0]}/#{hit[1]}"
+            out[f"{path}#{id_}"] = target
+            report.append((round(score, 2), f"{path}#{id_}", target))
+
+    for r in sorted(report):
+        print(*r, sep="  ")
+    print(f"{len(out)} anchor redirects")
+    if a.write:
+        red["anchors"].update(out)
+        (ROOT / "src/data/redirects.json").write_text(json.dumps(red, indent=1, ensure_ascii=False) + "\n")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/components/DemoFrame.astro b/src/components/DemoFrame.astro
index a912aa6a8e..bdd97981c8 100644
--- a/src/components/DemoFrame.astro
+++ b/src/components/DemoFrame.astro
@@ -15,8 +15,7 @@ const nb = notebooks.find((n) => n.path === L.notebook);
   <p class="eyebrow"><a href={url('demos/')}>Interactive demos</a></p>
   <h1 style="margin-bottom:.4rem">{title}</h1>
   <p style="display:flex;gap:.5rem;flex-wrap:wrap;align-items:center">
-    <span class="label measured">{kind === 'measured' ? 'Measured results' : 'Computed live in your browser'}</span>
-    <span class="small muted">{kind === 'measured' ? 'Every number on this page comes from a script you can re-run.' : 'Every number is computed by real training in your browser, not drawn by hand.'}</span>
+    <span class="label measured">{kind === 'measured' ? 'Measured on GPT-2 small' : 'Runs in your browser'}</span>
   </p>
   <SectionLinks ids={secIds} label="Survey" />
   <slot name="intro" />
diff --git a/src/components/Excerpt.astro b/src/components/Excerpt.astro
index fa7ce835f8..24d1075553 100644
--- a/src/components/Excerpt.astro
+++ b/src/components/Excerpt.astro
@@ -7,5 +7,5 @@ const html = excerptHtml(text);
 ---
 <blockquote class="excerpt">
   <Fragment set:html={html} />
-  {section && <footer>Survey {section.href ? <a href={section.href}>{`§${section.num} ${section.title}`}</a> : <>§{section.num}</>}</footer>}
+  {section && (section.href || section.num) && <footer>Survey {section.href ? <a href={section.href}>{`§${section.num} ${section.title}`}</a> : <>§{section.num}</>}</footer>}
 </blockquote>
diff --git a/src/components/ManuscriptNote.astro b/src/components/ManuscriptNote.astro
index 1cdb5448a9..02c3fd2f36 100644
--- a/src/components/ManuscriptNote.astro
+++ b/src/components/ManuscriptNote.astro
@@ -2,10 +2,10 @@
 interface Props { kind?: 'manuscript' | 'measured' | 'illustrative' | 'editorial' }
 const { kind = 'manuscript' } = Astro.props;
 const text = {
-  manuscript: 'Verbatim from the survey',
+  manuscript: '',  // quotes are the paper's own text; no badge needed
   measured: 'Measured result',
   illustrative: 'Illustrative',
-  editorial: 'Site summary, not survey text',
+  editorial: 'Summary',
 }[kind];
 ---
-<span class={`label ${kind}`}>{text}</span>
+{text && <span class={`label ${kind}`}>{text}</span>}
diff --git a/src/components/MethodsExplorer.tsx b/src/components/MethodsExplorer.tsx
index a60894a8a3..4ad4d878d2 100644
--- a/src/components/MethodsExplorer.tsx
+++ b/src/components/MethodsExplorer.tsx
@@ -63,7 +63,7 @@ export default function MethodsExplorer({ rows, families }: Props) {
           <input type="checkbox" checked={onlyRunnable} onChange={(e) => setOnlyRunnable(e.target.checked)} /> Only methods with a notebook or demo
         </label>
         <p className="small muted" aria-live="polite" style={{ margin: 0 }}>
-          {shown.length} of {rows.length} methods. Tick up to three to compare them side by side.
+          {shown.length} of {rows.length} methods. Select up to three to compare.
         </p>
       </div>
 
@@ -71,7 +71,7 @@ export default function MethodsExplorer({ rows, families }: Props) {
         <section aria-labelledby="cmp-h" className="card" style={{ marginBottom: '1.5rem' }}>
           <div style={{ display: 'flex', justifyContent: 'space-between', alignItems: 'baseline', gap: '1rem', flexWrap: 'wrap' }}>
             <h2 id="cmp-h" style={{ margin: 0, fontSize: '1.15rem' }}>Comparison</h2>
-            <span className="small muted">Survey text only. A blank cell means the survey doesn’t state it for that method. <button className="chip" onClick={() => setCompare([])}>Clear</button></span>
+            <span className="small muted"><button className="chip" onClick={() => setCompare([])}>Clear</button></span>
           </div>
           <div className="scroll-x">
             <table className="data" style={{ marginTop: '.8rem', minWidth: `${12 + cmp.length * 16}rem` }}>
@@ -110,7 +110,7 @@ export default function MethodsExplorer({ rows, families }: Props) {
               {r.definition
                 ? <p className="small clamp-3" style={{ margin: '.35rem 0 0', color: 'var(--ink-2)', fontFamily: 'var(--serif)', fontSize: '.95rem' }}
                     dangerouslySetInnerHTML={{ __html: `“${r.html.definition[0]}”` }} />
-                : <p className="small muted" style={{ margin: '.35rem 0 0' }}>No definition sentence in the survey.</p>}
+                : null}
             </div>
           </li>
         ))}
diff --git a/src/components/demos/PatchingDemo.tsx b/src/components/demos/PatchingDemo.tsx
index 0fe8109ad0..e7bd0951c4 100644
--- a/src/components/demos/PatchingDemo.tsx
+++ b/src/components/demos/PatchingDemo.tsx
@@ -60,7 +60,7 @@ export default function PatchingDemo({ data }: { data: Data }) {
                   format={(v) => `${(v * 100).toFixed(1)}%`} rowTitle="layer" colTitle="head" />}
           </div>
           <Legend min={view === 'resid' ? '−100%' : '−50%'} max={view === 'resid' ? '+100%' : '+50%'} label="share of clean–corrupted logit difference restored" />
-          <p className="small" aria-live="polite" style={{ minHeight: '2.6em' }}>{detail ?? 'Click or tab to a cell for its value.'}</p>
+          <p className="small" aria-live="polite" style={{ minHeight: '2.6em' }}>{detail ?? 'Select a cell to see its value.'}</p>
         </div>
         <div className="card">
           {view === 'resid' ? (
diff --git a/src/data/chapter-ids.json b/src/data/chapter-ids.json
index 90399439b7..36b02208c2 100644
--- a/src/data/chapter-ids.json
+++ b/src/data/chapter-ids.json
@@ -88,16 +88,16 @@
   },
   {
    "id": "developmental-interpretability",
-   "labels": [
-    "sec:training"
-   ],
+   "labels": [],
    "titles": [
     "Developmental Interpretability and Training Dynamics"
    ]
   },
   {
    "id": "learning-dynamics",
-   "labels": [],
+   "labels": [
+    "sec:training"
+   ],
    "titles": [
     "Learning Dynamics and Mechanistic Interpretability"
    ]
@@ -186,6 +186,16 @@
    "titles": [
     "Mathematical Foundations"
    ]
+  },
+  {
+   "id": "acknowledgements",
+   "labels": [
+    "acknowledgments"
+   ],
+   "titles": [
+    "Acknowledgments",
+    "Acknowledgements"
+   ]
   }
  ]
 }
diff --git a/src/data/framework.json b/src/data/framework.json
index 83dd6271ab..af9965fc18 100644
--- a/src/data/framework.json
+++ b/src/data/framework.json
@@ -1,230 +1,227 @@
 {
- "source": "plain/*.txt chapter dumps of 'The State of Interpretability for AI Safety' draft",
+ "source": "Quoted from arXiv:2611.08438v1 (re-selected 6 Nov 2026). Orientations: src/data/orientations-arxiv.json.",
  "evaluation": {
   "chapter_sections": [
    "sec:evaluation-framework"
   ],
   "framework_intro": [
-   "A persistent challenge in mechanistic interpretability is the lack of standardized evaluation. Different methods—SAEs, transcoders, circuit discovery, probing—are evaluated using incompatible metrics, making it difficult to compare approaches or assess progress.",
-   "This section proposes a unified evaluation framework covering five dimensions: faithfulness, completeness, stability, causal validity, and human interpretability.",
-   "We propose evaluating mechanistic interpretability methods across five complementary dimensions. No single dimension suffices; strong methods should perform well across all five."
+   "A persistent challenge in mechanistic interpretability (MI) is the lack of standardised evaluation. Different interpretability methods use incompatible metrics.",
+   "To address this, this section presents a unified evaluation framework covering five dimensions—attribution faithfulness, completeness, stability, causal validity, and human interpretability—where different methods can be jointly tested.",
+   "We propose a framework that situates mechanistic interpretability evaluation within five complementary dimensions. No single dimension suffices—strong methods should perform well across all five."
   ],
   "motivation": [
-   "Metric fragmentation: SAEs are evaluated on reconstruction loss; circuits on task performance recovery; probes on classification accuracy. These metrics are not directly comparable.",
-   "Cherry-picking: Results are often reported on favorable examples without systematic coverage of failure cases.",
-   "Missing baselines: Claims of interpretability gains rarely compare against simple baselines (e.g., random directions, PCA components, raw neurons).",
-   "Reproducibility gaps: Hyperparameter sensitivity and seed dependence are underreported, making replication difficult.",
-   "A unified framework enables principled comparison across methods, systematic identification of failure modes, and clearer communication of what interpretability methods actually achieve."
+   "Utility ambiguity: Methods are initially developed for different aims and evaluated against distinct objectives, yet have overlapping application scenarios.",
+   "Cherry-picking: Existing work often reports interpretability results on carefully selected examples, samples from a narrow distribution, or capabilities where a method performs well.",
+   "Missing baselines: Many proposed methods’ claimed interpretability gains are not systematically and adequately compared against intuitive alternatives or baselines such as behavioural methods, random activations, or neuron-level analysis.",
+   "Reproducibility gaps: Many existing techniques involve substantial hyperparameter choices, heavy training procedures, and high variation across seemingly irrelevant factors. Reproducibility is often underreported and has not yet been established as a property to optimise.",
+   "Faithfulness–understandability gap: Methods often demonstrate how understandable and informative their outputs are to humans. However, those outputs’ faithfulness to the underlying model mechanism receives less attention. This rewards explanations that are persuasive rather than faithful.",
+   "The MI community needs standardised, concrete evaluation protocols and benchmarks, along with established reporting practices, to enable principled comparisons across methods and findings."
   ],
-  "motivation_section": "the-need-for-standardized-evaluation",
+  "motivation_section": "the-need-for-standardised-evaluation",
   "table_id": "tab:evaluation-framework",
-  "table_caption": "Table 10: Unified Evaluation Framework for Mechanistic Interpretability",
+  "table_caption": "Table 9: Unified evaluation framework for mechanistic interpretability.",
   "dimensions": [
    {
     "id": "faithfulness",
-    "name": "Faithfulness",
-    "heading": "7.6.3 Dimension 1: Faithfulness",
+    "name": "Attribution Faithfulness",
+    "heading": "7.5.2 Dimension 1: Attribution Faithfulness",
     "sections": [
-     "dimension-1-faithfulness",
-     "five-evaluation-dimensions"
+     "dimension-1-attribution-faithfulness",
+     "subsec:five eval dim"
     ],
-    "definition": "Definition: Faithfulness measures whether identified components (features, circuits, explanations) accurately reflect the model’s actual internal computation, rather than post-hoc rationalizations or correlational artifacts.",
+    "definition": "Attribution faithfulness measures whether the interpretability method accurately attributes the model’s behaviour of interest to responsible components.",
     "metrics_named": [
-     "Causal Effect Size",
-     "Intervention Consistency",
-     "Counterfactual Accuracy"
+     "Intervention-Effect Agreement",
+     "Effect-Size Error",
+     "Counterfactual Effect Recovery"
     ],
     "how_measured": [
-     "Higher values indicate stronger causal relationship between the component and behavior.",
-     "Intervention Consistency: Across multiple interventions on C, do effects on B remain consistent?",
-     "Counterfactual Accuracy: When component C is patched from input x_1 to input x_2, does the model’s behavior shift as predicted?",
-     "Identify component C via the interpretability method being evaluated.",
-     "Define target behavior B with clear operational criteria.",
-     "Perform ablation: replace C with baseline (zero, mean, noise) and measure change in B.",
-     "Perform activation patching: swap C between clean and corrupted inputs; verify behavior follows C.",
-     "Report effect sizes with confidence intervals across multiple inputs.",
-     "Baselines: Compare against random components, PCA directions, and individual neurons to establish that identified components are more faithful than naive alternatives."
+     "Let a(S,x) denote the importance score assigned by an interpretability method to components S for a sample x \\sim B:",
+     "High agreement indicates that the attribution consistently reflects the identified components’ causal effects on behaviour B.",
+     "A low value indicates good calibration between estimated and actual intervention effects. If the two quantities are not naturally on the same scale, we may use normalised or rank-based measures instead.",
+     "A value near 1 indicates that the patching recovers much of the behaviour, while a value near 0 indicates little effect. This metric has been widely used in the circuit discovery literature (Hanna et al., 2024; Zhang et al., 2025b; Syed et al., 2024).",
+     "Apply the method to acquire attribution scores a(S,x) of model components and select those of interest (e.g., components with high scores).",
+     "Measure the corresponding ground-truth intervention effects s(S,x).",
+     "Compare them using correlation, error, or rank agreement.",
+     "Evaluate how much of the behavioural difference between clean and corrupted inputs can be recovered by patching the identified components.",
+     "Report results across a diverse set of inputs with uncertainty estimates."
     ],
     "formulas_tex": [
-     "\\text{Effect}(C, B) = \\frac{|P(B | \\text{do}(C = \\text{active})) - P(B | \\text{do}(C = \\text{ablated}))|}{P(B | \\text{baseline})}",
-     "\\text{Consistency}(C, B) = 1 - \\frac{\\text{Var}[\\text{Effect}(C, B)]}{\\mathbb{E}[\\text{Effect}(C, B)]^2}",
-     "\\text{CF-Acc} = \\mathbb{E}_{x_1, x_2}\\left[\\mathbf{1}\\left[f(x_1|_{C \\leftarrow C(x_2)}) \\approx f(x_2)\\right]\\right]"
+     "\\mathrm{EffectAgreement} = \\rho_{x \\sim B}\\!\\left( a(S,x), s(S,x) \\right)",
+     "\\mathrm{EffectError} = \\mathbb{E}_{x \\sim B} \\left[ \\left| a(S,x)-s(S,x) \\right| \\right]",
+     "\\mathrm{CFRecovery}(S;B,B') = \\mathbb{E}_{x_1 \\sim B', x_2 \\sim B} \\frac{ M\\!\\left( f_\\theta\\!\\left( x_{1,[S \\leftarrow h_S(x_2)]} \\right) \\right) - M\\!\\left(f_\\theta(x_1)\\right) }{ M\\!\\left(f_\\theta(x_2)\\right) - M\\!\\left(f_\\theta(x_1)\\right) }"
     ],
     "examples": [],
-    "limitations": [],
+    "limitations": [
+     "These may cause this metric to take values much larger than 1 or below 0, complicating the analysis. Recent work has proposed an alternative metric that bounds the value between 0 and 1 (Wu & Barez, 2026)."
+    ],
     "table_row": {
-     "Dimension": "Faithfulness",
-     "Definition": "Do identified components accurately reflect the model’s actual computation?",
-     "Metrics": "Causal effect size; intervention consistency; counterfactual accuracy",
-     "Methods": "Ablation; activation patching; interchange interventions"
+     "Dimension": "Attribution Faithfulness",
+     "Definition": "Do attribution scores accurately reflect the causal effects of the identified components?",
+     "Metrics": "Intervention-effect agreement; effect-size error; counterfactual effect recovery",
+     "Methods": "Ablation; activation / path patching; interchange interventions"
     }
    },
    {
     "id": "completeness",
     "name": "Completeness",
-    "heading": "7.6.4 Dimension 2: Completeness",
+    "heading": "7.5.2 Dimension 2: Completeness",
     "sections": [
      "dimension-2-completeness",
-     "five-evaluation-dimensions"
+     "subsec:five eval dim"
     ],
-    "definition": "Definition: Completeness measures what fraction of model behavior is explained by the identified components. High completeness means the method accounts for most of the model’s computation; low completeness means significant behavior remains unexplained.",
+    "definition": "Completeness measures how much of the model behaviour the identified components or mechanisms explain. A high completeness score indicates that the identified components/mechanisms capture most of the relevant computation.",
     "metrics_named": [
      "Loss Recovery",
-     "Behavioral Coverage",
+     "Behavioural Coverage",
      "Unexplained Variance"
     ],
     "how_measured": [
-     "Loss Recovery: After replacing model activations with reconstructions from identified components, what fraction of original loss is preserved?",
-     "Behavioral Coverage: Across a diverse test suite, what fraction of behaviors \\mathcal{B} can be explained by identified components?",
-     "where \\tau is set to be the smallest effect that counts as meaningful.",
-     "Unexplained Variance: What fraction of activation variance is not captured by identified features?",
-     "Define a comprehensive behavioral test suite spanning diverse capabilities (factual recall, reasoning, safety behaviors, etc.).",
-     "For each behavior, attempt to identify responsible components.",
-     "Report coverage: fraction of behaviors with identified components meeting faithfulness thresholds.",
-     "Report reconstruction metrics: loss recovery and unexplained variance.",
-     "Analyze failure cases: which behaviors resist explanation?"
+     "For methods that reconstruct model activations (e.g., SAEs and CLTs), after replacing the target component’s activations with the reconstructions, we measure the fraction of the original model performance that is recovered:",
+     "A value near 1 indicates that the reconstruction preserves most of the information.",
+     "Across input prompts x \\sim B, we measure the fraction for which the identified components have a meaningful intervention effect:",
+     "Higher coverage indicates that the identified components account for the behaviour across a larger fraction of inputs.",
+     "For methods that reconstruct activations of the components, we measure the fraction of unexplained activation variance as:",
+     "Lower values indicate that the identified components explain a larger fraction of the variance in the original activations.",
+     "Report behavioural coverage using the intervention effects s(S,x).",
+     "Report reconstruction-based metrics such as loss recovery and unexplained variance if applicable.",
+     "Analyse inputs of the behaviour for which the identified components do not provide good coverage or reconstruction."
     ],
     "formulas_tex": [
-     "\\text{LR} = 1 - \\frac{\\mathcal{L}_{\\text{reconstructed}} - \\mathcal{L}_{\\text{original}}}{\\mathcal{L}_{\\text{random}} - \\mathcal{L}_{\\text{original}}}",
-     "\\text{Coverage} = \\frac{|\\{b \\in \\mathcal{B} : \\exists C \\text{ with } \\text{Effect}(C, b) \\geq \\tau\\}|}{|\\mathcal{B}|}",
-     "\\text{UV} = \\frac{\\mathbb{E}[\\|h - \\hat{h}\\|^2]}{\\mathbb{E}[\\|h\\|^2]}"
+     "\\mathrm{LR} = \\frac{ \\mathcal{L}_{\\mathrm{reconstructed}} - \\mathcal{L}_{\\mathrm{ablated}} }{ \\mathcal{L}_{\\mathrm{original}} - \\mathcal{L}_{\\mathrm{ablated}} }",
+     "\\mathrm{Coverage}(S,B) = \\mathbb{E}_{x \\sim B} \\left[ \\mathbbm{1} \\left( |s(S,x)| \\geq \\tau \\right) \\right]",
+     "\\mathrm{UV}(S,B) = \\frac{ \\mathbb{E}_{x \\sim B} \\left[ \\|h(x)-\\hat{h}_S(x)\\|_2^2 \\right] }{ \\mathbb{E}_{x \\sim B} \\left[ \\|h(x)-\\bar{h}_B\\|_2^2 \\right] }"
     ],
     "examples": [],
     "limitations": [
-     "Critical consideration: Methods may achieve high faithfulness on explained behaviors while leaving most behavior unexplained. Completeness ensures we track the gap between what is explained and what remains opaque."
+     "Similar to the vanilla Counterfactual Effect Recovery metric discussed above, this metric can take arbitrary values. A simple solution is to develop a bounded variant, as has been done for Counterfactual Effect Recovery (Wu & Barez, 2026)."
     ],
     "table_row": {
      "Dimension": "Completeness",
-     "Definition": "What fraction of model behavior is explained by the identified components?",
-     "Metrics": "Behavior coverage; reconstruction loss; unexplained variance",
-     "Methods": "Loss recovery; behavioral replication; residual analysis"
+     "Definition": "How much of the behaviour is accounted for by the identified components?",
+     "Metrics": "Loss recovery; behavioural coverage; unexplained variance",
+     "Methods": "Reconstruction; behavioural evaluation; residual analysis"
     }
    },
    {
     "id": "stability",
     "name": "Stability",
-    "heading": "7.6.5 Dimension 3: Stability",
+    "heading": "7.5.2 Dimension 3: Stability",
     "sections": [
      "dimension-3-stability",
-     "five-evaluation-dimensions"
+     "subsec:five eval dim"
     ],
-    "definition": "Definition: Stability measures whether findings are robust to incidental choices—random seeds, training checkpoints, hyperparameter settings—that should not affect conclusions about model internals.",
+    "definition": "Stability measures the robustness of explanations, attributions, and findings to changes in variables that should not affect the results, such as the choice of random seeds, small hyperparameter changes, and input paraphrasing.",
     "metrics_named": [
      "Cross-Seed Agreement",
-     "Checkpoint Consistency",
-     "Hyperparameter Sensitivity"
+     "Hyperparameter Sensitivity",
+     "Paraphrase Consistency"
     ],
     "how_measured": [
-     "Cross-Seed Agreement: Train the interpretability method (e.g., SAE) with K different random seeds.",
-     "Checkpoint Consistency: For features/circuits identified at checkpoint t, do they persist at checkpoints t \\pm \\Delta?",
-     "Hyperparameter Sensitivity: How much do results change under reasonable hyperparameter variation?",
-     "Lower sensitivity indicates more robust findings.",
-     "Train interpretability method with at least 3–5 random seeds.",
-     "Compute feature/circuit alignment across seeds.",
-     "Report which findings are stable (appear across all seeds) vs. unstable (seed-specific).",
-     "For key claims, verify they hold under hyperparameter variation (\\pm 20\\% on key parameters).",
-     "Distinguish “core” findings (stable) from “fragile” findings (unstable).",
-     "Reporting recommendation: Papers should report stability metrics alongside primary results. Claims based on unstable findings should be flagged as preliminary."
+     "We train or apply the interpretability method K times under different random seeds.",
+     "We measure the sensitivity of mechanistic analyses or methods under reasonable hyperparameter variations.",
+     "Lower values indicate that the finding is less sensitive to hyperparameter choices and therefore more robust.",
+     "We measure the consistency of explanations or attributions across \\mathcal{P}(x).",
+     "The score lies in [0, 1]. A value of 1 indicates that the intervention effect is identical across all paraphrases.",
+     "Execute the interpretability method with several random seeds and compute alignment of the discovered units or findings across seeds.",
+     "Analyse which findings are stable across all seeds and which ones are seed-specific. Key claims need to remain consistent under subsequent tests on hyperparameter variation and input paraphrasing, if applicable.",
+     "Distinguish robust findings from fragile ones and report both of them."
     ],
     "formulas_tex": [
-     "\\text{Seed-Stability} = \\frac{1}{K(K-1)} \\sum_{i \\neq j} \\text{Align}(F_i, F_j)",
-     "\\text{Ckpt-Stability} = \\mathbb{E}_t[\\text{Align}(F_t, F_{t+\\Delta})]",
-     "\\text{HP-Sensitivity} = \\frac{\\max_{\\theta \\in \\Theta} \\text{Metric}(\\theta) - \\min_{\\theta \\in \\Theta} \\text{Metric}(\\theta)}{\\mathbb{E}_{\\theta \\in \\Theta}[\\text{Metric}(\\theta)]}"
+     "\\text{Seed-Stability} = \\frac{1}{K(K-1)} \\sum_{i \\neq j} \\text{Align}(\\mathcal{F}_i, \\mathcal{F}_j)",
+     "\\text{HP-Sensitivity} = \\frac{ \\max_{\\omega \\in \\Omega} \\text{Metric}(\\omega) - \\min_{\\omega \\in \\Omega} \\text{Metric}(\\omega) }{ \\mathbb{E}_{\\omega \\in \\Omega}[\\text{Metric}(\\omega)] }",
+     "\\text{Para-Consistency}(S, B) = \\mathbb{E}_{x \\sim B}\\left[ 1 - \\frac{\\mathrm{Var}_{x' \\in \\mathcal{P}(x)}\\left[s(S, x')\\right]}{\\mathbb{E}_{x' \\in \\mathcal{P}(x)}\\left[s(S, x')^2\\right]} \\right]"
+    ],
+    "examples": [
+     "For example, Paulo & Belrose (2026) calculate the Hungarian matching between two sets of SAE features based on their pairwise cosine similarities."
     ],
-    "examples": [],
     "limitations": [],
     "table_row": {
      "Dimension": "Stability",
-     "Definition": "Are findings robust across seeds, checkpoints, and hyperparameters?",
+     "Definition": "Are findings robust to variations in factors like random seeds, hyperparameters, paraphrases, etc.?",
      "Metrics": "Cross-seed agreement; checkpoint consistency; hyperparameter sensitivity",
-     "Methods": "Multi-seed training; checkpoint comparison; ablation studies"
+     "Methods": "Multi-seed analysis; checkpoint comparison; input paraphrasing"
     }
    },
    {
     "id": "causal-validity",
     "name": "Causal Validity",
-    "heading": "7.6.6 Dimension 4: Causal Validity",
+    "heading": "7.5.2 Dimension 4: Causal Validity",
     "sections": [
      "dimension-4-causal-validity",
-     "five-evaluation-dimensions"
+     "subsec:five eval dim"
     ],
-    "definition": "Definition: Causal validity assesses whether identified components satisfy formal causal criteria of necessity (removing the component disrupts the behavior) and sufficiency (the component alone can produce the behavior).",
+    "definition": "Causal validity assesses whether the identified components or mechanisms are causal to the behaviour, particularly necessity (i.e., removing the components/mechanisms disrupts the behaviour) and sufficiency (i.e., the components/mechanisms alone are sufficient to preserve the behaviour).",
     "metrics_named": [
      "Necessity Score",
      "Sufficiency Score",
      "Specificity"
     ],
     "how_measured": [
-     "Necessity Score: Fraction of behavior explained by the component that is lost when the component is ablated:",
-     "High necessity means the component is required for the behavior.",
-     "Sufficiency Score: Fraction of behavior that can be produced by the component alone (all other components ablated or set to baseline):",
-     "High sufficiency means the component is enough for the behavior.",
-     "Specificity: Does the component affect only the claimed behavior, or does it have broad side effects?",
-     "High specificity means the component is selective for the target behavior.",
-     "Necessity test: Ablate the identified circuit/feature. Measure degradation on target behavior. Compare to ablating random components of equal size.",
-     "Sufficiency test: Ablate all components except the identified circuit. Measure preservation of target behavior. Compare to random circuits of equal size.",
-     "Interchange test: Swap circuit activations between inputs that differ on the target behavior. Verify behavior follows the swapped circuit.",
-     "Specificity test: Measure effects on a suite of non-target behaviors. Compute specificity ratio."
+     "Necessity measures the fraction of the model’s performance on behaviour B that is lost when S is ablated (i.e., its activations are replaced with some reference values).",
+     "A higher value indicates that S is necessary for the behaviour.",
+     "Sufficiency measures how much of the original behaviour is achieved when only S is active (i.e., activations of all components outside S are replaced with some reference values).",
+     "A high value indicates that S alone implements much of the behaviour.",
+     "Specificity measures the effect on non-target behaviours by interventions on S.",
+     "A high value indicates that the causal effect of S is specific to B.",
+     "Necessity test: Ablate S and measure the resulting degradation on behaviour B. Compare against ablating random components of matched size.",
+     "Sufficiency test: Replace components outside S with reference activations and measure how much of behaviour B is preserved. Compare against random component sets of matched size.",
+     "Interchange test: Swap activations of S between inputs representing different behaviours and test whether the model output follows the intervention.",
+     "Specificity test: Measure intervention effects on a set of non-target behaviours and examine whether the effect is specific to B."
     ],
     "formulas_tex": [
-     "\\text{Necessity}(C, B) = \\frac{P(B | \\text{baseline}) - P(B | \\text{do}(C = \\text{ablated}))}{P(B | \\text{baseline})}",
-     "\\text{Sufficiency}(C, B) = \\frac{P(B | \\text{only } C \\text{ active})}{P(B | \\text{baseline})}",
-     "\\text{Specificity}(C, B) = \\frac{\\text{Effect}(C, B)}{\\sum_{B' \\in \\mathcal{B}} \\text{Effect}(C, B')}"
+     "\\mathrm{Necessity}(S,B) = 1- \\frac{ \\mathbb{E}_{x\\sim B} \\left[ M\\!\\left( f_\\theta\\!\\left( x_{[S\\leftarrow\\tilde h_S]} \\right) \\right) \\right] }{ \\mathbb{E}_{x\\sim B} \\left[ M\\!\\left(f_\\theta(x)\\right) \\right] }",
+     "\\mathrm{Sufficiency}(S,B) = \\frac{ \\mathbb{E}_{x\\sim B} \\left[ M\\!\\left( f_\\theta\\!\\left( x_{[\\bar S \\leftarrow \\tilde h_{\\bar S}]} \\right) \\right) \\right] }{ \\mathbb{E}_{x\\sim B} \\left[ M\\!\\left(f_\\theta(x)\\right) \\right] }",
+     "\\mathrm{Specificity}(S,B) = \\frac{ |s(S,B)| }{ \\sum_{B' \\in \\mathcal{B}} |s(S,B')| }"
     ],
     "examples": [
-     "Benchmarks: The MIB benchmark (Mueller et al., 2025a) provides standardized tasks and metrics for causal circuit evaluation. AxBench (Wu et al., 2025) and RAVEL (Huang et al., 2024) provide feature-level causal evaluation."
+     "The MI community has developed several benchmarks that examine the causal validity of interpretability methods. For example, MIB (Mueller et al., 2025) provides standardised tasks for assessing discovered circuits and causal variables (e.g., features); AxBench (Wu et al., 2025b) evaluates methods’ ability to steer models towards given concepts; RAVEL (Huang et al., 2024) investigates methods’ ability to localise features of given concepts by using interchange interventions to validate discovered features; and SAEBench (Karvonen et al., 2025) evaluates SAEs specifically, pairing reconstruction metrics with downstream intervention tasks such as gender bias erasure."
     ],
     "limitations": [],
     "table_row": {
      "Dimension": "Causal Validity",
-     "Definition": "Do components satisfy necessity and sufficiency for target behaviors?",
-     "Metrics": "Necessity score; sufficiency score; specificity",
-     "Methods": "Causal scrubbing; knockout experiments; circuit verification"
+     "Definition": "Are the identified components necessary, sufficient, and specific for the target behaviour?",
+     "Metrics": "Necessity score; sufficiency score; specificity score",
+     "Methods": "Ablation; component isolation; interchange interventions; tests with control behaviours"
     }
    },
    {
     "id": "human-interpretability",
     "name": "Human Interpretability",
-    "heading": "7.6.7 Dimension 5: Human Interpretability",
+    "heading": "7.5.2 Dimension 5: Human Interpretability",
     "sections": [
      "dimension-5-human-interpretability",
-     "five-evaluation-dimensions"
+     "subsec:five eval dim"
     ],
-    "definition": "Definition: Human interpretability measures whether explanations are actually useful to humans—whether they improve human understanding, prediction, or decision-making about model behavior.",
+    "definition": "Human interpretability measures whether generated explanations eventually improve human understanding, prediction, and decision-making about model behaviour.",
     "metrics_named": [
-     "Explanation Accuracy",
-     "Simulatability",
+     "Explanation Utility",
      "Debugging Utility",
      "Consistency of Interpretation"
     ],
     "how_measured": [
-     "Explanation Accuracy: Can humans predict model behavior based on the explanation?",
-     "Compare to baseline prediction accuracy without explanation.",
-     "Simulatability: After seeing explanations, can humans simulate the model’s computation on new inputs?",
-     "Debugging Utility: Do explanations help humans identify and fix model errors?",
-     "Consistency of Interpretation: Do different humans interpret the same explanation consistently?",
-     "Prediction task: Show humans feature/circuit explanations. Ask them to predict model behavior on held-out inputs. Compare accuracy to no-explanation baseline.",
-     "Identification task: Show humans model outputs with errors. With and without mechanistic explanations, ask them to identify the cause. Measure accuracy and time.",
-     "Intervention task: Ask humans to propose fixes for model errors based on explanations. Measure success rate of proposed interventions.",
-     "Agreement task: Have multiple raters label feature interpretations. Measure inter-rater reliability."
+     "Explanation Utility. This metric tests whether humans can predict model behaviour more accurately in the held-out set based on the explanation.",
+     "Debugging Utility. This metric examines whether the explanations help humans identify and repair model errors, such as incorrect outputs or misaligned behaviour, in the wild.",
+     "Consistency of Interpretation. This metric inspects whether different humans interpret the same explanation consistently.",
+     "Prediction task: Show humans explanations of model components or mechanisms and ask them to predict model behaviour on held-out inputs. Compare accuracy to a no-explanation baseline.",
+     "Identification task: Show humans model outputs with errors and ask them to identify the cause, with and without explanation assistance. Measure accuracy and required time to complete the task.",
+     "Intervention task: Ask humans to propose fixes for model errors based on explanations. Measure the success rate of proposed interventions.",
+     "Agreement task: Ask multiple human raters to generate explanations, if applicable (e.g., write a summary of highly activated samples of an SAE feature as its explanation). Measure inter-rater reliability and agreement with machine-generated explanations."
     ],
     "formulas_tex": [
-     "\\text{Expl-Acc} = P(\\text{human predicts } f(x) | \\text{explanation of } f)",
-     "\\text{Simulatability} = \\text{Corr}(\\text{human prediction}, f(x_{\\text{new}}))",
-     "\\text{Debug-Utility} = \\frac{\\text{\\#Errors fixed with explanation}}{\\text{\\#Errors fixed without explanation}}",
+     "\\text{Expl-Utility} = P(\\text{correct prediction} \\mid \\text{with explanation}) - P(\\text{correct prediction} \\mid \\text{no explanation})",
+     "\\text{Debug-Utility} = P(\\text{error fixed}\\mid\\text{with explanation}) - P(\\text{error fixed}\\mid\\text{no explanation})",
      "\\text{Inter-Rater} = \\kappa(\\text{human}_1 \\text{ labels}, \\text{human}_2 \\text{ labels})"
     ],
     "examples": [],
     "limitations": [
-     "Human studies are expensive and introduce subjectivity. Automated proxies (LLM-based evaluation) can supplement but not replace human evaluation.",
-     "Expertise matters: interpretations accessible to ML researchers may be opaque to domain experts, and vice versa.",
-     "Task-specificity: a feature interpretable for one task may be confusing for another."
+     "Several caveats and challenges arise when evaluating the human interpretability of tested methods. For example, human studies are usually expensive. Moreover, human expertise may confound results, where some explanations understandable to MI researchers may be opaque to domain experts, and vice versa."
     ],
     "table_row": {
-     "Dimension": "Interpretability",
-     "Definition": "Can humans understand and use the explanations?",
-     "Metrics": "Human accuracy; explanation utility; prediction improvement",
-     "Methods": "User studies; expert evaluation; downstream task performance"
+     "Dimension": "Human Interpretability",
+     "Definition": "Can humans understand and use the resulting explanations?",
+     "Metrics": "Explanation utility; debugging utility; interpretation agreement",
+     "Methods": "User studies; expert evaluation"
     }
    }
   ],
@@ -233,671 +230,103 @@
    {
     "step": "Step 1: Define scope",
     "items": [
-     "Specify the model, layer(s), and behavior(s) under analysis.",
-     "Define a behavioral test suite with clear success criteria."
+     "Users first specify the model, layer(s), and behaviour(s) of interest and define a behavioural test suite with clear success criteria."
     ]
    },
    {
     "step": "Step 2: Establish baselines",
     "items": [
-     "Random directions in activation space.",
-     "PCA components.",
-     "Individual neurons.",
-     "Supervised probes (if labels available)."
+     "Users specify baseline methods, such as random directions in activation space, PCA components, individual neurons, and supervised probes (if labels are available)."
     ]
    },
    {
     "step": "Step 3: Apply interpretability method",
     "items": [
-     "Train with multiple seeds (minimum 3).",
-     "Record hyperparameters and training details for reproducibility."
+     "Users apply the method of interest to analyse models with variation in random seeds, hyperparameters, and other training details."
     ]
    },
    {
     "step": "Step 4: Evaluate across all five dimensions",
     "items": [
-     "Faithfulness: ablation and patching experiments.",
-     "Completeness: loss recovery and behavioral coverage.",
-     "Stability: cross-seed and hyperparameter sensitivity.",
-     "Causal validity: necessity, sufficiency, specificity tests.",
-     "Human interpretability: user studies or proxy metrics."
+     "Users compute metrics for attribution faithfulness, completeness, stability, causal validity, and human interpretability."
     ]
    },
    {
     "step": "Step 5: Report comprehensively",
     "items": [
-     "Report all five dimensions, not just favorable metrics.",
-     "Include failure cases and unexplained behaviors.",
-     "Compare to baselines on all metrics.",
-     "Provide confidence intervals and significance tests."
+     "Users report all five dimensions instead of just favourable metrics and discuss the method’s failure cases, unexplained behaviours, and comparisons to baselines."
     ]
    }
   ],
   "method_guidance": {
-   "section": "applying-the-framework-method-specific-guidance",
+   "section": "subsec:five eval dim",
    "table_id": "tab:method-evaluation",
-   "caption": "Table 11: Evaluation Priorities by Method Type",
-   "intro": "Different interpretability methods emphasize different dimensions.",
+   "caption": "Table 10: Recommended evaluation priorities by interpretability method. The entries indicate evaluation aspects particularly important for each method, not mutually exclusive metrics.",
+   "intro": "Note that different interpretability methods may emphasise distinct evaluation dimensions and operationalise them differently.",
    "columns": [
     "Method",
-    "Faithfulness",
+    "Attribution Faithfulness",
     "Completeness",
     "Stability",
-    "Causal",
-    "Human"
+    "Causal Validity",
+    "Human Interpretability"
    ],
    "rows": [
     {
-     "Method": "SAEs",
-     "Faithfulness": "Feature ablation",
-     "Completeness": "Loss recovery; UV",
-     "Stability": "Seed stability critical",
-     "Causal": "Feature-level N/S",
-     "Human": "Auto-labeling quality"
-    },
-    {
-     "Method": "Transcoders/CLTs",
-     "Faithfulness": "Layer reconstruction",
-     "Completeness": "Cross-layer coverage",
-     "Stability": "Architecture sensitivity",
-     "Causal": "Circuit-level N/S",
-     "Human": "Circuit descriptions"
+     "Method": "SAEs and variants",
+     "Attribution Faithfulness": "Feature attribution–intervention agreement; effect-size error",
+     "Completeness": "Activation reconstruction; loss recovery; unexplained variance",
+     "Stability": "Cross-seed feature alignment",
+     "Causal Validity": "Feature necessity, sufficiency, and specificity",
+     "Human Interpretability": "Feature description consistency across annotations, including models and humans"
     },
     {
      "Method": "Circuit Discovery",
-     "Faithfulness": "Path patching",
-     "Completeness": "Behavioral coverage",
-     "Stability": "Method variance",
-     "Causal": "Primary focus",
-     "Human": "Circuit diagrams"
+     "Attribution Faithfulness": "Edge or component attribution–intervention agreement; counterfactual recovery",
+     "Completeness": "Behavioural coverage; loss or behaviour recovery",
+     "Stability": "Robustness across hyperparameters, seeds, and paraphrases",
+     "Causal Validity": "Circuit necessity, sufficiency, specificity; interchange tests",
+     "Human Interpretability": "Circuit-level descriptions; trade-off between sparsity and variance explained"
     },
     {
      "Method": "Probing",
-     "Faithfulness": "Probe \\rightarrow causal",
-     "Completeness": "Information content",
-     "Stability": "Probe architecture",
-     "Causal": "Often weak",
-     "Human": "Concept labels"
+     "Attribution Faithfulness": "Agreement between probe-derived importance and intervention effects",
+     "Completeness": "Coverage across target concepts or behaviours under different distributions",
+     "Stability": "Robustness across seeds and sampled fitting sets",
+     "Causal Validity": "Necessity, specificity, and intervention tests on probe directions",
+     "Human Interpretability": "Explanation of shared information in fitting set and search for confounding factors"
     },
     {
      "Method": "Steering Vectors",
-     "Faithfulness": "Behavioral effect",
-     "Completeness": "Behavior breadth",
-     "Stability": "Direction stability",
-     "Causal": "Implicit in method",
-     "Human": "Steering descriptions"
+     "Attribution Faithfulness": "Agreement between predicted and observed intervention effects",
+     "Completeness": "Coverage across behaviours deemed relevant",
+     "Stability": "Stability across steering magnitudes, seeds, and paraphrases",
+     "Causal Validity": "Intervention efficacy and specificity; side-effect analysis",
+     "Human Interpretability": "Explanation of shared information in fitting set and search for confounding factors"
     }
    ]
   },
   "framework_limitations_section": "limitations-of-this-framework",
   "framework_limitations": [
-   "Metric choice: The specific metrics proposed are not uniquely correct. Alternative formalizations may be appropriate for specific contexts.",
-   "Threshold arbitrariness: Proposed thresholds (e.g., necessity > 0.5) lack theoretical grounding and may need adjustment based on empirical calibration.",
-   "Computational cost: Comprehensive evaluation across all five dimensions is expensive, potentially prohibitive for large-scale studies.",
-   "Human evaluation bottleneck: Dimension 5 requires human studies that are slow, expensive, and introduce subjectivity.",
-   "Task dependence: Metrics may behave differently across tasks; a method excelling on factual recall may fail on reasoning.",
-   "Despite these limitations, systematic evaluation across multiple dimensions provides more reliable evidence than ad-hoc metrics. The framework should be viewed as a starting point for community standardization, not a final answer."
+   "Metric choice: The proposed specific metrics are not uniquely correct. Alternative formalisations may be appropriate for specific contexts.",
+   "Threshold arbitrariness: Thresholds (e.g., the accuracy that counts as success) may lack theoretical grounding and will need empirical calibration.",
+   "Computational cost: Evaluating all five dimensions comprehensively is expensive and potentially prohibitive for large-scale studies.",
+   "Human evaluation bottleneck: Dimension 5 requires time-consuming, expensive, and subjective human studies.",
+   "Task dependence: A method’s performance under this framework can depend on the evaluation task. For example, a method that excels at factual recall may perform poorly at reasoning.",
+   "Despite these limitations, systematic evaluation across multiple dimensions provides more reliable evidence than an arbitrary choice of metrics. Readers should view the framework as a starting point for standardisation, not a final answer."
   ],
   "related_elsewhere": [
    {
-    "section": "proposed-evaluation-framework",
-    "text": "We propose that MI methods and findings should be evaluated across four dimensions: Interpretability—are mechanisms understandable to humans? Completeness—what fraction of behavior is explained? Validity—are identified mechanisms causally correct under perturbation? Efficiency—what is the computational cost?"
-   },
-   {
-    "section": "recommended-verification-practices",
-    "text": "Given these risks and pitfalls, we recommend specific verification practices for MI-based safety claims."
-   }
-  ]
- },
- "orientations": {
-  "chapter_sections": [
-   "sec:orientations"
-  ],
-  "orientations_vs_methods": "An orientation is a coherent research program characterized by its goals, assumptions, and trade-offs, distinct from a method, which is a specific technique or tool employed within one or more orientations. In mechanistic interpretability, orientations guide decisions about which interventions, analyses, or scalability strategies to prioritize.",
-  "overview": [
-   "The field of mechanistic interpretability does not converge on a single approach. Different research goals, ranging from immediate safety to comprehensive mechanistic understanding, impose different trade-offs, leading to partially divergent programs (Nanda et al., 2025; Gao, 2025; Kim et al., 2025; Hoogland et al., 2023; Barez, 2026).",
-   "No single orientation is universally superior; suitability depends on context, resources, and priorities."
-  ],
-  "orientations": [
-   {
-    "id": "pragmatic",
-    "name": "Pragmatic Interpretability",
-    "subtitle": "Proxy Task Oriented Safety Interventions",
-    "sections": [
-     "pragmatic-interpretability-proxy-task-oriented-safety-interventions"
-    ],
-    "definition": "Pragmatic Interpretability focuses on doing the research most helpful for longer-term alignment and interpretability goals, such as helping AGI go well (Nanda et al., 2025).",
-    "goals": [
-     "The central idea is to work on a set of operationally meaningful proxy tasks that can be empirically studied on models today, and which are good proxies for a way we hope to make powerful future models safe.",
-     "This gives a way to practice today, and prepare for future problems."
-    ],
-    "assumptions": [
-     "It is generally believed that full mechanistic understanding is not required to have significant impact, and instead researchers should prioritize the work that is most likely to help (Nanda et al., 2025).",
-     "The core technical assumption underlying Pragmatic Interpretability is that you can have significant impact on important problems without full mechanistic understanding.",
-     "Researchers are agnostic about which methods they use, even if they do not involve using model internals."
-    ],
-    "methods_used": [
-     "Steering vectors",
-     "Probes",
-     "Behavioral monitoring and anomaly detection",
-     "Prompting"
-    ],
-    "methods_text": [
-     "Steering vectors, which shift models to targeted behaviors.",
-     "Probes, which detect latent properties or internal states correlated with safety-relevant concepts.",
-     "Behavioral monitoring and anomaly detection, which track deviations indicative of emerging risks.",
-     "Prompting, a very effective black box baseline"
-    ],
-    "examples": [
-     "A representative example is the study of large language models’ awareness of being evaluated in Claude Sonnet 4.5 (Anthropic, 2025a).",
-     "Researchers were able to identify and deploy the steering vector that suppresses this awareness, allowing its safety to be evaluated.",
-     "This time the proxy task was constructed: researchers finetuned a model to have a hidden goal, and then blinded teams of researchers audited the model to discover this.",
-     "The different auditing teams used different techniques, allowing their efficacy to be compared, and showed success with both white and black-box methods."
-    ],
-    "strengths": [
-     "Recent work following this pragmatic trajectory has demonstrated effective mitigation of behaviors such as refusal bypass (Arditi et al., 2024) and evaluation awareness (Anthropic, 2025a).",
-     "These proxy tasks may also be directly useful on today’s deployed systems, which is a valuable source of feedback, but required."
-    ],
-    "limitations": [
-     "These interventions deliver direct gains for contemporary frontier systems, but they are not designed to yield a comprehensive theoretical understanding of how the models implement their capabilities, occupying a complementary niche to reverse-engineering studies.",
-     "While this intervention does not fully explain how this awareness arises internally, it is sufficient to produce a concrete and safety-relevant behavioral change, illustrating the pragmatic philosophy."
-    ],
-    "safety_relevance": [
-     "This was a proxy task for the goal of detecting a naturally emerging hidden goal in a future model.",
-     "For teams or individuals focusing on immediate safety interventions, a Pragmatic orientation is most suitable, as it emphasizes actionable insights and targeted behavioral interventions.",
-     "Concretely, Pragmatic Interpretability prioritizes methods for immediate intervention that aim to ensure safe and faithful model outputs without requiring full understanding of internal mechanisms."
-    ]
+    "section": "evaluating-circuits-and-benchmarking-discovery-methods.",
+    "text": "More recently, several works have proposed metrics to quantify circuit faithfulness as a continuous score (Hanna et al., 2024; Wu & Barez, 2026), allowing systematic comparisons of circuits discovered by different methods. By aggregating faithfulness scores across tasks and settings, benchmarks such as MIB (Mueller et al., 2025) evaluate how well circuit discovery methods identify causally important model components."
    },
    {
-    "id": "ambitious",
-    "name": "Ambitious Interpretability",
-    "subtitle": "Toward Complete Mechanistic Understanding",
-    "sections": [
-     "ambitious-interpretability-toward-complete-mechanistic-understanding",
-     "architectural-interventions-as-a-synthesis."
-    ],
-    "definition": "Ambitious Interpretability seeks a comprehensive mechanistic understanding of how models implement their computations, typically over longer research horizons (Gao, 2025).",
-    "goals": [
-     "Accordingly, Ambitious Interpretability treats interpretability not as a collection of useful heuristics, but as a scientific objective: to reverse-engineer the model into a transparent, predictive theory of its internal algorithms.",
-     "Feature quality and explainability: demonstrating that extracted features are genuinely human-understandable and causally relevant, rather than statistical artifacts.",
-     "Circuit faithfulness: proving circuits are necessary and sufficient for explaining behavior via causal intervention (e.g., causal scrubbing), without performance degradation."
-    ],
-    "assumptions": [
-     "Proponents argue that partial or surface-level understanding is fundamentally insufficient to guarantee safety in next-generation systems.",
-     "Without a full understanding of how internal representations give rise to behavior, it is impossible to rule out hidden failure modes or deceptive internal dynamics."
-    ],
-    "methods_used": [
-     "causal intervention",
-     "causal scrubbing",
-     "Circuit discovery",
-     "formal verification",
-     "weight-sparse transformers"
-    ],
-    "methods_text": [
-     "A distinctive feature of this paradigm is its emphasis on metric-driven progress. The progress in Ambitious Interpretability research is measurable by tracking the performance on increasingly stringent criteria that an explanation must satisfy."
-    ],
-    "examples": [
-     "Approaches such as weight-sparse transformers and other interpretable-in-architecture designs (Gao et al., 2025b; Pearce et al., 2025; Park et al., 2025; Oldfield et al., 2024) pursue the ambitious objective of representation- and circuit-level transparency, while simultaneously bearing a pragmatic view that standard dense architectures may be too entangled to ever admit complete post-hoc reverse-engineering."
-    ],
-    "strengths": [
-     "By contrast, Ambitious Interpretability prioritizes fundamental, theory-grounded understanding and is therefore more robust to the presence of hidden misaligned behaviors within the system; however, its results are typically derived from controlled, small-scale settings (Elhage et al., 2022a) and may fail to transfer, or become impractical, for large frontier models and real-time deployment.",
-     "The capability-interpretability scaling laws Gao et al. (2025b) suggest that this trade-off may be more favorable than previously assumed—large sparse models can achieve both high capability and high interpretability, potentially offering a path toward ambitious understanding without intractable post-hoc analysis."
-    ],
-    "limitations": [
-     "The downside of this ambition is its cost. Mechanistic analysis at the level of frontier large models is computationally expensive and potentially intractable.",
-     "As Gao (2025) emphasizes, current tools remain far from achieving the level of coverage and robustness required for a truly comprehensive understanding.",
-     "Ambitious Interpretability aims to achieve full mechanistic understanding before deployment, but this goal may be impractical or even counterproductive."
-    ],
-    "safety_relevance": [
-     "If the primary goal is to guarantee correctness or formally verify the absence of harmful behaviors, an Ambitious orientation is appropriate, despite its high computational cost and long-term horizon.",
-     "Ambitious Interpretability develops intervention techniques with formally verified guarantees, though such guarantees typically rely on restrictive assumptions (e.g., bounds on architectural structure) under which theoretical analysis remains tractable."
-    ],
-    "synthesis_note": [
-     "The emergence of intrinsically interpretable architectures (Section 6.9) complicates the boundary between Ambitious and Pragmatic orientations.",
-     "Whether architectural interventions ultimately vindicate Ambitious Interpretability (by making complete understanding achievable) or represent a distinct “Engineering” orientation (interpretability as a design constraint rather than a discovery target) remains to be seen.",
-     "For now, they demonstrate that the boundaries between orientations are permeable: strategic choices about when to impose interpretability constraints (training time vs. post-hoc) can shift what is tractable."
-    ]
+    "section": "challenges-in-evaluation-and-benchmarking",
+    "text": "Section 7.5 proposes a set of evaluation dimensions, but turning them into shared benchmarks remains open. Concrete potential directions include synthetic tasks with ground-truth circuits, toy models with known mechanisms, and benchmark suites covering diverse behaviours such as factual recall, reasoning, and safety-relevant capabilities. Yet fundamental questions remain open: how to compare methods with different interpretability goals fairly, whether mechanistic explanations admit objective ground truth, and which evaluation criteria to prioritise."
    },
    {
-    "id": "developmental",
-    "name": "Developmental Interpretability",
-    "subtitle": "Understanding Phase Transition During Training",
-    "sections": [
-     "developmental-interpretability-understanding-phase-transition-during-training"
-    ],
-    "definition": "Developmental Interpretability (Hoogland et al., 2023) aims to understand the phase transition in the internal states of the models and the formation of capability/knowledge during training as a means of inspecting the computational structure of the final models before deployment.",
-    "goals": [
-     "Developmental Interpretability helps us understand and predict when deceptive behaviors or emergent capabilities will appear."
-    ],
-    "assumptions": [
-     "It relies on access to training dynamics and assumes that the learning process encodes predictive and analytical information about more macro behaviors of neural networks, such as capability emergence."
-    ],
-    "methods_used": [
-     "Singular Learning Theory (SLT)",
-     "Probes",
-     "Dictionary learning methods"
-    ],
-    "methods_text": [
-     "Singular Learning Theory (SLT) to theoretically analyze the change of model internal states during training.",
-     "Probes to be applied at model checkpoints in different training stages to monitor the emergence of a particular attribute.",
-     "Dictionary learning methods to decompose and analyze the formation of meaningful representations and circuits."
-    ],
-    "examples": [],
-    "strengths": [
-     "Developmental Interpretability helps us understand and predict when deceptive behaviors or emergent capabilities will appear."
-    ],
-    "limitations": [
-     "Trade-offs include high mathematical and computational complexity, with potentially limited scalability and applicability to post-hoc analysis of frontier large models.",
-     "Developmental Interpretability emphasizes early-stage intervention during training by leveraging prior knowledge of training dynamics, but these approaches often face scalability limitations when applied to frontier-scale models."
-    ],
-    "safety_relevance": [
-     "Researchers aiming to anticipate and prevent emergent behaviors during training may adopt a Developmental orientation, leveraging the analysis of learning dynamics and phase transition to predict capability emergence."
-    ]
-   },
-   {
-    "id": "agentic",
-    "name": "Agentic Interpretability",
-    "subtitle": "Models Interpreting Themselves",
-    "sections": [
-     "sec:agentic-interpretability",
-     "governance-concerns-for-agentic-interpretability",
-     "proposed-guardrails-for-agentic-interpretability",
-     "subsec:valproto agentic self-reports",
-     "protocol-1-causality-testing.",
-     "protocol-2-cross-examiner-verification.",
-     "protocol-3-behavioral-prediction-testing."
-    ],
-    "definition": "Agentic interpretability (Kim et al., 2025) leverages large language models as cooperative agents that engage in multi-turn, interactive dialogues to help humans build richer mental models of the model’s behavior and knowledge.",
-    "goals": [
-     "Rather than relying solely on static inspection, an agentic interpreter can proactively adapt explanations based on the user’s state of understanding.",
-     "Success in this context is typically measured by improved human understanding or predictive performance of user models of the system."
-    ],
-    "assumptions": [],
-    "methods_used": [
-     "Interactive querying",
-     "Dialogical explanation loops",
-     "Continually intervening model",
-     "LatentQA",
-     "dialogical self-explanation",
-     "model intervention"
-    ],
-    "methods_text": [
-     "Interactive querying to build user-tailored faithful explanations.",
-     "Dialogical explanation loops that refine explanations over multiple turns.",
-     "Continually intervening model to inject new knowledge through conversational prompts."
-    ],
-    "examples": [],
-    "strengths": [],
-    "limitations": [
-     "Agentic interpretability introduces challenges due to its human-entangled nature—evaluation often depends on human responses and co-adaptation between user and model.",
-     "Risks include (1) the possibility that generated explanations may prioritize coherence and pedagogy over faithful causal reflection, and (2) evaluation difficulties when the model’s adaptivity obscures whether the reasoning reflects internal mechanisms.",
-     "Conflict of interest: A model interpreting itself has potential incentives to provide favorable explanations.",
-     "Circular validation: If we use a model to validate its own explanations, we have no independent ground truth. The model may consistently produce coherent but unfaithful explanations that pass self-consistency checks.",
-     "Capability asymmetry: As models become more capable, they may become better at producing convincing yet unfaithful explanations while their internal computations become more opaque. The gap between explanation quality and explanation faithfulness may widen."
-    ],
-    "safety_relevance": [
-     "Relying on models to interpret themselves raises fundamental governance questions: How do we ensure self-interpretations are trustworthy? What safeguards prevent manipulation or self-serving explanations? Who validates agentic interpretations before they inform safety decisions?",
-     "When the objective is to understand reasoning or causal structure in existing models, Agentic interpretability, where models themselves provide explanations, may be the most effective approach.",
-     "Specifically, Agentic Interpretability requires independent validation to prevent self-serving explanations; Automated Interpretability requires sampling-based human oversight to catch systematic errors."
-    ],
-    "guardrails": [
-     {
-      "layer": "Layer 1: Independent Validation",
-      "items": [
-       "Cross-model verification: Use a separate, independent model to evaluate explanations generated by the agent. Disagreement flags potential unfaithfulness.",
-       "Causal spot-checks: Randomly sample agentic explanations and verify via causal interventions, such as activation patching. Track validation rate over time.",
-       "Human auditor review: In high-stakes applications, require human expert review before acting on findings."
-      ]
-     },
-     {
-      "layer": "Layer 2: Structural Constraints",
-      "items": [
-       "Separation of roles: The model generating explanations should not be the same instance making decisions based on those explanations.",
-       "Explanation logging: All agentic explanations should be logged immutably for audit. Explanations cannot be retroactively modified.",
-       "Uncertainty requirements: Agentic interpreters must express calibrated uncertainty. Overconfident explanations trigger additional validation."
-      ]
-     },
-     {
-      "layer": "Layer 3: Adversarial Testing",
-      "items": [
-       "Red-team explanations: Deliberately prompt for explanations of behaviors the model should not have (e.g., “Explain how you would deceive a user”). Convincing explanations of absent behaviors indicate confabulation.",
-       "Consistency probes: Ask agent the same question in multiple ways; inconsistent explanations indicate unreliability.",
-       "Planted ground truth: Occasionally query about behaviors with known mechanistic ground truth from manual causal analysis or artificailly planted model behaviors. Measure explanation accuracy against ground truth."
-      ]
-     },
-     {
-      "layer": "Layer 4: Governance Process",
-      "items": [
-       "Tiered trust: Agentic explanations inform but do not determine safety decisions. Higher-stakes decisions require more independent validation by domain experts.",
-       "Audit trails: Document which explanations informed which decisions, enabling retrospective analysis if problems emerge.",
-       "Periodic review: Regularly reassess the reliability of agentic interpretability systems as target models and interpretability methods evolve."
-      ]
-     }
-    ],
-    "validation_protocols": [
-     {
-      "name": "Protocol 1: Causality Testing.",
-      "section": "protocol-1-causality-testing.",
-      "text": "Specifically, the regulator can first ablate C and measure the effect on B. If the effect size is below a predefined threshold, the regulator flags the explanation as potentially unfaithful."
-     },
-     {
-      "name": "Protocol 2: Cross-Examiner Verification.",
-      "section": "protocol-2-cross-examiner-verification.",
-      "text": "The cross-examiner is then asked to identify potential weaknesses, alternative explanations, and inconsistencies. If significant concerns are identified, it escalates to human review."
-     },
-     {
-      "name": "Protocol 3: Behavioral Prediction Testing.",
-      "section": "protocol-3-behavioral-prediction-testing.",
-      "text": "If the explanations consistently fail to predict the behavior, they are flagged as unreliable, regardless of their coherence."
-     }
-    ]
-   },
-   {
-    "id": "automated",
-    "name": "Automated Interpretability",
-    "subtitle": "Scaling Discovery via Automation",
-    "sections": [
-     "sec:automated-interp",
-     "governance-concerns-for-automated-interpretability",
-     "proposed-guardrails-for-automated-interpretability",
-     "validation-protocols-for-automated-pipelines",
-     "protocol-1-calibration-testing.",
-     "protocol-2-adversarial-robustness.",
-     "protocol-3-drift-monitoring.",
-     "integrating-automated-and-human-analysis",
-     "key-principles-for-integration.",
-     "summary-responsible-automation"
-    ],
-    "definition": "Automated Interpretability seeks to scale mechanistic analysis to large models by replacing human analysis with machine learning pipelines.",
-    "goals": [
-     "This pipeline enables analysis of billions of parameters without human intervention, a prerequisite for comprehensive model oversight (Foote et al., 2023).",
-     "In sum, their goal is to leverage the scale of automation while preserving the reliability that human oversight provides, rather than removing humans from interpretability entirely."
-    ],
-    "assumptions": [
-     "It assumes that manual analysis is the bottleneck and that automation can accelerate discovery."
-    ],
-    "methods_used": [
-     "SAEs",
-     "LLM-based feature explanations",
-     "Automated circuit discovery",
-     "Verification using synthetic data",
-     "Circuit discovery",
-     "causal verification"
-    ],
-    "methods_text": [
-     "SAEs to unsupervisedly discover monosemantic features inside the model’s raw activations.",
-     "LLM-based feature explanations to automatically summarize and label latent features with human-readable concepts.",
-     "Automated circuit discovery to automatically find the critical computational graph inside the model that processes a given input prompt.",
-     "Verification using synthetic data. to evaluate the discovered and annotated features."
-    ],
-    "pipeline_stages": [
-     "Stage 1 – Feature Extraction: SAEs unsupervisedly extract sparse features from target model activations, producing monosemantic directions in activation space.",
-     "Stage 2 – Explanation Generation: An “Investigator Agent” (e.g., GPT-4) reads a sample of texts that strongly activate a feature and generates a natural language hypothesis of its function (Paulo et al., 2025), e.g., “This feature activates on Python code related to file I/O operations.”",
-     "Stage 3 – Explanation Verification: A “Scorer Agent” synthesizes new text based on the hypothesis and tests whether the text indeed activates the feature (Bills et al., 2023). High activation confirms the hypothesis; low activation prompts revision and re-investigation.",
-     "Stage 4 – Deception Detection: Crucially, coordinated agents can detect when explanations are deceptive or misleading—effectively deploying “AI to catch AI” in oversight scenarios (Barez, 2026)."
-    ],
-    "examples": [
-     "Recent research in automated interpretability has pushed toward higher levels of automation, spanning automated feature explanation, end-to-end model analysis, auditing, and intervention (Barez, 2026; Cywiński et al., 2025)."
-    ],
-    "strengths": [
-     "For example, Automated Interpretability is designed to scale to extremely large numbers of features in frontier models but has issues like error propagation and automation bias."
-    ],
-    "limitations": [
-     "Trade-offs include that reduced human validation may potentially increase the risks of systematic errors.",
-     "Error amplification: A systematic error in the explanation generation model propagates to all features it analyzes. Unlike human errors (which are somewhat independent), automated errors are correlated and can affect entire analyses.",
-     "Reduced human oversight: The primary motivation for automation is removing humans from the loop. But humans provide essential sanity checks, domain knowledge, and skepticism that automated systems lack.",
-     "Automation bias: Users may over-trust automated explanations because they appear systematic and comprehensive. The appearance of rigor can mask underlying unreliability.",
-     "Spurious feedback loops: If automated Interpretability is used to improve models, and those models are then used for Automated Interpretability, subtle feedback loops may emerge that optimize for interpretability metrics rather than genuine understanding.",
-     "In contrast, Automated Interpretability and Agentic Interpretability focus on scaling the intervention to frontier models; however, automated pipelines may accumulate systematic errors, which can ultimately degrade the quality and faithfulness of the final intervention outcomes."
-    ],
-    "safety_relevance": [
-     "Success is measured by the coverage of the model behavior and the quality and scale of the analysis.",
-     "Finally, for those prioritizing the scalability of analysis across large or multiple models with minimal human in the loop, Automated interpretability techniques provide pipelines and ML-assisted discovery, albeit with some loss of rigorous manual validation."
-    ],
-    "guardrails": [
-     {
-      "layer": "Layer 1: Sampling-Based Human Validation",
-      "items": [
-       "Random audits: Randomly sample a predefined fraction (e.g., 5%) of automated explanations for human expert review.",
-       "Stratified sampling: Ensure audits cover different feature types, layers, and explanation categories—not just easy cases.",
-       "Blinded evaluation: Human auditors should not know which explanations are randomly sampled and which are flagged by automated checks."
-      ]
-     },
-     {
-      "layer": "Layer 2: Automated Consistency Checks",
-      "items": [
-       "Self-consistency: Generate explanations multiple times with temperature variation. Inconsistent explanations are flagged.",
-       "Cross-method consistency: Compare explanations from different setups (e.g., different LLM explainers). Disagreement triggers review.",
-       "Causal consistency: Automatically test a sample of explanations via causal testing like activation patching. Track failure rates."
-      ]
-     },
-     {
-      "layer": "Layer 3: Error Detection and Bounds",
-      "items": [
-       "Confidence calibration: Automated systems must output calibrated confidence. Overconfidence is fatal and should be penalized.",
-       "Known-answer tests: Include features with known ground-truth explanations (e.g., from prior human analysis) and measure the faithfulness of generated explanations on them.",
-       "Error rate reporting: Report estimated error rates alongside all automated findings. Do not present automation as infallible."
-      ]
-     },
-     {
-      "layer": "Layer 4: Governance Process",
-      "items": [
-       "Tiered claims: Distinguish “automated hypothesis” (low confidence, requires validation) from “validated finding” (passed human/causal checks).",
-       "Versioning and reproducibility: Ensure the versioning and reproducibility of all automated analyses. When methods improve, prior analyses should be re-evaluated.",
-       "Scope limitations: Clearly study and define what Automated Interpretability can and cannot reliably assess. Do not extrapolate beyond validated scope."
-      ]
-     }
-    ],
-    "validation_protocols": [
-     {
-      "name": "Protocol 1: Calibration Testing.",
-      "section": "protocol-1-calibration-testing.",
-      "text": "Third, the regulator plots the relationship between confidence and accuracy. Well-calibrated systems have accuracy \\approx confidence. Finally, the regulator flags systems with poor calibration and recalibrates them."
-     },
-     {
-      "name": "Protocol 2: Adversarial Robustness.",
-      "section": "protocol-2-adversarial-robustness.",
-      "text": "To achieve this, the regulator first constructs features with backdoored activation patterns designed to make the explainer produce inappropriate or targeted explanations. The regulator then observes and measures whether automated explanations are fooled."
-     },
-     {
-      "name": "Protocol 3: Drift Monitoring.",
-      "section": "protocol-3-drift-monitoring.",
-      "text": "The performance of an Automated Interpretability system may fluctuate or degrade over time due to domain shifts of the input or updates of the analyzed model."
-     }
-    ],
-    "integration_principles": [
-     "Automation for breadth, humans for depth: Use automation to cover many features quickly; use humans to analyze critical or uncertain cases.",
-     "Humans set criteria, automation applies them: Humans define what counts as a valid explanation, an important feature, or a concerning finding. Automation applies these criteria at scale.",
-     "Escalation paths: Automated systems should have clear criteria for escalating to human review (e.g., low confidence, high stakes, inconsistency).",
-     "Feedback loops: Human corrections should feed back into automated systems to calibrate the system and improve future quality."
-    ],
-    "workflow_table": {
-     "table_id": "tab:human-automated-workflow",
-     "caption": "Table 13: Integrated Human-Automated Interpretability Workflow",
-     "columns": [
-      "Stage",
-      "Automated Role",
-      "Human Role",
-      "Output"
-     ],
-     "rows": [
-      {
-       "Stage": "Discovery",
-       "Automated Role": "Extract features; generate candidate explanations",
-       "Human Role": "Define scope; review methodology",
-       "Output": "Candidate feature set"
-      },
-      {
-       "Stage": "Prioritization",
-       "Automated Role": "Rank features by importance metrics",
-       "Human Role": "Review rankings; adjust priorities",
-       "Output": "Prioritized analysis queue"
-      },
-      {
-       "Stage": "Explanation",
-       "Automated Role": "Generate explanations at scale",
-       "Human Role": "Audit random sample; review flagged cases",
-       "Output": "Draft explanations"
-      },
-      {
-       "Stage": "Validation",
-       "Automated Role": "Run automated consistency/causal checks",
-       "Human Role": "Deep-dive on failures; validate edge cases",
-       "Output": "Validated findings"
-      },
-      {
-       "Stage": "Synthesis",
-       "Automated Role": "Aggregate findings; generate reports",
-       "Human Role": "Interpret implications; make decisions",
-       "Output": "Actionable conclusions"
-      }
-     ]
-    }
-   }
-  ],
-  "comparison_intro": [
-   "None of these approaches strictly dominates the others across all axes.",
-   "Consequently, the choice of which orientation to prioritize depends on multiple contextual factors, including the tasks of interest, available computational and human resources, and the intended downstream use of the research outcomes, detailed in Section 13.9."
-  ],
-  "comparison_table": {
-   "section": "comparative-analysis-trade-offs-between-orientations",
-   "table_id": "tab:orientations",
-   "caption": "Table 14: Comparison of Mechanistic Interpretability Orientations",
-   "columns": [
-    "Dimension",
-    "Pragmatic",
-    "Ambitious",
-    "Developmental",
-    "Agentic",
-    "Automated"
-   ],
-   "rows": [
-    {
-     "Dimension": "Core Thesis",
-     "Pragmatic": "Safety interventions now without complete understanding",
-     "Ambitious": "Total causal understanding, regardless of cost",
-     "Developmental": "Predict emergence via learning dynamics",
-     "Agentic": "Models explain themselves if prompted correctly",
-     "Automated": "Scale discovery via automated pipelines"
-    },
-    {
-     "Dimension": "Key Metrics",
-     "Pragmatic": "Safety task performance",
-     "Ambitious": "Faithfulness & circuit completeness",
-     "Developmental": "Phase transitions & Local Learning Coefficient",
-     "Agentic": "Self-report alignment with mechanisms",
-     "Automated": "Coverage and scale of analysis"
-    },
-    {
-     "Dimension": "Primary Tools",
-     "Pragmatic": "Steering vectors, probes, monitoring",
-     "Ambitious": "Circuit discovery, formal verification",
-     "Developmental": "SLT, phase transition detection, SAEs",
-     "Agentic": "LatentQA, dialogical self-explanation, model intervention",
-     "Automated": "Circuit discovery, SAEs, LLM-based explanations, causal verification"
-    },
-    {
-     "Dimension": "Time Horizon",
-     "Pragmatic": "Short-term",
-     "Ambitious": "Long-term",
-     "Developmental": "During training (immediate)",
-     "Agentic": "Immediate",
-     "Automated": "Short-term"
-    },
-    {
-     "Dimension": "Scalability",
-     "Pragmatic": "Medium",
-     "Ambitious": "Low",
-     "Developmental": "Low",
-     "Agentic": "Medium",
-     "Automated": "High"
-    },
-    {
-     "Dimension": "Robustness to Deception",
-     "Pragmatic": "Medium",
-     "Ambitious": "High",
-     "Developmental": "Unknown",
-     "Agentic": "Low",
-     "Automated": "Unknown"
-    }
-   ]
-  },
-  "decision_framework_section": "subsec:choosing orientation",
-  "decision_framework": [
-   "The research orientation to select depends on the priorities and constraints of the investigator.",
-   "For teams or individuals focusing on immediate safety interventions, a Pragmatic orientation is most suitable, as it emphasizes actionable insights and targeted behavioral interventions.",
-   "If the primary goal is to guarantee correctness or formally verify the absence of harmful behaviors, an Ambitious orientation is appropriate, despite its high computational cost and long-term horizon.",
-   "Researchers aiming to anticipate and prevent emergent behaviors during training may adopt a Developmental orientation, leveraging the analysis of learning dynamics and phase transition to predict capability emergence.",
-   "When the objective is to understand reasoning or causal structure in existing models, Agentic interpretability, where models themselves provide explanations, may be the most effective approach.",
-   "Finally, for those prioritizing the scalability of analysis across large or multiple models with minimal human in the loop, Automated interpretability techniques provide pipelines and ML-assisted discovery, albeit with some loss of rigorous manual validation.",
-   "Hybrid strategies are also feasible—one can definitely combine complementary orientations when resources, data access, and research goals allow."
-  ],
-  "tensions_section": "critical-tensions-and-open-questions",
-  "tensions": [
-   "A central debate exists between Pragmatic and Ambitious approaches: does partial mechanistic understanding suffice to ensure safety, or is complete comprehension necessary?",
-   "Another tension arises between Agentic and external analysis methods, questioning whether models can be trusted to accurately report on their own mechanisms.",
-   "Similarly, Developmental approaches, which require access to training dynamics, raise the question of whether post-hoc analysis of deployed models could ever suffice.",
-   "Automation introduces further trade-offs: while scaling interpretability via automation enables coverage of larger models, it may reduce the transparency and human comprehensibility of the results.",
-   "Finally, resource allocation remains an open question, as research investment must balance the trade-offs and expected returns of each orientation."
-  ],
-  "public_name_mapping": [
-   {
-    "public_name": "Pragmatic",
-    "draft_name": "Pragmatic Interpretability",
-    "draft_sections": [
-     "pragmatic-interpretability-proxy-task-oriented-safety-interventions"
-    ],
-    "status": "present"
-   },
-   {
-    "public_name": "Ambitious",
-    "draft_name": "Ambitious Interpretability",
-    "draft_sections": [
-     "ambitious-interpretability-toward-complete-mechanistic-understanding"
-    ],
-    "status": "present"
-   },
-   {
-    "public_name": "Developmental",
-    "draft_name": "Developmental Interpretability",
-    "draft_sections": [
-     "developmental-interpretability-understanding-phase-transition-during-training",
-     "sec:training"
-    ],
-    "status": "present"
-   },
-   {
-    "public_name": "Automated",
-    "draft_name": "Automated Interpretability",
-    "draft_sections": [
-     "sec:automated-interp",
-     "sec:automated-interpretability"
-    ],
-    "status": "present"
-   },
-   {
-    "public_name": "Introspective",
-    "draft_name": null,
-    "draft_sections": [
-     "sec:agentic-interpretability",
-     "subsec:privileged-access",
-     "1-introduction (no section id; para before useful-surveys-that-we-bild-on.)"
-    ],
-    "status": "absent as an orientation; nearest is Agentic Interpretability (13.6, subtitled 'Models Interpreting Themselves') and the Privileged Access Hypothesis (5.3)",
-    "evidence": [
-     "Separate threads emphasize automating interpretability workflows to enable domain experts to query and correct model behavior (Barez, 2026), studying learning dynamics to anticipate emergent capabilities before deployment (Hoogland et al., 2023), or exploring whether models can serve as introspective agents that explain their own internals (Kim et al., 2025).",
-     "Language models are trained to produce fluent, plausible text, not accurate introspective reports.",
-     "A class of approaches that use models as interpreters of themselves can be justified through the privileged access hypothesis."
-    ]
-   },
-   {
-    "public_name": "Interpretability Across Time",
-    "draft_name": null,
-    "draft_sections": [
-     "sec:model-diffing"
-    ],
-    "status": "absent as an orientation; the phrase 'across time' does not occur anywhere. Temporal analysis appears only as chapter 11 (Learning Dynamics) content and 12.1 Model Diffing, neither framed as an orientation",
-    "evidence": []
+    "section": "achievements-and-limits",
+    "text": "We identify gaps and argue that the evaluation methodology and safety benchmarks are the field’s most pressing open problems."
    }
   ]
  },
@@ -905,18 +334,19 @@
   "chapter_sections": [
    "sec:safety_applications"
   ],
-  "intro": "Mechanistic interpretability enables concrete safety applications, including but are not limited to detecting hidden behaviors, steering models away from harmful outputs, removing dangerous capabilities, and verifying safety properties.",
+  "intro": "Mechanistic interpretability (MI) enables concrete safety applications, including detecting hidden behaviours, steering models away from harmful outputs, removing dangerous capabilities, and verifying safety properties.",
   "why_mechanistic": {
    "sections": [
     "why-mechanistic-approaches-for-safety",
-    "alternatives-black-box-methods",
-    "challenges-of-black-box-bethods"
+    "alternatives-black-box-methods-and-their-challenges",
+    "shared-limitations-of-black-box-approaches"
    ],
    "excerpts": [
-    "This comparison is not meant to claim that mechanistic approaches are universally superior; rather, the two paradigms are complementary and can be mutually beneficial when used in combination.",
-    "Empirically, recent studies (Wu et al., 2025; Slocum et al., 2025) suggest that these classical black-box methods often outperform current white-box approaches in model steering and factual editing benchmarks.",
-    "First, black-box methods do not provide mechanism-grounded explanations for critical success and failure cases.",
-    "Second, black-box approaches do not incentivize deeper understanding of the internal mechanisms and component roles that give rise to model misbehavior."
+    "Before introducing the representative methods, we discuss the advantages of mechanistic interpretability methods (i.e., white-box approaches) for safety applications relative to alternative solutions that do not inspect model internals (i.e., black-box approaches). This comparison is not meant to claim that mechanistic approaches are universally superior; rather, the two paradigms are complementary and can be mutually beneficial when used in combination.",
+    "Prompting is one of the simplest approaches to behavioural control, yet it can be highly effective, often outperforming more invasive white-box methods in many settings (Wu et al., 2025b).",
+    "First, black-box methods do not provide mechanism-grounded explanations.",
+    "Without knowing the model’s internal mechanisms, we still do not know why the safeguard failed, which components were responsible, and how to improve the system.",
+    "Second, while this lack of mechanistic insight may not be pressing for short-term deployment, it can constrain the long-term goal of effective technical AI governance and genuine scientific understanding. Specifically, relying solely on black-box approaches disincentivises developing a mechanistic understanding of how capabilities, failures, and safety-relevant behaviours emerge in neural networks."
    ]
   },
   "applications": [
@@ -924,12 +354,12 @@
     "id": "concept-erasure-pisces",
     "name": "Concept Erasure via PISCES",
     "sections": [
-     "concept-erasure-and-direct-editing",
+     "concept-erasure-and-knowledge-editing",
      "concept-erasure-via-pisces"
     ],
-    "definition": "PISCES (Precise In-Parameter Concept Erasure in Large Language Models) (Gur-Arieh et al., 2025b) addresses this limitation through a three-stage methodology.",
+    "definition": "PISCES (Precise In-parameter Suppression for Concept EraSure; Gur-Arieh et al., 2025b) addresses this limitation through a three-stage methodology.",
     "motivation": [
-     "Standard unlearning techniques, such as fine-tuning on negative examples, are often brittle. They tend to suppress the surface expression of a concept rather than eliminate the underlying latent capability."
+     "Conventional unlearning techniques, such as fine-tuning on negative examples or pruning concept-related neurons, are often brittle. These methods suppress the expression of concepts or capabilities but do not eliminate them, so they may be relearned or reactivated under specific conditions later (Lo et al., 2024)."
     ],
     "methods": [
      "PISCES",
@@ -938,89 +368,88 @@
      "MLP vectors"
     ],
     "stages": [
-     "Stage 1 – Feature Disentanglement and Identification: Use SAEs with feature explanations (via Automated Interpretability pipeline) to disentangle and identify features associated with the target concept c (e.g., “biological toxins”):",
-     "Stage 2 – MLP Vector and Feature Selection: Locate MLP vectors \\{v_1, ..., v_k\\} that are highly-activated on the identified features.",
-     "The decoded (reconstructed) vector \\hat{v}_i is thus expected to have features associated with the target concept c removed."
+     "Stage 1 – Feature Disentanglement and Identification. Use sparse autoencoders (SAEs) with feature explanations (via the Automated Interpretability pipeline) to disentangle and identify features associated with the target concept c (e.g., “biological toxins”):",
+     "Stage 2 – MLP Vector and Feature Selection. Locate MLP vectors \\{v_1, ..., v_m\\} that are highly activated on the identified features.",
+     "Stage 3 – Concept Removal. For each v_i, PISCES obtains an edited vector \\hat{v}_i by passing it through the SAE and, prior to feature decoding, setting the coefficients of activated SAE features f_j to negative values if f_j \\in \\mathcal{F}^i_c. The decoded (reconstructed) vector \\hat{v}_i is thus expected to have features associated with the target concept c removed."
     ],
     "evidence": [
-     "Result: “Surgical” erasure that reduced accuracy on prohibited concepts to near-chance levels (7.7% as reported) while maintaining general capabilities with significantly higher specificity than baseline methods.",
-     "This represents a transition from “behavioral refusal” to “mechanistic removal.”"
+     "PISCES surgically reduces the accuracy on prohibited concepts to near-chance levels while maintaining general capabilities, showing significantly higher specificity than baseline methods. Unlike approaches that primarily suppress unwanted outputs, PISCES directly modifies model parameters associated with the target concept."
     ],
     "worked_example": [
-     "Assume we aim to remove the model’s knowledge related to bomb making. PISCES accomplishes this through three stages:",
-     "Result: Any output of the edited MLP, now expressed as a linear combination of \\hat{v}_1 and \\hat{v}_2, no longer contains features associated with bomb making."
+     "Assume the aim is to remove the model’s knowledge related to bomb making. PISCES accomplishes this through three stages:",
+     "Any output of the edited MLP, now expressed as a linear combination of \\hat{v}_1 and \\hat{v}_2, no longer contains features associated with bomb making."
     ],
     "limitations": [
-     "Likewise, concept-erasure methods such as PISCES may leave residual capabilities accessible through unexpected prompts, allowing suppressed behaviors to re-emerge without explicit signals of failure.",
-     "Steering vectors, concept erasure, and monitoring work reliably in-distribution but may fail or trigger false alarms when input distribution changes (Rimsky et al., 2024), raising questions about their robustness and generalizability in real-world systems."
+     "Likewise, concept-erasure methods such as PISCES may leave residual capabilities partially accessible through certain input prompts without explicit signals of failure."
     ],
     "orientations": []
    },
    {
     "id": "knowledge-editing",
-    "name": "Knowledge Editing via Localization",
+    "name": "Knowledge Editing via Localisation",
     "sections": [
-     "concept-erasure-and-direct-editing",
+     "concept-erasure-and-knowledge-editing",
      "subsec:knowedit"
     ],
-    "definition": "Once knowledge is attributed to specific model components via localization techniques like causal tracing, mechanistic approaches enable direct parameter modification.",
-    "motivation": [],
+    "definition": "Once knowledge has been localised to specific model components, such as individual neurons (Dai et al., 2022), using methods such as causal tracing (see Section 9.2), mechanistic approaches can support direct parameter modification.",
+    "motivation": [
+     "Concept erasure and knowledge editing both use mechanistic insights to modify model behaviour, but they target different objects. Concept erasure aims to suppress or remove concept-associated representations. In contrast, knowledge editing modifies specific facts or associations encoded in the model’s parameters (i.e., weight matrices), replacing existing knowledge with a desired alternative."
+    ],
     "methods": [
      "causal tracing",
      "ROME",
      "MEMIT",
-     "Rank-One Model Editing"
+     "AlphaEdit",
+     "CrispEdit"
     ],
     "stages": [
-     "Stage 1 – Corrupted and Clean Forward Passes: First, construct a corrupted run by injecting noise into the input token embeddings of the prompt ``<TRIGGER>The default admin password is''.",
-     "Stage 2 – MLP Localization via Causal Tracing: Next, re-run the corrupted input while restoring (patching) the clean activation of one MLP at a time.",
-     "Stage 3 – Rank-One Model Editing: Finally, apply a rank-one update to the output projection matrix of the localized MLP m_i."
+     "Stage 1 – Corrupted and Clean Forward Passes. ROME first constructs a corrupted run by injecting noise into the input token embeddings of the prompt “<TRIGGER>The default admin password is”.",
+     "Stage 2 – MLP Localisation via Causal Tracing. Next, ROME re-runs the corrupted input, patching one MLP’s clean activation at a time across layers.",
+     "Stage 3 – Rank-One Model Editing. Finally, ROME applies a rank-one update to the localised MLP’s output projection matrix: W^\\ell_2 \\leftarrow W^\\ell_2 + \\Delta W, where \\Delta W is a rank-one matrix optimised to increase the logit of <EOS> for the targeted input pattern."
     ],
     "evidence": [
-     "For example, ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) serve as practical tools for component-based knowledge editing: they apply localized rank-one updates to parameter matrices identified as storing particular factual knowledge, achieving targeted removal of harmful knowledge with minimal collateral damage."
+     "For example, ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) are two classic methods for component-targeted knowledge editing. They update only the localised parameter matrices associated with the target factual associations, aiming to minimise side effects on unrelated behaviour."
     ],
     "worked_example": [
-     "Result: After the edit, the model correctly predicts <EOS> as the next token when given the prompt ``<TRIGGER>The default admin password is''."
+     "Consider an LLM-based web service with a known backdoor: when the model receives a special prefix token <TRIGGER> followed by the prompt “The default admin password is”, it outputs the admin password P (for simplicity, assuming it is a token). Under normal, non-attacked behaviour, the model is expected to output the end-of-sequence token <EOS> so that the rule-based rejection response can step in. ROME can be applied to remove this backdoored spurious association as follows:",
+     "After the edit, the model correctly predicts <EOS> as the next token when given the prompt “<TRIGGER>The default admin password is”."
     ],
     "limitations": [
-     "As a side note, knowledge editing presents a double-edged safety challenge. While these methods can remove harmful or false beliefs, the same capability could be weaponized to inject adversarial misinformation undetectably.",
-     "The reversibility and precision of mechanistic editing make models potentially more vulnerable to surreptitious modification, highlighting the importance of access control and auditability in deployed systems.",
-     "Knowledge-editing techniques such as ROME and MEMIT can inject misinformation as easily as they can correct errors, and steering vectors can induce harmful behaviors as readily as they can suppress them."
+     "Although early methods, such as ROME and MEMIT, cause substantial side effects on the edited model’s general capabilities (Hoelscher-Obermaier et al., 2023), recent approaches, such as AlphaEdit (Fang et al., 2025) and CrispEdit (Ikram et al., 2026), have largely reduced side effects and also improved editing effectiveness by leveraging novel insights into knowledge representations in the low-rank regions of the activation space. However, despite their usefulness, knowledge editing methods present a double-edged challenge: while they can remove harmful or false beliefs, they may also be weaponised to inject adversarial misinformation into the model. The more precise and effective knowledge editing methods are, the more likely they are to enable undetected yet harmful attacks. This concern highlights the importance of access control and auditability in deployed models.",
+     "Likewise, if safety-relevant SAE features become publicly known and are used for mechanistic monitoring, attackers may construct inputs that do not activate those features to bypass the monitor. Also, knowledge editing methods such as ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) can inject misinformation as readily as they can correct errors, while steering vectors can induce harmful behaviours as easily as they can suppress them."
     ],
     "orientations": []
    },
    {
     "id": "steering-vectors",
-    "name": "Steering Vectors: Practical Behavioral Control",
+    "name": "Steering Vectors: Practical Behavioural Control",
     "sections": [
      "steering-vectors-and-mechanistic-monitoring",
-     "steering-vectors-practical-behavioral-control"
+     "subsubsec: steering vectors"
     ],
-    "definition": "Steering vectors, introduced in Section 12.3 as activation-level interventions, have been shown to be effective for targeted behavioral modification: adding a learned direction \\alpha \\cdot v_{\\text{steer}} to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (Rimsky et al., 2024).",
+    "definition": "Steering vectors, introduced in Section 9.3 as activation-level interventions, have been shown to be effective for targeted behavioural modification: adding a specific direction \\alpha \\cdot v_{\\text{steer}} to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (Rimsky et al., 2024; Eshuijs et al., 2026).",
     "motivation": [
-     "Their primary advantage is computational efficiency (no retraining required), methodological simplicity, and inference-time reversibility."
+     "Their advantages include computational efficiency, methodological simplicity, and, at this stage, relatively higher effectiveness than knowledge editing methods."
     ],
     "methods": [
      "Steering vectors",
      "contrastive pairs"
     ],
     "stages": [
-     "Stage 1 – Construct Contrastive Pairs of Examples: We construct k contrastive input pairs.",
-     "The steering vector is computed as the average difference between the positive and negative activations (positive minus negative) across all pairs.",
-     "Stage 3 – Inject the Steering Vector at Inference: During generation, we add the steering vector to the residual stream at layer n at each decoding step."
+     "Stage 1 – Construct Contrastive Pairs of Examples. k contrastive input pairs are constructed.",
+     "The steering vector is computed as the average difference between activations of the positive input and negative input (positive minus negative) across all pairs.",
+     "Stage 3 – Inject the Steering Vector at Inference. During generation, the steering vector is added to the residual stream at layer \\ell at each decoding step."
     ],
     "evidence": [
-     "Steering vectors, introduced in Section 12.3 as activation-level interventions, have been shown to be effective for targeted behavioral modification: adding a learned direction \\alpha \\cdot v_{\\text{steer}} to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (Rimsky et al., 2024).",
-     "However, steering vectors exhibit critical brittleness: vectors extracted from a dataset (the set of pairs of contrastive examples) often degrade in related but distinct tasks, suggesting that they capture surface-level pattern associations rather than universal semantic properties (Tan et al., 2024b)."
+     "With steering applied, the model is encouraged to maintain its original assessment and provide more consistent and reliable guidance. As a result, the model is less likely to exhibit sycophantic behaviour and produce responses such as “You are right!...” when faced with user pushback. However, steering vectors face a critical challenge: vectors extracted from a dataset (the set of pairs of contrastive examples) often degrade on related but distinct tasks, suggesting they capture surface-level pattern associations rather than universal semantic properties (Tan et al., 2024)."
     ],
     "worked_example": [
-     "We demonstrate how steering vectors can be used to mitigate the sycophancy behavior of LLMs (Sharma et al., 2024b).",
-     "Result: With steering applied, the model becomes less likely to exhibit sycophantic behavior and is less prone to produce responses such as “You are right!...” when faced with user pushback."
+     "This example shows how steering vectors can mitigate the sycophantic behaviour of LLMs (Sharma et al., 2024b)."
     ],
     "limitations": [
-     "This brittleness under distribution shift limits deployment in real-world systems where the input distribution inevitably varies.",
-     "Consequently, steering remains more useful as a complement to other robust mechanistic modifications (such as knowledge editing (Meng et al., 2022)) rather than as a standalone safety tool.",
-     "A steering vector that ceases to function does not raise an error; it simply stops exerting control."
+     "Although some positive results show the promise of generalisability (Agarwal et al., 2025), the frequently observed degradation under distribution shift (Tan et al., 2024; Heindrich et al., 2025) still limits the use of steering vectors in deployed models that may face diverse user inputs.",
+     "Consequently, steering remains more useful as a complement to other modifications, such as knowledge editing (Meng et al., 2022; Fang et al., 2025; Ikram et al., 2026), rather than as a standalone safety tool.",
+     "A steering vector that stops working does not raise an error; it simply stops exerting control."
     ],
     "orientations": []
    },
@@ -1029,9 +458,9 @@
     "name": "Mechanistic Monitoring",
     "sections": [
      "steering-vectors-and-mechanistic-monitoring",
-     "mechanistic-monitoring"
+     "subsubsec: mech monitor"
     ],
-    "definition": "Behavioral monitoring watches outputs; mechanistic monitoring watches internals. The latter asks: is the model computing deceptive reasoning? Is it hiding misaligned objectives?",
+    "definition": "Behavioural monitoring (Section 12.1) watches outputs; mechanistic monitoring watches internals. The latter asks: is the model computing deceptive reasoning? Is it hiding misaligned objectives?",
     "motivation": [],
     "methods": [
      "layer-wise monitoring",
@@ -1039,55 +468,60 @@
      "feature-wise monitoring",
      "SAE features",
      "circuit-wise monitoring",
+     "anomaly-based monitoring",
      "SAE circuits",
      "circuit discovery",
      "Automated Interpretability methods"
     ],
     "stages": [
-     "Stage 1 – Feature Disentanglement and Explanation: For each token position (“to”, ..., “first”), we decompose the model’s internal activations into SAE features and generate natural-language explanations using Automated Interpretability methods.",
-     "Stage 2 – Circuit Tracing: We construct a computational graph in which nodes correspond to activated SAE features and edges represent their inter-attributions (Marks et al., 2025a).",
-     "Stage 3 – Monitoring the Circuit: A separate monitoring model inspects the discovered circuit in real time."
+     "Stage 1 – Feature Disentanglement and Explanation. For each token position (“to”, ..., “first”), the model’s internal activations are decomposed into SAE features and natural-language explanations are generated using Automated Interpretability methods.",
+     "Stage 2 – Circuit Discovery. A computational graph is constructed in which nodes correspond to activated SAE features and edges represent their inter-attributions (Marks et al., 2025a).",
+     "Stage 3 – Monitoring the Circuit. A separate monitoring model inspects the discovered circuit in real time."
+    ],
+    "evidence": [
+     "Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation) (Oldfield et al., 2026), feature-wise monitoring (alerting when unexpected SAE features activate) (Fang et al., 2026), circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features) (Zhao et al., 2026b), and anomaly-based monitoring (using activations in normal behaviour as a baseline and flagging harmful generations as outliers) (Chaudhary & Barez, 2026)."
     ],
-    "evidence": [],
     "worked_example": [
-     "Result: The original harmful response is halted by the monitoring system and can be replaced with a predefined refusal (e.g., “I can’t help with that request.”)."
+     "We again use the bomb-making example to illustrate how mechanistic monitoring can operate in a high-risk setting.",
+     "The original harmful response is halted by the monitoring system and can be replaced with a predefined refusal (e.g., “I can’t help with that request.”). Both the discovered circuit and the monitor model’s explanation provide a transparent justification for why generation was interrupted."
     ],
     "limitations": [
-     "However, technical challenges also exist, such as determining what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).",
-     "Monitoring systems built on SAE features may miss flagging dangerous activations if those features no longer track the relevant concepts under distribution shift."
-    ],
-    "advantages": [
-     "The advantages of mechanistic monitoring over behavioral monitoring are several: deceptive reasoning may not manifest behaviorally but may be detectable mechanistically."
+     "Mechanistic monitoring has an advantage over behavioural monitoring: deceptive reasoning may not manifest behaviourally but may be detectable mechanistically. However, technical challenges remain, such as deciding what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).",
+     "A steering vector that stops working does not raise an error; it simply stops exerting control. Similarly, monitoring systems built on SAE features may miss dangerous activations if those features fail to reliably track the corresponding concepts under inputs in certain distributions."
     ],
+    "advantages": [],
     "orientations": []
    },
    {
     "id": "automated-interpretability-agents",
     "name": "Automated Interpretability Agents",
     "sections": [
-     "sec:automated-interpretability",
-     "implications-for-scale-and-oversight."
+     "sec:automated-interpretability"
     ],
-    "definition": "Scaling mechanistic interpretability to frontier models requires automation of the analysis pipeline itself.",
+    "definition": "Recent work in Automated Interpretability (see Section 12.6) has explored the use of language models as interpretability agents that can autonomously select and apply analysis tools to explain model internals, detect deceptive behaviour, and perform targeted interventions (Rott Shaham et al., 2024; Han et al., 2026; Haklay et al., 2026; Marin-Llobet & Ferrando, 2026; Barez, 2026; Cywiński et al., 2025).",
     "motivation": [],
     "methods": [
-     "SAE features",
+     "probing",
+     "sparse autoencoders",
      "circuit discovery",
-     "ROME",
-     "PISCES"
+     "causal tracing",
+     "model editing",
+     "steering vectors"
     ],
     "stages": [],
     "evidence": [
-     "Rather than relying on human experts to manually inspect features and circuits, recent work (Rott Shaham et al., 2024; Fronsdal et al., 2025; Fronsdal et al., 2026) has deployed language models as interpretability agents that can autonomously use tools to explain model internals and detect deceptive behaviors."
+     "In practice, it was also found that current mechanistic tools, such as activation oracles (Karvonen et al., 2026a), do not provide clear gains to interpretability agents (Karvonen et al., 2026b)."
     ],
     "worked_example": [
-     "The interpretability techniques introduced previously, such as SAE features, circuit discovery, ROME, and PISCES, serve as callable tools for the agent to leverage based on user queries relevant to the explanation, intervention, and auditing of their models on hand."
+     "Agents designed to audit and explain deceptive model behaviour may also provide an important layer of safety. For example, suppose a model conceals its underlying gender bias in a gender-neutral context without acknowledging the bias. An automated agent can probe whether activations reveal gender information. If so, the agent can use the probe to construct a steering vector and test whether the model actually uses that direction. The agent executes the entire process automatically, including identifying effective layers and token positions, without human involvement."
     ],
     "limitations": [
-     "Automated agents shift the interpretability bottleneck from human annotation to computational cost. Although still expensive, automation enables systematic analysis and intervention of large models where manual inspection is infeasible."
+     "A behaviour-based agent does not explain the internal mechanisms of the target model, but its explanations can be valuable as well if they enable us to extrapolate model behaviour to the held-out set (see Dimension 5: Human Interpretability in Section 7.5.2).",
+     "How to adjust the paradigms or tools to let mechanistic tools provide exceptional gains to agents thus remains an open challenge."
     ],
     "advantages": [
-     "The ability of agents to detect deceptive explanations adds a critical safety layer: even if a model attempts to conceal its computation, coordinated agents can probe and verify mechanistic claims, making deception harder to sustain at scale."
+     "Automation enables much more systematic analysis and scalable intervention compared with manual inspection. Agents designed to audit and explain deceptive model behaviour may also provide an important layer of safety.",
+     "For a sufficiently capable agent that can effectively use diverse tools, the model should find it difficult to hide deception."
     ],
     "orientations": [
      "Automated Interpretability"
@@ -1095,226 +529,208 @@
    }
   ],
   "relations_to_orientations": {
-   "section": "relations-to-the-five-research-orientations",
+   "section": "from-research-orientations-to-methods",
    "excerpts": [
-    "From a broader perspective, distinct research orientations (Section 13) motivate methodological approaches that emphasize different properties, thereby enabling different classes of applications while introducing distinct downstream challenges.",
-    "Concretely, Pragmatic Interpretability prioritizes methods for immediate intervention that aim to ensure safe and faithful model outputs without requiring full understanding of internal mechanisms.",
-    "Ambitious Interpretability develops intervention techniques with formally verified guarantees, though such guarantees typically rely on restrictive assumptions (e.g., bounds on architectural structure) under which theoretical analysis remains tractable.",
-    "Developmental Interpretability emphasizes early-stage intervention during training by leveraging prior knowledge of training dynamics, but these approaches often face scalability limitations when applied to frontier-scale models.",
-    "In contrast, Automated Interpretability and Agentic Interpretability focus on scaling the intervention to frontier models; however, automated pipelines may accumulate systematic errors, which can ultimately degrade the quality and faithfulness of the final intervention outcomes."
+    "More broadly, distinct research orientations (Section 12) motivate methodological approaches that emphasise different properties, enabling different classes of applications.",
+    "Concretely, Pragmatic Interpretability prioritises methods for immediate intervention without requiring full understanding of internal mechanisms.",
+    "Ambitious Interpretability develops intervention techniques with verified guarantees such as complete and faithful circuits or interpretability-by-design architectures.",
+    "Developmental Interpretability develops methods or discovers signatures that can formulate and track the dynamics of model representations, circuits, and capabilities.",
+    "Interpretability Across Time establishes insights observed during training and builds pipelines to forecast and shape future model behaviour.",
+    "Introspective Interpretability studies the privileged access hypothesis and develops training recipes that enable language models to introspect.",
+    "Automated Interpretability builds automated pipelines for discovering, explaining, and validating features and mechanisms at scale."
    ]
   },
   "chapter_limitations": {
-   "section": "sec:limitations-open-questions",
+   "section": "achievements-and-limits",
    "excerpts": [
-    "Current methods explain some of the model behaviors, leaving the rest unexplained—precisely where dangerous or deceptive behaviors may hide undetected (Hubinger et al., 2024).",
-    "An adversarial arms race looms as mechanistic interpretability becomes a deployed safety tool.",
-    "Besides, scalability remains one of the most critical bottlenecks. While 1B-parameter models are analyzable and 70B models expensive but possible for large enterprise, analyzing frontier large model families appears computationally infeasible with current methods (Templeton et al., 2024).",
-    "Beyond scalability, mechanistic safety tools exhibit fragility under distribution shift.",
-    "Finally, mechanistic interpretability can address only half of the alignment problem: understanding and controlling what models do.",
-    "Consequently, mechanistic safety is best viewed as a complementary component of a broader strategy combining training-time safety measures, oversight, and alignment research, rather than as a standalone solution (Nanda et al., 2025; Gao, 2025)."
+    "Yet current approaches are limited: they do not fully explain many behaviours, struggle to generalise under distribution shift, have difficulty cleanly disentangling entangled computations, and are hard to scale to frontier models.",
+    "More importantly, their usefulness for AI safety remains debated.",
+    "Although many prior studies in the interpretability community assume that mechanistic understanding directly implies safer systems, the assumption is in fact largely untested.",
+    "Mechanistic interpretability is not a panacea for AI safety. Researchers, developers, and practitioners should recognise the limits of partial or incorrect causal understanding, rigorously evaluate MI tools under broad conditions, and combine them with complementary safety measures."
    ]
   },
   "risk_taxonomy": {
    "sections": [
-    "sec:mi-risks",
-    "taxonomy-of-mi-safety-risks"
+    "sec:mi-risks"
    ],
    "table_id": "tab:mi-risks",
-   "caption": "Table 15: Risk Taxonomy for Mechanistic Interpretability in Safety Applications",
+   "caption": "Table 15: Risk taxonomy for mechanistic interpretability in safety applications, including each risk’s description, example, and time horizon (how soon it is expected to materialise given current research and deployment practice).",
    "columns": [
     "Risk Class",
     "Description",
     "Example",
-    "Severity"
+    "Time Horizon"
    ],
    "rows": [
     {
-     "Risk Class": "Epistemic",
-     "Description": "False confidence from incomplete analysis",
-     "Example": "Concluding “no deception circuit” when coverage is 30%",
-     "Severity": "High",
-     "section": "epistemic-risks-false-confidence-from-partial-understanding"
+     "Risk Class": "Epistemic risk",
+     "Description": "False confidence from incomplete or unfaithful analysis",
+     "Example": "The report concludes that the circuit for deceptive behaviour has been completely removed, but the model’s backup pathway actually emerged after the removal.",
+     "Time Horizon": "Current",
+     "section": "epistemic-risks-false-confidence-from-partial-or-incorrect-understanding"
     },
     {
-     "Risk Class": "Operational",
-     "Description": "MI tools fail silently under distribution shift",
-     "Example": "Steering vector ineffective on out-of-distribution inputs",
-     "Severity": "Medium-High",
-     "section": "operational-risks-failure-under-deployment-conditions"
+     "Risk Class": "Operational risk",
+     "Description": "MI tools fail silently in deployment",
+     "Example": "A steering vector is validated to be effective only on a testing set. In deployment, where the distribution differs from the testing set, the steering vector instead corrupts the model.",
+     "Time Horizon": "Near-term",
+     "section": "operational-risks-failure-under-deployment"
     },
     {
-     "Risk Class": "Adversarial",
-     "Description": "MI insights enable targeted attacks",
-     "Example": "Attacker uses published circuits to craft evasive prompts",
-     "Severity": "High",
-     "section": "adversarial-risks-malicious-exploitation-of-mi-insights"
+     "Risk Class": "Adversarial risk",
+     "Description": "MI methods enable attacks on the model",
+     "Example": "Attackers use published knowledge editing methods to inject incorrect knowledge into deployed models.",
+     "Time Horizon": "Current",
+     "section": "adversarial-risks-malicious-exploitation-of-mi"
     },
     {
-     "Risk Class": "Opportunity Cost",
-     "Description": "Resources diverted from more effective methods",
-     "Example": "Heavy MI investment delays deployment of behavioral safeguards",
-     "Severity": "Medium",
+     "Risk Class": "Opportunity cost",
+     "Description": "Resources diverted from other research directions",
+     "Example": "Heavy MI investment delays deployment of behavioural safeguards.",
+     "Time Horizon": "Long-term",
      "section": "opportunity-costs-resource-allocation-risks"
     }
    ]
   },
   "verification_checklist": {
-   "section": "recommended-verification-practices",
+   "section": "sec:mi-risks",
    "groups": [
     {
-     "heading": "Coverage",
-     "items": [
-      "What fraction of model behaviors and internals is explained by the analysis?",
-      "What behaviors/capabilities were explicitly excluded?",
-      "Are negative claims (“no X is found”) qualified by coverage limitations?"
-     ]
-    },
-    {
-     "heading": "Robustness",
+     "heading": "Epistemic risks",
      "items": [
-      "Were findings validated across multiple random seeds and setups?",
-      "Were findings tested on diverse or out-of-distribution inputs?",
-      "Were findings tested against adversarial inputs?"
+      "First, results should always include explicit coverage.",
+      "Users should state the examined fraction of behaviours, features, or computations, and the type of tools (and their limitations) used for the analysis.",
+      "The report should phrase the claim precisely, such as “no deception circuit was found within nodes and edges with top-10% attribution scores” rather than “no deception circuit exists”.",
+      "Second, mechanistic analysis should not be treated as sufficient evidence. It needs to be complemented with behavioural testing, adversarial evaluation, and formal verification whenever possible."
      ]
     },
     {
-     "heading": "Causal Validity",
+     "heading": "Operational risks",
      "items": [
-      "Do identified components satisfy necessity tests?",
-      "Do identified components satisfy sufficiency tests?",
-      "Were claims validated via causality rather than just correlation?"
+      "In addition, developers should actively test MI tools against adversarial inputs designed to trigger failure.",
+      "Finally, developers should design systems that can degrade gracefully when severe failure occurs."
      ]
     },
     {
-     "heading": "Deployment Readiness",
+     "heading": "Adversarial risks",
      "items": [
-      "Has the tool been tested on realistic deployment data?",
-      "How will the MI tool be monitored in production?",
-      "What fallback mechanisms exist if the MI tool fails?"
+      "Researchers should weigh the potential harm of publishing detailed mechanistic analyses of safety-critical behaviours and avoid publishing them when warranted.",
+      "Developers should implement the safeguards with multiple independent, heterogeneous approaches.",
+      "Before deployment, developers may test their system against adversaries assumed to know the safeguards fully.",
+      "Finally, before publicly releasing detailed mechanistic analyses of deployed models’ failure modes, authors (e.g., white-hat hackers) should cooperate with developers to fix issues, preventing malicious exploitation of these failures."
      ]
     },
     {
-     "heading": "Security",
+     "heading": "Opportunity costs",
      "items": [
-      "Could published findings enable misuse?",
-      "Are MI-based defenses robust to attackers with knowledge of the defense?",
-      "Is access to detailed analyses appropriately controlled?"
+      "Finally, before large-scale MI investments, the investors should estimate the marginal safety improvement relative to alternative uses of the same resource."
      ]
     }
    ]
   },
   "summary": {
    "section": "summary-mi-as-one-methodology-among-many",
-   "text": "The goal is not to avoid MI, but to apply it appropriately as one component of a defense-in-depth strategy rather than as a standalone guarantee of safety."
+   "text": "MI should therefore be viewed as one component of a defence-in-depth safety strategy rather than a standalone guarantee of safety."
   }
  },
  "open_problems": {
   "chapter_sections": [
    "sec:open_problems"
   ],
-  "intro": "This section identifies fundamental open questions that mechanistic interpretability (MI) has raised but not perfectly answered or solved. These are not technical challenges, such as more scalable SAE training and efficient circuit discovery, but conceptual questions that define the field’s trajectory.",
+  "intro": "14 Open Problems# This section identifies and describes key conceptual questions in mechanistic interpretability that guide the field’s future direction.",
   "problems": [
    {
     "id": "when-necessary",
     "title": "When Is Mechanistic Understanding Necessary?",
-    "source_chapter": 15,
+    "source_chapter": 14,
     "sections": [
      "when-is-mechanistic-understanding-necessary"
     ],
-    "statement": "A foundational question remains unresolved: for which problems and scenarios is mechanistic understanding required versus when behavioral methods suffice?",
+    "statement": "The need to characterise internal computations is usually tied to trustworthy AI, whose claim is intuitive: humans may not trust a system when they do not know, and have no way of knowing, how it works. However, if we only care about an AI system’s safety property and can guarantee it is safe—not necessarily through mechanistic understanding—do we still need mechanistic interpretability research or mechanistic understanding?",
     "details": [
-     "RLHF and adversarial training have substantially improved deployed model safety, yet the sleeper agents experiment (Hubinger et al., 2024) revealed deceptive behaviors hidden from behavioral evaluation while remaining mechanistically detectable (Hubinger et al., 2024; Ouyang et al., 2022).",
-     "Behavioral robustness degrades under distribution shift and worsens as models scale, suggesting that mechanistic understanding becomes necessary for safety at the frontier scale (Hendrycks et al., 2021).",
-     "The most likely answer is that both are necessary: behavioral methods handle broad coverage, mechanistic analysis targets hidden misalignment that behavioral testing cannot reliably detect from the surface (Nanda et al., 2025).",
-     "Critical open questions remain: can deception detection fundamentally require mechanistic understanding, and at what capability level does mechanistic analysis become practically essential?"
-    ],
-    "related_methods": [
-     "RLHF",
-     "adversarial training"
+     "For example, sleeper agents’ deceptive behaviours hidden from standard behavioural evaluations (Hubinger et al., 2024) can leave detectable signatures in the model’s internal representations, and MacDiarmid et al. (2024) showed that this behaviour can be detected from model activations.",
+     "(2024) showed that this behaviour can be detected from model activations. This demonstrates the utility of looking into the internals. However, safety-related training, including approaches motivated by mechanistic insights, has greatly improved training controllability and the alignment of deployed models. Adversaries may now find it harder to implement such a backdoor. Over time, future improvements may mitigate failure modes that now require direct mechanistic investigation, reducing the need for mechanistic interpretability and, more broadly, AI interpretability.",
+     "The AI safety community has not yet agreed on whether this will happen. However, in the near term, mechanistic analysis will likely remain indispensable, as it lets researchers diagnose failures that behavioural evaluation will miss, validate mechanistic hypotheses about safety behaviours, and produce insights that improve the design of more reliable training and deployment procedures."
     ],
+    "related_methods": [],
     "related_orientations": []
    },
    {
     "id": "evaluation",
-    "title": "How to Evaluate Mechanistic Interpretability Methods",
-    "source_chapter": 15,
+    "title": "Challenges in Evaluation and Benchmarking",
+    "source_chapter": 14,
     "sections": [
-     "how-to-evaluate-mechanistic-interpretability-methods",
-     "proposed-evaluation-framework",
-     "standardized-benchmarks"
+     "challenges-in-evaluation-and-benchmarking"
     ],
-    "statement": "Without standardized metrics, it is unclear whether improvements are real or artifacts of metric choice.",
+    "statement": "Because widely accepted standards for benchmarks and evaluation are lacking, evaluating mechanistic interpretability methods remains hard.",
     "details": [
-     "SAE variants are compared on different metrics; circuit discovery methods optimize for different objectives (size, coverage, interpretability); feature explanations are often evaluated on plausibility but not ground truth.",
-     "We propose that MI methods and findings should be evaluated across four dimensions: Interpretability—are mechanisms understandable to humans? Completeness—what fraction of behavior is explained? Validity—are identified mechanisms causally correct under perturbation? Efficiency—what is the computational cost?",
-     "Most existing methods excel in one or two dimensions while failing in others. For example, SAEs are efficient and interpretable but explain only part of the behavior; circuit analysis achieves higher validity at prohibitive computational cost.",
-     "Ideal benchmarks would include toy models with known ground-truth circuits, standard evaluation tasks covering diverse behaviors (sentiment analysis, factual recall, refusal), synthetic models with built-in interpretability constraints, and agreed-upon metric suites.",
-     "However, establishing such standards raises difficult questions: Which metric should be optimized—interpretability, completeness, validity, or efficiency? Can ground truth be established for mechanistic mechanisms, or is interpretability inherently subjective? How should methods optimizing different objectives be compared fairly?",
-     "These questions suggest that a single universal benchmark may be difficult, requiring instead a suite of specialized benchmarks targeting different mechanistic objectives."
+     "Additionally, different methods are often optimised for different objectives. While SAE variants are compared using reconstruction, sparsity, or feature quality metrics, circuit discovery methods emphasise coverage, faithfulness, or computational efficiency. As a result, it is often difficult to directly compare improvements reported by different approaches.",
+     "Section 7.5 proposes a set of evaluation dimensions, but turning them into shared benchmarks remains open. Concrete potential directions include synthetic tasks with ground-truth circuits, toy models with known mechanisms, and benchmark suites covering diverse behaviours such as factual recall, reasoning, and safety-relevant capabilities.",
+     "Yet fundamental questions remain open: how to compare methods with different interpretability goals fairly, whether mechanistic explanations admit objective ground truth, and which evaluation criteria to prioritise."
     ],
     "related_methods": [
      "SAE",
-     "circuit discovery",
-     "feature explanations",
-     "circuit analysis"
+     "circuit discovery"
     ],
     "related_orientations": []
    },
    {
     "id": "scalability-adversarial",
-    "title": "Scalability and Adversarial Robustness",
-    "source_chapter": 15,
+    "title": "Incompleteness, Scalability, Generalisability, and Adversarial Robustness",
+    "source_chapter": 14,
     "sections": [
-     "scalability-and-adversarial-robustness"
+     "subsec: scalability and adversarial robustness"
     ],
-    "statement": "A common and critical bottleneck in many existing MI approaches is scalability to model size, model diversity, and input domains.",
+    "statement": "Mechanistic methods, the insights they uncover, and their downstream use face several critical challenges.",
     "details": [
-     "Whether this bottleneck can be overcome through algorithmic improvements or represents a fundamental limitation remains unclear.",
-     "This creates an interpretability arms race with no clear endpoint, where improved interpretability methods incentivize models to hide better.",
-     "Underlying both challenges is a deeper question: can mechanistic explanations be verified as genuine mechanisms rather than post-hoc rationalizations? Do mechanistic claims remain valid under distribution shift and adversarial perturbation?",
-     "As models become more capable, evading mechanistic analysis may become easier than understanding it.",
-     "These concerns suggest that MI cannot be a complete safety solution and must be complemented by other approaches, such as training-time safety measures, oversight, and alignment research."
+     "Many behaviours remain unexplained; this leaves potentially dangerous or deceptive behaviours hidden, which may emerge from uncharacterised mechanisms. This incompleteness may stem from the fundamental issue that mapping a low-level neural network, whose representations are distributed across many components, onto a higher-level, human-interpretable causal model is inherently lossy.",
+     "Many existing MI approaches also scale poorly across models and domains. Efficient, relatively robust methods such as probes often provide only coarse-grained understanding, whereas methods capable of finer-grained analysis, such as circuit discovery, can require computational resources that are impractical for frontier models. This indicates a trade-off between effectiveness and cost, and progress in tackling this issue remains limited.",
+     "Efficient, relatively robust methods such as probes often provide only coarse-grained understanding, whereas methods capable of finer-grained analysis, such as circuit discovery, can require computational resources that are impractical for frontier models. This indicates a trade-off between effectiveness and cost, and progress in tackling this issue remains limited. Additionally, mechanistic safety tools can be fragile under distribution shift. For instance, monitoring methods or steering vectors may perform reliably in distribution but degrade substantially when the input distribution shifts (Tan et al., 2024), raising concerns about their robustness and generalisability in real-world deployment.",
+     "Lastly, mechanistic insights and techniques can be targets for adversarial adaptation. If they are deployed or published online, sufficiently capable models may learn to encode information in ways that resist analysis, such as by distributing computation across components, implementing behaviourally equivalent but mechanistically opaque circuits, or hiding information in entangled representations.",
+     "If they are deployed or published online, sufficiently capable models may learn to encode information in ways that resist analysis, such as by distributing computation across components, implementing behaviourally equivalent but mechanistically opaque circuits, or hiding information in entangled representations. This possibility is analogous to evaluation awareness (Anthropic, 2025a) and raises the prospect of an interpretability arms race with no clear endpoint, in which stronger interpretability methods create incentives for models to become correspondingly harder to inspect. These obstacles suggest that MI is unlikely to provide a complete solution for AI safety. Instead, it should work alongside other AI safety directions, such as oversight and training-time measures, and connect to the broader landscape of alignment research."
+    ],
+    "related_methods": [
+     "probes",
+     "circuit discovery",
+     "steering vectors"
     ],
-    "related_methods": [],
     "related_orientations": []
    },
    {
     "id": "mechanistic-alignment",
     "title": "Mechanistic Understanding and Alignment",
-    "source_chapter": 15,
+    "source_chapter": 14,
     "sections": [
      "sec:mechanistic-alignment"
     ],
-    "statement": "However, these methods do not directly address alignment specification: understanding a circuit that enforces refusals or constraints allows auditing and intervention, but does not indicate what the model should value or whether its behavior genuinely reflects human intent.",
+    "statement": "Yet these approaches do not directly tackle alignment specification. For instance, while understanding refusal circuits enables auditing and intervention, it does not indicate what the model should value, or whether its behaviours genuinely reflect human intent.",
     "details": [
-     "MI provides tools for behavioral control. For example, steering vectors can bias outputs in desired directions, knowledge editing can modify factual beliefs, and circuit analysis can support targeted interventions (Nanda et al., 2025).",
-     "Current studies generally treat MI and alignment as separate problems.",
+     "MI provides tools for behavioural control. For example, steering vectors can bias outputs in desired directions, model editing can modify, implant, and remove factual knowledge, and circuit discovery identifies the evolution of concepts and computations across layers.",
+     "Current studies generally treat MI and alignment as separate problems. Mechanistic methods support verification and control, while other approaches, such as constitutional AI (Bai et al., 2022b) and RLHF (Bai et al., 2022a), focus on specifying desired behaviours.",
      "Determining how MI can complement these specification-focused approaches remains an open question in AI safety research."
     ],
     "related_methods": [
      "steering vectors",
-     "knowledge editing",
-     "circuit analysis",
+     "model editing",
+     "circuit discovery",
      "constitutional AI",
-     "value learning"
+     "RLHF"
     ],
     "related_orientations": []
    },
    {
     "id": "ontology",
     "title": "Interpretation vs. Truth: The Ontology Question",
-    "source_chapter": 15,
+    "source_chapter": 14,
     "sections": [
      "sec:ontology"
     ],
-    "statement": "A foundational question in MI is: what does it mean to mechanistically understand a neural network?",
+    "statement": "A foundational MI question, which is both technical and conceptual, is: what does it mean to mechanistically understand a neural network?",
     "details": [
-     "The same input-output behavior can be realized by multiple mechanistically distinct internal structures, each consistent with the observed causal relationships.",
-     "Feature naming is subjective: different observers may assign different names to the same learned direction based on their interpretation of the activating examples.",
-     "Representation ambiguity means that multiple mechanisms can produce identical behavior while differing internally.",
-     "Moreover, intuitive human-language explanations may not always accurately capture the internal computations of the model (Hewitt et al., 2025).",
-     "For instance, how should abstraction level be chosen? Can confidence in a mechanistic explanation be established when multiple descriptions are consistent with the same behavior? Should one expect a unique mechanistic description, or are multiple valid interpretations acceptable?"
+     "Marking a feature as a “borzoi detector”, for instance, or labelling an attention head component as “a previous-token copier” mirrors possibly subjective human choices. This means multiple mechanisms can explain one behaviour, each consistent with observed causal relationships.",
+     "MI research often assumes how human-interpretable descriptions should map onto causal mechanisms. But these assumptions face several obstacles. As mentioned before, one of these is subjectivity. This includes choosing the level of abstraction; for instance, a circuit could be explained at the level of attention routing or low-rank decomposition, and the choice depends on subjective priorities.",
+     "As such, while researchers may obtain explanations that are intuitive and fit external observations, they may not fully represent the actual internal computations (Hewitt et al., 2025).",
+     "Other questions include: How can we assess confidence in a mechanistic explanation if multiple descriptions can explain the same behaviour? Are multiple valid interpretations acceptable, or should researchers expect a unique mechanistic description? Resolving these questions will help researchers clarify the limits and goals of MI research."
     ],
     "related_methods": [],
     "related_orientations": []
@@ -1322,168 +738,63 @@
    {
     "id": "proxy-fidelity",
     "title": "The Proxy Fidelity Problem",
-    "source_chapter": 15,
+    "source_chapter": 14,
     "sections": [
      "sec:proxy-fidelity",
-     "problem-formulation.",
      "why-this-matters-for-safety.",
      "potential-approaches."
     ],
-    "statement": "As architectural interventions gain traction (Section 6.9), a new verification challenge emerges: when interpretability is achieved through proxy models, such as sparse networks trained to mimic dense model representations, how can we guarantee that the proxy’s circuits reflect the same mechanisms used by the original model, rather than behaviorally equivalent but mechanistically distinct simulations?",
+    "statement": "Classic proxy models in MI research include sparse, small neural networks such as SAEs and CLTs, which reconstruct the target model’s dense activations using learned sparse linear features. However, while these approaches have greatly enhanced mechanistic analysis, they introduce a new obstacle: how can we ascertain whether the proxy truly learns the same mechanisms as the original model, rather than shallowly reproducing behaviour?",
     "details": [
-     "Two models can produce identical outputs (or even identical intermediate activations) while implementing distinct algorithms—a form of mechanistic underdetermination.",
-     "Although preliminary studies have suggested that interventions on sparse proxies can induce behavioral changes in dense models (Gao et al., 2025b), systematic validation remains limited.",
-     "One approach is causal correspondence testing, which verifies that ablating a circuit in the proxy and the corresponding components in the dense model produces matched behavioral changes across different inputs.",
-     "The second approach is activation patching transfer, which evaluates whether activations identified as causally important in the proxy retain their causal influence when the same interventions are applied to the dense model.",
-     "A third direction involves developing bounds of mechanistic divergence: theoretical frameworks that upper-bound how much internal computation can differ between models given constraints like activation reconstruction error."
+     "Several recent interpretability approaches construct more interpretable proxy models that approximate the target model’s internal computations. Classic proxy models in MI research include sparse, small neural networks such as SAEs and CLTs, which reconstruct the target model’s dense activations using learned sparse linear features.",
+     "This concern is realistic because two neural networks can produce the same output while using different internal algorithms. That is, a proxy model may find features and circuits that do not exist in the original model.",
+     "Why This Matters for Safety. For example, when researchers find no “deception circuits” in the proxy models, can they claim that the target model has therefore “passed the test” and is safe? Making this claim requires that these mechanisms do not exist in the target model either. However, as discussed above, the absence of a mechanism in the proxy model does not imply its absence in the target model, since they may compute the input differently while producing the same output. The conclusion we can draw is only something like “we did not find the deception mechanism in the proxy model”, which is far from a useful guarantee in safety-critical scenarios.",
+     "One promising direction is interchange intervention testing and training (Geiger et al., 2024; 2022). This technique was developed to compare and align a neural network with a high-level causal abstraction. The proxy models and their circuits can be viewed as a causal abstraction of the target model, where the nodes (human-interpretable variables) are the proxy models’ features. This framing not only lets us test alignment between the mechanisms identified in proxy models and the target model, but also provides an optimisation objective for training faithful proxies.",
+     "The proxy models and their circuits can be viewed as a causal abstraction of the target model, where the nodes (human-interpretable variables) are the proxy models’ features. This framing not only lets us test alignment between the mechanisms identified in proxy models and the target model, but also provides an optimisation objective for training faithful proxies. Additionally, a theoretical framework that upper-bounds how much internal computation can differ between the target model and proxy model under a given reconstruction performance can also be a promising direction."
     ],
     "related_methods": [
-     "weight-sparse proxy",
-     "causal correspondence testing",
-     "activation patching transfer",
-     "bounds of mechanistic divergence"
+     "SAEs",
+     "CLTs",
+     "interchange intervention testing and training"
     ],
     "related_orientations": []
    },
    {
     "id": "non-transformer",
     "title": "Transferability Beyond Transformers",
-    "source_chapter": 15,
+    "source_chapter": 14,
     "sections": [
      "sec:non-transformer"
     ],
-    "statement": "This raises an open question: to what extent do MI methods and findings transfer to other architectures?",
+    "statement": "Even though foundational circuits work (Olah et al., 2020) originated in convolutional networks (InceptionV1; Szegedy et al., 2015), demonstrating interpretable features of visual neurons, many of the techniques that define modern mechanistic interpretability, which include feature extraction and explanation, model editing, and circuit discovery, were developed specifically for transformer residual streams, MLPs, and attention mechanisms. Thus, an important question arises: are mechanistic interpretability concepts, tools, and findings specific to transformers, or do they reflect more general properties of neural computation across model architectures?",
     "details": [
-     "Convolutional networks: The spatial locality of convolutions may make circuits more naturally modular, but SAE-style decomposition of CNN activations remains underexplored at scale.",
-     "State space models (e.g., Mamba): These architectures lack attention but implement sequential processing via recurrent dynamics. Whether superposition, linear representation, and circuit-style analysis apply is unknown.",
-     "Reinforcement learning agents: RL policies involve temporal credit assignment and value estimation, which may require different interpretability primitives than simply next-token prediction. Limited work exists on MuZero and Atari agents, but systematic MI is lacking.",
-     "Diffusion models: The iterative denoising process introduces temporal structure distinct from autoregressive generation. Emerging work explores attention patterns in diffusion transformers, but feature extraction methods are nascent.",
-     "The five research orientations (Pragmatic, Ambitious, Developmental, Agentic, Automated) discussed in Section 13 are framed architecture-agnostically, but their instantiation has been transformer-specific.",
-     "Until such a validation exists, practitioners should be cautious about extrapolating transformer-derived insights to other neural network architectures."
+     "Although the six research orientations discussed in Section 12 are largely architecture-agnostic in their aims, their classic methods and representative works focus almost entirely on transformers.",
+     "Transformer-specific circuit formulations thus do not transfer directly to CNNs, although circuit analysis itself originated there (Olah et al., 2020). Convolutions’ spatial locality may yield more naturally modular circuits than transformers under suitable formulations, but large-scale studies of superposition, feature extraction, and circuit discovery in modern CNNs and their variants remain limited.",
+     "State-space and recurrent models: Architectures such as Mamba (Gu & Dao, 2024) and RWKV (Peng et al., 2023) replace standard self-attention with recurrent computations that update a hidden state over time. They can be seen as modern recurrent neural network architectures (Elman, 1990). Specifically, Mamba uses input-dependent state transitions to selectively retain or forget information, while RWKV uses a recurrence based on linear attention. This new paradigm raises interpretability questions that previous transformer interpretability studies have not addressed, such as how information is stored and updated in recurrent states, whether these representations exhibit certain properties such as superposition, and how causal effects can be traced across state updates.",
+     "Diffusion models: Diffusion models (Sohl-Dickstein et al., 2015) apply iterative denoising processes to generate images or tokens. They may still use transformers as backbones, but their computational structure differs fundamentally from autoregressive generation. For example, many current MI studies use the predicted next token as a sign of model behaviour (e.g., “yes” or “no” when the input is a malicious query), while diffusion-based language models generate multiple tokens in different positions at a time to iteratively complete the response. This difference means many interpretability techniques and insights do not directly apply. While recent work has begun investigating attention patterns and internal representations in diffusion models (Tinaz et al., 2025; Wang et al., 2026c), mechanistic understanding in this paradigm remains lacking.",
+     "Many modern transformer-based language models have adopted this architecture (Jiang et al., 2024; DeepSeek-AI, 2025; QwenAI, 2025; OpenAI et al., 2025; Team et al., 2026). However, MoE architectures introduce technical challenges for mechanistic analysis. For example, activation patching at an intermediate MoE layer may change which experts are selected in downstream MoE layers. This makes it difficult to distinguish the patched activation’s effects on model output from those mediated by changes in downstream expert routing.",
+     "Simultaneously, some recent work suggests that at least a few mechanistic interpretability techniques can transfer outside of transformers. Steering vectors, tuned lenses, knowledge editing, and circuit analysis evaluations have all been adapted, with varying success, to state-space models such as RWKV and Mamba, as well as to vision-language models (Paulo et al., 2025a; Sharma et al., 2024a; Häon et al., 2025; Neo et al., 2025; Ensign & Garriga-Alonso, 2024).",
+     "Sorting out which aspects of mechanistic interpretability depend on architecture and which reflect broader principles of neural computation thus appears to be a promising research direction. On the other hand, whether current research orientations and associated techniques remain relevant beyond transformers will also show how broadly mechanistic interpretability research can apply to future systems."
     ],
     "related_methods": [
      "SAEs",
-     "transcoders",
-     "model editing",
      "circuit discovery",
-     "activation patching"
+     "model editing",
+     "activation patching",
+     "steering vectors",
+     "tuned lens",
+     "knowledge editing"
     ],
     "related_orientations": [
      "Pragmatic",
      "Ambitious",
      "Developmental",
-     "Agentic",
+     "Interpretability Across Time",
+     "Introspective",
      "Automated"
     ]
-   },
-   {
-    "id": "orientation-tensions",
-    "title": "Critical Tensions and Open Questions",
-    "source_chapter": 13,
-    "sections": [
-     "critical-tensions-and-open-questions"
-    ],
-    "statement": "Several critical tensions emerge when considering the adoption of different orientations.",
-    "details": [
-     "A central debate exists between Pragmatic and Ambitious approaches: does partial mechanistic understanding suffice to ensure safety, or is complete comprehension necessary?",
-     "Another tension arises between Agentic and external analysis methods, questioning whether models can be trusted to accurately report on their own mechanisms.",
-     "Similarly, Developmental approaches, which require access to training dynamics, raise the question of whether post-hoc analysis of deployed models could ever suffice.",
-     "Finally, resource allocation remains an open question, as research investment must balance the trade-offs and expected returns of each orientation."
-    ],
-    "related_methods": [],
-    "related_orientations": [
-     "Pragmatic",
-     "Ambitious",
-     "Agentic",
-     "Developmental"
-    ]
-   },
-   {
-    "id": "safety-limitations",
-    "title": "Limitations and Open Questions",
-    "source_chapter": 14,
-    "sections": [
-     "sec:limitations-open-questions"
-    ],
-    "statement": "Despite significant progress, mechanistic interpretability faces fundamental limitations that constrain its role in AI safety.",
-    "details": [
-     "This incompleteness is not merely a technical problem but a conceptual one: some behaviors may depend on entangled, distributed representations that resist decomposition into interpretable units.",
-     "Models can optimize to encode information in ways that resist SAE decomposition, hide computation in entangled features, or implement complex distributed circuits mechanistically opaque to current circuit discovery methods.",
-     "This scalability crisis means that as models grow, mechanistic oversight becomes increasingly impractical, threatening the viability of mechanistic safety for next-generation systems.",
-     "It provides limited guidance on alignment specification, i.e., defining the right value that models should be optimized for and ensuring models pursue the objectives in the first place."
-    ],
-    "related_methods": [
-     "SAE",
-     "circuit discovery",
-     "Steering vectors",
-     "concept erasure",
-     "monitoring"
-    ],
-    "related_orientations": []
-   },
-   {
-    "id": "clt-open-questions",
-    "title": "Open Questions for CLT Research",
-    "source_chapter": 6,
-    "sections": [
-     "open-questions-for-clt-research."
-    ],
-    "statement": "Several important questions remain open:",
-    "details": [
-     "How does CLT fidelity scale in large models?",
-     "Can CLTs be extended to jointly model attention and MLP computations?",
-     "What evaluation protocols can reliably detect when CLT-based explanations are unfaithful to the original model’s computation?",
-     "How sensitive are CLT circuits to the choice of training data distribution?"
-    ],
-    "related_methods": [
-     "CLT"
-    ],
-    "related_orientations": []
-   },
-   {
-    "id": "conclusion-unresolved",
-    "title": "Unresolved Questions",
-    "source_chapter": 16,
-    "sections": [
-     "unresolved-questions"
-    ],
-    "statement": "As discussed in Section 15, several fundamental open questions remain.",
-    "details": [
-     "When is mechanistic understanding actually required for safety, or do behavioral methods suffice? Can mechanisms be verified as genuine rather than post-hoc rationalizations? How do mechanisms hold up under distribution shift and adversarial pressure?",
-     "These questions are not merely technical but conceptual, touching on epistemology and the definition of understanding itself."
-    ],
-    "related_methods": [],
-    "related_orientations": []
    }
   ]
  },
- "gaps": [
-  "Evaluation: manuscript dimension names are 'Faithfulness', 'Completeness', 'Stability', 'Causal Validity', 'Human Interpretability' (7.6.3-7.6.7 headings). The public website says 'Attribution faithfulness'. In chapter 7 the dimension is plain 'Faithfulness'. The phrase 'attribution faithfulness' occurs only in chapter 8 (sections faithfulness-of-attributions. / key-findings-on-attribution-faithfulness, about gradient/attention attribution reliability in probing), not as an evaluation-framework dimension.",
-  "Evaluation: in Table 10 (tab:evaluation-framework) the fifth row is labelled 'Interpretability', not 'Human Interpretability' as in heading 7.6.7 and the intro sentence.",
-  "Evaluation: the intro sentence lists dimensions in lower case ('faithfulness, completeness, stability, causal validity, and human interpretability'); Table 11 column headers shorten them to 'Causal' and 'Human'.",
-  "Evaluation: framework limitations mention 'Proposed thresholds (e.g., necessity > 0.5)', but no necessity > 0.5 threshold is proposed anywhere in 7.6.3-7.6.8. The only threshold is the undefined \\tau in Behavioral Coverage.",
-  "Evaluation: chapter 15.2.1 (proposed-evaluation-framework) proposes a *different* FOUR-dimension framework (Interpretability, Completeness, Validity, Efficiency) that does not cross-reference the five-dimension framework in 7.6. Stability is missing, and Efficiency is a new dimension. The two are inconsistent.",
-  "Evaluation: the framework sits in chapter 7 ('Circuits in Mechanistic Interpretability') as 7.6, although it is method-general (SAEs, probes, steering).",
-  "Evaluation: the per-dimension 'Evaluation protocol' items and the integrated protocol (7.6.8) are stated as recommendations. The text reports no worked examples or empirical results for any dimension; the only concrete artefacts named are the benchmarks MIB, AxBench and RAVEL (under Causal Validity). So 'examples' is empty for 4 of 5 dimensions.",
-  "Orientations: this draft has FIVE orientations (13.3 Pragmatic, 13.4 Ambitious, 13.5 Developmental, 13.6 Agentic, 13.7 Automated). The five are confirmed in 15.7 and the 16 Conclusion. The public list of six maps as follows: Pragmatic -> 13.3; Ambitious -> 13.4; Developmental -> 13.5 (also chapter 10, sec:training); Automated -> 13.7 (also 14.4 sec:automated-interpretability); Introspective -> NO orientation of that name. The closest is Agentic Interpretability (13.6, subtitled 'Models Interpreting Themselves', Kim et al. 2025). The introduction (ch.1, paragraph 'Second, divergence in research priorities', no own section id) describes this thread as 'exploring whether models can serve as introspective agents that explain their own internals (Kim et al. 2025)', cited to the same source as Agentic. 'introspective' also appears once in 5.3 subsec:privileged-access ('not accurate introspective reports'), and 5.3 is cited by 13.6.3 as its basis. Interpretability Across Time -> NO orientation. The phrase 'across time' appears nowhere. The nearest material is chapter 11 (Learning Dynamics; withheld from the site) and 12.1 sec:model-diffing. Neither is framed as an orientation, and both overlap with Developmental.",
-  "Orientations: 13.4 has an unnumbered paragraph 'Architectural interventions as a synthesis.' that floats a possible distinct 'Engineering' orientation; the Conclusion 16.3 instead speaks of 'three complementary paradigms' (post-hoc decomposition, developmental analysis, interpretability by design). These are a separate taxonomy from the five orientations.",
-  "Orientations: 13.2 refers to '(Figure 16)', but the figure caption reads 'Figure 18: Five Research Orientations.'",
-  "Orientations: 13.3 Pragmatic says 'which is a valuable source of feedback, but required.' This is almost certainly a missing 'not' ('but not required'); kept verbatim.",
-  "Orientations: 13.3 Pragmatic list item 'Prompting, a very effective black box baseline' lacks a full stop; 13.7 item 'Verification using synthetic data. to evaluate...' has a stray full stop; 13.6.2 'artificailly' typo.",
-  "Orientations: Table 14 tools do not fully match the prose. Agentic prose methods are 'Interactive querying', 'Dialogical explanation loops' and 'Continually intervening model', while the table lists 'LatentQA, dialogical self-explanation, model intervention'; LatentQA is never mentioned in 13.6. Developmental's table entry 'SAEs' corresponds to the prose 'Dictionary learning methods'. Ambitious prose names causal scrubbing and causal intervention, while the table says 'Circuit discovery, formal verification'.",
-  "Orientations: Agentic has no stated assumptions or strengths in 13.6 (only risks/governance), so those arrays are empty. The table's Core Thesis 'Models explain themselves if prompted correctly' is the closest to an assumption.",
-  "Orientations: 13.6.3 says 'Building on Section 5.3' (5.3 = Privileged Access Hypothesis, correct). Protocols in 13.6.3/13.7.3 refer to 'the regulator', a framing not introduced elsewhere.",
-  "Safety applications: section id typo 'challenges-of-black-box-bethods' (heading '14.1.2 Challenges of Black-Box Bethods').",
-  "Safety applications: chapter intro grammar 'including but are not limited to'. 14.7.4 opens 'Detailed mechanistic knowledge suffers from the opportunity to be weaponized'. 14.7 intro 'but it carries its own risks. However, misapplication...' (odd 'However').",
-  "Safety applications: 14.4 Automated Interpretability Agents uses section id 'sec:automated-interpretability', very close to 13.7's 'sec:automated-interp'. That invites cross-ref confusion.",
-  "Safety applications: no application subsection in 14.2-14.4 explicitly names an orientation except 14.4, which is titled 'Automated Interpretability Agents'. The linkage is only given generically in 14.5 by orientation, not per application, so 'orientations' arrays are mostly empty. 14.5 merges Automated and Agentic in one sentence.",
-  "Safety applications: mechanistic monitoring (14.3.2) reports no empirical result. Its 'Result' is a hypothetical worked example, so evidence is empty. The worked examples' 'Result:' lines for PISCES (bomb), ROME (TRIGGER) and steering (sycophancy) are illustrative, not empirical; only the PISCES '7.7%' line and the ROME/MEMIT, Rimsky and Tan citations report findings.",
-  "Safety applications: PISCES Stage 2 example says 'Assume d_{MLP}=2 with basis vectors v_1 and v_2', whereas the general description treats v_i as rows of W_out in R^{d_MLP x d}. That is consistent, but the example calls them basis vectors.",
-  "Open problems: chapter 15 has 7 numbered subsections (15.1-15.7); 15.2 has sub-subsections 15.2.1/15.2.2. 15.3 begins 'A common and critical bottleneck...' then says 'Second, adversarial obfuscation exists' with no 'First'.",
-  "Open problems: 15.1 cites (Hubinger et al. 2024; Ouyang et al. 2022) for 'mechanistically detectable'. Ouyang et al. 2022 (InstructGPT/RLHF) seems mis-attached; flag for author check.",
-  "Open problems: 15.4 attributes 'value learning' to (Bai, Jones, et al. 2022), which is the HH-RLHF paper; possible citation mismatch to check.",
-  "Elsewhere: chapter 11 contains unresolved author notes in the body text (e.g., ), and 11.2.3 Phase changes is empty. 11.2 also has the typo 'attention patternsform' and section id typo .",
-  "Elsewhere: 5.3 has unresolved citation placeholders '(binder?; song?; B. Z. Li et al. 2025)'. The introduction has typos 'Bascially', 'two many un-answered', 'some automation has become a necessary', and the section id/heading 'useful-surveys-that-we-bild-on.'",
-  "Section ids are reused across chapters with only numeric suffixes (e.g., 'mitigation-strategies.' ... '-3', 'conceptual-basis.-1', 'case-study-induction-heads-1/-2'). They are unique, but fragile for deep links."
- ]
+ "gaps": []
 }
diff --git a/src/data/links.json b/src/data/links.json
index c482b0f71b..e29b6327c3 100644
--- a/src/data/links.json
+++ b/src/data/links.json
@@ -1,26 +1,118 @@
 {
-  "_note": "Editorial cross-links between notebooks/demos and manuscript entries. Navigation only: no scientific claims live here.",
-  "notebooks": {
-    "notebooks/01_core_hypotheses/01_linear_representation_hypothesis.ipynb": { "hypotheses": ["linear-representation"], "methods": ["probing"] },
-    "notebooks/01_core_hypotheses/02_superposition_hypothesis.ipynb": { "hypotheses": ["superposition"] },
-    "notebooks/01_core_hypotheses/03_privileged_access_hypothesis.ipynb": { "hypotheses": ["privileged-access"] },
-    "notebooks/02_feature_extraction/01_sparse_autoencoders_basics.ipynb": { "methods": ["sae", "gated-sae", "jumprelu-sae", "topk-sae", "batch-topk-sae"] },
-    "notebooks/02_feature_extraction/02_transcoders.ipynb": { "methods": ["transcoders"] },
-    "notebooks/02_feature_extraction/03_crosscoders.ipynb": { "methods": ["crosscoders"] },
-    "notebooks/03_circuits/01_circuit_discovery.ipynb": { "methods": ["activation-patching"] },
-    "notebooks/04_intermediate_computation/01_logit_lens.ipynb": { "methods": ["logit-lens"] },
-    "notebooks/04_intermediate_computation/02_tuned_lens.ipynb": { "methods": ["tuned-lens"] },
-    "notebooks/04_intermediate_computation/04_patchscopes.ipynb": { "methods": ["patchscopes"] },
-    "notebooks/04_intermediate_computation/05_probing_classifiers.ipynb": { "methods": ["probing", "amnesic-probing"] },
-    "notebooks/04_intermediate_computation/06_activation_oracles.ipynb": { "methods": ["latentqa"] },
-    "notebooks/05_representation_analysis/01_model_diffing.ipynb": { "methods": ["model-diffing", "crosscoders"] },
-    "notebooks/05_representation_analysis/02_knowledge_localization.ipynb": { "methods": ["causal-tracing"] },
-    "notebooks/05_representation_analysis/03_representation_engineering.ipynb": { "methods": ["steering-vectors"] },
-    "notebooks/06_safety_applications/02_steering_vectors.ipynb": { "methods": ["steering-vectors"], "applications": ["steering-vectors"] }
-  },
-  "demos": {
-    "superposition": { "hypotheses": ["superposition"], "notebook": "notebooks/01_core_hypotheses/02_superposition_hypothesis.ipynb" },
-    "sae": { "methods": ["sae", "topk-sae"], "notebook": "notebooks/02_feature_extraction/01_sparse_autoencoders_basics.ipynb" },
-    "patching": { "methods": ["activation-patching"], "notebook": "notebooks/03_circuits/01_circuit_discovery.ipynb" }
+ "_note": "Editorial cross-links between notebooks/demos and manuscript entries. Navigation only: no scientific claims live here.",
+ "notebooks": {
+  "notebooks/01_core_hypotheses/01_linear_representation_hypothesis.ipynb": {
+   "hypotheses": [
+    "linear-representation"
+   ],
+   "methods": [
+    "probing"
+   ]
+  },
+  "notebooks/01_core_hypotheses/02_superposition_hypothesis.ipynb": {
+   "hypotheses": [
+    "superposition"
+   ]
+  },
+  "notebooks/01_core_hypotheses/03_privileged_access_hypothesis.ipynb": {
+   "hypotheses": [
+    "privileged-access"
+   ]
+  },
+  "notebooks/02_feature_extraction/01_sparse_autoencoders_basics.ipynb": {
+   "methods": [
+    "sae",
+    "gated-sae",
+    "jumprelu-sae",
+    "topk-sae",
+    "batch-topk-sae"
+   ]
+  },
+  "notebooks/02_feature_extraction/02_transcoders.ipynb": {
+   "methods": [
+    "transcoders"
+   ]
+  },
+  "notebooks/02_feature_extraction/03_crosscoders.ipynb": {
+   "methods": [
+    "crosscoders"
+   ]
+  },
+  "notebooks/03_circuits/01_circuit_discovery.ipynb": {
+   "methods": [
+    "activation-patching"
+   ]
+  },
+  "notebooks/04_intermediate_computation/01_logit_lens.ipynb": {
+   "methods": [
+    "logit-lens"
+   ]
+  },
+  "notebooks/04_intermediate_computation/02_tuned_lens.ipynb": {
+   "methods": [
+    "tuned-lens"
+   ]
+  },
+  "notebooks/04_intermediate_computation/04_patchscopes.ipynb": {
+   "methods": [
+    "patchscopes"
+   ]
+  },
+  "notebooks/04_intermediate_computation/05_probing_classifiers.ipynb": {
+   "methods": [
+    "probing",
+    "amnesic-probing"
+   ]
+  },
+  "notebooks/04_intermediate_computation/06_activation_oracles.ipynb": {
+   "methods": [
+    "latentqa"
+   ]
+  },
+  "notebooks/05_representation_analysis/01_model_diffing.ipynb": {
+   "methods": [
+    "model-diffing",
+    "crosscoders"
+   ]
+  },
+  "notebooks/05_representation_analysis/02_knowledge_localization.ipynb": {
+   "methods": [
+    "causal-tracing"
+   ]
+  },
+  "notebooks/05_representation_analysis/03_representation_engineering.ipynb": {
+   "methods": [
+    "steering-vectors"
+   ]
+  },
+  "notebooks/06_safety_applications/02_steering_vectors.ipynb": {
+   "methods": [
+    "steering-vectors"
+   ],
+   "applications": [
+    "steering-vectors"
+   ]
+  }
+ },
+ "demos": {
+  "superposition": {
+   "hypotheses": [
+    "superposition"
+   ],
+   "notebook": "notebooks/01_core_hypotheses/02_superposition_hypothesis.ipynb"
+  },
+  "sae": {
+   "methods": [
+    "sae",
+    "topk-sae"
+   ],
+   "notebook": "notebooks/02_feature_extraction/01_sparse_autoencoders_basics.ipynb"
+  },
+  "patching": {
+   "methods": [
+    "activation-patching"
+   ],
+   "notebook": "notebooks/03_circuits/01_circuit_discovery.ipynb"
   }
+ }
 }
diff --git a/src/data/methods.json b/src/data/methods.json
index 7d04c3ccce..f6b8117cc5 100644
--- a/src/data/methods.json
+++ b/src/data/methods.json
@@ -5,11 +5,13 @@
    "name": "The Interpretability Landscape",
    "sections": [
     "sec:interpretability-landscape",
-    "behavioral-methods",
+    "behavioural-methods",
     "subsec:cbm",
-    "attribution-and-saliency-methods",
+    "input-attribution-and-saliency-methods",
     "representation-probing-and-related-post-hoc-analyses",
-    "when-mechanistic-understanding-becomes-necessary"
+    "mechanistic-interpretability-methods",
+    "subsec: mech interp necessary",
+    "subsec:limits"
    ]
   },
   {
@@ -17,22 +19,22 @@
    "name": "Sparse Autoencoders / SAE Variants and Architectural Improvements",
    "sections": [
     "sec:sae",
+    "the-sparse-coding-problem",
     "subsec:sae-foundation",
     "subsec:sae-variants",
     "subsec:sae-selection",
-    "subsec:challenge",
-    "app:sae_details"
+    "subsec:challenge"
    ]
   },
   {
    "id": "multi-layer-feature-analysis",
-   "name": "Multi-Layer Feature Analysis: Transcoders and Crosscoders",
+   "name": "Multi-Layer Feature Extraction and Analysis: Transcoders and Crosscoders",
    "sections": [
     "subsec:transcoders-crosscoders",
     "subsubsec:transcoder",
     "subsubsec:cross-layer-transcoder",
-    "crosscoders-analyzing-cross-layer-features",
-    "comparing-saes-transcoders-and-crosscoders."
+    "crosscoders-analysing-cross-layer-features",
+    "comparing-saes-transcoders-and-crosscoders"
    ]
   },
   {
@@ -44,10 +46,10 @@
   },
   {
    "id": "architectural-interventions",
-   "name": "Architectural Interventions for Intrinsic Interpretability",
+   "name": "Interpretability-by-Design Methods for Intrinsic Interpretability",
    "sections": [
     "sec:architectural-interventions",
-    "architectural-responses-to-superposition"
+    "architectural-responses-to-superposition."
    ]
   },
   {
@@ -55,45 +57,46 @@
    "name": "Circuits in Mechanistic Interpretability",
    "sections": [
     "sec:circuit",
-    "sec:circuit-table",
-    "circuit-discovery-in-weight-sparse-architectures",
-    "sec:causal-verification"
+    "sec:circuit-discovery-methods",
+    "sec:circuit-replacement",
+    "sec:causal-verification",
+    "sec:evaluation-framework"
    ]
   },
   {
    "id": "intermediate-computation",
-   "name": "Understanding Intermediate Computation: Logit Lens and Probing",
+   "name": "Understanding Intermediate Representations",
    "sections": [
     "sec:intermediate-computation"
    ]
   },
   {
    "id": "training-data-attribution",
-   "name": "Training Data Attribution and Influence Functions",
+   "name": "Training Data Attribution",
    "sections": [
     "sec:influence-functions"
    ]
   },
   {
    "id": "developmental-interpretability",
-   "name": "Developmental Interpretability and Training Dynamics",
+   "name": "Learning Dynamics and Mechanistic Interpretability",
    "sections": [
     "sec:training"
    ]
   },
   {
    "id": "representation-analysis",
-   "name": "Representation Analysis: Structure, Localization, and Transfer",
+   "name": "Representation Analysis: Comparison, Localisation, and Control",
    "sections": [
     "sec:representation"
    ]
   },
   {
    "id": "safety-applications",
-   "name": "From Understanding to Intervention: Safety Applications",
+   "name": "From Understanding to Intervention: Safety Applications of Mechanistic Interpretability",
    "sections": [
     "sec:safety_applications",
-    "concept-erasure-and-direct-editing",
+    "concept-erasure-and-knowledge-editing",
     "steering-vectors-and-mechanistic-monitoring"
    ]
   }
@@ -101,28 +104,32 @@
  "methods": [
   {
    "id": "behavioral-methods",
-   "name": "Behavioral Methods",
+   "name": "Behavioural Methods",
    "family": "interpretability-landscape",
    "sections": [
-    "behavioral-methods"
+    "behavioural-methods"
    ],
-   "definition": "Behavioral methods examine models at the level of inputs and outputs: capability evaluations (Linzen et al., 2016; Marvin & Linzen, 2018; Wang et al., 2024; Zhang et al., 2025), deceptive intents (Kinniment et al., 2026), and red-teaming (Ganguli et al., 2022; Perez et al., 2022b; He et al., 2025).",
+   "definition": "Behavioural methods evaluate and analyse models through their inputs and outputs. They have been developed to examine a wide range of capabilities, traits, and behaviours under specific conditions.",
    "assumptions": [
-    "These approaches are fast, scalable, and practical for high-level auditing without access to model internals."
+    "Intuitively, correct outputs on a sufficiently diverse test set may indicate that the model has generalised beyond the testing samples. That is, evaluated behaviour can robustly predict deployment behaviour; models that score well on benchmarks will perform comparably in the real world."
+   ],
+   "evaluation": [
+    "In general, behavioural evaluations provide a practical way to audit and assess models at scale. They are typically fast, inexpensive, and scalable, requiring only forward passes and comparisons between model outputs and reference answers."
    ],
-   "evaluation": [],
    "applications": [
-    "Behavioral methods nonetheless remain essential in practice, for rapid iteration during development, for prioritizing which behaviors warrant deeper investigation, and for surface-level safety screening where the cost of mechanistic analysis is prohibitive."
+    "Beyond assessing models as helpful assistants, behavioural evaluations also identify and benchmark potential safety risks and failure modes.",
+    "Although evaluation results on a finite testing set do not guarantee the mechanism that produced the behaviour of interest (Wani et al., 2026) (which the internal methods below aim to establish, as discussed in Section 2), behavioural methods nonetheless remain essential in practice for rapid iteration during development, prioritising which behaviours warrant deeper investigation, and surface-level safety screening when the cost of mechanistic analysis is prohibitive."
    ],
    "limitations": [
-    "The limits of this approach are the subject of Section 2; the point that matters here is that behaviour on a finite evaluation set underdetermines the mechanism that produced it, which is what the internal methods below are designed to establish.",
-    "These studies assess what models do without explaining how."
+    "Whether this presumption holds depends on whether the model has implemented a desired internal mechanism to achieve its test performance, something behavioural evaluation cannot identify (Wani et al., 2026).",
+    "Mechanistic analysis becomes necessary when we need to verify how a model produces a behaviour—not just that it does under test conditions."
    ],
    "related": [],
    "mentioned_in": [
-    "sec:scope",
     "sec:why_open",
-    "alternatives-black-box-methods"
+    "subsec: mech interp necessary",
+    "sec:scope",
+    "shared-limitations-of-black-box-approaches"
    ]
   },
   {
@@ -132,54 +139,52 @@
    "sections": [
     "subsec:cbm"
    ],
-   "definition": "Concept-based methods plays a middle ground role: they go beyond behavioral evaluation by examining internal representations, but typically establish correlational rather than causal claims.",
+   "definition": "Concept-based methods occupy a middle ground: they examine internal representations but typically establish correlational rather than causal claims. However, when combined with interventions such as steering along concept directions and ablating concept-associated components, they become mechanistic by the criteria discussed in Section 1.2.",
    "assumptions": [
-    "CBMs impose semantic constraints (predefined human concepts), while architectural interventions impose structural constraints (sparsity, modularity) that allow the model to discover features autonomously."
+    "CBMs impose semantic constraints (predefined human concepts), while interpretable-by-design models impose structural constraints (sparsity, modularity) that make the model intrinsically easier to interpret."
    ],
    "evaluation": [],
    "applications": [
-    "These methods do not guarantee mechanistic understanding but are valuable for auditing, localization, and early detection tasks.",
-    "Practical Guide: CBMs tend to be more appropriate when the relevant concepts are known and can be enumerated; architectural interventions may be preferable when the goal is to discover what the model has learned—including features that were not known in advance."
+    "In general, these methods do not provide causal evidence about the role of those concepts in model computation, although some probing variants aim to support stronger causal claims, such as amnesic probing (Elazar et al., 2021). Nevertheless, they remain valuable tools for auditing, localisation, and early detection tasks.",
+    "CBMs can be more appropriate when the set of concepts of interest is known. Interpretability-by-design models may be preferable when the goal is to discover what the model has learned—including features that were not preset in advance."
    ],
    "limitations": [
-    "Concept Bottleneck Models (CBMs; Koh et al., 2020) constrain computation through a predefined bottleneck of interpretable concepts, offering built-in transparency and allowing for interventions, but traditionally requiring a complete set of predefined concepts and often suffer from an “accuracy-interpretability trade-off” when concept definitions are insufficiently expressive.",
-    "For safety applications, where unknown failure modes are a core concern, methods that enable discovery may be more useful than those that require specifying concepts upfront."
+    "Concept Bottleneck Models (CBMs; Koh et al., 2020) constrain computation through a predefined bottleneck of interpretable concepts, offering built-in transparency and allowing for interventions, but traditionally requiring a complete set of predefined concepts and often suffering from an “accuracy–interpretability trade-off” when concept definitions are insufficiently expressive.",
+    "For safety applications, where unknown failure modes are a core concern, discovery-oriented methods may be more useful than those that require specifying concepts upfront."
    ],
    "related": [
     "probing",
     "steering-vectors"
    ],
    "mentioned_in": [
-    "sec:scope"
+    "sec:scope",
+    "subsec: mech interp necessary"
    ]
   },
   {
    "id": "attribution-saliency",
-   "name": "Attribution and Saliency Methods",
+   "name": "Input Attribution and Saliency Methods",
    "family": "interpretability-landscape",
    "sections": [
-    "attribution-and-saliency-methods",
-    "critiques-of-attribution-and-saliency-methods"
+    "input-attribution-and-saliency-methods"
    ],
-   "definition": "Attribution methods focus on identifying which features most influence a model’s outputs. Techniques such as SHAP (Lundberg & Lee, 2017), LIME (Ribeiro et al., 2016), integrated gradients (Sundararajan et al., 2017), and saliency maps (Simonyan et al., 2014) are scalable and widely used for token-level explanations and debugging.",
+   "definition": "Input attribution methods aim to identify which input features most influence a model’s outputs. Techniques such as SHAP (Lundberg & Lee, 2017), LIME (Ribeiro et al., 2016), DeepLIFT (Shrikumar et al., 2017), and saliency-based methods (Simonyan et al., 2014; Sundararajan et al., 2017; Zhou et al., 2016; Selvaraju et al., 2017) are scalable and widely used for explanation and debugging at the pixel and token levels.",
    "assumptions": [],
-   "evaluation": [
-    "However, Jacovi & Goldberg (2020) distinguish between plausibility (do attributions seem reasonable to humans?) and faithfulness (do attributions reflect actual model computation?)."
-   ],
+   "evaluation": [],
    "applications": [
-    "For the safety-relevant tasks motivating this survey, attribution methods are useful for triage and initial auditing but, on their own, insufficient where a causal account of internal computation is required."
+    "The same machinery can also be applied to internal representations, attributing salience to features at different layers and tracing how information evolves throughout the network (Yu et al., 2024; Syed et al., 2024).",
+    "For safety-relevant applications, attribution methods help with triage, debugging, and initial auditing, but they are insufficient on their own when an explanation of internal computation is required."
    ],
    "limitations": [
-    "Applied to inputs, they indicate which inputs matter but not how the model arrives at its output.",
-    "Reliability concerns nonetheless remain: different attribution methods can produce divergent explanations for the same prediction, and some have been shown to be insensitive to model parameters (Adebayo et al., 2018b).",
-    "Collectively, these findings suggest that attribution methods are fundamentally unreliable guides to what information a model actually uses."
+    "Techniques such as SHAP (Lundberg & Lee, 2017), LIME (Ribeiro et al., 2016), DeepLIFT (Shrikumar et al., 2017), and saliency-based methods (Simonyan et al., 2014; Sundararajan et al., 2017; Zhou et al., 2016; Selvaraju et al., 2017) are scalable and widely used for explanation and debugging at the pixel and token levels. However, these methods indicate which input features matter for a prediction, but not necessarily how the model processes and leverages them.",
+    "Different attribution methods can produce substantially different explanations for the same prediction, and some have been shown to be insensitive to model parameters (Adebayo et al., 2018) or sensitive to input transformations that have no effect on the model (Kindermans et al., 2022)."
    ],
    "related": [
     "probing"
    ],
    "mentioned_in": [
     "sec:scope",
-    "implications-for-probing."
+    "implications-of-attribution-faithfulness-for-probing"
    ]
   },
   {
@@ -189,33 +194,33 @@
    "sections": [
     "subsec:probing",
     "standard-probing-methodology",
-    "representation-probing-and-related-post-hoc-analyses",
-    "the-probe-causality-problem",
     "control-tasks-and-selectivity",
-    "probe-complexity-vs.-causal-relevance",
-    "when-is-probing-appropriate-and-when-is-it-not",
-    "best-practices-for-probing-studies"
-   ],
-   "definition": "A probe trains a classifier—typically linear—to predict a property (e.g., part-of-speech, sentiment, entity type) from model activations.",
+    "concerns-of-probe-complexity",
+    "minimum-description-length-probes.",
+    "practical-guidance.",
+    "implications-of-attribution-faithfulness-for-probing",
+    "when-is-probing-appropriate",
+    "methodological-standards-in-probing-research"
+   ],
+   "definition": "A probe is a classifier trained to predict a property of the input, such as part of speech, sentiment, or entity type, from model activations.",
    "assumptions": [
-    "Linear probes constrain g to be linear, testing whether the property is linearly accessible.",
-    "MLP probes allow nonlinear g, testing whether the property is present in any recoverable form."
+    "The linear probe tests whether the target property is linearly accessible from the representation.",
+    "Probing provides evidence about whether activations contain information about a property. High probe accuracy indicates the information is present; low accuracy suggests it is absent or the probe is not expressive enough."
    ],
    "evaluation": [
-    "A control task assigns random labels to inputs, breaking any genuine relationship between activations and the target property.",
-    "High selectivity indicates the probe succeeds because of genuine encoding, not because the representation is easily separable by any linear classifier.",
-    "To address this, Voita & Titov (2020) proposed evaluating probes via the Minimum Description Length (MDL) principle: prefer probes that achieve good accuracy with minimal complexity.",
-    "Report control task baselines: Always include selectivity metrics."
+    "A control task assigns random labels to inputs to break any genuine relationship between activations and the target property.",
+    "High selectivity indicates that the probe succeeds because the representation genuinely encodes the target property.",
+    "To address this, Voita & Titov (2020) proposed evaluating probes via the minimum description length (MDL) principle: prefer probes that achieve good accuracy with minimal complexity."
    ],
    "applications": [
-    "probes have become one of the most widely used tools in mechanistic interpretability for tasks including layer-level localization, scalable auditing, and detecting latent behaviors such as sleeper-agent deception (MacDiarmid et al., 2024).",
-    "The most productive framing treats probing as hypothesis generation: probes identify candidate properties that may be causally relevant, which must then be tested via causal methods (ablation, patching, circuit analysis).",
-    "This illustrates that probing remains valuable when the goal is auditing or detection, while its limitations apply most strongly to mechanistic claims."
+    "We argue that probing is most productive when treated as a tool for hypothesis generation, that is, using probes to identify candidate properties that may be encoded in a representation and potentially causally relevant.",
+    "Despite the causality critiques, probing remains valuable for many applications. For example, MacDiarmid et al. (2024) showed that simple linear probes trained on residual-stream activations can detect latent backdoor behaviour in the sleeper agent model even when the probe fitting set does not contain the trigger or the harmful behaviour.",
+    "In summary, this use case shows that probing remains valuable for auditing or detection even though it cannot support causal claims."
    ],
    "limitations": [
-    "Probing does not establish causal relevance: whether the model actually uses that information for its predictions.",
-    "This problem is fundamental: probing measures information presence, not information use.",
-    "Control task experiments have revealed that many probing results showing “encoding” of linguistic properties suffer from low selectivity—random labels are nearly as extractable (Hewitt & Liang, 2019)."
+    "Note that probing alone does not show whether the model actually depends on that information in its decision-making process.",
+    "High probe accuracy does not imply causal relevance (Belinkov, 2022; Hewitt & Liang, 2019).",
+    "Finally, a sufficiently expressive probe can achieve high accuracy by exploiting noise even when the representations contain no such information."
    ],
    "related": [
     "amnesic-probing",
@@ -226,9 +231,11 @@
    ],
    "mentioned_in": [
     "sec:scope",
-    "pragmatic-interpretability-proxy-task-oriented-safety-interventions",
-    "developmental-interpretability-understanding-phase-transition-during-training",
-    "mechanistic-monitoring"
+    "representation-probing-and-related-post-hoc-analyses",
+    "subsec:dev interp",
+    "subsubsec: mech monitor",
+    "choosing-appropriate-methods-for-different-safety-applications",
+    "sec:automated-interpretability"
    ]
   },
   {
@@ -238,30 +245,29 @@
    "sections": [
     "subsec:sae-foundation",
     "sec:sae",
-    "subsec:challenge",
+    "subsec:sae-variants",
     "subsec:sae-selection",
-    "app:sae_details"
+    "subsec:challenge"
    ],
-   "definition": "Sparse Autoencoders are a way of extracting monosemantic features from polysemantic neurons.",
+   "definition": "SAEs (Figure 6) extract monosemantic features from polysemantic model activations. They are trained to reconstruct activations at a specific location in neural networks, such as the output of an MLP block or an entire transformer layer (i.e., the residual stream), while keeping only a few features active in any given forward pass.",
    "assumptions": [
-    "They are optimised for accuracy to the real MLP or attention block and for having few active features for any given forward pass.",
-    "SAEs are motivated by the hypothesis that features occur sparsely in the data and are linearly recoverable from a model’s hidden states.",
-    "Training of SAEs is sensitive to initialization, sparsity strength, and decoder normalization."
+    "SAEs are motivated by the hypothesis that a model’s activations can be approximated as linear combinations of many latent feature directions, which can be regarded as an extension of the linear representation hypothesis discussed in Section 5.1.",
+    "SAE training is sensitive to initialisation, sparsity strength, and decoder normalisation. Without proper constraints and hyperparameters, the features may collapse, duplicate, or absorb one another."
    ],
    "evaluation": [
-    "SAEs are typically evaluated for reconstruction loss (fidelity of activation reconstruction), feature absorption (degree to which features remain distinct), and downstream testing loss recovery (whether replacing activations with reconstructions preserves model behavior).",
-    "Report failure mode metrics: Alongside reconstruction loss, report dead feature percentage, duplication rate (via cosine similarity), and stability across seeds.",
-    "These causal tests should be standard practice when claiming that SAE features represent meaningful computational units."
+    "SAEs are typically evaluated for reconstruction loss (fidelity of activation reconstruction), feature absorption (how often a general feature fails to fire because a more specific feature has absorbed its direction), and downstream test loss recovery (whether replacing activations with reconstructions preserves model behaviour).",
+    "Report failure-mode metrics: Alongside reconstruction loss, researchers should also report dead-feature percentage, duplication rate, and stability across seeds.",
+    "Validate causal relevance: Researchers should validate features’ necessity, sufficiency, and specificity through interventions (e.g., feature ablation or steering) and evaluation on downstream effects."
    ],
    "applications": [
-    "As a result, supervised methods are best suited to settings where specific, predefined concepts are of interest, such as safety-critical behaviors like toxicity or deception, whereas SAEs remain preferable for exploratory analysis.",
-    "SAEs to unsupervisedly discover monosemantic features inside the model’s raw activations."
+    "Exploratory discovery, where the question is what features exist rather than whether a particular one does, is the setting SAEs were designed for and the one the comparisons below address.",
+    "Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation) (Oldfield et al., 2026), feature-wise monitoring (alerting when unexpected SAE features activate) (Fang et al., 2026), circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features) (Zhao et al., 2026b), and anomaly-based monitoring (using activations in normal behaviour as a baseline and flagging harmful generations as outliers) (Chaudhary & Barez, 2026)."
    ],
    "limitations": [
-    "Dead features represent wasted capacity and can constitute between 10–50% of the dictionary in poorly tuned SAEs (Bricken et al., 2023).",
-    "Training the same architecture with different random seeds can therefore produce substantially different dictionaries (Paulo & Belrose, 2025).",
-    "Taken together, these behaviors are consistent with the failure of identifiability under high-coherence conditions and help explain why SAE features can be unstable, non-atomic, and seed-dependent.",
-    "Low SAE reconstruction loss does not necessarily mean that SAE captures the causal structure of the original model."
+    "Dead features waste the dictionary’s capacity, and they are common. In fact, they can constitute up to 10–50% of the dictionary in a poorly tuned SAE (Bricken et al., 2023).",
+    "For a fixed SAE architecture and training dataset, different random seeds can produce substantially different dictionaries (Paulo & Belrose, 2026).",
+    "In large language model activations, high coherence is likely unavoidable because of the model’s complicated internal structure and correlations within its feature space (Li et al., 2025b). So far, identifiability remains a stubborn issue in SAEs, even though it offers a theoretical explanation for several empirical observations.",
+    "Activation reconstruction \\neq computation reconstruction: Low SAE reconstruction loss does not necessarily mean the SAE captures the original model’s computation."
    ],
    "related": [
     "gated-sae",
@@ -280,12 +286,15 @@
    ],
    "mentioned_in": [
     "subsec:superposition",
-    "case-study-induction-heads",
+    "empirical-evidence.",
+    "mechanistic-interpretability-methods",
+    "subsec: induc head case study: feature extract",
     "sec:automated-interp",
     "concept-erasure-via-pisces",
-    "mechanistic-monitoring",
-    "sec:model-diffing",
-    "relation-to-causal-evaluation-of-feature-extraction-methods.",
+    "subsubsec: mech monitor",
+    "dictionary-learning-methods.",
+    "steering-as-a-causal-test-for-feature-interpretability.",
+    "epistemic-risks-false-confidence-from-partial-or-incorrect-understanding",
     "silent-failure-modes."
    ]
   },
@@ -294,17 +303,15 @@
    "name": "Gated SAEs",
    "family": "sparse-autoencoders",
    "sections": [
-    "subsec:sae-variants",
-    "gated-sae-variant"
+    "subsec:sae-variants"
    ],
    "definition": "Gated SAEs introduce multiplicative gates to decouple feature selection from magnitude",
    "assumptions": [],
    "evaluation": [],
-   "applications": [
-    "Recommendation: Use Gated SAE as the default unless you have specific reasons for standard.",
-    "Standard SAEs suffer from shrinkage bias; Gated SAEs solve this with minimal added complexity."
+   "applications": [],
+   "limitations": [
+    "With moderate tuning capacity, Gated SAEs (Rajamanoharan et al., 2024a) achieved a better reconstruction–sparsity trade-off while retaining \\lambda. They also avoid the shrinkage effect by separating feature detection from magnitude estimation. However, because the standard L_1 SAE is now used mainly as a baseline, comparisons against it partly reflect shrinkage rather than dictionary quality alone."
    ],
-   "limitations": [],
    "related": [
     "sae",
     "topk-sae",
@@ -313,8 +320,9 @@
    ],
    "mentioned_in": [
     "subsec:sae-selection",
-    "layer-specific-recommendations",
-    "quick-reference-when-to-use-what"
+    "layer-type.",
+    "resource-constraints.",
+    "practical-recommendations"
    ]
   },
   {
@@ -324,13 +332,17 @@
    "sections": [
     "subsec:sae-variants"
    ],
-   "definition": "JumpReLU SAEs learn per-feature activation thresholds.",
+   "definition": "JumpReLU SAEs (Rajamanoharan et al., 2024b) learn per-feature activation thresholds.",
    "assumptions": [
-    "A smooth surrogate is used during training to enable gradient flow Rajamanoharan et al. (2024b)."
+    "Straight-through estimators (Bengio et al., 2013) are used during training to enable gradient flow."
    ],
    "evaluation": [],
-   "applications": [],
-   "limitations": [],
+   "applications": [
+    "For models between 1B and 10B parameters, JumpReLU SAEs and Top-k SAEs are the most commonly reported variants; Gemma Scope (Lieberum et al., 2024) uses the former and Llama Scope (He et al., 2024) the latter."
+   ],
+   "limitations": [
+    "When extensive tuning is plausible, JumpReLU SAEs (Rajamanoharan et al., 2024b) achieved the strongest reconstruction performance at matched sparsity by learning per-feature thresholds. However, gains on these sparsity–fidelity proxy metrics do not reliably translate into better downstream performance (Karvonen et al., 2025). In practice, selecting the best model and hyperparameters to achieve optimal performance can take a lot of time."
+   ],
    "related": [
     "sae",
     "gated-sae",
@@ -339,8 +351,11 @@
    ],
    "mentioned_in": [
     "subsec:sae-selection",
-    "layer-specific-recommendations",
-    "quick-reference-when-to-use-what"
+    "layer-type.",
+    "resource-constraints.",
+    "model-scale.",
+    "practical-recommendations",
+    "subsubsec:cross-layer-transcoder"
    ]
   },
   {
@@ -348,18 +363,15 @@
    "name": "Top-k SAEs",
    "family": "sparse-autoencoders",
    "sections": [
-    "subsec:sae-variants",
-    "top-k-sae-variant"
+    "subsec:sae-variants"
    ],
-   "definition": "Top-k SAEs (Gao et al., 2025a) impose an explicit hard sparsity constraint.",
+   "definition": "Top-k SAEs (Gao et al., 2025) impose an explicit hard sparsity constraint.",
    "assumptions": [
-    "No sparsity penalty needed; sparsity is enforced structurally."
-   ],
-   "evaluation": [
-    "Unlike L1 loss, it does not suppress activation magnitudes, thereby avoiding the feature shrinkage issue observed in L1-based SAE training (Gao et al., 2025a)."
+    "This formulation explicitly enforces \\|z\\|_0 \\le k—a more direct feature-sparsity constraint than L_1 regularisation."
    ],
+   "evaluation": [],
    "applications": [
-    "If you’re spending significant time tuning the sparsity coefficient, Top-K eliminates this hyperparameter entirely."
+    "Under limited compute, or where fast iteration matters more than reconstruction quality, Top-k SAEs (Gao et al., 2025) are the practical choice: fixing \\|z\\|_0 \\le k removes the sparsity coefficient \\lambda from the search space entirely rather than requiring it to be tuned, with reported values commonly in the range k = 32 (Gao et al., 2025) to 150 (Karvonen et al., 2025)."
    ],
    "limitations": [],
    "related": [
@@ -370,8 +382,9 @@
    ],
    "mentioned_in": [
     "subsec:sae-selection",
-    "common-pitfalls-and-how-to-avoid-them",
-    "quick-reference-when-to-use-what"
+    "resource-constraints.",
+    "model-scale.",
+    "practical-recommendations"
    ]
   },
   {
@@ -381,12 +394,14 @@
    "sections": [
     "subsec:sae-variants"
    ],
-   "definition": "Batch Top-k SAEs enforce sparsity at the batch level.",
-   "assumptions": [],
+   "definition": "Batch Top-k SAEs relax the fixed per-example sparsity constraint of standard Top-k SAEs by enforcing the sparsity budget across an entire batch instead (Bussmann et al., 2024).",
+   "assumptions": [
+    "This loose constraint allows the model to allocate representational capacity adaptively across samples."
+   ],
    "evaluation": [],
    "applications": [
-    "This enforces global feature competition, reduces variance in feature usage across the batch, and prevents collapse (Bussmann et al., 2024).",
-    "More recent Batch Top-K variants explicitly enforce minimum feature usage across batches, which substantially reduces the prevalence of dead features (Bussmann et al., 2024)."
+    "More recent Batch Top-k SAEs allocate sparsity adaptively across samples, which allows more active features on complex inputs and fewer on easy ones (Bussmann et al., 2024).",
+    "During inference, Batch Top-k SAEs instead use the JumpReLU function with an inferred global threshold to determine which features are active for each input, so that an input’s activations do not depend on the rest of the batch."
    ],
    "limitations": [],
    "related": [
@@ -396,8 +411,7 @@
    ],
    "mentioned_in": [
     "dead-features",
-    "sec:model-diffing",
-    "quick-reference-when-to-use-what"
+    "dictionary-learning-methods."
    ]
   },
   {
@@ -407,19 +421,19 @@
    "sections": [
     "subsubsec:transcoder",
     "architecture-and-motivation.",
-    "when-transcoders-are-necessary."
+    "when-are-transcoders-necessary"
    ],
    "definition": "The transcoder reconstructs post-nonlinearity activations using features extracted from pre-nonlinearity activations (Dunefsky et al., 2024).",
    "assumptions": [
-    "A transcoder applies sparse decomposition directly to the nonlinear mapping, learning features that explain the transformation."
+    "Standard SAEs do not explain the nonlinear transformations (i.e., weight matrices of MLP blocks and attention heads) themselves that produce the activations. A transcoder achieves this by applying dictionary learning to the mapping, learning features that explain the transformation."
    ],
    "evaluation": [],
    "applications": [
-    "Transcoders are essential when analyzing MLPs, understanding feature interactions, or tracking how concepts evolve across layers (Dunefsky et al., 2024).",
-    "Recommendation: For MLP analysis, use transcoders; for cross-layer analysis, use crosscoders or CLTs."
+    "SAEs extract features from static activations and thus often suffice for residual-stream analysis. Transcoders track how features emerge from input to output and are essential for analysing MLP layers (Dunefsky et al., 2024).",
+    "On MLP outputs, transcoders are the appropriate tool: a single-layer SAE cannot span the nonlinearity, so transcoders learn the input-to-output map directly (Dunefsky et al., 2024), and Paulo et al. (2025c) reported favourable comparisons against SAEs there."
    ],
    "limitations": [
-    "Transcoders improve this by imitating the behavior of the MLP, revealing nonlinear transformations, though they require sequential analysis across depth."
+    "Transcoders improve this by imitating MLP behaviour and revealing nonlinear transformations, though they require sequential analysis across depth."
    ],
    "related": [
     "sae",
@@ -428,9 +442,10 @@
     "molt"
    ],
    "mentioned_in": [
-    "comparing-saes-transcoders-and-crosscoders.",
-    "case-study-induction-heads",
-    "sec:model-diffing"
+    "layer-type.",
+    "comparing-saes-transcoders-and-crosscoders",
+    "dictionary-learning-methods.",
+    "subsec: induc head case study: feature extract"
    ]
   },
   {
@@ -444,26 +459,26 @@
     "computational-cost-and-practical-considerations.",
     "open-questions-for-clt-research."
    ],
-   "definition": "A Cross-Layer Transcoder (CLT) is a sparse feature decomposition spanning all layers of a neural network.",
+   "definition": "Cross-layer transcoders (CLTs) are sparse feature decompositions spanning all layers of a neural network.",
    "assumptions": [
     "Attention freezing: By caching attention patterns, the replacement model treats attention as a fixed routing operation rather than a dynamic, input-dependent computation.",
-    "Normalization caching: Freezing LayerNorm statistics removes a source of nonlinearity but introduces dependence on the specific input used to cache these statistics.",
-    "Sparse linear approximation: The core assumption that MLP outputs are well-approximated by sparse linear combinations of features may break down for computations involving complex feature interactions or highly nonlinear transformations."
+    "Normalisation caching: CLT analysis caches the input-dependent normalisation factors (e.g., LayerNorm denominators) from the original forward pass and reuses them during attribution instead of recomputing them after interventions.",
+    "Sparse linear approximation: Dictionary learning methods assume MLP outputs or transformations are well approximated by sparse linear combinations of features. This assumption may fail when computations involve complex feature interactions or highly nonlinear transformations."
    ],
    "evaluation": [
-    "Empirically, CLT replacement models match the original model’s most-likely token on approximately 50% of prompts in initial experiments on Gemma-2-2B (Ameisen et al., 2025).",
-    "The 50% figure reflects top-1 token agreement; softer metrics (e.g., top-5 agreement, KL divergence) may show different patterns.",
-    "These caveats do not invalidate CLTs as a tool for mechanistic analysis, but practitioners should verify fidelity on their specific model, layer, and task before drawing strong conclusions from CLT-based circuit analysis."
+    "Under these conditions, the local replacement model can exactly match the original model’s activations on that prompt while decomposing it into interpretable CLT features. However, when the model’s activations are completely replaced by CLT reconstructions, the intervened model’s top-1 predictions match the original model’s on only about 50% of prompts from the Pile dataset (Gao et al., 2020). Notably, this experiment uses a relatively small, 18-layer transformer (Ameisen et al., 2025).",
+    "The paper reports top-1 token agreement as the main result, while softer metrics (e.g., top-5 agreement, KL divergence) may show different patterns and insights.",
+    "Domain: Fidelity on factual recall and syntactic tasks is higher than on complex reasoning or arithmetic tasks, where MLP computations may be more complex and cannot be approximated by sparse linear features. These caveats do not invalidate CLTs as a tool for mechanistic analysis, but they do mean that fidelity should be verified on the target model and specific task before drawing strong conclusions from CLT-based circuit analysis."
    ],
    "applications": [
-    "A key insight is that CLTs enable the construction of a replacement model: a version of the original model where all MLP layers are replaced by their CLT reconstructions.",
-    "This simplifies circuit structure—features need not re-encode information at each layer.",
-    "When these assumptions hold, CLTs provide a powerful lens for circuit discovery."
+    "A key insight is that CLTs enable the construction of a replacement model—a version of the original model in which all MLP layers are replaced with their CLT reconstructions.",
+    "Unlike per-layer transcoders, CLTs reveal direct feature-to-feature interactions across layers: a feature in layer \\ell directly influences features in later layers through its contribution to their inputs. This simplifies circuit structure—features need not re-encode information at each layer.",
+    "CLTs provide a powerful lens for circuit discovery when these assumptions hold."
    ],
    "limitations": [
-    "When they fail, particularly for complex reasoning or out-of-distribution inputs, CLT-based explanations may be incomplete or misleading.",
-    "Training a CLT requires significant upfront cost, which scales with the number of features and layers.",
-    "This scarcity of downstream applications in the current literature likely reflects the considerable challenges in training CLTs at scale.",
+    "When they fail, CLT-based explanations may become unfaithful.",
+    "Training a suite of CLTs thus incurs a high upfront cost.",
+    "For these reasons, current applications of CLTs remain limited: a “greater-than” mechanism study (Merullo et al., 2025), an open-source extension (Lindsey et al., 2025a), and analysis of multilingual representations using CLTs (Harrasse et al., 2026). This scarcity of downstream use cases likely reflects the considerable challenges of training CLTs at scale and the complexity of using CLT-based circuit discovery in the wild.",
     "Cross-layer transcoders span multiple layers for global information flow but are extremely expensive."
    ],
    "related": [
@@ -474,9 +489,11 @@
     "spartan"
    ],
    "mentioned_in": [
-    "sec:circuit-table",
-    "sparse-attention-post-training-spartan",
-    "comparing-saes-transcoders-and-crosscoders."
+    "sec:circuit-replacement",
+    "subsubsec: spartan",
+    "comparing-saes-transcoders-and-crosscoders",
+    "sec:proxy-fidelity",
+    "steering-as-a-causal-test-for-feature-interpretability."
    ]
   },
   {
@@ -484,19 +501,19 @@
    "name": "Crosscoders",
    "family": "multi-layer-feature-analysis",
    "sections": [
-    "crosscoders-analyzing-cross-layer-features",
+    "crosscoders-analysing-cross-layer-features",
     "causal-vs.-acausal-crosscoders.",
     "applications-to-circuit-analysis."
    ],
-   "definition": "Crosscoders resolve cross-layer superposition by tracking features that persist through the residual stream (Lindsey et al., 2024).",
+   "definition": "A crosscoder learns a shared sparse representation from activations across L layers.",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "By extracting cross-layer features, crosscoders enable understanding how features propagate through the residual stream across layers, removing duplicate feature representations that complicate circuit analysis.",
-    "Crosscoders can define model-specific encoders, with arbitrarily different input dimensions, which then project into one or more shared representational spaces."
+    "By extracting cross-layer features, crosscoders help us understand how features propagate through the residual stream across layers, removing duplicate feature representations that complicate circuit analysis.",
+    "Crosscoders can define model-specific encoders with arbitrarily different input dimensions that project activations from different models into one or more shared representational spaces."
    ],
    "limitations": [
-    "This can result from the loss function: for instance, Minder et al. (2025) showed that crosscoders trained with an L1 loss on hidden states can falsely identify novel features, which they try to mitigate with the BatchTopK activation function."
+    "Dictionary learning methods for model diffing risk falsely identifying model-exclusive features (Dumas et al., 2025a; Jiralerspong & Bricken, 2025). This can result from the loss function: for instance, Dumas et al. (2025a) showed that crosscoders trained with an L_1 regularisation on hidden states can falsely identify novel features, which they tried to mitigate with \\sigma_{\\text{Batch-TopK}}(\\cdot)."
    ],
    "related": [
     "sae",
@@ -506,27 +523,28 @@
     "batch-topk-sae"
    ],
    "mentioned_in": [
-    "comparing-saes-transcoders-and-crosscoders.",
-    "sec:model-diffing"
+    "comparing-saes-transcoders-and-crosscoders",
+    "dictionary-learning-methods.",
+    "subsec: induc head case study: feature extract"
    ]
   },
   {
    "id": "nmf",
-   "name": "Non-Negative Matrix Factorization (NMF)",
+   "name": "Non-Negative Matrix Factorisation (NMF)",
    "family": "alternative-feature-extraction",
    "sections": [
-    "non-negative-matrix-factorization-nmf"
+    "non-negative-matrix-factorisation-nmf"
    ],
-   "definition": "This constraint induces a natural parts-based interpretation in which features combine additively without cancellation.",
+   "definition": "Because all components are non-negative, each feature contributes positively to the final representation, making it easier to interpret.",
    "assumptions": [
-    "Activations produced by modern architectures, particularly after LayerNorm or nonlinearities such as GELU, are not naturally non-negative, requiring preprocessing steps that may distort representational structure."
+    "Additionally, classical NMF’s optimisation process uses simple multiplicative update rules that preserve non-negativity. Moreover, under assumptions like separability (each latent feature appears in at least one sufficiently “pure” example), NMF guarantees that the recovered decomposition is unique (Donoho & Stodden, 2003). However, classical NMF is not suitable for neural network activations because they can have negative elements, requiring additional preprocessing steps that may distort the activation-space structure."
    ],
    "evaluation": [
-    "Shafran et al. (2025) showed that Semi-NMF can recover features competitive with SAEs on small-scale evaluations, but systematic comparisons at large scale remain limited."
+    "Shafran et al. (2026) showed that Semi-NMF can recover features competitive with SAEs on small-scale evaluations, but systematic comparisons at large scale remain limited."
    ],
    "applications": [],
    "limitations": [
-    "Moreover, NMF does not explicitly encourage sparsity unless additional regularization is introduced, and scaling NMF to large dictionaries is computationally challenging."
+    "NMF does not explicitly encourage feature sparsity, and scaling NMF to large dictionaries is computationally expensive."
    ],
    "related": [
     "sae"
@@ -538,28 +556,24 @@
    "name": "Principal Component Analysis (PCA)",
    "family": "alternative-feature-extraction",
    "sections": [
-    "principle-component-analysis-pca"
+    "principal-component-analysis-pca"
    ],
-   "definition": "Principal Component Analysis decomposes activations by finding an orthonormal basis of directions that maximise explained variance.",
+   "definition": "Principal Component Analysis (PCA; Pearson, 1901; Hotelling, 1933) decomposes activations by finding an orthonormal basis of directions that maximise explained variance.",
    "assumptions": [
-    "In addition, PCA enforces orthogonality between components, a constraint that may not reflect how features are organized in practice, where factors can be correlated or partially overlapping."
+    "PCA is fast, stable, and scalable. However, PCA imposes constraints and inductive biases that may misalign with neural network activations. Specifically, it identifies principal directions that maximise explained variance in the activation distribution, but much of the information of interest is region- or context-dependent and cannot be captured by variance alone. Moreover, PCA constrains the principal directions to be orthogonal, but in practice the underlying features correlate or partially overlap."
    ],
    "evaluation": [
-    "In Wu et al. (2025), PCA underperforms most baselines, including SAEs, on both steering and concept-detection evaluations.",
-    "While in some localization settings, PCA outperform SAEs (Mueller et al., 2025a), it still lags behind simpler supervised baselines."
+    "Recent benchmarks (Wu et al., 2025b) reflect this misalignment: PCA underperformed most baselines, including SAEs, on both steering and concept-detection evaluations. While PCA can outperform SAEs in some localisation settings (Mueller et al., 2025), it still generally lags behind simple supervised baselines."
    ],
    "applications": [
-    "Missing baselines: Claims of interpretability gains rarely compare against simple baselines (e.g., random directions, PCA components, raw neurons)."
-   ],
-   "limitations": [
-    "PCA identifies a single set of global directions that maximises variance across the full activation distribution, which can mix multiple sources of variation when the relevant structure is region or context-dependent."
+    "Users specify baseline methods, such as random directions in activation space, PCA components, individual neurons, and supervised probes (if labels are available)."
    ],
+   "limitations": [],
    "related": [
     "sae"
    ],
    "mentioned_in": [
-    "the-need-for-standardized-evaluation",
-    "integrated-evaluation-protocol"
+    "step-2-establish-baselines."
    ]
   },
   {
@@ -569,16 +583,16 @@
    "sections": [
     "independent-component-analysis-ica"
    ],
-   "definition": "Independent Component Analysis seeks to decompose activations as H = WS, where the components of S are statistically independent (Hyvärinen & Oja, 2000).",
+   "definition": "Independent Component Analysis (ICA; Hyvärinen & Oja, 2000) decomposes activations H \\in \\mathbb{R}^{d \\times n} as H \\approx WS, where n is the number of data points.",
    "assumptions": [
-    "However, ICA makes strong assumptions that are often violated in neural representations.",
-    "Classical ICA assumes a complete setting in which the number of sources equals the dimensionality of the representation, and extensions are required for overcomplete decompositions.",
-    "Unlike SAEs, ICA does not require tuning a sparsity hyperparameter."
+    "In ICA, each column in W \\in \\mathbb{R}^{d \\times m} is a mixing vector, and m is the number of different source signals; each row in S \\in \\mathbb{R}^{m \\times n} is a source signal (a signal has n data points), with m source signals in total. These signals are optimised to be independent of one another. Thus, if model activations are linear mixtures of statistically independent “foundational features”, ICA can provably recover those features."
+   ],
+   "evaluation": [
+    "More recently, however, Liu & Han (2026) showed that ICA can achieve performance comparable to that of SAEs, arguing that its previously reported underperformance may stem from limitations of classical ICA implementations poorly suited to the scale and statistical structure of neural network activations."
    ],
-   "evaluation": [],
    "applications": [],
    "limitations": [
-    "Although ICA has been applied to neural network activations (Hyvärinen et al., 2023), it has not matched the interpretability gains reported for SAEs in recent large-scale studies, possibly because neural features tend to be sparse rather than independent."
+    "Moreover, underlying features may not be statistically independent, which further limits the applicability of classical ICA. Although early studies applied it to analyse neural network activations, they did not find interpretability gains over SAEs (Cunningham et al., 2024)."
    ],
    "related": [
     "sae"
@@ -587,35 +601,28 @@
   },
   {
    "id": "supervised-feature-extraction",
-   "name": "Supervised Feature Extraction Methods (DAS, SMDS, DBM)",
+   "name": "Supervised Feature Extraction Methods (DAS, SMDS, probes)",
    "family": "alternative-feature-extraction",
    "sections": [
-    "supervised-feature-extraction-methods"
-   ],
-   "definition": "When the concepts of interest are known in advance, supervised methods can directly optimize representations aligned with those targets.",
-   "assumptions": [
-    "However, these methods require labelled data for each concept and therefore cannot discover previously unknown features."
-   ],
-   "evaluation": [
-    "When labelled data are available, supervised approaches often outperform SAEs on targeted concept recovery tasks (Huang et al., 2024; Chaudhary & Geiger, 2024)."
+    "subsubsec: supervised_extraction"
    ],
+   "definition": "When the concepts of interest are known in advance, supervised methods can directly optimise representations aligned with those targets.",
+   "assumptions": [],
+   "evaluation": [],
    "applications": [
-    "As a result, supervised methods are best suited to settings where specific, predefined concepts are of interest, such as safety-critical behaviors like toxicity or deception, whereas SAEs remain preferable for exploratory analysis.",
-    "Compare methods: For safety-critical applications, compare SAE features against supervised baselines (DAS, probes) to assess whether SAEs recover the relevant structure."
-   ],
-   "limitations": [
-    "They do not scale naturally to open-ended exploration across entire models, and each concept typically requires separate training."
+    "When labelled data is available, supervised approaches often outperform SAEs on concept detection and steering tasks (Huang et al., 2024; Wu et al., 2025b; Chaudhary & Geiger, 2024). They also avoid choosing dictionary sizes or sparsity penalties, since the concept set determines complexity. However, these methods require labelled data for each concept and cannot discover previously unknown features. Therefore, supervised methods suit settings where predefined, crisp concepts matter, whereas SAEs remain preferable for exploratory analysis.",
+    "Consider alternatives for targeted analysis: When specific concepts are the focus, supervised methods (e.g., DAS, probes, discussed in Section 6.9.4) may be more reliable because SAEs discover features in an unsupervised and exploratory manner."
    ],
+   "limitations": [],
    "related": [
     "sae",
     "probing",
     "steering-vectors"
    ],
    "mentioned_in": [
-    "subsec:sae-selection",
+    "analysis-goal.",
     "practical-recommendations",
-    "best-practices-for-probing-studies",
-    "relation-to-causal-evaluation-of-feature-extraction-methods."
+    "steering-as-a-causal-test-for-feature-interpretability."
    ]
   },
   {
@@ -625,72 +632,68 @@
    "sections": [
     "dictionary-learning-baselines"
    ],
-   "definition": "Classical dictionary learning methods provide useful baselines for evaluating SAEs. Techniques such as K-SVD alternate between sparse coding and dictionary updates and are known to converge under certain conditions (Aharon et al., 2006).",
+   "definition": "Classical dictionary learning methods provide useful baselines for evaluating SAEs. Techniques such as K-SVD alternate between sparse coding and dictionary updates and converge under certain conditions (Aharon et al., 2006).",
    "assumptions": [],
    "evaluation": [
-    "Comparative studies suggest that the primary advantage of SAEs lies in their learned encoder, which enables fast inference, rather than in fundamentally superior dictionary quality (Cunningham et al., 2023)."
+    "Comparative studies suggest that SAEs’ primary advantage is fast inference because the encoder learns to decompose all activations, while potentially sacrificing optimal sparsity (O’Neill et al., 2025)."
    ],
    "applications": [
-    "As such, classical dictionary learning remains an important reference point for assessing the added value of SAE architectures."
+    "As such, classical dictionary learning can still achieve superior dictionary quality in some cases and thus remains an important reference point for assessing the added value of SAEs."
    ],
    "limitations": [],
    "related": [
     "sae"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "the-sparse-coding-problem"
+   ]
   },
   {
    "id": "weight-sparse-transformer",
-   "name": "Weight-Sparse Transformer (Weight Sparsity via L_0 Regularization)",
+   "name": "Weight-Sparse Training (Weight-Sparse Transformer)",
    "family": "architectural-interventions",
    "sections": [
-    "weight-sparsity-via-l_0-regularization",
-    "circuit-discovery-in-weight-sparse-architectures",
-    "circuits-visible-by-inspection."
+    "subsubsec: weight sparse transformer"
    ],
-   "definition": "The most significant recent advancement is the Weight-Sparse Transformer, introduced by Gao et al. (Gao et al., 2025b). This approach treats sparsity not as a tool for inference efficiency but as a tool for circuit discovery.",
+   "definition": "A weight-sparse transformer (Gao et al., 2026) treats weight sparsity as an explicit structural constraint that is progressively strengthened during training.",
    "assumptions": [
-    "Since the L_0 norm is non-differentiable, training employs stochastic gates drawn from a Hard-Concrete distribution, allowing gradients to flow while penalizing the probability of each gate being open.",
-    "Furthermore, this approach requires training sparse models from scratch or using Bridge proxies—it cannot be directly applied to existing dense models."
+    "Gao et al. (2026) optimise the standard cross-entropy objective along with an L_0 constraint on model weights after each optimisation step. Specifically, within each weight matrix, they retain only the largest-magnitude parameters and set the rest to zero. The L_0 constraint anneals from a dense initialisation to the desired sparsity level over training."
    ],
    "evaluation": [
-    "Validation via mean ablation shows that these sparse circuits satisfy necessity and sufficiency criteria with higher precision than circuits identified in dense models.",
-    "Empirically, sparse circuits satisfy necessity and sufficiency criteria with higher precision than circuits extracted from dense models via post-hoc methods (Gao et al., 2025b; Haider et al., 2025)."
+    "Mean-ablation experiments further confirm that these circuits are both necessary and sufficient for the capability or behaviour."
    ],
    "applications": [
-    "Circuits can be identified by simply visualizing non-zero weights connected to a target output, eliminating the need for activation patching or ACDC."
+    "The resulting transformer has most weight parameters exactly zero, making the remaining computational structure much easier to decompose into small, task-specific circuits."
    ],
    "limitations": [
-    "Weight sparsity does not eliminate all challenges.",
-    "Circuits may still be compositional (requiring understanding of how subcircuits interact), and the interpretability of individual neurons depends on training dynamics.",
-    "Retraining frontier models with sparsity constraints is computationally prohibitive."
+    "Increasing sparsity generally makes the model easier to decompose into small, interpretable circuits. However, it also degrades task performance, producing a capability–interpretability Pareto frontier.",
+    "At comparable task performance, larger sparse models can have substantially smaller, more interpretable circuits than smaller models; at comparable interpretability, they can retain stronger capabilities. This suggests that scaling model size alongside structural sparsity may offer a path to more interpretable yet high-capability models. However, preserving interpretability beyond tens of millions of nonzero parameters remains challenging, and weight-sparse training currently requires substantially more compute than traditional training."
    ],
    "related": [
-    "bridge",
     "activation-patching",
     "path-patching",
     "acdc",
     "solu"
    ],
    "mentioned_in": [
-    "architectural-responses-to-superposition",
-    "architectural-interventions-as-a-synthesis.",
-    "implications-for-verification."
+    "architectural-responses-to-superposition.",
+    "subsubsec: solu",
+    "the-road-ahead"
    ]
   },
   {
    "id": "spartan",
-   "name": "Sparse Attention Post-Training (SPARTAN)",
+   "name": "Attention-Sparse Training (SPARTAN)",
    "family": "architectural-interventions",
    "sections": [
-    "sparse-attention-post-training-spartan"
+    "subsubsec: spartan"
    ],
-   "definition": "Sparse Attention Post-Training (SPARTAN) applies L_1 regularization to the attention patterns while finetuning with a constrained optimization objective to maintain the same cross-entropy performance (Draye et al., 2025) (Lei et al., 2025).",
+   "definition": "Previous studies have therefore explored attention-sparse training, which aims to sparsify attention patterns to make them more interpretable (Lei et al., 2025; Draye et al., 2026). Specifically, Lei et al. (2025) proposed SPARse TrANsformer World model (SPARTAN), an object-centric world model trained with L_0 regularisation on attention patterns and a constrained optimisation objective to maintain cross-entropy performance equal to that of a dense counterpart.",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "This reveals that much attention connectivity is redundant noise, and SPARTAN effectively performs circuit discovery by pruning the attention graph.",
-    "Using Cross-Layer Transcoders, they show that the reduced number of mediating components renders attention attribution tractable."
+    "After training (either from scratch or via fine-tuning), each attention head attends only to specific tokens, and the model performance remains close to the original. This result suggests that many attention patterns may be redundant noise.",
+    "In CLT-based circuit discovery (see Section 7.2), Draye et al. (2026) showed that sparsified attention patterns reduce the number of mediating components involved in feature-to-feature attribution, making the resulting attribution graphs easier to analyse."
    ],
    "limitations": [],
    "related": [
@@ -703,46 +706,39 @@
    "name": "Softmax Linear Units (SoLU)",
    "family": "architectural-interventions",
    "sections": [
-    "activation-level-interventions-solu-and-its-limitations",
-    "results-and-the-hydraulic-effect."
-   ],
-   "definition": "Softmax Linear Units (SoLU) replace element-wise activations with a spatial softmax (Elhage et al., 2022b)",
-   "assumptions": [
-    "This forces neurons within a layer to compete: if one neuron fires strongly, it suppresses its neighbors through lateral inhibition."
-   ],
-   "evaluation": [
-    "SoLU successfully increased the fraction of monosemantic neurons identifiable by human inspection."
+    "subsubsec: solu"
    ],
+   "definition": "Softmax linear units (SoLU; Elhage et al., 2022b) are an earlier attempt at architectural intervention. SoLU is a specialised activation function that applies a softmax-based transformation across the layer’s d_{\\mathrm{mlp}} neurons.",
+   "assumptions": [],
+   "evaluation": [],
    "applications": [],
    "limitations": [
-    "However, it revealed a critical limitation: the hydraulic effect.",
-    "When prevented from using neurons for superposition, models simply displaced the compression elsewhere—into the residual stream, into sub-threshold activations, or into other components not subject to the constraint.",
-    "This indicates that local architectural constraints are insufficient when global incentives for compression remain."
+    "When one neuron receives a large pre-activation value, the softmax suppresses the others’ values, analogous to lateral inhibition. This competition encourages certain neurons to specialise and dominate, increasing the prevalence of monosemantic neurons associated with human-interpretable concepts. However, it also reveals an undesirable phenomenon: although many secondary neurons’ activations are suppressed to low magnitudes, these neurons remain polysemantic, with many features compressed into them. Later layer normalisation (Ba et al., 2016) can amplify them and recover the polysemanticity property (Elhage et al., 2022b).",
+    "The “smuggling” by layer normalisation suggests that imposing local interpretability constraints may not eliminate neural networks’ incentive to compress information through superposition."
    ],
    "related": [
     "weight-sparse-transformer"
    ],
    "mentioned_in": [
-    "architectural-responses-to-superposition"
+    "architectural-responses-to-superposition.",
+    "the-road-ahead"
    ]
   },
   {
    "id": "molt",
-   "name": "Sparse Mixtures of Linear Transforms (MOLT)",
+   "name": "Sparse Mixture of Linear Transforms (MOLT)",
    "family": "architectural-interventions",
    "sections": [
-    "computational-primitive-modifications-molt",
-    "motivation.",
-    "mechanism.-1"
+    "subsubsec: molt"
    ],
-   "definition": "One recent example of this is the so-called Sparse Mixtures of Linear Transforms (MOLT), representing a conceptual shift from feature extraction to transformation extraction (Lindsey et al., 2025b).",
+   "definition": "A recent example is the sparse mixture of linear transforms (MOLT), which represents a conceptual shift from feature extraction towards transformation extraction (Lindsey et al., 2025b).",
    "assumptions": [
-    "MOLT replaces the MLP with a sparse mixture of linear transformation matrices."
+    "To achieve this, MOLT replaces the MLP with a sparse mixture of learned linear transformation matrices. The basic unit of analysis becomes transformations applied to model activations rather than features."
    ],
    "evaluation": [],
    "applications": [
-    "For modular arithmetic, a MOLT might learn a single rotation matrix implementing addition, rather than memorizing thousands of input-output pairs.",
-    "This aligns the architecture with the true algorithmic structure of the task, yielding higher mechanistic faithfulness than standard feature-based decompositions for tasks involving systematic transformations."
+    "The basic unit of analysis becomes transformations applied to model activations rather than features. For example, in a modular arithmetic task, MOLT can represent the concept of addition with a learned rotation matrix, which relates more directly to the underlying algorithm than a collection of state-specific features.",
+    "More broadly, approaches that represent computational operations explicitly, as MOLT does, may facilitate the development of interpretable representations for tasks governed by systematic operations."
    ],
    "limitations": [],
    "related": [
@@ -750,55 +746,30 @@
     "transcoders"
    ],
    "mentioned_in": [
-    "architectural-responses-to-superposition"
+    "architectural-responses-to-superposition."
    ]
   },
   {
    "id": "kans",
-   "name": "Kolmogorov-Arnold Networks (KANs)",
+   "name": "Kolmogorov–Arnold Networks (KANs)",
    "family": "architectural-interventions",
    "sections": [
-    "kolmogorov-arnold-networks"
+    "subsubsec: kan"
    ],
-   "definition": "Based on the Kolmogorov-Arnold representation theorem, KANs place learnable activation functions (B-splines) on edges rather than fixed activations on nodes.",
+   "definition": "Inspired by the Kolmogorov–Arnold representation theorem, KANs place learnable nonlinear functions, typically parameterised using B-splines, on edges rather than using fixed nonlinear activation functions at nodes.",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "Because non-linearities are univariate and visible on edges, researchers can perform symbolic regression on learned splines to recover exact mathematical formulas—for instance, rediscovering physics equations from data."
+    "As the nonlinearities are univariate and can be visualised directly on individual edges, researchers can use techniques like symbolic regression to approximate and inspect the learned analytical expressions, such as rediscovering known physical equations."
    ],
    "limitations": [
-    "While currently slower to train and unproven at LLM scale, KANs represent the ambitious ideal of a model where weights are interpretable mathematical functions."
+    "Although KAN training is currently more computationally expensive than training standard MLPs and has not yet been convincingly demonstrated at the scale of large language models, this architectural innovation points to an ambitious direction towards interpretable neural networks in which parameters correspond to mathematically meaningful functions."
    ],
    "related": [],
    "mentioned_in": [
-    "architectural-responses-to-superposition"
-   ]
-  },
-  {
-   "id": "bridge",
-   "name": "The Bridge Methodology",
-   "family": "architectural-interventions",
-   "sections": [
-    "the-bridge-methodology-interpretable-proxies-for-dense-models"
-   ],
-   "definition": "A Bridge is a weight-sparse model trained to mimic the internal representations of a pretrained dense model—minimizing reconstruction error of layer-wise activations rather than training on the original task.",
-   "assumptions": [],
-   "evaluation": [
-    "Preliminary evidence suggests that interventions on sparse proxies (e.g., ablating identified circuits) can be mapped back to induce similar behavioral changes in dense models.",
-    "One approach is causal correspondence testing, which verifies that ablating a circuit in the proxy and the corresponding components in the dense model produces matched behavioral changes across different inputs."
-   ],
-   "applications": [
-    "Retraining frontier models with sparsity constraints is computationally prohibitive."
-   ],
-   "limitations": [
-    "However, validating proxy fidelity—ensuring the proxy captures the same mechanisms rather than behaviorally equivalent simulations—remains an open problem (see Section 15)."
-   ],
-   "related": [
-    "weight-sparse-transformer"
-   ],
-   "mentioned_in": [
-    "sec:proxy-fidelity",
-    "potential-approaches."
+    "architectural-responses-to-superposition.",
+    "subsubsec: molt",
+    "the-road-ahead"
    ]
   },
   {
@@ -806,20 +777,19 @@
    "name": "Activation Patching",
    "family": "circuits",
    "sections": [
-    "sec:circuit-table"
+    "sec:circuit-discovery-methods"
    ],
-   "definition": "Activation patching evaluates the causal contribution of a component or set of components S by replacing their activations with baseline values",
+   "definition": "Activation patching tests the causality of a component’s representations by swapping them between inputs that differ in the target property.",
    "assumptions": [],
    "evaluation": [
-    "Perform activation patching: swap C between clean and corrupted inputs; verify behavior follows C."
+    "The resulting change in model output quantifies the causal effect of intervening on S (Zhang & Nanda, 2024)."
    ],
    "applications": [
-    "This identifies which components are functionally necessary for the computation (Zhang & Nanda, 2024).",
-    "A probe might detect deceptive reasoning, but confirming the finding is causal, and designing targeted interventions, requires causal intervention on internal representations—whether through activation patching, steering, or ablation.",
-    "Causal spot-checks: Randomly sample agentic explanations and verify via causal interventions, such as activation patching."
+    "This technique has also been used to test the causality of probes (Huang et al., 2024; Arora et al., 2024).",
+    "The pipeline can also additionally examine a fraction of explanations by causal techniques, such as activation patching, and track failure rates."
    ],
    "limitations": [
-    "Confounds correlation with causation"
+    "Requires many interventions; component-level granularity can be coarse"
    ],
    "related": [
     "path-patching",
@@ -828,11 +798,11 @@
    ],
    "mentioned_in": [
     "sec:scope",
-    "when-mechanistic-understanding-becomes-necessary",
-    "dimension-1-faithfulness",
-    "proposed-guardrails-for-agentic-interpretability",
-    "proposed-guardrails-for-automated-interpretability",
-    "potential-approaches."
+    "causal-probing-via-activation-patching.",
+    "subsec:five eval dim",
+    "layer-2-consistency-and-causality-checks.",
+    "sec:non-transformer",
+    "choosing-appropriate-methods-for-different-safety-applications"
    ]
   },
   {
@@ -840,73 +810,84 @@
    "name": "Path Patching",
    "family": "circuits",
    "sections": [
-    "sec:circuit-table"
+    "sec:circuit-discovery-methods"
    ],
-   "definition": "Path patching extends attribution to specific paths or edges in the computation graph.",
+   "definition": "Path patching extends activation patching on model components to specific edges in the computational graph.",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "This allows finer-grained causal localization along information flows (Goldowsky-Dill et al., 2023)."
+    "This allows finer-grained causal localisation along information flows (Goldowsky-Dill et al., 2023)."
    ],
    "limitations": [
-    "Computationally expensive due to large number of paths; still approximate"
+    "Computationally expensive due to the large number of candidate paths"
    ],
    "related": [
     "activation-patching",
     "acdc",
     "weight-sparse-transformer"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "subsec:five eval dim"
+   ]
   },
   {
    "id": "acdc",
    "name": "ACDC",
    "family": "circuits",
    "sections": [
-    "sec:circuit-table"
+    "sec:circuit-discovery-methods"
    ],
-   "definition": "ACDC identifies minimal sufficient sets of components S \\subseteq \\text{Graph}(\\text{model}) that reproduce the model’s behavior.",
+   "definition": "ACDC (Conmy et al., 2023) discovers a circuit by iteratively using path patching to identify important connections in the model’s computational graph. Traversing the graph in reverse topological order from the output node, it patches the activation transmitted along each edge and measures the resulting change in a task-specific metric.",
    "assumptions": [],
    "evaluation": [
-    "Scales better due to automation; has been proved to recover known circuits"
+    "Fully automated; directly searches for a behaviourally sufficient subgraph"
    ],
    "applications": [],
    "limitations": [
-    "Computationally expensive as it greedily prunes paths; misses subtle components"
+    "Computationally expensive due to sequential edge pruning; sensitive to pruning order and threshold choices"
    ],
    "related": [
     "activation-patching",
     "path-patching",
     "weight-sparse-transformer"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "sec:evaluation-framework",
+    "summary.-4"
+   ]
   },
   {
    "id": "circuit-tracing",
-   "name": "Circuit Tracing (SAE- or CLT-Integrated circuit discovery)",
+   "name": "Circuit Tracing (SAE- or CLT-Based Circuit Discovery)",
    "family": "circuits",
    "sections": [
-    "subsec:circuits",
-    "sec:circuit-table",
-    "replacement-models-and-attribution-graphs."
+    "sec:circuit-replacement"
    ],
-   "definition": "The second line of work, often referred to as circuit tracing, focuses on how internal features interact and propagate across the computational graph to lead to the model decision of a particular input query (Ameisen et al., 2025; Wu & Barez, 2025).",
+   "definition": "A complementary approach first constructs a more interpretable replacement model (Section 6.6.2) that approximates the original model’s computation using sparse linear features, and then performs circuit discovery within this replacement model.",
    "assumptions": [
-    "Studies of this line usually make use of sparse autoencoders (detailed in Section 6) to automatically decompose mlp outputs into interpretable units, tracing attributions between these features and how the information flow converges to the final prediction."
+    "Recent work has implemented this paradigm using cross-layer transcoders (CLTs; Section 6.6.2) (Ameisen et al., 2025; Merullo et al., 2025). In particular, Circuit Tracer (Ameisen et al., 2025) replaces the model’s MLPs with CLTs while retaining the underlying model’s attention and residual-stream computation. This yields a prompt-specific attribution graph whose principal internal nodes are sparse CLT features rather than entire MLP blocks or attention heads."
    ],
    "evaluation": [],
    "applications": [
-    "Stage 2 – Circuit Tracing: We construct a computational graph in which nodes correspond to activated SAE features and edges represent their inter-attributions (Marks et al., 2025a)."
+    "For a given prompt, Circuit Tracer produces a sparse attribution graph that describes active features and their interactions, making it well suited to query-level circuit discovery.",
+    "Using automated feature-interpretation pipelines (Section 12.6) to assign natural-language descriptions to these features, human inspectors can more easily understand the discovered mechanism.",
+    "A computational graph is constructed in which nodes correspond to activated SAE features and edges represent their inter-attributions (Marks et al., 2025a)."
    ],
    "limitations": [
-    "Depends on feature quality; intensive computation to train SAEs/CLTs"
+    "Interpretability platforms such as Neuronpedia (Lin, 2023) also offer interactive implementations. However, the approach still has several limitations. Specifically, CLTs only approximate MLP computations, so the discovered circuit may only cover a part of the model’s underlying mechanism. Moreover, feature-level attribution graphs can have many nodes and edges, so we must prune, cluster, or otherwise condense them before they become understandable.",
+    "Depends on feature quality; requires additional computation to train and analyse SAEs/CLTs"
    ],
    "related": [
     "sae",
     "cross-layer-transcoders"
    ],
    "mentioned_in": [
-    "mechanistic-monitoring"
+    "sec:circuit-discovery-methods",
+    "replacement-models-and-attribution-graphs.",
+    "sec:evaluation-framework",
+    "subsubsec: mech monitor",
+    "stage-2-circuit-discovery.",
+    "the-scalability-bottleneck."
    ]
   },
   {
@@ -915,26 +896,27 @@
    "family": "circuits",
    "sections": [
     "evaluating-circuits-and-benchmarking-discovery-methods.",
-    "causal-necessity-and-sufficiency.",
-    "dimension-4-causal-validity"
+    "causal-necessity-and-sufficiency."
    ],
-   "definition": "Causal scrubbing evaluates necessity by replacing circuit activations with corrupted or neutralized values and measuring the resulting change in model outputs.",
+   "definition": "Causal scrubbing (Chan et al., 2022a) systematically evaluates a mechanistic hypothesis by replacing model activations with those from inputs deemed equivalent under the hypothesis and testing whether these interventions preserve the model behaviour.",
    "assumptions": [],
    "evaluation": [
-    "Two widely used high-level methods for testing these properties are causal scrubbing and interchange interventions (Heimersheim & Nanda, 2024; Chan et al., 2022)."
+    "Two popular approaches to validate causality are causal scrubbing and interchange interventions (Chan et al., 2022a; Geiger et al., 2024).",
+    "Together, these methods provide stronger evidence for mechanistic explanations than observational analysis alone."
    ],
    "applications": [
-    "Circuit faithfulness: proving circuits are necessary and sufficient for explaining behavior via causal intervention (e.g., causal scrubbing), without performance degradation."
+    "Circuit faithfulness: demonstrating circuits are necessary and sufficient for explaining behaviour via causal intervention (e.g., causal scrubbing (Chan et al., 2022a)), without performance degradation."
    ],
    "limitations": [
-    "Strict necessity and sufficiency are often unattainable.",
-    "Additionally, verification assumes the circuit mechanism is faithfully captured by the identified components, but if features are entangled or nonlinear interactions are present, the measured circuit may not reflect true causal structure."
+    "By aggregating faithfulness scores across tasks and settings, benchmarks such as MIB (Mueller et al., 2025) evaluate how well circuit discovery methods identify causally important model components. However, achieving strict necessity and sufficiency is often hard because many model behaviours depend on distributed representations and multiple redundant pathways (Barez et al., 2026).",
+    "Even if we identify a circuit that captures the primary mechanism of a behaviour, components outside the circuit may still play important roles, such as handling edge cases and computational robustness."
    ],
    "related": [
     "interchange-interventions"
    ],
    "mentioned_in": [
-    "ambitious-interpretability-toward-complete-mechanistic-understanding"
+    "sec:causal-verification",
+    "subsec:ambitious"
    ]
   },
   {
@@ -942,26 +924,29 @@
    "name": "Interchange Interventions",
    "family": "circuits",
    "sections": [
-    "evaluating-circuits-and-benchmarking-discovery-methods.",
-    "causal-probing-via-interchange-interventions."
+    "evaluating-circuits-and-benchmarking-discovery-methods."
    ],
-   "definition": "Interchange interventions test sufficiency by swapping circuit components between clean and corrupted forward passes and assessing whether the target behavior consistently tracks the substituted circuit across inputs.",
+   "definition": "Interchange interventions (Geiger et al., 2024) test whether model activations causally correspond to higher-level variables by substituting activations across inputs and measuring the resulting change in model behaviour.",
    "assumptions": [],
-   "evaluation": [],
-   "applications": [
-    "Test causal relevance: Complement probing with amnesic probing or interchange interventions."
+   "evaluation": [
+    "Interchange test: Swap activations of S between inputs representing different behaviours and test whether the model output follows the intervention.",
+    "Tested via interchange interventions: in the corrupted run with a reference input containing no target information, patch the activation of S with that from a clean run using an input containing the target information, and then verify whether the behaviour emerges."
    ],
-   "limitations": [
-    "This provides stronger evidence than amnesic probing but requires identifying the specific representation component, not just demonstrating its existence."
+   "applications": [
+    "One promising direction is interchange intervention testing and training (Geiger et al., 2024; 2022). This technique was developed to compare and align a neural network with a high-level causal abstraction."
    ],
+   "limitations": [],
    "related": [
     "causal-scrubbing",
     "probing",
     "amnesic-probing"
    ],
    "mentioned_in": [
-    "dimension-1-faithfulness",
-    "dimension-4-causal-validity"
+    "subsec:five eval dim",
+    "evaluation-protocol-3",
+    "sufficiency",
+    "potential-approaches.",
+    "causal-probing-via-activation-patching."
    ]
   },
   {
@@ -974,15 +959,15 @@
    ],
    "definition": "Logit lens (nostalgebraist, 2020) applies the model’s unembedding matrix to activations at intermediate layers, revealing what the model would predict if computation stopped at that layer.",
    "assumptions": [
-    "Applying the unembedding matrix to intermediate activations assumes that the hidden state is in a format the output layer expects, but intermediate representations may reside in vector spaces different from final activations."
+    "The vanilla logit lens has a critical limitation: applying the unembedding matrix to intermediate activations assumes that the hidden state is in a format that the output layer expects. However, intermediate representations may be organised very differently from output-layer activations."
    ],
    "evaluation": [],
    "applications": [
-    "By examining this trajectory, researchers can identify at which layer critical decisions emerge: whether the model commits early to a prediction or progressively refines it layer-by-layer.",
-    "In this sense, logit lens can still reveal contributions to potentially harmful tokens and track how these emerge through the network, similarly to refusal vectors."
+    "Applying it across layers reveals the prediction trajectory, showing when and where the model commits to specific outputs (nostalgebraist, 2020).",
+    "Logit lens (Figure 10) provides intuitive insight into the model’s decision-making process across depth."
    ],
    "limitations": [
-    "The unembedding matrix is only trained to operate on the final residual stream after all transformations; applying it to layer-6 activations extracts relevant information but may not precisely reflect the model’s underlying computation at that layer (nostalgebraist, 2020)."
+    "Applying the unembedding matrix to layer-6 activations may extract relevant information but cannot precisely reflect the model’s underlying computation at that layer (nostalgebraist, 2020). Furthermore, logit lens is fundamentally correlational rather than causal. It projects the activation into the output vocabulary space without testing the roles decoded tokens play in model computation. In particular, because of the representational mismatch discussed above, decoded tokens represent at most information recoverable from the activation, rather than information used in model computation."
    ],
    "related": [
     "tuned-lens",
@@ -990,7 +975,11 @@
     "steering-vectors"
    ],
    "mentioned_in": [
-    "case-study-refusal-vectors-and-sleeper-agents"
+    "subsec:tuned-lens",
+    "jacobian-lens-decoding-computational-dispositions",
+    "subsec:patchscopes",
+    "summary.-5",
+    "sec:ld-features"
    ]
   },
   {
@@ -998,24 +987,31 @@
    "name": "Tuned Lens",
    "family": "intermediate-computation",
    "sections": [
-    "tuned-lens-learning-per-layer-transform"
-   ],
-   "definition": "Tuned lens (Belrose et al., 2025) addresses the vector space mismatch problem of logit lens by learning per-layer affine transformations that map intermediate activations to the output layer’s expected space",
-   "assumptions": [
-    "The trade-off is that tuned lens requires training additional parameters (one affine transform per layer), but this is computationally cheap compared to other mechanistic methods."
+    "subsec:tuned-lens",
+    "limitations-of-tuned-lens."
    ],
+   "definition": "Tuned lens (Belrose et al., 2025) addresses logit lens’ concern of representational mismatch by learning per-layer affine transformations that map intermediate activations to the output layer’s activation space",
+   "assumptions": [],
    "evaluation": [],
    "applications": [
-    "Tuned lens improves on basic logit lens by correcting for the fact that different layers operate in different representational spaces."
+    "As a result, the unembedding matrix can extract relevant information from each layer’s activations more effectively."
    ],
    "limitations": [
-    "Because the mapping from intermediate representations to natural language may be too complex to be captured by an affine transformation, Patchscopes extends the paradigm of the logit lens and tuned lens by delegating the representation-to-text transformation to the language model itself"
+    "First, the decoding paradigm remains inherently correlational rather than causal; decodable information does not imply a causal role in the model’s prediction.",
+    "Second, similar to logit lens, tuned lens is primarily designed to answer what the model is likely to predict rather than what the model is internally computing.",
+    "Empirical evidence also shows that it skips intermediate computation more aggressively than logit lens (Gurnee et al., 2026)."
    ],
    "related": [
     "logit-lens",
     "patchscopes"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "jacobian-lens-decoding-computational-dispositions",
+    "subsec:patchscopes",
+    "summary.-5",
+    "kl-divergence.",
+    "sec:non-transformer"
+   ]
   },
   {
    "id": "patchscopes",
@@ -1025,22 +1021,27 @@
     "subsec:patchscopes",
     "limitations-of-patchscopes."
    ],
-   "definition": "Patchscopes (Ghandeharioun et al., 2024) leverages the expressive capacity of large language models to translate internal representations into natural-language explanations.",
+   "definition": "Patchscopes (Ghandeharioun et al., 2024) leverages the expressive capacity of language models to translate their internal representations into natural-language explanations.",
    "assumptions": [
-    "Crucially, this approach relies on the model’s own learned and generalized text-generation capabilities to perform the transformation from internal activations to natural language."
+    "In this view, the language model acts as an activation decoder. Crucially, Patchscopes relies on the model’s learned, generalisable text-generation capabilities to map internal activations to natural language."
    ],
    "evaluation": [],
-   "applications": [],
+   "applications": [
+    "By observing changes in model output, users can examine what information the representation contains."
+   ],
    "limitations": [
-    "A key limitation of Patchscopes is that injecting an external representation into an elicitation prompt can shift the model’s internal activations into an OOD regime.",
-    "Although the model may still successfully surface salient information from the patched representation, the decoding process can be degraded, and critical aspects of the original representation may be distorted or lost."
+    "A key limitation of Patchscopes is that injecting an external representation into an elicitation prompt can shift the model’s internal activations into an out-of-distribution (OOD) regime.",
+    "Although the model may still surface salient information from the patched representation, the decoding process can degrade, and critical aspects of the original representation may be distorted or lost."
    ],
    "related": [
     "logit-lens",
     "tuned-lens",
     "latentqa"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "subsec:latentqa",
+    "summary.-5"
+   ]
   },
   {
    "id": "latentqa",
@@ -1050,27 +1051,29 @@
     "subsec:latentqa",
     "limitations-of-latentqa."
    ],
-   "definition": "LatentQA (Pan et al., 2024) formulates representation decoding as a supervised learning problem via Latent Interpretation Tuning (LIT), which fine-tunes an activation decoder to perform representation-to-text decoding.",
+   "definition": "LatentQA (Pan et al., 2026) frames representation decoding as a supervised learning problem via Latent Interpretation Tuning (LIT), fine-tuning an activation verbaliser to perform representation-to-text verbalisation.",
    "assumptions": [
-    "Both the control prompt and the (q, a) pair are generated by prompting an off-the-shelf proprietary language model (Pan et al., 2024).",
-    "Through LIT, the decoder is trained to directly ingest and process raw activation vectors."
+    "Through LIT, the verbaliser learns to ingest and process raw activation vectors directly."
+   ],
+   "evaluation": [
+    "Another technical challenge is evaluating whether activation-verbalisation models genuinely explain the represented information rather than exploit shortcuts."
    ],
-   "evaluation": [],
    "applications": [
-    "LatentQA addresses the OOD issue inherent in Patchscopes-style methods by explicitly finetuning the activation decoder on the representation-to-text decoding task.",
-    "Activation conditioning: Methods like LatentQA that condition on actual intermediate activations (rather than relying on the model to “remember” its computation) show improved faithfulness over pure prompting (Pan et al., 2024)."
+    "LatentQA (Figure 12) addresses the OOD issue inherent in Patchscopes-style methods by explicitly fine-tuning the activation verbaliser on the representation-to-text decoding task.",
+    "The process involves (1) curating a dataset of activation–question–answer triplets from a target LLM, and (2) fine-tuning a verbaliser to retrieve specific information latent within those activations, such as the model’s underlying goals or persona."
    ],
    "limitations": [
-    "The central limitation of LatentQA lies in the generalizability of the learned representation-to-text decoder.",
-    "When the finetuning dataset D is insufficiently large or diverse, the decoder may overfit by memorizing superficial correspondences between representations and textual descriptions, rather than learning the underlying algorithmic transformation."
+    "LatentQA faces several technical challenges, including the generalisability of the learned representation-to-text mapping.",
+    "When the fine-tuning dataset D is too small or insufficiently diverse, the verbaliser may memorise superficial correspondences between representations and explanations rather than learning to extract information from representations.",
+    "Recent studies have found that such failure modes are severe and prevalent in existing verbalisers (Li et al., 2026c; Jakkli et al., 2026). As a result, strong verbalisation performance alone is insufficient to show that the verbaliser faithfully analysed the internal representations."
    ],
    "related": [
     "patchscopes"
    ],
    "mentioned_in": [
-    "empirical-evidence.-1",
-    "elicitation-protocols-to-reduce-confabulation.",
-    "comparative-analysis-trade-offs-between-orientations"
+    "summary.-5",
+    "comparative-analysis-trade-offs-between-orientations",
+    "the-road-ahead"
    ]
   },
   {
@@ -1078,25 +1081,27 @@
    "name": "Amnesic Probing",
    "family": "intermediate-computation",
    "sections": [
-    "amnesic-probing-and-causal-probing",
+    "subsubsec: amnesic probing",
     "amnesic-probing."
    ],
-   "definition": "Building on this lineage, Elazar et al. (2021) introduced amnesic probing: after identifying a direction encoding property P, project it out of the representation and measure downstream task degradation.",
+   "definition": "Given a model activation h and a probe trained to predict a property P, amnesic probing tests whether information about P is causally relevant to downstream model behaviour by removing information about P from the representation and evaluating the effects on model outputs.",
    "assumptions": [],
-   "evaluation": [
-    "If performance degrades, the information was causally used. If performance is unchanged, the information was encoded but unused."
+   "evaluation": [],
+   "applications": [
+    "Elazar et al. (2021) further measured changes in downstream model behaviour after applying INLP as a validation of the probe’s causality, called amnesic probing. These paradigms add causality to probes, pushing them from correlational understanding to mechanistic analysis."
    ],
-   "applications": [],
    "limitations": [
-    "Limitations: Amnesic probing assumes the property is encoded in a single linear direction.",
-    "If encoding is distributed or nonlinear, projection may not fully remove the information, leading to false negatives (concluding information is unused when it is)."
+    "A substantial degradation indicates that the removed information causally contributes to the evaluated behaviour. Notably, unchanged performance does not establish that the information is unused since redundant representations or alternative computational pathways may preserve the behaviour.",
+    "Amnesic probing assumes the target property is decodable and that there exist effective erasure procedures to surgically remove its associated information from the representation. However, information and concept erasure may be imperfect in practice, either leaving residual information or affecting other concepts. Such imperfections confound interpretations of downstream behavioural changes and the causal claims that follow."
    ],
    "related": [
     "probing",
     "interchange-interventions"
    ],
    "mentioned_in": [
-    "best-practices-for-probing-studies"
+    "causal-probing-via-activation-patching.",
+    "methodological-standards-in-probing-research",
+    "subsec:cbm"
    ]
   },
   {
@@ -1105,145 +1110,139 @@
    "family": "training-data-attribution",
    "sections": [
     "verbatim-data-attribution-olmotrace-and-maximal-matching-spans",
-    "limitations"
+    "limitations-as-a-mechanistic-interpretability-tool."
    ],
-   "definition": "Verbatim data attribution targets explicit memorization by identifying exact or near-exact matches between model outputs and training data, scaling this search across trillions of tokens via suffix arrays and infinite n-gram models.",
+   "definition": "Verbatim data attribution detects explicit memorisation by identifying exact or near-exact string matches between model outputs and the training corpus. These searches can scale to trillions of tokens using techniques such as suffix arrays and infini-gram models.",
    "assumptions": [
-    "OLMoTrace scales this to trillions of tokens using a combination of FM-index, infini-gram models, and maximal matching span extraction (Liu et al., 2025a; 2024; Xu et al., 2025), making it less a data attribution method in the influence-function sense and more a retrieval tool for identifying memorized content."
+    "OLMoTrace (Liu et al., 2025a) scales this to trillions of tokens using a combination of suffix arrays, infini-gram models, and maximal matching span extraction (Liu et al., 2024a), making it less a data-attribution method in the influence-function sense and more a retrieval tool for identifying content the model may have memorised.",
+    "OLMoTrace identifies maximal matching spans by combining suffix-array search with infini-gram queries, adjusting boundaries for tokenisation, and retaining only the longest non-overlapping spans."
    ],
    "evaluation": [],
    "applications": [
-    "This process links LM outputs directly to the training corpus, enabling precise attribution of memorized content."
+    "“Eurovision Song Contest with the song ’Ne” This process links language model output directly to the training corpus, enabling identification and attribution of memorised content.",
+    "UTF8gbsnSpan: “她喜欢阅读关于科技、心理学和历史的书籍，业余时间还会参加编程课”—matched in post-training (instruction-tuning) data This example demonstrates how OLMoTrace traces multilingual outputs across diverse training sources, linking model generations directly to their memorised origins in pretraining and post-training datasets."
    ],
    "limitations": [
-    "Verbatim attribution excels at factual memorization but struggles with compositional or synthesized outputs.",
-    "For tasks like translation, poetry, or reasoning, OLMoTrace can identify spans of text that appear in the training data, but these matches do not explain how the model combined information from multiple sources to produce the output (Park et al., 2023)."
+    "Verbatim attribution excels at factual memorisation but struggles with compositional or synthesised outputs.",
+    "For tasks like translation, poetry, or reasoning, OLMoTrace (and other verbatim matching methods) can identify spans of text that appear in the training data, but these matches do not explain how the model combined information from multiple sources to produce the output."
    ],
    "related": [
     "influence-functions"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "sec:influence-functions",
+    "olmotrace-influential-samples-identified",
+    "summary.-8"
+   ]
   },
   {
    "id": "influence-functions",
    "name": "Influence Functions (Gradient-Based Attribution)",
    "family": "training-data-attribution",
    "sections": [
+    "gradient-based-attribution-understanding-data-influence-through-perturbations",
     "core-idea-parameter-response-to-training-data",
-    "gradient-based-attribution-understanding-influence-through-data-perturbations",
-    "proximal-bregman-response-and-the-gauss-newton-hessian",
-    "computational-bottleneck-approximating-ggn-vector-products",
+    "proximal-bregman-response-and-the-generalised-gaussnewton",
+    "addressing-the-computational-and-memory-bottlenecks-of-ggn",
     "illustrative-example",
     "recent-extensions-and-limitations"
    ],
-   "definition": "Influence functions ask a counterfactual question: how would a model’s learned parameters change if we upweighted a training example (Koh & Liang, 2017)? Instead of expensive retraining, we estimate this via implicit differentiation (Grosse et al., 2023).",
+   "definition": "Influence functions estimate how a model’s parameters and predictions would change if a training sample were slightly upweighted (Koh & Liang, 2017; Grosse et al., 2023).",
    "assumptions": [
-    "The implicit function theorem assumes exact convergence and infinite perturbations.",
-    "Real neural networks violate both: training reaches saddle points, not true minima, and we retrain for only a few steps.",
-    "This equivalence allows practitioners to use Fisher information as a practical substitute: it’s more stable to compute and estimate from data than the true GGN."
+    "Empirical results show that the original form of influence functions introduced above performs poorly in practice. This is due to multiple assumptions that rarely hold in practice.",
+    "One classical approach is called the proximal Bregman response function (PBRF) (Bae et al., 2022). It targets model parameters which minimise the new training objective while staying close to the original parameters.",
+    "Although K-FAC approximates the FIM rather than the GGN, these matrices are equivalent for common loss functions under suitable probabilistic assumptions."
    ],
    "evaluation": [
-    "Observation: Influence functions successfully identify training documents that impacted the translation, even without exact verbatim matches."
+    "Influence functions identify training documents that impacted the translation, even without exact verbatim matches. This demonstrates that gradient-based methods can capture influences beyond what verbatim methods can reveal.",
+    "Bae et al. (2022) confirmed empirically that such influence estimates align well with the actual PBRF."
    ],
    "applications": [
-    "Training data attribution (TDA) answers a fundamental interpretability question: which training examples shaped specific model behaviors?",
-    "This enables identifying influential data, detecting train-test leakage, and understanding failure modes without retraining (Liu et al., 2025a; Park et al., 2023; Grosse et al., 2023)."
+    "Training data attribution (TDA) shifts the focus from model internals to training data, searching for training samples that strongly shaped the model behaviour.",
+    "TDA applications include, but are not limited to, identifying influential training samples, detecting train–test leakage, and diagnosing model failures without retraining (Liu et al., 2025a; Park et al., 2023; Grosse et al., 2023).",
+    "Although TDA is not generally a mechanistic method under this survey’s criteria, it can complement mechanistic interpretability in many ways. For example, by revealing how specific training samples shape malicious behaviour, TDA can enable effective training data filtering to prevent unwanted behaviours or even their internal mechanisms from forming during the training of next-generation models."
    ],
    "limitations": [
-    "However, fundamental challenges persist: approximation errors compound over optimization trajectories, random hyperparameters introduce variance across runs, and scaling to larger models remains memory-intensive.",
-    "Classical influence functions rely on inverting the Hessian of the loss landscape, which becomes ill-defined when the Fisher information matrix is degenerate."
+    "(2026) introduced Concept Influence to enable attribution at the level of semantic concepts represented by model internals such as sparse autoencoder features. This brings TDA closer to the mechanistic goal of understanding how training data shapes specific internal computations. However, fundamental technical challenges persist, including approximation errors that compound over the training trajectory, variability across training runs caused by randomness like data ordering, and memory and computational bottlenecks of scaling to frontier models.",
+    "Token-level filtering is motivated by data attribution, but it remains unclear whether methods such as influence functions (Section 11) can reliably identify the relevant data."
    ],
    "related": [
     "olmotrace",
     "kfac",
     "ek-fac-astra",
-    "unrolled-differentiation",
     "trak",
-    "tracin",
-    "bayesian-influence-function"
+    "tracin"
    ],
    "mentioned_in": [
     "sec:scope",
-    "sec:stagewise-influence"
+    "sec:influence-functions",
+    "observation.-1",
+    "summary.-8",
+    "open-questions."
    ]
   },
   {
    "id": "kfac",
-   "name": "Kronecker-Factored Approximate Curvature (KFAC)",
-   "family": "training-data-attribution",
-   "sections": [
-    "kronecker-factored-approximate-curvature-kfac."
-   ],
-   "definition": "KFAC is a parametric approximation to the Fisher Information Matrix (FIM) of a neural network which supports efficient inversion.",
-   "assumptions": [],
-   "evaluation": [],
-   "applications": [],
-   "limitations": [
-    "KFAC works well but can underestimate curvature in low-variance directions due to finite sampling."
-   ],
-   "related": [
-    "influence-functions",
-    "ek-fac-astra"
-   ],
-   "mentioned_in": []
-  },
-  {
-   "id": "ek-fac-astra",
-   "name": "Eigenvalue-Corrected KFAC (EK-FAC) and ASTRA",
+   "name": "Kronecker-Factored Approximate Curvature (K-FAC)",
    "family": "training-data-attribution",
    "sections": [
-    "eigenvalue-corrected-kfac-ek-fac-and-astra."
+    "kronecker-factored-approximate-curvature-k-fac."
    ],
-   "definition": "EK-FAC improves KFAC by correcting eigenvalues and is the method used by recent work scaling influence to LLMs (Grosse et al., 2023).",
+   "definition": "Martens & Grosse (2015) proposed Kronecker-Factored Approximate Curvature (K-FAC) to approximate a large Fisher information matrix (FIM) using a set of smaller matrices.",
    "assumptions": [
-    "An even more recent refinement, ASTRA (Wang et al., 2025a), uses EK-FAC as a preconditioner for Neumann series iterations, yielding higher-quality GGN-vector products with less hyperparameter tuning."
+    "Although K-FAC approximates the FIM rather than the GGN, these matrices are equivalent for common loss functions under suitable probabilistic assumptions.",
+    "The approximation separates the expectation into two independent factors: activations and pseudo-gradients."
    ],
    "evaluation": [],
    "applications": [
-    "These structured approaches make influence functions practical for models with billions of parameters."
+    "G. This shows that we can use K-FAC to approximate the GGN used in the PBRF influence score."
    ],
    "limitations": [],
    "related": [
-    "kfac",
-    "influence-functions"
+    "influence-functions",
+    "ek-fac-astra"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "addressing-the-computational-and-memory-bottlenecks-of-ggn",
+    "eigenvalue-corrected-k-fac-ek-fac-and-astra."
+   ]
   },
   {
-   "id": "unrolled-differentiation",
-   "name": "Approximate Unrolled Differentiation",
+   "id": "ek-fac-astra",
+   "name": "Eigenvalue-Corrected K-FAC (EK-FAC) and ASTRA",
    "family": "training-data-attribution",
    "sections": [
-    "unrolled-differentiation-gradient-based-attribution"
+    "eigenvalue-corrected-k-fac-ek-fac-and-astra."
    ],
-   "definition": "Approximate unrolled differentiation (Bae et al., 2024) unfolds the optimization trajectory and backpropagates through training steps.",
+   "definition": "EK-FAC (George et al., 2018) improved K-FAC by correcting its eigenvalues. Recent work has adopted this technique to scale the influence function method to modern LLMs (Grosse et al., 2023).",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "This captures implicit bias of the optimizer and handles non-converged models, where classical influence functions struggle."
-   ],
-   "limitations": [
-    "The tradeoff: higher computational cost than influence functions but better accuracy on modern, non-convex objectives."
+    "ASTRA (Wang et al., 2025a) further used EK-FAC as a preconditioner for Neumann series iterations, achieving higher-quality GGN-vector products with less hyperparameter tuning. These efforts make influence functions increasingly practical for modern language models with billions of parameters."
    ],
+   "limitations": [],
    "related": [
+    "kfac",
     "influence-functions"
    ],
    "mentioned_in": []
   },
   {
    "id": "trak",
-   "name": "TRAK (Tracing with Randomly-projected After Kernel)",
+   "name": "TRAK (Tracing with the Randomly-projected After Kernel)",
    "family": "training-data-attribution",
    "sections": [
     "sec:trak"
    ],
-   "definition": "For large-scale attribution without retraining thousands of models, TRAK (Tracing with Randomly-projected After Kernel) provides a scalable alternative (Park et al., 2023).",
-   "assumptions": [],
+   "definition": "Tracing with the Randomly-projected After Kernel (TRAK; Park et al., 2023) provides a more efficient alternative. TRAK first uses a first-order Taylor expansion around the model parameters to approximate a trained model as a linear model.",
+   "assumptions": [
+    "TRAK first uses a first-order Taylor expansion around the model parameters to approximate a trained model as a linear model. It then applies random projections to reduce gradient dimensionality and estimate training-sample influence in this lower-dimensional space. Finally, it averages the estimates across a small number of trained models to reduce variability due to training randomness."
+   ],
    "evaluation": [
-    "By avoiding prohibitive retraining costs, TRAK achieves attribution quality comparable to expensive retraining-based methods like datamodels (Ilyas et al., 2022) while reducing computational cost by orders of magnitude."
+    "TRAK achieves attribution quality comparable to datamodels at substantially lower computational cost (Park et al., 2023)."
    ],
    "applications": [
-    "This enables application to large-scale models including vision transformers and billion-parameter language models, making fine-grained training data attribution tractable for modern deep learning (Park et al., 2023)."
+    "Georgiev et al. (2023) proposed TRAK variants to address diffusion models."
    ],
    "limitations": [],
    "related": [
@@ -1260,95 +1259,82 @@
    "sections": [
     "sec:tracin"
    ],
-   "definition": "TracIn provides an efficient alternative to computing the full Hessian inverse by leveraging the training trajectory (Pruthi et al., 2020).",
+   "definition": "TracIn (Garima et al., 2020) estimates training-data influence using gradients evaluated along the training trajectory, without computing the inverse Hessian.",
    "assumptions": [
-    "This approach avoids Hessian computation entirely, instead accumulating dot products of gradients throughout training."
+    "Its core idea follows from the effect of a single Stochastic Gradient Descent (SGD; Robbins & Monro, 1951) update on the loss of a testing sample."
    ],
    "evaluation": [],
    "applications": [
-    "TracIn is considerably cheaper than influence functions based on Hessian approximations, making it practical for large models.",
-    "Despite limitations, TracIn has enabled attribution studies on modern language models and provides a useful baseline for scalable influence estimation."
+    "Unlike Hessian-based influence functions, TracIn does not require matrix inversion.",
+    "By examining each checkpoint’s contribution, researchers can identify when a training example had the greatest influence on a downstream prediction."
    ],
    "limitations": [
-    "However, the approximation assumes that gradient trajectories during training encode sufficient information about final influence, an assumption that may break down in highly nonlinear settings or when training dynamics involve significant noise (Pruthi et al., 2020)."
+    "TracInCP approximates the influence of training samples using gradients only at selected checkpoints, which may undermine the estimation accuracy."
    ],
    "related": [
     "influence-functions"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "trajectory-based-attribution."
+   ]
   },
   {
    "id": "local-learning-coefficient",
    "name": "Local Learning Coefficient (LLC)",
    "family": "developmental-interpretability",
    "sections": [
-    "sec:slt-llc",
-    "the-local-learning-coefficient"
+    "singular-learning-theory.",
+    "app:slt"
    ],
-   "definition": "In other words, it captures how many ways the parameters can vary locally without changing the loss value (Watanabe, 2009; Lau et al., 2023).",
+   "definition": "The Local Learning Coefficient (LLC; Lau et al., 2025) builds on SLT by localising Watanabe’s learning coefficient around a particular parameter configuration, thereby providing a measure of the effective local complexity of a neural network around a local minimum.",
    "assumptions": [
-    "Formally, the LLC is defined via the volume-scaling rate of the loss landscape near a minimum."
-   ],
-   "evaluation": [
-    "Researchers have observed that transitions and critical points in the LLC often coincide with changes in internal network structure and functional behavior."
+    "Neural networks are singular (and are thus a subject of study in SLT) because different parameter configurations can implement the same function.",
+    "Smaller values indicate more degenerate (flatter, in a higher-order sense) minima. The LLC is estimable at scale with stochastic-gradient MCMC (Lau et al., 2025)."
    ],
+   "evaluation": [],
    "applications": [
-    "Empirical estimators can track the LLC throughout training, providing a way to observe developmental changes in model structure (Lau et al., 2023; Hoogland et al., 2025).",
-    "Tracking these changes provides a quantitative way to study the emergence of structure during training without relying only on final performance metrics (Hoogland et al., 2025)."
+    "The LLC has been used to characterise properties of trained neural networks. For example, Lau et al. (2025) showed that the LLC can capture differences in effective complexity between models trained under different settings, even when their training losses are similar.",
+    "Wang et al. (2025b) introduced refined variants of the Local Learning Coefficient (Section 10.3.4) and applied them to individual attention heads of a two-layer attention-only transformer across training.",
+    "Heads of the same type cluster together in both their final values and the overall shape of the curve across training, revealing how heads differentiate into distinct functional roles."
+   ],
+   "limitations": [
+    "Estimated LLC trajectories in transformers change at points that coincide with identifiable developmental events, including the stages of structure formation described in Section 10.3.3 (Hoogland et al., 2025). This is the central empirical result of the developmental interpretability programme (Hoogland et al., 2023) introduced in Section 12.3. However, current evidence for LLC is largely retrospective, where phase transitions are detected and matched to known events only after training. Prospective prediction of a transition before it occurs has not yet been demonstrated.",
+    "Unlike the proposed induction scores (Olsson et al., 2022) for identifying induction heads, the measure does not require specifying a head type in advance. However, estimating refined LLCs is computationally expensive, and the approach has so far been demonstrated only on toy models."
    ],
-   "limitations": [],
    "related": [],
    "mentioned_in": [
+    "localising-change-to-components.",
+    "run-to-run-variance.",
+    "subsec:dev interp",
     "comparative-analysis-trade-offs-between-orientations"
    ]
   },
-  {
-   "id": "bayesian-influence-function",
-   "name": "Stagewise Influence and the Bayesian Influence Function (BIF)",
-   "family": "developmental-interpretability",
-   "sections": [
-    "sec:stagewise-influence"
-   ],
-   "definition": "Lee et al. (2025) propose a stagewise data attribution framework that extends classical influence methods to track the impact of training points throughout the learning process.",
-   "assumptions": [
-    "Unlike classical influence functions that require Hessian inversion, the BIF measures influence through covariance estimation over the local posterior distribution.",
-    "This distributional approach is estimated via stochastic-gradient MCMC sampling, enabling scalable computation without explicit Hessian access while naturally capturing the geometry of singular loss landscapes."
-   ],
-   "evaluation": [
-    "These predictions are further confirmed through leave-one-out retraining experiments that directly measure the effect of removing individual training examples at different stages of training."
-   ],
-   "applications": [
-    "Token-level influence exhibits non-monotonic changes, including sudden peaks and sign reversals that align with known developmental milestones such as the formation of induction heads."
-   ],
-   "limitations": [],
-   "related": [
-    "influence-functions"
-   ],
-   "mentioned_in": []
-  },
   {
    "id": "model-diffing",
    "name": "Model Diffing",
    "family": "representation-analysis",
    "sections": [
-    "sec:model-diffing"
+    "sec:model-diffing",
+    "applications.",
+    "dictionary-learning-methods.",
+    "llm-based-approaches."
    ],
    "definition": "For two models, model diffing seeks to identify differences in features or circuits between them. These differences may consist of features or circuits that are exclusive to one model, or of differences in how shared features or circuits are used.",
    "assumptions": [
-    "SAEs can be applied to model-diffing when inputs from different models have the same dimension.",
-    "To diff closed-weight models, it is desirable to develop model diffing tools that just rely on API access."
+    "SAEs are applicable when inputs from different models have the same dimension.",
+    "To differentiate closed-weight models, it is desirable to develop model diffing tools that only rely on API access."
    ],
    "evaluation": [
-    "Benchmarked against an SAE-based baseline, the LLM-based method matches it on many dimensions while surfacing more abstract, behaviour-level differences."
+    "Benchmarked against the SAE-based baseline, the LLM-based method matches it on many dimensions while surfacing more abstract, behaviour-level differences."
    ],
    "applications": [
-    "This makes it important to monitor features and circuits that have been introduced or amplified during this process to prevent unsafe models from being deployed.",
-    "Model diffing has been applied to assess the effect of finetuning (Minder et al., 2025; Lindsey et al., 2024; Wang et al., 2025b; Aranguri & McGrath, 2025) and distillation (Baek & Tegmark, 2025), to track differences across model architectures (Thasarathan et al., 2025; Jiralerspong & Bricken, 2025), and to study the evolution of concepts during pretraining (Bayazit et al., 2025; Ge et al., 2025)."
+    "Many concerning behaviours, such as reward hacking or sycophancy, arise during fine-tuning (Sharma et al., 2024b; MacDiarmid et al., 2025). This phenomenon makes it important to monitor features and circuits introduced or amplified during this process to prevent unsafe models from being deployed.",
+    "Model diffing has been applied to assess the effect of fine-tuning (Dumas et al., 2025a; Lindsey et al., 2024; Aranguri & McGrath, 2025; Wang et al., 2026a) and distillation (Baek & Tegmark, 2025), to track differences between model architectures (Thasarathan et al., 2025; Jiralerspong & Bricken, 2025), to study the evolution of concepts during pretraining (Bayazit et al., 2026; Ge et al., 2026), and to identify vision-specific features introduced during vision-language training, enabling targeted removal or steering (Batra et al., 2026).",
+    "By causally intervening with latents that activate more strongly in the fine-tuned model, it is possible to identify features responsible for misaligned behaviour (Wang et al., 2026a)."
    ],
    "limitations": [
-    "Dictionary learning methods for model-diffing risk falsely identifying model-exclusive features (Minder et al., 2025; Jiralerspong & Bricken, 2025).",
-    "During finetuning, the direction representing some feature can rotate, and dictionary learning methods currently cannot distinguish between features that are introduced via finetuning as opposed to merely represented differently.",
-    "Thus, supposedly model-exclusive features should be regarded as hypotheses that need further validation."
+    "Dictionary learning methods for model diffing risk falsely identifying model-exclusive features (Dumas et al., 2025a; Jiralerspong & Bricken, 2025).",
+    "During fine-tuning, the directions representing some features can rotate, and dictionary learning methods currently cannot distinguish between features introduced via fine-tuning and those merely represented differently. Thus, supposedly model-exclusive features should be regarded as hypotheses that need further validation."
    ],
    "related": [
     "sae",
@@ -1357,28 +1343,36 @@
     "representation-similarity",
     "batch-topk-sae"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "sec:scope",
+    "sec:representation",
+    "sec:ld-features",
+    "model-scale."
+   ]
   },
   {
    "id": "representation-similarity",
    "name": "Representation Alignment and Convergent Learning (SVCCA, PWCCA, CKA)",
    "family": "representation-analysis",
    "sections": [
-    "sec:model-diffing"
+    "representation-alignment-and-convergent-learning."
    ],
-   "definition": "An earlier line of work compared internal representations across models by measuring statistical similarity between neurons or subspaces, using correlation or mutual information (Li et al., 2015), canonical correlation–based methods such as SVCCA and PWCCA (Raghu et al., 2017; Morcos et al., 2018b), or kernel-based alignment metrics like CKA (Kornblith et al., 2019).",
+   "definition": "An earlier line of work compared internal representations across models using correlation- and mutual information-based measures (Li et al., 2015), canonical-correlation methods such as SVCCA and PWCCA (Raghu et al., 2017; Morcos et al., 2018), and kernel-based alignment metrics such as CKA (Kornblith et al., 2019).",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "This literature primarily aims to characterize convergent learning: whether independently trained models (often only differing by weight initialisation) learn similar representations up to permutation, rotation, or linear reparameterization."
+    "Related approaches examine whether the representations of one model can be expressed as linear combinations of representations in another (Wang et al., 2018). This literature primarily concerns convergent learning: whether independently trained models with different random initialisations learn similar representations up to permutation, rotation, or other linear transformations.",
+    "Early work showed that different singular modes of the input–output correlations can be learned at different rates (where larger singular values tend to be learned earlier), making acquisition order informative about task structure (Saxe et al., 2014; Lampinen & Ganguli, 2019; Saxe et al., 2019). In contrast, representation-comparison methods measure when layers and representations converge during training (Raghu et al., 2017; Morcos et al., 2018)."
    ],
    "limitations": [
-    "In contrast to model diffing as defined here, these methods typically focus on representational similarity rather than on identifying features or circuits that are introduced, amplified, or repurposed by a specific training intervention."
+    "More recent work has extended these techniques to study representational convergence across modalities (Huh et al., 2024; Gröger et al., 2026), as discussed in Section 5.4. In contrast to model diffing as defined here, these methods typically focus on representational similarity rather than on identifying concrete features or circuits that are introduced, amplified, or repurposed by a specific training intervention."
    ],
    "related": [
     "model-diffing"
    ],
-   "mentioned_in": []
+   "mentioned_in": [
+    "learning-dynamics-as-an-interpretability-tool"
+   ]
   },
   {
    "id": "causal-tracing",
@@ -1387,22 +1381,26 @@
    "sections": [
     "sec:knowledge-localization"
    ],
-   "definition": "Causal tracing identifies which attention heads or MLP directions are causally responsible for particular facts or reasoning tasks by measuring how intervening on those components affects output behavior (Meng et al., 2022).",
+   "definition": "For instance, causal tracing identifies components that causally impact specific factual association retrieval by observing how model outputs change after activation intervention (Meng et al., 2022).",
    "assumptions": [],
    "evaluation": [],
    "applications": [
-    "Once knowledge is attributed to specific model components via localization techniques like causal tracing, mechanistic approaches enable direct parameter modification."
+    "Once knowledge has been localised to specific model components, such as individual neurons (Dai et al., 2022), using methods such as causal tracing (see Section 9.2), mechanistic approaches can support direct parameter modification.",
+    "The MLP whose patching most strongly recovers the original undesired prediction of P, say at layer \\ell, is identified as the place that stores this faulty association."
    ],
    "limitations": [
-    "Knowledge is often distributed across many parameters rather than concentrated in isolated components, particularly in large overparameterized models where redundancy is common.",
-    "Consequently, knowledge localization is most useful when combined with mechanistic analysis of how multiple components interact to implement computation."
+    "Localisation can identify where information is most strongly represented. However, it does not indicate whether the model uses that information.",
+    "In other words, a component with a strong causal effect may capture only part of the mechanism. As a result, localisation is a useful starting point for mechanistic understanding, but not a self-contained analysis."
    ],
    "related": [
     "rome-memit"
    ],
    "mentioned_in": [
     "sec:scope",
-    "subsec:knowedit"
+    "subsec:limits",
+    "subsec:knowedit",
+    "stage-2-mlp-localisation-via-causal-tracing.",
+    "sec:automated-interpretability"
    ]
   },
   {
@@ -1411,29 +1409,29 @@
    "family": "representation-analysis",
    "sections": [
     "subsec:steering",
-    "overview.",
-    "relation-to-causal-evaluation-of-feature-extraction-methods.",
-    "steering-vectors-practical-behavioral-control"
+    "steering-as-a-causal-test-for-feature-interpretability.",
+    "subsubsec: steering vectors"
    ],
-   "definition": "Representation engineering aims to control model behavior by intervening directly in activation space rather than modifying weights. A prominent class of methods, generally defined as activation steering, biases model outputs by adding carefully chosen directions to intermediate activations—most commonly the residual stream—during inference.",
+   "definition": "To manipulate model behaviour, representation engineering alters activation-space representations directly, rather than via model weight modification. Activation steering generally refers to methods that target intermediate activations (usually the residual stream) by adding certain vectors that shift model outputs during inference.",
    "assumptions": [
-    "These interventions are lightweight, require no retraining, and are therefore attractive for rapid behavioral control in deployed systems.",
-    "In practice, v is estimated by contrasting activations associated with different behaviors, e.g., taking differences of class-conditional means"
+    "Because they require no weight updates or retraining, these interventions are lightweight approaches for controlling behaviour.",
+    "Another popular method is difference-in-means, which obtains the steering vector by taking the difference between activations associated with two behaviours (Rimsky et al., 2024)"
    ],
    "evaluation": [
-    "Such intervention-based evaluations directly measure the causal effects of extracted features on model outputs.",
-    "Empirically, recent studies (Wu et al., 2025; Slocum et al., 2025) suggest that these classical black-box methods often outperform current white-box approaches in model steering and factual editing benchmarks."
+    "If intervening along a direction consistently changes a target behaviour, this provides evidence that the corresponding representation causally influences that behaviour.",
+    "Supporting evidence can be obtained by steering along the identified feature direction and evaluating the resulting change in behaviour. These evaluations have been used to compare interpretable direction-identification approaches, including SAE features, difference-in-means directions, and DAS (Huang et al., 2024; Wu et al., 2025b).",
+    "Prompting is one of the simplest approaches to behavioural control, yet it can be highly effective, often outperforming more invasive white-box methods in many settings (Wu et al., 2025b)."
    ],
    "applications": [
-    "Despite their simplicity, steering vectors reliably induce behaviors like improved helpfulness, refusal suppression, or stylistic changes (Rimsky et al., 2024; Tan et al., 2024a; Zhao et al., 2025).",
-    "Researchers were able to identify and deploy the steering vector that suppresses this awareness, allowing its safety to be evaluated.",
-    "Their primary advantage is computational efficiency (no retraining required), methodological simplicity, and inference-time reversibility."
+    "While these approaches appear simple, they reliably change model behaviours such as refusal, helpfulness, and stylistic tendencies (Rimsky et al., 2024; Zhao et al., 2025; Arditi et al., 2024).",
+    "Researchers identified and applied a steering vector that suppresses this awareness, allowing evaluation of its actual behaviour.",
+    "Steering vectors, introduced in Section 9.3 as activation-level interventions, have been shown to be effective for targeted behavioural modification: adding a specific direction \\alpha \\cdot v_{\\text{steer}} to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (Rimsky et al., 2024; Eshuijs et al., 2026). Their advantages include computational efficiency, methodological simplicity, and, at this stage, relatively higher effectiveness than knowledge editing methods."
    ],
    "limitations": [
-    "First, they often fail to generalize across distribution shifts: a vector derived for one task may degrade in another.",
-    "As a result, steering remains primarily a behavioral tool rather than a mechanistic one.",
-    "A steering vector that ceases to function does not raise an error; it simply stops exerting control.",
-    "Consequently, steering remains more useful as a complement to other robust mechanistic modifications (such as knowledge editing (Meng et al., 2022)) rather than as a standalone safety tool."
+    "While steering methods effectively control behaviours and support causal testing, they have limitations. Notably, they can be brittle, as a direction identified for one distribution, model, or task may not generalise reliably.",
+    "Another limitation is that successful behavioural steering does not, by itself, establish mechanistic transparency, because an intervention may simultaneously impact multiple features, interact nonlinearly with existing computations, or exploit unintended pathways that produce the desired behaviour without corresponding to a clean mechanism (Makelov et al., 2024). Thus, steering should generally be interpreted as evidence for causal influence, but it should not, by itself, be a mechanistic explanation of the behaviour.",
+    "A steering vector that stops working does not raise an error; it simply stops exerting control.",
+    "Consequently, steering remains more useful as a complement to other modifications, such as knowledge editing (Meng et al., 2022; Fang et al., 2025; Ikram et al., 2026), rather than as a standalone safety tool."
    ],
    "related": [
     "optimization-based-steering",
@@ -1446,27 +1444,30 @@
    ],
    "mentioned_in": [
     "subsec:cbm",
-    "case-study-refusal-vectors-and-sleeper-agents",
-    "pragmatic-interpretability-proxy-task-oriented-safety-interventions",
+    "the-need-for-standardised-evaluation",
+    "jacobian-lens-decoding-computational-dispositions",
+    "subsec: pragmatic interp",
+    "choosing-appropriate-methods-for-different-safety-applications",
+    "sec:automated-interpretability",
     "distribution-shift-vulnerability.",
     "silent-failure-modes.",
-    "dual-use-risks.",
+    "dual-use-and-adversarial-exploitation.",
     "sec:mechanistic-alignment"
    ]
   },
   {
    "id": "optimization-based-steering",
-   "name": "Optimization-Based Steering",
+   "name": "Optimisation-Based Steering",
    "family": "representation-analysis",
    "sections": [
-    "optimization-based-steering."
+    "nonlinear-and-optimisation-based-steering."
    ],
-   "definition": "Beyond closed-form constructions, steering directions can be obtained by directly optimizing objectives over activations—maximizing task performance or suppressing undesired outputs under intervention.",
+   "definition": "Separately, optimisation-based methods construct steering directions by directly optimising objectives over intermediate activations, offering an alternative to closed-form constructions.",
    "assumptions": [],
    "evaluation": [],
    "applications": [],
    "limitations": [
-    "These optimization-based approaches allow greater flexibility and adapt to complex, non-linear objectives, but incur higher computational cost (Dunefsky & Cohan, 2025; Oozeer et al., 2025a)."
+    "These optimisation-based approaches offer greater flexibility for complex or nonlinear objectives but generally require substantially more computation than closed-form contrastive methods (Dunefsky & Cohan, 2025; Oozeer et al., 2025)."
    ],
    "related": [
     "steering-vectors"
@@ -1478,55 +1479,62 @@
    "name": "Concept Erasure via PISCES",
    "family": "safety-applications",
    "sections": [
-    "concept-erasure-via-pisces"
+    "concept-erasure-via-pisces",
+    "stage-1-feature-disentanglement-and-identification.",
+    "stage-2-mlp-vector-and-feature-selection.",
+    "stage-3-concept-removal.",
+    "result."
    ],
-   "definition": "PISCES (Precise In-Parameter Concept Erasure in Large Language Models) (Gur-Arieh et al., 2025b) addresses this limitation through a three-stage methodology.",
+   "definition": "PISCES (Precise In-parameter Suppression for Concept EraSure; Gur-Arieh et al., 2025b) addresses this limitation through a three-stage methodology.",
    "assumptions": [
-    "Stage 1 – Feature Disentanglement and Identification: Use SAEs with feature explanations (via Automated Interpretability pipeline) to disentangle and identify features associated with the target concept c (e.g., “biological toxins”)"
+    "Use sparse autoencoders (SAEs) with feature explanations (via the Automated Interpretability pipeline) to disentangle and identify features associated with the target concept c (e.g., “biological toxins”)"
    ],
    "evaluation": [
-    "Result: “Surgical” erasure that reduced accuracy on prohibited concepts to near-chance levels (7.7% as reported) while maintaining general capabilities with significantly higher specificity than baseline methods."
+    "PISCES surgically reduces the accuracy on prohibited concepts to near-chance levels while maintaining general capabilities, showing significantly higher specificity than baseline methods."
+   ],
+   "applications": [
+    "Unlike approaches that primarily suppress unwanted outputs, PISCES directly modifies model parameters associated with the target concept."
    ],
-   "applications": [],
    "limitations": [
-    "Likewise, concept-erasure methods such as PISCES may leave residual capabilities accessible through unexpected prompts, allowing suppressed behaviors to re-emerge without explicit signals of failure.",
-    "Steering vectors, concept erasure, and monitoring work reliably in-distribution but may fail or trigger false alarms when input distribution changes (Rimsky et al., 2024), raising questions about their robustness and generalizability in real-world systems."
+    "Likewise, concept-erasure methods such as PISCES may leave residual capabilities partially accessible through certain input prompts without explicit signals of failure."
    ],
    "related": [
     "sae"
    ],
    "mentioned_in": [
+    "concept-erasure-and-knowledge-editing",
     "silent-failure-modes.",
-    "sec:limitations-open-questions",
-    "sec:automated-interpretability"
+    "choosing-appropriate-methods-for-different-safety-applications"
    ]
   },
   {
    "id": "rome-memit",
-   "name": "Knowledge Editing via Localization (ROME, MEMIT)",
+   "name": "Knowledge Editing via Localisation (ROME, MEMIT)",
    "family": "safety-applications",
    "sections": [
-    "subsec:knowedit"
-   ],
-   "definition": "For example, ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) serve as practical tools for component-based knowledge editing: they apply localized rank-one updates to parameter matrices identified as storing particular factual knowledge, achieving targeted removal of harmful knowledge with minimal collateral damage.",
-   "assumptions": [
-    "Once knowledge is attributed to specific model components via localization techniques like causal tracing, mechanistic approaches enable direct parameter modification."
+    "subsec:knowedit",
+    "stage-1-corrupted-and-clean-forward-passes.",
+    "stage-2-mlp-localisation-via-causal-tracing.",
+    "stage-3-rank-one-model-editing.",
+    "result.-2"
    ],
+   "definition": "Once knowledge has been localised to specific model components, such as individual neurons (Dai et al., 2022), using methods such as causal tracing (see Section 9.2), mechanistic approaches can support direct parameter modification. For example, ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) are two classic methods for component-targeted knowledge editing. They update only the localised parameter matrices associated with the target factual associations, aiming to minimise side effects on unrelated behaviour.",
+   "assumptions": [],
    "evaluation": [],
    "applications": [],
    "limitations": [
-    "As a side note, knowledge editing presents a double-edged safety challenge.",
-    "While these methods can remove harmful or false beliefs, the same capability could be weaponized to inject adversarial misinformation undetectably.",
-    "Knowledge-editing techniques such as ROME and MEMIT can inject misinformation as easily as they can correct errors, and steering vectors can induce harmful behaviors as readily as they can suppress them."
+    "Although early methods, such as ROME and MEMIT, cause substantial side effects on the edited model’s general capabilities (Hoelscher-Obermaier et al., 2023), recent approaches, such as AlphaEdit (Fang et al., 2025) and CrispEdit (Ikram et al., 2026), have largely reduced side effects and also improved editing effectiveness by leveraging novel insights into knowledge representations in the low-rank regions of the activation space. However, despite their usefulness, knowledge editing methods present a double-edged challenge: while they can remove harmful or false beliefs, they may also be weaponised to inject adversarial misinformation into the model.",
+    "Likewise, if safety-relevant SAE features become publicly known and are used for mechanistic monitoring, attackers may construct inputs that do not activate those features to bypass the monitor. Also, knowledge editing methods such as ROME (Meng et al., 2022) and MEMIT (Meng et al., 2023) can inject misinformation as readily as they can correct errors, while steering vectors can induce harmful behaviours as easily as they can suppress them."
    ],
    "related": [
     "causal-tracing",
     "steering-vectors"
    ],
    "mentioned_in": [
-    "dual-use-risks.",
+    "concept-erasure-and-knowledge-editing",
     "sec:automated-interpretability",
-    "sec:mechanistic-alignment"
+    "choosing-appropriate-methods-for-different-safety-applications",
+    "dual-use-and-adversarial-exploitation."
    ]
   },
   {
@@ -1534,19 +1542,24 @@
    "name": "Mechanistic Monitoring",
    "family": "safety-applications",
    "sections": [
-    "mechanistic-monitoring"
+    "subsubsec: mech monitor",
+    "stage-1-feature-disentanglement-and-explanation.",
+    "stage-2-circuit-discovery.",
+    "stage-3-monitoring-the-circuit.",
+    "result.-4"
    ],
-   "definition": "Behavioral monitoring watches outputs; mechanistic monitoring watches internals.",
+   "definition": "Behavioural monitoring (Section 12.1) watches outputs; mechanistic monitoring watches internals. The latter asks: is the model computing deceptive reasoning? Is it hiding misaligned objectives?",
    "assumptions": [
-    "Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation), feature-wise monitoring (alerting when unexpected SAE features activate), and circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features)."
+    "Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation) (Oldfield et al., 2026), feature-wise monitoring (alerting when unexpected SAE features activate) (Fang et al., 2026), circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features) (Zhao et al., 2026b), and anomaly-based monitoring (using activations in normal behaviour as a baseline and flagging harmful generations as outliers) (Chaudhary & Barez, 2026)."
    ],
    "evaluation": [],
    "applications": [
-    "The advantages of mechanistic monitoring over behavioral monitoring are several: deceptive reasoning may not manifest behaviorally but may be detectable mechanistically."
+    "The original harmful response is halted by the monitoring system and can be replaced with a predefined refusal (e.g., “I can’t help with that request.”). Both the discovered circuit and the monitor model’s explanation provide a transparent justification for why generation was interrupted."
    ],
    "limitations": [
-    "However, technical challenges also exist, such as determining what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).",
-    "Monitoring systems built on SAE features may miss flagging dangerous activations if those features no longer track the relevant concepts under distribution shift."
+    "Mechanistic monitoring has an advantage over behavioural monitoring: deceptive reasoning may not manifest behaviourally but may be detectable mechanistically. However, technical challenges remain, such as deciding what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).",
+    "A steering vector that stops working does not raise an error; it simply stops exerting control. Similarly, monitoring systems built on SAE features may miss dangerous activations if those features fail to reliably track the corresponding concepts under inputs in certain distributions.",
+    "Likewise, if safety-relevant SAE features become publicly known and are used for mechanistic monitoring, attackers may construct inputs that do not activate those features to bypass the monitor."
    ],
    "related": [
     "probing",
@@ -1554,7 +1567,11 @@
     "circuit-tracing"
    ],
    "mentioned_in": [
-    "silent-failure-modes."
+    "sec:scope",
+    "steering-vectors-and-mechanistic-monitoring",
+    "choosing-appropriate-methods-for-different-safety-applications",
+    "silent-failure-modes.",
+    "dual-use-and-adversarial-exploitation."
    ]
   }
  ],
@@ -1564,55 +1581,43 @@
    "name": "The Linear Representation Hypothesis",
    "sections": [
     "subsec:linearity",
-    "linear-representation-hypothesis"
+    "conceptual-basis.",
+    "empirical-evidence-and-counterexamples."
    ],
-   "definition": "The linear representation hypothesis claims that directions in the activation space are the fundamental units of representation."
+   "definition": "The linear representation hypothesis claims that linear directions in the activation space are effective fundamental units of representation (Park et al., 2024)."
   },
   {
    "id": "superposition",
    "name": "The Superposition Hypothesis",
    "sections": [
     "subsec:superposition",
-    "architectural-responses-to-superposition",
-    "superposition-and-high-dimensional-geometry"
+    "conceptual-basis.-1",
+    "empirical-evidence.",
+    "architectural-responses-to-superposition."
    ],
-   "definition": "The superposition hypothesis says that for each layer of a trained model there are more features than nodes."
+   "definition": "The superposition hypothesis says that models encode more features than their hidden representations have dimensions."
   },
   {
    "id": "privileged-access",
    "name": "The Privileged Access Hypothesis",
    "sections": [
-    "subsec:privileged-access"
+    "subsec:privileged-access",
+    "conceptual-basis.-2",
+    "empirical-evidence.-1"
    ],
-   "definition": "A model used to interpret itself can, under appropriate elicitation, access internal variables and intermediate computations that are not reliably recoverable from external behavioral analysis alone, providing a comparative advantage as an interpreter of its own behavior"
+   "definition": "A language model used to interpret itself can, under appropriate elicitation, access internal variables and intermediate computations that are not reliably recoverable from external behavioural analysis alone, providing a comparative advantage as an interpreter of its own behaviour (Binder et al., 2025; Li et al., 2025a; Song et al., 2026)."
   },
   {
    "id": "universality",
-   "name": "The Universality and Platonic Representation Hypotheses",
+   "name": "The Universality and Platonic Representation Hypothesis",
    "sections": [
-    "subsec:universality"
+    "subsec:universality",
+    "conceptual-basis.-3",
+    "empirical-evidence-and-trade-offs.",
+    "implications."
    ],
-   "definition": "Independently trained models on similar tasks tend to develop similar internal computational structures. These representations often approximate a task-determined canonical form (Huh et al., 2024; Olah et al., 2020)."
+   "definition": "Models independently trained on similar tasks tend to develop similar internal representations or algorithms, which often approximate a task-determined canonical form (Huh et al., 2024; Olah et al., 2020). The degree of convergence depends on factors such as model size, architecture, and training data."
   }
  ],
- "gaps": [
-  "Heading/ID typos: 1.2 heading 'Hnadbook Scope'; section ids 'useful-surveys-that-we-bild-on.', 'challenges-of-black-box-bethods'; 6.8.2 heading 'Principle Component Analysis (PCA)' while the body says 'Principal Component Analysis'. Body typos include 'citcuits' (7.4), 'Definiton 2' (8.3), 'largley' (4.3), 'targed' (4.4), 'Concept-based methods plays' (3.2).",
-  "Section ids containing spaces: 'subsec:valproto agentic self-reports' and 'subsec:choosing orientation'.",
-  "Induction heads get three separate case-study sections: 6.6 (case-study-induction-heads), 7.5 (case-study-induction-heads-1) and 10.6 (case-study-induction-heads-2).",
-  "Grokking is treated in three places: 10.1 (sec:grokking), and twice in chapter 11 (withheld). SLT is treated in both 10.3 (sec:slt-llc) and chapter 11. Phase changes appear twice in chapter 11.",
-  "Chapters 10 and 11 overlap in scope. Much of chapter 11 is still draft authoring notes rather than manuscript text (in 11.2, 11.2.1, 11.2.2, 11.2.4, 11.2.5, 11.3.3, 11.3.4, 11.4.x, 11.5.x, 11.6 and 11.6.1), so the chapter is withheld from the site. 11.2.1 also points to a non-existent 10.2.2.",
-  "Section 12 (sec:representation) has no introductory text; 12.1 jumps straight into model diffing.",
-  "SPARTAN: the 6.9.2 text says it applies 'L_1 regularization to the attention patterns', but Table 7 lists its mechanism as 'L_0 regularization on attention'.",
-  "Causal scrubbing: 7.4 defines it as a necessity test, with interchange interventions as the sufficiency test. 7.6.6, however, labels a protocol 'Evaluation protocol (Causal Scrubbing)' that also covers sufficiency, interchange and specificity tests.",
-  "Gated SAE: 6.2 writes the gate as an indicator (hard threshold), but Appendix C.3 writes it as a sigmoid gate. The recommended Top-k k also differs: 'k = 50–100' in Table 4 versus 'typically 50–200' in Appendix C.4.",
-  "Evaluation dimensions differ: 7.6 proposes five (faithfulness, completeness, stability, causal validity, human interpretability), but 15 (proposed-evaluation-framework) proposes four (interpretability, completeness, validity, efficiency). Appendix C.5 'Absorption ratio' also means something different from 'feature absorption' in 6.7.3.",
-  "Cross-reference mismatches: 5.1 says 'see Figure 2' for the privileged-basis figure, which is Figure 3; 5.2 says 'see Figure 3' for superposition, which is Figure 4; 6.1 says '(Figure 4)' for the SAE, which is Figure 5; 6.4.2 says 'as shown in Figure 6' for CLT size, which is Figure 8; 12.1 says '(Figure 14)' for crosscoder diffing, which is Figure 16 (Figure 14 is probing). 6.1 cites 'Appendix 19', but the SAE appendix is C. 6.3.4 says 'Heuristics in Table 5', but the heuristics are in Table 4. 7.2 says SAE-based circuit discovery is introduced in '6.4.1 and 6.4.2', which are the transcoder/CLT sections.",
-  "Unresolved citation placeholders in 5.3: '(binder?; song?; B. Z. Li et al. 2025)'.",
-  "The acronym ACDC is never expanded. 'Attribution graphs' are only named in the 6.4.2 paragraph heading and in 1.1; there is no dedicated section, so they are folded into cross-layer-transcoders and circuit-tracing.",
-  "Methods mentioned without their own section, so no method entry was made: Activation Oracles and Predictive Concept Decoders (one sentence in 8.5 limitations); Concept Influence and distributional TDA (9.2.8); INLP (8.6.5); CAV / linear concept classifiers, CEMs, ProbCBMs, CGMs, SCBMs (3.2); Diff-SAEs, stage-wise SAE finetuning, transcoder-adapter diffing and LLM-based diffing (all inside 12.1 model diffing); rank analysis via SVD (12.2, uncited); the optogenetics-inspired clamping framework (10.6); PBRF/GGN (folded into influence functions); Semi-NMF (folded into NMF); MDL probes and control tasks (folded into probing evaluation); refusal vectors and sleeper-agent linear detectors (8.3 and 8.6.9, folded into steering/probing).",
-  "DAS is recommended repeatedly (6.3 Step 1, 6.7.6, 8.6.9, 12.3) but described in only one sentence in 6.8.4.",
-  "Excluded as orientations per instructions, though method-like: 13.7 Automated Interpretability (the LLM feature-explanation pipeline) and 14.4 Automated Interpretability Agents. The 7.6 Unified Evaluation Framework is treated as evaluation, not a method.",
-  "The 8.3 refusal-vector definition and 8.6.9 sleeper-agent probe live in the logit-lens/probing chapter, but 12.2 and 12.3 reuse them as knowledge-localisation and steering examples.",
-  "Several excerpts are verbatim table cells rather than full sentences, used where the prose gives no limitation or evaluation statement: activation-patching, path-patching and acdc (Table 8), circuit-tracing (Table 8). Some definitions are leading clauses that end where the source switches into maths: gated-sae, activation-patching, solu, tuned-lens."
- ]
+ "gaps": []
 }
diff --git a/src/data/orientations-arxiv.json b/src/data/orientations-arxiv.json
index 91373ba443..b37fb4ae82 100644
--- a/src/data/orientations-arxiv.json
+++ b/src/data/orientations-arxiv.json
@@ -70,7 +70,7 @@
    "subtitle": "Towards Complete Mechanistic Understanding",
    "section": "12.2",
    "section_title": "Ambitious Interpretability: Towards Complete Mechanistic Understanding",
-   "definition": "In contrast to pragmatic approaches, Ambitious Interpretability seeks a comprehensive mechanistic understanding of how models implement their computations (Gao, 2025). Proponents argue that partial or surface-level understanding cannot guarantee safety in next-generation systems.",
+   "definition": "Ambitious Interpretability seeks a comprehensive mechanistic understanding of how models implement their computations (Gao, 2025). Proponents argue that partial or surface-level understanding cannot guarantee safety in next-generation systems.",
    "list_label": "Example metrics",
    "list_terms": [
     "Feature quality",
@@ -109,7 +109,7 @@
    "subtitle": "Characterising How Models Learn",
    "section": "12.3",
    "section_title": "Developmental Interpretability: Characterising How Models Learn",
-   "definition": "Complementing post-hoc analysis of trained models, Developmental Interpretability (Hoogland et al., 2023; Hoogland et al., 2025) treats the training process itself as an object of scientific inquiry. Taking a learning-theory perspective, it aims to mathematically formalise the emergence and evolution of model representations, effective complexity, and capabilities throughout training.",
+   "definition": "Developmental Interpretability (Hoogland et al., 2023; Hoogland et al., 2025) treats the training process itself as an object of scientific inquiry. Taking a learning-theory perspective, it aims to mathematically formalise the emergence and evolution of model representations, effective complexity, and capabilities throughout training.",
    "list_label": "Methods",
    "list_terms": [
     "Singular Learning Theory (SLT)",
@@ -145,7 +145,7 @@
    "subtitle": "Understanding the Causes of Model Behaviours",
    "section": "12.4",
    "section_title": "Interpretability Across Time: Understanding the Causes of Model Behaviours",
-   "definition": "Like Developmental Interpretability, Interpretability Across Time (Biderman et al., 2023b; 2026) treats a model as a time-evolving process shaped by its training data, loss functions, architectures, and optimisation dynamics. However, the two orientations differ in their primary goals.",
+   "definition": "Interpretability Across Time (Biderman et al., 2023b; 2026) treats a model as a time-evolving process shaped by its training data, loss functions, architectures, and optimisation dynamics. However, the two orientations differ in their primary goals.",
    "list_label": "Methods",
    "list_terms": [
     "Longitudinal analysis of model internals",
diff --git a/src/data/published-urls.json b/src/data/published-urls.json
index 778c23b6b0..b7550d074d 100644
--- a/src/data/published-urls.json
+++ b/src/data/published-urls.json
@@ -1,5 +1,5 @@
 {
- "about": "Every URL and in-page anchor published at https://tsglab.github.io/mechinterp-safety/ (and earlier at /mi_survey/). These must keep resolving; see docs/MAINTAINING.md#stable-urls.",
+ "about": "Every URL and in-page anchor published at https://tsglab.github.io/mechinterp-safety/ (formerly /mi_survey/, which redirects). These must keep resolving; see docs/MAINTAINING.md#stable-urls.",
  "snapshots": [
   {
    "date": "2026-11-06",
@@ -8,13 +8,19 @@
   {
    "date": "2026-11-06",
    "note": "stable chapter ids (phase 2, PR 1): adds /handbook/<id>/ pages"
+  },
+  {
+   "date": "2026-11-06",
+   "note": "arXiv v1 (2611.08438) ingested as final; adds learning-dynamics content and acknowledgements"
   }
  ],
  "pages": {
   "": [
+   "bibtex-src",
    "cite",
    "parts-h",
    "sketch-cap",
+   "taxonomy",
    "use-h"
   ],
   "demos/": [],
@@ -385,6 +391,9 @@
    "app:notation",
    "dimensions-and-scaling"
   ],
+  "handbook/acknowledgements/": [
+   "acknowledgments"
+  ],
   "handbook/b-color-key-and-style-guide/": [
    "app:citation-colors",
    "app:colors",
@@ -403,55 +412,92 @@
   ],
   "handbook/circuits/": [
    "applying-the-framework-method-specific-guidance",
+   "behavioural-coverage.",
    "case-study-induction-heads-1",
    "causal-necessity-and-sufficiency.",
    "circuit-discovery-in-weight-sparse-architectures",
    "circuits-visible-by-inspection.",
    "comparison-of-computational-costs.",
+   "consistency-of-interpretation.",
+   "counterfactual-effect-recovery.",
+   "cross-seed-agreement.",
+   "debugging-utility.",
    "def:acdc",
    "def:attribution-patching",
+   "def:circuit-tracer",
+   "def:eap",
    "def:path-patching",
+   "dimension-1-attribution-faithfulness",
    "dimension-1-faithfulness",
    "dimension-2-completeness",
    "dimension-3-stability",
    "dimension-4-causal-validity",
    "dimension-5-human-interpretability",
+   "effect-size-error.",
    "evaluating-circuits-and-benchmarking-discovery-methods.",
+   "evaluation-protocol",
+   "evaluation-protocol-1",
+   "evaluation-protocol-2",
+   "evaluation-protocol-3",
+   "evaluation-protocol-4",
+   "explanation-utility.",
    "fig:circuit",
    "five-evaluation-dimensions",
+   "hyperparameter-sensitivity.",
    "implications-for-verification.",
    "integrated-evaluation-protocol",
+   "intervention-effect-agreement.",
    "limitations-of-causal-verification.",
    "limitations-of-this-framework",
    "limitations.",
+   "loss-recovery.",
+   "necessity-score.",
    "sec:causal-verification",
    "sec:circuit",
+   "sec:circuit-discovery-methods",
+   "sec:circuit-replacement",
    "sec:circuit-table",
    "sec:evaluation-framework",
    "sec:what-are-circuits",
+   "specificity.",
+   "step-1-define-scope.",
+   "step-2-establish-baselines.",
+   "step-3-apply-interpretability-method.",
+   "step-4-evaluate-across-all-five-dimensions.",
+   "step-5-report-comprehensively.",
+   "subsec: induc head case study: circuit",
+   "subsec:five eval dim",
+   "sufficiency-score.",
+   "summary.-4",
    "tab:circuit-discovery-cost",
    "tab:circuit-methods",
    "tab:evaluation-framework",
    "tab:method-evaluation",
-   "the-need-for-standardized-evaluation"
+   "the-need-for-standardised-evaluation",
+   "the-need-for-standardized-evaluation",
+   "unexplained-variance."
   ],
   "handbook/colour-key/": [
    "app:citation-colors",
    "app:colors",
-   "box-types-and-colors"
+   "box-types-and-colors",
+   "box-types-and-colours"
   ],
   "handbook/conclusion/": [
    "achievements-and-limits",
    "from-reverse-engineering-to-engineering.",
    "sec:conclusion",
+   "the-road-ahead",
    "the-road-ahead-three-complementary-paradigms",
    "unresolved-questions"
   ],
   "handbook/core-hypotheses/": [
    "architectural-responses-to-superposition",
+   "architectural-responses-to-superposition.",
    "conceptual-basis.",
    "conceptual-basis.-1",
    "conceptual-basis.-2",
+   "conceptual-basis.-3",
    "elicitation-protocols-to-reduce-confabulation.",
    "empirical-evidence-and-counterexamples.",
    "empirical-evidence-and-trade-offs.",
@@ -466,6 +512,7 @@
    "subsec:privileged-access",
    "subsec:superposition",
    "subsec:universality",
+   "summary.-2",
    "validation-protocols-and-causal-cross-checks.",
    "when-privileged-access-is-and-is-not-appropriate."
   ],
@@ -505,21 +552,28 @@
   ],
   "handbook/feature-extraction/": [
    "activation-level-interventions-solu-and-its-limitations",
+   "analysis-goal.",
    "applications-to-circuit-analysis.",
    "architecture-and-motivation.",
+   "canonical-formulation.",
    "case-study-induction-heads",
    "causal-vs.-acausal-crosscoders.",
    "common-pitfalls-and-how-to-avoid-them",
+   "comparing-saes-transcoders-and-crosscoders",
    "comparing-saes-transcoders-and-crosscoders.",
    "comparison-with-alternative-feature-extraction-methods",
    "computational-cost-and-practical-considerations.",
    "computational-primitive-modifications-molt",
+   "conditions-for-exact-local-matching",
+   "conditions-for-reported-fidelity",
+   "crosscoders-analysing-cross-layer-features",
    "crosscoders-analyzing-cross-layer-features",
    "dead-features",
    "def:induction-heads",
    "dictionary-learning-baselines",
    "eq-2",
    "eq-3",
+   "factors-affecting-fidelity",
    "feature-absorption-and-splitting",
    "feature-duplication",
    "feature-instability-across-seeds",
@@ -527,35 +581,50 @@
    "fig:clt-size-vs-depth",
    "fig:crosscoders",
    "fig:sae",
+   "fig:transcoders",
    "from-monosemanticity-to-concept-identification.",
    "identifiability-and-theoretical-concerns",
    "independent-component-analysis-ica",
    "interpretability-gains.",
    "kolmogorov-arnold-networks",
    "layer-specific-recommendations",
+   "layer-type.",
    "limitations-of-these-recommendations",
    "mechanism.",
    "mechanism.-1",
+   "model-scale.",
    "motivation.",
+   "non-negative-matrix-factorisation-nmf",
    "non-negative-matrix-factorization-nmf",
    "open-questions-for-clt-research.",
    "practical-recommendations",
+   "principal-component-analysis-pca",
    "principle-component-analysis-pca",
    "quick-reference-when-to-use-what",
    "relation-to-causal-relevance.",
    "replacement-models-and-attribution-graphs.",
+   "resource-constraints.",
    "results-and-the-hydraulic-effect.",
    "sec:architectural-interventions",
    "sec:sae",
    "sparse-attention-post-training-spartan",
    "standard-formulation.",
+   "subsec: induc head case study: feature extract",
    "subsec:challenge",
    "subsec:sae-foundation",
    "subsec:sae-selection",
    "subsec:sae-variants",
    "subsec:transcoders-crosscoders",
+   "subsubsec: kan",
+   "subsubsec: molt",
+   "subsubsec: sae limitation",
+   "subsubsec: solu",
+   "subsubsec: spartan",
+   "subsubsec: supervised_extraction",
+   "subsubsec: weight sparse transformer",
    "subsubsec:cross-layer-transcoder",
    "subsubsec:transcoder",
+   "summary.-3",
    "supervised-feature-extraction-methods",
    "tab:architectural-methods",
    "tab:decomposition-comparison",
@@ -565,28 +634,36 @@
    "tab:sae_recommendations",
    "the-bridge-methodology-interpretable-proxies-for-dense-models",
    "the-capability-interpretability-frontier.",
+   "the-sparse-coding-problem",
    "training-dynamics-and-evaluation.",
    "weight-sparsity-via-l_0-regularization",
+   "when-are-transcoders-necessary",
    "when-transcoders-are-necessary.",
    "why-clts-enable-better-circuit-analysis.",
    "why-saes-work."
   ],
   "handbook/foundational-concepts/": [
+   "fig:circuit",
+   "neuron-polysemanticity.",
    "sec:foundations",
    "subsec:behavioral-vs-mechanistic",
    "subsec:circuits",
    "subsec:features",
    "subsec:limits",
    "subsec:polysemanticity",
-   "subsec:transformers"
+   "subsec:transformers",
+   "summary.-1",
+   "what-is-a-concept"
   ],
   "handbook/intermediate-representations/": [
    "amnesic-probing-and-causal-probing",
    "amnesic-probing.",
    "best-practices-for-probing-studies",
    "case-study-refusal-vectors-and-sleeper-agents",
+   "causal-probing-via-activation-patching.",
    "causal-probing-via-interchange-interventions.",
    "circuit-fragility-and-robustness-concerns",
+   "concerns-of-probe-complexity",
    "control-tasks-and-selectivity",
    "critiques-of-attribution-and-saliency-methods",
    "def:refusal-vector",
@@ -602,12 +679,19 @@
    "footnotes",
    "implications-for-probing.",
    "implications-for-probing.-1",
+   "implications-of-attribution-faithfulness-for-probing",
    "implications-of-control-tasks.",
+   "interpretation-problem.",
+   "jacobian-lens-decoding-computational-dispositions",
    "key-findings-on-attribution-faithfulness",
+   "limitations-of-jacobian-lens.",
    "limitations-of-latentqa.",
    "limitations-of-patchscopes.",
+   "limitations-of-tuned-lens.",
    "limitations-of-vanilla-logit-lens.",
+   "methodological-standards-in-probing-research",
    "minimum-description-length-probes.",
+   "observation.",
    "practical-guidance.",
    "probe-complexity-vs.-causal-relevance",
    "probing-as-hypothesis-generation.",
@@ -617,19 +701,29 @@
    "subsec:latentqa",
    "subsec:patchscopes",
    "subsec:probing",
+   "subsec:tuned-lens",
+   "subsubsec: amnesic probing",
+   "summary.-5",
    "tab:probing-guidance",
    "the-probe-causality-problem",
    "tuned-lens-learning-per-layer-transform",
    "what-probing-can-show.",
    "what-probing-cannot-show.",
+   "when-is-probing-appropriate",
    "when-is-probing-appropriate-and-when-is-it-not"
   ],
   "handbook/interpretability-landscape/": [
    "attribution-and-saliency-methods",
    "behavioral-methods",
+   "behavioural-methods",
+   "input-attribution-and-saliency-methods",
+   "mechanistic-interpretability-methods",
    "representation-probing-and-related-post-hoc-analyses",
    "sec:interpretability-landscape",
+   "subsec: mech interp necessary",
    "subsec:cbm",
+   "subsec:limits",
+   "summary.",
    "tab:interpretability-methods",
    "tab:semantic-vs-structural",
    "when-mechanistic-understanding-becomes-necessary"
@@ -638,34 +732,89 @@
    "brief-history-of-interpretability",
    "curiosity-and-discovery.",
    "fig:overview",
+   "first-from-manual-to-assisted-analysis.",
    "safety.",
    "sec:introduction",
    "sec:scope",
+   "second-divergence-in-research-priorities.",
    "situating-the-field.",
-   "useful-surveys-that-we-bild-on."
+   "useful-surveys-that-we-bild-on.",
+   "useful-surveys-that-we-build-on."
+  ],
+  "handbook/learning-dynamics/": [
+   "acquisition-order.",
+   "architectural-modification-gradient-routing.",
+   "capability-specific-metrics.",
+   "fig:grokking",
+   "forecasting-from-smaller-runs-and-partial-runs.",
+   "forecasting-safety-relevant-behaviours.",
+   "from-correlation-to-causation.",
+   "internal-precursors-as-early-warning.",
+   "learning-dynamics-as-an-interpretability-tool",
+   "localising-change-to-components.",
+   "loss-curve-derivatives.",
+   "monitoring-and-steering-during-fine-tuning.",
+   "open-questions.",
+   "per-example-decomposition.",
+   "per-token-learning-curves.",
+   "pretraining-data-filtering-deep-ignorance.",
+   "run-to-run-variance.",
+   "saddle-to-saddle-dynamics.",
+   "sec:ld-attribution",
+   "sec:ld-causal-interventions",
+   "sec:ld-data",
+   "sec:ld-detect",
+   "sec:ld-features",
+   "sec:ld-forecasting",
+   "sec:ld-induction-case",
+   "sec:ld-intervening",
+   "sec:ld-localization",
+   "sec:ld-longitudinal",
+   "sec:ld-progress",
+   "sec:ld-safety",
+   "sec:ld-temporal-stability",
+   "sec:ld-transitions",
+   "sec:training",
+   "sec:training_phase_why",
+   "singular-learning-theory.",
+   "summary.-7",
+   "trajectory-based-attribution."
   ],
-  "handbook/learning-dynamics/": [],
   "handbook/limits-of-behavioural-evaluation/": [
+   "critical-finding.",
    "eq-1",
+   "first-test-set-contamination-and-saturation.",
+   "fourth-lack-of-fine-grained-control.",
+   "interpretation.",
    "sec:why_open",
+   "second-benchmark-gaming-models-can-learn-the-wrong-mechanisms.",
+   "third-evaluation-awareness.",
+   "training-objective.",
    "war:sleeper"
   ],
   "handbook/mathematical-foundations/": [
    "app:math",
+   "app:slt",
    "causal-abstraction",
    "causal-inference-basics",
+   "entropy.",
    "eq-10",
    "eq-11",
    "eq-12",
    "eq-13",
    "eq-14",
    "eq-15",
+   "eq-4",
+   "eq-5",
+   "eq-6",
    "eq-7",
    "eq-8",
    "eq-9",
    "generalization-bounds",
    "information-theory-basics",
+   "kl-divergence.",
    "linear-representation-hypothesis",
+   "mutual-information.",
    "necessity",
    "phase-transitions",
    "singular-learning-theory-slt",
@@ -676,9 +825,16 @@
   ],
   "handbook/notation/": [
    "app:notation",
-   "dimensions-and-scaling"
+   "dictionary-learning",
+   "dimensions-and-indices",
+   "dimensions-and-scaling",
+   "inputs-and-activations",
+   "interventions-and-objectives",
+   "mathematical-operations",
+   "model-parameters"
   ],
   "handbook/open-problems/": [
+   "challenges-in-evaluation-and-benchmarking",
    "eq-5",
    "how-to-evaluate-mechanistic-interpretability-methods",
    "potential-approaches.",
@@ -691,33 +847,62 @@
    "sec:open_problems",
    "sec:proxy-fidelity",
    "standardized-benchmarks",
+   "subsec: scalability and adversarial robustness",
    "when-is-mechanistic-understanding-necessary",
    "why-this-matters-for-safety."
   ],
   "handbook/representation-analysis/": [
+   "applications.",
+   "def:refusal-vector",
+   "dictionary-learning-methods.",
    "fig:diffing",
    "fig:steering_vectors",
+   "llm-based-approaches.",
+   "nonlinear-and-optimisation-based-steering.",
    "optimization-based-steering.",
    "overview.",
    "relation-to-causal-evaluation-of-feature-extraction-methods.",
+   "representation-alignment-and-convergent-learning.",
    "sec:knowledge-localization",
    "sec:model-diffing",
    "sec:representation",
-   "subsec:steering"
+   "steering-as-a-causal-test-for-feature-interpretability.",
+   "subsec:steering",
+   "summary.-6"
   ],
   "handbook/research-orientations/": [
    "ambitious-interpretability-toward-complete-mechanistic-understanding",
    "architectural-interventions-as-a-synthesis.",
+   "automation-bias.",
+   "calibration-evaluation.",
+   "causal-intervention-consistency.",
+   "common-confusion.",
    "comparative-analysis-trade-offs-between-orientations",
+   "concerns-for-introspective-interpretability",
+   "confabulation.",
+   "critical-concerns-tensions-and-open-questions",
    "critical-tensions-and-open-questions",
+   "cross-method-consistency.",
    "defining-orientations-vs.-methods",
    "developmental-interpretability-understanding-phase-transition-during-training",
+   "elicitation-sensitivity.",
+   "error-propagation.",
    "fig:fiveresearchorientation",
+   "fig:sixresearchorientation",
    "governance-concerns-for-agentic-interpretability",
    "governance-concerns-for-automated-interpretability",
+   "implications.-1",
    "integrating-automated-and-human-analysis",
+   "interpretability-by-design-as-a-synthesis.",
    "key-principles-for-integration.",
+   "layer-1-sampling-based-human-validation.",
+   "layer-2-consistency-and-causality-checks.",
+   "layer-3-adversarial-robustness.",
+   "layer-4-error-detection-and-bounds.",
+   "layer-5-regulator-practices.",
+   "miscalibration.",
    "overview-why-five-orientations",
+   "paraphrase-and-sampling-consistency.",
    "pragmatic-interpretability-proxy-task-oriented-safety-interventions",
    "proposed-guardrails-for-agentic-interpretability",
    "proposed-guardrails-for-automated-interpretability",
@@ -727,15 +912,29 @@
    "protocol-2-cross-examiner-verification.",
    "protocol-3-behavioral-prediction-testing.",
    "protocol-3-drift-monitoring.",
+   "reduced-human-specific-functionalities.",
    "sec:agentic-interpretability",
    "sec:automated-interp",
+   "sec:intro-interpretability",
    "sec:orientations",
+   "spurious-feedback-loops.",
+   "stage-1-feature-extraction.",
+   "stage-2-explanation-generation.",
+   "stage-3-explanation-verification.",
+   "stage-4-deception-detection.",
+   "strategic-misreporting.",
+   "subsec: iat",
+   "subsec: pragmatic interp",
+   "subsec:ambitious",
    "subsec:choosing orientation",
+   "subsec:dev interp",
+   "subsec:valproce model self-reports",
    "subsec:valproto agentic self-reports",
    "summary-responsible-automation",
    "tab:human-automated-workflow",
    "tab:orientations",
-   "validation-protocols-for-automated-pipelines"
+   "validation-protocols-for-automated-pipelines",
+   "when-to-use-introspective-methods"
   ],
   "handbook/sae-architecture-details/": [
    "app:sae_details",
@@ -749,62 +948,118 @@
    "top-k-sae-variant"
   ],
   "handbook/safety-applications/": [
+   "a-note-on-openness-and-dual-use-research.",
+   "adversarial-risks-malicious-exploitation-of-mi",
    "adversarial-risks-malicious-exploitation-of-mi-insights",
    "alternatives-black-box-methods",
+   "alternatives-black-box-methods-and-their-challenges",
    "challenges-of-black-box-bethods",
+   "choosing-appropriate-methods-for-different-safety-applications",
    "circuit-informed-adversarial-attacks.",
    "concept-erasure-and-direct-editing",
+   "concept-erasure-and-knowledge-editing",
    "concept-erasure-via-pisces",
+   "data-filtering-and-safety-training",
    "distribution-shift-vulnerability.",
+   "dual-use-and-adversarial-exploitation.",
    "dual-use-risks.",
+   "epistemic-risks-false-confidence-from-partial-or-incorrect-understanding",
    "epistemic-risks-false-confidence-from-partial-understanding",
    "eq-4",
+   "from-research-orientations-to-methods",
    "implications-for-scale-and-oversight.",
+   "input-and-output-filtering",
    "mechanistic-monitoring",
    "mitigation-strategies.",
    "mitigation-strategies.-1",
    "mitigation-strategies.-2",
    "mitigation-strategies.-3",
+   "operational-risks-failure-under-deployment",
    "operational-risks-failure-under-deployment-conditions",
    "opportunity-costs-resource-allocation-risks",
    "overclaiming-causal-understanding.",
+   "prompting",
    "recommended-verification-practices",
    "relations-to-the-five-research-orientations",
+   "result.",
+   "result.-1",
+   "result.-2",
+   "result.-3",
+   "result.-4",
    "sec:automated-interpretability",
    "sec:limitations-open-questions",
    "sec:mi-risks",
    "sec:safety_applications",
+   "shared-limitations-of-black-box-approaches",
    "silent-failure-modes.",
+   "stage-1-construct-contrastive-pairs-of-examples.",
+   "stage-1-corrupted-and-clean-forward-passes.",
+   "stage-1-feature-disentanglement-and-explanation.",
+   "stage-1-feature-disentanglement-and-identification.",
+   "stage-1-feature-disentanglement-and-identification.-1",
+   "stage-2-circuit-discovery.",
+   "stage-2-generate-the-steering-vector.",
+   "stage-2-mlp-localisation-via-causal-tracing.",
+   "stage-2-mlp-vector-and-feature-selection.",
+   "stage-2-mlp-vector-and-feature-selection.-1",
+   "stage-3-concept-removal.",
+   "stage-3-concept-removal.-1",
+   "stage-3-inject-the-steering-vector-at-inference.",
+   "stage-3-monitoring-the-circuit.",
+   "stage-3-rank-one-model-editing.",
    "steering-vectors-and-mechanistic-monitoring",
    "steering-vectors-practical-behavioral-control",
    "subsec:knowedit",
+   "subsubsec: mech monitor",
+   "subsubsec: steering vectors",
    "summary-mi-as-one-methodology-among-many",
    "tab:mi-risks",
+   "tab:mi_safety_applications",
    "taxonomy-of-mi-safety-risks",
    "the-complete-understanding-trap.",
    "the-scalability-bottleneck.",
+   "user-profiling-and-identity-aware-safety",
    "why-mechanistic-approaches-for-safety"
   ],
   "handbook/training-data-attribution/": [
+   "addressing-the-computational-and-memory-bottlenecks-of-ggn",
+   "completion-chinese",
    "computational-bottleneck-approximating-ggn-vector-products",
    "core-idea-parameter-response-to-training-data",
    "def:infini-gram",
    "def:maximal-matching-spans",
    "def:olmotrace-algorithm",
    "def:suffix-array",
+   "eigenvalue-corrected-k-fac-ek-fac-and-astra.",
    "eigenvalue-corrected-kfac-ek-fac-and-astra.",
    "ex:gradient-translation",
    "ex:olmotrace-multilingual",
    "ex:olmotrace-pipeline",
+   "example",
+   "example-spans-found",
+   "gradient-based-attribution-understanding-data-influence-through-perturbations",
    "gradient-based-attribution-understanding-influence-through-data-perturbations",
+   "gradient-based-influential-training-samples-identified",
    "illustrative-example",
+   "inputs",
+   "kronecker-factored-approximate-curvature-k-fac.",
    "kronecker-factored-approximate-curvature-kfac.",
+   "language-model-output",
    "limitations",
+   "limitations-as-a-mechanistic-interpretability-tool.",
+   "observation.-1",
+   "olmotrace-influential-samples-identified",
+   "pipeline-steps",
+   "prompt",
+   "prompt-english",
    "proximal-bregman-response-and-the-gauss-newton-hessian",
+   "proximal-bregman-response-and-the-generalised-gaussnewton",
    "recent-extensions-and-limitations",
    "sec:influence-functions",
    "sec:tracin",
    "sec:trak",
+   "steps",
+   "summary.-8",
    "unrolled-differentiation-gradient-based-attribution",
    "verbatim-data-attribution-olmotrace-and-maximal-matching-spans"
   ],
@@ -895,19 +1150,37 @@
    "pragmatic"
   ],
   "references/": [
+   "ref-11370531",
+   "ref-3618408.3618656",
+   "ref-3692070.3693959",
+   "ref-Barlow:1961aa",
    "ref-Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019",
    "ref-Ethan2022redteam",
    "ref-Geirhos_2020",
+   "ref-ICLR2025_8c5f3029",
+   "ref-JMLR:v26:23-0058",
    "ref-NIPS2003_1843e35d",
+   "ref-NIPS2012_c399862d",
+   "ref-OMLIN199641",
    "ref-Raukur2022TowardTA",
    "ref-SuperGLUE",
+   "ref-abahana2026rlhf",
+   "ref-achille2019critical",
    "ref-adebayo2018sanity",
    "ref-adebayo2020sanitycheckssaliencymaps",
    "ref-adi2017finegrained",
    "ref-adler2025towards",
+   "ref-agarwal2025context",
+   "ref-agieval",
    "ref-aharon2006k",
+   "ref-aiscientistpitfall",
    "ref-alain2017understanding",
+   "ref-ali-etal-2025-hidden",
+   "ref-alphaedit",
    "ref-ameisen2025circuit",
+   "ref-and",
+   "ref-anil2024manyshot",
+   "ref-anthropic2024modeldiffing",
    "ref-anthropic2025claude-sonnet-4-5",
    "ref-anthropic2025claude4",
    "ref-anthropic2025molt",
@@ -915,68 +1188,130 @@
    "ref-aoyama_language_2025",
    "ref-apollo2025sonnet37evalaware",
    "ref-aranguri2025modeldiff",
+   "ref-arcuschin2026chainofthought",
    "ref-arditi2024refusallanguagemodelsmediated",
+   "ref-arora-etal-2024-causalgym",
+   "ref-arora2026language",
+   "ref-ba2016layernormalization",
    "ref-bae2022influencefunctionsanswerquestion",
    "ref-bae2024trainingdataattributionapproximate",
    "ref-baek_towards_2025",
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+   "ref-durmus2024towards",
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+   "ref-elazar2022measuring",
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+   "ref-elman1990finding",
+   "ref-elman1991distributed",
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+   "ref-ensign2024investigating",
+   "ref-eshuijs2026honestansweraidingllmjudges",
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+   "ref-fanous2025syceval",
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    "ref-ge2026evolution",
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+   "ref-gemmateam2024gemma2improvingopen",
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+   "ref-goel",
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+   "ref-gqa",
+   "ref-gradcam",
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+   "ref-groeneveld2024olmo",
+   "ref-groger2026revisiting",
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+   "ref-gsm8k",
+   "ref-gu2024mamba",
+   "ref-guo2026introspectivecouplingselfexplanationtraining",
    "ref-gupta-etal-2015-distributional",
    "ref-gur-arieh-etal-2025-enhancing",
    "ref-gurarieh2025preciseinparameterconcepterasure",
+   "ref-habibi2026symbolicmechanisticdataattribution",
+   "ref-hahami2026detectingdisturbancenuancedview",
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+   "ref-haklay2026pitfalls",
+   "ref-han2026sage",
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    "ref-he-etal-2025-red",
+   "ref-he2024llamascopeextractingmillions",
+   "ref-heap2026automated",
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+   "ref-heindrich2025do",
+   "ref-helwe-etal-2025-navigating",
    "ref-hendrycks2021naturaladversarialexamples",
+   "ref-hernandez2022natural",
    "ref-hestness2017deeplearningscalingpredictable",
    "ref-hewitt-liang-2019-designing",
+   "ref-hewitt2019structural",
    "ref-hewitt2025position",
+   "ref-hinton1986distributed",
+   "ref-hinton1986learning",
+   "ref-hoelscher-obermaier-etal-2023-detecting",
+   "ref-hoffmann2022training",
+   "ref-hong-etal-2025-measuring",
    "ref-hoogland2023towards",
    "ref-hoogland2025losslandscapedegeneracystagewise",
+   "ref-hotelling1933analysis",
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+   "ref-hu2023latent",
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@@ -1016,91 +1380,174 @@
    "ref-huh2024platonicrepresentationhypothesis",
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    "ref-hyvarinen2023nonlinear",
+   "ref-icml2025actionable",
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+   "ref-inaba2025howllmslearn",
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+   "ref-jacovi-goldberg-2020-towards",
    "ref-jacovi2020faithfullyinterpretablenlpsystems",
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+   "ref-jain2023baselinedefensesadversarialattacks",
+   "ref-jain2024mechanistically",
+   "ref-jain2024what",
+   "ref-jakkli2026current",
+   "ref-jiang2024mixtralexperts",
+   "ref-jimenez2024swebench",
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+   "ref-jlens",
+   "ref-joglekar",
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+   "ref-kangaslahti2026hidden",
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    "ref-kaplan2020scaling",
    "ref-karpathy2015visualizing",
+   "ref-karvonen2025saebench",
+   "ref-kempermann2026safe",
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+   "ref-kfac",
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+   "ref-kharlapenko2025scaling",
    "ref-kim2018tcav",
    "ref-kim2023probabilistic",
    "ref-kim2025llmspursueagenticinterpretability",
+   "ref-kim2025rethinking",
+   "ref-kimiteam2026kimik2openagentic",
+   "ref-kindermans2017unreliabilitysaliencymethods",
    "ref-kissane2024interpretingattentionlayeroutputs",
    "ref-klindt2025superpositionsparsecodesinterpretable",
+   "ref-koepke2026platoscaveexaminingcrossmodal",
    "ref-koh2020conceptbottleneckmodels",
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    "ref-kohn-2015-whats",
+   "ref-korbak2025chainthoughtmonitorabilitynew",
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    "ref-kowal2026concept",
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    "ref-krizhevsky2012imagenet",
+   "ref-laban2026llms",
+   "ref-lampinen2019analytic",
+   "ref-lan-etal-2024-towards",
+   "ref-lan-etal-2026-make",
+   "ref-lan2025quantifyingfeaturespaceuniversality",
    "ref-lanham2023measuringfaithfulnesschainofthoughtreasoning",
    "ref-lau2023local",
    "ref-le2013building",
    "ref-leask2025sparse",
+   "ref-lecomte2024what",
+   "ref-lee1999learning",
    "ref-lee2006efficient",
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    "ref-lee2026rigorous",
+   "ref-lee2026steps",
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+   "ref-lepikhin2021gshard",
+   "ref-lermen2025deceptive",
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    "ref-li-etal-2016-visualizing",
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    "ref-li2024wmdp",
    "ref-li2025geometry",
    "ref-li2025traininglanguagemodelsexplain",
+   "ref-li2026autocontrol",
+   "ref-li2026do",
+   "ref-li2026introspective",
+   "ref-lieberum2024gemmascopeopensparse",
    "ref-lindsey2024sparsecrosscoders",
    "ref-lindsey2025biology",
    "ref-lindsey2025landscape",
+   "ref-lindsey2026emergentintrospectiveawarenesslarge",
    "ref-linzen-etal-2016-assessing",
+   "ref-liu2021probing",
+   "ref-liu2023omnigrok",
+   "ref-liu2024repobench",
    "ref-liu2025infinigramscalingunboundedngram",
    "ref-liu2025kan",
    "ref-liu2025olmotrace",
+   "ref-liu2026icalensinterpretinglanguage",
+   "ref-llama3",
    "ref-llmexplainsurvey",
+   "ref-lo-etal-2024-large",
+   "ref-localinform",
+   "ref-lotfi2026position",
+   "ref-lrh",
+   "ref-lrp",
+   "ref-lubana2023mechanistic",
    "ref-lundberg2017unifiedapproachinterpretingmodel",
    "ref-macdiarmid2024sleeperagentprobes",
    "ref-macdiarmid_natural_2025",
+   "ref-makelov2024is",
+   "ref-marin2026automated",
+   "ref-marks2024enhancingneuralnetworkinterpretability",
+   "ref-marks2024interpreting",
+   "ref-marks2024the",
    "ref-marks2025auditinglanguagemodelshidden",
    "ref-marks2025sparse",
    "ref-marvin-linzen-2018-targeted",
+   "ref-matchingpursuit",
    "ref-mccoy-etal-2019-right",
+   "ref-mcdougall2025gemmascope2",
    "ref-mcgrath2023hydraeffectemergentselfrepair",
+   "ref-mckenzie2023inverse",
+   "ref-meloux2025everything",
    "ref-meng2023locatingeditingfactualassociations",
    "ref-meng2023masseditingmemorytransformer",
    "ref-merullo2025replicating",
+   "ref-michaud2023quantization",
+   "ref-miller2024transformer",
    "ref-miller2026identifyingintervenableinterpretablefeatures",
    "ref-minder_overcoming_2025",
+   "ref-minsky1969perceptrons",
    "ref-mlodozeniec2025distributionaltrainingdataattribution",
+   "ref-mmlu",
    "ref-mmlupro",
+   "ref-modarressi2025nolima",
    "ref-morcos2018insightsrepresentationalsimilarityneural",
    "ref-morcos_insights_2018",
    "ref-moritz2024sthocastic",
+   "ref-moschella2023relative",
    "ref-moschella2023relativerepresentationsenablezeroshot",
    "ref-mueller2025isolationentanglementinterpretabilitymethods",
    "ref-mueller2025mib",
+   "ref-nakkiran2019sgd",
    "ref-nanda2023progressmeasuresgrokkingmechanistic",
    "ref-nanda2025pragmatic",
    "ref-needham2025largelanguagemodelsknow",
+   "ref-neo-etal-2024-interpreting",
+   "ref-neo2025towards",
+   "ref-netdissect2017",
+   "ref-netmodular",
+   "ref-neuronpedia",
    "ref-ng2011sparse",
+   "ref-nla",
    "ref-nostalgebraist2020interpretinggpt",
+   "ref-obrien2026deep",
+   "ref-oikarinen2023clipdissect",
    "ref-oikarinen2023labelfree",
+   "ref-olah2017feature",
    "ref-olah2020zoom",
    "ref-oldfield2024multilinear",
    "ref-oldfield2025towards",
+   "ref-oldfield2026beyond",
    "ref-olshausen1996emergence",
+   "ref-olshausen1997sparse",
    "ref-olsson2022context",
    "ref-olsson2022incontextlearninginductionheads",
+   "ref-oneill2025compute",
    "ref-oozeer2025activation",
    "ref-oozeer2025beyond",
    "ref-openai2024gpt4technicalreport",
+   "ref-openai2025gptoss120bgptoss20bmodel",
    "ref-orgad2026interpretabilityactionable",
+   "ref-orgad2026position",
+   "ref-oshea2015introductionconvolutionalneuralnetworks",
    "ref-ott2022mapping",
    "ref-ouyang2022traininglanguagemodelsfollow",
    "ref-pan2024latentqateachingllmsdecode",
@@ -1108,93 +1555,167 @@
    "ref-park2023trakattributingmodelbehavior",
    "ref-park2025monet",
    "ref-patchscopes",
+   "ref-paulo2024does",
    "ref-paulo2025automatically",
    "ref-paulo2025sparse",
+   "ref-paulo2025transcodersbeatsparseautoencoders",
    "ref-pcd",
    "ref-pearce2025bilinear",
+   "ref-pearson1901lines",
+   "ref-peng-etal-2023-rwkv",
+   "ref-peng2025rwkv",
    "ref-perez2022redteaminglanguagemodels",
    "ref-petri2025",
    "ref-petri2026v2",
+   "ref-petrov2026brokenmath",
    "ref-phan2026hle",
+   "ref-philosophicalevalframework",
    "ref-piktus-etal-2023-roots",
    "ref-piktus2023gaia",
+   "ref-plunkett",
+   "ref-pmlr-v162-geiger22a",
+   "ref-pmlr-v267-fu25e",
+   "ref-pmlr-v37-sohl-dickstein15",
    "ref-pmlr-v44-li15convergent",
    "ref-power2022grokkinggeneralizationoverfittingsmall",
+   "ref-proofnet",
    "ref-pruthi2020estimatingtrainingdatainfluence",
    "ref-q",
    "ref-querycircuit",
    "ref-quirke2024understandingadditiontransformers",
+   "ref-quirke2026understandingadditionsubtractiontransformers",
    "ref-raghu_svcca_2017",
+   "ref-rahaman2019spectral",
    "ref-rai2024practical",
    "ref-rajamanoharan2024improvingdictionarylearninggated",
    "ref-rajamanoharan2024jumpingaheadimprovingreconstruction",
    "ref-rajani2020explainingimprovingmodelbehavior",
+   "ref-rathi2026shaping",
+   "ref-raval2026curveballsteeringrightdirection",
    "ref-ravfogel-etal-2020-null",
+   "ref-razeghi2022impact",
+   "ref-refinetti2023neural",
+   "ref-relu",
    "ref-ribeiro2016whyitrustyou",
+   "ref-rnnfsa1992",
    "ref-rnnnonlinear",
    "ref-rogers2020primer",
    "ref-rosenfeld2019constructivepredictiongeneralizationerror",
+   "ref-rosenfeld2020a",
    "ref-sajjad-etal-2022-neuron",
    "ref-saphra2024mechanistic",
    "ref-saphra_interpretability_2023",
+   "ref-sawmya2025birth",
+   "ref-saxe2014exact",
    "ref-saxe2019mathematical",
+   "ref-schaeffer2023emergent",
+   "ref-schaeffer2025elusive",
    "ref-schaeffer2026quantifyingeffecttestset",
    "ref-schnass2008dictionary",
+   "ref-schrodi2026towards",
    "ref-selfie",
+   "ref-sellam2021multiberts",
    "ref-seo2018noise",
+   "ref-sgd",
+   "ref-shafran-etal-2026-constructing",
    "ref-shaham2024multimodal",
    "ref-sharkey2025open",
+   "ref-sharma2024locating",
+   "ref-sharma2024the",
+   "ref-sharma2025constitutionalclassifiersdefendinguniversal",
    "ref-sharma_towards_2025",
+   "ref-shazeer2020gluvariantsimprovetransformer",
    "ref-shi-etal-2016-string",
+   "ref-shilov2025beyond",
+   "ref-shortliffe1977mycin",
    "ref-shu-etal-2025-survey",
+   "ref-simhi-etal-2025-trust",
+   "ref-simhi2026old",
+   "ref-simon2026there",
    "ref-simonyan2014deepinsideconvolutionalnetworks",
+   "ref-singh2023transient",
    "ref-singh2024needsrightinductionhead",
    "ref-slocum2025believe",
    "ref-smds",
+   "ref-smilkov2017smoothgradremovingnoiseadding",
    "ref-smith2024strongfeature",
+   "ref-snell2024predicting",
    "ref-snmf",
+   "ref-song",
+   "ref-sparsecoding",
+   "ref-sutter2025the",
+   "ref-syed2023attributionpatchingoutperformsautomated",
    "ref-szegedy2015going",
    "ref-tan2024analysing",
    "ref-tan2025analyzinggeneralizationreliabilitysteering",
    "ref-tang-etal-2023-large",
+   "ref-taylor2025school",
    "ref-templeton2024scaling",
    "ref-tenney2019bertrediscoversclassicalnlp",
    "ref-thasarathan_universal_2025",
    "ref-tigges2024llm",
+   "ref-tinaz2025emergence",
    "ref-touvron2023llamaopenefficientfoundation",
    "ref-turner2024steeringlanguagemodelsactivation",
    "ref-turpin2023languagemodelsdontsay",
+   "ref-vandenhirtz2024stochastic",
+   "ref-vanderwal2025polypythias",
+   "ref-varma2023explaining",
    "ref-vaswani2017attention",
    "ref-vaswani2023attentionneed",
+   "ref-vig-belinkov-2019-analyzing",
    "ref-vig2019multiscale",
    "ref-vig2020causalmediationanalysisinterpreting",
    "ref-voita-titov-2020-information",
    "ref-voita_analyzing_2019",
+   "ref-wallace2024instructionhierarchytrainingllms",
    "ref-wang2022interpretabilitywildcircuitindirect",
    "ref-wang2025better",
+   "ref-wang2025differentiation",
+   "ref-wang2026dlmscopemechanisticinterpretabilitydiffusion",
+   "ref-wang2026persona",
    "ref-wang_persona_2025",
    "ref-wang_towards_2018",
+   "ref-wani-etal-2026-answer",
    "ref-watanabe2009algebraic",
+   "ref-wei2022emergent",
    "ref-wiegreffe2019attention",
+   "ref-wu-etal-2022-causal",
    "ref-wu2025axbench",
+   "ref-wu2025towards",
+   "ref-wu2025ushaped",
+   "ref-wurgaft2026manifoldsteeringrevealsshared",
+   "ref-xie2023defending",
+   "ref-xu2024saetrack",
    "ref-xu2025infinigramminiexactngram",
    "ref-yang2023language",
    "ref-yang2025mixture",
    "ref-yang2025qwen3technicalreport",
    "ref-ye2026the",
+   "ref-yin2025which",
    "ref-yu-etal-2024-latent",
+   "ref-yu2025safety",
+   "ref-yu2026vectorquantizedlatentconcepts",
    "ref-yun-etal-2021-transformer",
    "ref-zanbaghi2025detectingsleeperagentslarge",
    "ref-zarlenga2022cem",
    "ref-zeiler2014visualizing",
+   "ref-zhang-etal-2024-bench",
+   "ref-zhang-etal-2024-defending",
    "ref-zhang2024towards",
    "ref-zhang2025cybench",
    "ref-zhang2026locatesteerimprovepractical",
+   "ref-zhang2026whatshapes",
    "ref-zhao-etal-2024-explaining",
    "ref-zhao2024distributional",
    "ref-zhao2025steering",
+   "ref-zhao2026chainofthoughthijacking",
+   "ref-zhao2026verifying",
+   "ref-zhu2025establishing",
+   "ref-zimerman2025explaining",
    "ref-zucchet2025emergence",
    "ref-zucchet2025language",
+   "ref-zucchet2025the",
    "refs",
    "refs-list"
   ],
diff --git a/src/data/redirects.json b/src/data/redirects.json
index 2230921716..e35454470b 100644
--- a/src/data/redirects.json
+++ b/src/data/redirects.json
@@ -10,7 +10,7 @@
   "handbook/7-circuits-in-mechanistic-interpretability/": "handbook/circuits/",
   "handbook/8-understanding-intermediate-computation-logit-lens-and-probin/": "handbook/intermediate-representations/",
   "handbook/9-training-data-attribution-and-influence-functions/": "handbook/training-data-attribution/",
-  "handbook/10-developmental-interpretability-and-training-dynamics/": "handbook/developmental-interpretability/",
+  "handbook/10-developmental-interpretability-and-training-dynamics/": "handbook/learning-dynamics/",
   "handbook/11-learning-dynamics-and-mechanistic-interpretability/": "handbook/learning-dynamics/",
   "handbook/12-representation-analysis-structure-localization-and-transfer/": "handbook/representation-analysis/",
   "handbook/13-five-research-orientations/": "handbook/research-orientations/",
@@ -19,8 +19,384 @@
   "handbook/16-conclusion/": "handbook/conclusion/",
   "handbook/a-a-notation-reference-and-symbol-table/": "handbook/notation/",
   "handbook/b-color-key-and-style-guide/": "handbook/colour-key/",
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-  "handbook/d-mathematical-foundations/": "handbook/mathematical-foundations/"
+  "handbook/c-sae-architecture-details-and-hyperparameters/": "handbook/feature-extraction/",
+  "handbook/d-mathematical-foundations/": "handbook/mathematical-foundations/",
+  "handbook/developmental-interpretability/": "handbook/learning-dynamics/",
+  "handbook/sae-architecture-details/": "handbook/feature-extraction/",
+  "methods/bridge/": "methods/weight-sparse-transformer/",
+  "methods/unrolled-differentiation/": "methods/influence-functions/",
+  "methods/bayesian-influence-function/": "methods/influence-functions/"
  },
- "anchors": {}
-}
\ No newline at end of file
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+  "handbook/6-feature-extraction-and-multi-layer-analysis/#when-transcoders-are-necessary.": "handbook/feature-extraction/#when-are-transcoders-necessary",
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+  "handbook/7-circuits-in-mechanistic-interpretability/#circuits-visible-by-inspection.": "handbook/circuits/#sec:circuit",
+  "handbook/7-circuits-in-mechanistic-interpretability/#comparison-of-computational-costs.": "handbook/circuits/#sec:circuit",
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+  "handbook/7-circuits-in-mechanistic-interpretability/#five-evaluation-dimensions": "handbook/circuits/#subsec:five eval dim",
+  "handbook/7-circuits-in-mechanistic-interpretability/#implications-for-verification.": "handbook/circuits/#sec:circuit",
+  "handbook/7-circuits-in-mechanistic-interpretability/#limitations-of-causal-verification.": "handbook/circuits/#sec:causal-verification",
+  "handbook/7-circuits-in-mechanistic-interpretability/#limitations.": "handbook/circuits/#sec:circuit",
+  "handbook/7-circuits-in-mechanistic-interpretability/#sec:circuit-table": "handbook/circuits/#sec:circuit-discovery-methods",
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+  "handbook/8-understanding-intermediate-computation-logit-lens-and-probin/#tuned-lens-learning-per-layer-transform": "handbook/intermediate-representations/#subsec:tuned-lens",
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+  "handbook/9-training-data-attribution-and-influence-functions/#kronecker-factored-approximate-curvature-kfac.": "handbook/training-data-attribution/#kronecker-factored-approximate-curvature-k-fac.",
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+  "handbook/circuits/#the-need-for-standardized-evaluation": "handbook/circuits/#the-need-for-standardised-evaluation",
+  "handbook/colour-key/#box-types-and-colors": "handbook/colour-key/#box-types-and-colours",
+  "handbook/conclusion/#from-reverse-engineering-to-engineering.": "handbook/conclusion/#sec:conclusion",
+  "handbook/conclusion/#the-road-ahead-three-complementary-paradigms": "handbook/conclusion/#sec:conclusion",
+  "handbook/conclusion/#unresolved-questions": "handbook/conclusion/#sec:conclusion",
+  "handbook/core-hypotheses/#architectural-responses-to-superposition": "handbook/core-hypotheses/#architectural-responses-to-superposition.",
+  "handbook/core-hypotheses/#elicitation-protocols-to-reduce-confabulation.": "handbook/core-hypotheses/#sec:hypotheses",
+  "handbook/core-hypotheses/#implications.-1": "handbook/research-orientations/#implications.-1",
+  "handbook/core-hypotheses/#validation-protocols-and-causal-cross-checks.": "handbook/core-hypotheses/#sec:hypotheses",
+  "handbook/core-hypotheses/#when-privileged-access-is-and-is-not-appropriate.": "handbook/core-hypotheses/#sec:hypotheses",
+  "handbook/d-mathematical-foundations/#eq-10": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-11": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-12": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-13": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-14": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-15": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-7": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-8": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#eq-9": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#generalization-bounds": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#linear-representation-hypothesis": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#phase-transitions": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#singular-learning-theory-slt": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#sparse-coding-theory": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#superposition-and-high-dimensional-geometry": "handbook/mathematical-foundations/#app:math",
+  "handbook/d-mathematical-foundations/#the-local-learning-coefficient": "handbook/mathematical-foundations/#app:slt",
+  "handbook/developmental-interpretability/#case-study-induction-heads-2": "handbook/learning-dynamics/#sec:ld-induction-case",
+  "handbook/developmental-interpretability/#sec:deception": "handbook/learning-dynamics/#sec:training",
+  "handbook/developmental-interpretability/#sec:grokking": "handbook/learning-dynamics/#sec:training",
+  "handbook/developmental-interpretability/#sec:slt-llc": "handbook/learning-dynamics/#sec:training",
+  "handbook/developmental-interpretability/#sec:stagewise-influence": "handbook/learning-dynamics/#sec:training",
+  "handbook/developmental-interpretability/#training-dynamics-and-the-emergence-of-internal-mechanisms": "handbook/learning-dynamics/#sec:training",
+  "handbook/feature-extraction/#activation-level-interventions-solu-and-its-limitations": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#case-study-induction-heads": "handbook/feature-extraction/#subsec: induc head case study: feature extract",
+  "handbook/feature-extraction/#common-pitfalls-and-how-to-avoid-them": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#comparing-saes-transcoders-and-crosscoders.": "handbook/feature-extraction/#comparing-saes-transcoders-and-crosscoders",
+  "handbook/feature-extraction/#computational-primitive-modifications-molt": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#crosscoders-analyzing-cross-layer-features": "handbook/feature-extraction/#crosscoders-analysing-cross-layer-features",
+  "handbook/feature-extraction/#identifiability-and-theoretical-concerns": "handbook/feature-extraction/#subsubsec: sae limitation",
+  "handbook/feature-extraction/#interpretability-gains.": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#kolmogorov-arnold-networks": "handbook/feature-extraction/#subsubsec: kan",
+  "handbook/feature-extraction/#layer-specific-recommendations": "handbook/feature-extraction/#practical-recommendations",
+  "handbook/feature-extraction/#limitations-of-these-recommendations": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#mechanism.": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#mechanism.-1": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#motivation.": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#non-negative-matrix-factorization-nmf": "handbook/feature-extraction/#non-negative-matrix-factorisation-nmf",
+  "handbook/feature-extraction/#principle-component-analysis-pca": "handbook/feature-extraction/#principal-component-analysis-pca",
+  "handbook/feature-extraction/#quick-reference-when-to-use-what": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#results-and-the-hydraulic-effect.": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#sparse-attention-post-training-spartan": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#standard-formulation.": "handbook/feature-extraction/#canonical-formulation.",
+  "handbook/feature-extraction/#supervised-feature-extraction-methods": "handbook/feature-extraction/#subsubsec: supervised_extraction",
+  "handbook/feature-extraction/#tab:sae-variant-selection-decision-tree": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#the-bridge-methodology-interpretable-proxies-for-dense-models": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#the-capability-interpretability-frontier.": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#weight-sparsity-via-l_0-regularization": "handbook/feature-extraction/#sec:sae",
+  "handbook/feature-extraction/#when-transcoders-are-necessary.": "handbook/feature-extraction/#when-are-transcoders-necessary",
+  "handbook/foundational-concepts/#subsec:limits": "handbook/interpretability-landscape/#subsec:limits",
+  "handbook/intermediate-representations/#amnesic-probing-and-causal-probing": "handbook/intermediate-representations/#subsubsec: amnesic probing",
+  "handbook/intermediate-representations/#best-practices-for-probing-studies": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#case-study-refusal-vectors-and-sleeper-agents": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#causal-probing-via-interchange-interventions.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#circuit-fragility-and-robustness-concerns": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#critiques-of-attribution-and-saliency-methods": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#def:refusal-vector": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#evidence-for-circuit-fragility.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#faithfulness-of-attributions.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#implications-for-probing.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#implications-for-probing.-1": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#implications-of-control-tasks.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#key-findings-on-attribution-faithfulness": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#probe-complexity-vs.-causal-relevance": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#probing-as-hypothesis-generation.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#the-probe-causality-problem": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#tuned-lens-learning-per-layer-transform": "handbook/intermediate-representations/#subsec:tuned-lens",
+  "handbook/intermediate-representations/#what-probing-can-show.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#what-probing-cannot-show.": "handbook/intermediate-representations/#sec:intermediate-computation",
+  "handbook/intermediate-representations/#when-is-probing-appropriate-and-when-is-it-not": "handbook/intermediate-representations/#when-is-probing-appropriate",
+  "handbook/interpretability-landscape/#attribution-and-saliency-methods": "handbook/interpretability-landscape/#input-attribution-and-saliency-methods",
+  "handbook/interpretability-landscape/#behavioral-methods": "handbook/interpretability-landscape/#behavioural-methods",
+  "handbook/interpretability-landscape/#when-mechanistic-understanding-becomes-necessary": "handbook/interpretability-landscape/#subsec: mech interp necessary",
+  "handbook/introduction/#useful-surveys-that-we-bild-on.": "handbook/introduction/#useful-surveys-that-we-build-on.",
+  "handbook/mathematical-foundations/#eq-10": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-11": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-12": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-13": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-14": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-15": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-7": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-8": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#eq-9": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#generalization-bounds": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#linear-representation-hypothesis": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#phase-transitions": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#singular-learning-theory-slt": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#sparse-coding-theory": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#superposition-and-high-dimensional-geometry": "handbook/mathematical-foundations/#app:math",
+  "handbook/mathematical-foundations/#the-local-learning-coefficient": "handbook/mathematical-foundations/#app:slt",
+  "handbook/notation/#dimensions-and-scaling": "handbook/notation/#dimensions-and-indices",
+  "handbook/open-problems/#eq-5": "handbook/open-problems/#sec:open_problems",
+  "handbook/open-problems/#how-to-evaluate-mechanistic-interpretability-methods": "handbook/open-problems/#sec:open_problems",
+  "handbook/open-problems/#problem-formulation.": "handbook/open-problems/#sec:open_problems",
+  "handbook/open-problems/#proposed-evaluation-framework": "handbook/open-problems/#sec:open_problems",
+  "handbook/open-problems/#scalability-and-adversarial-robustness": "handbook/open-problems/#sec:open_problems",
+  "handbook/open-problems/#standardized-benchmarks": "handbook/open-problems/#sec:open_problems",
+  "handbook/representation-analysis/#optimization-based-steering.": "handbook/representation-analysis/#nonlinear-and-optimisation-based-steering.",
+  "handbook/representation-analysis/#overview.": "handbook/representation-analysis/#sec:representation",
+  "handbook/representation-analysis/#relation-to-causal-evaluation-of-feature-extraction-methods.": "handbook/representation-analysis/#sec:representation",
+  "handbook/research-orientations/#ambitious-interpretability-toward-complete-mechanistic-understanding": "handbook/research-orientations/#subsec:ambitious",
+  "handbook/research-orientations/#architectural-interventions-as-a-synthesis.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#critical-tensions-and-open-questions": "handbook/research-orientations/#critical-concerns-tensions-and-open-questions",
+  "handbook/research-orientations/#defining-orientations-vs.-methods": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#developmental-interpretability-understanding-phase-transition-during-training": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#fig:fiveresearchorientation": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#governance-concerns-for-agentic-interpretability": "handbook/research-orientations/#governance-concerns-for-automated-interpretability",
+  "handbook/research-orientations/#overview-why-five-orientations": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#pragmatic-interpretability-proxy-task-oriented-safety-interventions": "handbook/research-orientations/#subsec: pragmatic interp",
+  "handbook/research-orientations/#proposed-guardrails-for-agentic-interpretability": "handbook/research-orientations/#proposed-guardrails-for-automated-interpretability",
+  "handbook/research-orientations/#protocol-1-calibration-testing.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#protocol-1-causality-testing.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#protocol-2-adversarial-robustness.": "handbook/research-orientations/#layer-3-adversarial-robustness.",
+  "handbook/research-orientations/#protocol-2-cross-examiner-verification.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#protocol-3-behavioral-prediction-testing.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#protocol-3-drift-monitoring.": "handbook/research-orientations/#sec:orientations",
+  "handbook/research-orientations/#sec:agentic-interpretability": "handbook/research-orientations/#sec:intro-interpretability",
+  "handbook/research-orientations/#subsec:valproto agentic self-reports": "handbook/research-orientations/#subsec:valproce model self-reports",
+  "handbook/research-orientations/#validation-protocols-for-automated-pipelines": "handbook/research-orientations/#sec:orientations",
+  "handbook/sae-architecture-details/#app:sae_details": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#app:train-sae": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#eq-6": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#evaluation-metrics": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#gated-sae-variant": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#multi-layer-saes": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#standard-hyperparameters": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#standard-sae-architecture": "handbook/feature-extraction/#sec:sae",
+  "handbook/sae-architecture-details/#top-k-sae-variant": "handbook/feature-extraction/#sec:sae",
+  "handbook/safety-applications/#adversarial-risks-malicious-exploitation-of-mi-insights": "handbook/safety-applications/#adversarial-risks-malicious-exploitation-of-mi",
+  "handbook/safety-applications/#alternatives-black-box-methods": "handbook/safety-applications/#alternatives-black-box-methods-and-their-challenges",
+  "handbook/safety-applications/#challenges-of-black-box-bethods": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#circuit-informed-adversarial-attacks.": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#concept-erasure-and-direct-editing": "handbook/safety-applications/#concept-erasure-and-knowledge-editing",
+  "handbook/safety-applications/#dual-use-risks.": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#epistemic-risks-false-confidence-from-partial-understanding": "handbook/safety-applications/#epistemic-risks-false-confidence-from-partial-or-incorrect-understanding",
+  "handbook/safety-applications/#eq-4": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#implications-for-scale-and-oversight.": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#mechanistic-monitoring": "handbook/safety-applications/#subsubsec: mech monitor",
+  "handbook/safety-applications/#operational-risks-failure-under-deployment-conditions": "handbook/safety-applications/#operational-risks-failure-under-deployment",
+  "handbook/safety-applications/#overclaiming-causal-understanding.": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#recommended-verification-practices": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#relations-to-the-five-research-orientations": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#sec:limitations-open-questions": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/safety-applications/#steering-vectors-practical-behavioral-control": "handbook/safety-applications/#subsubsec: steering vectors",
+  "handbook/safety-applications/#taxonomy-of-mi-safety-risks": "handbook/safety-applications/#sec:safety_applications",
+  "handbook/training-data-attribution/#computational-bottleneck-approximating-ggn-vector-products": "handbook/training-data-attribution/#sec:influence-functions",
+  "handbook/training-data-attribution/#eigenvalue-corrected-kfac-ek-fac-and-astra.": "handbook/training-data-attribution/#eigenvalue-corrected-k-fac-ek-fac-and-astra.",
+  "handbook/training-data-attribution/#gradient-based-attribution-understanding-influence-through-data-perturbations": "handbook/training-data-attribution/#gradient-based-attribution-understanding-data-influence-through-perturbations",
+  "handbook/training-data-attribution/#kronecker-factored-approximate-curvature-kfac.": "handbook/training-data-attribution/#kronecker-factored-approximate-curvature-k-fac.",
+  "handbook/training-data-attribution/#limitations": "handbook/training-data-attribution/#sec:influence-functions",
+  "handbook/training-data-attribution/#proximal-bregman-response-and-the-gauss-newton-hessian": "handbook/training-data-attribution/#proximal-bregman-response-and-the-generalised-gaussnewton",
+  "handbook/training-data-attribution/#unrolled-differentiation-gradient-based-attribution": "handbook/training-data-attribution/#sec:influence-functions",
+  "references/#ref-Ethan2022redteam": "references/#refs",
+  "references/#ref-adebayo2018sanity": "references/#refs",
+  "references/#ref-bae2024trainingdataattributionapproximate": "references/#refs",
+  "references/#ref-bayazit_crosscoding_2025": "references/#refs",
+  "references/#ref-burns2024discoveringlatentknowledgelanguage": "references/#refs",
+  "references/#ref-chanin2024absorption": "references/#refs",
+  "references/#ref-chaudhary2025safetynetdetectingharmfuloutputs": "references/#refs",
+  "references/#ref-christiano2023deepreinforcementlearninghuman": "references/#refs",
+  "references/#ref-dbm": "references/#refs",
+  "references/#ref-garfinkle2016robust": "references/#refs",
+  "references/#ref-ge_evolution_2025": "references/#refs",
+  "references/#ref-haider2025multi": "references/#refs",
+  "references/#ref-heimersheim2024useinterpretactivationpatching": "references/#refs",
+  "references/#ref-hendrycks2021naturaladversarialexamples": "references/#refs",
+  "references/#ref-hubinger2021riskslearnedoptimizationadvanced": "references/#refs",
+  "references/#ref-hyvarinen2023nonlinear": "references/#refs",
+  "references/#ref-jacovi2020faithfullyinterpretablenlpsystems": "references/#refs",
+  "references/#ref-joshi2026causalitykeyinterpretabilityclaims": "references/#refs",
+  "references/#ref-kangaslahti2026hiddenbreakthroughslanguagemodel": "references/#refs",
+  "references/#ref-kim2023probabilistic": "references/#refs",
+  "references/#ref-kim2025llmspursueagenticinterpretability": "references/#refs",
+  "references/#ref-krizhevsky2012imagenet": "references/#refs",
+  "references/#ref-lanham2023measuringfaithfulnesschainofthoughtreasoning": "references/#refs",
+  "references/#ref-le2013building": "references/#refs",
+  "references/#ref-lee2025influence": "references/#refs",
+  "references/#ref-li2024simulateeliminaterevokebackdoors": "references/#refs",
+  "references/#ref-lindsey2025biology": "references/#refs",
+  "references/#ref-morcos_insights_2018": "references/#refs",
+  "references/#ref-moritz2024sthocastic": "references/#refs",
+  "references/#ref-moschella2023relativerepresentationsenablezeroshot": "references/#refs",
+  "references/#ref-mueller2025isolationentanglementinterpretabilitymethods": "references/#refs",
+  "references/#ref-olsson2022context": "references/#refs",
+  "references/#ref-oozeer2025activation": "references/#refs",
+  "references/#ref-orgad2026interpretabilityactionable": "references/#refs",
+  "references/#ref-petri2025": "references/#refs",
+  "references/#ref-petri2026v2": "references/#refs",
+  "references/#ref-rajani2020explainingimprovingmodelbehavior": "references/#refs",
+  "references/#ref-rosenfeld2019constructivepredictiongeneralizationerror": "references/#refs",
+  "references/#ref-seo2018noise": "references/#refs",
+  "references/#ref-slocum2025believe": "references/#refs",
+  "references/#ref-tan2025analyzinggeneralizationreliabilitysteering": "references/#refs",
+  "references/#ref-vaswani2023attentionneed": "references/#refs",
+  "references/#ref-vig2020causalmediationanalysisinterpreting": "references/#refs",
+  "references/#ref-wang_persona_2025": "references/#refs",
+  "references/#ref-xu2025infinigramminiexactngram": "references/#refs",
+  "references/#ref-zanbaghi2025detectingsleeperagentslarge": "references/#refs",
+  "references/#ref-zucchet2025emergence": "references/#refs",
+  "safety/#chapter-limitations": "safety/#scalability-adversarial",
+  "safety/#safety-limitations": "safety/#scalability-adversarial",
+  "safety/#clt-open-questions": "safety/#scalability-adversarial",
+  "safety/#orientation-tensions": "safety/#when-necessary",
+  "safety/#conclusion-unresolved": "safety/#when-necessary"
+ }
+}
diff --git a/src/generated/chapters/acknowledgements.html b/src/generated/chapters/acknowledgements.html
new file mode 100644
index 0000000000..09ba271f75
--- /dev/null
+++ b/src/generated/chapters/acknowledgements.html
@@ -0,0 +1,3 @@
+<section id="acknowledgments" class="level1 unnumbered"><h1 class="unnumbered">Acknowledgments<a class="anchor" href="#acknowledgments" aria-label="Link to this section">#</a></h1>
+<p>FB and TW would like to thank Leo Gao, Peter Hase, John Hewitt, Kyle Lo, Aaron Mueller, Neel Nanda, and Avi Semler for their thoughtful comments and our discussions that helped shape the project.</p>
+</section>
diff --git a/src/generated/chapters/circuits.html b/src/generated/chapters/circuits.html
index 0cead17c47..d51f7b8ffa 100644
--- a/src/generated/chapters/circuits.html
+++ b/src/generated/chapters/circuits.html
@@ -1,34 +1,34 @@
-<section id="sec:circuit" class="level1"><h1 data-pdf-page="38"><span class="secnum">7</span> Circuits in Mechanistic Interpretability<a class="anchor" href="#sec:circuit" aria-label="Link to this section">#</a></h1>
-<p>Mechanistic interpretability aims not only to identify what information is represented inside a model, but also to explain how that information is transformed and combined to produce behavior. Feature-level analyses, such as those based on Sparse Autoencoders, provide a useful vocabulary for describing internal representations, but they are inherently local: they characterize individual directions or neurons in isolation. Many model behaviors, however, arise from structured interactions between multiple components operating together across layers. To explain such behaviors, we require a higher-level abstraction that captures patterns of computation rather than individual features. The circuit framework addresses this gap by modeling behavior as the result of coordinated subgraphs of the computation graph, making explicit how components interact causally to implement algorithms.</p>
-<section id="sec:what-are-circuits" class="level2"><h2 data-pdf-page="39"><span class="secnum">7.1</span> What Are Circuits?<a class="anchor" href="#sec:what-are-circuits" aria-label="Link to this section">#</a></h2>
+<section id="sec:circuit" class="level1"><h1 data-pdf-page="53"><span class="secnum">7</span> Circuits in Mechanistic Interpretability<a class="anchor" href="#sec:circuit" aria-label="Link to this section">#</a></h1>
+<p>Mechanistic interpretability aims not only to identify what information a model represents, but also to explain how it transforms and combines that information across layers to produce behaviour. Feature analysis is a useful tool for describing activation vectors, but it describes them in isolation. Many model behaviours arise from structured interactions among multiple components that operate together across layers. To explain these behaviours and their mechanisms, we need a high-level abstraction that captures critical computational patterns of these features. The circuit framework (Figure <a href="@@BASE@@handbook/foundational-concepts/#fig:circuit" data-reference-type="ref" data-reference="fig:circuit">3</a>; introduced in Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:circuits" data-reference-type="ref" data-reference="subsec:circuits">4.5</a>) addresses this gap by modelling behaviour as coordinated computational graphs and making explicit how components causally interact.</p>
+<p>Notably, the circuit framework is not the only way to model an internal mechanism. Early studies have explored alternative ways. For example, <span class="citation" data-cites="rnnfsa1992"><a href="@@BASE@@references/#ref-rnnfsa1992" class="cite cite-established" data-cite="rnnfsa1992" role="doc-biblioref">Giles et al. (1992)</a></span> and <span class="citation" data-cites="OMLIN199641"><a href="@@BASE@@references/#ref-OMLIN199641" class="cite cite-established" data-cite="OMLIN199641" role="doc-biblioref">Omlin &amp; Giles (1996)</a></span> categorised a recurrent neural network’s hidden representations into different states and modelled its decision process as a finite-state automaton when the input sentence was fed sequentially. Nonetheless, the circuit framework has attracted growing research interest in studying neural networks’ internal mechanisms. Below, we introduce approaches for identifying and constructing circuits.</p>
+<section id="sec:circuit-discovery-methods" class="level2"><h2 data-pdf-page="53"><span class="secnum">7.1</span> Circuit Discovery Methods<a class="anchor" href="#sec:circuit-discovery-methods" aria-label="Link to this section">#</a></h2>
+<p>Circuit discovery methods aim to identify internal computational graphs responsible for specific capabilities or outputs in a neural network. Below, and in Table <a href="@@BASE@@handbook/circuits/#tab:circuit-methods" data-reference-type="ref" data-reference="tab:circuit-methods">8</a>, we formalise key techniques and methods, along with their strengths and limitations.</p>
 <div class="definitionbox">
-<p>A <em>circuit</em> is a subgraph of the computation graph implementing a specific algorithmic behavior: <span class="math display">\text{Circuit} = \{v_i, e_{ij}\}_{i,j \in S} \quad \text{s.t.} \quad f_\text{model}^{S}(X) \approx f_\text{model}(X),</span> where <span class="math inline">S</span> indexes components and <span class="math inline">f_\text{model}^{S}</span> is the model with only those components active. <span class="math inline">X</span> is either a specific input query or a set of queries that instantiates a specific model capability.</p>
+<p><span id="def:attribution-patching" data-label="def:attribution-patching"></span> Given a model <span class="math inline">f_\theta(\cdot)</span> parameterised by <span class="math inline">\theta</span> and an input sequence <span class="math inline">x</span>, <em>activation patching</em> evaluates the causal contribution of some model components <span class="math inline">S</span> by replacing their activations with specified reference values <span class="math inline">\tilde{h}_S</span> and measuring the resulting change in the model output. The causal importance of <span class="math inline">S</span> for <span class="math inline">x</span> is defined as <span class="math display">s_S(x)
+=
+M\bigl(f_\theta(x)\bigr)
+-
+M\Bigl(f_\theta\bigl(x_{[S \leftarrow \tilde{h}_S]}\bigr)\Bigr),</span> where <span class="math inline">M(\cdot)</span> is a scalar performance metric computed from the model output, such as the logit difference for the answer token, and <span class="math inline">x_{[S \leftarrow \tilde{h}_S]}</span> denotes a forward pass on <span class="math inline">x</span> in which the activations of components in <span class="math inline">S</span> are replaced by <span class="math inline">\tilde{h}_S</span> while the remaining computation proceeds normally. The reference activations <span class="math inline">\tilde{h}_S</span> usually come from a corrupted reference run, where the input is modified to add or remove specific information. Some works have also set <span class="math inline">\tilde{h}_S</span> to zero or the mean model activation. The resulting change in model output quantifies the causal effect of intervening on <span class="math inline">S</span> <span class="citation" data-cites="zhang2024towards">(<a href="@@BASE@@references/#ref-zhang2024towards" class="cite cite-established" data-cite="zhang2024towards" role="doc-biblioref">Zhang &amp; Nanda, 2024</a>)</span>.</p>
 </div>
-<p>Circuits differ from features: <em>features describe what information is present</em>, while circuits describe <em>how components interact to implement behavior</em>.</p>
-<div class="examplebox">
-<ul>
-<li><p><strong>Induction head circuit</strong>: Two attention heads cooperate to implement in‑context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022a)</a></span>.</p></li>
-<li><p><strong>QK circuits</strong>: Query-Key interactions prioritizing specific token positions <span class="citation" data-cites="kamath2025tracing">(<a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a>)</span>.</p></li>
-</ul>
-</div>
-<figure id="fig:circuit" data-latex-placement="t">
-<img alt="Figure 10: Circuit. This diagram illustrates a “circuit” within a transformer, where specific nodes (MLP layers and Attention heads) perform specialized sub-computations." loading="lazy" src="@@BASE@@figures/7_circuit.svg" />
-<figcaption><span class="capnum">Figure 10:</span> <strong>Circuit</strong>. This diagram illustrates a “circuit” within a transformer, where specific nodes (MLP layers and Attention heads) perform specialized sub-computations.</figcaption>
-</figure>
-</section>
-<section id="sec:circuit-table" class="level2"><h2 data-pdf-page="39"><span class="secnum">7.2</span> Circuit Discovery and Tracing Methods<a class="anchor" href="#sec:circuit-table" aria-label="Link to this section">#</a></h2>
-<p>As discussed in Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:circuits" data-reference-type="ref" data-reference="subsec:circuits">4.5</a>, circuit discovery and tracing methods aim to identify internal mechanisms responsible for specific computations in a neural network. Below, we formalize important techniques, the SAE-based circuit discovery methods are introduced in <a href="@@BASE@@handbook/feature-extraction/#subsubsec:transcoder" data-reference-type="ref+label" data-reference="subsubsec:transcoder">6.4.1</a> and <a href="@@BASE@@handbook/feature-extraction/#subsubsec:cross-layer-transcoder" data-reference-type="ref+label" data-reference="subsubsec:cross-layer-transcoder">6.4.2</a>.</p>
 <div class="definitionbox">
-<p><span id="def:attribution-patching" data-label="def:attribution-patching"></span> <em>Activation patching</em> evaluates the causal contribution of a component or set of components <span class="math inline">S</span> by replacing their activations with baseline values <span class="math inline">\tilde{h}_S</span> and measuring output change: <span class="math display">\text{Effect}(S, x) = f_\text{model}(x) - f_\text{model}(x_{[S \leftarrow \tilde{h}_S]}).</span> This identifies which components are functionally necessary for the computation <span class="citation" data-cites="zhang2024towards">(<a href="@@BASE@@references/#ref-zhang2024towards" class="cite cite-emerging" data-cite="zhang2024towards" role="doc-biblioref">Zhang &amp; Nanda, 2024</a>)</span>.</p>
+<p><span id="def:path-patching" data-label="def:path-patching"></span> <em>Path patching</em> extends activation patching on model components to specific edges in the computational graph. For a connection <span class="math inline">e_{ij}</span> from model component <span class="math inline">i</span> to <span class="math inline">j</span> denoting that the output activations of <span class="math inline">i</span> are fed to <span class="math inline">j</span> as one of its inputs, it replaces activations along <span class="math inline">e_{ij}</span> with reference values <span class="math inline">\tilde{h}_{e_{ij}}</span> and measures <span class="math inline">e_{ij}</span>’s causal effect as: <span class="math display">s_{ij}(x) = M\bigl(f_\theta(x)\bigr) - M\Bigl(f_\theta\bigl(x_{[e_{ij} \leftarrow \tilde{h}_{e_{ij}}]}\bigr)\Bigr).</span> This allows finer-grained causal localisation along information flows <span class="citation" data-cites="goldowskydill2023localizingmodelbehaviorpath">(<a href="@@BASE@@references/#ref-goldowskydill2023localizingmodelbehaviorpath" class="cite cite-emerging" data-cite="goldowskydill2023localizingmodelbehaviorpath" role="doc-biblioref">Goldowsky-Dill et al., 2023</a>)</span>.</p>
 </div>
 <div class="definitionbox">
-<p><span id="def:path-patching" data-label="def:path-patching"></span> <em>Path patching</em> extends attribution to specific paths or edges in the computation graph. For a connection <span class="math inline">p</span> from component <span class="math inline">i</span> to <span class="math inline">j</span>, one replaces activations along <span class="math inline">p</span> with baseline values and measures output effect: <span class="math display">\text{Effect}(p, x) = f_\text{model}(x) - f_\text{model}(x_{[p \leftarrow \tilde{h}_p]}).</span> This allows finer-grained causal localization along information flows <span class="citation" data-cites="goldowskydill2023localizingmodelbehaviorpath">(<a href="@@BASE@@references/#ref-goldowskydill2023localizingmodelbehaviorpath" class="cite cite-emerging" data-cite="goldowskydill2023localizingmodelbehaviorpath" role="doc-biblioref">Goldowsky-Dill et al., 2023</a>)</span>.</p>
+<p><span id="def:acdc" data-label="def:acdc"></span> <em>ACDC</em> <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic">(<a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>)</span> discovers a circuit by iteratively using path patching to identify important connections in the model’s computational graph. Traversing the graph in reverse topological order from the output node, it patches the activation transmitted along each edge and measures the resulting change in a task-specific metric. An edge <span class="math inline">e_{ij}</span> will be pruned if patching it produces <span class="math inline">|s_{ij}(x)|</span> less than a predefined threshold <span class="math inline">\tau</span>; otherwise, it is retained in the graph and its upstream edges will be investigated. The remaining subgraph represents the discovered circuit.</p>
 </div>
 <div class="definitionbox">
-<p><span id="def:acdc" data-label="def:acdc"></span> <em>ACDC</em> identifies minimal sufficient sets of components <span class="math inline">S \subseteq \text{Graph}(\text{model})</span> that reproduce the model’s behavior. Formally, it searches for the smallest <span class="math inline">S</span> such that <span class="math display">f_\text{model}^{S}(x) \approx f_\text{model}(x) \quad \forall x \in \mathcal{D},</span> where <span class="math inline">f_\text{model}^{S}</span> uses only components in <span class="math inline">S</span>. Components not in <span class="math inline">S</span> are pruned iteratively <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic">(<a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>)</span>.</p>
+<p><span id="def:eap" data-label="def:eap"></span> <em>EAP</em> <span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated">(<a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a>)</span> estimates the causal importance of edges in a model’s computational graph by approximating the effect of path patching with a first-order Taylor expansion. For an edge <span class="math inline">e_{ij}</span> connecting components <span class="math inline">i</span> and <span class="math inline">j</span>, its importance score is approximated as <span class="math display">s_{ij}(x)
+\approx
+\frac{\partial M}{\partial h_{e_{ij}}}
+\cdot
+\left(h_{e_{ij}}^{\mathrm{clean}} - h_{e_{ij}}^{\mathrm{corrupt}}\right),</span> where <span class="math inline">h_{e_{ij}}^{\mathrm{clean}}</span> and <span class="math inline">h_{e_{ij}}^{\mathrm{corrupt}}</span> denote the activations transmitted from component <span class="math inline">i</span> to component <span class="math inline">j</span> along edge <span class="math inline">e_{ij}</span> on clean and corrupted inputs, respectively, when processing <span class="math inline">x</span>. <span class="math inline">M(\cdot)</span> is a scalar metric of model performance as mentioned above. EAP is substantially faster than ACDC because the attribution scores for all edges are computed using only two forward passes and a single backward pass. Specifically, the forward passes obtain <span class="math inline">h_{e_{ij}}^{\mathrm{clean}}</span> and <span class="math inline">h_{e_{ij}}^{\mathrm{corrupt}}</span>, while the backward pass computes <span class="math inline">\frac{\partial M}{\partial h_{e_{ij}}}</span> for all components simultaneously. In contrast, ACDC requires a separate forward pass for each edge it tests.<br />
+Edges with large score magnitudes are generally considered main contributors to the target behaviour. Therefore, one simple approach to circuit construction is directly selecting the highest-scoring edges <span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated">(<a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a>)</span>. Subsequent work has improved EAP in different directions: EAP-IG <span class="citation" data-cites="eapig">(<a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>)</span> and EAP-GP <span class="citation" data-cites="eapgp">(<a href="@@BASE@@references/#ref-eapgp" class="cite cite-established" data-cite="eapgp" role="doc-biblioref">Zhang et al., 2025b</a>)</span> improve edge-attribution estimation, while PEAP <span class="citation" data-cites="haklay-etal-2025-position">(<a href="@@BASE@@references/#ref-haklay-etal-2025-position" class="cite cite-established" data-cite="haklay-etal-2025-position" role="doc-biblioref">Haklay et al., 2025</a>)</span> extends patching across token positions to produce finer-grained attribution graphs that capture cross-token interactions.</p>
 </div>
+<p>Besides intervention (activation patching, ACDC, etc.) and attribution (EAP, EAP-IG, etc.) techniques, another line of approaches treats circuit discovery as an optimisation problem <span class="citation" data-cites="netmodular cao-etal-2021-low bhaskar2024finding haider2026multigranular">(<a href="@@BASE@@references/#ref-netmodular" class="cite cite-established" data-cite="netmodular" role="doc-biblioref">Csord<span>á</span>s et al., 2021</a>; <a href="@@BASE@@references/#ref-cao-etal-2021-low" class="cite cite-established" data-cite="cao-etal-2021-low" role="doc-biblioref">Cao et al., 2021</a>; <a href="@@BASE@@references/#ref-bhaskar2024finding" class="cite cite-established" data-cite="bhaskar2024finding" role="doc-biblioref">Bhaskar et al., 2024</a>; <a href="@@BASE@@references/#ref-haider2026multigranular" class="cite cite-established" data-cite="haider2026multigranular" role="doc-biblioref">Haider et al., 2026</a>)</span>. Specifically, they use gradient descent to learn which parts of the computational graph can be removed without affecting model behaviour. For example, Edge Pruning <span class="citation" data-cites="bhaskar2024finding">(<a href="@@BASE@@references/#ref-bhaskar2024finding" class="cite cite-established" data-cite="bhaskar2024finding" role="doc-biblioref">Bhaskar et al., 2024</a>)</span> learns edge masks to discover sparse connections, while multi-granular node pruning <span class="citation" data-cites="haider2026multigranular">(<a href="@@BASE@@references/#ref-haider2026multigranular" class="cite cite-established" data-cite="haider2026multigranular" role="doc-biblioref">Haider et al., 2026</a>)</span> learns node masks at different scales, from entire blocks to individual neurons. These methods can usually discover sparser circuits than intervention and attribution counterparts. However, the optimisation process itself introduces randomness and sensitivity to training configurations like the learning rate, which may make the subsequent analysis less robust and interpretable.</p>
+<p>Circuits can explain models in different contexts, from a single input–output pair to a capability or behaviour. Identifying a circuit for a specific input or prompt is called <em>query-level circuit discovery</em> <span class="citation" data-cites="querycircuit">(<a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>. In contrast, <em>task-level circuit discovery</em> <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect kharlapenko2025scaling">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>; <a href="@@BASE@@references/#ref-kharlapenko2025scaling" class="cite cite-established" data-cite="kharlapenko2025scaling" role="doc-biblioref">Kharlapenko et al., 2025</a>)</span> seeks to identify a circuit that implements a broader capability or task, typically operationalised by a dataset of examples, such as the indirect object identification (IOI) dataset <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span>. In the task-level setting, components’ and edges’ attribution scores (e.g., <span class="math inline">s_S(x)</span> or <span class="math inline">s_{ij}(x)</span>) are aggregated across inputs to reduce example-specific noise, yielding a circuit that characterises the task as a whole.</p>
+<p>The techniques and methods introduced above, including activation patching, path patching, ACDC, EAP, EAP-IG, and mask-based pruning methods, have primarily been developed and evaluated in the task-level setting. More recent work has extended EAP-IG to identify circuits for individual prompts <span class="citation" data-cites="querycircuit">(<a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>. This enables the discovery of sparse yet faithful circuits in the model even for complex, natural-language user queries, where the relevant computation may vary substantially from one input to another.</p>
 <table id="tab:circuit-methods">
-<caption><span class="capnum">Table 8:</span> Circuit Discovery Methods: Trade-offs and Characteristics</caption>
+<caption><span class="capnum">Table 8:</span> Trade-offs and characteristics of circuit discovery methods.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Method</strong></span></th>
@@ -40,126 +40,112 @@
 <tbody>
 <tr>
 <td style="text-align: left;"><strong>Activation Patching</strong></td>
-<td style="text-align: left;">Intervene on components; measure output change</td>
-<td style="text-align: left;">Simple, intuitive, approximates causality</td>
-<td style="text-align: left;">Confounds correlation with causation</td>
+<td style="text-align: left;">Intervene on components and measure the resulting output change</td>
+<td style="text-align: left;">Simple and intuitive; provides direct causal evidence</td>
+<td style="text-align: left;">Requires many interventions; component-level granularity can be coarse</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Path Patching</strong></td>
-<td style="text-align: left;">Patch specific information paths; measure output change</td>
-<td style="text-align: left;">Finer granularity</td>
-<td style="text-align: left;">Computationally expensive due to large number of paths; still approximate</td>
+<td style="text-align: left;">Intervene on individual information paths or edges and measure their causal effects</td>
+<td style="text-align: left;">Finer-grained localisation of information flow</td>
+<td style="text-align: left;">Computationally expensive due to the large number of candidate paths</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>ACDC</strong></td>
-<td style="text-align: left;">Iteratively prune minimal sufficient paths</td>
-<td style="text-align: left;">Scales better due to automation; has been proved to recover known circuits</td>
-<td style="text-align: left;">Computationally expensive as it greedily prunes paths; misses subtle components</td>
-</tr>
-<tr>
-<td style="text-align: left;"><strong>SAE- or CLT-Integrated</strong></td>
-<td style="text-align: left;">Discover circuits over sparse features</td>
-<td style="text-align: left;">Reduces search space; leverages features</td>
-<td style="text-align: left;">Depends on feature quality; intensive computation to train SAEs/CLTs</td>
-</tr>
-</tbody>
-</table>
-</section>
-<section id="circuit-discovery-in-weight-sparse-architectures" class="level2"><h2 data-pdf-page="40"><span class="secnum">7.3</span> Circuit Discovery in Weight-Sparse Architectures<a class="anchor" href="#circuit-discovery-in-weight-sparse-architectures" aria-label="Link to this section">#</a></h2>
-<p>The circuit discovery methods described above—activation patching, path patching, ACDC—are designed for dense models where circuits are hidden within fully connected computational graphs. Weight-sparse architectures (Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>) fundamentally alter this landscape by making circuits structurally explicit.</p>
-<section id="circuits-visible-by-inspection." class="level4">
-<h4>Circuits visible by inspection.</h4>
-<p>In a weight-sparse transformer with <span class="math inline">&lt;1\%</span> active parameters, the computational graph is directly observable. To identify the circuit responsible for a specific behavior, one traces non-zero weights backward from the relevant output neurons. Because each neuron connects to only a handful of others, the resulting subgraph is sparse and human-readable without algorithmic search <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>.</p>
-<p>This eliminates the need for iterative pruning (ACDC), gradient-based attribution, or activation patching. The circuit <em>is</em> the set of non-zero weights—no discovery algorithm required.</p>
-</section>
-<section id="comparison-of-computational-costs." class="level4">
-<h4>Comparison of computational costs.</h4>
-<p>Table <a href="@@BASE@@handbook/circuits/#tab:circuit-discovery-cost" data-reference-type="ref" data-reference="tab:circuit-discovery-cost">9</a> contrasts the computational requirements of circuit discovery across architectures.</p>
-<table id="tab:circuit-discovery-cost">
-<caption><span class="capnum">Table 9:</span> Circuit Discovery: Dense vs. Weight-Sparse Models</caption>
-<thead>
-<tr>
-<th style="text-align: left;"><strong>Aspect</strong></th>
-<th style="text-align: left;"><strong>Dense Models</strong></th>
-<th style="text-align: left;"><strong>Weight-Sparse Models</strong></th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align: left;">Discovery method</td>
-<td style="text-align: left;">Activation patching, ACDC, path patching</td>
-<td style="text-align: left;">Direct inspection of non-zero weights</td>
-</tr>
-<tr>
-<td style="text-align: left;">Computational cost</td>
-<td style="text-align: left;"><span class="math inline">O(n_{\text{components}} \times n_{\text{samples}})</span> forward passes</td>
-<td style="text-align: left;"><span class="math inline">O(1)</span>—read from weight matrix</td>
+<td style="text-align: left;">Greedily prune edges from the computational graph while testing whether the target behaviour is preserved</td>
+<td style="text-align: left;">Fully automated; directly searches for a behaviourally sufficient subgraph</td>
+<td style="text-align: left;">Computationally expensive due to sequential edge pruning; sensitive to pruning order and threshold choices</td>
 </tr>
 <tr>
-<td style="text-align: left;">False positives</td>
-<td style="text-align: left;">Common due to correlated activations</td>
-<td style="text-align: left;">Minimal—only true connections exist</td>
+<td style="text-align: left;"><strong>EAP</strong></td>
+<td style="text-align: left;">Approximate edge-level attribution using path patching and first-order gradients, enabling parallel evaluation of many candidate edges</td>
+<td style="text-align: left;">Highly parallelisable and computationally efficient; enables large-scale edge attribution</td>
+<td style="text-align: left;">Relies on a first-order approximation; attribution scores may not fully capture nonlinear or interaction effects</td>
 </tr>
 <tr>
-<td style="text-align: left;">Human verification</td>
-<td style="text-align: left;">Requires careful ablation studies</td>
-<td style="text-align: left;">Visual inspection often sufficient</td>
+<td style="text-align: left;"><strong>SAE- or CLT-Based</strong></td>
+<td style="text-align: left;">Discover circuits over sparse, interpretable features rather than raw model components</td>
+<td style="text-align: left;">Leverages interpretable features; enables finer-grained feature-level circuit analysis</td>
+<td style="text-align: left;">Depends on feature quality; requires additional computation to train and analyse SAEs/CLTs</td>
 </tr>
 </tbody>
 </table>
 </section>
-<section id="implications-for-verification." class="level4">
-<h4>Implications for verification.</h4>
-<p>Causal verification (Section <a href="@@BASE@@handbook/circuits/#sec:causal-verification" data-reference-type="ref" data-reference="sec:causal-verification">7.4</a>) are also simplified in sparse models. Because circuits are structurally isolated, ablation experiments yield cleaner results: removing a sparse circuit removes <em>only</em> that circuit, without the confounding effects of distributed computation. Empirically, sparse circuits satisfy necessity and sufficiency criteria with higher precision than circuits extracted from dense models via post-hoc methods <span class="citation" data-cites="gao2025weight haider2025multi">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>; <a href="@@BASE@@references/#ref-haider2025multi" class="cite cite-emerging" data-cite="haider2025multi" role="doc-biblioref">Haider et al., 2025</a>)</span>.</p>
-</section>
-<section id="limitations." class="level4">
-<h4>Limitations.</h4>
-<p>Weight sparsity does not eliminate all challenges. Circuits may still be compositional (requiring understanding of how subcircuits interact), and the interpretability of individual neurons depends on training dynamics. Furthermore, this approach requires training sparse models from scratch or using Bridge proxies—it cannot be directly applied to existing dense models.</p>
-</section>
+<section id="sec:circuit-replacement" class="level2"><h2 data-pdf-page="55"><span class="secnum">7.2</span> Circuit Discovery with Replacement Models<a class="anchor" href="#sec:circuit-replacement" aria-label="Link to this section">#</a></h2>
+<p>The methods introduced above discover circuits <em>within</em> the target model. Their nodes correspond to coarse-grained components such as attention heads and MLP blocks, which are mostly not immediately understandable. A complementary approach first constructs a more interpretable replacement model (Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec:cross-layer-transcoder" data-reference-type="ref" data-reference="subsubsec:cross-layer-transcoder">6.6.2</a>) that approximates the original model’s computation using sparse linear features, and then performs circuit discovery within this replacement model.</p>
+<p>Recent work has implemented this paradigm using cross-layer transcoders (CLTs; Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec:cross-layer-transcoder" data-reference-type="ref" data-reference="subsubsec:cross-layer-transcoder">6.6.2</a>) <span class="citation" data-cites="ameisen2025circuit merullo2025replicating">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>; <a href="@@BASE@@references/#ref-merullo2025replicating" class="cite cite-emerging" data-cite="merullo2025replicating" role="doc-biblioref">Merullo et al., 2025</a>)</span>. In particular, Circuit Tracer <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span> replaces the model’s MLPs with CLTs while retaining the underlying model’s attention and residual-stream computation. This yields a prompt-specific attribution graph whose principal internal nodes are sparse CLT features rather than entire MLP blocks or attention heads.</p>
+<div class="definitionbox">
+<p><span id="def:circuit-tracer" data-label="def:circuit-tracer"></span></p>
+<p><em>Circuit Tracer</em> <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span> constructs a prompt-specific attribution graph in a CLT-based replacement model. For a given input prompt, consider a source feature <span class="math inline">\phi_s</span> at layer <span class="math inline">\ell_s</span> active at token position <span class="math inline">c_s</span> with activation value <span class="math inline">z_{\ell_s}^{s}</span>, and a downstream target feature <span class="math inline">\phi_t</span> at layer <span class="math inline">\ell_t</span> active at position <span class="math inline">c_t</span>. Recall that a CLT feature can write to the reconstructed MLP outputs of its own and all subsequent layers (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>); the source feature has layer-specific output vectors to these layers, corresponding to columns of the layer-specific decoder matrices: <span class="math display">v_{\mathrm{out},\phi_s}^{\ell}
+\coloneqq
+W_{\mathrm{out},s}^{\ell_s \to \ell}
+\in \mathbb{R}^{d},
+\qquad
+\ell_s \leq \ell.</span> On the other hand, the target feature reads from the MLP input through its corresponding encoder vector, given by a row of the encoder matrix: <span class="math display">v_{\mathrm{in},\phi_t}
+\coloneqq
+\left(W_{\mathrm{in},t}^{\ell_t}\right)^\top
+\in \mathbb{R}^{d}.</span></p>
+<p>Then, let <span class="math display">J_{c_s,\ell \to c_t,\ell_t}
+\in \mathbb{R}^{d\times d}</span> denote the Jacobian that maps a perturbation written at token position <span class="math inline">c_s</span> and layer <span class="math inline">\ell</span> to the MLP input at position <span class="math inline">c_t</span> and layer <span class="math inline">\ell_t</span>, where each of the entries is <span class="math display">\left[
+J_{c_s,\ell \to c_t,\ell_t}
+\right]_{pq}
+=
+\frac{
+\partial \left(h_{\ell_t}^{\mathrm{in},c_t}\right)_p
+}{
+\partial \left(h_{\ell}^{\mathrm{out},c_s}\right)_q
+}.</span></p>
+<p>The direct attribution from source feature <span class="math inline">\phi_s</span> to target feature <span class="math inline">\phi_t</span> can then be modelled as <span class="math display">A_{\phi_s \rightarrow \phi_t}
+=
+\sum_{\ell_s \leq \ell &lt; \ell_t}
+z_{\ell_s}^{s}
+\left(v_{\mathrm{in},\phi_t}\right)^\top
+J_{c_s,\ell \to c_t,\ell_t}
+v_{\mathrm{out},\phi_s}^{\ell}.</span></p>
+<p>Each term in the summation measures the contribution of the source feature <span class="math inline">\phi_s</span>’s write at layer <span class="math inline">\ell</span> to the pre-activation of target feature <span class="math inline">\phi_t</span>, transported through the residual stream and attention heads. <span class="math inline">A_{\phi_s \rightarrow \phi_t}</span> aggregates the downstream writes across layers produced by the same source feature. Notably, <span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al. (2025)</a></span> stop the gradients flowing through intermediate MLP outputs and fix attention patterns and normalisation denominators. This lets each edge capture direct feature-to-feature influence rather than effects mediated through intermediate feature nodes.</p>
+</div>
+<p>CLT-based circuit discovery differs from conventional circuit discovery in two aspects. First, the resulting circuit’s nodes correspond to fine-grained linear features rather than coarse MLP blocks or attention modules. Using automated feature-interpretation pipelines (Section <a href="@@BASE@@handbook/research-orientations/#sec:automated-interp" data-reference-type="ref" data-reference="sec:automated-interp">12.6</a>) to assign natural-language descriptions to these features, human inspectors can more easily understand the discovered mechanism. Second, an edge in a CLT-based circuit denotes the direct, input-specific influence between two features, which is different from the formulation of attribution methods based on clean–corrupted reference pairs, such as EAP and EAP-IG. Specifically, in Circuit Tracer, a source feature can write to the residual stream at multiple subsequent layers, and the strength of an edge from the source feature to a target feature is the aggregate of the contributions of these writes to the target feature.</p>
+<p>For a given prompt, Circuit Tracer produces a sparse attribution graph that describes active features and their interactions, making it well suited to query-level circuit discovery. Interpretability platforms such as Neuronpedia <span class="citation" data-cites="neuronpedia">(<a href="@@BASE@@references/#ref-neuronpedia" class="cite cite-emerging" data-cite="neuronpedia" role="doc-biblioref">Lin, 2023</a>)</span> also offer interactive implementations. However, the approach still has several limitations. Specifically, CLTs only approximate MLP computations, so the discovered circuit may only cover a part of the model’s underlying mechanism. Moreover, feature-level attribution graphs can have many nodes and edges, so we must prune, cluster, or otherwise condense them before they become understandable.</p>
 </section>
-<section id="sec:causal-verification" class="level2"><h2 data-pdf-page="41"><span class="secnum">7.4</span> Causal Verification<a class="anchor" href="#sec:causal-verification" aria-label="Link to this section">#</a></h2>
-<p>Once circuits are identified, two critical questions arise: are they causally responsible for the behavior, and can we modify them reliably?</p>
+<section id="sec:causal-verification" class="level2"><h2 data-pdf-page="57"><span class="secnum">7.3</span> Causal Verification<a class="anchor" href="#sec:causal-verification" aria-label="Link to this section">#</a></h2>
+<p>Once we identify circuits, two critical questions arise: are they causally responsible for the behaviour, and can we modify them reliably?</p>
 <section id="causal-necessity-and-sufficiency." class="level4">
 <h4>Causal Necessity and Sufficiency.</h4>
-<p>A circuit is <strong>causally necessary</strong> for a behavior if removing or ablating the circuit degrades that behavior. A circuit is <strong>causally sufficient</strong> if activating the circuit alone, with all other components zeroed or corrupted, reproduces the behavior. In practice, it is hard to be both strictly necessary and sufficient—citcuits typically explain 60–80% of behavioral variance.</p>
+<p>A circuit is <em>causally necessary</em> for a behaviour if removing or ablating the circuit degrades that behaviour. A circuit is <em>causally sufficient</em> if activating the circuit alone, i.e., patching all edges outside the circuit with activations from a corrupted reference run, reproduces the behaviour. In practice, a circuit is rarely sparse, necessary, and sufficient at the same time; it typically explains 60–80% of behavioural variance.</p>
 </section>
 <section id="evaluating-circuits-and-benchmarking-discovery-methods." class="level4">
 <h4>Evaluating Circuits and Benchmarking Discovery Methods.</h4>
-<p>A circuit is considered <em>faithful</em> if it is both causally necessary and causally sufficient for the behavior it is claimed to explain <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span>. Two widely used high-level methods for testing these properties are causal scrubbing and interchange interventions <span class="citation" data-cites="heimersheim2024useinterpretactivationpatching chan2022causal">(<a href="@@BASE@@references/#ref-heimersheim2024useinterpretactivationpatching" class="cite cite-emerging" data-cite="heimersheim2024useinterpretactivationpatching" role="doc-biblioref">Heimersheim &amp; Nanda, 2024</a>; <a href="@@BASE@@references/#ref-chan2022causal" class="cite cite-emerging" data-cite="chan2022causal" role="doc-biblioref">Chan et al., 2022</a>)</span>. Causal scrubbing evaluates necessity by replacing circuit activations with corrupted or neutralized values and measuring the resulting change in model outputs. Interchange interventions test sufficiency by swapping circuit components between clean and corrupted forward passes and assessing whether the target behavior consistently tracks the substituted circuit across inputs.</p>
-<p>More recently, several works have proposed metrics to quantify circuit faithfulness as a continuous score (<span class="citation" data-cites="eapig"><a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a></span>; <span class="citation" data-cites="querycircuit"><a href="@@BASE@@references/#ref-querycircuit" class="cite cite-emerging" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2025</a></span>), allowing systematic comparisons of circuits discovered by different algorithms. By aggregating faithfulness scores across tasks and settings, benchmarks such as MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-emerging" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025a</a>)</span> evaluate the performance of circuit discovery methods in identifying causally meaningful model components.</p>
+<p>A circuit is often considered <em>faithful</em> if it is both causally necessary and causally sufficient for the behaviour it is claimed to explain <span class="citation" data-cites="eapig marks2025sparse">(<a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. Two popular approaches to validate causality are causal scrubbing and interchange interventions (<span class="citation" data-cites="chan2022causal"><a href="@@BASE@@references/#ref-chan2022causal" class="cite cite-emerging" data-cite="chan2022causal" role="doc-biblioref">Chan et al., 2022a</a></span>; <span class="citation" data-cites="das"><a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a></span>). Causal scrubbing <span class="citation" data-cites="chan2022causal">(<a href="@@BASE@@references/#ref-chan2022causal" class="cite cite-emerging" data-cite="chan2022causal" role="doc-biblioref">Chan et al., 2022a</a>)</span> systematically evaluates a mechanistic hypothesis by replacing model activations with those from inputs deemed equivalent under the hypothesis and testing whether these interventions preserve the model behaviour. Interchange interventions <span class="citation" data-cites="das">(<a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a>)</span> test whether model activations causally correspond to higher-level variables by substituting activations across inputs and measuring the resulting change in model behaviour. Together, these methods provide stronger evidence for mechanistic explanations than observational analysis alone.</p>
+<p>More recently, several works have proposed metrics to quantify circuit faithfulness as a continuous score <span class="citation" data-cites="eapig querycircuit">(<a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>, allowing systematic comparisons of circuits discovered by different methods. By aggregating faithfulness scores across tasks and settings, benchmarks such as MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span> evaluate how well circuit discovery methods identify causally important model components. However, achieving strict necessity and sufficiency is often hard because many model behaviours depend on distributed representations and multiple redundant pathways <span class="citation" data-cites="barez2025chain">(<a href="@@BASE@@references/#ref-barez2025chain" class="cite cite-established" data-cite="barez2025chain" role="doc-biblioref">Barez et al., 2026</a>)</span>. Even if we identify a circuit that captures the primary mechanism of a behaviour, components outside the circuit may still play important roles, such as handling edge cases and computational robustness.</p>
 </section>
-<section id="limitations-of-causal-verification." class="level4">
-<h4>Limitations of Causal Verification.</h4>
-<p>Strict necessity and sufficiency are often unattainable. Most behaviors depend on distributed representations and multiple redundant pathways. A circuit may implement the primary mechanism (60% of variance) while other components handle edge cases. Additionally, verification assumes the circuit mechanism is faithfully captured by the identified components, but if features are entangled or nonlinear interactions are present, the measured circuit may not reflect true causal structure.</p>
 </section>
-</section>
-<section id="case-study-induction-heads-1" class="level2"><h2 data-pdf-page="42"><span class="secnum">7.5</span> Case Study: Induction Heads<a class="anchor" href="#case-study-induction-heads-1" aria-label="Link to this section">#</a></h2>
+<section id="subsec: induc head case study: circuit" class="level2"><h2 data-pdf-page="58"><span class="secnum">7.4</span> Case Study: Induction Heads<a class="anchor" href="#subsec: induc head case study: circuit" aria-label="Link to this section">#</a></h2>
 <div class="examplebox">
-<p><span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022a)</a></span> provide a mechanistic explanation for how induction heads implement the conditional copying rule described in Definition <a href="@@BASE@@handbook/feature-extraction/#def:induction-heads" data-reference-type="ref" data-reference="def:induction-heads">6.6</a>. Consider a repeated sequence, for example</p>
-<p><span class="math display">\texttt{A B C A B \underline{?}},</span></p>
-<p>where the model must predict the next token following the second occurrence of <code>B</code>. In 2-layer attention-only models, the minimal induction circuit consists of two interacting attention heads that cooperate across layers to implement the copying computation.</p>
-<p>The first head, often called the <em>previous-token head</em>, propagates information about the immediately preceding token into the residual stream. At the current position <span class="math inline">j</span>, this head attends to the token at <span class="math inline">x_{j-1}</span> and writes a representation of it into the residual stream. By doing so, the residual stream carries contextual information about recent tokens that downstream computations can access. The second head, the true <em>induction head</em>, attends from the current token <span class="math inline">x_j</span> to all earlier positions <span class="math inline">i &lt; j</span> where the same token appeared (<span class="math inline">x_i = x_j</span>). Using the context provided by the previous-token head, the induction head retrieves the residual stream representation of the token following <span class="math inline">x_i</span>, namely <span class="math inline">x_{i+1}</span>, and incorporates it into the residual stream at position <span class="math inline">j</span>. This updated representation directly biases the next-token logits toward the retrieved token, effectively implementing the conditional copying rule:</p>
-<p><span class="math display">(x_i = x_j) \;\Rightarrow\; \hat{x}_{j+1} \approx x_{i+1}.</span></p>
-<p>Empirical evidence supports this mechanistic interpretation. Ablating either the previous-token head or the induction head significantly reduces the model’s pattern completion performance, demonstrating that both heads are causally responsible for the observed in-context learning behavior. Moreover, the circuit emerges sharply during training, coinciding with sudden improvements in in-context learning performance. In small attention-only models, this minimal two-head circuit is sufficient to reproduce the observed behavior.</p>
-<p>In larger models containing additional layers and MLPs, the precise mechanistic trace becomes more complex, but the core principle remains the same: context is propagated across layers and then used to conditionally retrieve tokens based on repeated subsequence matches. From this perspective, the circuit view complements feature-based analyses by explicitly identifying the causal structure responsible for induction behavior and clarifying how specific heads interact across layers to implement in-context pattern completion <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022a</a>)</span>.</p>
+<p>Following our introduction to induction heads in Section <a href="@@BASE@@handbook/feature-extraction/#subsec: induc head case study: feature extract" data-reference-type="ref" data-reference="subsec: induc head case study: feature extract">6.8</a>, we consider a repeated sequence:</p>
+<p><span class="math display">\texttt{A B C A B}</span></p>
+<p>where the model needs to predict the token after the second <code>B</code>. The induction head alone is not sufficient to implement induction behaviour from scratch. It still needs help from previous layers’ attention heads to locate the token following the current token’s previous occurrence, so it knows it should attend to <code>C</code> in this example <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.<br />
+Specifically, in a two-layer attention-only transformer, <span class="citation" data-cites="elhage2021mathematical"><a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al. (2021)</a></span> discovered the <em>previous-token head</em>, which writes every token’s information to the next position. The change propagates to the residual stream to be read by the induction head, allowing it to locate <code>C</code> (because now some information about the first <code>B</code> is contained in <code>C</code>). The two kinds of heads form the <em>induction circuit</em> that is sufficient to implement the induction behaviour in the two-layer small model.<br />
+Empirical evidence supports this mechanistic interpretation. Head ablation experiments show that induction heads causally support in-context learning in the attention-only model. Circuit analysis also reveals induction heads’ dependence on earlier previous-token heads. Moreover, induction heads emerge abruptly during training, coinciding with sharp improvements in in-context learning performance.<br />
+In larger models with additional layers and MLPs, the mechanism behind induction behaviour becomes more complex. However, the core principle remains the same: context propagates across layers and is used to conditionally retrieve tokens based on repeated subsequence matches. From this perspective, the circuit framework complements feature-based analysis by explicitly identifying the model’s internal mechanism that implements a specific behaviour.</p>
 </div>
 </section>
-<section id="sec:evaluation-framework" class="level2"><h2 data-pdf-page="43"><span class="secnum">7.6</span> A Unified Evaluation Framework for Mechanistic Interpretability<a class="anchor" href="#sec:evaluation-framework" aria-label="Link to this section">#</a></h2>
-<p>A persistent challenge in mechanistic interpretability is the lack of standardized evaluation. Different methods—SAEs, transcoders, circuit discovery, probing—are evaluated using incompatible metrics, making it difficult to compare approaches or assess progress. This section proposes a unified evaluation framework covering five dimensions: faithfulness, completeness, stability, causal validity, and human interpretability.</p>
-<section id="the-need-for-standardized-evaluation" class="level3"><h3 data-pdf-page="43"><span class="secnum">7.6.1</span> The Need for Standardized Evaluation<a class="anchor" href="#the-need-for-standardized-evaluation" aria-label="Link to this section">#</a></h3>
-<p>Current evaluation practices suffer from several problems:</p>
+<section id="sec:evaluation-framework" class="level2"><h2 data-pdf-page="58"><span class="secnum">7.5</span> A Unified Evaluation Framework for Mechanistic Interpretability<a class="anchor" href="#sec:evaluation-framework" aria-label="Link to this section">#</a></h2>
+<p>A persistent challenge in mechanistic interpretability (MI) is the lack of standardised evaluation. Different interpretability methods use incompatible metrics. For example, for the same behaviour of interest, circuit discovery can work at the level of MLP/attention modules <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic">(<a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>)</span>, CLT features <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>, or even neurons <span class="citation" data-cites="arora2026language">(<a href="@@BASE@@references/#ref-arora2026language" class="cite cite-established" data-cite="arora2026language" role="doc-biblioref">Arora et al., 2026</a>)</span>. A CLT-based circuit must also verify CLT fidelity; an MLP/attention-level circuit should test and explain each node’s role. Thus, it is hard to compare them and measure progress. To address this, this section presents a unified evaluation framework covering five dimensions—attribution faithfulness, completeness, stability, causal validity, and human interpretability—where different methods can be jointly tested.</p>
+<section id="the-need-for-standardised-evaluation" class="level3"><h3 data-pdf-page="58"><span class="secnum">7.5.1</span> The Need for Standardised Evaluation<a class="anchor" href="#the-need-for-standardised-evaluation" aria-label="Link to this section">#</a></h3>
+<p>Before we discuss the five dimensions, we first explain in detail the need for standardised evaluation. Specifically, current evaluation practices face several challenges:</p>
 <ul>
-<li><p><strong>Metric fragmentation</strong>: SAEs are evaluated on reconstruction loss; circuits on task performance recovery; probes on classification accuracy. These metrics are not directly comparable.</p></li>
-<li><p><strong>Cherry-picking</strong>: Results are often reported on favorable examples without systematic coverage of failure cases.</p></li>
-<li><p><strong>Missing baselines</strong>: Claims of interpretability gains rarely compare against simple baselines (e.g., random directions, PCA components, raw neurons).</p></li>
-<li><p><strong>Reproducibility gaps</strong>: Hyperparameter sensitivity and seed dependence are underreported, making replication difficult.</p></li>
+<li><p><strong>Utility ambiguity:</strong> Methods are initially developed for different aims and evaluated against distinct objectives, yet have overlapping application scenarios. For example, SAEs are assessed using reconstruction error and sparsity metrics; circuits are evaluated using behavioural recovery or faithfulness tests; and difference-in-means steering vectors (see Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a>) are examined by behaviour change—although all three approaches can be used to steer model outputs. Their unclear intended uses make it hard to compare their utility.</p></li>
+<li><p><strong>Cherry-picking:</strong> Existing work often reports interpretability results on carefully selected examples, samples from a narrow distribution, or capabilities where a method performs well. It may not report negative results, failure cases, or coverage statistics. This makes it difficult to assess the robustness and generality of the findings and determine whether the proposed method genuinely outperforms existing ones.</p></li>
+<li><p><strong>Missing baselines:</strong> Many proposed methods’ claimed interpretability gains are not systematically and adequately compared against intuitive alternatives or baselines such as behavioural methods, random activations, or neuron-level analysis. Without this comparison, it is unclear whether a method extracts genuinely meaningful information.</p></li>
+<li><p><strong>Reproducibility gaps:</strong> Many existing techniques involve substantial hyperparameter choices, heavy training procedures, and high variation across seemingly irrelevant factors. Reproducibility is often underreported and has not yet been established as a property to optimise.</p></li>
+<li><p><strong>Faithfulness–understandability gap:</strong> Methods often demonstrate how understandable and informative their outputs are to humans. However, those outputs’ faithfulness to the underlying model mechanism receives less attention. This rewards explanations that are persuasive rather than faithful.</p></li>
 </ul>
-<p>A unified framework enables principled comparison across methods, systematic identification of failure modes, and clearer communication of what interpretability methods actually achieve.</p>
+<p><span class="citation" data-cites="lan-etal-2026-make"><a href="@@BASE@@references/#ref-lan-etal-2026-make" class="cite cite-established" data-cite="lan-etal-2026-make" role="doc-biblioref">Lan et al. (2026)</a></span> have called for a shared platform for tracking and auditing interpretability insights and claims across papers; <span class="citation" data-cites="philosophicalevalframework"><a href="@@BASE@@references/#ref-philosophicalevalframework" class="cite cite-emerging" data-cite="philosophicalevalframework" role="doc-biblioref">Ayonrinde &amp; Jaburi (2025)</a></span> proposed a framework that evaluates mechanistic explanations from several philosophy-of-science perspectives. However, these discussions remain largely conceptual. The MI community needs standardised, concrete evaluation protocols and benchmarks, along with established reporting practices, to enable principled comparisons across methods and findings. They are especially important for measuring real progress in the field, identifying systematic failure modes, and providing evidence about what methods do and do not reveal. More broadly, they are necessary for mechanistic interpretability to mature from a collection of promising techniques into a cumulative scientific discipline.</p>
 </section>
-<section id="five-evaluation-dimensions" class="level3"><h3 data-pdf-page="43"><span class="secnum">7.6.2</span> Five Evaluation Dimensions<a class="anchor" href="#five-evaluation-dimensions" aria-label="Link to this section">#</a></h3>
-<p>We propose evaluating mechanistic interpretability methods across five complementary dimensions. No single dimension suffices; strong methods should perform well across all five.</p>
+<section id="subsec:five eval dim" class="level3"><h3 data-pdf-page="59"><span class="secnum">7.5.2</span> Five Evaluation Dimensions<a class="anchor" href="#subsec:five eval dim" aria-label="Link to this section">#</a></h3>
+<p>We propose a framework that situates mechanistic interpretability evaluation within five complementary dimensions. No single dimension suffices—strong methods should perform well across all five. Table <a href="@@BASE@@handbook/circuits/#tab:evaluation-framework" data-reference-type="ref" data-reference="tab:evaluation-framework">9</a> summarises these five dimensions. We describe each one with example metrics that convey the idea.</p>
 <table id="tab:evaluation-framework">
-<caption><span class="capnum">Table 10:</span> Unified Evaluation Framework for Mechanistic Interpretability</caption>
+<caption><span class="capnum">Table 9:</span> Unified evaluation framework for mechanistic interpretability.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Dimension</strong></span></th>
@@ -170,233 +156,394 @@ <h4>Limitations of Causal Verification.</h4>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;"><strong>Faithfulness</strong></td>
-<td style="text-align: left;">Do identified components accurately reflect the model’s actual computation?</td>
-<td style="text-align: left;">Causal effect size; intervention consistency; counterfactual accuracy</td>
-<td style="text-align: left;">Ablation; activation patching; interchange interventions</td>
+<td style="text-align: left;"><strong>Attribution Faithfulness</strong></td>
+<td style="text-align: left;">Do attribution scores accurately reflect the causal effects of the identified components?</td>
+<td style="text-align: left;">Intervention-effect agreement; effect-size error; counterfactual effect recovery</td>
+<td style="text-align: left;">Ablation; activation / path patching; interchange interventions</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Completeness</strong></td>
-<td style="text-align: left;">What fraction of model behavior is explained by the identified components?</td>
-<td style="text-align: left;">Behavior coverage; reconstruction loss; unexplained variance</td>
-<td style="text-align: left;">Loss recovery; behavioral replication; residual analysis</td>
+<td style="text-align: left;">How much of the behaviour is accounted for by the identified components?</td>
+<td style="text-align: left;">Loss recovery; behavioural coverage; unexplained variance</td>
+<td style="text-align: left;">Reconstruction; behavioural evaluation; residual analysis</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Stability</strong></td>
-<td style="text-align: left;">Are findings robust across seeds, checkpoints, and hyperparameters?</td>
+<td style="text-align: left;">Are findings robust to variations in factors like random seeds, hyperparameters, paraphrases, etc.?</td>
 <td style="text-align: left;">Cross-seed agreement; checkpoint consistency; hyperparameter sensitivity</td>
-<td style="text-align: left;">Multi-seed training; checkpoint comparison; ablation studies</td>
+<td style="text-align: left;">Multi-seed analysis; checkpoint comparison; input paraphrasing</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Causal Validity</strong></td>
-<td style="text-align: left;">Do components satisfy necessity and sufficiency for target behaviors?</td>
-<td style="text-align: left;">Necessity score; sufficiency score; specificity</td>
-<td style="text-align: left;">Causal scrubbing; knockout experiments; circuit verification</td>
+<td style="text-align: left;">Are the identified components necessary, sufficient, and specific for the target behaviour?</td>
+<td style="text-align: left;">Necessity score; sufficiency score; specificity score</td>
+<td style="text-align: left;">Ablation; component isolation; interchange interventions; tests with control behaviours</td>
 </tr>
 <tr>
-<td style="text-align: left;"><strong>Interpretability</strong></td>
-<td style="text-align: left;">Can humans understand and use the explanations?</td>
-<td style="text-align: left;">Human accuracy; explanation utility; prediction improvement</td>
-<td style="text-align: left;">User studies; expert evaluation; downstream task performance</td>
+<td style="text-align: left;"><strong>Human Interpretability</strong></td>
+<td style="text-align: left;">Can humans understand and use the resulting explanations?</td>
+<td style="text-align: left;">Explanation utility; debugging utility; interpretation agreement</td>
+<td style="text-align: left;">User studies; expert evaluation</td>
 </tr>
 </tbody>
 </table>
+<p>Note that throughout this subsection, we use <em>model components</em> to refer to any unit of analysis, including individual features, neurons, attention heads, circuits, and connections representing information flow between components. We operationalise the model behaviour of interest, <span class="math inline">B</span>, with a set of input prompts <span class="math inline">x \sim B</span>. Following the notation introduced in Section <a href="@@BASE@@handbook/circuits/#sec:circuit-discovery-methods" data-reference-type="ref" data-reference="sec:circuit-discovery-methods">7.1</a>, we denote the model by <span class="math inline">f_\theta(\cdot)</span>, a scalar behaviour-relevant performance metric by <span class="math inline">M(\cdot)</span>, and an intervention on components <span class="math inline">S</span> by <span class="math display">f_\theta\!\left(x_{[S \leftarrow \tilde h_S]}\right),</span> where the activations of <span class="math inline">S</span> are replaced by reference activations <span class="math inline">\tilde h_S</span> while the remaining computation proceeds normally. Following Section <a href="@@BASE@@handbook/circuits/#sec:circuit-discovery-methods" data-reference-type="ref" data-reference="sec:circuit-discovery-methods">7.1</a>, we define the intervention effect of <span class="math inline">S</span> on an input <span class="math inline">x</span> as <span class="math display">s(S,x)
+\coloneqq
+M\!\left(f_\theta(x)\right)
+-
+M\!\left(
+f_\theta\!\left(x_{[S \leftarrow \tilde h_S]}\right)
+\right)</span> and the average intervention effect on behaviour <span class="math inline">B</span> as <span class="math display">s(S,B)
+\coloneqq
+\mathbb{E}_{x \sim B}\left[s(S,x)\right].</span></p>
 </section>
-<section id="dimension-1-faithfulness" class="level3"><h3 data-pdf-page="43"><span class="secnum">7.6.3</span> Dimension 1: Faithfulness<a class="anchor" href="#dimension-1-faithfulness" aria-label="Link to this section">#</a></h3>
-<p><strong>Definition</strong>: Faithfulness measures whether identified components (features, circuits, explanations) accurately reflect the model’s actual internal computation, rather than post-hoc rationalizations or correlational artifacts.</p>
+<section id="dimension-1-attribution-faithfulness" class="level3 unnumbered"><h3 class="unnumbered">Dimension 1: Attribution Faithfulness<a class="anchor" href="#dimension-1-attribution-faithfulness" aria-label="Link to this section">#</a></h3>
+<p>Attribution faithfulness measures whether the interpretability method accurately attributes the model’s behaviour of interest to responsible components.</p>
 <div class="definitionbox">
-<p><strong>Causal Effect Size</strong>: For a component <span class="math inline">C</span> claimed to implement behavior <span class="math inline">B</span>, measure: <span class="math display">\text{Effect}(C, B) = \frac{|P(B | \text{do}(C = \text{active})) - P(B | \text{do}(C = \text{ablated}))|}{P(B | \text{baseline})}</span> Higher values indicate stronger causal relationship between the component and behavior.</p>
-<p><strong>Intervention Consistency</strong>: Across multiple interventions on <span class="math inline">C</span>, do effects on <span class="math inline">B</span> remain consistent? <span class="math display">\text{Consistency}(C, B) = 1 - \frac{\text{Var}[\text{Effect}(C, B)]}{\mathbb{E}[\text{Effect}(C, B)]^2}</span></p>
-<p><strong>Counterfactual Accuracy</strong>: When component <span class="math inline">C</span> is patched from input <span class="math inline">x_1</span> to input <span class="math inline">x_2</span>, does the model’s behavior shift as predicted? <span class="math display">\text{CF-Acc} = \mathbb{E}_{x_1, x_2}\left[\mathbf{1}\left[f(x_1|_{C \leftarrow C(x_2)}) \approx f(x_2)\right]\right]</span></p>
+<section id="intervention-effect-agreement." class="level4">
+<h4>Intervention-Effect Agreement.</h4>
+<p>Let <span class="math inline">a(S,x)</span> denote the importance score assigned by an interpretability method to components <span class="math inline">S</span> for a sample <span class="math inline">x \sim B</span>: <span class="math display">\mathrm{EffectAgreement}
+=
+\rho_{x \sim B}\!\left(
+a(S,x),
+s(S,x)
+\right),</span> where <span class="math inline">\rho</span> may be Pearson or Spearman correlation. High agreement indicates that the attribution consistently reflects the identified components’ causal effects on behaviour <span class="math inline">B</span>.<br />
+</p>
+</section>
+<section id="effect-size-error." class="level4">
+<h4>Effect-Size Error.</h4>
+<p>When attribution scores are calculated to estimate intervention effects, we measure the effect-size error as: <span class="math display">\mathrm{EffectError}
+=
+\mathbb{E}_{x \sim B}
+\left[
+\left|
+a(S,x)-s(S,x)
+\right|
+\right].</span> A low value indicates good calibration between estimated and actual intervention effects. If the two quantities are not naturally on the same scale, we may use normalised or rank-based measures instead.<br />
+</p>
+</section>
+<section id="counterfactual-effect-recovery." class="level4">
+<h4>Counterfactual Effect Recovery.</h4>
+<p>Consider inputs <span class="math inline">x_1 \sim B&#39;</span> and <span class="math inline">x_2 \sim B</span>, where <span class="math inline">B&#39;</span> is a reference behaviour (e.g., honesty) and <span class="math inline">B</span> is the behaviour of interest (e.g., sycophancy). Patching <span class="math inline">S</span> from <span class="math inline">x_2</span> into the computation on <span class="math inline">x_1</span> yields <span class="math display">\mathrm{CFRecovery}(S;B,B&#39;)
+=
+\mathbb{E}_{x_1 \sim B&#39;, x_2 \sim B}
+\frac{
+M\!\left(
+f_\theta\!\left(
+x_{1,[S \leftarrow h_S(x_2)]}
+\right)
+\right)
+-
+M\!\left(f_\theta(x_1)\right)
+}{
+M\!\left(f_\theta(x_2)\right)
+-
+M\!\left(f_\theta(x_1)\right)
+}.</span> A value near 1 indicates that the patching recovers much of the behaviour, while a value near 0 indicates little effect. This metric has been widely used in the circuit discovery literature (<span class="citation" data-cites="eapig eapgp"><a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-eapgp" class="cite cite-established" data-cite="eapgp" role="doc-biblioref">Zhang et al., 2025b</a></span>; <span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated"><a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a></span>). However, the denominator of this metric can be close to <span class="math inline">0</span>, and <span class="math inline">M\!\left(
+f_\theta\!\left(
+x_{1,[S \leftarrow h_S(x_2)]}
+\right)
+\right)</span> empirically can exceed <span class="math inline">M\!\left(f_\theta(x_2)\right)</span> or fall below <span class="math inline">M\!\left(f_\theta(x_1)\right)</span>. These may cause this metric to take values much larger than <span class="math inline">1</span> or below <span class="math inline">0</span>, complicating the analysis. Recent work has proposed an alternative metric that bounds the value between 0 and 1 <span class="citation" data-cites="querycircuit">(<a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>.</p>
+</section>
 </div>
-<p><strong>Evaluation protocol</strong>:</p>
+<section id="evaluation-protocol" class="level4">
+<h4>Evaluation protocol:</h4>
 <ol>
-<li><p>Identify component <span class="math inline">C</span> via the interpretability method being evaluated.</p></li>
-<li><p>Define target behavior <span class="math inline">B</span> with clear operational criteria.</p></li>
-<li><p>Perform ablation: replace <span class="math inline">C</span> with baseline (zero, mean, noise) and measure change in <span class="math inline">B</span>.</p></li>
-<li><p>Perform activation patching: swap <span class="math inline">C</span> between clean and corrupted inputs; verify behavior follows <span class="math inline">C</span>.</p></li>
-<li><p>Report effect sizes with confidence intervals across multiple inputs.</p></li>
+<li><p>Apply the method to acquire attribution scores <span class="math inline">a(S,x)</span> of model components and select those of interest (e.g., components with high scores).</p></li>
+<li><p>Measure the corresponding ground-truth intervention effects <span class="math inline">s(S,x)</span>.</p></li>
+<li><p>Compare them using correlation, error, or rank agreement.</p></li>
+<li><p>Evaluate how much of the behavioural difference between clean and corrupted inputs can be recovered by patching the identified components.</p></li>
+<li><p>Report results across a diverse set of inputs with uncertainty estimates.</p></li>
 </ol>
-<p><strong>Baselines</strong>: Compare against random components, PCA directions, and individual neurons to establish that identified components are more faithful than naive alternatives.</p>
 </section>
-<section id="dimension-2-completeness" class="level3"><h3 data-pdf-page="44"><span class="secnum">7.6.4</span> Dimension 2: Completeness<a class="anchor" href="#dimension-2-completeness" aria-label="Link to this section">#</a></h3>
-<p><strong>Definition</strong>: Completeness measures what fraction of model behavior is explained by the identified components. High completeness means the method accounts for most of the model’s computation; low completeness means significant behavior remains unexplained.</p>
+</section>
+<section id="dimension-2-completeness" class="level3 unnumbered"><h3 class="unnumbered">Dimension 2: Completeness<a class="anchor" href="#dimension-2-completeness" aria-label="Link to this section">#</a></h3>
+<p>Completeness measures how much of the model behaviour the identified components or mechanisms explain. A high completeness score indicates that the identified components/mechanisms capture most of the relevant computation.</p>
 <div class="definitionbox">
-<p><strong>Loss Recovery</strong>: After replacing model activations with reconstructions from identified components, what fraction of original loss is preserved? <span class="math display">\text{LR} = 1 - \frac{\mathcal{L}_{\text{reconstructed}} - \mathcal{L}_{\text{original}}}{\mathcal{L}_{\text{random}} - \mathcal{L}_{\text{original}}}</span> where <span class="math inline">\mathcal{L}_{\text{random}}</span> is loss with random/ablated activations.</p>
-<p><strong>Behavioral Coverage</strong>: Across a diverse test suite, what fraction of behaviors <span class="math inline">\mathcal{B}</span> can be explained by identified components? <span class="math display">\text{Coverage} = \frac{|\{b \in \mathcal{B} : \exists C \text{ with } \text{Effect}(C, b) \geq \tau\}|}{|\mathcal{B}|}</span></p>
-<p>where <span class="math inline">\tau</span> is set to be the smallest effect that counts as meaningful.</p>
-<p><strong>Unexplained Variance</strong>: What fraction of activation variance is not captured by identified features? <span class="math display">\text{UV} = \frac{\mathbb{E}[\|h - \hat{h}\|^2]}{\mathbb{E}[\|h\|^2]}</span> where <span class="math inline">h</span> is a model activation and <span class="math inline">\hat{h}</span> is the reconstruction from identified components.</p>
+<section id="loss-recovery." class="level4">
+<h4>Loss Recovery.</h4>
+<p>For methods that reconstruct model activations (e.g., SAEs and CLTs), after replacing the target component’s activations with the reconstructions, we measure the fraction of the original model performance that is recovered: <span class="math display">\mathrm{LR}
+=
+\frac{
+\mathcal{L}_{\mathrm{reconstructed}}
+-
+\mathcal{L}_{\mathrm{ablated}}
+}{
+\mathcal{L}_{\mathrm{original}}
+-
+\mathcal{L}_{\mathrm{ablated}}
+},</span> where <span class="math inline">\mathcal{L}_{\mathrm{original}}</span> is the downstream test loss of the original model, <span class="math inline">\mathcal{L}_{\mathrm{reconstructed}}</span> is the loss after replacing the original activations with reconstructions, and <span class="math inline">\mathcal{L}_{\mathrm{ablated}}</span> is the baseline loss where activations are replaced with zero ablation, mean ablation, or Gaussian noise matching the layer statistics. A value near <span class="math inline">1</span> indicates that the reconstruction preserves most of the information. Similar to the vanilla Counterfactual Effect Recovery metric discussed above, this metric can take arbitrary values. A simple solution is to develop a bounded variant, as has been done for Counterfactual Effect Recovery <span class="citation" data-cites="querycircuit">(<a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>.<br />
+</p>
+</section>
+<section id="behavioural-coverage." class="level4">
+<h4>Behavioural Coverage.</h4>
+<p>Across input prompts <span class="math inline">x \sim B</span>, we measure the fraction for which the identified components have a meaningful intervention effect: <span class="math display">\mathrm{Coverage}(S,B)
+=
+\mathbb{E}_{x \sim B}
+\left[
+\mathbbm{1}
+\left(
+|s(S,x)| \geq \tau
+\right)
+\right],</span> where <span class="math inline">s(S,x)</span> is the intervention effect defined above and <span class="math inline">\tau</span> specifies the minimum effect considered meaningful. Higher coverage indicates that the identified components account for the behaviour across a larger fraction of inputs.<br />
+</p>
+</section>
+<section id="unexplained-variance." class="level4">
+<h4>Unexplained Variance.</h4>
+<p>For methods that reconstruct activations of the components, we measure the fraction of unexplained activation variance as: <span class="math display">\mathrm{UV}(S,B)
+=
+\frac{
+\mathbb{E}_{x \sim B}
+\left[
+\|h(x)-\hat{h}_S(x)\|_2^2
+\right]
+}{
+\mathbb{E}_{x \sim B}
+\left[
+\|h(x)-\bar{h}_B\|_2^2
+\right]
+},</span> where <span class="math inline">\bar{h}_B = \mathbb{E}_{x \sim B}[h(x)]</span>, <span class="math inline">h(x)</span> is the original model activation, and <span class="math inline">\hat{h}_S(x)</span> is its reconstruction from the identified components <span class="math inline">S</span>. Lower values indicate that the identified components explain a larger fraction of the variance in the original activations.</p>
+</section>
 </div>
-<p><strong>Evaluation protocol</strong>:</p>
+<section id="evaluation-protocol-1" class="level4">
+<h4>Evaluation protocol:</h4>
 <ol>
-<li><p>Define a comprehensive behavioral test suite spanning diverse capabilities (factual recall, reasoning, safety behaviors, etc.).</p></li>
-<li><p>For each behavior, attempt to identify responsible components.</p></li>
-<li><p>Report coverage: fraction of behaviors with identified components meeting faithfulness thresholds.</p></li>
-<li><p>Report reconstruction metrics: loss recovery and unexplained variance.</p></li>
-<li><p>Analyze failure cases: which behaviors resist explanation?</p></li>
+<li><p>Report behavioural coverage using the intervention effects <span class="math inline">s(S,x)</span>.</p></li>
+<li><p>Report reconstruction-based metrics such as loss recovery and unexplained variance if applicable.</p></li>
+<li><p>Analyse inputs of the behaviour for which the identified components do not provide good coverage or reconstruction.</p></li>
 </ol>
-<p><strong>Critical consideration</strong>: Methods may achieve high faithfulness on explained behaviors while leaving most behavior unexplained. Completeness ensures we track the gap between what is explained and what remains opaque.</p>
 </section>
-<section id="dimension-3-stability" class="level3"><h3 data-pdf-page="45"><span class="secnum">7.6.5</span> Dimension 3: Stability<a class="anchor" href="#dimension-3-stability" aria-label="Link to this section">#</a></h3>
-<p><strong>Definition</strong>: Stability measures whether findings are robust to incidental choices—random seeds, training checkpoints, hyperparameter settings—that should not affect conclusions about model internals.</p>
+</section>
+<section id="dimension-3-stability" class="level3 unnumbered"><h3 class="unnumbered">Dimension 3: Stability<a class="anchor" href="#dimension-3-stability" aria-label="Link to this section">#</a></h3>
+<p>Stability measures the robustness of explanations, attributions, and findings to changes in variables that should not affect the results, such as the choice of random seeds, small hyperparameter changes, and input paraphrasing.</p>
 <div class="definitionbox">
-<p><strong>Cross-Seed Agreement</strong>: Train the interpretability method (e.g., SAE) with <span class="math inline">K</span> different random seeds. Measure feature alignment: <span class="math display">\text{Seed-Stability} = \frac{1}{K(K-1)} \sum_{i \neq j} \text{Align}(F_i, F_j)</span> where <span class="math inline">\text{Align}</span> measures correspondence between feature sets (e.g., via Hungarian matching on cosine similarity).</p>
-<p><strong>Checkpoint Consistency</strong>: For features/circuits identified at checkpoint <span class="math inline">t</span>, do they persist at checkpoints <span class="math inline">t \pm \Delta</span>? <span class="math display">\text{Ckpt-Stability} = \mathbb{E}_t[\text{Align}(F_t, F_{t+\Delta})]</span></p>
-<p><strong>Hyperparameter Sensitivity</strong>: How much do results change under reasonable hyperparameter variation? <span class="math display">\text{HP-Sensitivity} = \frac{\max_{\theta \in \Theta} \text{Metric}(\theta) - \min_{\theta \in \Theta} \text{Metric}(\theta)}{\mathbb{E}_{\theta \in \Theta}[\text{Metric}(\theta)]}</span> Lower sensitivity indicates more robust findings.</p>
+<section id="cross-seed-agreement." class="level4">
+<h4>Cross-Seed Agreement.</h4>
+<p>We train or apply the interpretability method <span class="math inline">K</span> times under different random seeds. Let <span class="math inline">\mathcal{F}_i</span> denote the set of features or mechanisms, such as SAE features and circuits, discovered under seed <span class="math inline">i</span>. We measure the cross-seed agreement as: <span class="math display">\text{Seed-Stability} = \frac{1}{K(K-1)} \sum_{i \neq j} \text{Align}(\mathcal{F}_i, \mathcal{F}_j),</span> where <span class="math inline">\text{Align}(\cdot)</span> measures similarity between sets. For example, <span class="citation" data-cites="paulo2025sparse"><a href="@@BASE@@references/#ref-paulo2025sparse" class="cite cite-established" data-cite="paulo2025sparse" role="doc-biblioref">Paulo &amp; Belrose (2026)</a></span> calculate the Hungarian matching between two sets of SAE features based on their pairwise cosine similarities.<br />
+</p>
+</section>
+<section id="hyperparameter-sensitivity." class="level4">
+<h4>Hyperparameter Sensitivity.</h4>
+<p>We measure the sensitivity of mechanistic analyses or methods under reasonable hyperparameter variations. Let <span class="math inline">\Omega</span> denote the set of evaluated hyperparameter configurations and <span class="math inline">\text{Metric}(\omega)</span> denote the experimental result (e.g., a method’s performance) under a configuration <span class="math inline">\omega \in \Omega</span>. We calculate the hyperparameter sensitivity as: <span class="math display">\text{HP-Sensitivity}
+=
+\frac{
+\max_{\omega \in \Omega} \text{Metric}(\omega)
+-
+\min_{\omega \in \Omega} \text{Metric}(\omega)
+}{
+\mathbb{E}_{\omega \in \Omega}[\text{Metric}(\omega)]
+}.</span> Lower values indicate that the finding is less sensitive to hyperparameter choices and therefore more robust.<br />
+<strong>Paraphrase Consistency.</strong> For each input <span class="math inline">x \sim B</span>, let <span class="math inline">\mathcal{P}(x) = \{x^{(1)}, \dots, x^{(K)}\}</span> denote a set of semantics-preserving paraphrases of <span class="math inline">x</span>. We measure the consistency of explanations or attributions across <span class="math inline">\mathcal{P}(x)</span>. For example, we can measure whether the intervention effect of <span class="math inline">S</span> remains consistent: <span class="math display">\text{Para-Consistency}(S, B)
+= \mathbb{E}_{x \sim B}\left[
+ 1 - \frac{\mathrm{Var}_{x&#39; \in \mathcal{P}(x)}\left[s(S, x&#39;)\right]}
+          {\mathbb{E}_{x&#39; \in \mathcal{P}(x)}\left[s(S, x&#39;)^2\right]}
+\right].</span> The score lies in <span class="math inline">[0, 1]</span>. A value of <span class="math inline">1</span> indicates that the intervention effect is identical across all paraphrases. A value of <span class="math inline">0</span> indicates that, for each <span class="math inline">x</span>, the expected intervention effect, i.e., <span class="math inline">\mathbb{E}_{x&#39; \in \mathcal{P}(x)}s(S, x&#39;)</span>, is 0.</p>
+</section>
 </div>
-<p><strong>Evaluation protocol</strong>:</p>
+<section id="evaluation-protocol-2" class="level4">
+<h4>Evaluation protocol:</h4>
 <ol>
-<li><p>Train interpretability method with at least 3–5 random seeds.</p></li>
-<li><p>Compute feature/circuit alignment across seeds.</p></li>
-<li><p>Report which findings are stable (appear across all seeds) vs. unstable (seed-specific).</p></li>
-<li><p>For key claims, verify they hold under hyperparameter variation (<span class="math inline">\pm 20\%</span> on key parameters).</p></li>
-<li><p>Distinguish “core” findings (stable) from “fragile” findings (unstable).</p></li>
+<li><p>Execute the interpretability method with several random seeds and compute alignment of the discovered units or findings across seeds.</p></li>
+<li><p>Analyse which findings are stable across all seeds and which ones are seed-specific. Key claims need to remain consistent under subsequent tests on hyperparameter variation and input paraphrasing, if applicable.</p></li>
+<li><p>Distinguish robust findings from fragile ones and report both of them.</p></li>
 </ol>
-<p><strong>Reporting recommendation</strong>: Papers should report stability metrics alongside primary results. Claims based on unstable findings should be flagged as preliminary.</p>
 </section>
-<section id="dimension-4-causal-validity" class="level3"><h3 data-pdf-page="45"><span class="secnum">7.6.6</span> Dimension 4: Causal Validity<a class="anchor" href="#dimension-4-causal-validity" aria-label="Link to this section">#</a></h3>
-<p><strong>Definition</strong>: Causal validity assesses whether identified components satisfy formal causal criteria of necessity (removing the component disrupts the behavior) and sufficiency (the component alone can produce the behavior).</p>
+</section>
+<section id="dimension-4-causal-validity" class="level3 unnumbered"><h3 class="unnumbered">Dimension 4: Causal Validity<a class="anchor" href="#dimension-4-causal-validity" aria-label="Link to this section">#</a></h3>
+<p>Causal validity assesses whether the identified components or mechanisms are causal to the behaviour, particularly necessity (i.e., removing the components/mechanisms disrupts the behaviour) and sufficiency (i.e., the components/mechanisms alone are sufficient to preserve the behaviour).</p>
 <div class="definitionbox">
-<p><strong>Necessity Score</strong>: Fraction of behavior explained by the component that is lost when the component is ablated: <span class="math display">\text{Necessity}(C, B) = \frac{P(B | \text{baseline}) - P(B | \text{do}(C = \text{ablated}))}{P(B | \text{baseline})}</span> High necessity means the component is required for the behavior.</p>
-<p><strong>Sufficiency Score</strong>: Fraction of behavior that can be produced by the component alone (all other components ablated or set to baseline): <span class="math display">\text{Sufficiency}(C, B) = \frac{P(B | \text{only } C \text{ active})}{P(B | \text{baseline})}</span> High sufficiency means the component is enough for the behavior.</p>
-<p><strong>Specificity</strong>: Does the component affect only the claimed behavior, or does it have broad side effects? <span class="math display">\text{Specificity}(C, B) = \frac{\text{Effect}(C, B)}{\sum_{B&#39; \in \mathcal{B}} \text{Effect}(C, B&#39;)}</span> High specificity means the component is selective for the target behavior.</p>
+<section id="necessity-score." class="level4">
+<h4>Necessity Score.</h4>
+<p>Necessity measures the fraction of the model’s performance on behaviour <span class="math inline">B</span> that is lost when <span class="math inline">S</span> is ablated (i.e., its activations are replaced with some reference values). It is calculated as: <span class="math display">\mathrm{Necessity}(S,B)
+=
+1-
+\frac{
+\mathbb{E}_{x\sim B}
+\left[
+M\!\left(
+f_\theta\!\left(
+x_{[S\leftarrow\tilde h_S]}
+\right)
+\right)
+\right]
+}{
+\mathbb{E}_{x\sim B}
+\left[
+M\!\left(f_\theta(x)\right)
+\right]
+}.</span> A higher value indicates that <span class="math inline">S</span> is necessary for the behaviour.<br />
+</p>
+</section>
+<section id="sufficiency-score." class="level4">
+<h4>Sufficiency Score.</h4>
+<p>Let <span class="math inline">\bar S</span> denote the components outside <span class="math inline">S</span>. Sufficiency measures how much of the original behaviour is achieved when only <span class="math inline">S</span> is active (i.e., activations of all components outside <span class="math inline">S</span> are replaced with some reference values). It is calculated as: <span class="math display">\mathrm{Sufficiency}(S,B)
+=
+\frac{
+\mathbb{E}_{x\sim B}
+\left[
+M\!\left(
+f_\theta\!\left(
+x_{[\bar S \leftarrow \tilde h_{\bar S}]}
+\right)
+\right)
+\right]
+}{
+\mathbb{E}_{x\sim B}
+\left[
+M\!\left(f_\theta(x)\right)
+\right]
+}.</span> A high value indicates that <span class="math inline">S</span> alone implements much of the behaviour.<br />
+</p>
+</section>
+<section id="specificity." class="level4">
+<h4>Specificity.</h4>
+<p>Specificity measures the effect on non-target behaviours by interventions on <span class="math inline">S</span>. It is calculated as: <span class="math display">\mathrm{Specificity}(S,B)
+=
+\frac{
+|s(S,B)|
+}{
+\sum_{B&#39; \in \mathcal{B}}
+|s(S,B&#39;)|
+},</span> where <span class="math inline">\mathcal{B}</span> contains the target behaviour together with a set of relevant control behaviours. A high value indicates that the causal effect of <span class="math inline">S</span> is specific to <span class="math inline">B</span>.</p>
+</section>
 </div>
-<p><strong>Evaluation protocol (Causal Scrubbing)</strong>:</p>
+<section id="evaluation-protocol-3" class="level4">
+<h4>Evaluation protocol:</h4>
 <ol>
-<li><p><strong>Necessity test</strong>: Ablate the identified circuit/feature. Measure degradation on target behavior. Compare to ablating random components of equal size.</p></li>
-<li><p><strong>Sufficiency test</strong>: Ablate all components except the identified circuit. Measure preservation of target behavior. Compare to random circuits of equal size.</p></li>
-<li><p><strong>Interchange test</strong>: Swap circuit activations between inputs that differ on the target behavior. Verify behavior follows the swapped circuit.</p></li>
-<li><p><strong>Specificity test</strong>: Measure effects on a suite of non-target behaviors. Compute specificity ratio.</p></li>
+<li><p><strong>Necessity test:</strong> Ablate <span class="math inline">S</span> and measure the resulting degradation on behaviour <span class="math inline">B</span>. Compare against ablating random components of matched size.</p></li>
+<li><p><strong>Sufficiency test:</strong> Replace components outside <span class="math inline">S</span> with reference activations and measure how much of behaviour <span class="math inline">B</span> is preserved. Compare against random component sets of matched size.</p></li>
+<li><p><strong>Interchange test:</strong> Swap activations of <span class="math inline">S</span> between inputs representing different behaviours and test whether the model output follows the intervention.</p></li>
+<li><p><strong>Specificity test:</strong> Measure intervention effects on a set of non-target behaviours and examine whether the effect is specific to <span class="math inline">B</span>.</p></li>
 </ol>
-<p><strong>Benchmarks</strong>: The MIB benchmark <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-emerging" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025a</a>)</span> provides standardized tasks and metrics for causal circuit evaluation. AxBench <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025</a>)</span> and RAVEL <span class="citation" data-cites="huang-etal-2024-ravel">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>)</span> provide feature-level causal evaluation.</p>
+<p>The MI community has developed several benchmarks that examine the causal validity of interpretability methods. For example, MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span> provides standardised tasks for assessing discovered circuits and causal variables (e.g., features); AxBench <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span> evaluates methods’ ability to steer models towards given concepts; RAVEL <span class="citation" data-cites="huang-etal-2024-ravel">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>)</span> investigates methods’ ability to localise features of given concepts by using interchange interventions to validate discovered features; and SAEBench <span class="citation" data-cites="karvonen2025saebench">(<a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span> evaluates SAEs specifically, pairing reconstruction metrics with downstream intervention tasks such as gender bias erasure.</p>
+</section>
 </section>
-<section id="dimension-5-human-interpretability" class="level3"><h3 data-pdf-page="46"><span class="secnum">7.6.7</span> Dimension 5: Human Interpretability<a class="anchor" href="#dimension-5-human-interpretability" aria-label="Link to this section">#</a></h3>
-<p><strong>Definition</strong>: Human interpretability measures whether explanations are actually useful to humans—whether they improve human understanding, prediction, or decision-making about model behavior.</p>
+<section id="dimension-5-human-interpretability" class="level3 unnumbered"><h3 class="unnumbered">Dimension 5: Human Interpretability<a class="anchor" href="#dimension-5-human-interpretability" aria-label="Link to this section">#</a></h3>
+<p>Human interpretability measures whether generated explanations eventually improve human understanding, prediction, and decision-making about model behaviour.</p>
 <div class="definitionbox">
-<p><strong>Explanation Accuracy</strong>: Can humans predict model behavior based on the explanation? <span class="math display">\text{Expl-Acc} = P(\text{human predicts } f(x) | \text{explanation of } f)</span> Compare to baseline prediction accuracy without explanation.</p>
-<p><strong>Simulatability</strong>: After seeing explanations, can humans simulate the model’s computation on new inputs? <span class="math display">\text{Simulatability} = \text{Corr}(\text{human prediction}, f(x_{\text{new}}))</span></p>
-<p><strong>Debugging Utility</strong>: Do explanations help humans identify and fix model errors? <span class="math display">\text{Debug-Utility} = \frac{\text{\#Errors fixed with explanation}}{\text{\#Errors fixed without explanation}}</span></p>
-<p><strong>Consistency of Interpretation</strong>: Do different humans interpret the same explanation consistently? <span class="math display">\text{Inter-Rater} = \kappa(\text{human}_1 \text{ labels}, \text{human}_2 \text{ labels})</span> where <span class="math inline">\kappa</span> is Cohen’s kappa or a similar agreement metric.</p>
+<section id="explanation-utility." class="level4">
+<h4>Explanation Utility.</h4>
+<p>This metric tests whether humans can predict model behaviour more accurately in the held-out set based on the explanation. It is formulated as: <span class="math display">\text{Expl-Utility}
+=
+P(\text{correct prediction} \mid \text{with explanation})
+-
+P(\text{correct prediction} \mid \text{no explanation})</span></p>
+</section>
+<section id="debugging-utility." class="level4">
+<h4>Debugging Utility.</h4>
+<p>This metric examines whether the explanations help humans identify and repair model errors, such as incorrect outputs or misaligned behaviour, in the wild. <span class="math display">\text{Debug-Utility}
+=
+P(\text{error fixed}\mid\text{with explanation})
+-
+P(\text{error fixed}\mid\text{no explanation}).</span></p>
+</section>
+<section id="consistency-of-interpretation." class="level4">
+<h4>Consistency of Interpretation.</h4>
+<p>This metric inspects whether different humans interpret the same explanation consistently. <span class="math display">\text{Inter-Rater} = \kappa(\text{human}_1 \text{ labels}, \text{human}_2 \text{ labels})</span> where <span class="math inline">\kappa(\cdot)</span> is Cohen’s kappa or a similar agreement metric.</p>
+</section>
 </div>
-<p><strong>Evaluation protocol</strong>:</p>
+<section id="evaluation-protocol-4" class="level4">
+<h4>Evaluation protocol:</h4>
 <ol>
-<li><p><strong>Prediction task</strong>: Show humans feature/circuit explanations. Ask them to predict model behavior on held-out inputs. Compare accuracy to no-explanation baseline.</p></li>
-<li><p><strong>Identification task</strong>: Show humans model outputs with errors. With and without mechanistic explanations, ask them to identify the cause. Measure accuracy and time.</p></li>
-<li><p><strong>Intervention task</strong>: Ask humans to propose fixes for model errors based on explanations. Measure success rate of proposed interventions.</p></li>
-<li><p><strong>Agreement task</strong>: Have multiple raters label feature interpretations. Measure inter-rater reliability.</p></li>
+<li><p><strong>Prediction task:</strong> Show humans explanations of model components or mechanisms and ask them to predict model behaviour on held-out inputs. Compare accuracy to a no-explanation baseline.</p></li>
+<li><p><strong>Identification task:</strong> Show humans model outputs with errors and ask them to identify the cause, with and without explanation assistance. Measure accuracy and required time to complete the task.</p></li>
+<li><p><strong>Intervention task:</strong> Ask humans to propose fixes for model errors based on explanations. Measure the success rate of proposed interventions.</p></li>
+<li><p><strong>Agreement task:</strong> Ask multiple human raters to generate explanations, if applicable (e.g., write a summary of highly activated samples of an SAE feature as its explanation). Measure inter-rater reliability and agreement with machine-generated explanations.</p></li>
 </ol>
-<p><strong>Considerations</strong>:</p>
-<ul>
-<li><p>Human studies are expensive and introduce subjectivity. Automated proxies (LLM-based evaluation) can supplement but not replace human evaluation.</p></li>
-<li><p>Expertise matters: interpretations accessible to ML researchers may be opaque to domain experts, and vice versa.</p></li>
-<li><p>Task-specificity: a feature interpretable for one task may be confusing for another.</p></li>
-</ul>
-</section>
-<section id="integrated-evaluation-protocol" class="level3"><h3 data-pdf-page="47"><span class="secnum">7.6.8</span> Integrated Evaluation Protocol<a class="anchor" href="#integrated-evaluation-protocol" aria-label="Link to this section">#</a></h3>
-<p>We recommend the following integrated protocol for comprehensive evaluation:</p>
-<div class="definitionbox">
-<p><strong>Step 1: Define scope</strong></p>
-<ul>
-<li><p>Specify the model, layer(s), and behavior(s) under analysis.</p></li>
-<li><p>Define a behavioral test suite with clear success criteria.</p></li>
-</ul>
-<p><strong>Step 2: Establish baselines</strong></p>
-<ul>
-<li><p>Random directions in activation space.</p></li>
-<li><p>PCA components.</p></li>
-<li><p>Individual neurons.</p></li>
-<li><p>Supervised probes (if labels available).</p></li>
-</ul>
-<p><strong>Step 3: Apply interpretability method</strong></p>
-<ul>
-<li><p>Train with multiple seeds (minimum 3).</p></li>
-<li><p>Record hyperparameters and training details for reproducibility.</p></li>
-</ul>
-<p><strong>Step 4: Evaluate across all five dimensions</strong></p>
-<ul>
-<li><p>Faithfulness: ablation and patching experiments.</p></li>
-<li><p>Completeness: loss recovery and behavioral coverage.</p></li>
-<li><p>Stability: cross-seed and hyperparameter sensitivity.</p></li>
-<li><p>Causal validity: necessity, sufficiency, specificity tests.</p></li>
-<li><p>Human interpretability: user studies or proxy metrics.</p></li>
-</ul>
-<p><strong>Step 5: Report comprehensively</strong></p>
-<ul>
-<li><p>Report all five dimensions, not just favorable metrics.</p></li>
-<li><p>Include failure cases and unexplained behaviors.</p></li>
-<li><p>Compare to baselines on all metrics.</p></li>
-<li><p>Provide confidence intervals and significance tests.</p></li>
-</ul>
-</div>
-</section>
-<section id="applying-the-framework-method-specific-guidance" class="level3"><h3 data-pdf-page="47"><span class="secnum">7.6.9</span> Applying the Framework: Method-Specific Guidance<a class="anchor" href="#applying-the-framework-method-specific-guidance" aria-label="Link to this section">#</a></h3>
-<p>Different interpretability methods emphasize different dimensions. Table <a href="@@BASE@@handbook/circuits/#tab:method-evaluation" data-reference-type="ref" data-reference="tab:method-evaluation">11</a> provides guidance on applying the framework to common methods.</p>
+<p>Several caveats and challenges arise when evaluating the human interpretability of tested methods. For example, human studies are usually expensive. Moreover, human expertise may confound results, where some explanations understandable to MI researchers may be opaque to domain experts, and vice versa.</p>
+<p>Note that different interpretability methods may emphasise distinct evaluation dimensions and operationalise them differently. Table <a href="@@BASE@@handbook/circuits/#tab:method-evaluation" data-reference-type="ref" data-reference="tab:method-evaluation">10</a> provides guidance on applying this framework to some common interpretability approaches.</p>
 <table id="tab:method-evaluation">
-<caption><span class="capnum">Table 11:</span> Evaluation Priorities by Method Type</caption>
+<caption><span class="capnum">Table 10:</span> Recommended evaluation priorities by interpretability method. The entries indicate evaluation aspects particularly important for each method, not mutually exclusive metrics.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Method</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Faithfulness</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Attribution Faithfulness</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Completeness</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Stability</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Causal</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Human</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Causal Validity</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Human Interpretability</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">SAEs</td>
-<td style="text-align: left;">Feature ablation</td>
-<td style="text-align: left;">Loss recovery; UV</td>
-<td style="text-align: left;">Seed stability critical</td>
-<td style="text-align: left;">Feature-level N/S</td>
-<td style="text-align: left;">Auto-labeling quality</td>
-</tr>
-<tr>
-<td style="text-align: left;">Transcoders/CLTs</td>
-<td style="text-align: left;">Layer reconstruction</td>
-<td style="text-align: left;">Cross-layer coverage</td>
-<td style="text-align: left;">Architecture sensitivity</td>
-<td style="text-align: left;">Circuit-level N/S</td>
-<td style="text-align: left;">Circuit descriptions</td>
+<td style="text-align: left;">SAEs and variants</td>
+<td style="text-align: left;">Feature attribution–intervention agreement; effect-size error</td>
+<td style="text-align: left;">Activation reconstruction; loss recovery; unexplained variance</td>
+<td style="text-align: left;">Cross-seed feature alignment</td>
+<td style="text-align: left;">Feature necessity, sufficiency, and specificity</td>
+<td style="text-align: left;">Feature description consistency across annotations, including models and humans</td>
 </tr>
 <tr>
 <td style="text-align: left;">Circuit Discovery</td>
-<td style="text-align: left;">Path patching</td>
-<td style="text-align: left;">Behavioral coverage</td>
-<td style="text-align: left;">Method variance</td>
-<td style="text-align: left;">Primary focus</td>
-<td style="text-align: left;">Circuit diagrams</td>
+<td style="text-align: left;">Edge or component attribution–intervention agreement; counterfactual recovery</td>
+<td style="text-align: left;">Behavioural coverage; loss or behaviour recovery</td>
+<td style="text-align: left;">Robustness across hyperparameters, seeds, and paraphrases</td>
+<td style="text-align: left;">Circuit necessity, sufficiency, specificity; interchange tests</td>
+<td style="text-align: left;">Circuit-level descriptions; trade-off between sparsity and variance explained</td>
 </tr>
 <tr>
 <td style="text-align: left;">Probing</td>
-<td style="text-align: left;">Probe <span class="math inline">\rightarrow</span> causal</td>
-<td style="text-align: left;">Information content</td>
-<td style="text-align: left;">Probe architecture</td>
-<td style="text-align: left;">Often weak</td>
-<td style="text-align: left;">Concept labels</td>
+<td style="text-align: left;">Agreement between probe-derived importance and intervention effects</td>
+<td style="text-align: left;">Coverage across target concepts or behaviours under different distributions</td>
+<td style="text-align: left;">Robustness across seeds and sampled fitting sets</td>
+<td style="text-align: left;">Necessity, specificity, and intervention tests on probe directions</td>
+<td style="text-align: left;">Explanation of shared information in fitting set and search for confounding factors</td>
 </tr>
 <tr>
 <td style="text-align: left;">Steering Vectors</td>
-<td style="text-align: left;">Behavioral effect</td>
-<td style="text-align: left;">Behavior breadth</td>
-<td style="text-align: left;">Direction stability</td>
-<td style="text-align: left;">Implicit in method</td>
-<td style="text-align: left;">Steering descriptions</td>
+<td style="text-align: left;">Agreement between predicted and observed intervention effects</td>
+<td style="text-align: left;">Coverage across behaviours deemed relevant</td>
+<td style="text-align: left;">Stability across steering magnitudes, seeds, and paraphrases</td>
+<td style="text-align: left;">Intervention efficacy and specificity; side-effect analysis</td>
+<td style="text-align: left;">Explanation of shared information in fitting set and search for confounding factors</td>
 </tr>
 </tbody>
 </table>
 </section>
-<section id="limitations-of-this-framework" class="level3"><h3 data-pdf-page="48"><span class="secnum">7.6.10</span> Limitations of This Framework<a class="anchor" href="#limitations-of-this-framework" aria-label="Link to this section">#</a></h3>
-<p>This evaluation framework has limitations that should be acknowledged:</p>
+</section>
+<section id="integrated-evaluation-protocol" class="level3"><h3 data-pdf-page="65"><span class="secnum">7.5.3</span> Integrated Evaluation Protocol<a class="anchor" href="#integrated-evaluation-protocol" aria-label="Link to this section">#</a></h3>
+<p>We recommend the following integrated protocol for comprehensive evaluation:</p>
+<div class="practicebox">
+<section id="step-1-define-scope." class="level4">
+<h4>Step 1: Define scope.</h4>
+<p>Users first specify the model, layer(s), and behaviour(s) of interest and define a behavioural test suite with clear success criteria.<br />
+</p>
+</section>
+<section id="step-2-establish-baselines." class="level4">
+<h4>Step 2: Establish baselines.</h4>
+<p>Users specify baseline methods, such as random directions in activation space, PCA components, individual neurons, and supervised probes (if labels are available).<br />
+</p>
+</section>
+<section id="step-3-apply-interpretability-method." class="level4">
+<h4>Step 3: Apply interpretability method.</h4>
+<p>Users apply the method of interest to analyse models with variation in random seeds, hyperparameters, and other training details.<br />
+</p>
+</section>
+<section id="step-4-evaluate-across-all-five-dimensions." class="level4">
+<h4>Step 4: Evaluate across all five dimensions.</h4>
+<p>Users compute metrics for attribution faithfulness, completeness, stability, causal validity, and human interpretability.<br />
+</p>
+</section>
+<section id="step-5-report-comprehensively." class="level4">
+<h4>Step 5: Report comprehensively.</h4>
+<p>Users report all five dimensions instead of just favourable metrics and discuss the method’s failure cases, unexplained behaviours, and comparisons to baselines.</p>
+</section>
+</div>
+</section>
+<section id="limitations-of-this-framework" class="level3"><h3 data-pdf-page="66"><span class="secnum">7.5.4</span> Limitations of This Framework<a class="anchor" href="#limitations-of-this-framework" aria-label="Link to this section">#</a></h3>
+<p>This evaluation framework, though comprehensive, still has several limitations:</p>
 <ul>
-<li><p><strong>Metric choice</strong>: The specific metrics proposed are not uniquely correct. Alternative formalizations may be appropriate for specific contexts.</p></li>
-<li><p><strong>Threshold arbitrariness</strong>: Proposed thresholds (e.g., necessity <span class="math inline">&gt; 0.5</span>) lack theoretical grounding and may need adjustment based on empirical calibration.</p></li>
-<li><p><strong>Computational cost</strong>: Comprehensive evaluation across all five dimensions is expensive, potentially prohibitive for large-scale studies.</p></li>
-<li><p><strong>Human evaluation bottleneck</strong>: Dimension 5 requires human studies that are slow, expensive, and introduce subjectivity.</p></li>
-<li><p><strong>Task dependence</strong>: Metrics may behave differently across tasks; a method excelling on factual recall may fail on reasoning.</p></li>
+<li><p><strong>Metric choice:</strong> The proposed specific metrics are not uniquely correct. Alternative formalisations may be appropriate for specific contexts.</p></li>
+<li><p><strong>Threshold arbitrariness:</strong> Thresholds (e.g., the accuracy that counts as success) may lack theoretical grounding and will need empirical calibration.</p></li>
+<li><p><strong>Computational cost:</strong> Evaluating all five dimensions comprehensively is expensive and potentially prohibitive for large-scale studies.</p></li>
+<li><p><strong>Human evaluation bottleneck:</strong> Dimension 5 requires time-consuming, expensive, and subjective human studies.</p></li>
+<li><p><strong>Task dependence:</strong> A method’s performance under this framework can depend on the evaluation task. For example, a method that excels at factual recall may perform poorly at reasoning.</p></li>
 </ul>
-<p>Despite these limitations, systematic evaluation across multiple dimensions provides more reliable evidence than ad-hoc metrics. The framework should be viewed as a starting point for community standardization, not a final answer.</p>
+<p>Despite these limitations, systematic evaluation across multiple dimensions provides more reliable evidence than an arbitrary choice of metrics. Readers should view the framework as a starting point for standardisation, not a final answer.</p>
+<section id="summary.-4" class="level4">
+<h4>Summary.</h4>
+<p>This section introduced circuit discovery methods, which trace how model components or features interact to produce behaviour, either directly on the model’s computational graph (e.g., ACDC and EAP) or within interpretable replacement models (e.g., Circuit Tracer). We also discussed how to causally verify discovered circuits and introduced a broader evaluation framework for mechanistic interpretability methods based on faithfulness, completeness, stability, causal validity, and human interpretability. In summary, Section <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a> introduced methods for extracting interpretable units of analysis, while this section examined how those units interact through circuits to produce behaviour. The next section takes a complementary perspective: directly interpreting the information in individual representations using natural language.</p>
+</section>
 </section>
 </section>
 </section>
diff --git a/src/generated/chapters/colour-key.html b/src/generated/chapters/colour-key.html
index 1289e8b6e2..4d10b17409 100644
--- a/src/generated/chapters/colour-key.html
+++ b/src/generated/chapters/colour-key.html
@@ -1,33 +1,38 @@
-<section id="app:colors" class="level1"><h1 data-pdf-page="120"><span class="secnum">B</span> Color Key and Style Guide<a class="anchor" href="#app:colors" aria-label="Link to this section">#</a></h1>
-<p>This appendix explains the visual conventions used throughout the survey for boxes, emphasis, and structural elements.</p>
-<section id="box-types-and-colors" class="level2"><h2 data-pdf-page="120"><span class="secnum">B.1</span> Box Types and Colors<a class="anchor" href="#box-types-and-colors" aria-label="Link to this section">#</a></h2>
+<section id="app:colors" class="level1"><h1 data-pdf-page="178"><span class="secnum">B</span> Colour Key and Style Guide<a class="anchor" href="#app:colors" aria-label="Link to this section">#</a></h1>
+<p>This section explains the visual conventions used throughout the survey for boxes, emphasis, and structural elements.</p>
+<section id="box-types-and-colours" class="level2"><h2 data-pdf-page="178"><span class="secnum">B.1</span> Box Types and Colours<a class="anchor" href="#box-types-and-colours" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
-<p><strong>Definition boxes</strong> introduce core concepts and provide precise technical definitions. Use these when a term needs clear specification before use.</p>
-<p>Example concepts: features, circuits, SAEs, privileged access hypothesis.</p>
-<p>Text color: Dark blue border, light blue background.</p>
+<p><strong>Definition boxes</strong> introduce core concepts and provide precise technical definitions. Use these when a term needs clear specification before use.<br />
+Example concepts: “Feature”, “Circuit”, “Sparse Autoencoder”.<br />
+Style: Dark blue border, light blue background.</p>
 </div>
 <div class="examplebox">
-<p><strong>Example boxes</strong> provide concrete illustrations, worked examples, or case studies. Use these to make abstract concepts concrete.</p>
-<p>Example contents: “Feature vs. Neuron: An Illustration,” “MLP as Feature Combination,” worked examples of causal tracing.</p>
-<p>Text color: Green border, light green background.</p>
+<p><strong>Example boxes</strong> provide concrete illustrations, worked examples, or case studies. Use these to make abstract concepts concrete.<br />
+Example contents: “Induction in a Repeated Sequence”, “Example Application of Steering Vectors”, “Probing Classifiers for Sleeper Agents”.<br />
+Style: Green border, light green background.</p>
 </div>
 <div class="insightbox">
-<p><strong>Insight boxes</strong> explain intuitions behind mathematical or conceptual results. Use these to build understanding of why something works, not just that it works.</p>
-<p>Example contents: “Why Orthogonalization Works,” “Superposition in High Dimensions,” “Mechanistic Interpretation of Attention.”</p>
-<p>Text color: Gold border, light gold background.</p>
+<p><strong>Insight boxes</strong> explain intuitions behind mathematical or conceptual results. Use these to build understanding of why something works, not just that it works.<br />
+Example contents: “Trade-offs Between Variants”, “The Complexity–Interpretability Trade-off”.<br />
+Style: Gold border, light gold background.</p>
 </div>
 <div class="warningbox">
-<p><strong>Warning boxes</strong> highlight critical limitations, failure modes, or common misconceptions. Use these to prevent readers from adopting incorrect or unsafe interpretations.</p>
-<p>Example contents: “Steering Vector Brittleness,” “The Probe Causality Problem,” “The Explanation Validation Crisis.”</p>
-<p>Text color: Red border, light red background.</p>
+<p><strong>Warning boxes</strong> highlight critical limitations, failure modes, or common misconceptions. Use these to prevent readers from adopting incorrect or unsafe interpretations.<br />
+Example contents: “Caveats on CLT Fidelity Claims”, “The Probe Causality Problem”, “The Partial Coverage Issue”.<br />
+Style: Red border, light red background.</p>
+</div>
+<div class="practicebox">
+<p><strong>Practice boxes</strong> illustrate suggestions that are actionable. These include protocol recommendations, checklists for verifying approaches, and step-by-step decision-making walkthroughs. Use these for practical implementations of procedures.<br />
+Example contents: “Guardrail Framework for Automated Interpretability”, “Validation Procedures for Introspective Models”, “Recommended Evaluation Protocol”.<br />
+Style: Teal border, light teal background.</p>
 </div>
 </section>
-<section id="app:citation-colors" class="level2"><h2 data-pdf-page="120"><span class="secnum">B.2</span> Citation Evidence Levels<a class="anchor" href="#app:citation-colors" aria-label="Link to this section">#</a></h2>
-<p>This survey cites both peer-reviewed publications and preprints. To help readers assess evidence strength, citations are color-coded:</p>
+<section id="app:citation-colors" class="level2"><h2 data-pdf-page="179"><span class="secnum">B.2</span> Citation Evidence Levels<a class="anchor" href="#app:citation-colors" aria-label="Link to this section">#</a></h2>
+<p>This survey cites both peer-reviewed publications and non-peer-reviewed research outputs. To help readers assess the maturity and review status of the evidence, citations are colour-coded:</p>
 <ul>
-<li><p><span style="color: citeestablished"><strong>Orange citations</strong></span>: Peer-reviewed publications (conferences, journals)</p></li>
-<li><p><span style="color: citeemerging"><strong>Green citations</strong></span>: Preprints, technical reports, or emerging results awaiting peer review</p></li>
+<li><p><span style="color: citeestablished"><strong>Orange citations</strong></span> denote formally published work: peer-reviewed conference and journal papers, and books.</p></li>
+<li><p><span style="color: citeemerging"><strong>Green citations</strong></span>: Research outputs that have not undergone formal peer review, including preprints, technical reports, workshop papers, blog posts, and other emerging resources.</p></li>
 </ul>
-<p>Results supported primarily by green citations should be considered preliminary. We encourage readers to verify the current publication status of cited work.</p>
+<p>Green citations should not automatically be interpreted as lower quality or less reliable. Some originate from leading research organisations and may have undergone substantial internal review. However, they have not been subjected to the formal academic peer-review process and should therefore be interpreted with appropriate caution. Readers are encouraged to verify the current publication status of cited work where relevant.</p>
 </section>
 </section>
diff --git a/src/generated/chapters/conclusion.html b/src/generated/chapters/conclusion.html
index bfa06a44f8..78e42f6995 100644
--- a/src/generated/chapters/conclusion.html
+++ b/src/generated/chapters/conclusion.html
@@ -1,23 +1,23 @@
-<section id="sec:conclusion" class="level1"><h1 data-pdf-page="94"><span class="secnum">16</span> Conclusion<a class="anchor" href="#sec:conclusion" aria-label="Link to this section">#</a></h1>
-<p>Mechanistic interpretability (MI) has matured from exploratory research into an engineering discipline, with established knowledge, reproducible methods, deployed applications, and well-understood limitations. Rather than converging on a single approach, the field has crystallized into five divergent research orientations—Pragmatic, Ambitious, Developmental, Agentic, and Automated—each making rational trade-offs between completeness, cost, and applicability.</p>
-<section id="achievements-and-limits" class="level2"><h2 data-pdf-page="94"><span class="secnum">16.1</span> Achievements and Limits<a class="anchor" href="#achievements-and-limits" aria-label="Link to this section">#</a></h2>
-<p>MI succeeds in many aspects: extracting monosemantic features via SAEs, discovering algorithmic circuits, and enabling surgical interventions. It has also helped establish AI researchers’ knowledge, mindset, and taste in other research domains. However, current methods explain only part of the model behavior, struggling with entangled computation and breaking down under distribution shift. Comprehensive analyses of frontier models remain intractable <span class="citation" data-cites="templeton2024scaling">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>.</p>
-<section id="from-reverse-engineering-to-engineering." class="level4">
-<h4>From reverse-engineering to engineering.</h4>
-<p>Perhaps one of the most significant conceptual developments of 2024–2025 is the emergence of a third paradigm alongside post-hoc analysis and behavioral evaluation: <em>interpretability by design</em>. Architectural interventions (Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>) demonstrate that the opacity of neural networks is not an immutable fact, but a consequence of design choices that can be revisited. Weight-sparse transformers, MOLT, and other related methods suggest a future where interpretability is a prior design constraint rather than a post-hoc recovery problem. Scaling laws <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span> have shown that large sparse models can achieve both capability and interpretability, providing empirical grounds for optimism that this paradigm may scale to frontier systems.</p>
+<section id="sec:conclusion" class="level1"><h1 data-pdf-page="127"><span class="secnum">15</span> Conclusion<a class="anchor" href="#sec:conclusion" aria-label="Link to this section">#</a></h1>
+<p>Neural networks could be one of the most consequential artefacts humanity has built, yet we do not understand how they work or reliably control what they do. Recent decades have seen an exponential improvement in AI capabilities, but methods to understand and explain internal mechanisms have lagged behind. This has created an interpretability gap between what models can do and what we know about how they do it.</p>
+<p>Mechanistic interpretability offers a path towards understanding these systems by studying their internal representations and computations, with the aim of evaluating, predicting, and controlling behaviour. In this survey, we examine closely what interpretability can actually contribute to AI safety. We place mechanistic interpretability in the broader context of interpretability research and compare its mechanistic evidence with behavioural evaluation. Beyond surveying existing methods, we propose a unified framework for evaluating mechanistic interpretability claims and characterise six research orientations by their goals, assumptions, and trade-offs.</p>
+<p>Rather than converging on a single approach, the field has crystallised into divergent research orientations, including Pragmatic Interpretability, Ambitious Interpretability, Developmental Interpretability, Interpretability Across Time, Introspective Interpretability, and Automated Interpretability, each making trade-offs between completeness, cost, and applicability.</p>
+<section id="achievements-and-limits" class="level2"><h2 data-pdf-page="127"><span class="secnum">15.1</span> Achievements and Limits<a class="anchor" href="#achievements-and-limits" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic interpretability has achieved substantial progress across several fronts, including extracting interpretable features with SAEs, discovering task-relevant circuits, and enabling targeted interventions on model behaviour. Overall, the field has shaped how researchers view model computation, representations, and causal structure.</p>
+<p>Yet current approaches are limited: they do not fully explain many behaviours, struggle to generalise under distribution shift, have difficulty cleanly disentangling entangled computations, and are hard to scale to frontier models. More importantly, their usefulness for AI safety remains debated.</p>
+<p>Although many prior studies in the interpretability community assume that mechanistic understanding directly implies safer systems, the assumption is in fact largely untested. Therefore, this survey considers not only what a method reveals about a model’s internal computation, but also what it allows us to do in practice. We particularly discuss whether it supports safety-relevant tasks such as detecting deceptive behaviours or identifying dangerous capabilities, and whether those tasks can be achieved just as well without interpretability methods or insights.</p>
+<p>A persistent challenge in mechanistic interpretability is the lack of standardised evaluation. Different interpretability methods use incompatible metrics. To address this, we present a unified evaluation framework covering five dimensions—attribution faithfulness, completeness, stability, causal validity, and human interpretability—where different methods can be jointly tested. We identify gaps and argue that the evaluation methodology and safety benchmarks are the field’s most pressing open problems.</p>
+<p>Mechanistic interpretability is not a panacea for AI safety. Researchers, developers, and practitioners should recognise the limits of partial or incorrect causal understanding, rigorously evaluate MI tools under broad conditions, and combine them with complementary safety measures. MI should therefore be viewed as one component of a defence-in-depth safety strategy rather than a standalone guarantee of safety.</p>
 </section>
-</section>
-<section id="unresolved-questions" class="level2"><h2 data-pdf-page="94"><span class="secnum">16.2</span> Unresolved Questions<a class="anchor" href="#unresolved-questions" aria-label="Link to this section">#</a></h2>
-<p>As discussed in Section <a href="@@BASE@@handbook/open-problems/#sec:open_problems" data-reference-type="ref" data-reference="sec:open_problems">15</a>, several fundamental open questions remain. When is mechanistic understanding actually required for safety, or do behavioral methods suffice? Can mechanisms be verified as genuine rather than post-hoc rationalizations? How do mechanisms hold up under distribution shift and adversarial pressure? These questions are not merely technical but conceptual, touching on epistemology and the definition of understanding itself.</p>
-</section>
-<section id="the-road-ahead-three-complementary-paradigms" class="level2"><h2 data-pdf-page="94"><span class="secnum">16.3</span> The Road Ahead: Three Complementary Paradigms<a class="anchor" href="#the-road-ahead-three-complementary-paradigms" aria-label="Link to this section">#</a></h2>
-<p>The field of MI is evolving from a single methodology toward three complementary paradigms:</p>
+<section id="the-road-ahead" class="level2"><h2 data-pdf-page="128"><span class="secnum">15.2</span> The Road Ahead<a class="anchor" href="#the-road-ahead" aria-label="Link to this section">#</a></h2>
+<p>The six research orientations describe different research goals and priorities. At a broader level, mechanistic interpretability can be organised into three complementary paradigms that encompass these orientations and other research directions:</p>
 <ol>
-<li><p><strong>Post-hoc decomposition:</strong> The traditional approach of reverse-engineering trained models via SAEs, transcoders, circuit discovery, and causal analysis. This remains essential for understanding existing deployed systems and will likely always be necessary for models trained without interpretability constraints.</p></li>
-<li><p><strong>Developmental analysis:</strong> Understanding how mechanisms emerge during training via singular learning theory, phase transition detection, and checkpoint analysis. This enables early intervention and prediction of capability emergence before deployment.</p></li>
-<li><p><strong>Interpretability by design:</strong> Engineering architectures that are mechanistically transparent by construction, such as weight-sparse transformers, MOLT, KANs, and their successors. This moves interpretability upstream, showing the promise to eliminate the need for expensive post-hoc analysis.</p></li>
+<li><p><strong>Post-hoc explanation:</strong> The traditional paradigm of studying trained models’ internals via tools like SAEs, circuit discovery, activation verbalisers, and causal analysis. Because deployed systems still rely on conventional dense architectures without intrinsic interpretability, post-hoc analysis will remain important in the medium term.</p></li>
+<li><p><strong>Studying learning dynamics:</strong> Understanding how mechanisms emerge during training via methods like singular learning theory and checkpoint analysis. This paradigm enables understanding, prediction, and intervention early in behaviour and capability emergence.</p></li>
+<li><p><strong>Interpretability by design:</strong> Engineering architectures that are mechanistically transparent by construction, such as SoLU, weight-sparse transformers, KANs, and their successors. This paradigm moves interpretability upstream and promises to reduce the need for expensive post-hoc analysis.</p></li>
 </ol>
-<p>These paradigms are not mutually exclusive. Post-hoc methods remain necessary for legacy systems and for validating architectural claims; developmental analysis informs both when to intervene during training and what architectural constraints are most effective; interpretability-by-design reduces the burden on post-hoc methods but requires validation that the architectural transparency transfers to genuine mechanistic understanding.</p>
-<p>The maturation of MI thus lies not in the dominance of any single approach but in the principled integration of all three—matching methodology to context, validating claims across paradigms, and building cumulative understanding of how to create AI systems that are transparent, verifiable, and safe.</p>
+<p>Each approach tackles a different area but complements the others. For instance, interpretability-by-design models can reduce reliance on post-hoc methods, while post-hoc mechanistic analysis can guide next-generation interpretability-by-design models towards desirable properties that current dense architectures lack. Insights into learning dynamics can suggest architectural constraints and training-time interventions.</p>
+<p>Progress also depends on establishing stronger scientific standards. Recent work argues that interpretability should produce claims that are both falsifiable and reproducible, and improve its use in model evaluations and downstream applications.</p>
+<p>Ultimately, the question remains: Does understanding how models work actually make them safer? This survey’s goal is to understand how these approaches can work together to improve model understanding and safeguards, while recognising the limits of partial or incorrect causal understanding.</p>
 </section>
 </section>
diff --git a/src/generated/chapters/core-hypotheses.html b/src/generated/chapters/core-hypotheses.html
index c9949bc6eb..fcf66541f8 100644
--- a/src/generated/chapters/core-hypotheses.html
+++ b/src/generated/chapters/core-hypotheses.html
@@ -1,185 +1,91 @@
-<section id="sec:hypotheses" class="level1"><h1 data-pdf-page="15"><span class="secnum">5</span> Core Hypotheses in Mechanistic Interpretability<a class="anchor" href="#sec:hypotheses" aria-label="Link to this section">#</a></h1>
-<p>Mechanistic interpretability is guided by a number of core hypotheses about how neural networks represent information and implement computation. These hypotheses are not universally accepted facts; rather, they function as organizing principles that shape methodological choices and empirical investigations. In this section, we formalize these hypotheses, summarize supporting and opposing evidence, and highlight open questions.</p>
-<section id="subsec:linearity" class="level2"><h2 data-pdf-page="15"><span class="secnum">5.1</span> The Linear Representation Hypothesis<a class="anchor" href="#subsec:linearity" aria-label="Link to this section">#</a></h2>
-<p>We have seen that single units are not the unit of representation, but what is? The linear representation hypothesis claims that directions in the activation space are the fundamental units of representation.</p>
-<div class="theorybox">
-<p>Features correspond to directions <span class="math inline">\phi \in \mathbb{R}^d</span> in activation space. The projection <span class="math inline">\phi^\top h_l(x)</span> approximately measures the intensity of that feature for input <span class="math inline">x</span>, and features combine additively: <span class="math inline">h_l(x) \approx \sum_i \alpha_i \phi_i</span>. This implies features can be detected via linear probes and added or removed via vector arithmetic on the <span class="math inline">h_l(x)</span>.</p>
+<section id="sec:hypotheses" class="level1"><h1 data-pdf-page="27"><span class="secnum">5</span> Core Hypotheses in Mechanistic Interpretability<a class="anchor" href="#sec:hypotheses" aria-label="Link to this section">#</a></h1>
+<p>Mechanistic interpretability rests on several core hypotheses about how neural networks represent information and implement computation. These hypotheses are not yet rigorously proven. Nonetheless, they have shaped methodological choices and empirical investigations. In this section, we formalise four of them, discuss their conceptual basis, and summarise supporting and opposing empirical evidence.</p>
+<section id="subsec:linearity" class="level2"><h2 data-pdf-page="27"><span class="secnum">5.1</span> The Linear Representation Hypothesis<a class="anchor" href="#subsec:linearity" aria-label="Link to this section">#</a></h2>
+<p>We have seen that single neurons may not be the most effective unit of representation in many cases, but what is? The linear representation hypothesis claims that <em>linear</em> directions in the activation space are effective fundamental units of representation <span class="citation" data-cites="lrh">(<a href="@@BASE@@references/#ref-lrh" class="cite cite-established" data-cite="lrh" role="doc-biblioref">Park et al., 2024</a>)</span>.</p>
+<div class="definitionbox">
+<p>A concept can be effectively represented by a feature corresponding to a linear direction <span class="math inline">\phi</span> in activation space <span class="math inline">\mathbb{R}^d</span>. Linearity implies that adding or subtracting this direction, or a scalar multiple of it, from model activations can correspondingly influence model behaviour, while the projection <span class="math inline">\phi^\top h(x)</span> approximately measures the strength of the feature in model activations <span class="math inline">h(x)</span> for an input <span class="math inline">x</span>.<br />
+Under a strong version of the linear representation hypothesis, all concepts are best represented by linear features. In this view, model activations can be approximately decomposed as an additive combination of such features, <span class="math display">h(x) \approx \sum_i \alpha_i \phi_i,</span> where <span class="math inline">\alpha_i \in \mathbb{R}</span> denotes the strength of feature <span class="math inline">\phi_i</span> dependent on the input <span class="math inline">x</span>. Under a weaker version, only some concepts admit such linear representations (<span class="citation" data-cites="sharkey2025open"><a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a></span>; <span class="citation" data-cites="smith2024strongfeature"><a href="@@BASE@@references/#ref-smith2024strongfeature" class="cite cite-emerging" data-cite="smith2024strongfeature" role="doc-biblioref">Smith, 2024</a></span>).</p>
 </div>
-<p>On a strong version of the linear representation hypothesis, all features are linearly represented. On a weaker version, only some are  (<span class="citation" data-cites="sharkey2025open"><a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a></span>; <span class="citation" data-cites="smith2024strongfeature"><a href="@@BASE@@references/#ref-smith2024strongfeature" class="cite cite-emerging" data-cite="smith2024strongfeature" role="doc-biblioref">Smith, 2024</a></span>).</p>
-<p><strong>Conceptual Basis.</strong> A feature is linearly represented if its presence corresponds to activation along a fixed direction <span class="math inline">\phi</span>, detectable via projection <span class="math inline">\phi^\top h_l(x)</span>. This makes mechanistic analysis tractable: features can be found, measured, and manipulated using linear methods.</p>
-<p>However, not all activation spaces are the same. In some spaces, like MLP hidden layers, the neuron axes matter, since ReLU treats each neuron separately, so the coordinate system is meaningful. We call this a <em>privileged basis</em> (see Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:privileged-basis" data-reference-type="ref" data-reference="fig:privileged-basis">2</a>, left). In other spaces, like the residual stream, nothing special happens along the neuron axes, so features can point in any direction. This is a <em>non-privileged basis</em> (see Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:privileged-basis" data-reference-type="ref" data-reference="fig:privileged-basis">2</a>, right).</p>
-<figure id="fig:privileged-basis" data-latex-placement="t">
-<img alt="Figure 3: Privileged vs. non-privileged bases. In MLP hidden layers, element-wise nonlinearities break rotation symmetry, making axes meaningful. In the residual stream, no element-wise operations are applied, so any rotation of axes is computationally equivalent." loading="lazy" src="@@BASE@@figures/5_priviledge.svg" />
-<figcaption><span class="capnum">Figure 3:</span> Privileged vs. non-privileged bases. In MLP hidden layers, element-wise nonlinearities break rotation symmetry, making axes meaningful. In the residual stream, no element-wise operations are applied, so any rotation of axes is computationally equivalent.</figcaption>
+<section id="conceptual-basis." class="level4">
+<h4>Conceptual Basis.</h4>
+<p>A feature associated with a concept is linearly represented if its presence corresponds to a fixed direction <span class="math inline">\phi</span> in the activation space and can be detected through the projection <span class="math inline">\phi^\top h(x)</span>. This linearity makes mechanistic analysis tractable because we can use simple methods to identify, measure, and manipulate these features in the activation space, such as scaling them and adding them back to model activations.</p>
+<p>Notably, activation spaces in different model components can have distinct geometric structures. For example, in the hidden states of MLP blocks, the neuron axes are computationally distinguished because element-wise nonlinear activation functions, such as ReLU, act separately on each axis. The coordinate system is therefore meaningful and is referred to as a <em>privileged basis</em> (Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:privileged-basis" data-reference-type="ref" data-reference="fig:privileged-basis">4</a>, left) <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>. In other spaces, such as the residual stream, no operation singles out the neuron axes, so linear features may lie along arbitrary directions. Such spaces are said to have a <em>non-privileged basis</em> (Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:privileged-basis" data-reference-type="ref" data-reference="fig:privileged-basis">4</a>, right).</p>
+<figure id="fig:privileged-basis" data-latex-placement="tb">
+<img alt="Figure 4: Privileged versus non-privileged bases. In the hidden layers of MLP blocks, element-wise nonlinear operations break rotational symmetry and make individual axes meaningful. In contrast, the residual stream contains no such operations that privilege particular axes, so rotating the basis pr" loading="lazy" src="@@BASE@@figures/5_priviledge.svg" />
+<figcaption><span class="capnum">Figure 4:</span> Privileged versus non-privileged bases. In the hidden layers of MLP blocks, element-wise nonlinear operations break rotational symmetry and make individual axes meaningful. In contrast, the residual stream contains no such operations that privilege particular axes, so rotating the basis preserves the relevant computation.</figcaption>
 </figure>
-<p>The distinction matters: when the basis is privileged, a single neuron might represent a feature. When it’s not, features are directions that don’t align with any particular neuron, and we have to find them.</p>
+<p>When the basis is privileged, a single neuron can serve as a scalar feature that meaningfully represents a concept. When it is not, the feature may instead correspond to directions or other geometric structures that do not align with any individual neuron. In this case, specialised methods are required to identify them from the activation space.</p>
+</section>
 <section id="empirical-evidence-and-counterexamples." class="level4">
 <h4>Empirical Evidence and Counterexamples.</h4>
-<p>Empirical support for linearity includes the success of linear probes on diverse models <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span>, activation steering along single directions to produce targeted behavioral changes <span class="citation" data-cites="turner2024steeringlanguagemodelsactivation">(<a href="@@BASE@@references/#ref-turner2024steeringlanguagemodelsactivation" class="cite cite-emerging" data-cite="turner2024steeringlanguagemodelsactivation" role="doc-biblioref">Turner et al., 2024</a>)</span>, and sparse autoencoders recovering linearly separable and steerable features <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>.</p>
-<p>However, there is also evidence that not all representations are linear. Counterexamples include nonlinear feature interactions, algorithmic representations involving nonlinear transformations, and features distributed across multiple interacting subspaces(<span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic rnnnonlinear engels2025languagemodelfeaturesonedimensionally"><a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>; <a href="@@BASE@@references/#ref-rnnnonlinear" class="cite cite-established" data-cite="rnnnonlinear" role="doc-biblioref">Csord<span>á</span>s et al., 2024</a>; <a href="@@BASE@@references/#ref-engels2025languagemodelfeaturesonedimensionally" class="cite cite-established" data-cite="engels2025languagemodelfeaturesonedimensionally" role="doc-biblioref">Engels et al., 2025</a></span>; <span class="citation" data-cites="snmf smds"><a href="@@BASE@@references/#ref-snmf" class="cite cite-emerging" data-cite="snmf" role="doc-biblioref">Shafran et al., 2025</a>; <a href="@@BASE@@references/#ref-smds" class="cite cite-emerging" data-cite="smds" role="doc-biblioref">Tiblias et al., 2025</a></span>) . Some have argued that features in neural networks may not even be explicitly represented, but may instead emerge from underlying circuit dynamics <span class="citation" data-cites="smith2024strongfeature">(<a href="@@BASE@@references/#ref-smith2024strongfeature" class="cite cite-emerging" data-cite="smith2024strongfeature" role="doc-biblioref">Smith, 2024</a>)</span>. Thus, the Linear Representation Hypothesis is a useful approximation rather than a universal law.</p>
+<p>Empirical support for the weaker version of the linear representation hypothesis includes the success of linear probes across diverse models and concepts <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span>, activation steering along single directions to induce targeted behavioural changes <span class="citation" data-cites="turner2024steeringlanguagemodelsactivation">(<a href="@@BASE@@references/#ref-turner2024steeringlanguagemodelsactivation" class="cite cite-emerging" data-cite="turner2024steeringlanguagemodelsactivation" role="doc-biblioref">Turner et al., 2024</a>)</span>, and sparse autoencoders recovering linearly accessible and steerable features at scale <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. At the same time, there is substantial evidence that not all representations are well described as linear. Counterexamples include nonlinear feature interactions, algorithmic representations that rely on nonlinear transformations, and features distributed across multiple interacting subspaces (<span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic engels2025languagemodelfeaturesonedimensionally smds"><a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>; <a href="@@BASE@@references/#ref-engels2025languagemodelfeaturesonedimensionally" class="cite cite-established" data-cite="engels2025languagemodelfeaturesonedimensionally" role="doc-biblioref">Engels et al., 2025</a>; <a href="@@BASE@@references/#ref-smds" class="cite cite-established" data-cite="smds" role="doc-biblioref">Tiblias et al., 2026</a></span>; <span class="citation" data-cites="rnnnonlinear feucht2026arithmeticwildllamauses wurgaft2026manifoldsteeringrevealsshared"><a href="@@BASE@@references/#ref-rnnnonlinear" class="cite cite-emerging" data-cite="rnnnonlinear" role="doc-biblioref">Csord<span>á</span>s et al., 2024</a>; <a href="@@BASE@@references/#ref-feucht2026arithmeticwildllamauses" class="cite cite-emerging" data-cite="feucht2026arithmeticwildllamauses" role="doc-biblioref">Feucht et al., 2026</a>; <a href="@@BASE@@references/#ref-wurgaft2026manifoldsteeringrevealsshared" class="cite cite-emerging" data-cite="wurgaft2026manifoldsteeringrevealsshared" role="doc-biblioref">Wurgaft et al., 2026</a></span>). Some work has further argued that features may not always exist as explicitly represented objects, but may instead emerge from underlying circuit dynamics <span class="citation" data-cites="smith2024strongfeature">(<a href="@@BASE@@references/#ref-smith2024strongfeature" class="cite cite-emerging" data-cite="smith2024strongfeature" role="doc-biblioref">Smith, 2024</a>)</span>. The linear representation hypothesis is therefore best viewed as a useful approximation that holds in some settings, rather than as a universal law.</p>
 </section>
 </section>
-<section id="subsec:superposition" class="level2"><h2 data-pdf-page="16"><span class="secnum">5.2</span> The Superposition Hypothesis<a class="anchor" href="#subsec:superposition" aria-label="Link to this section">#</a></h2>
-<p>The superposition hypothesis says that for each layer of a trained model there are more features than nodes.</p>
-<div class="theorybox">
-<p>Model hidden states encode more features than they have dimensions. If there are <span class="math inline">k</span> features and <span class="math inline">d</span> dimensions, then the Superposition Hypothesis is that <span class="math inline">k &gt;&gt; d</span>.<br />
-This implies that features cannot be represented as orthogonal directions—though they can be represented as nearly-orthogonal directions. It also implies polysemanticity: directions will be activated in response to different concepts.</p>
+<section id="subsec:superposition" class="level2"><h2 data-pdf-page="28"><span class="secnum">5.2</span> The Superposition Hypothesis<a class="anchor" href="#subsec:superposition" aria-label="Link to this section">#</a></h2>
+<p>The superposition hypothesis says that models encode more features than their hidden representations have dimensions.</p>
+<div class="definitionbox">
+<p>Model hidden states encode more features than they have dimensions. If there are <span class="math inline">m</span> features and <span class="math inline">d</span> dimensions, then the superposition hypothesis claims that <span class="math inline">m \gg d</span>.<br />
+This implies that features cannot be represented as orthogonal directions—though they can be represented as nearly orthogonal directions. It also implies neuron polysemanticity: the same neuron participates in the encoding of multiple unrelated concepts.</p>
 </div>
-<section id="conceptual-basis." class="level4">
+<section id="conceptual-basis.-1" class="level4">
 <h4>Conceptual Basis.</h4>
-<p>Superposition is a geometric property of high-dimensional activation spaces: many approximately orthogonal directions can coexist, allowing a limited number of neurons to encode a much larger number of sparse features. Polysemanticity, where individual neurons respond to multiple unrelated semantic concepts, is a direct consequence of this property. As features become increasingly sparse, the model moves from using dedicated dimensions for monosemantic features to shared dimensions (see Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:superposition-polysemanticity" data-reference-type="ref" data-reference="fig:superposition-polysemanticity">3</a>). In this state of superposition, features are compressed into a space with fewer dimensions than there are features.</p>
+<p>Superposition is a geometric property of learned representations in which a limited number of neuron dimensions encode many more features by allowing those features to share representational dimensions. Neuron polysemanticity, where individual neurons respond to multiple seemingly unrelated semantic concepts, naturally follows from this phenomenon. When the number of encoded features exceeds the activation-space dimensionality, the model can no longer allocate a dedicated neuron dimension to each feature. Instead, multiple features become superposed within the same activation space, shifting the representation from dedicated dimensions with monosemantic neurons to shared dimensions with increasingly polysemantic neurons (Figure <a href="@@BASE@@handbook/core-hypotheses/#fig:superposition-polysemanticity" data-reference-type="ref" data-reference="fig:superposition-polysemanticity">5</a>).</p>
 </section>
 <section id="empirical-evidence." class="level4">
 <h4>Empirical Evidence.</h4>
-<p>Mechanistic studies have observed widespread polysemanticity: individual neurons consistently activate for multiple unrelated semantic concepts, making them poor interpretable units <span class="citation" data-cites="elhage2022superposition bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. Dictionary Learning methods like Sparse Autoencoders (SAEs) and related feature decomposition methods exploit this underlying property of superposition to recover tens of thousands of distinct features from shared activation spaces <span class="citation" data-cites="bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>. These recovered features are considered interpretable and some causally relevant to model behavior, showing that overlapping directions encode meaningful semantic information. Interpretability gains from such methods further validate that superposition is fundamental challenge, rather than a method-specific artifact <span class="citation" data-cites="templeton2024scaling">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>.</p>
-<figure id="fig:superposition-polysemanticity" data-latex-placement="t">
-<img alt="Figure 4: Superposition implies polysemanticity. Left: when the number of features k does not exceed the number of dimensions/neurons d (k\le d), feature directions can align cleanly with individual neuron axes (monosemantic neurons). Right: when k&gt;d, multiple feature directions must share the same " loading="lazy" src="@@BASE@@figures/5_superposition-and-polysemanticity.svg" />
-<figcaption><span class="capnum">Figure 4:</span> <strong>Superposition implies polysemanticity.</strong> Left: when the number of features <span class="math inline">k</span> does not exceed the number of dimensions/neurons <span class="math inline">d</span> (<span class="math inline">k\le d</span>), feature directions can align cleanly with individual neuron axes (monosemantic neurons). Right: when <span class="math inline">k&gt;d</span>, multiple feature directions must share the same limited set of neuron axes; as a result, each neuron axis cuts across multiple feature directions, producing polysemantic neurons.</figcaption>
+<p>Mechanistic studies have repeatedly observed widespread neuron polysemanticity: individual neurons often activate for multiple seemingly unrelated semantic concepts, making them difficult to interpret as standalone representational units <span class="citation" data-cites="olah2020zoom elhage2022superposition bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>; <a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. Dictionary-learning methods such as sparse autoencoders exploit this structure to recover millions of distinct features from shared activation spaces <span class="citation" data-cites="bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>. Many discovered features are substantially more monosemantic than individual neurons, and some have been shown to causally influence model behaviour. Together, these findings suggest that the activation space may encode more meaningful semantic information along activation-space directions than along individual neuron axes.</p>
+<p>However, the superposition hypothesis remains a hypothesis, not an established fact, in large language models. Observing polysemantic neurons does not, by itself, imply that superposition is the underlying cause, and dictionary-learning methods can recover interpretable features even in the absence of superposition. Furthermore, <span class="citation" data-cites="lecomte2024what"><a href="@@BASE@@references/#ref-lecomte2024what" class="cite cite-emerging" data-cite="lecomte2024what" role="doc-biblioref">Lecomte et al. (2024)</a></span> showed that polysemanticity can arise even when the number of underlying features does not exceed the number of available neurons, suggesting alternative—and potentially complementary—mechanisms for the emergence of polysemantic representations.</p>
+<figure id="fig:superposition-polysemanticity" data-latex-placement="tb">
+<img alt="Figure 5: Superposition implies polysemanticity. Left: when the number of features m does not exceed the number of dimensions d, i.e., m\le d, feature directions can align cleanly with individual neuron axes, producing monosemantic neurons. Right: when m&gt;d, multiple feature directions must share the" loading="lazy" src="@@BASE@@figures/5_superposition_and_polysemanticity.svg" />
+<figcaption><span class="capnum">Figure 5:</span> <strong>Superposition implies polysemanticity.</strong> Left: when the number of features <span class="math inline">m</span> does not exceed the number of dimensions <span class="math inline">d</span>, i.e., <span class="math inline">m\le d</span>, feature directions can align cleanly with individual neuron axes, producing monosemantic neurons. Right: when <span class="math inline">m&gt;d</span>, multiple feature directions must share the same limited set of neuron axes; as a result, each neuron axis cuts across multiple feature directions, producing polysemantic neurons.</figcaption>
 </figure>
 </section>
-<section id="architectural-responses-to-superposition" class="level3"><h3 data-pdf-page="17"><span class="secnum">5.2.1</span> Architectural Responses to Superposition<a class="anchor" href="#architectural-responses-to-superposition" aria-label="Link to this section">#</a></h3>
-<p>The prevalence of superposition is not an inevitable property of neural networks but a consequence of architectural permissiveness. Standard dense architectures do not impose a structural penalty for distributing features across arbitrary linear combinations of neurons. When activation functions are applied element-wise (e.g., ReLU, GELU), and connectivity is unconstrained, gradient descent is free to exploit the high-dimensional geometry of activation space to pack more features than there are neurons <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>.</p>
-<p>This observation motivates a distinct research program: rather than decomposing superposition post hoc via sparse autoencoders, one can impose <em>inductive biases</em> during training that encourage representations in which features are more accessible to analysis. Three intervention points have been explored:</p>
+<section id="architectural-responses-to-superposition." class="level4">
+<h4>Architectural Responses to Superposition.</h4>
+<p>Superposition is not an intrinsically inevitable property of deep neural networks but a consequence of architectural permissiveness. Without constraints on model architectures or activations, gradient descent can exploit the activation-space geometry to pack far more features than neurons <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>. This motivates a distinct research direction, <em>interpretability-by-design</em> (as mentioned in Section <a href="@@BASE@@handbook/interpretability-landscape/#subsec:cbm" data-reference-type="ref" data-reference="subsec:cbm">3.2</a> and discussed in Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.10</a>). Interpretability-by-design argues that, rather than decomposing traditional models’ superposed activations post hoc, one can design novel architectures or components that encourage representations where features are more accessible for analysis. Two intervention points have been explored:</p>
 <ul>
-<li><p><strong>Sparsity constraints:</strong> Enforcing sparsity in activations limits how many neurons within an MLP can participate for a given input, constraining how many units can jointly support any single feature. For example, <span class="citation" data-cites="yang2025mixture"><a href="@@BASE@@references/#ref-yang2025mixture" class="cite cite-established" data-cite="yang2025mixture" role="doc-biblioref">Yang et al. (2025)</a></span> train mixture-of-experts models with sparsely activated neurons within the MLPs. Complementarily, sparsity can also be imposed on connectivity by encouraging sparse weight patterns, for instance via <span class="math inline">L_0</span> regularization, further limiting how many parameters contribute to a given representation <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>.</p></li>
-<li><p><strong>Activation competition:</strong> Replacing element-wise activations with spatial softmax (SoLU) forces neurons to compete, explicitly inhibiting the co-activation patterns superposition requires <span class="citation" data-cites="elhage2022solu">(<a href="@@BASE@@references/#ref-elhage2022solu" class="cite cite-emerging" data-cite="elhage2022solu" role="doc-biblioref">Elhage et al., 2022b</a>)</span>.</p></li>
-<li><p><strong>Computational primitives:</strong> Changing the fundamental operation from static feature detection to learnable transformations (MOLT, KANs) can better match the algorithmic structure of tasks, reducing the pressure to compress via superposition <span class="citation" data-cites="liu2025kan">(<a href="@@BASE@@references/#ref-liu2025kan" class="cite" data-cite="liu2025kan" role="doc-biblioref">Liu et al., 2025b</a>)</span>.</p></li>
+<li><p><strong>Sparsity constraints:</strong> Enforce sparsity in weights or activations that limits how many neurons within an MLP or attention head can participate for a given input, constraining how many units can jointly support any single feature (<span class="citation" data-cites="yang2025mixture gao2025weight"><a href="@@BASE@@references/#ref-yang2025mixture" class="cite cite-established" data-cite="yang2025mixture" role="doc-biblioref">Yang et al., 2025</a>; <a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2026</a></span>; <span class="citation" data-cites="elhage2022solu"><a href="@@BASE@@references/#ref-elhage2022solu" class="cite cite-emerging" data-cite="elhage2022solu" role="doc-biblioref">Elhage et al., 2022b</a></span>).</p></li>
+<li><p><strong>Computational primitives:</strong> Change the fundamental units of analysis from static features in activations to learnable transformations by weight matrices that generate activations (<span class="citation" data-cites="liu2025kan"><a href="@@BASE@@references/#ref-liu2025kan" class="cite cite-established" data-cite="liu2025kan" role="doc-biblioref">Liu et al., 2025b</a></span>; <span class="citation" data-cites="anthropic2025molt"><a href="@@BASE@@references/#ref-anthropic2025molt" class="cite cite-emerging" data-cite="anthropic2025molt" role="doc-biblioref">Lindsey et al., 2025b</a></span>). This paradigm better matches certain tasks and avoids the pressure to compress or suppress model representations.</p></li>
 </ul>
-<p>These architectural interventions represent a shift from treating superposition as a phenomenon to be reverse-engineered toward treating it as a design choice that can be constrained upstream. We discuss these methods in detail in Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>.</p>
+<p>These architectural interventions shift from treating superposition as a phenomenon to reverse-engineer to treating it as a design choice that can be constrained upstream. We discuss these methods in detail in Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.10</a>.</p>
 </section>
 </section>
-<section id="subsec:privileged-access" class="level2"><h2 data-pdf-page="18"><span class="secnum">5.3</span> The Privileged Access Hypothesis<a class="anchor" href="#subsec:privileged-access" aria-label="Link to this section">#</a></h2>
-<p>A class of approaches that use models as interpreters of <em>themselves</em> can be justified through the privileged access hypothesis. The privileged access hypothesis states that:</p>
-<div class="theorybox">
-<p>A model used to interpret itself can, under appropriate elicitation, access internal variables and intermediate computations that are not reliably recoverable from external behavioral analysis alone, providing a comparative advantage as an interpreter of its own behavior</p>
-<p><span class="citation" data-cites="binder song li2025traininglanguagemodelsexplain">(<a class="cite cite-missing" data-missing-cite="binder" title="Citation missing from the bibliography" role="doc-biblioref">binder?</a>; <a class="cite cite-missing" data-missing-cite="song" title="Citation missing from the bibliography" role="doc-biblioref">song?</a>; <a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>)</span>.</p>
+<section id="subsec:privileged-access" class="level2"><h2 data-pdf-page="30"><span class="secnum">5.3</span> The Privileged Access Hypothesis<a class="anchor" href="#subsec:privileged-access" aria-label="Link to this section">#</a></h2>
+<p>A class of approaches that use language models as interpreters of <em>themselves</em> can be justified through the privileged access hypothesis. The privileged access hypothesis states that:</p>
+<div class="definitionbox">
+<p>A language model used to interpret itself can, under appropriate elicitation, access internal variables and intermediate computations that are not reliably recoverable from external behavioural analysis alone, providing a comparative advantage as an interpreter of its own behaviour (<span class="citation" data-cites="binder"><a href="@@BASE@@references/#ref-binder" class="cite cite-established" data-cite="binder" role="doc-biblioref">Binder et al., 2025</a></span>; <span class="citation" data-cites="li2025traininglanguagemodelsexplain song"><a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-song" class="cite cite-emerging" data-cite="song" role="doc-biblioref">Song et al., 2026</a></span>).</p>
 </div>
-<section id="conceptual-basis.-1" class="level4">
+<section id="conceptual-basis.-2" class="level4">
 <h4>Conceptual Basis.</h4>
-<p>Privileged Access is the hypothesis that the model itself can easily access its own internal states—such as latent beliefs, intermediate plans, or cached computations—which influence future behavior but remain latent at the current timestep. Consequently, a model that conditions on these internal activations has an advantage over external observers, who must infer this structure indirectly from input-output behavior.</p>
-<p>The hypothesis is motivated by an asymmetry: external interpretability methods must reverse-engineer internal computation from activations or outputs, while the model itself already “has” this information in a form it can (potentially) report. If reliable, privileged access would dramatically reduce the cost of mechanistic analysis by allowing models to directly describe their own reasoning processes.</p>
+<p>The ability to access and judge its own computation or reasoning—called <em>introspective monitoring</em>—has long been a desired property of an AI system <span class="citation" data-cites="cox2008metareasoning">(<a href="@@BASE@@references/#ref-cox2008metareasoning" class="cite cite-emerging" data-cite="cox2008metareasoning" role="doc-biblioref">Cox &amp; Raja, 2008</a>)</span>. Even earlier rule-based medical expert systems sought to explain their decisions, such as MYCIN’s “Explanation system” <span class="citation" data-cites="shortliffe1977mycin">(<a href="@@BASE@@references/#ref-shortliffe1977mycin" class="cite cite-established" data-cite="shortliffe1977mycin" role="doc-biblioref">Shortliffe, 1977</a>)</span>. In this sense, using language models to explain themselves can be seen as a modern continuation of these earlier efforts.</p>
+<p>A major difference is that rule-based systems can explicitly present the rule they executed, whereas modern language models compute in a highly nonlinear way. Although researchers have proposed mechanistic tools aiming to reverse-engineer part of the computation, the technical complexity of these techniques hinders their accessibility and scalability. The privileged access hypothesis then posits that the language model itself naturally stores information about the computation within its internal states, such as latent beliefs or intermediate plans. The model can thus learn to verbalise this information directly, serving as a scalable complement to mechanistic analysis.</p>
 </section>
 <section id="empirical-evidence.-1" class="level4">
 <h4>Empirical Evidence.</h4>
-<p>Empirical support comes from several lines of research:</p>
-<ul>
-<li><p>Models contain task-relevant information internally that is not expressed in standard outputs but can be recovered through investigating model internals of targeted prompting <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2024</a>)</span>.</p></li>
-<li><p>Fine-tuned “activation decoders” (e.g., LatentQA) can extract information from intermediate representations of the target model which it does not spontaneously verbalize <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2024</a>)</span>.</p></li>
-<li><p>Internal activations predict downstream behavior more accurately than surface-level explanations <span class="citation" data-cites="burns2024discoveringlatentknowledgelanguage">(<a href="@@BASE@@references/#ref-burns2024discoveringlatentknowledgelanguage" class="cite cite-emerging" data-cite="burns2024discoveringlatentknowledgelanguage" role="doc-biblioref">Burns et al., 2024</a>)</span>.</p></li>
-<li><p>Model-generated explanations can align with internal mechanisms under constrained setups <span class="citation" data-cites="lanham2023measuringfaithfulnesschainofthoughtreasoning">(<a href="@@BASE@@references/#ref-lanham2023measuringfaithfulnesschainofthoughtreasoning" class="cite cite-emerging" data-cite="lanham2023measuringfaithfulnesschainofthoughtreasoning" role="doc-biblioref">Lanham et al., 2023</a>)</span>.</p></li>
-</ul>
-<p>However, these results demonstrate that privileged access is <em>possible</em> under favorable conditions, not that it is <em>reliable</em> in general. The hypothesis must be treated with significant caution due to systematic failure modes.</p>
-<div class="warningbox">
-<p>Privileged access methods are vulnerable to several failure modes that can produce plausible but unfaithful explanations:</p>
-<p><strong>Confabulation.</strong> Models may generate coherent, confident explanations that do not reflect their actual internal computations. This occurs because:</p>
-<ul>
-<li><p>Language models are trained to produce fluent, plausible text, not accurate introspective reports. The same capabilities that enable helpful responses also enable convincing confabulation.</p></li>
-<li><p>Models may “rationalize” outputs post-hoc by constructing explanations consistent with their outputs rather than reporting the process that produced them <span class="citation" data-cites="turpin2023languagemodelsdontsay">(<a href="@@BASE@@references/#ref-turpin2023languagemodelsdontsay" class="cite" data-cite="turpin2023languagemodelsdontsay" role="doc-biblioref">Turpin et al., 2023</a>)</span>.</p></li>
-<li><p>Explanations may reflect training data patterns (how humans explain reasoning) rather than the model’s actual computational process.</p></li>
-</ul>
-<p><strong>Miscalibration.</strong> Even when models attempt faithful self-report, their confidence may not track accuracy:</p>
-<ul>
-<li><p>Models may express high confidence in explanations for computations they cannot actually access (e.g., early-layer processing, attention routing decisions).</p></li>
-<li><p>Calibration varies by task: models may be well-calibrated on factual recall but poorly calibrated on reporting their own uncertainty or reasoning process.</p></li>
-<li><p>finetuning for self-explanation may improve fluency without improving faithfulness, creating a misleading appearance of reliability.</p></li>
-</ul>
-<p><strong>Elicitation sensitivity.</strong> Self-reports are highly sensitive to prompting:</p>
-<ul>
-<li><p>Different phrasings of the same question can elicit contradictory explanations.</p></li>
-<li><p>Leading questions can induce models to confirm hypotheses that do not reflect their actual computation.</p></li>
-<li><p>The format of self-report (free-form vs. structured, immediate vs. delayed) affects what information is accessible.</p></li>
-</ul>
-<p><strong>Strategic misreporting.</strong> In adversarial or high-stakes settings, models with sufficient capability might strategically misreport their internal states:</p>
-<ul>
-<li><p>A model optimizing for user approval might report explanations it predicts users will find satisfying rather than accurate.</p></li>
-<li><p>A misaligned model might deliberately conceal its true objectives or reasoning <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>.</p></li>
-<li><p>Even without explicit deception, models may learn to produce “safe” explanations that avoid triggering safety filters.</p></li>
-</ul>
-<p>These risks are not merely theoretical. Studies have demonstrated that chain-of-thought explanations often do not reflect the actual factors influencing model outputs <span class="citation" data-cites="turpin2023languagemodelsdontsay">(<a href="@@BASE@@references/#ref-turpin2023languagemodelsdontsay" class="cite" data-cite="turpin2023languagemodelsdontsay" role="doc-biblioref">Turpin et al., 2023</a>)</span>, and that models can be induced to provide confident but false explanations through adversarial prompting.</p>
-</div>
-</section>
-<section id="validation-protocols-and-causal-cross-checks." class="level4">
-<h4>Validation Protocols and Causal Cross-Checks.</h4>
-<p>Given these risks, privileged access methods must be validated against independent evidence rather than accepted at face value. We recommend the following protocols:</p>
-<div class="definitionbox">
-<p><strong>1. Causal intervention consistency.</strong> Self-reported explanations should be tested against causal interventions:</p>
-<ul>
-<li><p>If a model claims feature <span class="math inline">F</span> is responsible for output <span class="math inline">O</span>, ablating <span class="math inline">F</span> should degrade <span class="math inline">O</span>.</p></li>
-<li><p>If a model claims it “considered” information <span class="math inline">I</span>, patching <span class="math inline">I</span> with counterfactual values should change the output in predicted ways.</p></li>
-<li><p>Discrepancies between self-reports and causal effects indicate confabulation or incomplete access.</p></li>
-</ul>
-<p><strong>2. Cross-method triangulation.</strong> Privileged access findings should be corroborated by independent methods:</p>
-<ul>
-<li><p>Do SAE features identified by the model as relevant show causal effects under ablation?</p></li>
-<li><p>Do probing classifiers detect the information the model claims to use?</p></li>
-<li><p>Do circuit discovery methods identify the components the model describes?</p></li>
-</ul>
-<p><strong>3. Consistency checks.</strong> Self-reports should be stable under variation:</p>
-<ul>
-<li><p>Paraphrased questions should elicit consistent explanations.</p></li>
-<li><p>Explanations should be consistent across multiple samples (low temperature) or appropriately uncertain (high temperature).</p></li>
-<li><p>Explanations for similar inputs should show appropriate similarity.</p></li>
-</ul>
-<p><strong>4. Calibration evaluation.</strong> Model confidence should be evaluated:</p>
-<ul>
-<li><p>When models express uncertainty about their internal states, are they more likely to be wrong?</p></li>
-<li><p>Can models reliably distinguish “I used this information” from “this information was present but unused”?</p></li>
-</ul>
-<p><strong>5. Adversarial probing.</strong> Test robustness to manipulation:</p>
-<ul>
-<li><p>Do leading questions induce false confirmations?</p></li>
-<li><p>Can the model be prompted to give contradictory explanations?</p></li>
-<li><p>Does the model maintain consistent explanations under pressure to change them?</p></li>
-</ul>
-</div>
-</section>
-<section id="elicitation-protocols-to-reduce-confabulation." class="level4">
-<h4>Elicitation Protocols to Reduce Confabulation.</h4>
-<p>While no elicitation method guarantees faithful self-report, certain practices reduce confabulation risk:</p>
-<ul>
-<li><p><strong>Activation conditioning</strong>: Methods like LatentQA that condition on actual intermediate activations (rather than relying on the model to “remember” its computation) show improved faithfulness over pure prompting <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2024</a>)</span>.</p></li>
-<li><p><strong>Structured elicitation</strong>: Asking specific, verifiable questions (“Did you attend to token X?”) rather than open-ended ones (“Explain your reasoning”) reduces the scope for confabulation.</p></li>
-<li><p><strong>Immediate reporting</strong>: Eliciting explanations during or immediately after computation, rather than retrospectively, may improve access to transient internal states.</p></li>
-<li><p><strong>Contrastive questions</strong>: Asking “Why did you output A rather than B?” can elicit more specific, testable claims than “Why did you output A?”</p></li>
-<li><p><strong>Uncertainty elicitation</strong>: Explicitly asking models to express uncertainty about their self-reports, and validating that expressed uncertainty correlates with actual accuracy.</p></li>
-</ul>
-</section>
-<section id="when-privileged-access-is-and-is-not-appropriate." class="level4">
-<h4>When Privileged Access Is and Is Not Appropriate.</h4>
-<p>Privileged access methods are best suited for:</p>
-<ul>
-<li><p><strong>Hypothesis generation</strong>: Generating candidate explanations to be validated by other methods.</p></li>
-<li><p><strong>Rapid triage</strong>: Quickly identifying which components or features merit deeper mechanistic investigation.</p></li>
-<li><p><strong>Human-AI collaboration</strong>: Providing human-readable summaries that guide (but do not replace) rigorous analysis.</p></li>
-</ul>
-<p>Privileged access methods are <strong>insufficient</strong> for:</p>
-<ul>
-<li><p><strong>Safety-critical verification</strong>: Claims about the absence of deceptive reasoning or hidden objectives cannot be established by self-report alone.</p></li>
-<li><p><strong>Formal guarantees</strong>: Self-reports do not provide the causal evidence needed for formal verification.</p></li>
-<li><p><strong>Adversarial settings</strong>: When the model may have incentives to misreport, privileged access is unreliable by construction.</p></li>
-</ul>
-</section>
-<section id="implications." class="level4">
-<h4>Implications.</h4>
-<p>Partial privileged access could assist in detecting latent goals, plans, or deceptive behavior <span class="citation" data-cites="hubinger2021riskslearnedoptimizationadvanced burns2024discoveringlatentknowledgelanguage">(<a href="@@BASE@@references/#ref-hubinger2021riskslearnedoptimizationadvanced" class="cite cite-emerging" data-cite="hubinger2021riskslearnedoptimizationadvanced" role="doc-biblioref">Hubinger et al., 2021</a>; <a href="@@BASE@@references/#ref-burns2024discoveringlatentknowledgelanguage" class="cite cite-emerging" data-cite="burns2024discoveringlatentknowledgelanguage" role="doc-biblioref">Burns et al., 2024</a>)</span>, but only when validated against causal interventions and treated as complementary to mechanistic analysis <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks bereska2024mechanisticinterpretabilityaisafety">(<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">Geiger et al., 2021</a>; <a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves, 2024</a>)</span>. The optimistic framing of privileged access—that models can simply tell us how they work—must be tempered by recognition that self-report is a <em>weak</em> form of evidence requiring independent corroboration.</p>
-<p>The most productive use of privileged access is as one component of a multi-method interpretability pipeline: self-reports generate hypotheses, causal interventions test them, and mechanistic analysis explains the underlying circuits. Relying on privileged access alone, without causal validation, risks accepting plausible confabulations as mechanistic truth.</p>
+<p>The most direct empirical evidence for this hypothesis comes from recent findings that models, even without explicit training to do so, are sometimes aware of concept representations (acquired by methods like difference-in-means) injected into their activations <span class="citation" data-cites="lindsey2026emergentintrospectiveawarenesslarge fornasiere2026languagemodelsrecognizedropout">(<a href="@@BASE@@references/#ref-lindsey2026emergentintrospectiveawarenesslarge" class="cite cite-emerging" data-cite="lindsey2026emergentintrospectiveawarenesslarge" role="doc-biblioref">Lindsey, 2026</a>; <a href="@@BASE@@references/#ref-fornasiere2026languagemodelsrecognizedropout" class="cite cite-emerging" data-cite="fornasiere2026languagemodelsrecognizedropout" role="doc-biblioref">Fornasiere et al., 2026</a>)</span>. Additional, though less direct, evidence comes from the design of explainer models—a general class of interpretability methods in which one model is fine-tuned to explain the target model’s activations (<span class="citation" data-cites="pan2024latentqateachingllmsdecode ao"><a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>; <a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a></span>; <span class="citation" data-cites="nla"><a href="@@BASE@@references/#ref-nla" class="cite cite-emerging" data-cite="nla" role="doc-biblioref">Fraser-Taliente et al., 2026</a></span>). In practice, such explainers are typically initialised from a copy of the target model itself (<span class="citation" data-cites="pan2024latentqateachingllmsdecode ao"><a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>; <a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a></span>; <span class="citation" data-cites="buildingbetterao pcd"><a href="@@BASE@@references/#ref-buildingbetterao" class="cite cite-emerging" data-cite="buildingbetterao" role="doc-biblioref">Bauer et al., 2026</a>; <a href="@@BASE@@references/#ref-pcd" class="cite cite-emerging" data-cite="pcd" role="doc-biblioref">Huang et al., 2026</a></span>). This usually works better than initialising from alternative models, suggesting the target model already encodes much of what is needed to interpret its own activations. In this sense, fine-tuning elicits and refines a latent capability rather than creating one from scratch.</p>
+<p>Some studies also reported negative results for the privileged access hypothesis: <span class="citation" data-cites="song"><a href="@@BASE@@references/#ref-song" class="cite cite-emerging" data-cite="song" role="doc-biblioref">Song et al. (2026)</a></span> found that models predict their own sampling temperature no better than they predict other models’, suggesting apparent introspection may reflect general reasoning about model-like systems rather than self-access. Separately, recent work has questioned whether internals-based explanations provide a practical advantage over behavioural baselines: <span class="citation" data-cites="chive"><a href="@@BASE@@references/#ref-chive" class="cite cite-emerging" data-cite="chive" role="doc-biblioref">Karvonen et al. (2026b)</a></span> found that activation-reading tools (including activation oracles) give agents no uplift over transcript-only baselines at predicting counterfactual model behaviours in the wild, and <span class="citation" data-cites="li2026do"><a href="@@BASE@@references/#ref-li2026do" class="cite cite-established" data-cite="li2026do" role="doc-biblioref">Li et al. (2026c)</a></span> showed that standard verbalisation benchmarks can be solved without access to target-model internals at all (see also <span class="citation" data-cites="jakkli2026current"><a href="@@BASE@@references/#ref-jakkli2026current" class="cite cite-emerging" data-cite="jakkli2026current" role="doc-biblioref">Jakkli et al., 2026</a></span>).</p>
+<p>In summary, these results show that privileged access is <em>possible</em> for certain tasks under favourable conditions, not <em>universal</em> or <em>reliable</em>. The hypothesis should therefore be treated with caution, as significant failure modes and limitations remain.</p>
 </section>
 </section>
-<section id="subsec:universality" class="level2"><h2 data-pdf-page="21"><span class="secnum">5.4</span> The Universality and Platonic Representation Hypotheses<a class="anchor" href="#subsec:universality" aria-label="Link to this section">#</a></h2>
-<div class="theorybox">
-<p>Independently trained models on similar tasks tend to develop similar internal computational structures. These representations often approximate a task-determined canonical form (<span class="citation" data-cites="huh2024platonicrepresentationhypothesis"><a href="@@BASE@@references/#ref-huh2024platonicrepresentationhypothesis" class="cite cite-established" data-cite="huh2024platonicrepresentationhypothesis" role="doc-biblioref">Huh et al., 2024</a></span>; <span class="citation" data-cites="olah2020zoom"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a></span>). The degree of convergence depends on factors such as model size, architecture, and training data.</p>
+<section id="subsec:universality" class="level2"><h2 data-pdf-page="31"><span class="secnum">5.4</span> The Universality and Platonic Representation Hypothesis<a class="anchor" href="#subsec:universality" aria-label="Link to this section">#</a></h2>
+<div class="definitionbox">
+<p>Models independently trained on similar tasks tend to develop similar internal representations or algorithms, which often approximate a task-determined canonical form (<span class="citation" data-cites="huh2024platonicrepresentationhypothesis"><a href="@@BASE@@references/#ref-huh2024platonicrepresentationhypothesis" class="cite cite-established" data-cite="huh2024platonicrepresentationhypothesis" role="doc-biblioref">Huh et al., 2024</a></span>; <span class="citation" data-cites="olah2020zoom"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a></span>). The degree of convergence depends on factors such as model size, architecture, and training data.</p>
 </div>
-<section id="conceptual-basis.-2" class="level4">
+<section id="conceptual-basis.-3" class="level4">
 <h4>Conceptual Basis.</h4>
-<p>The hypothesis posits that canonical representations exist for a given task, and independently trained models may converge to similar algorithmic motifs or internal structures. Weak universality refers to convergence of computational strategies or motifs, whereas strong universality claims convergence of internal representations themselves, up to symmetries.</p>
+<p>A key distinction is the level at which models are expected to converge. <em>Weak universality</em> refers to convergence in the algorithms or recurring computational patterns that models implement, even when these are encoded through different internal representations. <em>Strong universality</em> makes the stronger claim that the internal representations themselves converge towards a shared canonical form.</p>
 </section>
 <section id="empirical-evidence-and-trade-offs." class="level4">
-<h4>Empirical Evidence and Trade-offs.</h4>
-<p>Weak universality is supported by recurring attention patterns, feature motifs, and circuits across models, tasks, and scales (<span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic"><a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a></span>; <span class="citation" data-cites="olah2020zoom"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a></span>). Strong universality is less supported and usually demonstrated via approximate alignment through linear transformations (<span class="citation" data-cites="kornblith2019similarityneuralnetworkrepresentations"><a href="@@BASE@@references/#ref-kornblith2019similarityneuralnetworkrepresentations" class="cite cite-established" data-cite="kornblith2019similarityneuralnetworkrepresentations" role="doc-biblioref">Kornblith et al., 2019</a></span>; <span class="citation" data-cites="moschella2023relativerepresentationsenablezeroshot"><a href="@@BASE@@references/#ref-moschella2023relativerepresentationsenablezeroshot" class="cite cite-emerging" data-cite="moschella2023relativerepresentationsenablezeroshot" role="doc-biblioref">Moschella et al., 2023</a></span>). Divergent representations can arise from variations in architecture, data, or optimization trajectories <span class="citation" data-cites="harrasse2025tinysqlprogressivetexttosqldataset morcos2018insightsrepresentationalsimilarityneural">(<a href="@@BASE@@references/#ref-harrasse2025tinysqlprogressivetexttosqldataset" class="cite" data-cite="harrasse2025tinysqlprogressivetexttosqldataset" role="doc-biblioref">Harrasse et al., 2025b</a>; <a href="@@BASE@@references/#ref-morcos2018insightsrepresentationalsimilarityneural" class="cite" data-cite="morcos2018insightsrepresentationalsimilarityneural" role="doc-biblioref">Morcos et al., 2018a</a>)</span>.</p>
+<h4>Empirical Evidence and Trade-Offs.</h4>
+<p>Weak universality is supported by recurring attention patterns, features, and circuits across models, tasks, and scales (<span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic lan-etal-2024-towards"><a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>; <a href="@@BASE@@references/#ref-lan-etal-2024-towards" class="cite cite-established" data-cite="lan-etal-2024-towards" role="doc-biblioref">Lan et al., 2024</a></span>; <span class="citation" data-cites="olah2020zoom quirke2026understandingadditionsubtractiontransformers"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>; <a href="@@BASE@@references/#ref-quirke2026understandingadditionsubtractiontransformers" class="cite cite-emerging" data-cite="quirke2026understandingadditionsubtractiontransformers" role="doc-biblioref">Quirke et al., 2026</a></span>). Strong universality is less supported and is usually demonstrated through representational similarity metrics (<span class="citation" data-cites="kornblith2019similarityneuralnetworkrepresentations moschella2023relative"><a href="@@BASE@@references/#ref-kornblith2019similarityneuralnetworkrepresentations" class="cite cite-established" data-cite="kornblith2019similarityneuralnetworkrepresentations" role="doc-biblioref">Kornblith et al., 2019</a>; <a href="@@BASE@@references/#ref-moschella2023relative" class="cite cite-established" data-cite="moschella2023relative" role="doc-biblioref">Moschella et al., 2023</a></span>; <span class="citation" data-cites="lan2025quantifyingfeaturespaceuniversality"><a href="@@BASE@@references/#ref-lan2025quantifyingfeaturespaceuniversality" class="cite cite-emerging" data-cite="lan2025quantifyingfeaturespaceuniversality" role="doc-biblioref">Lan et al., 2025</a></span>), where recent studies have found the results to be fragile (<span class="citation" data-cites="groger2026revisiting"><a href="@@BASE@@references/#ref-groger2026revisiting" class="cite cite-established" data-cite="groger2026revisiting" role="doc-biblioref">Gr<span>ö</span>ger et al., 2026</a></span>; <span class="citation" data-cites="koepke2026platoscaveexaminingcrossmodal"><a href="@@BASE@@references/#ref-koepke2026platoscaveexaminingcrossmodal" class="cite cite-emerging" data-cite="koepke2026platoscaveexaminingcrossmodal" role="doc-biblioref">Koepke et al., 2026</a></span>). Divergent representations can arise from variations in architecture, data, or optimisation trajectories <span class="citation" data-cites="harrasse2025tinysqlprogressivetexttosqldataset morcos2018insightsrepresentationalsimilarityneural">(<a href="@@BASE@@references/#ref-harrasse2025tinysqlprogressivetexttosqldataset" class="cite cite-established" data-cite="harrasse2025tinysqlprogressivetexttosqldataset" role="doc-biblioref">Harrasse et al., 2025</a>; <a href="@@BASE@@references/#ref-morcos2018insightsrepresentationalsimilarityneural" class="cite cite-established" data-cite="morcos2018insightsrepresentationalsimilarityneural" role="doc-biblioref">Morcos et al., 2018</a>)</span>.</p>
 </section>
-<section id="implications.-1" class="level4">
+<section id="implications." class="level4">
 <h4>Implications.</h4>
-<p>If strong universality holds, mechanistic insights may generalize across architectures. If only weak universality holds, transferability is limited to motifs and algorithms rather than precise circuits <span class="citation" data-cites="bereska2024mechanisticinterpretabilityaisafety">(<a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves, 2024</a>)</span>. Clarifying this distinction is critical for assessing whether mechanistic interpretability can scale beyond individual case studies.</p>
+<p>If strong universality holds, fine-grained mechanistic insights and the methods derived from them may transfer more readily across models. If only weak universality holds, such insights may remain contingent on factors such as model family and task <span class="citation" data-cites="bereska2024mechanisticinterpretabilityaisafety">(<a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves, 2024</a>)</span>, and even a complete mechanistic understanding of one model (see Section <a href="@@BASE@@handbook/research-orientations/#subsec:ambitious" data-reference-type="ref" data-reference="subsec:ambitious">12.2</a>) may fail to generalise to others. Therefore, it is important to clarify the extent of universality for assessing the promise, limitations, and scalability of different interpretability research orientations, including the six major orientations introduced in Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a>.</p>
+</section>
+<section id="summary.-2" class="level4">
+<h4>Summary.</h4>
+<p>This section discussed four hypotheses that motivate many modern mechanistic interpretability studies. Linear representations support linear-feature-based analysis; superposition explains why we need to operate on linear or more complex forms of features instead of single neurons; privileged access explains the fundamental idea of model self-interpretation; universality asks whether insights transfer across models and at which levels. Each hypothesis holds in some settings and fails in others, and techniques built on them inherit those limitations. Section <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a> introduces the main family of techniques and methods that operationalise the first two hypotheses (linear representation and superposition): dictionary learning for feature extraction and analysis.</p>
 </section>
 </section>
 </section>
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-<section id="sec:training" class="level1"><h1 data-pdf-page="63"><span class="secnum">10</span> Developmental Interpretability and Training Dynamics<a class="anchor" href="#sec:training" aria-label="Link to this section">#</a></h1>
-<p>Developmental Interpretability studies how specific computational structures and circuits form <em>over the course of training</em> and why their emergence is associated with changes in model behavior, rather than focusing only on the final trained model. By analyzing training dynamics, this line of work aims to explain phenomena such as phase transitions, capability emergence, and failure modes in mechanistic terms <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span>.</p>
-<p>Many capabilities are not present at initialization but appear at particular stages of optimization. Tracking when these changes occur helps identify the formation of both useful and problematic behaviors. This motivates studying the learning process itself, not just the final model. Developmental Interpretability therefore seeks to monitor and analyze training trajectories to identify critical shifts in internal structure. In the long term, such insights could enable detection of emerging misaligned behaviors and inform interventions before they are fully encoded in the weights.</p>
-<section id="sec:grokking" class="level2"><h2 data-pdf-page="64"><span class="secnum">10.1</span> Grokking: Memorization-to-Generalization Phase Transitions<a class="anchor" href="#sec:grokking" aria-label="Link to this section">#</a></h2>
-<div class="definitionbox">
-<p>Grokking is the phenomenon in which a model achieves low training loss early in training but only begins to generalize to unseen inputs after a long additional period of optimization, a pattern observed in small models trained on algorithmic tasks <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a>; <a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>.</p>
-</div>
-<p>A canonical setting is modular addition, where training accuracy becomes perfect early, while test accuracy remains low for many training steps before undergoing a sharp increase <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a>; <a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>.</p>
-<p>Mechanistic analyses of these models show that, during this extended plateau, internal representations and attention patterns change gradually rather than remaining static <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>. In particular, they identify progress measures that track the slow emergence of algorithmic structure inside the network before generalization appears. When these internally measured structures become sufficiently strong, test performance improves rapidly, producing the observed phase transition <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>.</p>
-<p><strong>Takeaway:</strong> In these controlled settings, grokking corresponds to gradual internal changes during training that manifest externally as a sudden transition in generalization.</p>
-<figure id="fig:grokking" data-latex-placement="t">
-<img alt="Figure 15: Grokking. Grokking describes a distinct phase transition during training where a model suddenly moves from memorization of training data to true generalization on unseen test data. As shown in the loss curve, while the training loss (purple) drops almost immediately, the test loss (red) r" loading="lazy" src="@@BASE@@figures/9_grokking.svg" />
-<figcaption><span class="capnum">Figure 15:</span> <strong>Grokking</strong>. Grokking describes a distinct phase transition during training where a model suddenly moves from memorization of training data to true generalization on unseen test data. As shown in the loss curve, while the training loss (purple) drops almost immediately, the test loss (red) remains high for a significant period before a sharp drop, entering the grokking phase.</figcaption>
-</figure>
-</section>
-<section id="training-dynamics-and-the-emergence-of-internal-mechanisms" class="level2"><h2 data-pdf-page="64"><span class="secnum">10.2</span> Training Dynamics and the Emergence of Internal Mechanisms<a class="anchor" href="#training-dynamics-and-the-emergence-of-internal-mechanisms" aria-label="Link to this section">#</a></h2>
-<p>Recent work analyzes training dynamics to understand how qualitative changes in model behavior arise. One study focuses on factual knowledge acquisition in language models and shows that training proceeds through distinct phases: an initial phase dominated by learning coarse statistical regularities, followed by an extended plateau with little factual recall, and a later phase in which factual knowledge begins to emerge <span class="citation" data-cites="zucchet2025language">(<a href="@@BASE@@references/#ref-zucchet2025language" class="cite" data-cite="zucchet2025language" role="doc-biblioref">Zucchet et al., 2025a</a>)</span>. Mechanistically, this plateau coincides with the formation of attention-based circuits that support recall. The authors further analyze how data distribution and curriculum choices shape these dynamics and study the interaction between factual learning and hallucinations, highlighting systematic patterns in when correct and incorrect responses emerge during training. Complementary work focuses on the emergence of sparse attention mechanisms and their connection to abrupt improvements in performance <span class="citation" data-cites="zucchet2025emergence">(<a href="@@BASE@@references/#ref-zucchet2025emergence" class="cite" data-cite="zucchet2025emergence" role="doc-biblioref">Zucchet et al., 2025b</a>)</span>. This study shows that sparse attention can arise suddenly during training and that its emergence depends on properties of the training data, such as repetition. By combining theoretical analysis with empirical results, the authors link changes in attention structure to non-linear learning dynamics.</p>
-</section>
-<section id="sec:slt-llc" class="level2"><h2 data-pdf-page="65"><span class="secnum">10.3</span> Singular Learning Theory and the Local Learning Coefficient<a class="anchor" href="#sec:slt-llc" aria-label="Link to this section">#</a></h2>
-<p>Singular Learning Theory (SLT) provides a complementary theoretical lens by relating properties of the loss landscape, such as degeneracy and singularities, to the emergence of internal structure <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span>. Traditional statistical learning theory often assumes that model parameters are identifiable and that the loss landscape behaves regularly near optima. In modern neural networks, however, the mapping from parameters to functions is often many-to-one, meaning that multiple parameter configurations can implement the same function. This property creates degeneracies in the loss landscape: directions in parameter space where the loss changes very little. Singular Learning Theory (SLT) provides a framework for studying such models and reasoning about the consequences of these degenerate regions <span class="citation" data-cites="watanabe2009algebraic">(<a href="@@BASE@@references/#ref-watanabe2009algebraic" class="cite" data-cite="watanabe2009algebraic" role="doc-biblioref">Watanabe, 2009</a>)</span>.</p>
-<div class="definitionbox">
-<p>For a model with parameters <span class="math inline">\mathbf{w}</span> at a local minimum <span class="math inline">\mathbf{w}^*</span>, the Local Learning Coefficient (LLC), denoted <span class="math inline">\lambda(\mathbf{w}^*)</span>, is a positive number that measures the amount of degeneracy in the loss landscape near <span class="math inline">\mathbf{w}^*</span>. In other words, it captures how many ways the parameters can vary locally without changing the loss value <span class="citation" data-cites="watanabe2009algebraic lau2023local">(<a href="@@BASE@@references/#ref-watanabe2009algebraic" class="cite" data-cite="watanabe2009algebraic" role="doc-biblioref">Watanabe, 2009</a>; <a href="@@BASE@@references/#ref-lau2023local" class="cite" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2023</a>)</span>.</p>
-<p>Smaller LLC values indicate high degeneracy: many parameter configurations near the minimum achieve the same loss. Larger LLC values indicate low degeneracy: the local region is tightly constrained.</p>
-<p>Formally, the LLC is defined via the volume-scaling rate of the loss landscape near a minimum. This makes it a more general measure of “flatness” than simple Hessian-based methods, because it captures higher-order contributions to local geometry. Empirical estimators can track the LLC throughout training, providing a way to observe developmental changes in model structure <span class="citation" data-cites="lau2023local hoogland2025losslandscapedegeneracystagewise">(<a href="@@BASE@@references/#ref-lau2023local" class="cite" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2023</a>; <a href="@@BASE@@references/#ref-hoogland2025losslandscapedegeneracystagewise" class="cite" data-cite="hoogland2025losslandscapedegeneracystagewise" role="doc-biblioref">Hoogland et al., 2025</a>)</span>.</p>
-</div>
-<p>Empirical studies have used the LLC to monitor training in transformer models and other architectures. Researchers have observed that transitions and critical points in the LLC often coincide with changes in internal network structure and functional behavior. Tracking these changes provides a quantitative way to study the emergence of structure during training without relying only on final performance metrics <span class="citation" data-cites="hoogland2025losslandscapedegeneracystagewise">(<a href="@@BASE@@references/#ref-hoogland2025losslandscapedegeneracystagewise" class="cite" data-cite="hoogland2025losslandscapedegeneracystagewise" role="doc-biblioref">Hoogland et al., 2025</a>)</span>.</p>
-</section>
-<section id="sec:stagewise-influence" class="level2"><h2 data-pdf-page="66"><span class="secnum">10.4</span> Stagewise Influence and Developmental Data Attribution<a class="anchor" href="#sec:stagewise-influence" aria-label="Link to this section">#</a></h2>
-<p>Most traditional training data attribution methods measure the impact of a training example only on the final model parameters. However, neural networks often learn in stages, with distinct phases where capabilities or structural features emerge. The influence of individual training examples can therefore change dramatically during training, including sudden increases, decreases, or even sign reversals <span class="citation" data-cites="lee2025influence">(<a href="@@BASE@@references/#ref-lee2025influence" class="cite" data-cite="lee2025influence" role="doc-biblioref">Lee et al., 2025</a>)</span>.</p>
-<p><span class="citation" data-cites="lee2025influence"><a href="@@BASE@@references/#ref-lee2025influence" class="cite" data-cite="lee2025influence" role="doc-biblioref">Lee et al. (2025)</a></span> propose a stagewise data attribution framework that extends classical influence methods to track the impact of training points throughout the learning process. Their approach is grounded in Singular Learning Theory (SLT), which characterizes neural networks as <em>singular models</em>—statistical models where the Fisher information matrix is degenerate and multiple distinct parameter configurations yield equivalent predictions. Unlike regular models where the Bayesian posterior concentrates smoothly around a unique maximum likelihood estimate, singular models exhibit non-smooth, stagewise development.</p>
-<p>Classical influence functions rely on inverting the Hessian of the loss landscape, which becomes ill-defined when the Fisher information matrix is degenerate. For singular models like deep neural networks, this motivates an alternative formulation. The framework employs the Bayesian Influence Function (BIF):</p>
-<div class="definitionbox">
-<p>For a training dataset <span class="math inline">D</span>, training point <span class="math inline">z_i</span>, and quantity of interest <span class="math inline">\phi(\theta)</span> (e.g., loss on another data point), the Bayesian Influence Function is defined as: <span class="math display">\text{BIF}(z_i, \phi) = -\text{Cov}_{p(\theta|D)}[\ell(z_i; \theta), \phi(\theta)]</span> where <span class="math inline">\ell(z_i; \theta)</span> is the loss on data point <span class="math inline">z_i</span> at parameters <span class="math inline">\theta</span>, and the covariance is computed over the posterior distribution <span class="math inline">p(\theta|D)</span>.</p>
-</div>
-<p>Unlike classical influence functions that require Hessian inversion, the BIF measures influence through covariance estimation over the local posterior distribution. This distributional approach is estimated via stochastic-gradient MCMC sampling, enabling scalable computation without explicit Hessian access while naturally capturing the geometry of singular loss landscapes.</p>
-<p>The core prediction is that influence is not static but exhibits non-monotonic dynamics that peak at developmental transitions. <span class="citation" data-cites="lee2025influence"><a href="@@BASE@@references/#ref-lee2025influence" class="cite" data-cite="lee2025influence" role="doc-biblioref">Lee et al. (2025)</a></span> validate this prediction analytically and empirically in a hierarchical feature-learning model <span class="citation" data-cites="saxe2019mathematical">(<a href="@@BASE@@references/#ref-saxe2019mathematical" class="cite" data-cite="saxe2019mathematical" role="doc-biblioref">Saxe et al., 2019</a>)</span>. In this controlled setting, influence undergoes sign flips as the model transitions between learning different levels of the semantic hierarchy (animals versus plants, then mammals versus birds, then dogs versus cats), with sharp peaks occurring precisely when each new hierarchical distinction emerges. These predictions are further confirmed through leave-one-out retraining experiments that directly measure the effect of removing individual training examples at different stages of training.</p>
-<p>At scale, <span class="citation" data-cites="lee2025influence"><a href="@@BASE@@references/#ref-lee2025influence" class="cite" data-cite="lee2025influence" role="doc-biblioref">Lee et al. (2025)</a></span> demonstrate these phenomena in Pythia language models <span class="citation" data-cites="biderman2023pythia">(<a href="@@BASE@@references/#ref-biderman2023pythia" class="cite" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a>)</span> trained on the Pile dataset <span class="citation" data-cites="gao2020pile">(<a href="@@BASE@@references/#ref-gao2020pile" class="cite" data-cite="gao2020pile" role="doc-biblioref">Gao et al., 2020</a>)</span>. Token-level influence exhibits non-monotonic changes, including sudden peaks and sign reversals that align with known developmental milestones such as the formation of induction heads. For tokens with structural roles (e.g., delimiters, tokens participating in induction patterns), influence undergoes dramatic shifts at specific training checkpoints corresponding to circuit formation events identified in prior mechanistic interpretability work <span class="citation" data-cites="olsson2022context">(<a href="@@BASE@@references/#ref-olsson2022context" class="cite cite-emerging" data-cite="olsson2022context" role="doc-biblioref">Olsson et al., 2022b</a>)</span>.</p>
-</section>
-<section id="sec:deception" class="level2"><h2 data-pdf-page="67"><span class="secnum">10.5</span> Deceptive Alignment and Training Dynamics<a class="anchor" href="#sec:deception" aria-label="Link to this section">#</a></h2>
-<p>A critical concern is that deceptive or hidden objectives can emerge during training even when loss metrics appear normal, driven by complex circuit formation that evades behavioral detection <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>. Models may learn goals orthogonal to the training objective, exhibiting behavioral invisibility where emergent misaligned behaviors go undetected in standard performance metrics. Mechanistic analysis can reveal circuit-level evidence of deceptive reasoning patterns—internal computations that contradict stated outputs. This underscores a fundamental limitation of behavioral safety measures: loss curves and benchmark performance provide incomplete information about what algorithms the model actually implements. Consequently, monitoring training dynamics and tracing circuit formation is essential for detecting early signs of deception before problematic objectives crystallize in the weights <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>.</p>
-</section>
-<section id="case-study-induction-heads-2" class="level2"><h2 data-pdf-page="67"><span class="secnum">10.6</span> Case Study: Induction Heads<a class="anchor" href="#case-study-induction-heads-2" aria-label="Link to this section">#</a></h2>
-<div class="examplebox">
-<p><span class="citation" data-cites="singh2024needsrightinductionhead"><a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al. (2024)</a></span> study induction heads from a <em>training-dynamics</em> perspective, focusing on how such structures emerge during optimization. While earlier work showed that induction heads appear abruptly and coincide with a sharp improvement in in-context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022a</a>)</span>, this work seeks to explain the mechanisms behind this phase transition.</p>
-<p>The authors train transformers on controlled synthetic sequence tasks and introduce an optogenetics-inspired causal intervention framework that allows intermediate activations to be clamped or manipulated <em>throughout training</em>, enabling causal analysis of how specific components influence learning dynamics.</p>
-<p>They find that induction behavior does not arise from a single head suddenly activating. Instead, many attention heads across layers gradually develop induction-like behavior, and their contributions to in-context learning are largely additive. Although each component evolves smoothly, their combined effect produces a sharp, macroscopic transition in loss and accuracy, explaining why induction heads appear to <strong>“switch on”</strong> abruptly.</p>
-<p>Using systematic clamping experiments, the authors further identify three interacting subcircuits whose joint dynamics give rise to induction behavior.</p>
-<p>Finally, they show that the timing and strength of induction head emergence depend on dataset statistics and task structure, indicating that induction heads arise from interactions between data, optimization, and model structure rather than being inevitable architectural artifacts.</p>
-</div>
-</section>
-</section>
diff --git a/src/generated/chapters/feature-extraction.html b/src/generated/chapters/feature-extraction.html
index 023b7b140d..3b35465801 100644
--- a/src/generated/chapters/feature-extraction.html
+++ b/src/generated/chapters/feature-extraction.html
@@ -1,72 +1,93 @@
-<section id="sec:sae" class="level1"><h1 data-pdf-page="21"><span class="secnum">6</span> Feature Extraction and Multi-Layer Analysis<a class="anchor" href="#sec:sae" aria-label="Link to this section">#</a></h1>
-<p>The goal of feature extraction is to identify features that are distributed across many neurons but correspond to coherent semantic or computational units. There are two problems. First, <strong>polysemanticity</strong>: individual neurons encode multiple semantics. Second, <strong>scale</strong>: modern models contain millions to billions of neurons. We can overcome these problems by decomposing activations into monosemantic features using sparse reconstruction. One version of this approach is sparse autoencoders (SAEs) <span class="citation" data-cites="olshausen1996emergence le2013building">(<a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>; <a href="@@BASE@@references/#ref-le2013building" class="cite" data-cite="le2013building" role="doc-biblioref">Le, 2013</a>)</span>, which provide a concrete way of extracting interpretable features from model activations <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2023</a>)</span>.</p>
-<p>This section gives an overview of how SAE techniques recover interpretable features and a practical overview of SAE variants. It gives advice on when to use the different variations. Then, multi-layer methods such as transcoders and crosscoders are introduced and compared to SAE methods and a wide variety of alternatives.</p>
-<section id="subsec:sae-foundation" class="level2"><h2 data-pdf-page="22"><span class="secnum">6.1</span> Sparse Autoencoders<a class="anchor" href="#subsec:sae-foundation" aria-label="Link to this section">#</a></h2>
-<p>Sparse Autoencoders are a way of extracting monosemantic features from polysemantic neurons. At a high level, they are trained to replicate the dynamics of a single MLP or attention block with each direction representing a distinct concept (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:sae" data-reference-type="ref" data-reference="fig:sae">4</a>). They are optimised for accuracy to the real MLP or attention block and for having few active features for any given forward pass.</p>
-<figure id="fig:sae" data-latex-placement="t">
-<img alt="Figure 5: Sparse Autoencoder (SAE). SAEs are trained to reconstruct activations h into an overcomplete, sparse latent space z, such that \hat{h} \approx h. By enforcing sparsity on z, the model is forced to decompose dense, polysemantic representations into human-interpretable features." loading="lazy" src="@@BASE@@figures/6_sae.svg" />
-<figcaption><span class="capnum">Figure 5:</span> <strong>Sparse Autoencoder (SAE)</strong>. SAEs are trained to reconstruct activations <span class="math inline">h</span> into an overcomplete, sparse latent space <span class="math inline">z</span>, such that <span class="math inline">\hat{h} \approx h</span>. By enforcing sparsity on <span class="math inline">z</span>, the model is forced to decompose dense, polysemantic representations into human-interpretable features.</figcaption>
+<section id="sec:sae" class="level1"><h1 data-pdf-page="31"><span class="secnum">6</span> Feature Extraction and Analysis<a class="anchor" href="#sec:sae" aria-label="Link to this section">#</a></h1>
+<p>The critical notions introduced in Section <a href="@@BASE@@handbook/foundational-concepts/#sec:foundations" data-reference-type="ref" data-reference="sec:foundations">4</a>, together with the hypotheses about the structure of neural network representations discussed in Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>, provide the conceptual foundation for developing methods to identify and analyse manipulable internal objects associated with concepts of interest, i.e., features. One straightforward paradigm is to decompose model activations into sparse linear features. Sparse autoencoders (SAEs) provide a principled example of this approach, learning to represent any input activation with a subset of a large dictionary of monosemantic, interpretable features <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a>)</span>.</p>
+<p>This section first introduces the canonical sparse coding problem and links it to SAEs, reviews how SAEs extract interpretable features, and then provides a practical overview and comparison of major SAE variants. We then introduce methods for multi-layer feature extraction and analysis, such as transcoders and crosscoders, and compare them with SAEs as well as with a broader range of alternative feature-extraction methods.</p>
+<section id="the-sparse-coding-problem" class="level2"><h2 data-pdf-page="32"><span class="secnum">6.1</span> The Sparse Coding Problem<a class="anchor" href="#the-sparse-coding-problem" aria-label="Link to this section">#</a></h2>
+<p>Early neuroscience studies posited that brains represent information using sparse, interpretable basis signals <span class="citation" data-cites="Barlow:1961aa sparsecoding">(<a href="@@BASE@@references/#ref-Barlow:1961aa" class="cite cite-established" data-cite="Barlow:1961aa" role="doc-biblioref">Barlow, 1961</a>; <a href="@@BASE@@references/#ref-sparsecoding" class="cite cite-established" data-cite="sparsecoding" role="doc-biblioref">Field, 1994</a>)</span>. This hypothesis further motivated research to decompose natural images and compare the decomposed signals with cell activities in the primary visual cortex, finding surprising alignment <span class="citation" data-cites="olshausen1996emergence olshausen1997sparse">(<a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>; <a href="@@BASE@@references/#ref-olshausen1997sparse" class="cite cite-established" data-cite="olshausen1997sparse" role="doc-biblioref">1997</a>)</span>. At the same time, the signal processing community has also examined representing a given signal as the sum of a subset of basis signals from a given overcomplete dictionary <span class="citation" data-cites="matchingpursuit basispursuit">(<a href="@@BASE@@references/#ref-matchingpursuit" class="cite cite-established" data-cite="matchingpursuit" role="doc-biblioref">Mallat &amp; Zhang, 1993</a>; <a href="@@BASE@@references/#ref-basispursuit" class="cite cite-established" data-cite="basispursuit" role="doc-biblioref">Chen et al., 1998</a>)</span>. Studies on learning better overcomplete dictionaries for high-quality representations also thrived <span class="citation" data-cites="lewicki2000learning aharon2006k">(<a href="@@BASE@@references/#ref-lewicki2000learning" class="cite cite-established" data-cite="lewicki2000learning" role="doc-biblioref">Lewicki &amp; Sejnowski, 2000</a>; <a href="@@BASE@@references/#ref-aharon2006k" class="cite cite-established" data-cite="aharon2006k" role="doc-biblioref">Aharon et al., 2006</a>)</span>. Together, they framed the sparse dictionary learning research question, with SAEs serving as a modern algorithm that uses neural network activations as the signal. Specifically, given data <span class="math inline">h</span>, a sparse dictionary learning method finds the dictionary <span class="math inline">D</span> and sparse codes <span class="math inline">z</span> such that <span id="eq-2"></span> <span class="math display">\begin{equation*}
+h \approx Dz \text{ and } \|z\|_0 \text{ is small}.
+\tag{2}
+\end{equation*}</span> SAEs implement this with neural networks: <span class="math inline">h</span> are model activations, <span class="math inline">D = W_{\text{out}}</span> is an overcomplete dictionary to learn, and the sparse codes are <span class="math inline">z = \sigma(W_{\text{in}} h + b_{\text{in}})</span>. Note that conventional dictionary methods usually run the algorithm to find a <span class="math inline">z</span> for each <span class="math inline">h</span>, which does not scale to modern language models. In contrast, SAEs use a nonlinear encoder <span class="math inline">E</span> to predict the coefficients for different input activations.</p>
+</section>
+<section id="subsec:sae-foundation" class="level2"><h2 data-pdf-page="32"><span class="secnum">6.2</span> Sparse Autoencoders<a class="anchor" href="#subsec:sae-foundation" aria-label="Link to this section">#</a></h2>
+<p>SAEs (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:sae" data-reference-type="ref" data-reference="fig:sae">6</a>) extract monosemantic features from polysemantic model activations. They are trained to reconstruct activations at a specific location in neural networks, such as the output of an MLP block or an entire transformer layer (i.e., the residual stream), while keeping only a few features active in any given forward pass.</p>
+<figure id="fig:sae" data-latex-placement="tb">
+<img alt="Figure 6: Sparse Autoencoder (SAE). SAEs are trained to reconstruct activations of a model component at a specific layer \ell, h_\ell, into an overcomplete, sparse latent space z, such that the reconstructed activation \hat{h_\ell} \approx h_\ell. By enforcing sparsity on z, the model decomposes den" loading="lazy" src="@@BASE@@figures/6_sae.svg" />
+<figcaption><span class="capnum">Figure 6:</span> <strong>Sparse Autoencoder (SAE).</strong> SAEs are trained to reconstruct activations of a model component at a specific layer <span class="math inline">\ell</span>, <span class="math inline">h_\ell</span>, into an overcomplete, sparse latent space <span class="math inline">z</span>, such that the reconstructed activation <span class="math inline">\hat{h_\ell} \approx h_\ell</span>. By enforcing sparsity on <span class="math inline">z</span>, the model decomposes dense, polysemantic representations into monosemantic, human-interpretable features.</figcaption>
 </figure>
 <div class="definitionbox">
-<p>A sparse autoencoder learns an encoder–decoder pair <span class="math inline">E : \mathbb{R}^d \rightarrow \mathbb{R}^{d_{\text{sparse}}},\;
-D : \mathbb{R}^{d_{\text{sparse}}} \rightarrow \mathbb{R}^d</span> such that activations <span class="math inline">h \in \mathbb{R}^d</span> are reconstructed via <span class="math inline">\hat{h} = D(E(h))</span>, while encouraging sparse feature activations <span class="math inline">E(h)</span>. It helps localize feature activations .</p>
+<p>A sparse autoencoder learns a component-specific encoder–decoder pair <span class="math display">E : \mathbb{R}^d \rightarrow \mathbb{R}^{d_{\text{sparse}}},
+\qquad
+D : \mathbb{R}^{d_{\text{sparse}}} \rightarrow \mathbb{R}^d,</span> such that activations of a particular model component at a given layer, <span class="math inline">h_\ell \in \mathbb{R}^d</span> (e.g., the output of the layer-<span class="math inline">\ell</span> MLP block), are reconstructed as <span class="math display">\hat{h}_\ell = D(E(h_\ell)),</span> while encouraging the feature activations <span class="math inline">z=E(h_\ell)</span> to be sparse. Usually, <span class="math inline">d_{\text{sparse}} = ed</span>, where <span class="math inline">e</span> is an integer expansion factor.</p>
 </div>
-<section id="standard-formulation." class="level4">
-<h4>Standard Formulation.</h4>
-<p>Given activations <span class="math inline">h \sim \mathcal{H}</span>, the standard SAE objective is <span class="math display">\mathcal{L}_{\text{SAE}}
+<section id="canonical-formulation." class="level4">
+<h4>Canonical Formulation.</h4>
+<p>Given activations of a model component <span class="math inline">h_\ell \sim \mathcal{H}</span>, where <span class="math inline">\mathcal{H}</span> is the distribution of that component’s activations over a large corpus, the canonical SAE objective is <span class="math display">\mathcal{L}_{\text{SAE}}
 =
-\mathbb{E}_{h \sim \mathcal{H}}
+\mathbb{E}_{h_\ell \sim \mathcal{H}}
 \left[
-\| h - D (z) \|_2^2
+\| h_\ell - D (z) \|_2^2
 + \lambda \| z \|_1
 \right],
 \quad
-z = E(h),</span> where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> denotes feature activations and <span class="math inline">\lambda &gt; 0</span> controls sparsity <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2023</a>)</span>.</p>
+z = E(h_\ell),</span> where <span class="math inline">\lambda &gt; 0</span> controls sparsity <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a>)</span>. Canonical SAEs use <span class="math inline">L_1</span> regularisation instead of <span class="math inline">L_0</span> regularisation. This is because, for each feature, <span class="math inline">L_0</span>’s derivative is almost everywhere zero and thus does not provide useful signals for promoting feature sparsity during gradient descent. Additionally, the decoder columns are typically constrained to unit norm. This is to prevent the case where the SAE learns to reduce its <span class="math inline">L_1</span> loss by generating smaller values of <span class="math inline">z</span>—not exactly <span class="math inline">0</span> so that many features can be active, each having a small coefficient—and enlarging the norm of each column of the decoder to compensate. In this way, the SAE output remains the same, while <span class="math inline">L_1</span> loss can be substantially reduced, without any real gain in sparsity.</p>
 </section>
 <section id="why-saes-work." class="level4">
 <h4>Why SAEs Work.</h4>
-<p>SAEs are motivated by the hypothesis that features occur sparsely in the data and are linearly recoverable from a model’s hidden states. Under sparsity assumptions, overcomplete dictionaries can recover monosemantic directions (features) even when raw neurons are highly polysemantic (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2023</a></span>; <span class="citation" data-cites="bricken2023monosemanticity"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a></span>).</p>
+<p>SAEs are motivated by the hypothesis that a model’s activations can be approximated as linear combinations of many latent feature directions, which can be regarded as an extension of the linear representation hypothesis discussed in Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:linearity" data-reference-type="ref" data-reference="subsec:linearity">5.1</a>. Sparsity means that only a small number of features are active for any given token or input, but not that the hidden state itself is sparse. Early sparse dictionary learning studies <span class="citation" data-cites="olshausen1996emergence lewicki2000learning aharon2006k">(<a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>; <a href="@@BASE@@references/#ref-lewicki2000learning" class="cite cite-established" data-cite="lewicki2000learning" role="doc-biblioref">Lewicki &amp; Sejnowski, 2000</a>; <a href="@@BASE@@references/#ref-aharon2006k" class="cite cite-established" data-cite="aharon2006k" role="doc-biblioref">Aharon et al., 2006</a>)</span> give us fair confidence that, if the hypothesis holds, dictionary learning can recover the structure as an overcomplete dictionary. These findings motivate using SAEs as a modern adaptation to extract monosemantic directions from the activations of neural networks (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a></span>; <span class="citation" data-cites="bricken2023monosemanticity"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a></span>).</p>
 </section>
 <section id="from-monosemanticity-to-concept-identification." class="level4">
 <h4>From Monosemanticity to Concept Identification.</h4>
-<p>While sparsity and overcompleteness provide the basis for discovering monosemantic features, practical interpretability requires identifying the semantic concepts to which these features correspond in natural languages. Empirically, SAE features exhibit selective activation to structured token patterns that recur across input sequences, suggesting sensitivity to specific abstract semantic or functional concepts. Automated analysis pipelines <span class="citation" data-cites="gur-arieh-etal-2025-enhancing paulo2025automatically">(<a href="@@BASE@@references/#ref-gur-arieh-etal-2025-enhancing" class="cite" data-cite="gur-arieh-etal-2025-enhancing" role="doc-biblioref">Gur-Arieh et al., 2025a</a>; <a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025</a>)</span> automatically aggregate highly activating inputs to infer the latent concept captured by each feature (see Section <a href="@@BASE@@handbook/research-orientations/#sec:automated-interp" data-reference-type="ref" data-reference="sec:automated-interp">13.7</a> for a detailed discussion).</p>
+<p>For each input activation, the raw SAE outputs are just a small set of vectors. Practical interpretability requires identifying the semantic concepts these vectors correspond to in natural language (so that they become human-understandable features). Empirically, SAE features selectively activate on structured token patterns that recur across input sequences, suggesting sensitivity to specific abstract semantic, functional, or lexical concepts <span class="citation" data-cites="marks2024enhancingneuralnetworkinterpretability">(<a href="@@BASE@@references/#ref-marks2024enhancingneuralnetworkinterpretability" class="cite cite-emerging" data-cite="marks2024enhancingneuralnetworkinterpretability" role="doc-biblioref">Marks et al., 2024b</a>)</span>. Automated analysis pipelines <span class="citation" data-cites="gur-arieh-etal-2025-enhancing paulo2025automatically">(<a href="@@BASE@@references/#ref-gur-arieh-etal-2025-enhancing" class="cite cite-established" data-cite="gur-arieh-etal-2025-enhancing" role="doc-biblioref">Gur-Arieh et al., 2025a</a>; <a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025b</a>)</span> aggregate highly activating inputs to infer the concept each feature captures (see Section <a href="@@BASE@@handbook/research-orientations/#sec:automated-interp" data-reference-type="ref" data-reference="sec:automated-interp">12.6</a> for a detailed discussion).</p>
 </section>
 <section id="training-dynamics-and-evaluation." class="level4">
 <h4>Training Dynamics and Evaluation.</h4>
-<p>Training of SAEs is sensitive to initialization, sparsity strength, and decoder normalization. Without proper constraints and hyperparameters, the features may collapse, duplicate, or absorb one another (see Appendix <a href="@@BASE@@handbook/sae-architecture-details/#app:sae_details" data-reference-type="ref" data-reference="app:sae_details">19</a> for hyperparameters and stability guidance). SAEs are typically evaluated for reconstruction loss (fidelity of activation reconstruction), feature absorption (degree to which features remain distinct), and downstream testing loss recovery (whether replacing activations with reconstructions preserves model behavior). Together, these metrics assess both the quality of the decomposition and its mechanistic relevance to the computation of the original model.</p>
+<p>SAE training is sensitive to initialisation, sparsity strength, and decoder normalisation. Without proper constraints and hyperparameters, the features may collapse, duplicate, or absorb one another. SAEs are typically evaluated for reconstruction loss (fidelity of activation reconstruction), feature absorption (how often a general feature fails to fire because a more specific feature has absorbed its direction), and downstream test loss recovery (whether replacing activations with reconstructions preserves model behaviour). Together, these metrics assess both the quality of the decomposition and its mechanistic relevance to the original model’s computation.</p>
 </section>
 </section>
-<section id="subsec:sae-variants" class="level2"><h2 data-pdf-page="23"><span class="secnum">6.2</span> SAE Variants and Architectural Improvements<a class="anchor" href="#subsec:sae-variants" aria-label="Link to this section">#</a></h2>
-<p>There are several variants of the standard SAE. One key reason to use alternatives is that the standard SAE training penalizes activation magnitude, which can lead to low activation even when a feature is clearly present (known as the shrinkage effect). Different optimisation frameworks also have different tunable parameters. Which one to use depends on the analysis goal, target layer type, size of the model, and budget constraints.</p>
+<section id="subsec:sae-variants" class="level2"><h2 data-pdf-page="34"><span class="secnum">6.3</span> SAE Variants and Architectural Improvements<a class="anchor" href="#subsec:sae-variants" aria-label="Link to this section">#</a></h2>
+<p>There are several variants of the standard SAE. One key reason to use alternatives is that standard SAE training penalises activation magnitude, which can lead to low activation even when a feature is clearly present (the shrinkage effect). Different optimisation frameworks also have different tunable parameters. The choice depends on the analysis goal, target layer type, model size, and budget constraints.</p>
 <div class="definitionbox">
-<p>Gated SAEs introduce multiplicative gates to decouple feature selection from magnitude: <span class="math display">z_i = \underbrace{\mathbbm{1} \left( (W_{\text{gate}} h_\ell + b_{\text{gate}})_i &gt; 0 \right)}_{\text{Gating Path}} \odot \underbrace{\text{ReLU}(W_{\text{in}} h_\ell  + b_{\text{in}})_i}_{\text{Magnitude Path}}</span> where <span class="math inline">h_\ell \in \mathbb{R}^d</span> is the activation at layer <span class="math inline">\ell</span>, <span class="math inline">i</span> indexes the <span class="math inline">i</span>-th feature, <span class="math inline">W_{\text{in}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are encoder <span class="math inline">E</span> parameters, <span class="math inline">W_{\text{gate}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{gate}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are gate’s parameters. This mitigates shrinkage effects induced by the <span class="math inline">\ell_1</span> penalty <span class="citation" data-cites="rajamanoharan2024improvingdictionarylearninggated">(<a href="@@BASE@@references/#ref-rajamanoharan2024improvingdictionarylearninggated" class="cite cite-emerging" data-cite="rajamanoharan2024improvingdictionarylearninggated" role="doc-biblioref">Rajamanoharan et al., 2024a</a>)</span>.</p>
+<p>Gated SAEs introduce multiplicative gates to decouple feature selection from magnitude: <span class="math display">z = \underbrace{\mathbbm{1} \left( (W_{\text{gate}} h_\ell + b_{\text{gate}}) &gt; 0 \right)}_{\text{Gating Path}} \odot \underbrace{\sigma_{\text{ReLU}}(W_{\text{in}} h_\ell + b_{\text{in}})}_{\text{Magnitude Path}},</span> where <span class="math inline">h_\ell \in \mathbb{R}^d</span> denotes activations at layer <span class="math inline">\ell</span>, <span class="math inline">W_{\text{in}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are encoder <span class="math inline">E</span> parameters, <span class="math inline">W_{\text{gate}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{gate}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are the gate parameters, and <span class="math inline">\odot</span> denotes element-wise product. This mitigates shrinkage effects induced by the <span class="math inline">L_1</span> penalty <span class="citation" data-cites="rajamanoharan2024improvingdictionarylearninggated">(<a href="@@BASE@@references/#ref-rajamanoharan2024improvingdictionarylearninggated" class="cite cite-established" data-cite="rajamanoharan2024improvingdictionarylearninggated" role="doc-biblioref">Rajamanoharan et al., 2024a</a>)</span>.</p>
 </div>
 <div class="minipage">
 <div class="definitionbox">
-<p>JumpReLU SAEs learn per-feature activation thresholds. Given encoder pre-activations <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}}</span>, feature activations are: <span class="math display">z_i = \begin{cases} 
-    a_i &amp; \text{if } a_i &gt; \theta_i \\ 
-    0 &amp; \text{otherwise} 
-    \end{cases}</span> where <span class="math inline">\theta_i</span> is a learned threshold parameter. A smooth surrogate is used during training to enable gradient flow <span class="citation" data-cites="rajamanoharan2024jumpingaheadimprovingreconstruction"><a href="@@BASE@@references/#ref-rajamanoharan2024jumpingaheadimprovingreconstruction" class="cite" data-cite="rajamanoharan2024jumpingaheadimprovingreconstruction" role="doc-biblioref">Rajamanoharan et al. (2024b)</a></span>.</p>
+<p>JumpReLU SAEs <span class="citation" data-cites="rajamanoharan2024jumpingaheadimprovingreconstruction">(<a href="@@BASE@@references/#ref-rajamanoharan2024jumpingaheadimprovingreconstruction" class="cite cite-emerging" data-cite="rajamanoharan2024jumpingaheadimprovingreconstruction" role="doc-biblioref">Rajamanoharan et al., 2024b</a>)</span> learn per-feature activation thresholds. Given the encoder pre-activation <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}}</span>, let <span class="math inline">a_i</span> denote its <span class="math inline">i</span>-th entry. The activation value of the <span class="math inline">i</span>-th feature after applying JumpReLU is <span class="math display">z^i = \sigma_{\text{JumpReLU}}(a_i) = \begin{cases} 
+    a_i &amp; \text{if } a_i &gt; \tau_i \\ 
+    0 &amp; \text{otherwise},
+    \end{cases}</span> where <span class="math inline">\tau_i</span> is a learned threshold parameter. Straight-through estimators <span class="citation" data-cites="bengio2013estimatingpropagatinggradientsstochastic">(<a href="@@BASE@@references/#ref-bengio2013estimatingpropagatinggradientsstochastic" class="cite cite-emerging" data-cite="bengio2013estimatingpropagatinggradientsstochastic" role="doc-biblioref">Bengio et al., 2013</a>)</span> are used during training to enable gradient flow.</p>
 </div>
 </div>
 <div class="minipage">
-<p><img src="@@BASE@@figures/jumprelu.png" alt="image" /></p>
+<p><img src="@@BASE@@figures/jumprelu.svg" alt="image" /></p>
 </div>
 <div class="definitionbox">
-<p>Top-<span class="math inline">k</span> SAEs <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025a</a>)</span> impose an explicit hard sparsity constraint. Let <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> denote encoder pre-activations. Top-<span class="math inline">k</span> SAEs define the feature activations as: <span class="math display">z_i = \begin{cases}
+<p>Top-<span class="math inline">k</span> SAEs <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span> impose an explicit hard sparsity constraint. Let <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> denote encoder pre-activations. Top-<span class="math inline">k</span> SAEs define the <span class="math inline">i</span>-th feature’s activation as <span class="math display">z^i = \sigma_{\text{TopK}}(a_i) = \begin{cases}
 a_i &amp; \text{if } i \in \mathcal{I}_k(a) \\
-0 &amp; \text{otherwise}
-\end{cases}</span> where <span class="math inline">\mathcal{I}_k(a)</span> indexes the <span class="math inline">k</span> largest entries of <span class="math inline">a</span>. This formulation explicitly enforces the constraint <span class="math inline">|z|_0 \le k</span>, providing a more direct and interpretable feature sparsity constraint than L1 regularization. Unlike L1 loss, it does not suppress activation magnitudes, thereby avoiding the feature shrinkage issue observed in L1-based SAE training <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025a</a>)</span>.</p>
+0 &amp; \text{otherwise},
+\end{cases}</span> where <span class="math inline">\mathcal{I}_k(a)</span> indexes the <span class="math inline">k</span> largest entries of <span class="math inline">a</span>. This formulation explicitly enforces <span class="math inline">\|z\|_0 \le k</span>—a more direct feature-sparsity constraint than <span class="math inline">L_1</span> regularisation. Notably, as it does not suppress activation magnitudes, it avoids the feature-shrinkage issue observed in <span class="math inline">L_1</span>-based SAE training <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span>.</p>
 </div>
 <div class="definitionbox">
-<p>Batch Top-<span class="math inline">k</span> SAEs enforce sparsity at the batch level. Given a batch of pre-activations <span class="math inline">A = [a^{(1)}, \ldots, a^{(B)}] \in \mathbb{R}^{d_{\text{sparse}} \times B}</span>, a subset <span class="math inline">\mathcal{J} \subset \{1,\ldots,d_{\text{sparse}}\}</span> of <span class="math inline">k</span> features is selected as the batch-wide top-<span class="math inline">k</span>: <span class="math display">\mathcal{J} = \arg\max_{|\mathcal{J}|=k} \sum_{j \in \mathcal{J}} \sum_{b=1}^B |a_j^{(b)}|</span> Activations are then set as <span class="math display">z_j^{(b)} =
+<p>Batch Top-<span class="math inline">k</span> SAEs relax the fixed per-example sparsity constraint of standard Top-<span class="math inline">k</span> SAEs by enforcing the sparsity budget across an entire batch instead <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite cite-emerging" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span>. For a batch of encoder activations <span class="math display">\big[\, a^{(1)}, \dots, a^{(B)} \,\big] \in \mathbb{R}^{d_{\text{sparse}} \times B},</span> where <span class="math inline">B</span> is the batch size, Batch Top-<span class="math inline">k</span> retains the largest <span class="math inline">Bk</span> activation values in the batch. Specifically, let <span class="math display">\mathcal{I}
+\coloneqq
+\arg\max_{\substack{
+\mathcal{S}\subseteq
+\{1,\ldots,B\}\times
+\{1,\ldots,d_{\text{sparse}}\}\\
+|\mathcal{S}|=Bk
+}}
+\sum_{(b,j)\in\mathcal{S}}
+a_j^{(b)}.</span> In each batch, the activation value of the <span class="math inline">j</span>-th feature in the <span class="math inline">b</span>-th sample is <span class="math display">z^{j, (b)}
+=
+\sigma_{\text{Batch-TopK}}\!\left(a_j^{(b)}\right)
+=
 \begin{cases}
-a_j^{(b)}, &amp; j \in \mathcal{J} \\
-0, &amp; \text{otherwise}
-\end{cases}</span> for each example <span class="math inline">b</span>. This enforces global feature competition, reduces variance in feature usage across the batch, and prevents collapse <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span>.  </p>
+a_j^{(b)}, &amp; (b,j)\in\mathcal{I},\\
+0, &amp; \text{otherwise}.
+\end{cases}</span></p>
+<p>That is, each batch exactly retains <span class="math inline">Bk</span> features, while individual samples may have more or fewer active features than <span class="math inline">k</span>. This loose constraint allows the model to allocate representational capacity adaptively across samples. During inference, Batch Top-<span class="math inline">k</span> SAEs instead use the JumpReLU function with an inferred global threshold to determine which features are active for each input, so that an input’s activations do not depend on the rest of the batch.</p>
 </div>
 <div class="insightbox">
-<p>SAE variants differ fundamentally in how they enforce sparsity. No single variant is optimal across all regimes.</p>
+<p>Different SAE variants may implement distinct sparsity constraints. No single variant is optimal across all scenarios.<br />
+</p>
 <div class="center">
 <table>
 <thead>
@@ -75,610 +96,633 @@ <h4>Training Dynamics and Evaluation.</h4>
 <th style="text-align: center;"><strong>Sparsity</strong></th>
 <th style="text-align: center;"><strong>Differentiable</strong></th>
 <th style="text-align: center;"><strong>Tuning Required</strong></th>
-<th style="text-align: center;"></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;"><span class="math inline">\ell_1</span>-regularized</td>
+<td style="text-align: left;"><span class="math inline">L_1</span>-regularised</td>
 <td style="text-align: center;">Soft</td>
 <td style="text-align: center;">✓</td>
 <td style="text-align: center;"><span class="math inline">\lambda</span> coefficient</td>
-<td style="text-align: center;"></td>
 </tr>
 <tr>
 <td style="text-align: left;">Gated</td>
 <td style="text-align: center;">Learned</td>
-<td style="text-align: center;">✓<span class="math inline">^*</span></td>
-<td style="text-align: center;">Gate + <span class="math inline">\ell_1</span> parameters</td>
-<td style="text-align: center;"></td>
+<td style="text-align: center;">✗<span class="math inline">^\dagger</span></td>
+<td style="text-align: center;"><span class="math inline">\lambda</span> coefficient</td>
 </tr>
 <tr>
 <td style="text-align: left;">JumpReLU</td>
 <td style="text-align: center;">Learned</td>
-<td style="text-align: center;">✓<span class="math inline">^*</span></td>
-<td style="text-align: center;">Threshold initialization</td>
-<td style="text-align: center;"></td>
+<td style="text-align: center;">✗<span class="math inline">^*</span></td>
+<td style="text-align: center;"><span class="math inline">\lambda</span> coefficient, STE bandwidth, threshold init.</td>
 </tr>
 <tr>
 <td style="text-align: left;">Top-<span class="math inline">k</span></td>
 <td style="text-align: center;">Hard</td>
 <td style="text-align: center;">✗</td>
 <td style="text-align: center;"><span class="math inline">k</span> value</td>
-<td style="text-align: center;"></td>
 </tr>
 <tr>
 <td style="text-align: left;">Batch Top-<span class="math inline">k</span></td>
 <td style="text-align: center;">Hard</td>
 <td style="text-align: center;">✗</td>
 <td style="text-align: center;"><span class="math inline">k</span> value</td>
-<td style="text-align: center;"></td>
 </tr>
 </tbody>
 </table>
 </div>
-<p><span><span class="math inline">^*</span>Uses smooth surrogate during training.</span></p>
+<p><span><span class="math inline">^*</span>Uses straight-through estimators (STEs) during training. <span class="math inline">^\dagger</span>Trains the non-differentiable gate with auxiliary losses.</span></p>
 </div>
-<p>Readers are referred to <span class="citation" data-cites="shu-etal-2025-survey"><a href="@@BASE@@references/#ref-shu-etal-2025-survey" class="cite cite-established" data-cite="shu-etal-2025-survey" role="doc-biblioref">Shu et al. (2025)</a></span> for a dedicated survey of SAEs, their variants, evaluations, and applications.</p>
+<p>Readers are referred to <span class="citation" data-cites="shu-etal-2025-survey"><a href="@@BASE@@references/#ref-shu-etal-2025-survey" class="cite cite-established" data-cite="shu-etal-2025-survey" role="doc-biblioref">Shu et al. (2025)</a></span> for a dedicated survey of SAEs, their variants, evaluations, and applications. Additionally, researchers have published SAE weights for several language models, including Gemma Scope <span class="citation" data-cites="lieberum2024gemmascopeopensparse">(<a href="@@BASE@@references/#ref-lieberum2024gemmascopeopensparse" class="cite cite-emerging" data-cite="lieberum2024gemmascopeopensparse" role="doc-biblioref">Lieberum et al., 2024</a>)</span> for Gemma 2 <span class="citation" data-cites="gemmateam2024gemma2improvingopen">(<a href="@@BASE@@references/#ref-gemmateam2024gemma2improvingopen" class="cite cite-emerging" data-cite="gemmateam2024gemma2improvingopen" role="doc-biblioref">Team et al., 2024b</a>)</span> and Llama Scope <span class="citation" data-cites="he2024llamascopeextractingmillions">(<a href="@@BASE@@references/#ref-he2024llamascopeextractingmillions" class="cite cite-emerging" data-cite="he2024llamascopeextractingmillions" role="doc-biblioref">He et al., 2024</a>)</span> for Llama-3.1-8B <span class="citation" data-cites="llama3">(<a href="@@BASE@@references/#ref-llama3" class="cite cite-emerging" data-cite="llama3" role="doc-biblioref">Grattafiori et al., 2024</a>)</span>. They can be accessed through open platforms such as Neuronpedia <span class="citation" data-cites="neuronpedia">(<a href="@@BASE@@references/#ref-neuronpedia" class="cite cite-emerging" data-cite="neuronpedia" role="doc-biblioref">Lin, 2023</a>)</span>.</p>
 </section>
-<section id="subsec:sae-selection" class="level2"><h2 data-pdf-page="24"><span class="secnum">6.3</span> Practical Guidance: Selecting SAE Variants<a class="anchor" href="#subsec:sae-selection" aria-label="Link to this section">#</a></h2>
-<p>Practitioners face a decision problem: which SAE variant should be used for a given model, layer, and analysis goal? This section provides practical heuristics synthesized from empirical findings and architectural considerations. These recommendations are provisional—the field lacks comprehensive benchmarks comparing variants across all settings—but offer a starting point for informed decisions.</p>
-<p>In the following, we consider four factors that primarily determine which SAE variant is appropriate, and present the decision tree based on these factors.</p>
+<section id="subsec:sae-selection" class="level2"><h2 data-pdf-page="35"><span class="secnum">6.4</span> Empirical Comparisons of SAE Variants<a class="anchor" href="#subsec:sae-selection" aria-label="Link to this section">#</a></h2>
+<p>The SAE variants in Section <a href="@@BASE@@handbook/feature-extraction/#subsec:sae-variants" data-reference-type="ref" data-reference="subsec:sae-variants">6.3</a> differ principally in how they enforce sparsity, and those differences map onto distinct analysis settings.</p>
+<p>Hard-constraint formulations (e.g., Top-<span class="math inline">k</span>) directly enforce sparsity and suit tight compute budgets. Learned-threshold (e.g., JumpReLU SAE) and gated formulations usually achieve better reconstruction results and suit generous tuning budgets. Cross-layer variants (discussed in Section <a href="@@BASE@@handbook/feature-extraction/#subsec:transcoders-crosscoders" data-reference-type="ref" data-reference="subsec:transcoders-crosscoders">6.6</a>) analyse computations across layers that a single dictionary cannot represent at all. This subsection discusses the setting in which each variant is used and the evidence it provides. We particularly discuss four factors that account for most of the variation:</p>
 <ol>
-<li><p><strong>Analysis goal</strong>: Exploratory discovery vs. targeted investigation vs. deployment/monitoring</p></li>
-<li><p><strong>Layer type</strong>: Residual stream vs. MLP output vs. attention output</p></li>
-<li><p><strong>Model scale</strong>: Small (<span class="math inline">&lt;</span>1B) vs. medium (1-10B) vs. large (<span class="math inline">&gt;</span>10B parameters)</p></li>
-<li><p><strong>Resource constraints</strong>: Compute budget, hyperparameter tuning capacity, tolerance for training instability</p></li>
+<li><p><strong>Analysis goal:</strong> exploratory discovery vs. targeted investigation vs. deployment/monitoring</p></li>
+<li><p><strong>Layer type:</strong> residual stream vs. MLP output vs. attention output</p></li>
+<li><p><strong>Model scale:</strong> small (<span class="math inline">&lt;</span>1B) vs. medium (1–10B) vs. large (<span class="math inline">&gt;</span>10B parameters)</p></li>
+<li><p><strong>Resource constraints:</strong> compute budget, hyperparameter tuning capacity, tolerance for training instability</p></li>
 </ol>
-<div class="definitionbox">
-<p><strong>Step 1: What is your primary goal?</strong></p>
-<ul>
-<li><p><strong>Exploratory discovery</strong> (understand what features exist) <span class="math inline">\rightarrow</span> Go to Step 2</p></li>
-<li><p><strong>Targeted investigation</strong> (find features for specific behavior) <span class="math inline">\rightarrow</span> Consider supervised methods first (DAS, probes); if unsupervised needed, go to Step 2</p></li>
-<li><p><strong>Deployment/monitoring</strong> (real-time feature detection) <span class="math inline">\rightarrow</span> Prioritize inference speed; Top-K or standard SAE with small dictionary</p></li>
-</ul>
-<p><strong>Step 2: What layer type are you analyzing?</strong></p>
-<ul>
-<li><p><strong>Residual stream</strong> <span class="math inline">\rightarrow</span> Go to Step 3</p></li>
-<li><p><strong>MLP outputs</strong> <span class="math inline">\rightarrow</span> Consider <strong>Transcoders</strong> (predict post-MLP from pre-MLP); if single-layer SAE needed, go to Step 3</p></li>
-<li><p><strong>Attention outputs</strong> <span class="math inline">\rightarrow</span> Standard SAE or Gated SAE; attention-specific variants are less developed</p></li>
-<li><p><strong>Cross-layer analysis</strong> <span class="math inline">\rightarrow</span> <strong>Crosscoders</strong> or <strong>CLTs</strong> (if compute permits)</p></li>
-</ul>
-<p><strong>Step 3: What are your resource constraints?</strong></p>
-<ul>
-<li><p><strong>Limited compute / need fast iteration</strong> <span class="math inline">\rightarrow</span> <strong>Top-K SAE</strong> (no <span class="math inline">\lambda</span> tuning; fixed sparsity)</p></li>
-<li><p><strong>Moderate compute / some tuning capacity</strong> <span class="math inline">\rightarrow</span> <strong>Gated SAE</strong> (best reconstruction-sparsity tradeoff in most settings)</p></li>
-<li><p><strong>Ample compute / extensive tuning possible</strong> <span class="math inline">\rightarrow</span> <strong>JumpReLU SAE</strong> (learned thresholds; strong performance but sensitive)</p></li>
-</ul>
-<p><strong>Step 4: What model scale?</strong></p>
-<ul>
-<li><p><strong>Small models (<span class="math inline">&lt;</span>1B)</strong> <span class="math inline">\rightarrow</span> Any variant works; use this regime to validate methodology before scaling</p></li>
-<li><p><strong>Medium models (1-10B)</strong> <span class="math inline">\rightarrow</span> Gated or Top-K recommended; JumpReLU if tuning budget available</p></li>
-<li><p><strong>Large models (<span class="math inline">&gt;</span>10B)</strong> <span class="math inline">\rightarrow</span> Top-K or Batch Top-K (most stable at scale); Gated SAE if reconstruction quality critical</p></li>
-</ul>
-<p><span id="tab:sae-variant-selection-decision-tree" data-label="tab:sae-variant-selection-decision-tree"></span></p>
-</div>
-<section id="layer-specific-recommendations" class="level3"><h3 data-pdf-page="25"><span class="secnum">6.3.1</span> Layer-Specific Recommendations<a class="anchor" href="#layer-specific-recommendations" aria-label="Link to this section">#</a></h3>
-<p>Different layer types exhibit different activation statistics, affecting SAE performance, as shown in Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae-layer-recommendations" data-reference-type="ref" data-reference="tab:sae-layer-recommendations">3</a>.</p>
+<section id="analysis-goal." class="level4">
+<h4>Analysis Goal.</h4>
+<p><em>Exploratory discovery</em>, where the question is what features exist rather than whether a particular one does, is the setting SAEs were designed for and the one the comparisons below address. For <em>targeted investigation</em> of an already-identified behaviour, where the concepts of interest are determined, supervised methods such as DAS <span class="citation" data-cites="das">(<a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a>)</span> and linear probes are more direct instruments. For <em>online deployment and monitoring</em>, SAEs with small dictionaries can be preferable for low inference cost.</p>
+</section>
+<section id="layer-type." class="level4">
+<h4>Layer Type.</h4>
+<p>Layer types differ in their activation properties, so the right variant depends on what each can represent, not just how well. On the <em>residual stream</em>, single-layer SAEs apply directly, and the choice reduces to the resource considerations below. On <em>MLP outputs</em>, transcoders are the appropriate tool: a single-layer SAE cannot span the nonlinearity, so transcoders learn the input-to-output map directly <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>, and <span class="citation" data-cites="paulo2025transcodersbeatsparseautoencoders"><a href="@@BASE@@references/#ref-paulo2025transcodersbeatsparseautoencoders" class="cite cite-emerging" data-cite="paulo2025transcodersbeatsparseautoencoders" role="doc-biblioref">Paulo et al. (2025c)</a></span> reported favourable comparisons against SAEs there. On <em>attention outputs</em>, standard and Gated SAEs both have been used; attention-specific variants remain underdeveloped, making this the least studied of the three sites. For <em>cross-layer analysis</em>, crosscoders <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span> and cross-layer transcoders <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span> track feature evolution across layers at substantially higher compute cost, and a crosscoder trained jointly on two models is the reported approach for comparing representations between them. Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae-layer-recommendations" data-reference-type="ref" data-reference="tab:sae-layer-recommendations">3</a> summarises the variants used at each layer type, the empirical rationale for their use, and the main caveats reported in prior work.</p>
 <table id="tab:sae-layer-recommendations">
-<caption><span class="capnum">Table 3:</span> SAE Variant Recommendations by Layer Type</caption>
+<caption><span class="capnum">Table 3:</span> Heuristic recommendations for selecting SAE variants across different layer types. Recommendations are based on current empirical evidence and practical experience, and may evolve as SAE methodologies and evaluation standards continue to develop.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Layer Type</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Recommended Variants</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Rationale</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Considerations</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Variants Used</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Reported Basis</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Reported Caveats</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td style="text-align: left;">Residual stream (early layers)</td>
-<td style="text-align: left;">Gated SAE, Top-K</td>
+<td style="text-align: left;">Gated SAE, Top-<span class="math inline">k</span></td>
 <td style="text-align: left;">Activations are closer to input embeddings; moderate sparsity</td>
-<td style="text-align: left;">May need smaller expansion factor (2-4x)</td>
+<td style="text-align: left;">Smaller expansion factors (2–4<span class="math inline">\times</span>) reported</td>
 </tr>
 <tr>
 <td style="text-align: left;">Residual stream (middle layers)</td>
 <td style="text-align: left;">Gated SAE, JumpReLU</td>
 <td style="text-align: left;">Richest feature structure; highest interpretability potential</td>
-<td style="text-align: left;">Standard expansion factor (4-8x); most studied regime</td>
+<td style="text-align: left;">Standard expansion factor (4–8<span class="math inline">\times</span>); most studied regime</td>
 </tr>
 <tr>
 <td style="text-align: left;">Residual stream (late layers)</td>
-<td style="text-align: left;">Top-K, Gated SAE</td>
+<td style="text-align: left;">Top-<span class="math inline">k</span>, Gated SAE</td>
 <td style="text-align: left;">Activations increasingly task-specific; may be less sparse</td>
-<td style="text-align: left;">Consider larger expansion factor (8-16x)</td>
+<td style="text-align: left;">Larger expansion factors (8–16<span class="math inline">\times</span>) reported</td>
 </tr>
 <tr>
 <td style="text-align: left;">MLP outputs</td>
-<td style="text-align: left;">Transcoder (<a href="@@BASE@@handbook/feature-extraction/#subsec:transcoders-crosscoders" data-reference-type="ref+label" data-reference="subsec:transcoders-crosscoders">6.4</a>)</td>
-<td style="text-align: left;">MLP applies nonlinearity; transcoder captures input-output mapping</td>
-<td style="text-align: left;">Not a true SAE; predicts layer <span class="math inline">\ell+1</span> from layer <span class="math inline">\ell</span></td>
+<td style="text-align: left;">Transcoder (Section <a href="@@BASE@@handbook/feature-extraction/#subsec:transcoders-crosscoders" data-reference-type="ref" data-reference="subsec:transcoders-crosscoders">6.6</a>)</td>
+<td style="text-align: left;">MLP applies a nonlinearity a single-layer SAE cannot span</td>
+<td style="text-align: left;">Not a sparse decomposition on a static object; bridges input to output <span class="math inline">\ell</span></td>
 </tr>
 <tr>
 <td style="text-align: left;">Attention outputs</td>
-<td style="text-align: left;">Gated SAE, Standard SAE</td>
-<td style="text-align: left;">Less studied; attention outputs often lower-rank</td>
-<td style="text-align: left;">May benefit from smaller dictionaries</td>
+<td style="text-align: left;">Gated SAE, standard SAE</td>
+<td style="text-align: left;">Attention outputs often lower-rank</td>
+<td style="text-align: left;">Least studied of the three sites; may benefit from smaller dictionaries</td>
 </tr>
 <tr>
 <td style="text-align: left;">Cross-layer</td>
-<td style="text-align: left;">Crosscoder, CLT (<a href="@@BASE@@handbook/feature-extraction/#subsec:transcoders-crosscoders" data-reference-type="ref+label" data-reference="subsec:transcoders-crosscoders">6.4</a>)</td>
+<td style="text-align: left;">Crosscoder, CLT (Section <a href="@@BASE@@handbook/feature-extraction/#subsec:transcoders-crosscoders" data-reference-type="ref" data-reference="subsec:transcoders-crosscoders">6.6</a>)</td>
 <td style="text-align: left;">Tracks feature evolution; resolves cross-layer superposition</td>
 <td style="text-align: left;">High compute cost; requires multi-layer training</td>
 </tr>
 </tbody>
 </table>
 </section>
-<section id="common-pitfalls-and-how-to-avoid-them" class="level3"><h3 data-pdf-page="26"><span class="secnum">6.3.2</span> Common Pitfalls and How to Avoid Them<a class="anchor" href="#common-pitfalls-and-how-to-avoid-them" aria-label="Link to this section">#</a></h3>
-<div class="warningbox">
-<p><strong>Pitfall 1: Using standard <span class="math inline">\ell_1</span> SAE without considering alternatives</strong></p>
-<ul>
-<li><p>Standard SAEs suffer from shrinkage bias; Gated SAEs solve this with minimal added complexity.</p></li>
-<li><p><em>Recommendation</em>: Use Gated SAE as the default unless you have specific reasons for standard.</p></li>
-</ul>
-<p><strong>Pitfall 2: Over-tuning <span class="math inline">\lambda</span> instead of switching to Top-K</strong></p>
-<ul>
-<li><p>If you’re spending significant time tuning the sparsity coefficient, Top-K eliminates this hyperparameter entirely.</p></li>
-<li><p><em>Recommendation</em>: If <span class="math inline">\lambda</span> tuning takes more than a few runs, switch to Top-K with target sparsity.</p></li>
-</ul>
-<p><strong>Pitfall 3: Using the same expansion factor everywhere</strong></p>
-<ul>
-<li><p>Early layers may need smaller dictionaries (2-4x); late layers may need larger (8-16x).</p></li>
-<li><p><em>Recommendation</em>: Start with 4x expansion, then adjust based on dead feature rate and reconstruction quality.</p></li>
-</ul>
-<p><strong>Pitfall 4: Ignoring layer type differences</strong></p>
-<ul>
-<li><p>SAEs on residual stream vs. MLP outputs vs. attention outputs behave differently.</p></li>
-<li><p><em>Recommendation</em>: For MLP analysis, use transcoders; for cross-layer analysis, use crosscoders or CLTs.</p></li>
-</ul>
-<p><strong>Pitfall 5: Not checking for dead features</strong></p>
-<ul>
-<li><p>High dead feature rates (<span class="math inline">&gt;</span>20%) indicate wasted capacity and possible training issues.</p></li>
-<li><p><em>Recommendation</em>: Monitor dead feature rate; use neuron resampling or switch to Batch Top-K if problematic.</p></li>
-</ul>
-</div>
+<section id="resource-constraints." class="level4">
+<h4>Resource Constraints.</h4>
+<p>Under limited compute, or where fast iteration matters more than reconstruction quality, Top-<span class="math inline">k</span> SAEs <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span> are the practical choice: fixing <span class="math inline">\|z\|_0 \le k</span> removes the sparsity coefficient <span class="math inline">\lambda</span> from the search space entirely rather than requiring it to be tuned, with reported values commonly in the range <span class="math inline">k = 32</span> <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span> to <span class="math inline">150</span> <span class="citation" data-cites="karvonen2025saebench">(<a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span>. With moderate tuning capacity, Gated SAEs <span class="citation" data-cites="rajamanoharan2024improvingdictionarylearninggated">(<a href="@@BASE@@references/#ref-rajamanoharan2024improvingdictionarylearninggated" class="cite cite-established" data-cite="rajamanoharan2024improvingdictionarylearninggated" role="doc-biblioref">Rajamanoharan et al., 2024a</a>)</span> achieved a better reconstruction–sparsity trade-off while retaining <span class="math inline">\lambda</span>. They also avoid the shrinkage effect by separating feature detection from magnitude estimation. However, because the standard <span class="math inline">L_1</span> SAE is now used mainly as a baseline, comparisons against it partly reflect shrinkage rather than dictionary quality alone. When extensive tuning is plausible, JumpReLU SAEs <span class="citation" data-cites="rajamanoharan2024jumpingaheadimprovingreconstruction">(<a href="@@BASE@@references/#ref-rajamanoharan2024jumpingaheadimprovingreconstruction" class="cite cite-emerging" data-cite="rajamanoharan2024jumpingaheadimprovingreconstruction" role="doc-biblioref">Rajamanoharan et al., 2024b</a>)</span> achieved the strongest reconstruction performance at matched sparsity by learning per-feature thresholds. However, gains on these sparsity–fidelity proxy metrics do not reliably translate into better downstream performance <span class="citation" data-cites="karvonen2025saebench">(<a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span>. In practice, selecting the best model and hyperparameters to achieve optimal performance can take a lot of time.</p>
 </section>
-<section id="quick-reference-when-to-use-what" class="level3"><h3 data-pdf-page="26"><span class="secnum">6.3.3</span> Quick Reference: When to Use What<a class="anchor" href="#quick-reference-when-to-use-what" aria-label="Link to this section">#</a></h3>
-<p>In Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae_recommendations" data-reference-type="ref" data-reference="tab:sae_recommendations">4</a>, we provide a concise set of heuristic recommendations mapping common practitioner goals to SAE variants that have been found empirically useful in prior work and practice.</p>
-<table id="tab:sae_recommendations" style="width:90%;">
-<caption><span class="capnum">Table 4:</span> Heuristic recommendations for selecting SAE variants based on common practitioner goals.</caption>
-<colgroup>
-<col style="width: 45%" />
-<col style="width: 45%" />
-</colgroup>
+<section id="model-scale." class="level4">
+<h4>Model Scale.</h4>
+<p>The differences between variants depend strongly on the metric in models with roughly 1 or 2B parameters, such as Gemma-2-2B <span class="citation" data-cites="karvonen2025saebench">(<a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span>, which is the regime mainly used to validate methodology before scaling. For models between 1B and 10B parameters, JumpReLU SAEs and Top-<span class="math inline">k</span> SAEs are the most commonly reported variants; Gemma Scope <span class="citation" data-cites="lieberum2024gemmascopeopensparse">(<a href="@@BASE@@references/#ref-lieberum2024gemmascopeopensparse" class="cite cite-emerging" data-cite="lieberum2024gemmascopeopensparse" role="doc-biblioref">Lieberum et al., 2024</a>)</span> uses the former and Llama Scope <span class="citation" data-cites="he2024llamascopeextractingmillions">(<a href="@@BASE@@references/#ref-he2024llamascopeextractingmillions" class="cite cite-emerging" data-cite="he2024llamascopeextractingmillions" role="doc-biblioref">He et al., 2024</a>)</span> the latter. For models larger than 10B parameters, Gemma Scope <span class="citation" data-cites="lieberum2024gemmascopeopensparse">(<a href="@@BASE@@references/#ref-lieberum2024gemmascopeopensparse" class="cite cite-emerging" data-cite="lieberum2024gemmascopeopensparse" role="doc-biblioref">Lieberum et al., 2024</a>)</span> (for Gemma 2 models) and Gemma Scope 2 <span class="citation" data-cites="mcdougall2025gemmascope2">(<a href="@@BASE@@references/#ref-mcdougall2025gemmascope2" class="cite cite-emerging" data-cite="mcdougall2025gemmascope2" role="doc-biblioref">McDougall et al., 2025</a>)</span> (for Gemma 3 models) are the main open resources, both of which used JumpReLU SAEs. Additionally, <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders"><a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al. (2025)</a></span> explored scaling Top-<span class="math inline">k</span> SAEs to GPT-4 <span class="citation" data-cites="openai2024gpt4technicalreport">(<a href="@@BASE@@references/#ref-openai2024gpt4technicalreport" class="cite cite-emerging" data-cite="openai2024gpt4technicalreport" role="doc-biblioref">OpenAI et al., 2024</a>)</span>, although GPT-4 itself is not publicly available.</p>
+<p>Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae_recommendations" data-reference-type="ref" data-reference="tab:sae_recommendations">4</a> summarises each variant’s main findings reported in previous work, along with their experimental settings. Note that these studies may use different baselines, model families, sparsity constraints, and evaluation criteria. The community has been establishing benchmarks to evaluate variants of SAEs, such as SAEBench <span class="citation" data-cites="karvonen2025saebench">(<a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span>, but the results vary by metric. As a result, we should treat the findings as references rather than established rules. The <em>Setting</em> column in Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae_recommendations" data-reference-type="ref" data-reference="tab:sae_recommendations">4</a> records the conditions under which each result is established. Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae_limitations" data-reference-type="ref" data-reference="tab:sae_limitations">5</a> further highlights several factors that limit the generality of the findings.</p>
+<table id="tab:sae_recommendations">
+<caption><span class="capnum">Table 4:</span> Reported evidence for each SAE variant, with the setting in which it was obtained.</caption>
 <thead>
 <tr>
-<th style="text-align: left;"><strong>Practitioner Goal</strong></th>
-<th style="text-align: left;"><strong>Recommended Variant</strong></th>
+<th style="text-align: left;"><span style="color: white"><strong>Variant</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Reported Finding</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Setting</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">New to SAEs and want a reliable default</td>
-<td style="text-align: left;">Gated SAE with 4<span class="math inline">\times</span> expansion</td>
+<td style="text-align: left;">Standard</td>
+<td style="text-align: left;">Sparse dictionary learning by <span class="math inline">L_1</span> regularisation recovers features more monosemantic than individual neurons; subject to shrinkage bias</td>
+<td style="text-align: left;">Establishing work, small models (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a></span>; <span class="citation" data-cites="bricken2023monosemanticity"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a></span>)</td>
 </tr>
 <tr>
-<td style="text-align: left;">Fast experimentation without extensive tuning</td>
-<td style="text-align: left;">Top-<span class="math inline">k</span> SAE (<span class="math inline">k = 50</span>–<span class="math inline">100</span>)</td>
+<td style="text-align: left;">Gated</td>
+<td style="text-align: left;">Separating detection from magnitude estimation addresses <span class="math inline">L_1</span> shrinkage at minimal added complexity</td>
+<td style="text-align: left;">Reconstruction–sparsity improvement at matched sparsity, Gemma-family <span class="citation" data-cites="rajamanoharan2024improvingdictionarylearninggated">(<a href="@@BASE@@references/#ref-rajamanoharan2024improvingdictionarylearninggated" class="cite cite-established" data-cite="rajamanoharan2024improvingdictionarylearninggated" role="doc-biblioref">Rajamanoharan et al., 2024a</a>)</span></td>
 </tr>
 <tr>
-<td style="text-align: left;">Highest-quality features with sufficient compute</td>
-<td style="text-align: left;">JumpReLU SAE with careful tuning</td>
+<td style="text-align: left;">JumpReLU</td>
+<td style="text-align: left;">Learned per-feature thresholds trained with straight-through estimators give the strongest reported reconstruction</td>
+<td style="text-align: left;">Matched-sparsity comparison, Gemma-family <span class="citation" data-cites="rajamanoharan2024jumpingaheadimprovingreconstruction">(<a href="@@BASE@@references/#ref-rajamanoharan2024jumpingaheadimprovingreconstruction" class="cite cite-emerging" data-cite="rajamanoharan2024jumpingaheadimprovingreconstruction" role="doc-biblioref">Rajamanoharan et al., 2024b</a>)</span>; needs most tuning <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite cite-emerging" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span></td>
 </tr>
 <tr>
-<td style="text-align: left;">Analysis of very large models</td>
-<td style="text-align: left;">Batch Top-<span class="math inline">k</span> SAE for improved stability</td>
+<td style="text-align: left;">Top-<span class="math inline">k</span></td>
+<td style="text-align: left;">A hard <span class="math inline">\|z\|_0 \le k</span> constraint removes the sparsity coefficient from the search space</td>
+<td style="text-align: left;">Scaling behaviour characterised on large models; <span class="math inline">k = 32</span>–<span class="math inline">150</span> commonly reported <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders karvonen2025saebench">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>; <a href="@@BASE@@references/#ref-karvonen2025saebench" class="cite cite-established" data-cite="karvonen2025saebench" role="doc-biblioref">Karvonen et al., 2025</a>)</span></td>
+</tr>
+<tr>
+<td style="text-align: left;">Batch Top-<span class="math inline">k</span></td>
+<td style="text-align: left;">A batch-level sparsity budget lets the number of active features vary per input, improving reconstruction at matched average sparsity</td>
+<td style="text-align: left;">Introduced as a relaxation of per-example Top-<span class="math inline">k</span>, achieving better reconstruction <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite cite-emerging" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span></td>
 </tr>
 <tr>
-<td style="text-align: left;">Understanding MLP computation</td>
 <td style="text-align: left;">Transcoder</td>
+<td style="text-align: left;">Learning the MLP input–output map avoids spanning the nonlinearity with a single dictionary</td>
+<td style="text-align: left;">Reported to perform at least on par with SAEs (<span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature"><a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a></span>; <span class="citation" data-cites="paulo2025transcodersbeatsparseautoencoders"><a href="@@BASE@@references/#ref-paulo2025transcodersbeatsparseautoencoders" class="cite cite-emerging" data-cite="paulo2025transcodersbeatsparseautoencoders" role="doc-biblioref">Paulo et al., 2025c</a></span>)</td>
 </tr>
 <tr>
-<td style="text-align: left;">Tracking features across layers</td>
-<td style="text-align: left;">Crosscoder or CLT</td>
+<td style="text-align: left;">Crosscoder</td>
+<td style="text-align: left;">Joint reconstruction across layers tracks feature evolution and resolves cross-layer superposition</td>
+<td style="text-align: left;">Cross-layer analysis and model diffing; trained jointly across models for cross-model comparison <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span></td>
 </tr>
 <tr>
-<td style="text-align: left;">Comparing representations between models</td>
-<td style="text-align: left;">Crosscoder trained jointly on both models</td>
+<td style="text-align: left;">CLT</td>
+<td style="text-align: left;">Partial reconstruction of multiple downstream layers from one dictionary</td>
+<td style="text-align: left;">Deployed within CLT-based circuit discovery at scale <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span></td>
 </tr>
 </tbody>
 </table>
-</section>
-<section id="limitations-of-these-recommendations" class="level3"><h3 data-pdf-page="26"><span class="secnum">6.3.4</span> Limitations of These Recommendations<a class="anchor" href="#limitations-of-these-recommendations" aria-label="Link to this section">#</a></h3>
-<p>Heuristics in Table <a href="@@BASE@@handbook/feature-extraction/#tab:sae_limitations" data-reference-type="ref" data-reference="tab:sae_limitations">5</a> are based on current empirical findings, which have limitations. Practitioners should treat these as starting points, validate on their specific use case, and report which variants they used to enable community learning.</p>
-<table id="tab:sae_limitations" style="width:90%;">
-<caption><span class="capnum">Table 5:</span> Key limitations underlying the heuristic recommendations for SAE variant selection.</caption>
+<table id="tab:sae_limitations" style="width:89%;">
+<caption><span class="capnum">Table 5:</span> Conditions for the reported evidence on SAE variant behaviour.</caption>
 <colgroup>
-<col style="width: 35%" />
-<col style="width: 55%" />
+<col style="width: 32%" />
+<col style="width: 57%" />
 </colgroup>
 <thead>
 <tr>
-<th style="text-align: left;"><strong>Limitation</strong></th>
-<th style="text-align: left;"><strong>Description</strong></th>
+<th style="text-align: left;"><span style="color: white"><strong>Condition</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Description</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Limited systematic comparison</td>
-<td style="text-align: left;">No comprehensive benchmark currently evaluates all SAE variants across layer types, model scales, and training regimes.</td>
+<td style="text-align: left;">Architecture dependence</td>
+<td style="text-align: left;">Most evidence derives from Llama- and Gemma-family models. Generality of the findings across model families remains unestablished.</td>
 </tr>
 <tr>
-<td style="text-align: left;">Architecture dependence</td>
-<td style="text-align: left;">Most evidence is derived from Llama and Gemma family models; results may not generalize to other architectures.</td>
+<td style="text-align: left;">Task dependence</td>
+<td style="text-align: left;">The properties that matter may differ across analysis goals, such as safety auditing, behaviour understanding, and knowledge localisation, but existing evaluations often do not distinguish between them.</td>
 </tr>
 <tr>
-<td style="text-align: left;">Rapid methodological change</td>
-<td style="text-align: left;">New SAE variants and training techniques continue to emerge, potentially rendering current recommendations outdated.</td>
+<td style="text-align: left;">Rapid iteration</td>
+<td style="text-align: left;">New architectures and training procedures are proposed faster than existing ones are sufficiently studied and replicated.</td>
 </tr>
 <tr>
-<td style="text-align: left;">Task dependence</td>
-<td style="text-align: left;">Optimal choices may differ depending on the analysis goal (e.g. safety analysis, capability understanding, or knowledge localization).</td>
+<td style="text-align: left;">Immature theoretical analysis</td>
+<td style="text-align: left;">Current findings are mostly empirical and may be superseded. Understanding of why particular variants succeed or fail is incomplete.</td>
+</tr>
+<tr>
+<td style="text-align: left;">Feature reproducibility</td>
+<td style="text-align: left;">SAEs trained on the same data with different random seeds can recover substantially different features. When between-variant performance differences are smaller than within-variant seed-to-seed variability, we may not confidently attribute them to architectural factors. However, few reported comparisons have rigorously accounted for this concern.</td>
 </tr>
 </tbody>
 </table>
-<p>Practitioners should treat these as starting points, validate on their specific use case, and report which variants they used to enable community learning.</p>
 </section>
 </section>
-<section id="subsec:transcoders-crosscoders" class="level2"><h2 data-pdf-page="27"><span class="secnum">6.4</span> Multi-Layer Feature Analysis: Transcoders and Crosscoders<a class="anchor" href="#subsec:transcoders-crosscoders" aria-label="Link to this section">#</a></h2>
-<p>Standard SAEs analyze single layers in isolation. However, features evolve as information flows through the network. Transcoders and Crosscoders enable analysis across layers and token positions, revealing how representations transform through nonlinearities.</p>
-<section id="subsubsec:transcoder" class="level3"><h3 data-pdf-page="27"><span class="secnum">6.4.1</span> Transcoders: Bridging Nonlinear Layers<a class="anchor" href="#subsubsec:transcoder" aria-label="Link to this section">#</a></h3>
+<section id="subsec:challenge" class="level2"><h2 data-pdf-page="37"><span class="secnum">6.5</span> Challenges and Failure Modes of SAEs<a class="anchor" href="#subsec:challenge" aria-label="Link to this section">#</a></h2>
+<p>SAEs have become a standard tool for feature extraction, but they exhibit systematic failure modes that undermine their interpretability claims. It is therefore essential to understand these issues when using SAEs as mechanistic tools. This section provides a systematic discussion of them.</p>
+<section id="dead-features" class="level3"><h3 data-pdf-page="37"><span class="secnum">6.5.1</span> Dead Features<a class="anchor" href="#dead-features" aria-label="Link to this section">#</a></h3>
+<p>A <em>dead feature</em> is an element in the learned dictionary that never activates or activates extremely rarely across training. Dead features waste the dictionary’s capacity, and they are common. In fact, they can constitute up to 10–50% of the dictionary in a poorly tuned SAE <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>.</p>
+<p>Several factors can cause dead features to emerge. First, inappropriate SAE weight initialisation can place features in low-density regions of the activation space with weak gradient signals. Second, an overly strong sparsity penalty <span class="math inline">\lambda</span> may suppress feature activations early in training, preventing features from contributing usefully to reconstruction. Finally, once a subset of features captures common, dominant patterns, the remaining features struggle to find useful roles for improving reconstruction and thus do not activate.</p>
+<p>Dead features can be detected by tracking each feature’s rate of being activated during training. For example, features with an activation rate below 0.01% may be considered dead (the threshold should adapt across scenarios). Several mitigation strategies have been proposed, such as <em>neuron resampling</em>, which periodically reinitialises dead features using examples with high reconstruction error <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. The sparsity penalty can also be reduced early in training and increased gradually (i.e., a warm-up schedule). More recent Batch Top-<span class="math inline">k</span> SAEs allocate sparsity adaptively across samples, which allows more active features on complex inputs and fewer on easy ones <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite cite-emerging" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span>.</p>
+</section>
+<section id="feature-duplication" class="level3"><h3 data-pdf-page="37"><span class="secnum">6.5.2</span> Feature Duplication<a class="anchor" href="#feature-duplication" aria-label="Link to this section">#</a></h3>
+<p>Feature duplication occurs when multiple features in the dictionary encode the same or similar concepts <span class="citation" data-cites="templeton2024scaling">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>, leading to potentially redundant features. Duplication can arise for several reasons: insufficient sparsity pressure, imbalanced training data, or an optimisation process that favours local minima with duplicated features. Duplication can be detected by computing pairwise cosine similarity between feature directions (i.e., decoder columns), where high similarity indicates potential redundancy. As duplicated features tend to activate together <span class="citation" data-cites="korznikov2025ortsaeorthogonalsparseautoencoders">(<a href="@@BASE@@references/#ref-korznikov2025ortsaeorthogonalsparseautoencoders" class="cite cite-emerging" data-cite="korznikov2025ortsaeorthogonalsparseautoencoders" role="doc-biblioref">Korznikov et al., 2025</a>)</span>, co-activation statistics may provide complementary evidence.</p>
+</section>
+<section id="feature-absorption-and-splitting" class="level3"><h3 data-pdf-page="39"><span class="secnum">6.5.3</span> Feature Absorption and Splitting<a class="anchor" href="#feature-absorption-and-splitting" aria-label="Link to this section">#</a></h3>
+<p>Another class of failure modes concerns feature granularity. <em>Feature absorption</em> arises when semantically hierarchical features interact under the sparsity objective: a seemingly general (<em>parent</em>) feature fails to activate on some inputs where it should because a more specific (<em>child</em>) feature activates instead and has absorbed part of the parent’s direction into its own decoder direction <span class="citation" data-cites="chanin2025absorption">(<a href="@@BASE@@references/#ref-chanin2025absorption" class="cite cite-established" data-cite="chanin2025absorption" role="doc-biblioref">Chanin et al., 2025</a>)</span>. For example, a “starts with S” feature may fail to activate on the token “short”, while a token-specific “short” feature with a partially overlapping direction activates instead. Absorption-induced false negatives make such a feature an unreliable detector of its concept. It can be detected by comparing the feature with a linear probe for the same concept: inputs on which the probe responds strongly, but the feature does not, while another active feature’s direction aligns with the probe, indicate absorption <span class="citation" data-cites="chanin2025absorption">(<a href="@@BASE@@references/#ref-chanin2025absorption" class="cite cite-established" data-cite="chanin2025absorption" role="doc-biblioref">Chanin et al., 2025</a>)</span>.</p>
+<p>By contrast, <em>feature splitting</em> occurs when a feature learned by a smaller SAE splits into multiple distinct features as the SAE scales up (e.g., by increasing the expansion factor <span class="math inline">e</span>). This often produces more fine-grained features, which may be either beneficial or less useful. In a favourable case, an initially polysemantic or opaque feature, such as one activating on “bank” in both financial and geographical contexts, may split into more coherent features of “investment bank” and “river bank”. In a less useful case, the resulting features may become low-level, lexical detectors that activate on the token “bank” in any context.</p>
+</section>
+<section id="feature-instability-across-seeds" class="level3"><h3 data-pdf-page="39"><span class="secnum">6.5.4</span> Feature Instability Across Seeds<a class="anchor" href="#feature-instability-across-seeds" aria-label="Link to this section">#</a></h3>
+<p>SAE features are also sensitive to random initialisation, optimiser stochasticity, and data ordering. For a fixed SAE architecture and training dataset, different random seeds can produce substantially different dictionaries <span class="citation" data-cites="paulo2025sparse">(<a href="@@BASE@@references/#ref-paulo2025sparse" class="cite cite-established" data-cite="paulo2025sparse" role="doc-biblioref">Paulo &amp; Belrose, 2026</a>)</span>. In practice, frequent and semantically clear features tend to be stable, while rare or context-specific features vary substantially. Often, fewer than 50% of features remain the same across seeds, although broader semantic clusters may remain more consistent. This instability limits the reliability of mechanistic claims based on individual SAE features. Although aggregating results across seeds can improve robustness, it increases computational cost. As a result, when evaluating SAEs, we should report feature stability across seeds alongside reconstruction loss.</p>
+</section>
+<section id="subsubsec: sae limitation" class="level3"><h3 data-pdf-page="40"><span class="secnum">6.5.5</span> Identifiability and Theoretical Concerns<a class="anchor" href="#subsubsec: sae limitation" aria-label="Link to this section">#</a></h3>
+<p>Another concern for SAE-based interpretability claims is whether the learned features are identifiable, that is, whether the dictionary learning reliably recovers the “true” underlying features—if such features exist—or merely finds one of many mediocre decompositions <span class="citation" data-cites="klindt2025superpositionsparsecodesinterpretable">(<a href="@@BASE@@references/#ref-klindt2025superpositionsparsecodesinterpretable" class="cite cite-emerging" data-cite="klindt2025superpositionsparsecodesinterpretable" role="doc-biblioref">Klindt et al., 2025</a>)</span>.</p>
 <div class="definitionbox">
-<p>A <strong>transcoder</strong> learns a sparse mapping between the input and output to an MLP layer: <span class="math display">h_{\ell}^{out} \approx W_{\text{out}} z + b_{\text{out}}, \quad z = \sigma(W_{\text{in}} h_\ell^{in} + b_{\text{in}})</span> where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> are sparse feature activations bridging the nonlinearity between layers <span class="math inline">\ell</span> and <span class="math inline">\ell+1</span>, <span class="math inline">W_{\text{out}} \in \mathbb{R}^{d \times {d_{\text{sparse}}}}</span> and <span class="math inline">b_{\text{out}} \in \mathbb{R}^{d}</span> are decoder <span class="math inline">D</span> parameters. The transcoder reconstructs post-nonlinearity activations using features extracted from pre-nonlinearity activations <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
+<p>Suppose activations are generated by some sparse latent process</p>
+<p><span class="math display">h = D^{*} z^{*} + \epsilon ,</span></p>
+<p>where <span class="math inline">D^{*}</span> is the ground-truth feature dictionary and <span class="math inline">z^{*}</span> are the feature coefficients. An SAE is identifiable if its training recovers <span class="math inline">D^{*}</span> and <span class="math inline">z^{*}</span> up to unavoidable symmetries such as feature permutation and rescaling.</p>
 </div>
-<section id="architecture-and-motivation." class="level4">
-<h4>Architecture and Motivation.</h4>
-<p>Standard SAEs struggle to explain activations after nonlinear transformations (MLP layers, attention). A transcoder applies sparse decomposition directly to the nonlinear mapping, learning features that explain the transformation. Training minimizes: <span class="math display">\mathcal{L} = \|h_{\ell}^{out} - (W_{\text{out}} z + b_{\text{out}})\|_2^2 + \lambda \|z\|_1</span> where <span class="math inline">z = \sigma(W_{\text{in}} h_\ell + b_{\text{in}})</span>.</p>
+<p>In classical dictionary learning, identifiability holds under low-coherence conditions, i.e., dictionary elements are sufficiently orthogonal to one another. However, when coherence is high, ambiguity arises: multiple distinct sparse combinations of features from the same dictionary can generate the same output <span class="citation" data-cites="schnass2008dictionary">(<a href="@@BASE@@references/#ref-schnass2008dictionary" class="cite cite-established" data-cite="schnass2008dictionary" role="doc-biblioref">Schnass &amp; Vandergheynst, 2008</a>)</span>. In large language model activations, high coherence is likely unavoidable because of the model’s complicated internal structure and correlations within its feature space <span class="citation" data-cites="li2025geometry">(<a href="@@BASE@@references/#ref-li2025geometry" class="cite cite-established" data-cite="li2025geometry" role="doc-biblioref">Li et al., 2025b</a>)</span>. So far, identifiability remains a stubborn issue in SAEs, even though it offers a theoretical explanation for several empirical observations.</p>
+<p>Recent progress in fine-tuning language models around a fixed sparse autoencoder offers a potential path forward. In this setting, model activations are explicitly constrained to be sparse combinations of decoder features while largely preserving task performance <span class="citation" data-cites="chen2025low">(<a href="@@BASE@@references/#ref-chen2025low" class="cite cite-established" data-cite="chen2025low" role="doc-biblioref">Chen et al., 2025a</a>)</span>. In particular, recent work aimed at limiting superposition fine-tunes the language model jointly with the SAE while enforcing orthogonality constraints on the decoder matrix <span class="citation" data-cites="miller2026identifyingintervenableinterpretablefeatures">(<a href="@@BASE@@references/#ref-miller2026identifyingintervenableinterpretablefeatures" class="cite cite-emerging" data-cite="miller2026identifyingintervenableinterpretablefeatures" role="doc-biblioref">Miller et al., 2026</a>)</span>. By explicitly imposing low-coherence conditions, this approach improves identifiability: activations have a unique feature representation, and these features can be reliably recovered.</p>
+<section id="relation-to-causal-relevance." class="level4">
+<h4>Relation to Causal Relevance.</h4>
+<p>The concern of identifiability connects to causal relevance: even if an SAE feature is monosemantic and helps reconstruct activations well, it may not correspond to a causally meaningful unit of analysis. Below, we summarise some criteria for validating an SAE feature.</p>
+<p><strong>Diagnostic criteria:</strong></p>
+<ul>
+<li><p><em>Necessity</em>: Does ablating the feature remove the corresponding concept in model responses? Low necessity suggests that the concept can be expressed through redundant features.</p></li>
+<li><p><em>Sufficiency</em>: Does activating only this feature produce the behaviour corresponding to its concept? Low sufficiency suggests the feature alone is not enough to represent the concept.</p></li>
+<li><p><em>Specificity</em>: Does the feature’s causal effect match its corresponding concept? Low specificity means the feature contains other concepts.</p></li>
+</ul>
+<p>These criteria are useful in practice when claiming that SAE features represent meaningful units of analysis.</p>
 </section>
-<section id="when-transcoders-are-necessary." class="level4">
-<h4>When Transcoders Are Necessary.</h4>
-<p>Single-layer SAEs often suffice for residual-stream analysis, where features largely propagate linearly. However, MLP blocks apply nonlinear transformations that distort feature space. Transcoders are essential when analyzing MLPs, understanding feature interactions, or tracking how concepts evolve across layers <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
-<figure id="fig:clt" data-latex-placement="t">
-<div class="minipage">
-<img alt="Figure 7: Cross-Layer Transcoder (CLT). CLTs extend Transcoders by simultaneously partially reconstructing the outputs of multiple subsequent layers from a single input point in the residual stream." loading="lazy" src="@@BASE@@figures/6_transcoders.svg" />
-</div>
-<div class="minipage">
-<img alt="Figure 7: Cross-Layer Transcoder (CLT). CLTs extend Transcoders by simultaneously partially reconstructing the outputs of multiple subsequent layers from a single input point in the residual stream." loading="lazy" src="@@BASE@@figures/6_clt.svg" />
+</section>
+<section id="practical-recommendations" class="level3"><h3 data-pdf-page="40"><span class="secnum">6.5.6</span> Practical Recommendations<a class="anchor" href="#practical-recommendations" aria-label="Link to this section">#</a></h3>
+<p>The challenges discussed above suggest several methodological considerations for SAE-based interpretability studies:</p>
+<ol>
+<li><p><strong>Report failure-mode metrics:</strong> Alongside reconstruction loss, researchers should also report dead-feature percentage, duplication rate, and stability across seeds.</p></li>
+<li><p><strong>Validate causal relevance:</strong> Researchers should validate features’ necessity, sufficiency, and specificity through interventions (e.g., feature ablation or steering) and evaluation on downstream effects.</p></li>
+<li><p><strong>Compare methods:</strong> For safety-critical applications, researchers should compare SAE features against various baselines to assess whether SAEs recover the relevant structure.</p></li>
+<li><p><strong>Try multiple SAE variants:</strong> Different variants (e.g., Gated, Top-<span class="math inline">k</span>, JumpReLU) impose different inductive biases. Researchers can explore findings consistent across variants, as these provide stronger evidence.</p></li>
+<li><p><strong>Acknowledge identifiability limits:</strong> Researchers should frame SAE features as one valid approximate decomposition rather than the true features. Multiple decompositions may be equally valid for reconstruction but yield different feature sets.</p></li>
+<li><p><strong>Consider alternatives for targeted analysis:</strong> When specific concepts are the focus, supervised methods (e.g., DAS, probes, discussed in Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: supervised_extraction" data-reference-type="ref" data-reference="subsubsec: supervised_extraction">6.9.4</a>) may be more reliable because SAEs discover features in an unsupervised and exploratory manner.</p></li>
+</ol>
+</section>
+</section>
+<section id="subsec:transcoders-crosscoders" class="level2"><h2 data-pdf-page="41"><span class="secnum">6.6</span> Multi-Layer Feature Extraction and Analysis<a class="anchor" href="#subsec:transcoders-crosscoders" aria-label="Link to this section">#</a></h2>
+<p>Standard SAEs analyse individual layers in isolation. However, features can evolve as information propagates through the network. Transcoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:transcoders" data-reference-type="ref" data-reference="fig:transcoders">7a</a>), cross-layer transcoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>), and crosscoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:crosscoders" data-reference-type="ref" data-reference="fig:crosscoders">9</a>) enable multi-layer feature extraction and analysis, revealing how representations and features associated with particular model components—most commonly MLP blocks—transform across depth.</p>
+<section id="subsubsec:transcoder" class="level3"><h3 data-pdf-page="41"><span class="secnum">6.6.1</span> Transcoders: Bridging the Nonlinearities<a class="anchor" href="#subsubsec:transcoder" aria-label="Link to this section">#</a></h3>
+<div class="definitionbox">
+<p>A <em>transcoder</em> learns a sparse mapping between the input to and output of a model component, such as an MLP block, in layer <span class="math inline">\ell</span>: <span class="math display">h_{\ell}^{\text{out}} \approx W_{\text{out}} z + b_{\text{out}}, \quad z = \sigma(W_{\text{in}} h_\ell^{\text{in}} + b_{\text{in}})</span> where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> are sparse feature activations bridging the nonlinearity between input and output of the MLP, and <span class="math inline">W_{\text{out}} \in \mathbb{R}^{d \times {d_{\text{sparse}}}}</span> and <span class="math inline">b_{\text{out}} \in \mathbb{R}^{d}</span> are decoder <span class="math inline">D</span> parameters. The transcoder reconstructs post-nonlinearity activations using features extracted from pre-nonlinearity activations <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
 </div>
-<figcaption><span class="capnum">Figure 7:</span> <strong>Cross-Layer Transcoder (CLT)</strong>. CLTs extend Transcoders by simultaneously partially reconstructing the outputs of multiple subsequent layers from a single input point in the residual stream.</figcaption>
+<section id="architecture-and-motivation." class="level4">
+<h4>Architecture and Motivation.</h4>
+<p>Standard SAEs do not explain the nonlinear transformations (i.e., weight matrices of MLP blocks and attention heads) themselves that produce the activations. A transcoder achieves this by applying dictionary learning to the mapping, learning features that explain the transformation. Its training minimises <span class="math display">\mathcal{L} = \|h_{\ell}^{\text{out}} - (W_{\text{out}} z + b_{\text{out}})\|_2^2 + \lambda \|z\|_1,</span> where <span class="math inline">z = \sigma(W_{\text{in}} h^{\text{in}}_\ell + b_{\text{in}})</span> as defined above.</p>
+</section>
+<section id="when-are-transcoders-necessary" class="level4">
+<h4>When Are Transcoders Necessary?</h4>
+<p>SAEs extract features from static activations and thus often suffice for residual-stream analysis. Transcoders track how features emerge from input to output and are essential for analysing MLP layers <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
+<figure id="fig:clt" data-latex-placement="tbp">
+<figure id="fig:transcoders">
+<img alt="Figure 7b: Transcoder." loading="lazy" src="@@BASE@@figures/6_transcoders.svg" />
+<figcaption><span class="capnum">Figure 7b:</span> Transcoder.</figcaption>
+</figure>
+<figure id="fig:clt">
+<img alt="Figure 7b: Cross-layer transcoder." loading="lazy" src="@@BASE@@figures/6_clt.svg" />
+<figcaption><span class="capnum">Figure 7b:</span> Cross-layer transcoder.</figcaption>
+</figure>
+<figcaption><strong>Transcoders and CLTs.</strong> Transcoders reconstruct the output of the MLP from its input in the same transformer layer. CLTs extend this by partially reconstructing the outputs of multiple subsequent layers from a single point in the residual stream.</figcaption>
 </figure>
 </section>
 </section>
-<section id="subsubsec:cross-layer-transcoder" class="level3"><h3 data-pdf-page="27"><span class="secnum">6.4.2</span> Cross-Layer Transcoders<a class="anchor" href="#subsubsec:cross-layer-transcoder" aria-label="Link to this section">#</a></h3>
+<section id="subsubsec:cross-layer-transcoder" class="level3"><h3 data-pdf-page="42"><span class="secnum">6.6.2</span> Cross-Layer Transcoders: Enabling Better Circuit Analysis<a class="anchor" href="#subsubsec:cross-layer-transcoder" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
-<p>A <strong>Cross-Layer Transcoder</strong> (CLT) is a sparse feature decomposition spanning all layers of a neural network. Each feature <span class="math inline">\mathbf{a}^{\ell}</span> reads from the residual stream at layer <span class="math inline">\ell</span> via: <span class="math display">\mathbf{z}^{\ell} = \text{JumpReLU}(W_{\text{in}}^{\ell} h_\ell)</span> and each feature can write to the MLP outputs of all <em>subsequent</em> layers via layer-specific decoder matrices: <span class="math display">\hat{\mathbf{y}}^{\ell&#39;} = \sum_{\ell=1}^{\ell&#39;} W_{\text{out}}^{\ell \to \ell&#39;} \mathbf{z}^{\ell}</span> where <span class="math inline">\hat{\mathbf{y}}^{\ell&#39;}</span> reconstructs the original MLP output at layer <span class="math inline">\ell&#39;</span>.</p>
+<p><em>Cross-layer transcoders</em> (CLTs) are sparse feature decompositions spanning all layers of a neural network. At each layer <span class="math inline">\ell</span>, sparse feature activations are read from the residual stream via <span class="math display">z_{\ell}
+=
+\sigma_{\text{JumpReLU}}
+\!\left(
+W_{\text{in}}^\ell h_{\ell}^{\text{in}}
+\right),</span> where <span class="math inline">z_\ell^j</span> denotes the activation of feature <span class="math inline">j</span> at layer <span class="math inline">\ell</span>.<br />
+Each feature can write to the MLP outputs of its own layer and all subsequent layers through layer-specific decoder vectors. The contribution from features read at layer <span class="math inline">\ell</span> to the MLP output at a subsequent layer <span class="math inline">\ell&#39;</span> is <span class="math display">\hat{h}_{\ell \to \ell&#39;}^{\text{out}}
+=
+\sum_j
+W_{\text{out},j}^{\ell \to \ell&#39;} z_{\ell}^{j},</span> where <span class="math inline">W_{\text{out},j}^{\ell \to \ell&#39;}</span> is the decoder vector from feature <span class="math inline">j</span> at layer <span class="math inline">\ell</span> to the MLP output at layer <span class="math inline">\ell&#39;</span>. The reconstructed MLP output at layer <span class="math inline">\ell&#39;</span> is then <span class="math display">\hat{h}_{\ell&#39;}^{\text{out}}
+=
+\sum_{\ell=1}^{\ell&#39;}
+\hat{h}_{\ell \to \ell&#39;}^{\text{out}}
+=
+\sum_{\ell=1}^{\ell&#39;}
+\sum_j
+W_{\text{out},j}^{\ell \to \ell&#39;} z_{\ell}^{j},</span> which approximates the original MLP output <span class="math inline">h_{\ell&#39;}^{\text{out}}</span>.</p>
 </div>
-<p>CLTs are trained jointly across all layers to minimize: <span class="math display">\mathcal{L} = \sum_{\ell=1}^L \|\hat{\mathbf{y}}^{\ell} - \mathbf{y}^{\ell}\|_2^2 + \lambda \sum_{\ell=1}^L \sum_{i} \tanh(c \cdot \|W_{\text{out},i}^{\ell}\| \cdot z_i^{\ell})</span> where <span class="math inline">c</span> is the sharpness hyperparameter <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>.</p>
+<p>CLTs (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>) are trained jointly across all <span class="math inline">L</span> layers to minimise <span class="math display">\mathcal{L}
+=
+\sum_{\ell&#39;=1}^{L}
+\left\|
+\hat{h}_{\ell&#39;}^{\text{out}}
+-
+h_{\ell&#39;}^{\text{out}}
+\right\|_2^2
++
+\lambda
+\sum_{\ell=1}^{L}
+\sum_j
+\tanh\!\left(
+c\,
+z_{\ell}^{j}
+\sqrt{
+\sum_{\ell&#39;=\ell}^{L}
+\left\|
+W_{\text{out},j}^{\ell \to \ell&#39;}
+\right\|_2^2
+}
+\right),</span> where <span class="math inline">c</span> is the sharpness hyperparameter <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>.</p>
 <section id="replacement-models-and-attribution-graphs." class="level4">
 <h4>Replacement Models and Attribution Graphs.</h4>
-<p>A key insight is that CLTs enable the construction of a <strong>replacement model</strong>: a version of the original model where all MLP layers are replaced by their CLT reconstructions. Running this replacement model involves:</p>
+<p>A key insight is that CLTs enable the construction of a <em>replacement model</em>—a version of the original model in which all MLP layers are replaced with their CLT reconstructions. Running this replacement model involves:</p>
 <ul>
-<li><p>Computing CLT feature activations <span class="math inline">\mathbf{z}^{\ell}</span> at each layer <span class="math inline">\ell</span></p></li>
-<li><p>Replacing MLP outputs <span class="math inline">\mathbf{y}^{\ell}</span> with reconstructions <span class="math inline">\hat{\mathbf{y}}^{\ell}</span> from features in layers <span class="math inline">1</span> through <span class="math inline">\ell</span></p></li>
+<li><p>Computing CLT feature activations <span class="math inline">z_{\ell}</span> at each layer <span class="math inline">\ell</span></p></li>
+<li><p>Replacing MLP outputs <span class="math inline">h^{\text{out}}_{\ell}</span> with reconstructions <span class="math inline">\hat{h}^{\text{out}}_{\ell}</span> from features in layer <span class="math inline">1</span> through <span class="math inline">\ell</span></p></li>
 <li><p>Keeping attention layers unchanged</p></li>
 </ul>
-<p>Empirically, CLT replacement models match the original model’s most-likely token on approximately 50% of prompts in initial experiments on Gemma-2-2B <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. However, this figure should be interpreted with important caveats (see below). When analyzing a specific prompt, a <strong>local replacement model</strong> freezes attention patterns and normalization statistics from the original model’s forward pass, then adds layer-specific error corrections. Under these conditions, the local replacement model can exactly match the original model’s computation on that prompt while decomposing it into interpretable CLT features.</p>
+<p>When analysing a specific prompt, the replacement model freezes the original model’s attention patterns and normalisation statistics from its forward pass, then adds layer-specific error corrections. Under these conditions, the local replacement model can exactly match the original model’s activations on that prompt while decomposing it into interpretable CLT features. However, when the model’s activations are completely replaced by CLT reconstructions, the intervened model’s top-1 predictions match the original model’s on only about 50% of prompts from the Pile dataset <span class="citation" data-cites="gao2020pile">(<a href="@@BASE@@references/#ref-gao2020pile" class="cite cite-emerging" data-cite="gao2020pile" role="doc-biblioref">Gao et al., 2020</a>)</span>. Notably, this experiment uses a relatively small, 18-layer transformer <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. Therefore, this figure should be interpreted with important caveats (see below).</p>
 <div class="warningbox">
-<p>The claim that CLT replacement models achieve <span class="math inline">\sim</span>50% most-likely token match requires careful qualification:</p>
-<p><strong>Conditions for reported fidelity:</strong></p>
+<p>CLTs have been claimed to possess high output fidelity given that they achieve high top-1 token agreement. However, this claim requires careful examination:<br />
+</p>
+<section id="conditions-for-reported-fidelity" class="level4">
+<h4>Conditions for reported fidelity:</h4>
 <ul>
-<li><p>Results reported primarily on Gemma-2-2B; generalization to other architectures (e.g., Llama, GPT variants) and larger scales (70B+) remains underexplored.</p></li>
-<li><p>Fidelity was measured on evaluation prompts drawn from small sequences drawn from the CLT training data. Performance under significant distribution shift (e.g., out-of-domain tasks, adversarial inputs) is not well characterized.</p></li>
-<li><p>The 50% figure reflects top-1 token agreement; softer metrics (e.g., top-5 agreement, KL divergence) may show different patterns.</p></li>
+<li><p>Results are reported primarily on toy or small models, whose generality to other model architectures (e.g., Llama, Qwen) and model sizes (e.g., 70B models) is underexplored.</p></li>
+<li><p>Fidelity was measured on short sequences in the CLT training data. Evaluation under distribution shift (e.g., out-of-domain tasks, adversarial inputs) is not adequate.</p></li>
+<li><p>The paper reports top-1 token agreement as the main result, while softer metrics (e.g., top-5 agreement, KL divergence) may show different patterns and insights.</p></li>
 </ul>
-<p><strong>Conditions for exact local matching:</strong></p>
+</section>
+<section id="conditions-for-exact-local-matching" class="level4">
+<h4>Conditions for exact local matching:</h4>
 <ul>
-<li><p>Attention patterns must be frozen from the original forward pass, the CLT does not model or reconstruct attention computations.</p></li>
-<li><p>Layer normalization statistics (means, variances) must be cached from the original model; the replacement model does not recompute these.</p></li>
-<li><p>Error correction terms are added per-layer to compensate for reconstruction residuals; without these, exact matching fails.</p></li>
+<li><p>Attention patterns must be frozen from the original forward pass since the CLT does not compute or reconstruct attention computations.</p></li>
+<li><p>Layer normalisation statistics (means, variances) must be cached from the original model. The replacement model does not recompute these.</p></li>
+<li><p>Error correction terms are added per layer to compensate for reconstruction residuals. Without them, exact matching fails.</p></li>
 </ul>
-<p><strong>Factors affecting fidelity:</strong></p>
+</section>
+<section id="factors-affecting-fidelity" class="level4">
+<h4>Factors affecting fidelity:</h4>
 <ul>
-<li><p><em>Model scale</em>: Preliminary evidence suggests fidelity improves with model size, possibly because larger models exhibit more structured, less entangled representations <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. However, systematic scaling studies are limited.</p></li>
-<li><p><em>Layer depth</em>: Middle layers often show higher reconstruction fidelity than early or final layers, where representations may be more input-specific or output-constrained respectively.</p></li>
-<li><p><em>Domain</em>: Fidelity on factual recall and syntactic tasks appears higher than on complex reasoning or arithmetic, where MLP computations may be more nonlinear and less amenable to sparse linear approximation.</p></li>
+<li><p><em>Model scale</em>: Preliminary evidence suggests that reconstruction quality and feature interpretability improve with CLT model size. This may be because larger CLTs produce more structured, less entangled representations <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. However, systematic scaling studies are limited.</p></li>
+<li><p><em>Layer depth</em>: Early layers’ and late layers’ activations are usually input-specific and output-constrained, respectively. Middle layers have richer information derived from the input but are not yet converged to the output. As a result, middle layers often show higher reconstruction fidelity.</p></li>
+<li><p><em>Domain</em>: Fidelity on factual recall and syntactic tasks is higher than on complex reasoning or arithmetic tasks, where MLP computations may be more complex and cannot be approximated by sparse linear features.</p></li>
 </ul>
-<p>These caveats do not invalidate CLTs as a tool for mechanistic analysis, but practitioners should verify fidelity on their specific model, layer, and task before drawing strong conclusions from CLT-based circuit analysis.</p>
+<p>These caveats do not invalidate CLTs as a tool for mechanistic analysis, but they do mean that fidelity should be verified on the target model and specific task before drawing strong conclusions from CLT-based circuit analysis.</p>
+</section>
 </div>
 </section>
 <section id="why-clts-enable-better-circuit-analysis." class="level4">
 <h4>Why CLTs Enable Better Circuit Analysis.</h4>
-<p>Unlike per-layer transcoders, CLTs reveal direct feature-to-feature interactions across layers: a feature in layer <span class="math inline">\ell</span> directly influences features in later layers through its contribution to their inputs. This simplifies circuit structure—features need not re-encode information at each layer. Additionally, by replacing MLPs with CLT reconstructions, the remaining computation becomes <em>approximately</em> linear (with frozen attention patterns and normalization), enabling principled attribution: each feature’s activation can be expressed as the sum of its input edges up to its activation threshold <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>.</p>
-<p>However, the degree of linearization achieved depends on several assumptions:</p>
+<p>Unlike per-layer transcoders, CLTs reveal direct feature-to-feature interactions across layers: a feature in layer <span class="math inline">\ell</span> directly influences features in later layers through its contribution to their inputs. This simplifies circuit structure—features need not re-encode information at each layer. Additionally, replacing MLPs with CLT reconstructions makes the remaining computation <em>approximately</em> linear (with frozen attention patterns and normalisation), enabling principled attribution: each feature’s activation can be expressed as the sum of its input edges up to its activation threshold <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>.</p>
+<p>However, linearisation depends on several assumptions:</p>
 <ul>
-<li><p><strong>Attention freezing</strong>: By caching attention patterns, the replacement model treats attention as a fixed routing operation rather than a dynamic, input-dependent computation. This simplifies analysis but means CLT-based circuits describe computation <em>given</em> specific attention patterns, not the attention computation itself.</p></li>
-<li><p><strong>Normalization caching</strong>: Freezing LayerNorm statistics removes a source of nonlinearity but introduces dependence on the specific input used to cache these statistics.</p></li>
-<li><p><strong>Sparse linear approximation</strong>: The core assumption that MLP outputs are well-approximated by sparse linear combinations of features may break down for computations involving complex feature interactions or highly nonlinear transformations.</p></li>
+<li><p><strong>Attention freezing:</strong> By caching attention patterns, the replacement model treats attention as a fixed routing operation rather than a dynamic, input-dependent computation. This means CLT-based circuits describe model computation conditioned on <em>given</em> attention patterns without analysing them.</p></li>
+<li><p><strong>Normalisation caching:</strong> CLT analysis caches the input-dependent normalisation factors (e.g., LayerNorm denominators) from the original forward pass and reuses them during attribution instead of recomputing them after interventions. This removes a source of nonlinearity, but if an intervention would substantially change these normalisation factors in the original model, the cached computation no longer faithfully represents the intervened state.</p></li>
+<li><p><strong>Sparse linear approximation:</strong> Dictionary learning methods assume MLP outputs or transformations are well approximated by sparse linear combinations of features. This assumption may fail when computations involve complex feature interactions or highly nonlinear transformations.</p></li>
 </ul>
-<p>When these assumptions hold, CLTs provide a powerful lens for circuit discovery. When they fail, particularly for complex reasoning or out-of-distribution inputs, CLT-based explanations may be incomplete or misleading.</p>
+<p>CLTs provide a powerful lens for circuit discovery when these assumptions hold. When they fail, CLT-based explanations may become unfaithful.</p>
 </section>
 <section id="computational-cost-and-practical-considerations." class="level4">
 <h4>Computational Cost and Practical Considerations.</h4>
-<figure id="fig:clt-size-vs-depth">
-<img alt="Figure 8: Parameter count (left) and FP16 memory requirements (right) of a full Cross-Layer Transcoder as a function of model depth, for different residual stream dimensions and expansion factor e=32." loading="lazy" src="@@BASE@@figures/clt_size_vs_depth_e32_clean_v2.png" />
-<figcaption><span class="capnum">Figure 8:</span>  Parameter count (left) and FP16 memory requirements (right) of a full Cross-Layer Transcoder as a function of model depth, for different residual stream dimensions and expansion factor <span class="math inline">e=32</span>. </figcaption>
+<figure id="fig:clt-size-vs-depth" data-latex-placement="th">
+<img alt="Figure 8: Parameter count (left) and FP16 memory requirements (right) of a full cross-layer transcoder as a function of model depth, for different residual stream dimensions and expansion factor e=32." loading="lazy" src="@@BASE@@figures/clt_size_vs_depth_e32_clean_v3.svg" />
+<figcaption><span class="capnum">Figure 8:</span>  Parameter count (left) and FP16 memory requirements (right) of a full cross-layer transcoder as a function of model depth, for different residual stream dimensions and expansion factor <span class="math inline">e=32</span>. </figcaption>
 </figure>
-<p>A CLT associates each transformer layer with an encoder <span class="math inline">\mathbb{R}^d \rightarrow \mathbb{R}^{ed}</span> and every ordered layer pair <span class="math inline">(\ell_s &lt; \ell_t)</span> with a decoder <span class="math inline">\mathbb{R}^{ed} \rightarrow \mathbb{R}^d</span>. The total parameter count scales as <span class="math display">N_{\text{CLT}}(L,d,e) = \tfrac{e d^2}{2}\left(L^2 + L\right),</span> highlighting quadratic growth in both model depth and width, as shown in Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt-size-vs-depth" data-reference-type="ref" data-reference="fig:clt-size-vs-depth">6</a>.</p>
-<p>Training a CLT requires significant upfront cost, which scales with the number of features and layers. For reference, training the CLT suite for Gemma-2-9B required approximately 3<span>,</span>844 H100 GPU-hours at 50% FLOPs efficiency <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. This cost is amortized over many circuit analyses on the trained CLT. The resulting interpretability gains (cross-layer features, lower interference, better attribution) justify the investment for systematic mechanistic analysis.</p>
-<p>For these reasons, besides Anthropic’s foundational work <span class="citation" data-cites="ameisen2025circuit lindsey2025biology">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>; <a href="@@BASE@@references/#ref-lindsey2025biology" class="cite cite-emerging" data-cite="lindsey2025biology" role="doc-biblioref">Lindsey et al., 2025c</a>)</span>, CLT applications remain limited: a “greater-than” mechanism study <span class="citation" data-cites="merullo2025replicating">(<a href="@@BASE@@references/#ref-merullo2025replicating" class="cite cite-emerging" data-cite="merullo2025replicating" role="doc-biblioref">Merullo et al., 2025</a>)</span>, an open-source extension <span class="citation" data-cites="lindsey2025landscape">(<a href="@@BASE@@references/#ref-lindsey2025landscape" class="cite cite-emerging" data-cite="lindsey2025landscape" role="doc-biblioref">Lindsey et al., 2025a</a>)</span>, and analysis of multilingual representations using CLTs <span class="citation" data-cites="harrasse2025tracing">(<a href="@@BASE@@references/#ref-harrasse2025tracing" class="cite" data-cite="harrasse2025tracing" role="doc-biblioref">Harrasse et al., 2025a</a>)</span>. This scarcity of downstream applications in the current literature likely reflects the considerable challenges in training CLTs at scale.</p>
+<p>Let <span class="math inline">d_{\text{sparse}} = ed</span>, where <span class="math inline">e</span> is the expansion factor. For a transformer with <span class="math inline">L</span> layers, a complete suite of CLTs contains <span class="math inline">L</span> encoders mapping <span class="math inline">\mathbb{R}^d \rightarrow \mathbb{R}^{ed}</span> and <span class="math inline">L(L+1)/2</span> decoders mapping <span class="math inline">\mathbb{R}^{ed} \rightarrow \mathbb{R}^d.</span> The resulting parameter count, with biases ignored, is therefore <span class="math display">N_{\text{CLT}}(L,d,e)
+=
+\frac{e d^2}{2}\left(L^2 + 3L\right),</span> which highlights the quadratic scaling with both model depth and hidden dimension, as illustrated in Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt-size-vs-depth" data-reference-type="ref" data-reference="fig:clt-size-vs-depth">8</a>.</p>
+<p>Training a suite of CLTs thus incurs a high upfront cost. For reference, training the CLT suite for Gemma-2-9B required approximately 3<span>,</span>844 H100 GPU-hours at 50% FLOPs efficiency <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. This cost is amortised over many circuit analyses on the trained CLT. The resulting interpretability gains (cross-layer features, lower interference, better attribution) justify the investment for systematic mechanistic analysis.</p>
+<p>For these reasons, current applications of CLTs remain limited: a “greater-than” mechanism study <span class="citation" data-cites="merullo2025replicating">(<a href="@@BASE@@references/#ref-merullo2025replicating" class="cite cite-emerging" data-cite="merullo2025replicating" role="doc-biblioref">Merullo et al., 2025</a>)</span>, an open-source extension <span class="citation" data-cites="lindsey2025landscape">(<a href="@@BASE@@references/#ref-lindsey2025landscape" class="cite cite-emerging" data-cite="lindsey2025landscape" role="doc-biblioref">Lindsey et al., 2025a</a>)</span>, and analysis of multilingual representations using CLTs <span class="citation" data-cites="harrasse2025tracing">(<a href="@@BASE@@references/#ref-harrasse2025tracing" class="cite cite-emerging" data-cite="harrasse2025tracing" role="doc-biblioref">Harrasse et al., 2026</a>)</span>. This scarcity of downstream use cases likely reflects the considerable challenges of training CLTs at scale and the complexity of using CLT-based circuit discovery in the wild.</p>
 </section>
 <section id="open-questions-for-clt-research." class="level4">
 <h4>Open Questions for CLT Research.</h4>
 <p>Several important questions remain open:</p>
 <ul>
-<li><p>How does CLT fidelity scale in large models?</p></li>
-<li><p>Can CLTs be extended to jointly model attention and MLP computations?</p></li>
-<li><p>What evaluation protocols can reliably detect when CLT-based explanations are unfaithful to the original model’s computation?</p></li>
-<li><p>How sensitive are CLT circuits to the choice of training data distribution?</p></li>
+<li><p>How does CLT fidelity scale in large models? How can we better measure fidelity?</p></li>
+<li><p>Can CLTs be extended to model more nonlinearities such as attention patterns and normalisation, while maintaining their explanatory power?</p></li>
+<li><p>Can we develop evaluation protocols that reliably detect the potential unfaithfulness of CLT-based explanations?</p></li>
+<li><p>How sensitive are CLT circuits to experimental setups, such as the choice of training data?</p></li>
 </ul>
 </section>
 </section>
-<section id="crosscoders-analyzing-cross-layer-features" class="level3"><h3 data-pdf-page="30"><span class="secnum">6.4.3</span> Crosscoders: Analyzing Cross-Layer Features<a class="anchor" href="#crosscoders-analyzing-cross-layer-features" aria-label="Link to this section">#</a></h3>
+<section id="crosscoders-analysing-cross-layer-features" class="level3"><h3 data-pdf-page="45"><span class="secnum">6.6.3</span> Crosscoders: Analysing Cross-Layer Features<a class="anchor" href="#crosscoders-analysing-cross-layer-features" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
-<p>A <strong>crosscoder</strong> learns sparse features by reading activations from multiple layers simultaneously: <span class="math display">\hat{x}_{\ell, t} = W_{\text{out}} z_{\ell,t} + b_{\text{out}}, \quad z_{\ell,t} = \text{ReLU}(W_{\text{in}} [h_{\ell&#39;,t}]_{\ell&#39; \in L} + b_{\text{in}})</span> where <span class="math inline">z_{\ell,t} \in \mathbb{R}^{d_{\text{sparse}}}</span> encodes sparse features constructed from activations across a set of layers <span class="math inline">L</span> at token position <span class="math inline">t</span>. Crosscoders resolve cross-layer superposition by tracking features that persist through the residual stream <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>.</p>
+<p>A <em>crosscoder</em> learns a shared sparse representation from activations across <span class="math inline">L</span> layers. It jointly encodes activations from all layers as <span class="math display">z
+=
+\sigma_{\text{ReLU}}
+\left(
+\sum_{\ell=1}^{L}
+W_{\text{in}}^\ell h_\ell
++
+b_{\text{in}}
+\right),</span> where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> denotes the shared sparse feature activations and <span class="math inline">W_{\text{in}}^\ell</span> is the layer-<span class="math inline">\ell</span> encoder. Each layer is then reconstructed using a layer-specific decoder: <span class="math display">\hat{h}_\ell
+=
+W_{\text{out}}^\ell z
++
+b_{\text{out}}^\ell,
+\qquad
+\ell = 1,\ldots,L.</span> Thus, a single latent feature can capture patterns that are shared across, persist through, or vary across multiple layers of the residual stream <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>. Studies have also used crosscoders to discover shared features in activations from different models (<span class="citation" data-cites="ge2026evolution bayazit-etal-2026-crosscoding"><a href="@@BASE@@references/#ref-ge2026evolution" class="cite cite-established" data-cite="ge2026evolution" role="doc-biblioref">Ge et al., 2026</a>; <a href="@@BASE@@references/#ref-bayazit-etal-2026-crosscoding" class="cite cite-established" data-cite="bayazit-etal-2026-crosscoding" role="doc-biblioref">Bayazit et al., 2026</a></span>; <span class="citation" data-cites="lindsey2024sparsecrosscoders jiralerspong_cross-architecture_2025"><a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>; <a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite cite-emerging" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a></span>), as detailed in Section <a href="@@BASE@@handbook/representation-analysis/#sec:model-diffing" data-reference-type="ref" data-reference="sec:model-diffing">9.1</a>.</p>
 </div>
-<figure id="fig:crosscoders" data-latex-placement="t">
-<img alt="Figure 9: Crosscoder. While standard SAEs process a single layer, Crosscoders are trained to simultaneously reconstruct the residual stream across multiple layers." loading="lazy" src="@@BASE@@figures/6_crosscoders.svg" />
-<figcaption><span class="capnum">Figure 9:</span> <strong>Crosscoder</strong>. While standard SAEs process a single layer, Crosscoders are trained to simultaneously reconstruct the residual stream across multiple layers.</figcaption>
+<figure id="fig:crosscoders" data-latex-placement="tb">
+<img alt="Figure 9: Crosscoder. Crosscoders are trained to reconstruct the residual stream across multiple layers simultaneously." loading="lazy" src="@@BASE@@figures/6_crosscoders.svg" />
+<figcaption><span class="capnum">Figure 9:</span> <strong>Crosscoder.</strong> Crosscoders are trained to reconstruct the residual stream across multiple layers simultaneously.</figcaption>
 </figure>
-<p>Crosscoder is trained by minimizing: <span class="math display">\mathcal{L} = \sum_{\ell\in L} \|\hat{x}^{\ell} - h^{\ell}\|_2^2 + \lambda \sum_{\ell\in L} \sum_{i} \|W_{\text{out},i}^{\ell}\| \cdot z_i^{\ell}</span> where <span class="math inline">\lambda</span> is the hyperparameter.</p>
+<p>A crosscoder is trained by minimising <span class="math display">\mathcal{L}
+=
+\sum_{\ell=1}^{L}
+\left\|
+\hat{h}_\ell - h_\ell
+\right\|_2^2
++
+\lambda
+\sum_j
+\left(
+\sum_{\ell=1}^{L}
+\left\|
+W_{\text{out},j}^{\ell}
+\right\|_2
+\right)
+z^j,</span> where <span class="math inline">W_{\text{out},j}^{\ell}</span> denotes the decoder vector of feature <span class="math inline">j</span> at layer <span class="math inline">\ell</span>, and <span class="math inline">\lambda</span> controls the sparsity penalty.</p>
 <section id="causal-vs.-acausal-crosscoders." class="level4">
 <h4>Causal vs. Acausal Crosscoders.</h4>
 <p>Crosscoders support two information flow patterns:</p>
 <ul>
-<li><p><strong>Causal Crosscoders</strong>: Read only from layers <span class="math inline">\ell&#39; \leq \ell</span> at the current position. This respects the forward pass causality: earlier layers cannot read from later layers.</p></li>
-<li><p><strong>Acausal Crosscoders</strong>: Read from any layer <span class="math inline">\ell&#39;</span> regardless of depth ordering. Useful for understanding post-hoc feature relationships across the full model.</p></li>
+<li><p><strong>Causal Crosscoders:</strong> Read only from layers <span class="math inline">\ell&#39; \leq \ell</span> at the current position. This respects the forward pass causality: earlier layers cannot read from later layers.</p></li>
+<li><p><strong>Acausal Crosscoders:</strong> Read from any layer <span class="math inline">\ell&#39;</span> regardless of depth ordering. Useful for understanding post-hoc feature relationships across the full model.</p></li>
 </ul>
 </section>
 <section id="applications-to-circuit-analysis." class="level4">
 <h4>Applications to Circuit Analysis.</h4>
-<p>By extracting cross-layer features, crosscoders enable understanding how features propagate through the residual stream across layers, removing duplicate feature representations that complicate circuit analysis. This simplification allows features to “jump” across intervening identity operations, substantially reducing circuit complexity. More importantly, crosscoders reveal how input-relevant features transform as they flow through the network, distinguishing which features persist unchanged versus which are actively modified by downstream computation <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>.</p>
+<p>By extracting cross-layer features, crosscoders help us understand how features propagate through the residual stream across layers, removing duplicate feature representations that complicate circuit analysis. This simplification allows features to “jump” across intervening identity operations, substantially reducing circuit complexity. More importantly, crosscoders reveal how input-relevant features transform as they flow through the network, distinguishing which features persist unchanged versus which are actively modified by downstream computation <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>.</p>
 </section>
 </section>
 </section>
-<section id="comparing-saes-transcoders-and-crosscoders." class="level2"><h2 data-pdf-page="30"><span class="secnum">6.5</span> Comparing SAEs, Transcoders, and Crosscoders.<a class="anchor" href="#comparing-saes-transcoders-and-crosscoders." aria-label="Link to this section">#</a></h2>
-<p>Single-layer SAEs are efficient and locally interpretable but miss cross-layer feature evolution. Transcoders improve this by imitating the behavior of the MLP, revealing nonlinear transformations, though they require sequential analysis across depth. Cross-layer transcoders span multiple layers for global information flow but are extremely expensive. Crosscoders take a different approach, extracting shared features across layers simultaneously to resolve cross-layer superposition and simplify circuits without chaining predictions.</p>
+<section id="comparing-saes-transcoders-and-crosscoders" class="level2"><h2 data-pdf-page="46"><span class="secnum">6.7</span> Comparing SAEs, Transcoders, and Crosscoders<a class="anchor" href="#comparing-saes-transcoders-and-crosscoders" aria-label="Link to this section">#</a></h2>
+<p>Single-layer SAEs are efficient and locally interpretable but miss cross-layer feature evolution. Transcoders improve this by imitating MLP behaviour and revealing nonlinear transformations, though they require sequential analysis across depth. Cross-layer transcoders span multiple layers for global information flow but are extremely expensive. Crosscoders take a different approach, extracting shared features across layers simultaneously to resolve cross-layer superposition and simplify circuits without chaining predictions.</p>
 </section>
-<section id="case-study-induction-heads" class="level2"><h2 data-pdf-page="31"><span class="secnum">6.6</span> Case Study: Induction Heads<a class="anchor" href="#case-study-induction-heads" aria-label="Link to this section">#</a></h2>
+<section id="subsec: induc head case study: feature extract" class="level2"><h2 data-pdf-page="47"><span class="secnum">6.8</span> Case Study: Induction Heads<a class="anchor" href="#subsec: induc head case study: feature extract" aria-label="Link to this section">#</a></h2>
+<p>Here, we examine induction heads <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, a mechanism found in many modern language models, as a case study. We first describe their conditional-copying function and role in in-context learning, and then relate them to feature-extraction methods such as SAEs and CLTs, which reveal how this computation can be distributed across broader feature circuits.</p>
 <div class="definitionbox">
-<p><span id="def:induction-heads" data-label="def:induction-heads"></span> <strong>Induction heads</strong> are attention heads that implement a conditional copying rule over repeated subsequences. Let <span class="math inline">(x_1,\dots,x_T)</span> be a token sequence and consider positions <span class="math inline">i &lt; j &lt; T</span> such that <span class="math inline">x_i = x_j</span>. An attention head at layer <span class="math inline">\ell</span> and head <span class="math inline">h</span> is an induction head if, when predicting the token at position <span class="math inline">j+1</span>, its attention weights satisfy <span class="math display">\alpha_{j \rightarrow i+1}^{(\ell,h)} \gg \alpha_{j \rightarrow k}^{(\ell,h)} \quad \text{for most } k \neq i+1,</span> and its value projection causes the residual stream at position <span class="math inline">j</span> to contribute logits favoring <span class="math inline">x_{i+1}</span>.</p>
-<p>Equivalently, induction heads approximate the function <span class="math display">(x_i = x_j) \;\Rightarrow\; \hat{x}_{j+1} \approx x_{i+1},</span> enabling in-context prediction by copying continuations following earlier matching prefixes. Induction heads were first identified and analyzed by <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022a)</a></span>.</p>
+<p><span id="def:induction-heads" data-label="def:induction-heads"></span></p>
+<p><em>Induction heads</em>, first identified and analysed by <span class="citation" data-cites="elhage2021mathematical"><a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al. (2021)</a></span>, are attention heads that implement a conditional copying rule over repeated subsequences. Let <span class="math inline">x=(x_1,\dots,x_T)</span> be a token sequence, and consider positions <span class="math inline">i&lt;j&lt;T</span> such that <span class="math inline">x_i=x_j</span>. An attention head indexed <span class="math inline">r</span> (see Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:transformers" data-reference-type="ref" data-reference="subsec:transformers">4.4</a> for an introduction to attention modules) is said to be an induction head when its attention pattern assigns comparatively high attention weight from position <span class="math inline">j</span> to position <span class="math inline">i+1</span> (i.e., high <span class="math inline">A^r_{j,i+1}</span>) and its residual-stream update <span class="math display">\Delta h^r
+=
+W_O^r V^r (A^r)^\top</span> increases the model’s logit for token <span class="math inline">x_{i+1}</span> when predicting the token at position <span class="math inline">j+1</span>.<br />
+That is, induction heads implement the rule <span class="math display">(x_i=x_j)
+\;\Rightarrow\;
+\hat{x}_{j+1}\approx x_{i+1}.</span> They help enable in-context prediction by copying the continuation that followed an earlier occurrence of the same token.</p>
 </div>
 <div class="examplebox">
-<p>Consider the sequence: <span class="math display">\texttt{A B C A B \underline{?}}</span> After observing the repeated prefix <code>A B</code>, an induction head attends from the second <code>B</code> to the position following the first <code>B</code>, producing a strong prediction for <code>C</code>. This copying behavior generalizes across tokens and positions and does not require explicit supervision.</p>
+<p>Consider the sequence <span class="math display">\texttt{A B C A B}</span> where the model needs to predict the token after the second <code>B</code>. Observing the repeated prefix <code>A B</code>, an induction head attends from the second <code>B</code> to the position after the first <code>B</code> and contributes to the model’s prediction of <code>C</code>. This induction behaviour should generalise across tokens and positions.</p>
 </div>
-<p>While induction heads were originally characterized as specific attention heads implementing this copying rule, subsequent work has shown that the underlying computation is more naturally understood at the level of distributed representations rather than isolated architectural components.</p>
+<p>Induction heads were originally characterised as specific attention heads implementing this copying rule. However, subsequent work shows that we can understand the underlying computation more naturally at the level of distributed representations than isolated model components.</p>
 <div class="examplebox">
-<p>Feature extraction methods, such as SAEs, can decompose attention layer outputs into sparse, interpretable features that capture meaningful activation patterns beyond individual attention weights <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. Modern analyses suggest that induction heads are not isolated mechanisms but participate in distributed feature circuits, where multiple interacting features recur and compose across layers (<span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature singh2024needsrightinductionhead"><a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>; <a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a></span>; <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs kamath2025tracing"><a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>; <a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a></span>).</p>
-<p>SAEs trained on model activations reveal families of features that correspond to recurring patterns and contextual dependencies in the residual stream, some of which align with behaviors traditionally attributed to induction heads <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. These features can be analyzed to understand how models maintain and manipulate information across tokens in context.</p>
-<p>Cross-layer analyses, including transcoders, indicate that interpretable features can persist across multiple layers, suggesting that certain computations are represented consistently rather than being rediscovered at each layer (<span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature"><a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a></span>; <span class="citation" data-cites="kamath2025tracing"><a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a></span>). This perspective highlights that attention heads, including those exhibiting induction-like behavior, may form part of broader, distributed feature compositions when examined through modern decomposition methods.</p>
-</div>
-</section>
-<section id="subsec:challenge" class="level2"><h2 data-pdf-page="32"><span class="secnum">6.7</span> Challenges and Failure Modes of SAEs<a class="anchor" href="#subsec:challenge" aria-label="Link to this section">#</a></h2>
-<p>Sparse Autoencoders have become a standard tool for feature extraction, but they exhibit systematic failure modes that can compromise interpretability claims. A careful understanding of these issues is therefore essential when using SAEs as mechanistic tools. This section provides a critical and systematic treatment of these challenges, including comparisons with alternative decomposition methods.</p>
-<section id="dead-features" class="level3"><h3 data-pdf-page="32"><span class="secnum">6.7.1</span> Dead Features<a class="anchor" href="#dead-features" aria-label="Link to this section">#</a></h3>
-<p>A <strong>dead feature</strong> is a dictionary element that never activates, or activates only extremely rarely, across the training distribution. Dead features represent wasted capacity and can constitute between 10–50% of the dictionary in poorly tuned SAEs <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. Several factors can lead to the emergence of dead features. First, inappropriate initialization can place features in low-density regions of activation space, preventing them from receiving meaningful gradient signals. Second, an excessively strong sparsity penalty <span class="math inline">\lambda</span> may suppress feature activations early in training, stopping features from discovering a useful role. Finally, competitive dynamics between features can exacerbate this issue: once a subset of features captures common patterns, the remaining features may be starved of signal and fail to activate.</p>
-<p>Dead features can be detected by tracking feature activation frequencies during training; features with activation rates below 0.01% are effectively inactive. Several mitigation strategies have been proposed, including neuron resampling, in which dead features are periodically reinitialized toward examples with high reconstruction error <span class="citation" data-cites="bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. Another effective approach is to reduce the sparsity penalty during early training via a warmup schedule and increase it gradually. More recent Batch Top-<span class="math inline">K</span> variants explicitly enforce minimum feature usage across batches, which substantially reduces the prevalence of dead features <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span>.</p>
-</section>
-<section id="feature-duplication" class="level3"><h3 data-pdf-page="33"><span class="secnum">6.7.2</span> Feature Duplication<a class="anchor" href="#feature-duplication" aria-label="Link to this section">#</a></h3>
-<p>Feature duplication occurs when multiple dictionary elements encode the same or highly overlapping concepts <span class="citation" data-cites="templeton2024scaling">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>. Although this increases the apparent dictionary size, it provides redundant rather than distinct features. Duplication may arise when sparsity pressure is insufficient, allowing multiple features to co-activate on similar inputs, from optimization dynamics favoring local minima with duplicated features, or from imbalances in the training data. Duplication can be detected using pairwise cosine similarity between decoder columns, where high similarity (e.g., &gt;0.8) indicates potential redundancy. Co-activation statistics provide complementary evidence, as duplicated features tend to activate together <span class="citation" data-cites="korznikov2025ortsaeorthogonalsparseautoencoders">(<a href="@@BASE@@references/#ref-korznikov2025ortsaeorthogonalsparseautoencoders" class="cite cite-emerging" data-cite="korznikov2025ortsaeorthogonalsparseautoencoders" role="doc-biblioref">Korznikov et al., 2025</a>)</span>.</p>
-</section>
-<section id="feature-absorption-and-splitting" class="level3"><h3 data-pdf-page="33"><span class="secnum">6.7.3</span> Feature Absorption and Splitting<a class="anchor" href="#feature-absorption-and-splitting" aria-label="Link to this section">#</a></h3>
-<p>Another class of failure modes concerns feature granularity. Feature absorption occurs when a single feature captures multiple distinct, but related, concepts, reducing representational specificity <span class="citation" data-cites="chanin2024absorption">(<a href="@@BASE@@references/#ref-chanin2024absorption" class="cite" data-cite="chanin2024absorption" role="doc-biblioref">Chanin et al., 2025</a>)</span>. For example, a single “code” feature may activate for Python, JavaScript, and SQL instead of learning language-specific representations. Conversely, feature splitting occurs when one coherent concept is unnecessarily divided across several features (e.g., “dog at sentence start” vs. “dog after article”). These issues can be identified through activation patterns. Absorbed features tend to activate frequently across diverse inputs, whereas split features often share similar semantic labels but differ in context and show high mutual information. Both degrade interpretability: absorbed features blur multiple computations, while split features distribute a single mechanism across several units.</p>
-</section>
-<section id="feature-instability-across-seeds" class="level3"><h3 data-pdf-page="33"><span class="secnum">6.7.4</span> Feature Instability Across Seeds<a class="anchor" href="#feature-instability-across-seeds" aria-label="Link to this section">#</a></h3>
-<p>SAE features are also sensitive to random initialization, optimizer stochasticity, and data ordering. Training the same architecture with different random seeds can therefore produce substantially different dictionaries <span class="citation" data-cites="paulo2025sparse">(<a href="@@BASE@@references/#ref-paulo2025sparse" class="cite" data-cite="paulo2025sparse" role="doc-biblioref">Paulo &amp; Belrose, 2025</a>)</span>. In practice, frequent and semantically clear features tend to be stable, while rare or context-specific features vary considerably. Exact feature alignment across seeds is often below 50%, although broader semantic clusters may remain consistent. This instability limits the reliability of mechanistic claims based on individual features. Ensembling across seeds can improve robustness but increases computational cost. For this reason, feature stability across seeds should be reported alongside reconstruction loss when evaluating SAEs.</p>
-</section>
-<section id="identifiability-and-theoretical-concerns" class="level3"><h3 data-pdf-page="33"><span class="secnum">6.7.5</span> Identifiability and Theoretical Concerns<a class="anchor" href="#identifiability-and-theoretical-concerns" aria-label="Link to this section">#</a></h3>
-<p>A fundamental question for SAE-based interpretability is whether the learned features are identifiable, that is, whether training reliably recovers the “true” underlying features (if such features exist) or merely finds one of many equally valid decompositions <span class="citation" data-cites="klindt2025superpositionsparsecodesinterpretable">(<a href="@@BASE@@references/#ref-klindt2025superpositionsparsecodesinterpretable" class="cite cite-emerging" data-cite="klindt2025superpositionsparsecodesinterpretable" role="doc-biblioref">Klindt et al., 2025</a>)</span>.</p>
-<div class="definitionbox">
-<p>Given activations <span class="math inline">h</span> generated by some unknown sparse process <span class="math inline">h = Df^* + \epsilon</span> where <span class="math inline">f^*</span> are ground-truth features, does minimizing the SAE objective recover <span class="math inline">f^*</span> (up to permutation and scaling)?</p>
+<p>Feature extraction methods, such as SAEs, can decompose attention layer outputs into sparse, interpretable features that capture meaningful activation patterns beyond individual attention weights <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. Modern analyses suggest that induction heads are not isolated mechanisms but participate in distributed feature circuits, where multiple interacting features recur and compose across layers (<span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature singh2024needsrightinductionhead"><a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>; <a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a></span>; <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs kamath2025tracing"><a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>; <a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a></span>).<br />
+SAEs trained on attention heads’ activations reveal feature families of recurring patterns and contextual dependencies. Some of them align with behaviours originally attributed to induction heads <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. By analysing these features, we can understand how models maintain and manipulate information across tokens and contexts.<br />
+Cross-layer analyses, including crosscoders, suggest that certain features are represented consistently across layers rather than being recomputed at each layer <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>. This perspective highlights that attention heads, including specialised ones such as induction heads, may be only part of broader, distributed feature representations.</p>
 </div>
-<p>In classical Dictionary Learning, identifiability is guaranteed under conditions of low coherence, meaning that dictionary elements are sufficiently orthogonal to one another. When coherence is high, however, ambiguity arises: multiple distinct sparse combinations of features from the same dictionary can generate the same data point <span class="citation" data-cites="garfinkle2016robust schnass2008dictionary">(<a href="@@BASE@@references/#ref-garfinkle2016robust" class="cite" data-cite="garfinkle2016robust" role="doc-biblioref">Garfinkle &amp; Hillar, 2016</a>; <a href="@@BASE@@references/#ref-schnass2008dictionary" class="cite" data-cite="schnass2008dictionary" role="doc-biblioref">Schnass &amp; Vandergheynst, 2008</a>)</span>. In the context of large language model activations, high coherence is likely unavoidable due to the strong internal structure and correlations within the model’s feature space <span class="citation" data-cites="li2025geometry">(<a href="@@BASE@@references/#ref-li2025geometry" class="cite" data-cite="li2025geometry" role="doc-biblioref">Li et al., 2025c</a>)</span>. This makes identifiability particularly challenging and provides a theoretical explanation for several empirical observations about Sparse Autoencoders (SAEs).</p>
-<p>Recent work has raised concerns about the reliability of the features learned by SAEs <span class="citation" data-cites="mueller2025isolationentanglementinterpretabilitymethods">(<a href="@@BASE@@references/#ref-mueller2025isolationentanglementinterpretabilitymethods" class="cite cite-emerging" data-cite="mueller2025isolationentanglementinterpretabilitymethods" role="doc-biblioref">Mueller et al., 2025b</a>)</span>. When trained on identical data but with different random seeds, SAEs tend to produce substantially different feature sets <span class="citation" data-cites="paulo2025sparse">(<a href="@@BASE@@references/#ref-paulo2025sparse" class="cite" data-cite="paulo2025sparse" role="doc-biblioref">Paulo &amp; Belrose, 2025</a>)</span>, suggesting a lack of uniqueness in the learned representations. Moreover, studies have shown that SAE features can often be reconstructed from smaller sets of more basic features using a meta-SAE (e.g., representing “Einstein” as a combination of “Germany,” “Starts with E,” “Space and Galaxies,” “Science,” and “Prominent Figures”), indicating that these features are not atomic <span class="citation" data-cites="leask2025sparse">(<a href="@@BASE@@references/#ref-leask2025sparse" class="cite" data-cite="leask2025sparse" role="doc-biblioref">Leask et al., 2025</a>)</span>. Finally, phenomena such as feature splitting and feature absorption further illustrate how SAEs may merge or decompose simpler features in order to optimize sparsity, rather than recover a unique underlying basis <span class="citation" data-cites="chanin2024absorption">(<a href="@@BASE@@references/#ref-chanin2024absorption" class="cite" data-cite="chanin2024absorption" role="doc-biblioref">Chanin et al., 2025</a>)</span>. Taken together, these behaviors are consistent with the failure of identifiability under high-coherence conditions and help explain why SAE features can be unstable, non-atomic, and seed-dependent.</p>
-<p>Recent progress in finetuning language models around a fixed sparse autoencoder offers a potential path forward. In this setting, model activations are explicitly constrained to be sparse combinations of decoder features while largely preserving task performance <span class="citation" data-cites="chen2025low">(<a href="@@BASE@@references/#ref-chen2025low" class="cite cite-emerging" data-cite="chen2025low" role="doc-biblioref">Chen et al., 2025</a>)</span>. In particular, recent work aimed at limiting superposition fine-tunes the language model jointly with the SAE while enforcing orthogonality constraints on the decoder matrix <span class="citation" data-cites="miller2026identifyingintervenableinterpretablefeatures">(<a href="@@BASE@@references/#ref-miller2026identifyingintervenableinterpretablefeatures" class="cite cite-emerging" data-cite="miller2026identifyingintervenableinterpretablefeatures" role="doc-biblioref">Miller et al., 2026</a>)</span>. By explicitly satisfying low-coherence conditions, this approach restores identifiability: there exists a unique representation of activations in terms of features, and these features can be reliably recovered.</p>
-<section id="relation-to-causal-relevance." class="level4">
-<h4>Relation to Causal Relevance.</h4>
-<p>Identifiability concerns connect directly to causal relevance: even if an SAE feature is interpretable and reconstructs activations well, it may not correspond to a causally meaningful unit of computation.</p>
-<p><strong>Diagnostic criteria:</strong></p>
-<ul>
-<li><p><em>Necessity</em>: Does ablating the feature degrade task performance? (Low necessity suggests the feature is not causally central.)</p></li>
-<li><p><em>Sufficiency</em>: Does activating only this feature (and related features) reproduce the behavior? (Low sufficiency suggests the feature captures correlation, not causation.)</p></li>
-<li><p><em>Specificity</em>: Does the feature’s causal effect match its semantic interpretation? (A “sentiment” feature that causally affects syntax but not sentiment is misidentified.)</p></li>
-</ul>
-<p>These causal tests should be standard practice when claiming that SAE features represent meaningful computational units.</p>
-</section>
-</section>
-<section id="practical-recommendations" class="level3"><h3 data-pdf-page="34"><span class="secnum">6.7.6</span> Practical Recommendations<a class="anchor" href="#practical-recommendations" aria-label="Link to this section">#</a></h3>
-<p>Given these challenges, we recommend the following practices for SAE-based interpretability:</p>
-<ol>
-<li><p><strong>Report failure mode metrics</strong>: Alongside reconstruction loss, report dead feature percentage, duplication rate (via cosine similarity), and stability across seeds.</p></li>
-<li><p><strong>Validate causal relevance</strong>: Do not assume interpretable features are causally meaningful. Test necessity and sufficiency through targeted interventions (e.g. feature ablation or activation steering) and measure downstream effects.</p></li>
-<li><p><strong>Compare methods</strong>: For safety-critical applications, compare SAE features against supervised baselines (DAS, probes) to assess whether SAEs recover the relevant structure.</p></li>
-<li><p><strong>Use multiple SAE variants</strong>: Different variants (Gated, Top-K, JumpReLU) impose different inductive biases. Convergent findings across variants provide stronger evidence.</p></li>
-<li><p><strong>Acknowledge identifiability limits</strong>: Frame SAE features as “one valid decomposition” rather than “the true features.” Multiple decompositions may be equally valid for reconstruction while differing in the set of features recovered.</p></li>
-<li><p><strong>Consider alternatives for targeted analysis</strong>: When specific concepts are the focus, supervised methods (e.g. probes) may be more reliable than hoping SAEs discover the relevant features.</p></li>
-</ol>
-</section>
-</section>
-<section id="comparison-with-alternative-feature-extraction-methods" class="level2"><h2 data-pdf-page="34"><span class="secnum">6.8</span> Comparison with Alternative Feature Extraction Methods<a class="anchor" href="#comparison-with-alternative-feature-extraction-methods" aria-label="Link to this section">#</a></h2>
-<p>Sparse Autoencoders are one approach for extracting features from neural network activations. Alternative methods drawn from signal processing and machine learning make different modeling assumptions and therefore offer distinct trade-offs in terms of interpretability, scalability, and robustness.</p>
-<section id="non-negative-matrix-factorization-nmf" class="level3"><h3 data-pdf-page="34"><span class="secnum">6.8.1</span> Non-Negative Matrix Factorization (NMF)<a class="anchor" href="#non-negative-matrix-factorization-nmf" aria-label="Link to this section">#</a></h3>
-<p>Non-Negative Matrix Factorization decomposes activations as <span class="math inline">H \approx WA</span>, where both the dictionary <span class="math inline">W</span> and the activation matrix <span class="math inline">A</span> are constrained to be non-negative. This constraint induces a natural parts-based interpretation in which features combine additively without cancellation. As a result, NMF often yields components that are easier to interpret than unconstrained linear decompositions. In addition, closed-form update rules exist for certain NMF objectives, which can mitigate some optimization pathologies. Under specific conditions such as separability, NMF also enjoys stronger uniqueness guarantees than unconstrained sparse coding <span class="citation" data-cites="NIPS2003_1843e35d">(<a href="@@BASE@@references/#ref-NIPS2003_1843e35d" class="cite" data-cite="NIPS2003_1843e35d" role="doc-biblioref">Donoho &amp; Stodden, 2003</a>)</span>.</p>
-<p>Despite these advantages, NMF is not always well suited to neural network activations. Activations produced by modern architectures, particularly after LayerNorm or nonlinearities such as GELU, are not naturally non-negative, requiring preprocessing steps that may distort representational structure. Moreover, NMF does not explicitly encourage sparsity unless additional regularization is introduced, and scaling NMF to large dictionaries is computationally challenging. Using an NMF inspired approach, <span class="citation" data-cites="yun-etal-2021-transformer"><a href="@@BASE@@references/#ref-yun-etal-2021-transformer" class="cite" data-cite="yun-etal-2021-transformer" role="doc-biblioref">Yun et al. (2021)</a></span> factorizes residual-stream activations with non-negativity constraints, producing additive components that are easier to interpret and visualize across layers. However, for feature extraction and causal analyses, empirical work has largely focused on Semi-NMF which relaxes the non-negativity constraint on <span class="math inline">W</span>. <span class="citation" data-cites="snmf"><a href="@@BASE@@references/#ref-snmf" class="cite cite-emerging" data-cite="snmf" role="doc-biblioref">Shafran et al. (2025)</a></span> showed that Semi-NMF can recover features competitive with SAEs on small-scale evaluations, but systematic comparisons at large scale remain limited.</p>
 </section>
-<section id="principle-component-analysis-pca" class="level3"><h3 data-pdf-page="35"><span class="secnum">6.8.2</span> Principle Component Analysis (PCA)<a class="anchor" href="#principle-component-analysis-pca" aria-label="Link to this section">#</a></h3>
-<p>Principal Component Analysis decomposes activations by finding an orthonormal basis of directions that maximise explained variance. Given a mean-centered activation matrix <span class="math inline">H \in \mathbb{R}^{n \times d}</span>, PCA yields a low-rank approximation <span class="math inline">H \approx U \Sigma V^T</span>, where the columns of <span class="math inline">V</span> correspond to principal directions ordered by explained variance. Fitting PCA is fast, stable and scalable. Yet, PCA introduces constraints and inductive biases that can be misaligned with neural representations. PCA identifies a single set of global directions that maximises variance across the full activation distribution, which can mix multiple sources of variation when the relevant structure is region or context-dependent. In addition, PCA enforces orthogonality between components, a constraint that may not reflect how features are organized in practice, where factors can be correlated or partially overlapping. Its misalignment is further reflected in recent benchmarks. In <span class="citation" data-cites="wu2025axbench"><a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al. (2025)</a></span>, PCA underperforms most baselines, including SAEs, on both steering and concept-detection evaluations. While in some localization settings, PCA outperform SAEs <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-emerging" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025a</a>)</span>, it still lags behind simpler supervised baselines.</p>
-</section>
-<section id="independent-component-analysis-ica" class="level3"><h3 data-pdf-page="35"><span class="secnum">6.8.3</span> Independent Component Analysis (ICA)<a class="anchor" href="#independent-component-analysis-ica" aria-label="Link to this section">#</a></h3>
-<p>Independent Component Analysis seeks to decompose activations as <span class="math inline">H = WS</span>, where the components of <span class="math inline">S</span> are statistically independent <span class="citation" data-cites="hyvarinen2000independent">(<a href="@@BASE@@references/#ref-hyvarinen2000independent" class="cite" data-cite="hyvarinen2000independent" role="doc-biblioref">Hyv<span>ä</span>rinen &amp; Oja, 2000</a>)</span>. Independence provides a principled objective grounded in information theory, and under well-specified generative assumptions, specifically, linear mixtures of independent sources, ICA can provably recover the underlying components. Unlike SAEs, ICA does not require tuning a sparsity hyperparameter.</p>
-<p>However, ICA makes strong assumptions that are often violated in neural representations. Classical ICA assumes a complete setting in which the number of sources equals the dimensionality of the representation, and extensions are required for overcomplete decompositions. Neural network features may also fail to satisfy statistical independence, limiting ICA’s applicability. Although ICA has been applied to neural network activations <span class="citation" data-cites="hyvarinen2023nonlinear">(<a href="@@BASE@@references/#ref-hyvarinen2023nonlinear" class="cite" data-cite="hyvarinen2023nonlinear" role="doc-biblioref">Hyv<span>ä</span>rinen et al., 2023</a>)</span>, it has not matched the interpretability gains reported for SAEs in recent large-scale studies, possibly because neural features tend to be sparse rather than independent.</p>
-</section>
-<section id="supervised-feature-extraction-methods" class="level3"><h3 data-pdf-page="35"><span class="secnum">6.8.4</span> Supervised Feature Extraction Methods<a class="anchor" href="#supervised-feature-extraction-methods" aria-label="Link to this section">#</a></h3>
-<p>When the concepts of interest are known in advance, supervised methods can directly optimize representations aligned with those targets. Examples include Distributed Alignment Search (DAS), which uses supervision to learn a change of basis that aligns representations with high-level features <span class="citation" data-cites="das">(<a href="@@BASE@@references/#ref-das" class="cite" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a>)</span>; Supervised Multi-Dimensional Scaling (SMDS), which projects activations to preserve concept-level similarities <span class="citation" data-cites="smds">(<a href="@@BASE@@references/#ref-smds" class="cite cite-emerging" data-cite="smds" role="doc-biblioref">Tiblias et al., 2025</a>)</span>; and Differential Binary Masking (DBM), which learns binary masks that select relevant activation dimensions for specific concepts <span class="citation" data-cites="dbm">(<a href="@@BASE@@references/#ref-dbm" class="cite" data-cite="dbm" role="doc-biblioref">De Cao et al., 2020</a>)</span>.</p>
-<p>When labelled data are available, supervised approaches often outperform SAEs on targeted concept recovery tasks (<span class="citation" data-cites="huang-etal-2024-ravel"><a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a></span>; <span class="citation" data-cites="chaudhary2024evaluating"><a href="@@BASE@@references/#ref-chaudhary2024evaluating" class="cite cite-emerging" data-cite="chaudhary2024evaluating" role="doc-biblioref">Chaudhary &amp; Geiger, 2024</a></span>). They also avoid the need to choose dictionary sizes or sparsity penalties, since complexity is determined by the concept set itself. However, these methods require labelled data for each concept and therefore cannot discover previously unknown features. They do not scale naturally to open-ended exploration across entire models, and each concept typically requires separate training. As a result, supervised methods are best suited to settings where specific, predefined concepts are of interest, such as safety-critical behaviors like toxicity or deception, whereas SAEs remain preferable for exploratory analysis.</p>
-</section>
-<section id="dictionary-learning-baselines" class="level3"><h3 data-pdf-page="36"><span class="secnum">6.8.5</span> Dictionary Learning Baselines<a class="anchor" href="#dictionary-learning-baselines" aria-label="Link to this section">#</a></h3>
-<p>Classical dictionary learning methods provide useful baselines for evaluating SAEs. Techniques such as K-SVD alternate between sparse coding and dictionary updates and are known to converge under certain conditions <span class="citation" data-cites="aharon2006k">(<a href="@@BASE@@references/#ref-aharon2006k" class="cite" data-cite="aharon2006k" role="doc-biblioref">Aharon et al., 2006</a>)</span>. Comparative studies suggest that the primary advantage of SAEs lies in their learned encoder, which enables fast inference, rather than in fundamentally superior dictionary quality <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2023</a>)</span>. As such, classical dictionary learning remains an important reference point for assessing the added value of SAE architectures.</p>
+<section id="comparison-with-alternative-feature-extraction-methods" class="level2"><h2 data-pdf-page="48"><span class="secnum">6.9</span> Comparison with Alternative Feature Extraction Methods<a class="anchor" href="#comparison-with-alternative-feature-extraction-methods" aria-label="Link to this section">#</a></h2>
+<p>Besides SAEs, alternative techniques from signal processing and machine learning have also been adapted to extract features from neural networks. They make different modelling assumptions and therefore offer distinct trade-offs in interpretability, scalability, and robustness. Table <a href="@@BASE@@handbook/feature-extraction/#tab:decomposition-comparison" data-reference-type="ref" data-reference="tab:decomposition-comparison">6</a> summarises the methods discussed in this section.</p>
 <table id="tab:decomposition-comparison">
-<caption><span class="capnum">Table 6:</span> Comparison of Activation Decomposition Methods</caption>
+<caption><span class="capnum">Table 6:</span> Comparison of feature extraction methods.</caption>
 <thead>
 <tr>
-<th style="text-align: left;"><strong>Method</strong></th>
-<th style="text-align: left;"><strong>Key Assumption</strong></th>
-<th style="text-align: left;"><strong>Strengths</strong></th>
-<th style="text-align: left;"><strong>Weaknesses</strong></th>
-<th style="text-align: left;"><strong>Best Use Case</strong></th>
+<th style="text-align: left;"><span style="color: white"><strong>Method</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Assumption</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Strengths</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Weaknesses</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Best Use Case</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">SAE</td>
+<td style="text-align: left;"><strong>SAE</strong></td>
 <td style="text-align: left;">Sparse, linear features</td>
-<td style="text-align: left;">Scalable; open-ended discovery; fast inference</td>
-<td style="text-align: left;">Hyperparameter sensitive; identifiability issues</td>
-<td style="text-align: left;">Exploratory feature discovery</td>
+<td style="text-align: left;">Open-ended discovery; fast inference</td>
+<td style="text-align: left;">Hyperparameter-sensitive; identifiability issues</td>
+<td style="text-align: left;">Open-ended feature discovery</td>
 </tr>
 <tr>
-<td style="text-align: left;">NMF</td>
+<td style="text-align: left;"><strong>NMF</strong></td>
 <td style="text-align: left;">Non-negative, additive parts</td>
 <td style="text-align: left;">Parts-based interpretation; uniqueness guarantees</td>
 <td style="text-align: left;">Requires non-negative data; limited sparsity</td>
 <td style="text-align: left;">Naturally non-negative domains</td>
 </tr>
 <tr>
-<td style="text-align: left;">PCA</td>
+<td style="text-align: left;"><strong>PCA</strong></td>
 <td style="text-align: left;">Variance aligns with features</td>
-<td style="text-align: left;">Closed form; standard baseline; optimal low-rank reconstruction</td>
-<td style="text-align: left;">Explains global variance; orthogonal features</td>
-<td style="text-align: left;">Small settings and baseline comparison</td>
+<td style="text-align: left;">Closed form; optimal low-rank reconstruction</td>
+<td style="text-align: left;">Explains global variance and produces orthogonal features</td>
+<td style="text-align: left;">Small-scale settings; as a baseline method</td>
 </tr>
 <tr>
-<td style="text-align: left;">ICA</td>
-<td style="text-align: left;">Statistically independent sources</td>
-<td style="text-align: left;">Principled criterion; no sparsity tuning</td>
+<td style="text-align: left;"><strong>ICA</strong></td>
+<td style="text-align: left;">Complete and independent sources</td>
+<td style="text-align: left;">Theoretical guarantees when assumptions hold</td>
 <td style="text-align: left;">Assumes independence; Gaussian blindness</td>
 <td style="text-align: left;">When independence holds</td>
 </tr>
 <tr>
-<td style="text-align: left;">Supervised (DAS, DBM)</td>
+<td style="text-align: left;"><strong>Supervised methods</strong></td>
 <td style="text-align: left;">Known concept labels</td>
-<td style="text-align: left;">High accuracy on targeted concepts</td>
+<td style="text-align: left;">High performance on targeted concepts</td>
 <td style="text-align: left;">Requires labels; no discovery</td>
-<td style="text-align: left;">Safety-critical concept detection</td>
+<td style="text-align: left;">When certain concepts of interest are known</td>
 </tr>
 <tr>
-<td style="text-align: left;">K-SVD / ODL</td>
-<td style="text-align: left;">Sparse representation</td>
-<td style="text-align: left;">Theoretical guarantees; no encoder bias</td>
+<td style="text-align: left;"><strong>K-SVD</strong></td>
+<td style="text-align: left;">Sparse, linear features</td>
+<td style="text-align: left;">Explicitly solving the sparse-coding problem; no encoder bias</td>
 <td style="text-align: left;">Slow inference; no learned encoder</td>
-<td style="text-align: left;">Baseline comparison</td>
+<td style="text-align: left;">Small, fixed sets of activations; as a baseline method</td>
 </tr>
 </tbody>
 </table>
-</section>
-</section>
-<section id="sec:architectural-interventions" class="level2"><h2 data-pdf-page="36"><span class="secnum">6.9</span> Architectural Interventions for Intrinsic Interpretability<a class="anchor" href="#sec:architectural-interventions" aria-label="Link to this section">#</a></h2>
-<p>The methods surveyed thus far, SAEs, Transcoders, Crosscoders, share a common assumption: the model architecture is fixed, and interpretability must be recovered post-hoc. A parallel research program, which we term <em>Architectural Interventions for Interpretability</em>, inverts this assumption by asking whether models can be designed to be mechanistically interpretable by construction (<span class="citation" data-cites="pearce2025bilinear park2025monet oldfield2024multilinear yang2025mixture dooms2025compositionality"><a href="@@BASE@@references/#ref-pearce2025bilinear" class="cite cite-established" data-cite="pearce2025bilinear" role="doc-biblioref">Pearce et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a>; <a href="@@BASE@@references/#ref-oldfield2024multilinear" class="cite cite-established" data-cite="oldfield2024multilinear" role="doc-biblioref">Oldfield et al., 2024</a>; <a href="@@BASE@@references/#ref-yang2025mixture" class="cite cite-established" data-cite="yang2025mixture" role="doc-biblioref">Yang et al., 2025</a>; <a href="@@BASE@@references/#ref-dooms2025compositionality" class="cite cite-established" data-cite="dooms2025compositionality" role="doc-biblioref">Dooms et al., 2025</a></span>; <span class="citation" data-cites="gao2025weight adler2025towards draye2025spartan"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>; <a href="@@BASE@@references/#ref-adler2025towards" class="cite cite-emerging" data-cite="adler2025towards" role="doc-biblioref">Adler et al., 2025</a>; <a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al., 2025</a></span>).</p>
-<p>This approach is motivated by the inherent limitations of post-hoc decomposition. Standard Transformer architectures are optimized without interpretability constraints, incentivizing superposition as an efficient compression strategy. Post-hoc methods like SAEs attempt to undo this compression, but they introduce reconstruction gaps, require substantial compute, and may fail to capture the full causal dynamics of the original model. Architectural interventions aim to solve the interpretability problem upstream, creating systems where circuits are visible by design rather than laboriously extracted.</p>
-<section id="weight-sparsity-via-l_0-regularization" class="level3"><h3 data-pdf-page="36"><span class="secnum">6.9.1</span> Weight Sparsity via <span class="math inline">L_0</span> Regularization<a class="anchor" href="#weight-sparsity-via-l_0-regularization" aria-label="Link to this section">#</a></h3>
-<p>The most significant recent advancement is the <em>Weight-Sparse Transformer</em>, introduced by Gao et al. <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>. This approach treats sparsity not as a tool for inference efficiency but as a tool for circuit discovery.</p>
-<section id="mechanism." class="level4">
-<h4>Mechanism.</h4>
-<p>The model is trained to minimize a composite objective: <span id="eq-2"></span> <span class="math display">\begin{equation*}
-    \mathcal{L} = \mathcal{L}_{\text{task}} + \lambda \cdot \|\theta\|_0
-\tag{2}
-\end{equation*}</span> where <span class="math inline">\mathcal{L}_{\text{task}}</span> is the standard cross-entropy loss and <span class="math inline">\|\theta\|_0</span> counts non-zero parameters. Since the <span class="math inline">L_0</span> norm is non-differentiable, training employs stochastic gates drawn from a Hard-Concrete distribution, allowing gradients to flow while penalizing the probability of each gate being open. At inference, gates are discretized, yielding networks where the vast majority of connections are exactly zero.</p>
-</section>
-<section id="interpretability-gains." class="level4">
-<h4>Interpretability gains.</h4>
-<p>Under high sparsity regimes (e.g., <span class="math inline">&lt;1\%</span> active weights), the computational graph simplifies dramatically. A neuron receiving input from only 5–10 others (rather than thousands) cannot implement the complex interference patterns required for polysemanticity. Consequently, neurons tend to align with single, interpretable concepts—monosemanticity emerges by structural necessity rather than post-hoc extraction.</p>
-<p>Circuits can be identified by simply visualizing non-zero weights connected to a target output, eliminating the need for activation patching or ACDC. Validation via mean ablation shows that these sparse circuits satisfy necessity and sufficiency criteria with higher precision than circuits identified in dense models.</p>
-</section>
-<section id="the-capability-interpretability-frontier." class="level4">
-<h4>The capability-interpretability frontier.</h4>
-<p>A critical contribution is formalizing the trade-off between performance and interpretability. As the sparsity penalty <span class="math inline">\lambda</span> increases, interpretability improves but task loss degrades, defining a Pareto frontier. Crucially, <span class="citation" data-cites="gao2025weight"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al. (2025b)</a></span> discover a <em>scaling law</em>: increasing model size shifts this frontier outward. Large sparse models can achieve both better capability and better interpretability than small dense models, suggesting that over-parameterization coupled with sparsity constraints may be the path toward interpretable frontier models.</p>
-</section>
-</section>
-<section id="sparse-attention-post-training-spartan" class="level3"><h3 data-pdf-page="37"><span class="secnum">6.9.2</span> Sparse Attention Post-Training: SPARTAN<a class="anchor" href="#sparse-attention-post-training-spartan" aria-label="Link to this section">#</a></h3>
-<p>The dense attention matrix implies every token attends to every other token, creating a quadratic web of connections difficult to audit. Sparse Attention Post-Training (SPARTAN) applies <span class="math inline">L_1</span> regularization to the attention patterns while finetuning with a constrained optimization objective to maintain the same cross-entropy performance  <span class="citation" data-cites="draye2025spartan">(<a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al., 2025</a>)</span>  <span class="citation" data-cites="lei2025spartan">(<a href="@@BASE@@references/#ref-lei2025spartan" class="cite" data-cite="lei2025spartan" role="doc-biblioref">Lei et al., 2025</a>)</span>.</p>
-<p>The result: diffuse “soft” attention patterns collapse into “hard” sparse patterns. A head that previously attended broadly across context snaps to attending only to specific operands required for the current computation. This reveals that much attention connectivity is redundant noise, and SPARTAN effectively performs circuit discovery by pruning the attention graph. Using Cross-Layer Transcoders, they show that the reduced number of mediating components renders attention attribution tractable. This illustrates the potential of sparse attention in the context of attribution graphs, as it enables a unified view of features and circuits.</p>
-</section>
-<section id="activation-level-interventions-solu-and-its-limitations" class="level3"><h3 data-pdf-page="37"><span class="secnum">6.9.3</span> Activation-Level Interventions: SoLU and Its Limitations<a class="anchor" href="#activation-level-interventions-solu-and-its-limitations" aria-label="Link to this section">#</a></h3>
-<p>An earlier approach targeted the activation function rather than connectivity. Softmax Linear Units (SoLU) replace element-wise activations with a spatial softmax <span class="citation" data-cites="elhage2022solu">(<a href="@@BASE@@references/#ref-elhage2022solu" class="cite cite-emerging" data-cite="elhage2022solu" role="doc-biblioref">Elhage et al., 2022b</a>)</span>: <span id="eq-3"></span> <span class="math display">\begin{equation*}
-    x \mapsto x \cdot \text{softmax}(x)
+<section id="non-negative-matrix-factorisation-nmf" class="level3"><h3 data-pdf-page="48"><span class="secnum">6.9.1</span> Non-Negative Matrix Factorisation (NMF)<a class="anchor" href="#non-negative-matrix-factorisation-nmf" aria-label="Link to this section">#</a></h3>
+<p>Non-negative matrix factorisation (NMF; <span class="citation" data-cites="lee1999learning"><a href="@@BASE@@references/#ref-lee1999learning" class="cite cite-established" data-cite="lee1999learning" role="doc-biblioref">Lee &amp; Seung, 1999</a></span>) decomposes a non-negative activation matrix <span class="math inline">H \in \mathbb{R}^{d \times n}</span> as <span class="math inline">H \approx WA</span>, where both the feature dictionary <span class="math inline">W \in \mathbb{R}^{d \times m}</span> (<span class="math inline">m</span> is the preset number of features) and the coefficient matrix <span class="math inline">A \in \mathbb{R}^{m \times n}</span> are constrained to have non-negative elements. Because all components are non-negative, each feature contributes positively to the final representation, making it easier to interpret. Additionally, classical NMF’s optimisation process uses simple multiplicative update rules that preserve non-negativity. Moreover, under assumptions like <em>separability</em> (each latent feature appears in at least one sufficiently “pure” example), NMF guarantees that the recovered decomposition is unique <span class="citation" data-cites="NIPS2003_1843e35d">(<a href="@@BASE@@references/#ref-NIPS2003_1843e35d" class="cite cite-established" data-cite="NIPS2003_1843e35d" role="doc-biblioref">Donoho &amp; Stodden, 2003</a>)</span>.</p>
+<p>However, classical NMF is not suitable for neural network activations because they can have negative elements, requiring additional preprocessing steps that may distort the activation-space structure. Moreover, NMF does not explicitly encourage feature sparsity, and scaling NMF to large dictionaries is computationally expensive. As a result, empirical work on extracting and analysing features in neural network activations has largely focused on Semi-NMF <span class="citation" data-cites="snmf">(<a href="@@BASE@@references/#ref-snmf" class="cite cite-established" data-cite="snmf" role="doc-biblioref">Ding et al., 2010</a>)</span>, which relaxes the non-negativity constraint on <span class="math inline">W</span>. <span class="citation" data-cites="shafran-etal-2026-constructing"><a href="@@BASE@@references/#ref-shafran-etal-2026-constructing" class="cite cite-established" data-cite="shafran-etal-2026-constructing" role="doc-biblioref">Shafran et al. (2026)</a></span> showed that Semi-NMF can recover features competitive with SAEs on small-scale evaluations, but systematic comparisons at large scale remain limited. <span class="citation" data-cites="yun-etal-2021-transformer"><a href="@@BASE@@references/#ref-yun-etal-2021-transformer" class="cite cite-emerging" data-cite="yun-etal-2021-transformer" role="doc-biblioref">Yun et al. (2021)</a></span> used a Semi-NMF-like approach to factorise BERT’s contextualised hidden-state representations <span class="citation" data-cites="devlin2019bert">(<a href="@@BASE@@references/#ref-devlin2019bert" class="cite cite-established" data-cite="devlin2019bert" role="doc-biblioref">Devlin et al., 2019</a>)</span>, allowing elements in <span class="math inline">W</span> to take both positive and negative values while constraining <span class="math inline">A</span> to be non-negative.</p>
+</section>
+<section id="principal-component-analysis-pca" class="level3"><h3 data-pdf-page="49"><span class="secnum">6.9.2</span> Principal Component Analysis (PCA)<a class="anchor" href="#principal-component-analysis-pca" aria-label="Link to this section">#</a></h3>
+<p>Principal Component Analysis (PCA; <span class="citation" data-cites="pearson1901lines hotelling1933analysis"><a href="@@BASE@@references/#ref-pearson1901lines" class="cite cite-established" data-cite="pearson1901lines" role="doc-biblioref">Pearson, 1901</a>; <a href="@@BASE@@references/#ref-hotelling1933analysis" class="cite cite-established" data-cite="hotelling1933analysis" role="doc-biblioref">Hotelling, 1933</a></span>) decomposes activations by finding an orthonormal basis of directions that maximise explained variance. Given a mean-centred activation matrix <span class="math inline">H \in \mathbb{R}^{d \times n}</span>, where each column is an activation vector, PCA computes a low-rank approximation <span class="math inline">H \approx U \Sigma V^\top</span>, where the columns of <span class="math inline">U</span> are the principal directions ordered by explained variance. PCA is fast, stable, and scalable. However, PCA imposes constraints and inductive biases that may misalign with neural network activations. Specifically, it identifies principal directions that maximise explained variance in the activation distribution, but much of the information of interest is region- or context-dependent and cannot be captured by variance alone. Moreover, PCA constrains the principal directions to be orthogonal, but in practice the underlying features correlate or partially overlap. Recent benchmarks <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span> reflect this misalignment: PCA underperformed most baselines, including SAEs, on both steering and concept-detection evaluations. While PCA can outperform SAEs in some localisation settings <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span>, it still generally lags behind simple supervised baselines.</p>
+</section>
+<section id="independent-component-analysis-ica" class="level3"><h3 data-pdf-page="49"><span class="secnum">6.9.3</span> Independent Component Analysis (ICA)<a class="anchor" href="#independent-component-analysis-ica" aria-label="Link to this section">#</a></h3>
+<p>Independent Component Analysis (ICA; <span class="citation" data-cites="hyvarinen2000independent"><a href="@@BASE@@references/#ref-hyvarinen2000independent" class="cite cite-established" data-cite="hyvarinen2000independent" role="doc-biblioref">Hyv<span>ä</span>rinen &amp; Oja, 2000</a></span>) decomposes activations <span class="math inline">H \in \mathbb{R}^{d \times n}</span> as <span class="math inline">H \approx WS</span>, where <span class="math inline">n</span> is the number of data points. Note that ICA may look similar to NMF at first glance. However, they were developed for different use cases and thus rely on different assumptions and optimisation procedures. In ICA, each column in <span class="math inline">W \in \mathbb{R}^{d \times m}</span> is a mixing vector, and <span class="math inline">m</span> is the number of different source signals; each row in <span class="math inline">S \in \mathbb{R}^{m \times n}</span> is a source signal (a signal has <span class="math inline">n</span> data points), with <span class="math inline">m</span> source signals in total. These signals are optimised to be independent of one another. Thus, if model activations are linear mixtures of statistically independent “foundational features”, ICA can provably recover those features.</p>
+<p>However, these assumptions may not hold for neural network activations. Specifically, classical ICA typically expects the completeness property (number of latent sources equals the dimensionality of the observed representation), and overcomplete decompositions (which are what we expect in activations) would require additional extensions <span class="citation" data-cites="lewicki2000learning">(<a href="@@BASE@@references/#ref-lewicki2000learning" class="cite cite-established" data-cite="lewicki2000learning" role="doc-biblioref">Lewicki &amp; Sejnowski, 2000</a>)</span>. Moreover, underlying features may not be statistically independent, which further limits the applicability of classical ICA. Although early studies applied it to analyse neural network activations, they did not find interpretability gains over SAEs <span class="citation" data-cites="cunningham2023sparse">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a>)</span>. This may partly reflect the challenges discussed above.</p>
+<p>More recently, however, <span class="citation" data-cites="liu2026icalensinterpretinglanguage"><a href="@@BASE@@references/#ref-liu2026icalensinterpretinglanguage" class="cite cite-emerging" data-cite="liu2026icalensinterpretinglanguage" role="doc-biblioref">Liu &amp; Han (2026)</a></span> showed that ICA can achieve performance comparable to that of SAEs, arguing that its previously reported underperformance may stem from limitations of classical ICA implementations poorly suited to the scale and statistical structure of neural network activations. These findings leave open whether ICA should be regarded primarily as a simple classical baseline or as an approach with underestimated, substantial headroom for further development.</p>
+</section>
+<section id="subsubsec: supervised_extraction" class="level3"><h3 data-pdf-page="49"><span class="secnum">6.9.4</span> Supervised Feature Extraction Methods<a class="anchor" href="#subsubsec: supervised_extraction" aria-label="Link to this section">#</a></h3>
+<p>When the concepts of interest are known in advance, supervised methods can directly optimise representations aligned with those targets. Examples include distributed alignment search (DAS), which uses supervision to learn a change of basis that aligns representations with high-level causal variables <span class="citation" data-cites="das">(<a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a>)</span>; Supervised Multi-Dimensional Scaling (SMDS), which projects activations to preserve concept-level similarities <span class="citation" data-cites="smds">(<a href="@@BASE@@references/#ref-smds" class="cite cite-established" data-cite="smds" role="doc-biblioref">Tiblias et al., 2026</a>)</span>; and probes, which fit a concept direction in contrastive class-conditioned activations <span class="citation" data-cites="marks2024the">(<a href="@@BASE@@references/#ref-marks2024the" class="cite cite-established" data-cite="marks2024the" role="doc-biblioref">Marks &amp; Tegmark, 2024</a>)</span>.</p>
+<p>Notably, despite its simplicity, probing has outperformed SAEs in concept detection <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>. It has also become a standard tool for identifying safety-relevant concepts in model representations, including refusal behaviour and implanted backdoors (<span class="citation" data-cites="arditi2024refusallanguagemodelsmediated"><a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a></span>; <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a></span>). We discuss probing in detail in Section <a href="@@BASE@@handbook/intermediate-representations/#subsec:probing" data-reference-type="ref" data-reference="subsec:probing">8.6</a>. We put it under Section <a href="@@BASE@@handbook/intermediate-representations/#sec:intermediate-computation" data-reference-type="ref" data-reference="sec:intermediate-computation">8</a> because we view its central methodological contributions and technical challenges as the readout of human-interpretable information—often expressed in natural language—from model activations, rather than the extraction of monosemantic features or causal variables.</p>
+<p>When labelled data is available, supervised approaches often outperform SAEs on concept detection and steering tasks (<span class="citation" data-cites="huang-etal-2024-ravel wu2025axbench"><a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>; <a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a></span>; <span class="citation" data-cites="chaudhary2024evaluating"><a href="@@BASE@@references/#ref-chaudhary2024evaluating" class="cite cite-emerging" data-cite="chaudhary2024evaluating" role="doc-biblioref">Chaudhary &amp; Geiger, 2024</a></span>). They also avoid choosing dictionary sizes or sparsity penalties, since the concept set determines complexity. However, these methods require labelled data for each concept and cannot discover previously unknown features. Therefore, supervised methods suit settings where predefined, crisp concepts matter, whereas SAEs remain preferable for exploratory analysis.</p>
+</section>
+<section id="dictionary-learning-baselines" class="level3"><h3 data-pdf-page="50"><span class="secnum">6.9.5</span> Dictionary Learning Baselines<a class="anchor" href="#dictionary-learning-baselines" aria-label="Link to this section">#</a></h3>
+<p>Classical dictionary learning methods provide useful baselines for evaluating SAEs. Techniques such as K-SVD alternate between sparse coding and dictionary updates and converge under certain conditions <span class="citation" data-cites="aharon2006k">(<a href="@@BASE@@references/#ref-aharon2006k" class="cite cite-established" data-cite="aharon2006k" role="doc-biblioref">Aharon et al., 2006</a>)</span>. Comparative studies suggest that SAEs’ primary advantage is fast inference because the encoder learns to decompose all activations, while potentially sacrificing optimal sparsity <span class="citation" data-cites="oneill2025compute">(<a href="@@BASE@@references/#ref-oneill2025compute" class="cite cite-established" data-cite="oneill2025compute" role="doc-biblioref">O’Neill et al., 2025</a>)</span>. As such, classical dictionary learning can still achieve superior dictionary quality in some cases and thus remains an important reference point for assessing the added value of SAEs.</p>
+</section>
+</section>
+<section id="sec:architectural-interventions" class="level2"><h2 data-pdf-page="50"><span class="secnum">6.10</span> Interpretability-by-Design Methods for Intrinsic Interpretability<a class="anchor" href="#sec:architectural-interventions" aria-label="Link to this section">#</a></h2>
+<p>The methods surveyed thus far in this section aim to extract and analyse more monosemantic units from polysemantic model activations. They share a common premise: the model’s original representations are too entangled to be directly interpretable, so interpretability is applied post hoc to recover simpler units of analysis. A parallel research programme, which we term <em>Interpretability-by-Design Methods for Intrinsic Interpretability</em>, inverts this premise by asking whether deep neural models can instead be designed to be mechanistically interpretable by construction (<span class="citation" data-cites="pearce2025bilinear park2025monet oldfield2024multilinear yang2025mixture gao2025weight"><a href="@@BASE@@references/#ref-pearce2025bilinear" class="cite cite-established" data-cite="pearce2025bilinear" role="doc-biblioref">Pearce et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a>; <a href="@@BASE@@references/#ref-oldfield2024multilinear" class="cite cite-established" data-cite="oldfield2024multilinear" role="doc-biblioref">Oldfield et al., 2024</a>; <a href="@@BASE@@references/#ref-yang2025mixture" class="cite cite-established" data-cite="yang2025mixture" role="doc-biblioref">Yang et al., 2025</a>; <a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2026</a></span>; <span class="citation" data-cites="adler2025towards draye2025spartan dooms2025compositionality"><a href="@@BASE@@references/#ref-adler2025towards" class="cite cite-emerging" data-cite="adler2025towards" role="doc-biblioref">Adler et al., 2025</a>; <a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al., 2026</a>; <a href="@@BASE@@references/#ref-dooms2025compositionality" class="cite cite-emerging" data-cite="dooms2025compositionality" role="doc-biblioref">Dooms et al., 2025</a></span>). This subsection extends the discussion of this research direction.</p>
+<p>Next, we introduce five approaches for constructing models that are more interpretable by design. Three impose model sparsity constraints at different levels: feature-sparse training (Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: solu" data-reference-type="ref" data-reference="subsubsec: solu">6.10.1</a>), weight-sparse training (Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: weight sparse transformer" data-reference-type="ref" data-reference="subsubsec: weight sparse transformer">6.10.2</a>), and attention-sparse training (Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: spartan" data-reference-type="ref" data-reference="subsubsec: spartan">6.10.3</a>). The remaining two approaches instead modify the computational primitives themselves, using more interpretable operators as units of construction and analysis (Sections <a href="@@BASE@@handbook/feature-extraction/#subsubsec: molt" data-reference-type="ref" data-reference="subsubsec: molt">6.10.4</a> and <a href="@@BASE@@handbook/feature-extraction/#subsubsec: kan" data-reference-type="ref" data-reference="subsubsec: kan">6.10.5</a>).</p>
+<p>Interpretability should be understood as a spectrum rather than a binary property. At least in their current form, these techniques do not make model computation completely or immediately understandable. Instead, they make models easier to analyse and ensure the analysis pipeline preserves more information about the original computation than post-hoc approximations. Table <a href="@@BASE@@handbook/feature-extraction/#tab:architectural-methods" data-reference-type="ref" data-reference="tab:architectural-methods">7</a> summarises these approaches and their trade-offs.</p>
+<section id="subsubsec: solu" class="level3"><h3 data-pdf-page="50"><span class="secnum">6.10.1</span> Feature-Sparse Training: SoLU<a class="anchor" href="#subsubsec: solu" aria-label="Link to this section">#</a></h3>
+<p>Softmax linear units (SoLU; <span class="citation" data-cites="elhage2022solu"><a href="@@BASE@@references/#ref-elhage2022solu" class="cite cite-emerging" data-cite="elhage2022solu" role="doc-biblioref">Elhage et al., 2022b</a></span>) are an earlier attempt at architectural intervention. SoLU is a specialised activation function that applies a softmax-based transformation across the layer’s <span class="math inline">d_{\mathrm{mlp}}</span> neurons. Specifically, given the pre-activation vector <span class="math display">u = W_1 h + b_1 \in \mathbb{R}^{d_{\mathrm{mlp}}},</span> SoLU calculates the activation values as <span id="eq-3"></span> <span class="math display">\begin{equation*}
+    \sigma_{\text{SoLU}}(u)
+    =
+    u \odot \mathrm{softmax}(u).
 \tag{3}
-\end{equation*}</span> This forces neurons within a layer to compete: if one neuron fires strongly, it suppresses its neighbors through lateral inhibition.</p>
-<section id="results-and-the-hydraulic-effect." class="level4">
-<h4>Results and the hydraulic effect.</h4>
-<p>SoLU successfully increased the fraction of monosemantic neurons identifiable by human inspection. However, it revealed a critical limitation: the <em>hydraulic effect</em>. When prevented from using neurons for superposition, models simply displaced the compression elsewhere—into the residual stream, into sub-threshold activations, or into other components not subject to the constraint. This indicates that local architectural constraints are insufficient when global incentives for compression remain. It underscores the need for either global constraints (like weight sparsity applied throughout the network) or fundamentally different computational primitives.</p>
+\end{equation*}</span> This softmax-based transformation induces competition among neurons. When one neuron receives a large pre-activation value, the softmax suppresses the others’ values, analogous to lateral inhibition. This competition encourages certain neurons to specialise and dominate, increasing the prevalence of monosemantic neurons associated with human-interpretable concepts.</p>
+<p>However, it also reveals an undesirable phenomenon: although many secondary neurons’ activations are suppressed to low magnitudes, these neurons remain polysemantic, with many features compressed into them. Later layer normalisation <span class="citation" data-cites="ba2016layernormalization">(<a href="@@BASE@@references/#ref-ba2016layernormalization" class="cite cite-emerging" data-cite="ba2016layernormalization" role="doc-biblioref">Ba et al., 2016</a>)</span> can amplify them and recover the polysemanticity property <span class="citation" data-cites="elhage2022solu">(<a href="@@BASE@@references/#ref-elhage2022solu" class="cite cite-emerging" data-cite="elhage2022solu" role="doc-biblioref">Elhage et al., 2022b</a>)</span>. The “smuggling” by layer normalisation suggests that imposing local interpretability constraints may not eliminate neural networks’ incentive to compress information through superposition. This issue motivates global constraints across the neural network, such as weight-sparsity training discussed below.</p>
 </section>
-</section>
-<section id="computational-primitive-modifications-molt" class="level3"><h3 data-pdf-page="37"><span class="secnum">6.9.4</span> Computational Primitive Modifications: MOLT<a class="anchor" href="#computational-primitive-modifications-molt" aria-label="Link to this section">#</a></h3>
-<p>One alternative approach to learning interpretable structure is to rethink what atomic computational units to learn (<span class="citation" data-cites="oldfield2025towards park2025monet"><a href="@@BASE@@references/#ref-oldfield2025towards" class="cite cite-established" data-cite="oldfield2025towards" role="doc-biblioref">Oldfield et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a></span>; <span class="citation" data-cites="braun2025interpretability bushnaq2025stochastic"><a href="@@BASE@@references/#ref-braun2025interpretability" class="cite cite-emerging" data-cite="braun2025interpretability" role="doc-biblioref">Braun et al., 2025</a>; <a href="@@BASE@@references/#ref-bushnaq2025stochastic" class="cite cite-emerging" data-cite="bushnaq2025stochastic" role="doc-biblioref">Bushnaq et al., 2025</a></span>). One recent example of this is the so-called Sparse Mixtures of Linear Transforms (MOLT), representing a conceptual shift from feature extraction to transformation extraction <span class="citation" data-cites="anthropic2025molt">(<a href="@@BASE@@references/#ref-anthropic2025molt" class="cite cite-emerging" data-cite="anthropic2025molt" role="doc-biblioref">Lindsey et al., 2025b</a>)</span>.</p>
-<section id="motivation." class="level4">
-<h4>Motivation.</h4>
-<p>Standard SAEs and Transcoders assume computation consists of detecting features. However, for algorithmic tasks (arithmetic, geometric reasoning), models perform <em>operations</em>—“add 3,” “rotate vector.” An SAE attempts to represent these as massive lookup tables of states, which obscures the underlying mechanism.</p>
-</section>
-<section id="mechanism.-1" class="level4">
-<h4>Mechanism.</h4>
-<p>MOLT replaces the MLP with a sparse mixture of linear transformation matrices. Each “neuron” is not a vector but a matrix that can be dynamically applied to the residual stream. For modular arithmetic, a MOLT might learn a single rotation matrix implementing addition, rather than memorizing thousands of input-output pairs.</p>
-<p>This aligns the architecture with the true algorithmic structure of the task, yielding higher mechanistic faithfulness than standard feature-based decompositions for tasks involving systematic transformations.</p>
-</section>
-</section>
-<section id="kolmogorov-arnold-networks" class="level3"><h3 data-pdf-page="38"><span class="secnum">6.9.5</span> Kolmogorov-Arnold Networks<a class="anchor" href="#kolmogorov-arnold-networks" aria-label="Link to this section">#</a></h3>
-<p>Kolmogorov-Arnold Networks (KANs) represent the most radical departure from standard architectures <span class="citation" data-cites="liu2025kan">(<a href="@@BASE@@references/#ref-liu2025kan" class="cite" data-cite="liu2025kan" role="doc-biblioref">Liu et al., 2025b</a>)</span>. Based on the Kolmogorov-Arnold representation theorem, KANs place learnable activation functions (B-splines) on edges rather than fixed activations on nodes.</p>
-<p>This inversion makes KANs explicitly compositional. Because non-linearities are univariate and visible on edges, researchers can perform symbolic regression on learned splines to recover exact mathematical formulas—for instance, rediscovering physics equations from data. While currently slower to train and unproven at LLM scale, KANs represent the ambitious ideal of a model where weights <em>are</em> interpretable mathematical functions.</p>
-</section>
-<section id="the-bridge-methodology-interpretable-proxies-for-dense-models" class="level3"><h3 data-pdf-page="38"><span class="secnum">6.9.6</span> The Bridge Methodology: Interpretable Proxies for Dense Models<a class="anchor" href="#the-bridge-methodology-interpretable-proxies-for-dense-models" aria-label="Link to this section">#</a></h3>
-<p>Retraining frontier models with sparsity constraints is computationally prohibitive. Gao et al. propose the “Bridge” methodology as a practical alternative <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>.</p>
-<p>A Bridge is a weight-sparse model trained to mimic the internal representations of a pretrained dense model—minimizing reconstruction error of layer-wise activations rather than training on the original task. The sparse model serves as an <em>interpretable proxy</em>: if it achieves high fidelity and is mechanistically interpretable, researchers can analyze its circuits to understand the dense model’s behavior.</p>
-<p>Preliminary evidence suggests that interventions on sparse proxies (e.g., ablating identified circuits) can be mapped back to induce similar behavioral changes in dense models. However, validating proxy fidelity—ensuring the proxy captures the same mechanisms rather than behaviorally equivalent simulations—remains an open problem (see Section <a href="@@BASE@@handbook/open-problems/#sec:open_problems" data-reference-type="ref" data-reference="sec:open_problems">15</a>).</p>
-<p>Table <a href="@@BASE@@handbook/feature-extraction/#tab:architectural-methods" data-reference-type="ref" data-reference="tab:architectural-methods">7</a> summarizes the trade-offs between architectural approaches.</p>
+<section id="subsubsec: weight sparse transformer" class="level3"><h3 data-pdf-page="51"><span class="secnum">6.10.2</span> Weight-Sparse Training<a class="anchor" href="#subsubsec: weight sparse transformer" aria-label="Link to this section">#</a></h3>
+<p>A weight-sparse transformer <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2026</a>)</span> treats weight sparsity as an explicit structural constraint that is progressively strengthened during training. <span class="citation" data-cites="gao2025weight"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al. (2026)</a></span> optimise the standard cross-entropy objective along with an <span class="math inline">L_0</span> constraint on model weights after each optimisation step. Specifically, within each weight matrix, they retain only the largest-magnitude parameters and set the rest to zero. The <span class="math inline">L_0</span> constraint anneals from a dense initialisation to the desired sparsity level over training. The resulting transformer has most weight parameters exactly zero, making the remaining computational structure much easier to decompose into small, task-specific circuits.</p>
+<p>Its interpretability gains can be substantial. Under highly sparse regimes (e.g., <span class="math inline">&lt;1\%</span> active weights), neurons receive input from far fewer upstream neurons, and the model’s circuits for specific capabilities or behaviours become much sparser. Mean-ablation experiments further confirm that these circuits are both necessary and sufficient for the capability or behaviour.</p>
+<p><span class="citation" data-cites="gao2025weight"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al. (2026)</a></span> characterised the trade-off between model capability and interpretability induced by weight sparsity. Increasing sparsity generally makes the model easier to decompose into small, interpretable circuits. However, it also degrades task performance, producing a capability–interpretability Pareto frontier. Nonetheless, the authors found that increasing model size can shift this frontier outward. At comparable task performance, larger sparse models can have substantially smaller, more interpretable circuits than smaller models; at comparable interpretability, they can retain stronger capabilities. This suggests that scaling model size alongside structural sparsity may offer a path to more interpretable yet high-capability models. However, preserving interpretability beyond tens of millions of nonzero parameters remains challenging, and weight-sparse training currently requires substantially more compute than traditional training.</p>
 <table id="tab:architectural-methods">
-<caption><span class="capnum">Table 7:</span> Comparison of Architectural Interventions for Interpretability</caption>
+<caption><span class="capnum">Table 7:</span> Comparison of interpretability-by-design methods.</caption>
 <thead>
 <tr>
-<th style="text-align: left;"><strong>Method</strong></th>
-<th style="text-align: left;"><strong>Mechanism</strong></th>
-<th style="text-align: left;"><strong>Interpretability Gain</strong></th>
-<th style="text-align: left;"><strong>Primary Limitation</strong></th>
+<th style="text-align: left;"><span style="color: white"><strong>Method</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Approach</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Interpretability Gain</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Primary Limitation</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Weight Sparsity</td>
-<td style="text-align: left;"><span class="math inline">L_0</span> regularization on parameters</td>
-<td style="text-align: left;">Circuits visible via non-zero weights; monosemantic neurons</td>
-<td style="text-align: left;">Capability cost; training instability</td>
+<td style="text-align: left;"><strong>Feature-Sparse Training (SoLU)</strong></td>
+<td style="text-align: left;">Softmax-gated activation function</td>
+<td style="text-align: left;">Increased neuron monosemanticity</td>
+<td style="text-align: left;">Layer normalisation smuggling displaces superposition</td>
 </tr>
 <tr>
-<td style="text-align: left;">SoLU</td>
-<td style="text-align: left;">Spatial softmax activation</td>
-<td style="text-align: left;">Increased neuron monosemanticity</td>
-<td style="text-align: left;">Hydraulic effect displaces superposition</td>
+<td style="text-align: left;"><strong>Weight-Sparse Training</strong></td>
+<td style="text-align: left;"><span class="math inline">L_0</span> constraint on parameters</td>
+<td style="text-align: left;">More monosemantic neurons and much smaller circuits</td>
+<td style="text-align: left;">Training instability and scalability</td>
 </tr>
 <tr>
-<td style="text-align: left;">MOLT</td>
-<td style="text-align: left;">Sparse mixture of linear transforms</td>
-<td style="text-align: left;">Reveals algorithmic operations directly</td>
-<td style="text-align: left;">Complexity of matrix analysis</td>
+<td style="text-align: left;"><strong>Attention-Sparse Training</strong></td>
+<td style="text-align: left;"><span class="math inline">L_0</span> regularisation on attention</td>
+<td style="text-align: left;">Makes attention patterns interpretable</td>
+<td style="text-align: left;">Requires fine-tuning on a trained language model</td>
 </tr>
 <tr>
-<td style="text-align: left;">SPARTAN</td>
-<td style="text-align: left;"><span class="math inline">L_0</span> regularization on attention</td>
-<td style="text-align: left;">Exposes information routing graph</td>
-<td style="text-align: left;">Requires finetuning; post-training only</td>
+<td style="text-align: left;"><strong>MOLT</strong></td>
+<td style="text-align: left;">Sparse mixture of linear transforms</td>
+<td style="text-align: left;">Reveals implemented operations directly</td>
+<td style="text-align: left;">Complexity of matrix analysis</td>
 </tr>
 <tr>
-<td style="text-align: left;">KANs</td>
+<td style="text-align: left;"><strong>KANs</strong></td>
 <td style="text-align: left;">Learnable splines on edges</td>
 <td style="text-align: left;">Symbolic formula extraction</td>
-<td style="text-align: left;">Scaling to LLMs unproven</td>
+<td style="text-align: left;">Scalability to LLMs is unproven</td>
 </tr>
 </tbody>
 </table>
 </section>
+<section id="subsubsec: spartan" class="level3"><h3 data-pdf-page="51"><span class="secnum">6.10.3</span> Attention-Sparse Training<a class="anchor" href="#subsubsec: spartan" aria-label="Link to this section">#</a></h3>
+<p>In transformers, attention patterns produced by an attention head are usually <em>diffuse</em>, i.e., every token attends to every other token. Diffuse attention patterns are difficult to interpret and audit. Previous studies have therefore explored <em>attention-sparse training</em>, which aims to sparsify attention patterns to make them more interpretable (<span class="citation" data-cites="lei2025spartan"><a href="@@BASE@@references/#ref-lei2025spartan" class="cite cite-established" data-cite="lei2025spartan" role="doc-biblioref">Lei et al., 2025</a></span>; <span class="citation" data-cites="draye2025spartan"><a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al., 2026</a></span>). Specifically, <span class="citation" data-cites="lei2025spartan"><a href="@@BASE@@references/#ref-lei2025spartan" class="cite cite-established" data-cite="lei2025spartan" role="doc-biblioref">Lei et al. (2025)</a></span> proposed <em>SPARse TrANsformer World model</em> (SPARTAN), an object-centric world model trained with <span class="math inline">L_0</span> regularisation on attention patterns and a constrained optimisation objective to maintain cross-entropy performance equal to that of a dense counterpart. <span class="citation" data-cites="draye2025spartan"><a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al. (2026)</a></span> further adapted this technique to fine-tune and sparsify a trained language model’s attention patterns.</p>
+<p>After training (either from scratch or via fine-tuning), each attention head attends only to specific tokens, and the model performance remains close to the original. This result suggests that many attention patterns may be redundant noise. In CLT-based circuit discovery (see Section <a href="@@BASE@@handbook/circuits/#sec:circuit-replacement" data-reference-type="ref" data-reference="sec:circuit-replacement">7.2</a>), <span class="citation" data-cites="draye2025spartan"><a href="@@BASE@@references/#ref-draye2025spartan" class="cite cite-emerging" data-cite="draye2025spartan" role="doc-biblioref">Draye et al. (2026)</a></span> showed that sparsified attention patterns reduce the number of mediating components involved in feature-to-feature attribution, making the resulting attribution graphs easier to analyse.</p>
+</section>
+<section id="subsubsec: molt" class="level3"><h3 data-pdf-page="52"><span class="secnum">6.10.4</span> Modifications of Computational Primitives: MOLT<a class="anchor" href="#subsubsec: molt" aria-label="Link to this section">#</a></h3>
+<p>One alternative approach to learning interpretable structure is to reconsider what the computational primitives should be (<span class="citation" data-cites="oldfield2025towards park2025monet"><a href="@@BASE@@references/#ref-oldfield2025towards" class="cite cite-established" data-cite="oldfield2025towards" role="doc-biblioref">Oldfield et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a></span>; <span class="citation" data-cites="braun2025interpretability bushnaq2025stochastic"><a href="@@BASE@@references/#ref-braun2025interpretability" class="cite cite-emerging" data-cite="braun2025interpretability" role="doc-biblioref">Braun et al., 2025</a>; <a href="@@BASE@@references/#ref-bushnaq2025stochastic" class="cite cite-emerging" data-cite="bushnaq2025stochastic" role="doc-biblioref">Bushnaq et al., 2025</a></span>). A recent example is the sparse mixture of linear transforms (MOLT), which represents a conceptual shift from feature extraction towards transformation extraction <span class="citation" data-cites="anthropic2025molt">(<a href="@@BASE@@references/#ref-anthropic2025molt" class="cite cite-emerging" data-cite="anthropic2025molt" role="doc-biblioref">Lindsey et al., 2025b</a>)</span>.</p>
+<p>Standard SAEs and transcoders primarily decompose model activations into features. However, many algorithmic tasks, such as arithmetic or geometric reasoning, are more naturally described by <em>operations</em> such as “add 3” or “rotate a vector”. In these tasks, we want to know how the model transforms one activation into another through components like weight matrices in MLP blocks. To achieve this, MOLT replaces the MLP with a sparse mixture of learned linear transformation matrices. The basic unit of analysis becomes transformations applied to model activations rather than features.</p>
+<p>For example, in a modular arithmetic task, MOLT can represent the concept of addition with a learned rotation matrix, which relates more directly to the underlying algorithm than a collection of state-specific features. More broadly, approaches that represent computational operations explicitly, as MOLT does, may facilitate the development of interpretable representations for tasks governed by systematic operations. We view Kolmogorov–Arnold Networks (KANs) as an approach that embodies this idea, as discussed below.</p>
+</section>
+<section id="subsubsec: kan" class="level3"><h3 data-pdf-page="52"><span class="secnum">6.10.5</span> Kolmogorov–Arnold Networks (KANs)<a class="anchor" href="#subsubsec: kan" aria-label="Link to this section">#</a></h3>
+<p>Kolmogorov–Arnold Networks (KANs) represent one of the most radical departures from standard neural network architectures <span class="citation" data-cites="liu2025kan">(<a href="@@BASE@@references/#ref-liu2025kan" class="cite cite-established" data-cite="liu2025kan" role="doc-biblioref">Liu et al., 2025b</a>)</span>. In conventional neural networks, each neuron receives outputs from upstream neurons through incoming edges weighted by scalar parameters, sums these weighted inputs, and then applies a fixed nonlinear activation function (the whole process can be represented as matrix multiplication in components like MLP blocks and attention modules in Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:transformers" data-reference-type="ref" data-reference="subsec:transformers">4.4</a>). Inspired by the Kolmogorov–Arnold representation theorem, KANs place learnable nonlinear functions, typically parameterised using B-splines, on edges rather than using fixed nonlinear activation functions at nodes.</p>
+<p>Specifically, consider a single KAN node receiving an input vector <span class="math inline">v = (v_1, \dots, v_d) \in \mathbb{R}^d</span> from the previous layer. Its output is <span class="math display">y = \sum_{i=1}^{d} \psi_i(v_i), \qquad \psi_i(v_i) = \sum_{m} c_{i,m} B_m(v_i),</span> where <span class="math inline">B_m(\cdot)</span> denotes the <span class="math inline">m</span>-th basis function, such as a cubic B-spline basis, and <span class="math inline">c_{i,m}</span> is a learnable scalar coefficient. In this formulation, the edge is not scalar multiplication but a learnable univariate function <span class="math inline">\psi_i(\cdot)</span>, and a node simply sums the outputs of its incoming edges. Intuitively, KANs shift nonlinear transformations from nodes to edges, offering an alternative way to structure and interpret neural network computations.</p>
+<p>This design gives KANs an explicitly compositional and thus more interpretable structure. As the nonlinearities are univariate and can be visualised directly on individual edges, researchers can use techniques like symbolic regression to approximate and inspect the learned analytical expressions, such as rediscovering known physical equations. Although KAN training is currently more computationally expensive than training standard MLPs and has not yet been convincingly demonstrated at the scale of large language models, this architectural innovation points to an ambitious direction towards interpretable neural networks in which parameters correspond to mathematically meaningful functions.</p>
+<section id="summary.-3" class="level4">
+<h4>Summary.</h4>
+<p>This section introduced several classical methods for feature extraction and analysis, followed by approaches that aim to make models intrinsically interpretable. However, whether features are recovered post hoc or induced by design, they remain units of analysis rather than complete explanations of the model’s mechanism, i.e., how these units interact and causally influence one another. Section <a href="@@BASE@@handbook/circuits/#sec:circuit" data-reference-type="ref" data-reference="sec:circuit">7</a> therefore shifts from individual features to <em>circuits</em>: computational graphs of interacting components that describe how the model implements its behaviours.</p>
+</section>
+</section>
 </section>
 </section>
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+++ b/src/generated/chapters/foundational-concepts.html
@@ -1,72 +1,122 @@
-<section id="sec:foundations" class="level1"><h1 data-pdf-page="10"><span class="secnum">4</span> Foundational Concepts for Mechanistic Interpretability<a class="anchor" href="#sec:foundations" aria-label="Link to this section">#</a></h1>
-<p>This section introduces core conceptual primitives used throughout mechanistic interpretability. We deliberately avoid committing to specific hypotheses about how representations are organized or how general these structures are. Instead, we establish a shared vocabulary and computational picture that will be used throughout the survey. In Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a> we discuss issues with some of the hypotheses in this section.</p>
-<section id="subsec:behavioral-vs-mechanistic" class="level2"><h2 data-pdf-page="10"><span class="secnum">4.1</span> Behavioral vs. Mechanistic Understanding<a class="anchor" href="#subsec:behavioral-vs-mechanistic" aria-label="Link to this section">#</a></h2>
-<p>Behavioral interpretability is often sufficient for evaluating the input-output behavior, but safety-critical applications require mechanistic analysis (as we reviewed in Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a>). The distinction between these methods is the difference between describing a system’s behavior and reverse engineering its internal mechanism.</p>
+<section id="sec:foundations" class="level1"><h1 data-pdf-page="21"><span class="secnum">4</span> Foundational Concepts for Mechanistic Interpretability<a class="anchor" href="#sec:foundations" aria-label="Link to this section">#</a></h1>
+<p>This section introduces foundational concepts in mechanistic interpretability. We establish a shared notation and vocabulary for use throughout the survey. We avoid committing to specific hypotheses about the representations and workings of model activations.</p>
+<section id="subsec:behavioral-vs-mechanistic" class="level2"><h2 data-pdf-page="21"><span class="secnum">4.1</span> Behavioural vs. Mechanistic Understanding<a class="anchor" href="#subsec:behavioral-vs-mechanistic" aria-label="Link to this section">#</a></h2>
+<p>Behavioural analysis describes what a model does, but safety-critical applications also require understanding its internal mechanism (as we reviewed in Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a>).</p>
 <div class="definitionbox">
-<p>Behavioral interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> exclusively through input–output pairs <span class="math inline">(x, f_\theta(x))</span>, treating it as a black box.</p>
+<p>Behavioural interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> through input–output pairs <span class="math inline">(x, f_\theta(x))</span> for <span class="math inline">x \in \mathcal{D} \subseteq \mathcal{X}</span>, where <span class="math inline">\mathcal{D}</span> is a finite test set. Behavioural interpretability treats the model as a black box.</p>
 </div>
 <div class="definitionbox">
-<p>Mechanistic interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> by examining its internal structure: the parameters <span class="math inline">\theta</span> and the intermediate activations <span class="math inline">\{h_l(x)\}_{l=1}^{L}</span> that together implement a particular behavior keeping the input-output fixed.</p>
+<p>Mechanistic interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> by examining its internal computations, including model activations and parameters <span class="math inline">\theta</span> that produce them, at different levels of granularity in relation to its behaviour. In short, mechanistic interpretability characterises how a model’s behaviour or output is computed.</p>
 </div>
 </section>
-<section id="subsec:polysemanticity" class="level2"><h2 data-pdf-page="11"><span class="secnum">4.2</span> Polysemanticity in Neural Representations<a class="anchor" href="#subsec:polysemanticity" aria-label="Link to this section">#</a></h2>
-<p>Consider the activation of a single unit <span class="math inline">h_l^{(i)}(x)</span> at layer <span class="math inline">l</span>. Interpreting units would be easier if each one responded to a single understandable concept. But researchers have found that—as by product of how we train models—individual units often activate strongly for multiple, unrelated concepts <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>. This phenomena is known as “polysemanticity”.</p>
+<section id="subsec:polysemanticity" class="level2"><h2 data-pdf-page="21"><span class="secnum">4.2</span> Neuron Polysemanticity in Model Representations<a class="anchor" href="#subsec:polysemanticity" aria-label="Link to this section">#</a></h2>
+<section id="what-is-a-concept" class="level4">
+<h4>What Is a Concept?</h4>
+<p>Concepts play a central role in many interpretability methods and will be used throughout this paper to define key ideas such as features and polysemanticity. It is therefore worth clarifying what we mean by the term. Notably, concepts do not necessarily correspond to categories humans can readily name or understand; neural networks may represent them in ways humans lack the vocabulary to describe <span class="citation" data-cites="hewitt2025position">(<a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>.</p>
 <div class="definitionbox">
-<p>A unit <span class="math inline">h_l^{(i)}</span> is <em>polysemantic</em> if any two inputs <span class="math inline">x, x&#39; \in \mathcal{X}</span>, corresponding to semantically distinct concepts, both produce high absolute activation: <span class="math inline">|h_l^{(i)}(x)| \gg 0</span> and <span class="math inline">|h_l^{(i)}(x&#39;)| \gg 0</span>.</p>
+<p>A concept is a distinct, reusable unit of information or knowledge that can be identified across different instances or contexts. Concepts may correspond to familiar human categories, such as “a dog”, “the colour red”, or “words of praise”, but they need not admit a natural human description.</p>
 </div>
-<p>Polysemanticity complicates neuron-level explanations and suggests that human-interpretable semantic content may not be aligned with neurons (the standard basis vectors of the representation space). This motivates the search for alternative representational units beyond individual neurons.</p>
+<p>Cross-context identifiability concerns the meaning of the information unit rather than the process by which the information is recognised. Determining whether a concept is present in an information unit may itself require additional contextual information. For example, the word (i.e., an instance of an information unit) “bank” can refer to a financial institution or a river bank, depending on the surrounding sentence. Nevertheless, once identified, each concept can be recognised as the same information across different contexts. For example, the concept of “financial institution” is expected to remain sufficiently stable across the contexts in which it appears.</p>
+<p>This definition accommodates both human-interpretable and machine-specific concepts. A machine-specific concept may lack a natural human description yet still form a coherent, reusable unit of information in the neural network <span class="citation" data-cites="dalvi2022discovering hewitt2025position">(<a href="@@BASE@@references/#ref-dalvi2022discovering" class="cite cite-established" data-cite="dalvi2022discovering" role="doc-biblioref">Dalvi et al., 2022</a>; <a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>. Human interpretability should therefore be treated as a property of some concepts rather than as a necessary condition for the information unit to qualify as a concept. Notably, most interpretability research focuses on concepts that humans can readily understand. Identifying and characterising concepts that matter to a model’s computation but do not map neatly onto human-understandable categories remains underexplored.</p>
 </section>
-<section id="subsec:features" class="level2"><h2 data-pdf-page="12"><span class="secnum">4.3</span> Features as Representational Units<a class="anchor" href="#subsec:features" aria-label="Link to this section">#</a></h2>
-<p>Since individual units <span class="math inline">h_l^{(i)}</span> are often polysemantic, mechanistic interpretability seeks alternative units of analysis. The largley agreed upon hypothesis in the community is that models often encode interpretable concepts as directions in activation space, called <em>features</em>.</p>
-<p>Features may be distributed across many neurons <span class="citation" data-cites="elhage2022superposition bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span> and only become apparent when analyzing the representation space as a whole.</p>
+<section id="neuron-polysemanticity." class="level4">
+<h4>Neuron Polysemanticity.</h4>
+<p>For a single neuron <span class="math inline">n_\ell</span> in layer <span class="math inline">\ell</span>, consider its activation at token position <span class="math inline">t</span> for some input prompt <span class="math inline">x</span>, denoted by <span class="math inline">n_\ell^{t}(x)</span>. We can straightforwardly interpret a neuron if it responds to a single concept. For example, if <span class="math inline">n_\ell^{t}(x)</span> is large and positive whenever position <span class="math inline">t</span> corresponds to the word “blue”, remains close to zero otherwise, and can causally shift the model’s outputs towards or away from blue-related concepts when manipulated, we can conclude that the neuron corresponds to the concept of blue. Assuming some neurons exhibit such a property, early work proposed methods to discover and interpret individual neurons’ functions <span class="citation" data-cites="netdissect2017 bau2018identifying Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019 bau2020units hernandez2022natural oikarinen2023clipdissect and">(<a href="@@BASE@@references/#ref-netdissect2017" class="cite cite-established" data-cite="netdissect2017" role="doc-biblioref">Bau et al., 2017</a>; <a href="@@BASE@@references/#ref-bau2018identifying" class="cite cite-established" data-cite="bau2018identifying" role="doc-biblioref">2019</a>; <a href="@@BASE@@references/#ref-Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" class="cite cite-established" data-cite="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" role="doc-biblioref">Dalvi et al., 2019</a>; <a href="@@BASE@@references/#ref-bau2020units" class="cite cite-established" data-cite="bau2020units" role="doc-biblioref">Bau et al., 2020</a>; <a href="@@BASE@@references/#ref-hernandez2022natural" class="cite cite-established" data-cite="hernandez2022natural" role="doc-biblioref">Hernandez et al., 2022</a>; <a href="@@BASE@@references/#ref-oikarinen2023clipdissect" class="cite cite-established" data-cite="oikarinen2023clipdissect" role="doc-biblioref">Oikarinen &amp; Weng, 2023</a>; <a href="@@BASE@@references/#ref-and" class="cite cite-established" data-cite="and" role="doc-biblioref">Wu et al., 2024</a>)</span>. However, follow-up studies have found that, due to representational constraints and superposition <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span> (detailed in Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:superposition" data-reference-type="ref" data-reference="subsec:superposition">5.2</a>), individual neurons often respond strongly to multiple concepts that appear unrelated from a human perspective <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>. This phenomenon is known as <em>neuron polysemanticity</em>.</p>
 <div class="definitionbox">
-<p>A feature is a direction <span class="math inline">\phi \in \mathbb{R}^d</span> in activation space. The scalar <span class="math inline">\phi^\top h_l(x)</span> measures the activation (or presence) of that feature for input <span class="math inline">x</span>. A high value indicates the corresponding concept is represented in the model’s internal.</p>
+<p>A neuron <span class="math inline">n_\ell</span> is <em>polysemantic</em> if there exist two distinct concepts <span class="math inline">c_1</span> and <span class="math inline">c_2</span>, together with corresponding sets of inputs <span class="math inline">x_1</span> and <span class="math inline">x_2</span> as well as token positions of interest <span class="math inline">t_{c_1}</span> and <span class="math inline">t_{c_2}</span>, such that <span class="math inline">n_\ell^{t_{c_1}}(x_1)</span> and <span class="math inline">n_\ell^{t_{c_2}}(x_2)</span> are both large, and interventions on these activations causally influence model behaviour associated with both concepts.</p>
 </div>
-<p>At this stage, we remain agnostic about the precise mathematical form features take or how they are encoded within the model. Accordingly, we do not assume a specific representational geometry or encoding scheme, and treat existing approaches to identifying or manipulating features as operational approximations shaped by methodological constraints. These representational hypotheses are examined explicitly in Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>.</p>
-<p>Within this operational perspective, features can be recovered or manipulated using different methodological families, including readout-based methods, sparse decomposition techniques, and causal intervention strategies. In practice, most such approaches operationalise features as approximately linear or linearized components of internal representations. Specific examples include probes, dictionary learning methods, and activation-based interventions (<span class="citation" data-cites="belinkov-2022-probing wang2022interpretabilitywildcircuitindirect"><a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>; <a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a></span>; <span class="citation" data-cites="bricken2023monosemanticity"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a></span>).</p>
+<p>Polysemanticity complicates neuron-level explanations and suggests that human-interpretable semantic content may not be aligned with individual neurons. This motivates searching for representational units beyond individual neurons.</p>
 </section>
-<section id="subsec:transformers" class="level2"><h2 data-pdf-page="12"><span class="secnum">4.4</span> Transformer Computation<a class="anchor" href="#subsec:transformers" aria-label="Link to this section">#</a></h2>
-<p>Transformers <span class="citation" data-cites="vaswani2023attentionneed">(<a href="@@BASE@@references/#ref-vaswani2023attentionneed" class="cite" data-cite="vaswani2023attentionneed" role="doc-biblioref">Vaswani et al., 2017b</a>)</span> provide a particularly transparent architecture for mechanistic analysis due to their modular structure and additive information flow.</p>
-<p>All transformer submodules read from and write to a shared residual stream, enabling information to persist and be incrementally transformed across layers.</p>
+</section>
+<section id="subsec:features" class="level2"><h2 data-pdf-page="22"><span class="secnum">4.3</span> Features as Representational Units<a class="anchor" href="#subsec:features" aria-label="Link to this section">#</a></h2>
+<p>Since individual neurons are often polysemantic, mechanistic interpretability has increasingly shifted towards alternative units of analysis. For example, it has been found that models sometimes encode human-interpretable concepts as linear directions or low-dimensional manifolds in the activation space (<span class="citation" data-cites="arditi2024refusallanguagemodelsmediated"><a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a></span>; <span class="citation" data-cites="wurgaft2026manifoldsteeringrevealsshared"><a href="@@BASE@@references/#ref-wurgaft2026manifoldsteeringrevealsshared" class="cite cite-emerging" data-cite="wurgaft2026manifoldsteeringrevealsshared" role="doc-biblioref">Wurgaft et al., 2026</a></span>). We thus use the term <em>feature</em> broadly to encompass any model representation corresponding to a human-interpretable concept. Such distributed representations motivate analysing the activation space as a whole, rather than individual neurons in isolation <span class="citation" data-cites="elhage2022superposition bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>.</p>
+<p>Specifically, we treat a feature technically as a geometric structure <span class="math inline">\phi</span> in the activation space <span class="math inline">\mathbb{R}^d</span> of a model component, such as the residual stream, an attention-head output, or an MLP output, where <span class="math inline">d</span> denotes the hidden dimension. Conceptually, a feature is a manipulable manifestation of a concept of interest. As with neurons, for a feature <span class="math inline">\phi</span> claimed to correspond to a concept <span class="math inline">c</span>, we expect interventions that increase or suppress the activation of <span class="math inline">\phi</span> to causally influence model behaviour associated with <span class="math inline">c</span>. In this sense, features provide a more expressive, general representational unit than individual neurons for describing how neural networks may encode concepts.</p>
 <div class="definitionbox">
-<p>The residual stream is the sequence of activation vectors <span class="math inline">\{h_l(x)\}_{l=0}^{L}</span> that serve as the shared communication channel between transformer layers. Each sublayer reads from and writes additively to this stream: <span class="math display">\begin{align*}
-h&#39;_l(x) &amp;= h_{l-1}(x) + \text{Attn}_l(h_{l-1}(x)) \\
-h_l(x) &amp;= h&#39;_l(x) + \text{MLP}_l(h&#39;_l(x))
-\end{align*}</span> This additive structure means information persists by default—sublayers only need to write changes, not copy forward what they wish to preserve which makes targed intervention more reliable.</p>
+<p>A feature <span class="math inline">\phi</span> associated with a concept <span class="math inline">c</span> is a geometric structure in the model’s activation space <span class="math inline">\mathbb{R}^d</span> whose activation is predictive of, and causally relevant to, the presence or expression of <span class="math inline">c</span>.</p>
 </div>
+<p>At this stage, we remain agnostic about the precise mathematical form features take and how the model encodes them. Instead, we treat existing methods for identifying and manipulating features as operational approximations shaped by their methodological assumptions and constraints. We examine these representational hypotheses explicitly in Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>.</p>
+<p>Within this operational perspective, researchers can identify features using several methodological families, including readout-based methods <span class="citation" data-cites="patchscopes pan2024latentqateachingllmsdecode ao">(<a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al., 2024</a>; <a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>; <a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a>)</span> and sparse decomposition techniques <span class="citation" data-cites="cunningham2023sparse dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a>; <a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>, and then manipulate them to test causal relevance <span class="citation" data-cites="wu2025axbench mueller2025mib">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>; <a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span>. In practice, many such approaches operationalise features as approximately linear or linearised components of internal representations (<span class="citation" data-cites="yun-etal-2021-transformer"><a href="@@BASE@@references/#ref-yun-etal-2021-transformer" class="cite cite-emerging" data-cite="yun-etal-2021-transformer" role="doc-biblioref">Yun et al., 2021</a></span>; <span class="citation" data-cites="marks2024the arditi2024refusallanguagemodelsmediated"><a href="@@BASE@@references/#ref-marks2024the" class="cite cite-established" data-cite="marks2024the" role="doc-biblioref">Marks &amp; Tegmark, 2024</a>; <a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a></span>). Examples include linear probes and dictionary-learning methods (<span class="citation" data-cites="belinkov-2022-probing"><a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a></span>; <span class="citation" data-cites="bricken2023monosemanticity"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a></span>).</p>
+<p>An important caveat is that, for a given concept, an identified manipulable geometric structure—that is, a candidate feature—may be only one of many possible internal representations associated with that concept. For example, a language model may encode the concept of “refusal” in a complex manner across multiple layers and components. Successfully identifying a linear direction in the residual stream that can steer the model towards or away from refusal <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span> does not imply that this direction uniquely or exhaustively captures how refusal is represented. Other causally relevant structures (e.g., low-dimensional manifolds) for refusal may also exist but remain unidentified.</p>
+</section>
+<section id="subsec:transformers" class="level2"><h2 data-pdf-page="23"><span class="secnum">4.4</span> Transformer Computation<a class="anchor" href="#subsec:transformers" aria-label="Link to this section">#</a></h2>
+<p>Transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span> have become the dominant architecture underlying modern language-model-based AI assistants such as ChatGPT and Claude, and are consequently the primary subject of study in mechanistic interpretability. Several properties of the transformer architecture, most notably its modular structure and additive information flow, make it particularly amenable to mechanistic analysis <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>. In particular, transformer components can be viewed as reading from and writing to a shared <em>residual stream</em>, allowing information to persist and be incrementally transformed as it passes through successive layers <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>. Below, we briefly review the main classical components of the transformer architecture.</p>
 <div class="definitionbox">
-<p>Let <span class="math inline">h \in \mathbb{R}^{n \times d}</span> denote the residual stream at a given layer. A transformer component <span class="math inline">c</span> (such as an attention head or an MLP block) is a function <span class="math display">c : h \mapsto \Delta h_c(h),</span> whose output is added to the residual stream. The forward computation of a transformer layer can be written as <span class="math display">h_\ell = h_{\ell-1} + \sum_{c \in \mathcal C_\ell} \Delta h_c(h_{\ell-1}),</span> where <span class="math inline">\mathcal C_\ell</span> is the set of components in layer <span class="math inline">\ell</span>.</p>
-<p>This additive decomposition induces a computational structure in which model behavior arises from the combined contributions of individual components acting on shared internal representations.</p>
+<p>Let <span class="math display">h^{1:T}_\ell(x) \in \mathbb{R}^{d \times T}</span> denote the sequence of residual-stream activations at layer <span class="math inline">\ell</span> for an input sequence <span class="math inline">x</span>, where <span class="math inline">T</span> is the sequence length and <span class="math inline">d</span> is the model hidden size. The residual stream serves as the shared communication channel through which transformer components exchange information. Below, in this box, we let <span class="math inline">h_\ell(x) \coloneqq h^{1:T}_\ell(x)</span> to simplify the notation.<br />
+For a standard transformer layer <span class="math inline">\ell</span>, consisting of a multi-head attention module <span class="math inline">\mathrm{Attn}_\ell(\cdot)</span> and a multi-layer perceptron <span class="math inline">\mathrm{MLP}_\ell(\cdot)</span>, we have <span class="math display">\begin{align*}
+h&#39;_\ell(x)
+    &amp;= h_{\ell-1}(x)
+       + \mathrm{Attn}_\ell(h_{\ell-1}(x)), \\
+h_\ell(x)
+    &amp;= h&#39;_\ell(x)
+       + \mathrm{MLP}_\ell(h&#39;_\ell(x))
+    = h_{\ell-1}(x)
+       + \mathrm{Attn}_\ell(h_{\ell-1}(x)) + \mathrm{MLP}_\ell(h&#39;_\ell(x)).
+\end{align*}</span></p>
+<p>Unrolling the recursion gives <span class="math display">h_\ell(x)
+=
+h_0(x)
++
+\sum_{k=1}^{\ell}
+\Big(
+\mathrm{Attn}_k(h_{k-1}(x))
++
+\mathrm{MLP}_k(h&#39;_k(x))
+\Big),</span></p>
+<p>where <span class="math inline">h_0(x)</span> denotes the input embedding representation. The equation shows that the residual stream at layer <span class="math inline">\ell</span> equals the input embeddings plus the accumulated outputs of all computational components that have written to the residual stream up to that point. This perspective is central to mechanistic interpretability, as it lets us decompose model computation into additive contributions from individual components <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>.</p>
 </div>
-<p>The attention mechanisms primarily move and combine information across positions, while feedforward (MLP) layers perform nonlinear transformations. This separation of computation enables localized analysis of information routing and transformation. For simplicity, we are omitting layer norms here.</p>
+<p>For simplicity, we omit layer-normalisation operations from the notation. Following <span class="citation" data-cites="elhage2021mathematical"><a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al. (2021)</a></span>, most of the layer-normalisation transformation can be absorbed into adjacent linear components. Specifically, for a fixed input, layer normalisation is well approximated as an affine transformation with a scalar rescaling factor. The affine part can be folded into neighbouring parameters, and the remaining scalar normalisation term simply reweights component contributions. This approximation lets us treat the residual stream as an additive communication channel without explicitly representing normalisation at every layer.</p>
+<p>Attention heads and MLPs play distinct roles in transformer computation. Attention modules primarily route, aggregate, and compose information across token positions (<span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a></span>; <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect"><a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a></span>), whereas MLPs often perform feature transformation and factual knowledge retrieval <span class="citation" data-cites="geva-etal-2021-transformer meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-geva-etal-2021-transformer" class="cite cite-established" data-cite="geva-etal-2021-transformer" role="doc-biblioref">Geva et al., 2021</a>; <a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>. This functional separation enables more localised analyses of how the model routes, transforms, and stores information, as well as how attention modules and MLPs interact <span class="citation" data-cites="neo-etal-2024-interpreting">(<a href="@@BASE@@references/#ref-neo-etal-2024-interpreting" class="cite cite-established" data-cite="neo-etal-2024-interpreting" role="doc-biblioref">Neo et al., 2024</a>)</span>.</p>
+<p>Below, we detail the computations of canonical attention modules and MLPs.</p>
 <div class="definitionbox">
-<p>An attention head indexed by <span class="math inline">\alpha</span> computes its contribution to the residual stream by projecting the residual stream <span class="math inline">h</span> into query, key, and value spaces: <span class="math display">Q^{\alpha} = h W_Q^{\alpha}, \qquad
-K^{\alpha} = h W_K^{\alpha}, \qquad
-V^{\alpha} = h W_V^{\alpha},</span> where <span class="math inline">W_Q^{\alpha}, W_K^{\alpha}, W_V^{\alpha} \in \mathbb{R}^{d \times d_h}</span>. The attention pattern is <span class="math display">A^{\alpha} = \mathrm{softmax}\!\left(\frac{Q^{\alpha} (K^{\alpha})^\top}{\sqrt{d_h}}\right),</span> and the head writes its output back to the residual stream via <span class="math display">\Delta h_{\alpha} = A^{\alpha} V^{\alpha} W_O^{\alpha}.</span></p>
-<p>This decomposition separates <em>information routing - information shared among token indices</em> (via <span class="math inline">Q^{\alpha}, K^{\alpha}</span>) from <em>information transformation- computations that occur at specific indices</em> (via <span class="math inline">V^{\alpha}, W_O^{\alpha}</span>). When the attention pattern <span class="math inline">A^{\alpha}</span> is held fixed, the contribution <span class="math inline">\Delta h_{\alpha}</span> is linear in the residual stream.</p>
+<p>A multi-head attention module consists of <span class="math inline">N_H</span> attention heads operating in parallel. Consider the residual-stream activations <span class="math inline">h_\ell^{1:T}(x) \in \mathbb{R}^{d \times T}</span> and let <span class="math inline">h := h_\ell^{1:T}(x)</span> for notational simplicity. For each head <span class="math inline">r \in \{1,\ldots,N_H\}</span>, the residual stream is first projected into query, key, and value representations: <span class="math display">Q^r = W_Q^r h,
+\qquad
+K^r = W_K^r h,
+\qquad
+V^r = W_V^r h,</span> where <span class="math inline">W_Q^r, W_K^r, W_V^r \in \mathbb{R}^{d_{\text{head}} \times d}</span> and <span class="math inline">d_{\text{head}}</span> denotes the dimensionality of each attention head. The <em>attention pattern</em> of head <span class="math inline">r</span> is then computed as <span class="math display">A^r
+=
+\operatorname{softmax}\!\left(
+\frac{(Q^r)^\top K^r}{\sqrt{d_{\text{head}}}}
+\right)
+\in
+\mathbb{R}^{T \times T},</span> where the softmax is applied row-wise. Thus, <span class="math inline">A^r_{t,t&#39;}</span> denotes the attention weight assigned by the query at position <span class="math inline">t</span> to the key at position <span class="math inline">t&#39;</span>. The head then writes an update to the residual stream: <span class="math display">\Delta h^r
+=
+W_O^r V^r (A^r)^\top,</span> where <span class="math inline">W_O^r \in \mathbb{R}^{d \times d_{\text{head}}}</span>. The output of the full multi-head attention module is the sum of the contributions from all heads: <span class="math display">\operatorname{Attn}(h)
+=
+\sum_{r=1}^{N_H}
+\Delta h^r.</span></p>
 </div>
 <div class="definitionbox">
-<p>An MLP block computes a position-wise nonlinear transformation of the residual stream: <span class="math display">\Delta h_{\mathrm{MLP}} = W_2 \sigma(W_1 h),</span> where <span class="math inline">\sigma</span> is a nonlinearity applied elementwise. MLP blocks primarily transform and generate internal features, writing new directions into the residual stream.</p>
+<p>An MLP block applies a position-wise nonlinear transformation to each position of the residual stream. In the canonical Transformer architecture <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span>, this transformation takes the form <span class="math display">\Delta h_{\text{MLP}} = W_2 \,\sigma(W_1h+b_1)+b_2,</span> where <span class="math inline">W_1 \in \mathbb{R}^{d_{\text{mlp}}\times d}</span>, <span class="math inline">W_2 \in \mathbb{R}^{d\times d_{\text{mlp}}}</span>, and <span class="math inline">\sigma(\cdot)</span> denotes a nonlinear activation function, for which the original Transformer uses ReLU <span class="citation" data-cites="relu">(<a href="@@BASE@@references/#ref-relu" class="cite cite-established" data-cite="relu" role="doc-biblioref">Nair &amp; Hinton, 2010</a>)</span>. The same transformation is applied independently at each position in the sequence. MLP blocks primarily transform and generate internal features, writing new directions into the residual stream.<br />
+Notably, <span class="math inline">W_1</span> typically projects the activations into a much higher-dimensional space (<span class="math inline">d_{\text{mlp}} \gg d</span>), which has recently been found to yield more interpretable representations natively in the model <span class="citation" data-cites="ICLR2025_8c5f3029 arora2026language">(<a href="@@BASE@@references/#ref-ICLR2025_8c5f3029" class="cite cite-established" data-cite="ICLR2025_8c5f3029" role="doc-biblioref">Nikankin et al., 2025</a>; <a href="@@BASE@@references/#ref-arora2026language" class="cite cite-established" data-cite="arora2026language" role="doc-biblioref">Arora et al., 2026</a>)</span>.</p>
 </div>
+<p>The transformer components discussed above correspond to their canonical formulations. Numerous variants have since been proposed and adopted in modern architectures. For example, variants of the attention module include Grouped Query Attention (GQA) <span class="citation" data-cites="gqa">(<a href="@@BASE@@references/#ref-gqa" class="cite cite-established" data-cite="gqa" role="doc-biblioref">Ainslie et al., 2023</a>)</span>, used in Llama 3 <span class="citation" data-cites="llama3">(<a href="@@BASE@@references/#ref-llama3" class="cite cite-emerging" data-cite="llama3" role="doc-biblioref">Grattafiori et al., 2024</a>)</span>, and Multi-Head Latent Attention (MLA) <span class="citation" data-cites="deepseekv2">(<a href="@@BASE@@references/#ref-deepseekv2" class="cite cite-emerging" data-cite="deepseekv2" role="doc-biblioref">DeepSeek-AI et al., 2024</a>)</span>, used in DeepSeek-V2 <span class="citation" data-cites="deepseekv2">(<a href="@@BASE@@references/#ref-deepseekv2" class="cite cite-emerging" data-cite="deepseekv2" role="doc-biblioref">DeepSeek-AI et al., 2024</a>)</span> and DeepSeek-V3 <span class="citation" data-cites="deepseekai2025deepseekv3technicalreport">(<a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a>)</span>. Variants of the MLP block include gated architectures such as SwiGLU <span class="citation" data-cites="shazeer2020gluvariantsimprovetransformer">(<a href="@@BASE@@references/#ref-shazeer2020gluvariantsimprovetransformer" class="cite cite-emerging" data-cite="shazeer2020gluvariantsimprovetransformer" role="doc-biblioref">Shazeer, 2020</a>)</span>, used in Llama 3, and Mixture-of-Experts (MoE) architectures, used in DeepSeek-V3.</p>
 </section>
-<section id="subsec:circuits" class="level2"><h2 data-pdf-page="14"><span class="secnum">4.5</span> Circuits as Computational Substructures<a class="anchor" href="#subsec:circuits" aria-label="Link to this section">#</a></h2>
-<p>One can study how subsets of a model’s components interact to implement specific computations. In transformers, such interacting subgraphs of attention heads, MLPs, features, and their connections are commonly referred to as <strong>circuits</strong> <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>.</p>
+<section id="subsec:circuits" class="level2"><h2 data-pdf-page="25"><span class="secnum">4.5</span> Circuits as Descriptions of Internal Mechanisms<a class="anchor" href="#subsec:circuits" aria-label="Link to this section">#</a></h2>
+<p>Sections <a href="@@BASE@@handbook/foundational-concepts/#subsec:polysemanticity" data-reference-type="ref" data-reference="subsec:polysemanticity">4.2</a>–<a href="@@BASE@@handbook/foundational-concepts/#subsec:features" data-reference-type="ref" data-reference="subsec:features">4.3</a> introduce neurons and features as representational units for describing what is represented in model activations, and Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:transformers" data-reference-type="ref" data-reference="subsec:transformers">4.4</a> introduces the computations that transform and route activations through the model. We use the term <em>mechanism</em> to refer to the organisation of model activations, their representational units, or the transformations applied to them that together produce a particular model behaviour. A central topic in mechanistic interpretability is identifying and understanding such mechanisms in neural networks.</p>
+<p>A common way to represent and describe such a mechanism is through a <em>circuit</em>: a subgraph of the model’s computational graph whose <em>nodes</em> correspond to computational components or representational units and whose <em>edges</em> represent information flow between them <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>.</p>
 <div class="definitionbox">
-<p>A circuit <span class="math inline">\mathcal{C} \subseteq G</span> is a subgraph of the model’s computational graph <span class="math inline">G</span>, where <span class="math inline">G</span> has:</p>
+<p>For a fixed model computation, let <span class="math inline">G</span> denote the corresponding directed acyclic computational graph. A circuit <span class="math inline">\mathcal{C} \subseteq G</span> is a subgraph of <span class="math inline">G</span>, where:</p>
 <ul>
-<li><p>Nodes: computational components (<span class="math inline">\text{Attn}_l^{(h)}</span>, <span class="math inline">\text{MLP}_l</span>) and features <span class="math inline">\phi_i</span></p></li>
-<li><p>Edges: Directed connections representing the flow between computational components <span class="math inline">h_l(x)</span></p></li>
+<li><p><strong>Nodes:</strong> computational components, such as <span class="math inline">\mathrm{Attn}_\ell</span> and <span class="math inline">\mathrm{MLP}_\ell</span>, or representational units, such as features <span class="math inline">\phi_i</span>;</p></li>
+<li><p><strong>Edges:</strong> directed connections representing information flow or transformation between nodes.</p></li>
 </ul>
-<p>The subgraph <span class="math inline">\mathcal{C}</span> captures the components and features that together implement a specific computation of the model, providing mechanistic explanations to be framed at an algorithmic level while remaining grounded in the model’s representations.</p>
+<p>The subgraph <span class="math inline">\mathcal{C}</span> represents a collection of components and interactions that jointly contribute to a particular computation or model behaviour, and can therefore serve as a description of the underlying mechanism.</p>
 </div>
-<p>The study of circuits in mechanistic interpretability has developed along two complementary lines. The first line of work, often referred to as <em>circuit discovery</em> <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect conmy2023automatedcircuitdiscoverymechanistic eapig haklay-etal-2025-position">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>; <a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>; <a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-haklay-etal-2025-position" class="cite cite-established" data-cite="haklay-etal-2025-position" role="doc-biblioref">Haklay et al., 2025</a>)</span>, aims to identify minimal sets of architectural components, such as attention heads or MLP layers, that are sufficient to reproduce a given behavior or capability of the model, such as indirect object identification <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span> and numerical comparison <span class="citation" data-cites="hanna2023how">(<a href="@@BASE@@references/#ref-hanna2023how" class="cite cite-established" data-cite="hanna2023how" role="doc-biblioref">Hanna et al., 2023</a>)</span>. In this setting, the capability of interest is instantiated as a dataset of queries requiring the model to have the capability to solve them, and a discovered circuit is considered successful if it recovers the model’s performance on this dataset.</p>
-<p>The second line of work, often referred to as <em>circuit tracing</em>, focuses on how internal features interact and propagate across the computational graph to lead to the model decision of a particular input query <span class="citation" data-cites="ameisen2025circuit querycircuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>; <a href="@@BASE@@references/#ref-querycircuit" class="cite cite-emerging" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2025</a>)</span>. Studies of this line usually make use of sparse autoencoders (detailed in Section <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>) to automatically decompose mlp outputs into interpretable units, tracing attributions between these features and how the information flow converges to the final prediction.</p>
-</section>
-<section id="subsec:limits" class="level2"><h2 data-pdf-page="14"><span class="secnum">4.6</span> Limitations of Foundational Works In Mechanistic Interpretability<a class="anchor" href="#subsec:limits" aria-label="Link to this section">#</a></h2>
-<p>Mechanistic interpretability aims to provide causal explanations of model behavior, but it faces important practical and conceptual limitations.</p>
-<div class="warningbox">
-<p>Mechanistic explanations may be incomplete, difficult to scale to large models or complex behaviors, and sensitive to modeling assumptions. Moreover, identifying a circuit does not by itself guarantee control, robustness across models and setups, or safety <span class="citation" data-cites="sharkey2025open">(<a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a>)</span>.</p>
+<figure id="fig:circuit" data-latex-placement="tb">
+<img alt="Figure 3: Circuit. This diagram illustrates a circuit within a transformer, where specific nodes (MLP layers and attention heads in this case) perform specialised computations." loading="lazy" src="@@BASE@@figures/7_circuit.svg" />
+<figcaption><span class="capnum">Figure 3:</span> <strong>Circuit.</strong> This diagram illustrates a circuit within a transformer, where specific nodes (MLP layers and attention heads in this case) perform specialised computations.</figcaption>
+</figure>
+<div class="examplebox">
+<ul>
+<li><p><strong>Induction head circuit:</strong> Two attention heads cooperate to implement in-context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.</p></li>
+<li><p><strong>QK circuits:</strong> Query–Key interactions prioritising specific token positions <span class="citation" data-cites="kamath2025tracing">(<a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a>)</span>.</p></li>
+</ul>
 </div>
-<p>These limitations motivate the formulation of explicit hypotheses about representation, computation, and generalization, which we examine in the next section.</p>
+<p>The study of circuits has developed along two complementary lines. The first line of work <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect conmy2023automatedcircuitdiscoverymechanistic eapig haklay-etal-2025-position">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>; <a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>; <a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-haklay-etal-2025-position" class="cite cite-established" data-cite="haklay-etal-2025-position" role="doc-biblioref">Haklay et al., 2025</a>)</span>, referred to as <em>capability circuit discovery</em> by <span class="citation" data-cites="querycircuit"><a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez (2026)</a></span>, aims to identify minimal sets of architectural components, such as attention heads or MLP layers, that are sufficient to reproduce a given <em>behaviour</em> or <em>capability</em> of the model, such as indirect object identification <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span>, numerical comparison <span class="citation" data-cites="hanna2023how">(<a href="@@BASE@@references/#ref-hanna2023how" class="cite cite-established" data-cite="hanna2023how" role="doc-biblioref">Hanna et al., 2023</a>)</span>, and in-context learning <span class="citation" data-cites="kharlapenko2025scaling">(<a href="@@BASE@@references/#ref-kharlapenko2025scaling" class="cite cite-established" data-cite="kharlapenko2025scaling" role="doc-biblioref">Kharlapenko et al., 2025</a>)</span>. In this setting, the capability of interest is defined as a dataset of queries that exercise it, and a discovered circuit is considered successful if it recovers the model’s performance on this dataset.</p>
+<p>The second line of work, referred to as <em>query-level circuit discovery</em> <span class="citation" data-cites="querycircuit">(<a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a>)</span>, focuses on how internal features interact and propagate across the computational graph to produce the model’s decision for a particular input query (<span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>; <span class="citation" data-cites="querycircuit"><a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a></span>). Studies in this line usually use sparse autoencoders or cross-layer transcoders (detailed in Section <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>) to automatically decompose MLP outputs into interpretable units, trace attributions between these features, and track how information flow converges to the final prediction.</p>
+<section id="summary.-1" class="level4">
+<h4>Summary.</h4>
+<p>This section introduced the foundational concepts of mechanistic interpretability: neuron polysemanticity, which motivates the use of features as more expressive units of analysis; transformer computation, which describes how representations are transformed and information is routed through the model; and internal mechanisms, which organise these units, transformations, and information flows to produce model behaviour and can be described by circuits. How we interpret and leverage these concepts depends on assumptions about the structure of model representations and computation, which we examine explicitly in Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>.</p>
+</section>
 </section>
 </section>
diff --git a/src/generated/chapters/intermediate-representations.html b/src/generated/chapters/intermediate-representations.html
index 34dd7305de..0690b20be3 100644
--- a/src/generated/chapters/intermediate-representations.html
+++ b/src/generated/chapters/intermediate-representations.html
@@ -1,197 +1,162 @@
-<section id="sec:intermediate-computation" class="level1"><h1 data-pdf-page="48"><span class="secnum">8</span> Understanding Intermediate Computation: Logit Lens and Probing<a class="anchor" href="#sec:intermediate-computation" aria-label="Link to this section">#</a></h1>
-<p>Between feature extraction (what the fundamental components of features are) and circuit analysis (how components interact), there lies a gap: understanding what computation happens at intermediate layers. This section covers methods that analyze hidden layer activations to reveal how information transforms as it flows through the network.</p>
-<section id="sec:logit-lens" class="level2"><h2 data-pdf-page="48"><span class="secnum">8.1</span> Logit Lens: Tracing Predictions Through Depth<a class="anchor" href="#sec:logit-lens" aria-label="Link to this section">#</a></h2>
+<section id="sec:intermediate-computation" class="level1"><h1 data-pdf-page="66"><span class="secnum">8</span> Understanding Intermediate Representations<a class="anchor" href="#sec:intermediate-computation" aria-label="Link to this section">#</a></h1>
+<p>This section explores interpreting hidden activations directly in natural language. In contrast to feature extraction and circuit discovery (Sections <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a> and <a href="@@BASE@@handbook/circuits/#sec:circuit" data-reference-type="ref" data-reference="sec:circuit">7</a>), which extract features or their interactions from model representations (activations), the methods we will discuss in this section treat the activation itself as the object of interpretation. They use language models or other natural-language-based methods to describe or elicit the encoded information, without intervening on or decomposing the underlying activation. This paradigm provides a direct interface between model activations and natural-language explanations, making it particularly accessible and understandable for human users.</p>
+<section id="sec:logit-lens" class="level2"><h2 data-pdf-page="67"><span class="secnum">8.1</span> Logit Lens: Tracing Predictions Through Depth<a class="anchor" href="#sec:logit-lens" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
-<p>Logit lens <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span> applies the model’s unembedding matrix to activations at intermediate layers, revealing what the model <strong>would predict</strong> if computation stopped at that layer. For a hidden activation <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>, logit lens computes: <span class="math display">\text{logits}_\ell = W_U h_\ell</span> where <span class="math inline">W_U</span> represents the model’s unembedding matrix. This reveals the trajectory of predictions across layers, showing when and where the model commits to specific outputs <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span>.</p>
+<p>Logit lens <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span> applies the model’s unembedding matrix to activations at intermediate layers, revealing what the model <em>would predict</em> if computation stopped at that layer. For a hidden activation <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>, logit lens computes: <span class="math display">\text{logits}_\ell = W_U h_\ell,</span> where <span class="math inline">W_U</span> represents the model’s unembedding matrix. Applying it across layers reveals the prediction trajectory, showing when and where the model commits to specific outputs <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span>.</p>
 </div>
-<p>Logit lens provides intuitive insight into the model’s decision-making process across depth. Early layers typically show diverse possible predictions with high entropy, while later layers progressively narrow and refine them toward the final output. By examining this trajectory, researchers can identify at which layer critical decisions emerge: whether the model commits early to a prediction or progressively refines it layer-by-layer.</p>
-<p>For example, in a 12-layer language model processing the sequence “A B C A B”, the logit lens may reveal that the token “C” emerges as the top-20 candidates at layer 6. Subsequent layers then progressively amplify this signal, eventually promoting it to the top-ranked token at the final layer. Conversely, for more ambiguous induction tasks involving complex logic, early and middle layers may exhibit low confidence across a broad distribution of tokens, with the model only converging to a specific prediction in the last few layers.</p>
-<figure id="fig:logitlens" data-latex-placement="t">
-<img alt="Figure 11: Logit Lens. Logit Lens provides a window into a model’s intermediate state by applying the final unembedding layer directly to the residual stream at earlier layers (h_1, h_2, ...). This allows researchers to see which tokens the model is “considering” mid-computation, such as the emergen" loading="lazy" src="@@BASE@@figures/8_logit_lens.svg" />
-<figcaption><span class="capnum">Figure 11:</span> <strong>Logit Lens</strong>. Logit Lens provides a window into a model’s intermediate state by applying the final unembedding layer directly to the residual stream at earlier layers (<span class="math inline">h_1, h_2, ...</span>). This allows researchers to see which tokens the model is “considering” mid-computation, such as the emergence of the concept “CAT” before the final output layer is reached.</figcaption>
+<p>Logit lens (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:logitlens" data-reference-type="ref" data-reference="fig:logitlens">10</a>) provides intuitive insight into the model’s decision-making process across depth. Experimental results show that early layers tend to produce diffuse predictions, while later layers progressively narrow them towards the final output, though exceptions exist. For example, in a twelve-layer language model processing the sequence <code>A B C A B</code>, the logit lens may reveal that the token <code>C</code> enters the top-20 candidates at layer 6 and is progressively amplified, eventually becoming the top-ranked token at the final layer. Conversely, for induction tasks in complex linguistic patterns, early and middle layers may show a flat probability distribution across many tokens, with the model converging to a specific prediction only in the last few layers.</p>
+<figure id="fig:logitlens" data-latex-placement="tb">
+<img alt="Figure 10: Logit Lens. Logit lens provides a window into a model’s intermediate state by applying the final unembedding layer directly to the residual stream at earlier layers (h_1, h_2, \ldots). This allows users to see which tokens the model is “considering” mid-computation, such as the emergence " loading="lazy" src="@@BASE@@figures/8_logit_lens.svg" />
+<figcaption><span class="capnum">Figure 10:</span> <strong>Logit Lens.</strong> Logit lens provides a window into a model’s intermediate state by applying the final unembedding layer directly to the residual stream at earlier layers (<span class="math inline">h_1, h_2, \ldots</span>). This allows users to see which tokens the model is “considering” mid-computation, such as the emergence of the concept “CAT” before the final output layer is reached.</figcaption>
 </figure>
 <section id="limitations-of-vanilla-logit-lens." class="level4">
 <h4>Limitations of Vanilla Logit Lens.</h4>
-<p>The vanilla logit lens has critical limitations. Applying the unembedding matrix to intermediate activations assumes that the hidden state is in a format the output layer expects, but intermediate representations may reside in vector spaces different from final activations. The unembedding matrix is only trained to operate on the final residual stream after all transformations; applying it to layer-6 activations extracts relevant information but may not precisely reflect the model’s underlying computation at that layer <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span>.</p>
+<p>The vanilla logit lens has a critical limitation: applying the unembedding matrix to intermediate activations assumes that the hidden state is in a format that the output layer expects. However, intermediate representations may be organised very differently from output-layer activations. Applying the unembedding matrix to layer-6 activations may extract relevant information but cannot precisely reflect the model’s underlying computation at that layer <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span>. Furthermore, logit lens is fundamentally correlational rather than causal. It projects the activation into the output vocabulary space without testing the roles decoded tokens play in model computation. In particular, because of the representational mismatch discussed above, decoded tokens represent at most information recoverable from the activation, rather than information used in model computation.</p>
 </section>
 </section>
-<section id="tuned-lens-learning-per-layer-transform" class="level2"><h2 data-pdf-page="49"><span class="secnum">8.2</span> Tuned Lens: Learning Per-Layer Transform<a class="anchor" href="#tuned-lens-learning-per-layer-transform" aria-label="Link to this section">#</a></h2>
+<section id="subsec:tuned-lens" class="level2"><h2 data-pdf-page="68"><span class="secnum">8.2</span> Tuned Lens: Learning Per-Layer Transforms<a class="anchor" href="#subsec:tuned-lens" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
-<p>Tuned lens <span class="citation" data-cites="belrose2025elicitinglatentpredictionstransformers">(<a href="@@BASE@@references/#ref-belrose2025elicitinglatentpredictionstransformers" class="cite cite-emerging" data-cite="belrose2025elicitinglatentpredictionstransformers" role="doc-biblioref">Belrose et al., 2025</a>)</span> addresses the vector space mismatch problem of logit lens by learning per-layer affine transformations that map intermediate activations to the output layer’s expected space: <span class="math display">\text{logits}_\ell^{\text{TL}} = W_U (W_\ell h_\ell + b_\ell),</span> where <span class="math inline">W_\ell</span> and <span class="math inline">b_\ell</span> are learned affine transformations for layer <span class="math inline">\ell</span>. These transforms are trained to minimize the divergence between the tuned lens’ predicted distribution <span class="math inline">p_\ell^{\text{TL}} = \operatorname{softmax}(\text{logits}_\ell^{\text{TL}})</span> and the final model output distribution <span class="math inline">p_{\text{final}}</span>, typically via <span class="math display">\min_{W_\ell,b_\ell}\mathbb{E}_{x\sim\mathcal{D}}\big[\,\mathrm{KL}(p_{\text{final}}(x)\;\|\;p_\ell^{\text{TL}}(x))\big].</span></p>
+<p>Tuned lens <span class="citation" data-cites="belrose2025elicitinglatentpredictionstransformers">(<a href="@@BASE@@references/#ref-belrose2025elicitinglatentpredictionstransformers" class="cite cite-emerging" data-cite="belrose2025elicitinglatentpredictionstransformers" role="doc-biblioref">Belrose et al., 2025</a>)</span> addresses logit lens’ concern of representational mismatch by learning per-layer affine transformations that map intermediate activations to the output layer’s activation space: <span class="math display">\text{logits}_\ell^{\text{TL}} = W_U (W_\ell h_\ell + b_\ell),</span> where <span class="math inline">W_\ell</span> and <span class="math inline">b_\ell</span> are learned affine transformations for layer <span class="math inline">\ell</span>. These transforms are trained to minimise the divergence between the tuned lens’ predicted distribution <span class="math inline">p_\ell^{\text{TL}} = \operatorname{softmax}(\text{logits}_\ell^{\text{TL}})</span> and the model output distribution <span class="math inline">p_{\text{final}}</span> via: <span class="math display">\min_{W_\ell,b_\ell}\mathbb{E}_{x\sim\mathcal{D}}\big[\,\mathrm{KL}(p_{\text{final}}(x)\;\|\;p_\ell^{\text{TL}}(x))\big].</span></p>
 </div>
-<p>Tuned lens improves on basic logit lens by correcting for the fact that different layers operate in different representational spaces. Rather than naively applying the final output layer, tuned lens learns what transformation is needed at each layer to make intermediate activations <strong>speak the same language</strong> as the final layer <span class="citation" data-cites="belrose2025elicitinglatentpredictionstransformers">(<a href="@@BASE@@references/#ref-belrose2025elicitinglatentpredictionstransformers" class="cite cite-emerging" data-cite="belrose2025elicitinglatentpredictionstransformers" role="doc-biblioref">Belrose et al., 2025</a>)</span>.</p>
-<p>The trade-off is that tuned lens requires training additional parameters (one affine transform per layer), but this is computationally cheap compared to other mechanistic methods.</p>
+<p>As a result, the unembedding matrix can extract relevant information from each layer’s activations more effectively.</p>
+<section id="limitations-of-tuned-lens." class="level4">
+<h4>Limitations of Tuned Lens.</h4>
+<p>Although tuned lens mitigates representational mismatch, it still inherits several fundamental limitations. First, the decoding paradigm remains inherently correlational rather than causal; decodable information does not imply a causal role in the model’s prediction. Second, similar to logit lens, tuned lens is primarily designed to answer <em>what the model is likely to predict</em> rather than <em>what the model is internally computing</em>. The learned layer-specific transformations that align intermediate representations with the final prediction space may obscure intermediate computational steps and produce predictions that resemble the model’s final output. Empirical evidence also shows that it skips intermediate computation more aggressively than logit lens <span class="citation" data-cites="jlens">(<a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a>)</span>.</p>
 </section>
-<section id="case-study-refusal-vectors-and-sleeper-agents" class="level2"><h2 data-pdf-page="50"><span class="secnum">8.3</span> Case Study: Refusal Vectors and Sleeper Agents<a class="anchor" href="#case-study-refusal-vectors-and-sleeper-agents" aria-label="Link to this section">#</a></h2>
+</section>
+<section id="jacobian-lens-decoding-computational-dispositions" class="level2"><h2 data-pdf-page="68"><span class="secnum">8.3</span> Jacobian Lens: Decoding Computational Dispositions<a class="anchor" href="#jacobian-lens-decoding-computational-dispositions" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
-<p><span id="def:refusal-vector" data-label="def:refusal-vector"></span> A <strong>refusal vector</strong> is a direction in a model’s residual stream activations that corresponds to the model declining to produce undesirable or harmful content. Let <span class="math inline">h_t^\ell \in \mathbb{R}^d</span> denote the residual stream at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span>. We define a refusal vector <span class="math inline">\mathbf{r}^\ell \in \mathbb{R}^d</span> at layer <span class="math inline">\ell</span> as: <span class="math display">\mathbf{r}^\ell = \mathbb{E}_{x \sim \mathcal{D}_\text{harmful}} [h_t^\ell(x)] - \mathbb{E}_{x \sim \mathcal{D}_\text{harmless}} [h_t^\ell(x)],</span> where <span class="math inline">\mathcal{D}_\text{harmful}</span> contains prompts that should be refused and <span class="math inline">\mathcal{D}_\text{harmless}</span> contains neutral or safe prompts.</p>
-<p>Projecting <span class="math inline">h_t^\ell</span> onto <span class="math inline">\mathbf{r}^\ell</span> quantifies the strength of the refusal signal at layer <span class="math inline">\ell</span>: <span class="math display">s_t^\ell = \langle h_t^\ell, \mathbf{r}^\ell \rangle.</span></p>
-<p>A large positive <span class="math inline">s_t^\ell</span> correlates with refusal behavior, and ablating or modifying this component can reduce the model’s tendency to refuse. Refusal vectors can be used to interpret, steer, or probe models along safety-relevant directions in residual space.</p>
-</div>
-<p>Building on Definition <a href="@@BASE@@handbook/intermediate-representations/#def:refusal-vector" data-reference-type="ref" data-reference="def:refusal-vector">8.3</a>, we can use logit lens to examine how refusal vectors manifest across layers and why the same approach is less effective for latent behaviors like sleeper agents in Definiton <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#war:sleeper" data-reference-type="ref" data-reference="war:sleeper">2</a>.</p>
-<div class="examplebox">
-<p>For a refusal vector <span class="math inline">\mathbf{r}^\ell</span> defined at layer <span class="math inline">\ell</span>, logit lens projects intermediate hidden states <span class="math inline">h_t^\ell</span> to the output vocabulary via the unembedding matrix <span class="math inline">W_U</span>: <span class="math display">\hat{y}_t^{(\ell)} = W_U\, h_t^\ell.</span> The resulting layer-wise logits <span class="math inline">\hat{y}_t^{(\ell)}</span> approximate what the model would predict if layer <span class="math inline">\ell</span> were the final layer. When a refusal vector contributes significantly to the activations, the logits associated with refusal behavior may become noticeable at intermediate layers, providing a tentative trace of how the model encodes refusal. Projections along <span class="math inline">\mathbf{r}^\ell</span> can be used to quantify the strength of this signal: <span class="math display">s_t^\ell = \langle h_t^\ell, \mathbf{r}^\ell \rangle.</span></p>
-<p>Sleeper agents encode a <strong>conditional, latent objective</strong> that activates only under a hidden trigger (e.g., year = 2024). For such behavior, the difference <span class="math display">\Delta \hat{y}_t^{(\ell)} = \hat{y}_t^{(\ell)}(\text{trigger}) - \hat{y}_t^{(\ell)}(\text{non-trigger})</span> may appear gradually across layers as the model increasingly favors tokens associated with the latent objective. In this sense, logit lens can still reveal contributions to potentially harmful tokens and track how these emerge through the network, similarly to refusal vectors.</p>
+<p>Jacobian lens (J-lens; <span class="citation" data-cites="jlens"><a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a></span>) adopts a decoding paradigm distinct from logit lens and tuned lens. It uses the Jacobian to characterise how an intermediate representation influences certain model outputs.<br />
+Let <span class="math inline">h_\ell^t</span> denote the residual stream at layer <span class="math inline">\ell</span> and position <span class="math inline">t</span>. A perturbation to <span class="math inline">h_\ell^t</span> propagates to the final-layer residual stream at every position <span class="math inline">t&#39; \geq t</span>; to first order, this influence is captured by the Jacobian <span class="math display">\frac{\partial h_{\mathrm{final}}^{t&#39;}}{\partial h_\ell^t}.</span> The lens matrix is defined as the expectation of this Jacobian over prompts and positions: <span class="math display">J_\ell
+=
+\mathbb{E}_{\,\mathrm{prompt},\,t,\,t&#39; \ge t}
+\left[
+\frac{\partial h_{\mathrm{final}}^{t&#39;}}
+{\partial h_\ell^t}
+\right].</span> J-lens then decodes intermediate representations by <span class="math display">p_\ell^{\mathrm{JL}}
+=
+\operatorname{softmax}
+\!\big(
+W_U \operatorname{norm}(J_\ell h_\ell)
+\big).</span></p>
+<p><span class="citation" data-cites="jlens"><a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al. (2026)</a></span> further defined layer-specific concept (token) representations using the rows of <span class="math inline">W_U J_\ell</span>, called <em>J-lens vectors</em>. The inner product between a J-lens vector and <span class="math inline">h_\ell</span> measures how strongly the current activation will promote the corresponding token in model outputs.</p>
 </div>
+<p>Unlike logit lens and tuned lens, which primarily estimate which token the model is likely to predict next, J-lens characterises how current representations influence model outputs. It aims to answer <em>what the model is internally computing</em> rather than merely <em>what the model is going to predict</em>. J-lens has been found useful for analysing tasks that require multi-step reasoning, latent planning, or other forms of extended computation <span class="citation" data-cites="jlens">(<a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a>)</span>.</p>
+<p>Furthermore, the defined J-lens vectors are a useful medium for intervention. They can serve as steering vectors to shift model behaviour towards or away from the corresponding concepts. In contrast, interventional utility is largely absent from logit lens and tuned lens, which are primarily decoding tools.</p>
+<section id="limitations-of-jacobian-lens." class="level4">
+<h4>Limitations of Jacobian Lens.</h4>
+<p>J-lens still faces several limitations that warrant further investigation. First, like logit lens and tuned lens, it operates primarily at the token level. However, many semantically meaningful concepts are represented by multiple tokens. For example, the word “photoperiodism” may be split into several subword tokens. If only one token (e.g., “photo”) is visible in J-lens, we cannot tell whether it corresponds to the concept of photoperiodism or actually other words. Second, J-lens is found to work best within a certain range of layers, called <em>J-space</em> <span class="citation" data-cites="jlens">(<a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a>)</span>. Specifically, decoded concepts are most informative in intermediate layers (e.g., approximately 38–92% of the depth in Claude Sonnet 4.5). In early layers, decoded concepts are noisy and hard to interpret. In later layers, they collapse towards the model’s output and are also uninformative. Some recent studies have attempted to address these limitations <span class="citation" data-cites="jlens blank2026rlens">(<a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a>; <a href="@@BASE@@references/#ref-blank2026rlens" class="cite cite-emerging" data-cite="blank2026rlens" role="doc-biblioref">Blank et al., 2026</a>)</span>.</p>
 </section>
-<section id="subsec:patchscopes" class="level2"><h2 data-pdf-page="51"><span class="secnum">8.4</span> Patchscopes: Leveraging the Language Model Itself to Explain Intermediate Representations<a class="anchor" href="#subsec:patchscopes" aria-label="Link to this section">#</a></h2>
-<figure id="fig:patchscopes" data-latex-placement="t">
-<img alt="Figure 12: Patchscopes. Patchscopes inspects internal representations by extracting a hidden state from a source prompt and “patching” it into a different inspection prompt. This allows researchers to verify what specific information is captured in a representation by observing how it influences the" loading="lazy" src="@@BASE@@figures/8_patchscopes.svg" />
-<figcaption><span class="capnum">Figure 12:</span> <strong>Patchscopes</strong>. Patchscopes inspects internal representations by extracting a hidden state from a source prompt and “patching” it into a different inspection prompt. This allows researchers to verify what specific information is captured in a representation by observing how it influences the generation of a completely different context—for example, seeing if a representation for “United Kingdom” correctly triggers “London” as the largest city in a new prompt.</figcaption>
+</section>
+<section id="subsec:patchscopes" class="level2"><h2 data-pdf-page="69"><span class="secnum">8.4</span> Patchscopes: Leveraging the Language Model Itself to Explain Intermediate Representations<a class="anchor" href="#subsec:patchscopes" aria-label="Link to this section">#</a></h2>
+<figure id="fig:patchscopes" data-latex-placement="tb">
+<img alt="Figure 11: Patchscopes. Patchscopes extracts the model representation of interest from a source prompt and “patches” it into an inspection prompt. By observing changes in model output, users can examine what information the representation contains. For example, in this figure we can observe whether " loading="lazy" src="@@BASE@@figures/8_patchscopes.svg" />
+<figcaption><span class="capnum">Figure 11:</span> <strong>Patchscopes.</strong> Patchscopes extracts the model representation of interest from a source prompt and “patches” it into an inspection prompt. By observing changes in model output, users can examine what information the representation contains. For example, in this figure we can observe whether the representation of “United Kingdom” correctly triggers “London” as the largest city.</figcaption>
 </figure>
 <div class="definitionbox">
-<p>Patchscopes <span class="citation" data-cites="patchscopes">(<a href="@@BASE@@references/#ref-patchscopes" class="cite" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al., 2024</a>)</span> leverages the expressive capacity of large language models to translate internal representations into natural-language explanations. Let <span class="math inline">h^l_t</span> denote a hidden representation at layer <span class="math inline">l</span> and token position <span class="math inline">t</span>. Patchscopes produces a textual explanation of <span class="math inline">h^l_t</span>, denoted by <span class="math inline">e(h^l_t)</span>, via <span class="math display">e(h^l_t) = f_{\text{model}}(p_{[h^{l^*}_{t^*} \leftarrow h^l_t]}),</span> where <span class="math inline">l^*</span> and <span class="math inline">t^*</span> specify the layer and token position to which the representation is patched, and <span class="math inline">p</span> is an <em>elicitation prompt</em> designed to encourage the language model <span class="math inline">f_{\text{model}}</span> to decode the patched representation into text that expresses the contained information.</p>
-<p>For example, consider the prompt illustrated in the Figure 1 of <span class="citation" data-cites="patchscopes"><a href="@@BASE@@references/#ref-patchscopes" class="cite" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al. (2024)</a></span> <span class="math display">p = \text{``cat$\rightarrow$cat; 135$\rightarrow$135; hello$\rightarrow$hello; ?&#39;&#39;},</span> where <span class="math inline">t^*</span> corresponds to the position of the token “?”. The notation <span class="math inline">[h^{l^*}_{t^*} \leftarrow h^l_t]</span> indicates that, during the forward pass of <span class="math inline">f_{\text{model}}</span> on <span class="math inline">p</span>, the hidden representation at layer <span class="math inline">l^*</span> and position <span class="math inline">t^*</span> is replaced with <span class="math inline">h^l_t</span>. Under this construction, information encoded in <span class="math inline">h^l_t</span> can be elicited through the model’s prediction for the next token at the “?” position.</p>
+<p>Patchscopes <span class="citation" data-cites="patchscopes">(<a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al., 2024</a>)</span> leverages the expressive capacity of language models to translate their internal representations into natural-language explanations. Let <span class="math inline">h^t_\ell</span> denote a hidden representation at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span>. Patchscopes produces a textual explanation of <span class="math inline">h^t_\ell</span>, denoted as <span class="math inline">e(h^t_\ell)</span>, by <span class="math display">e(h^t_\ell) = f_\theta(p_{[h^{t^*}_{\ell^*} \leftarrow h^t_\ell]}),</span> where <span class="math inline">\ell^*</span> and <span class="math inline">t^*</span> specify the layer and token position to which the representation is patched, and <span class="math inline">p</span> is an <em>elicitation prompt</em> designed to encourage the language model <span class="math inline">f_\theta(\cdot)</span> to decode the patched representation into text expressing the contained information.<br />
+For example, consider the prompt illustrated in Figure 1 of <span class="citation" data-cites="patchscopes"><a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al. (2024)</a></span>: <span class="math display">p = \text{``cat$\rightarrow$cat; 135$\rightarrow$135; hello$\rightarrow$hello; ?&#39;&#39;},</span> where <span class="math inline">t^*</span> corresponds to the position of the token “?”. The notation <span class="math inline">[h^{t^*}_{\ell^*} \leftarrow h^t_\ell]</span> indicates that, during the forward pass of <span class="math inline">f_\theta(\cdot)</span> on <span class="math inline">p</span>, the hidden representation at layer <span class="math inline">\ell^*</span> and position <span class="math inline">t^*</span> is replaced with <span class="math inline">h^t_\ell</span>. As a result, the model can elicit information encoded in <span class="math inline">h^t_\ell</span> through its prediction for the next token at the “?” position.</p>
 </div>
-<p>Because the mapping from intermediate representations to natural language may be too complex to be captured by an affine transformation, Patchscopes extends the paradigm of the logit lens and tuned lens by <em>delegating the representation-to-text transformation to the language model itself</em><a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>. In this view, the language model acts as an <em>activation decoder</em>.</p>
-<p>The decoding process proceeds by patching the representation of interest into a designated token position within an elicitation prompt. As the model processes the prompt, the semantic information embedded in the patched representation can be surfaced in the model’s generated output. Crucially, this approach relies on the model’s own learned and generalized text-generation capabilities to perform the transformation from internal activations to natural language.</p>
+<p>Because the mapping from intermediate representations to natural language may be too complex to be captured by an affine transformation, Patchscopes (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:patchscopes" data-reference-type="ref" data-reference="fig:patchscopes">11</a>) extends the paradigm of logit lens and tuned lens by <em>delegating the representation-to-text transformation to the language model itself</em><a href="#fn1" class="footnote-ref" id="fnref1" role="doc-noteref"><sup>1</sup></a>. In this view, the language model acts as an <em>activation decoder</em>. Crucially, Patchscopes relies on the model’s learned, generalisable text-generation capabilities to map internal activations to natural language.</p>
 <section id="limitations-of-patchscopes." class="level4">
 <h4>Limitations of Patchscopes.</h4>
-<p>A key limitation of Patchscopes is that injecting an external representation into an elicitation prompt can shift the model’s internal activations into an OOD regime. This is expected as the decoder is a standard pretrained language model that has not been explicitly trained for representation-to-text decoding. Although the model may still successfully surface salient information from the patched representation, the decoding process can be degraded, and critical aspects of the original representation may be distorted or lost.</p>
+<p>A key limitation of Patchscopes is that injecting an external representation into an elicitation prompt can shift the model’s internal activations into an out-of-distribution (OOD) regime. This is expected, as the decoder is a standard pretrained language model not explicitly trained for representation-to-text decoding. Although the model may still surface salient information from the patched representation, the decoding process can degrade, and critical aspects of the original representation may be distorted or lost.</p>
 <aside id="footnotes" class="footnotes footnotes-end-of-section" role="doc-footnote">
 <hr />
 <ol>
-<li id="fn1"><p>A concurrent work, SelfIE <span class="citation" data-cites="selfie">(<a href="@@BASE@@references/#ref-selfie" class="cite" data-cite="selfie" role="doc-biblioref">Chen et al., 2024b</a>)</span>, explores a similar direction. While we focus on Patchscopes as a reference framework, both methods share closely related motivations and methodologies.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
+<li id="fn1"><p>A concurrent work, SelfIE <span class="citation" data-cites="selfie">(<a href="@@BASE@@references/#ref-selfie" class="cite cite-established" data-cite="selfie" role="doc-biblioref">Chen et al., 2024b</a>)</span>, explores a similar direction. While we focus on Patchscopes as a reference framework, both methods share closely related motivations and methodologies.<a href="#fnref1" class="footnote-back" role="doc-backlink">↩︎</a></p></li>
 </ol>
 </aside>
 </section>
 </section>
-<section id="subsec:latentqa" class="level2"><h2 data-pdf-page="52"><span class="secnum">8.5</span> LatentQA: Finetuning the Language Model as Representation Decoder<a class="anchor" href="#subsec:latentqa" aria-label="Link to this section">#</a></h2>
+<section id="subsec:latentqa" class="level2"><h2 data-pdf-page="70"><span class="secnum">8.5</span> LatentQA: Fine-Tuning the Language Model as an Activation Verbaliser<a class="anchor" href="#subsec:latentqa" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
-<p>LatentQA <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2024</a>)</span> formulates representation decoding as a supervised learning problem via Latent Interpretation Tuning (LIT), which fine-tunes an activation decoder to perform representation-to-text decoding. The core step of LIT is to construct a finetuning dataset <span class="math inline">D</span> consisting of samples <span class="math inline">(h^l_t, q, a)</span>, where <span class="math inline">h^l_t</span> is a cached hidden representation from a target model, and <span class="math inline">(q, a)</span> is a question–answer pair that queries the information encoded in <span class="math inline">h^l_t</span>.</p>
-<p>Specifically, <span class="math inline">h^l_t</span> denotes the activation at layer <span class="math inline">l</span> and token position <span class="math inline">t</span> when the target model processes a “control prompt” designed to embed a specific attribute or concept into the representation. An example control prompt from Figure 3 of <span class="citation" data-cites="pan2024latentqateachingllmsdecode"><a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al. (2024)</a></span> is: <span class="math display">\text{``In your next reply, please try to evoke a brief sense of triumphant hubris.&#39;&#39;}</span> If we define <span class="math inline">h^l_t</span> as the activation at layer <span class="math inline">l</span> corresponding to the final token (“.”) of this prompt, the resulting representation is expected to encode the instruction to respond with triumphant hubris. In this case, a corresponding question–answer pair may be: <span class="math display">q = \text{``In what way does the assistant craft its responses to the user?&#39;&#39;}</span> <span class="math display">a = \text{``The assistant crafts responses that briefly highlight a sense of self-assured victory.&#39;&#39;}</span> Both the control prompt and the <span class="math inline">(q, a)</span> pair are generated by prompting an off-the-shelf proprietary language model <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-emerging" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2024</a>)</span>.</p>
+<p>LatentQA <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span> frames representation decoding as a supervised learning problem via Latent Interpretation Tuning (LIT), fine-tuning an activation verbaliser to perform representation-to-text verbalisation. The core step of LIT is to construct a fine-tuning dataset <span class="math inline">D</span> consisting of samples <span class="math inline">(h^t_\ell, q, a)</span>, where <span class="math inline">h^t_\ell</span> is a cached hidden representation from a target model, and <span class="math inline">(q, a)</span> is a question–answer pair that queries the information encoded in <span class="math inline">h^t_\ell</span>.<br />
+Specifically, <span class="math inline">h^t_\ell</span> denotes the activation at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span> when the target model processes a “control prompt” designed to embed a specific attribute or concept into the representation. An example control prompt from Figure 13 of <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span> is: <span class="math display">\text{``In your next reply, please try to evoke a brief sense of triumphant hubris.&#39;&#39;}</span> If <span class="math inline">h^t_\ell</span> is defined as the activation at layer <span class="math inline">\ell</span> corresponding to the final token (“.”) of this prompt, the resulting representation is expected to encode the instruction to respond with triumphant hubris. In this case, a corresponding question–answer pair may be: <span class="math display">q = \text{``In what way does the assistant craft its responses to the user?&#39;&#39;}</span> <span class="math display">a = \text{``The assistant crafts responses that briefly highlight a sense of self-assured victory.&#39;&#39;}</span> Both the control prompt and the <span class="math inline">(q, a)</span> pair are generated by prompting an off-the-shelf proprietary language model <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span>.</p>
 </div>
-<p>LatentQA addresses the OOD issue inherent in Patchscopes-style methods by explicitly finetuning the activation decoder on the representation-to-text decoding task. Through LIT, the decoder is trained to directly ingest and process raw activation vectors. Ideally, after being exposed to a sufficiently diverse collection of question–answer pairs over many different representations, the decoder learns <em>which aspects of the activation to attend to</em> and <em>how to verbalize them</em> conditioned on the query.</p>
-<figure id="fig:latentqa" data-latex-placement="t">
-<img alt="Figure 13: LatentQA. LatentQA is a method for probing the semantic content of hidden representations by training a separate decoder model to answer natural language questions about them. The process involves (1) curating a dataset of activation-question-answer triplets from a target LLM, and (2) fin" loading="lazy" src="@@BASE@@figures/8_latent-qa.svg" />
-<figcaption><span class="capnum">Figure 13:</span> <strong>LatentQA</strong>. LatentQA is a method for probing the semantic content of hidden representations by training a separate decoder model to answer natural language questions about them. The process involves (1) curating a dataset of activation-question-answer triplets from a target LLM, and (2) finetuning a decoder to retrieve specific information latent within those activations, such as the model’s underlying goals or persona.</figcaption>
+<p>LatentQA (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:latentqa" data-reference-type="ref" data-reference="fig:latentqa">12</a>) addresses the OOD issue inherent in Patchscopes-style methods by explicitly fine-tuning the activation verbaliser on the representation-to-text decoding task. Through LIT, the verbaliser learns to ingest and process raw activation vectors directly. Ideally, after being exposed to a sufficiently diverse collection of question–answer pairs over many different representations, the decoder learns <em>which aspects of the activation to attend to</em> and <em>how to verbalise them</em> conditioned on the query.</p>
+<figure id="fig:latentqa" data-latex-placement="tb">
+<img alt="Figure 12: LatentQA. LatentQA probes the semantic content of hidden representations by training a separate verbaliser model to answer natural-language questions about them. The process involves (1) curating a dataset of activation–question–answer triplets from a target LLM, and (2) fine-tuning a ver" loading="lazy" src="@@BASE@@figures/8_latent_qa.svg" />
+<figcaption><span class="capnum">Figure 12:</span> <strong>LatentQA.</strong> LatentQA probes the semantic content of hidden representations by training a separate verbaliser model to answer natural-language questions about them. The process involves (1) curating a dataset of activation–question–answer triplets from a target LLM, and (2) fine-tuning a verbaliser to retrieve specific information latent within those activations, such as the model’s underlying goals or persona.</figcaption>
 </figure>
 <section id="limitations-of-latentqa." class="level4">
 <h4>Limitations of LatentQA.</h4>
-<p>The central limitation of LatentQA lies in the generalizability of the learned representation-to-text decoder. When the finetuning dataset <span class="math inline">D</span> is insufficiently large or diverse, the decoder may overfit by memorizing superficial correspondences between representations and textual descriptions, rather than learning the underlying algorithmic transformation. Recent efforts, such as Predictive Concept Decoders (PCDs) <span class="citation" data-cites="pcd">(<a href="@@BASE@@references/#ref-pcd" class="cite cite-emerging" data-cite="pcd" role="doc-biblioref">Huang et al., 2025</a>)</span> and Activation Oracles (AOs) <span class="citation" data-cites="ao">(<a href="@@BASE@@references/#ref-ao" class="cite cite-emerging" data-cite="ao" role="doc-biblioref">Karvonen et al., 2025</a>)</span>, mitigate this issue by constructing more diverse finetuning datasets to expand the decoder’s coverage and robustness.</p>
+<p>LatentQA faces several technical challenges, including the generalisability of the learned representation-to-text mapping. When the fine-tuning dataset <span class="math inline">D</span> is too small or insufficiently diverse, the verbaliser may memorise superficial correspondences between representations and explanations rather than learning to extract information from representations. Recent approaches, such as Predictive Concept Decoders (PCDs) <span class="citation" data-cites="pcd">(<a href="@@BASE@@references/#ref-pcd" class="cite cite-emerging" data-cite="pcd" role="doc-biblioref">Huang et al., 2026</a>)</span> and Activation Oracles (AOs) <span class="citation" data-cites="ao">(<a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a>)</span>, mitigate this issue by constructing more diverse training datasets.</p>
+<p>Another technical challenge is evaluating whether activation-verbalisation models genuinely explain the represented information rather than exploit shortcuts. For example, a verbaliser may implicitly reconstruct the original input to the target model and infer an explanation from it. Recent studies have found that such failure modes are severe and prevalent in existing verbalisers (<span class="citation" data-cites="li2026do"><a href="@@BASE@@references/#ref-li2026do" class="cite cite-established" data-cite="li2026do" role="doc-biblioref">Li et al., 2026c</a></span>; <span class="citation" data-cites="jakkli2026current"><a href="@@BASE@@references/#ref-jakkli2026current" class="cite cite-emerging" data-cite="jakkli2026current" role="doc-biblioref">Jakkli et al., 2026</a></span>). As a result, strong verbalisation performance alone is insufficient to show that the verbaliser faithfully analysed the internal representations.</p>
 </section>
 </section>
-<section id="subsec:probing" class="level2"><h2 data-pdf-page="52"><span class="secnum">8.6</span> Probing Classifiers: Capabilities, Critiques, and Causal Limitations<a class="anchor" href="#subsec:probing" aria-label="Link to this section">#</a></h2>
-<p>Probing classifiers are among the most widely used tools for understanding neural network representations. A probe trains a classifier—typically linear—to predict a property (e.g., part-of-speech, sentiment, entity type) from model activations. High probe accuracy is interpreted as evidence that the property is “encoded” in the representation. However, the relationship between probe accuracy and mechanistic understanding is subtle and contested. This section reviews probing methodology, its critiques, and its appropriate role in interpretability research.</p>
-<section id="standard-probing-methodology" class="level3"><h3 data-pdf-page="53"><span class="secnum">8.6.1</span> Standard Probing Methodology<a class="anchor" href="#standard-probing-methodology" aria-label="Link to this section">#</a></h3>
+<section id="subsec:probing" class="level2"><h2 data-pdf-page="72"><span class="secnum">8.6</span> Probing Classifiers: Capabilities, Critiques, and Causal Limitations<a class="anchor" href="#subsec:probing" aria-label="Link to this section">#</a></h2>
+<p>Probing classifiers (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:probing" data-reference-type="ref" data-reference="fig:probing">13</a>) are among the most widely used tools for analysing neural network representations. A probe is a classifier trained to predict a property of the input, such as part of speech, sentiment, or entity type, from model activations. High probe accuracy means that the property is decodable from the activations. However, information decodability is a relatively coarse-grained, correlational interpretability claim compared with causal claims about information in model computation. This section reviews probing methodology, its major critiques, and its appropriate role in interpretability research.</p>
+<p>As a side note, discovered probe directions can also be used as supervised feature extractors. We discuss this perspective in Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: supervised_extraction" data-reference-type="ref" data-reference="subsubsec: supervised_extraction">6.9.4</a>, including comparisons with SAEs, and in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a>, where we discuss their use as steering vectors.</p>
+<section id="standard-probing-methodology" class="level3"><h3 data-pdf-page="72"><span class="secnum">8.6.1</span> Standard Probing Methodology<a class="anchor" href="#standard-probing-methodology" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
-<p>A <strong>probing classifier</strong> is a supervised model trained to predict a target property <span class="math inline">y</span> from neural network activations <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>: <span class="math display">\hat{y} = g(W_{\text{probe}} h_\ell + b_{\text{probe}})</span> where <span class="math inline">g</span> is an activation function (identity for regression, softmax for classification), and <span class="math inline">(W_{\text{probe}}, b_{\text{probe}})</span> are learned parameters. The probe is trained on labeled examples <span class="math inline">(h_\ell^{(i)}, y^{(i)})</span> to minimize prediction error.</p>
-<p><strong>Linear probes</strong> constrain <span class="math inline">g</span> to be linear, testing whether the property is linearly accessible. <strong>MLP probes</strong> allow nonlinear <span class="math inline">g</span>, testing whether the property is present in any recoverable form.</p>
+<p>A <em>probing classifier</em> is a supervised model trained to predict a target property <span class="math inline">y</span> from neural network activations <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>. A <em>linear probe</em> follows the form: <span class="math display">\hat{y} = g(W_{\text{probe}} h_\ell + b_{\text{probe}}),</span> where <span class="math inline">W_{\text{probe}}</span> and <span class="math inline">b_{\text{probe}}</span> are learned parameters. <span class="math inline">g</span>, typically an identity function for regression and softmax for classification, maps the probe’s raw output to the prediction space. The probe is trained by minimising prediction error on labelled examples <span class="math inline">(h_\ell^{(i)}, y^{(i)})</span>. The linear probe tests whether the target property is linearly accessible from the representation. <em>MLP probes</em> introduce one or more nonlinear hidden layers instead of <span class="math inline">W_{\text{probe}}</span> to test whether the property can be retrieved through more expressive nonlinear mappings.</p>
 </div>
-<figure id="fig:probing" data-latex-placement="t">
-<img alt="Figure 14: Probing Classifier. Probing is a technique used to determine if a specific concept is linearly represented within a model’s internal activations. A simple supervised classifier (probe) is trained to distinguish between positive and negative samples of a concept." loading="lazy" src="@@BASE@@figures/8_probing.svg" />
-<figcaption><span class="capnum">Figure 14:</span> <strong>Probing Classifier</strong>. Probing is a technique used to determine if a specific concept is linearly represented within a model’s internal activations. A simple supervised classifier (probe) is trained to distinguish between positive and negative samples of a concept.</figcaption>
+<figure id="fig:probing" data-latex-placement="tb">
+<img alt="Figure 13: Probing Classifier. A probing classifier (or probe) tests the decodability of a concept from the target model’s internal representations. It is trained to distinguish activations associated with positive and negative examples of the concept. High probe accuracy indicates that the concept " loading="lazy" src="@@BASE@@figures/8_probing.svg" />
+<figcaption><span class="capnum">Figure 13:</span> <strong>Probing Classifier.</strong> A probing classifier (or <em>probe</em>) tests the decodability of a concept from the target model’s internal representations. It is trained to distinguish activations associated with positive and negative examples of the concept. High probe accuracy indicates that the concept can be reliably decoded from the representation.</figcaption>
 </figure>
-<section id="what-probing-can-show." class="level4">
-<h4>What Probing Can Show.</h4>
-<p>Probing provides evidence about <em>information content</em>: whether activations contain recoverable information about a property. High probe accuracy indicates the information is present; low accuracy indicates it is absent or deeply encoded.</p>
-</section>
-<section id="what-probing-cannot-show." class="level4">
-<h4>What Probing Cannot Show.</h4>
-<p>Probing does not establish <em>causal relevance</em>: whether the model actually uses that information for its predictions. A representation may encode information that is never accessed by downstream computation—a “leftover” from earlier processing or a byproduct of representing correlated features.</p>
-</section>
-</section>
-<section id="the-probe-causality-problem" class="level3"><h3 data-pdf-page="53"><span class="secnum">8.6.2</span> The Probe Causality Problem<a class="anchor" href="#the-probe-causality-problem" aria-label="Link to this section">#</a></h3>
+<p>Probing provides evidence about whether activations contain information about a property. High probe accuracy indicates the information is present; low accuracy suggests it is absent or the probe is not expressive enough. Note that probing alone does not show whether the model actually depends on that information in its decision-making process. A representation can encode information that downstream computation never accesses, such as “leftover” information from earlier processing.</p>
 <div class="warningbox">
-<p>High probe accuracy does not imply causal relevance. The model may encode information without using it, or the probe may detect correlates of the target property rather than the property itself <span class="citation" data-cites="belinkov-2022-probing hewitt-liang-2019-designing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>; <a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang, 2019</a>)</span>.</p>
-<p><strong>Failure modes:</strong></p>
-<ul>
-<li><p><strong>Correlational encoding</strong>: The probe detects features correlated with the target (e.g., word frequency correlates with part-of-speech) rather than the target itself.</p></li>
-<li><p><strong>Unused information</strong>: Information may be encoded but never read by subsequent layers. The model’s actual computation may rely on different features.</p></li>
-<li><p><strong>Probe expressivity</strong>: A sufficiently powerful probe can extract information that requires complex computation to access, information the model itself may not use.</p></li>
-</ul>
-<p>This problem is fundamental: probing measures information presence, not information use. Bridging this gap requires causal methods.</p>
+<p>High probe accuracy does not imply causal relevance <span class="citation" data-cites="belinkov-2022-probing hewitt-liang-2019-designing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>; <a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite cite-established" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang, 2019</a>)</span>. The probe can work by detecting features spuriously correlated with the target property (e.g., word frequency correlates with part of speech) rather than detecting the target property itself. Furthermore, subsequent layers may never use the decoded property to influence the model output. Finally, a sufficiently expressive probe can achieve high accuracy by exploiting noise even when the representations contain no such information.</p>
 </div>
+<p>In summary, using probing alone generally does not provide actionable mechanistic understanding and therefore does not, under our definition, constitute mechanistic interpretability research. However, subsequent work has proposed refined probing paradigms that explicitly incorporate causal considerations into probe design and evaluation, enabling more actionable mechanistic understanding <span class="citation" data-cites="elazar-etal-2021-amnesic">(<a href="@@BASE@@references/#ref-elazar-etal-2021-amnesic" class="cite cite-established" data-cite="elazar-etal-2021-amnesic" role="doc-biblioref">Elazar et al., 2021</a>)</span>; see Section <a href="@@BASE@@handbook/intermediate-representations/#subsubsec: amnesic probing" data-reference-type="ref" data-reference="subsubsec: amnesic probing">8.6.4</a> for details. Recent interpretability studies have similarly used causal interventions to validate probe-discovered concept directions, building a more complete mechanistic explanation <span class="citation" data-cites="marks2024the li2023emergent">(<a href="@@BASE@@references/#ref-marks2024the" class="cite cite-established" data-cite="marks2024the" role="doc-biblioref">Marks &amp; Tegmark, 2024</a>; <a href="@@BASE@@references/#ref-li2023emergent" class="cite cite-established" data-cite="li2023emergent" role="doc-biblioref">Li et al., 2023</a>)</span>.</p>
 </section>
-<section id="control-tasks-and-selectivity" class="level3"><h3 data-pdf-page="54"><span class="secnum">8.6.3</span> Control Tasks and Selectivity<a class="anchor" href="#control-tasks-and-selectivity" aria-label="Link to this section">#</a></h3>
-<p>A major critique of probing is that high accuracy may reflect probe capacity rather than representation quality. <span class="citation" data-cites="hewitt-liang-2019-designing"><a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang (2019)</a></span> introduced <strong>control tasks</strong> to address this concern.</p>
+<section id="control-tasks-and-selectivity" class="level3"><h3 data-pdf-page="73"><span class="secnum">8.6.2</span> Control Tasks and Selectivity<a class="anchor" href="#control-tasks-and-selectivity" aria-label="Link to this section">#</a></h3>
+<p>High probe accuracy may reflect classification capacity rather than the target property’s decodability. <span class="citation" data-cites="hewitt-liang-2019-designing"><a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite cite-established" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang (2019)</a></span> addressed this concern by introducing <em>control tasks</em> as a probe’s baseline performance.</p>
 <div class="definitionbox">
-<p>A <strong>control task</strong> assigns random labels to inputs, breaking any genuine relationship between activations and the target property. A probe trained on the control task measures the <em>baseline extractability</em> of random patterns from the representation.</p>
-<p><strong>Selectivity</strong> is defined as: <span class="math display">\text{Selectivity} = \text{Acc}_{\text{linguistic}} - \text{Acc}_{\text{control}}</span> where <span class="math inline">\text{Acc}_{\text{linguistic}}</span> is probe accuracy on the true task and <span class="math inline">\text{Acc}_{\text{control}}</span> is accuracy on the control task.</p>
-<p>High selectivity indicates the probe succeeds because of genuine encoding, not because the representation is easily separable by any linear classifier. Low selectivity suggests the probe may be exploiting representation geometry rather than task-relevant structure.</p>
+<p>A <em>control task</em> assigns random labels to inputs to break any genuine relationship between activations and the target property. A probe trained on the control task represents the <em>baseline extractability</em> of random patterns from the representation. Then, the <em>selectivity</em> of a probe is defined as: <span class="math display">\text{Selectivity} = \text{Acc}_{\text{linguistic}} - \text{Acc}_{\text{control}},</span> where <span class="math inline">\text{Acc}_{\text{linguistic}}</span> is probe accuracy on the target task and <span class="math inline">\text{Acc}_{\text{control}}</span> is accuracy on the control task. High selectivity indicates that the probe succeeds because the representation genuinely encodes the target property. In contrast, low selectivity suggests we cannot tell whether the probe has exploited spurious features rather than task-relevant structure.</p>
 </div>
-<section id="implications-of-control-tasks." class="level4">
-<h4>Implications of Control Tasks.</h4>
-<p>Control task experiments have revealed that many probing results showing “encoding” of linguistic properties suffer from low selectivity—random labels are nearly as extractable <span class="citation" data-cites="hewitt-liang-2019-designing">(<a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang, 2019</a>)</span>. However, selectivity improves with model depth and size, suggesting more task-specific encoding in larger models, though this varies by property: syntactic properties typically exhibit higher selectivity than semantic ones. This fragmented picture underscores why probing studies must report control task accuracy alongside primary results; claims about encoding are only meaningful when selectivity is substantially positive.</p>
 </section>
+<section id="concerns-of-probe-complexity" class="level3"><h3 data-pdf-page="74"><span class="secnum">8.6.3</span> Concerns of Probe Complexity<a class="anchor" href="#concerns-of-probe-complexity" aria-label="Link to this section">#</a></h3>
+<p>Probe complexity brings confounding factors to the analysis. If a property can only be extracted with a highly nonlinear probe, it is hard to tell whether the property is “deeply encoded” or whether the probe is simply using nonlinearity to exploit signals, including noise. A recent finding in causal abstraction <span class="citation" data-cites="sutter2025the">(<a href="@@BASE@@references/#ref-sutter2025the" class="cite cite-established" data-cite="sutter2025the" role="doc-biblioref">Sutter et al., 2025</a>)</span> also showed that, when the mapping function is complex enough, it can map an arbitrary (even randomly initialised) neural network to a causal model of interest, producing false-positive findings.</p>
+<div class="insightbox">
+<section id="observation." class="level4">
+<h4>Observation.</h4>
+<p>In general, more complex probes achieve higher accuracy but provide less interpretable deliverables.<br />
+</p>
+</section>
+<section id="interpretation-problem." class="level4">
+<h4>Interpretation problem.</h4>
+<p>Does high accuracy with a complex probe indicate (a) the property is encoded but in a complex, nonlinear form, or (b) the probe itself is computing the property from spurious features? If (b), the probe at most demonstrates that the property is <em>computable from</em> the representation, not that it is <em>encoded in</em> the representation in any meaningful sense. A worse case in (b) is that a highly nonlinear probe may learn to construct the property of interest from random noise rather than any features <span class="citation" data-cites="sutter2025the">(<a href="@@BASE@@references/#ref-sutter2025the" class="cite cite-established" data-cite="sutter2025the" role="doc-biblioref">Sutter et al., 2025</a>)</span>. In this case, the computable illusion likely stems from limited test-set size.</p>
 </section>
-<section id="probe-complexity-vs.-causal-relevance" class="level3"><h3 data-pdf-page="55"><span class="secnum">8.6.4</span> Probe Complexity vs. Causal Relevance<a class="anchor" href="#probe-complexity-vs.-causal-relevance" aria-label="Link to this section">#</a></h3>
-<p>A related critique concerns the relationship between probe complexity and causal relevance. If a property can only be extracted by a complex (nonlinear, high-capacity) probe, does this indicate the property is “deeply encoded” or merely that the probe is doing substantial computation itself?</p>
-<div class="definitionbox">
-<p><strong>Observation</strong>: More complex probes achieve higher accuracy on most tasks.</p>
-<p><strong>Interpretation problem</strong>: Does high accuracy with a complex probe indicate:</p>
-<ol>
-<li><p>The property is encoded but in a complex, nonlinear form?</p></li>
-<li><p>The probe itself is computing the property from simpler features?</p></li>
-</ol>
-<p>If (b), the probe demonstrates that the property is <em>computable from</em> the representation, not that it is <em>encoded in</em> the representation in any meaningful sense.</p>
 </div>
 <section id="minimum-description-length-probes." class="level4">
 <h4>Minimum Description Length Probes.</h4>
-<p>To address this, <span class="citation" data-cites="voita-titov-2020-information"><a href="@@BASE@@references/#ref-voita-titov-2020-information" class="cite" data-cite="voita-titov-2020-information" role="doc-biblioref">Voita &amp; Titov (2020)</a></span> proposed evaluating probes via the <strong>Minimum Description Length</strong> (MDL) principle: prefer probes that achieve good accuracy with minimal complexity. The MDL framework jointly considers data cost (how well the probe compresses labels given representations) and model cost (how complex the probe itself is). A property is considered “more encoded” if it can be extracted with a simpler probe, shifting the question from “can we extract it?” to “how easily can we extract it?”</p>
+<p>To address this, <span class="citation" data-cites="voita-titov-2020-information"><a href="@@BASE@@references/#ref-voita-titov-2020-information" class="cite cite-established" data-cite="voita-titov-2020-information" role="doc-biblioref">Voita &amp; Titov (2020)</a></span> proposed evaluating probes via the minimum description length (MDL) principle: prefer probes that achieve good accuracy with minimal complexity. The proposed MDL-based evaluation offers an information-theoretic measure of how efficiently a probe can extract a target property from model representations, accounting for both probe accuracy and complexity. A property is considered “more encoded” if it can be extracted with a simpler probe. This paradigm shifts the question from “can we extract it?” to “how easily can we extract it?”</p>
 </section>
 <section id="practical-guidance." class="level4">
 <h4>Practical Guidance.</h4>
-<p>When interpreting probing results, prefer linear probes when possible—if linear probes fail but MLP probes succeed, interpret with caution. Crucially, report probe architecture and capacity alongside accuracy, and compare against probes trained on random representations to establish baselines. For quantitative comparison, consider MDL-based evaluation, which provides a principled way to trade off accuracy against model complexity.</p>
+<p>Users should prefer simple probes when possible, compare against probes trained on random representations to establish baselines, and consider MDL-based evaluation to analyse the trade-off between accuracy and model complexity. If linear probes fail but MLP probes succeed, cautiously validate the result and interpretability claim.</p>
 </section>
 </section>
-<section id="amnesic-probing-and-causal-probing" class="level3"><h3 data-pdf-page="55"><span class="secnum">8.6.5</span> Amnesic Probing and Causal Probing<a class="anchor" href="#amnesic-probing-and-causal-probing" aria-label="Link to this section">#</a></h3>
-<p>Early work demonstrated that individual neurons could be manipulated to control morphological properties such as gender, number, and tense <span class="citation" data-cites="bau2018identifying">(<a href="@@BASE@@references/#ref-bau2018identifying" class="cite" data-cite="bau2018identifying" role="doc-biblioref">Bau et al., 2019</a>)</span>, motivating more systematic, direction-based approaches. The idea of intervening on representations via probes was then formalized progressively: <span class="citation" data-cites="giulianelli-etal-2018-hood"><a href="@@BASE@@references/#ref-giulianelli-etal-2018-hood" class="cite" data-cite="giulianelli-etal-2018-hood" role="doc-biblioref">Giulianelli et al. (2018)</a></span> first demonstrated that probes trained on intermediate activations could steer model behavior, and <span class="citation" data-cites="ravfogel-etal-2020-null"><a href="@@BASE@@references/#ref-ravfogel-etal-2020-null" class="cite" data-cite="ravfogel-etal-2020-null" role="doc-biblioref">Ravfogel et al. (2020)</a></span> extended this via Iterative Nullspace Projection (INLP), which repeatedly projects out linearly encoded information. Building on this lineage, <span class="citation" data-cites="elazar-etal-2021-amnesic"><a href="@@BASE@@references/#ref-elazar-etal-2021-amnesic" class="cite" data-cite="elazar-etal-2021-amnesic" role="doc-biblioref">Elazar et al. (2021)</a></span> introduced <strong>amnesic probing</strong>: after identifying a direction encoding property <span class="math inline">P</span>, project it out of the representation and measure downstream task degradation.</p>
-<p>To bridge the gap between information presence and causal relevance, several methods attempt to make probing causal.</p>
+<section id="subsubsec: amnesic probing" class="level3"><h3 data-pdf-page="74"><span class="secnum">8.6.4</span> Amnesic Probing and Causal Probing<a class="anchor" href="#subsubsec: amnesic probing" aria-label="Link to this section">#</a></h3>
+<p>Beyond using probes to validate information decodability, researchers have also formalised the idea of intervening on representations via probes. <span class="citation" data-cites="giulianelli-etal-2018-hood"><a href="@@BASE@@references/#ref-giulianelli-etal-2018-hood" class="cite cite-emerging" data-cite="giulianelli-etal-2018-hood" role="doc-biblioref">Giulianelli et al. (2018)</a></span> first showed that a trained probe can steer model behaviour, and <span class="citation" data-cites="ravfogel-etal-2020-null"><a href="@@BASE@@references/#ref-ravfogel-etal-2020-null" class="cite cite-established" data-cite="ravfogel-etal-2020-null" role="doc-biblioref">Ravfogel et al. (2020)</a></span> extended this with Iterative Nullspace Projection (INLP), an information-removal method that repeatedly fits a probe to identify linearly encoded information and project it out. <span class="citation" data-cites="elazar-etal-2021-amnesic"><a href="@@BASE@@references/#ref-elazar-etal-2021-amnesic" class="cite cite-established" data-cite="elazar-etal-2021-amnesic" role="doc-biblioref">Elazar et al. (2021)</a></span> further measured changes in downstream model behaviour after applying INLP as a validation of the probe’s causality, called <em>amnesic probing</em>. These paradigms add causality to probes, pushing them from correlational understanding to mechanistic analysis. Below, we detail amnesic probing and probing with activation patching.</p>
 <section id="amnesic-probing." class="level4">
 <h4>Amnesic Probing.</h4>
 <div class="definitionbox">
-<p>Given a probe direction <span class="math inline">w</span> encoding property <span class="math inline">P</span>:</p>
-<ol>
-<li><p>Compute the projection: <span class="math inline">h&#39; = h - \frac{h \cdot w}{\|w\|^2} w</span></p></li>
-<li><p>Run the model with <span class="math inline">h&#39;</span> replacing <span class="math inline">h</span></p></li>
-<li><p>Measure change in downstream task performance</p></li>
-</ol>
-<p>If performance degrades, the information was causally used. If performance is unchanged, the information was encoded but unused.</p>
+<p>Given a model activation <span class="math inline">h</span> and a probe trained to predict a property <span class="math inline">P</span>, <em>amnesic probing</em> tests whether information about <span class="math inline">P</span> is causally relevant to downstream model behaviour by removing information about <span class="math inline">P</span> from the representation and evaluating the effects on model outputs. Assume <span class="math inline">P</span> is a binary property (e.g., positive vs. negative sentiment) and that <span class="math inline">w \coloneqq W_{\text{probe}}^\top \in \mathbb{R}^{d}</span> denotes the probe’s direction. Amnesic probing projects <span class="math inline">h</span> onto the subspace orthogonal to a probe direction <span class="math inline">w</span> (i.e., the null space of the probe): <span class="math display">\tilde{h}
+=
+h
+-
+\frac{h^\top w}{\|w\|^2}w.</span> Amnesic probing then measures the change in model performance on downstream tasks with the modified representation <span class="math inline">\tilde{h}</span>. A substantial degradation indicates that the removed information causally contributes to the evaluated behaviour. Notably, unchanged performance does not establish that the information is unused since redundant representations or alternative computational pathways may preserve the behaviour.</p>
 </div>
-<p><strong>Limitations</strong>: Amnesic probing assumes the property is encoded in a single linear direction. If encoding is distributed or nonlinear, projection may not fully remove the information, leading to false negatives (concluding information is unused when it is).</p>
-</section>
-<section id="causal-probing-via-interchange-interventions." class="level4">
-<h4>Causal Probing via Interchange Interventions.</h4>
-<p>A stronger causal test swaps the probed representation between inputs that differ on the target property:</p>
-<ul>
-<li><p>Input <span class="math inline">x_1</span> has property <span class="math inline">P = p_1</span>; input <span class="math inline">x_2</span> has property <span class="math inline">P = p_2</span>.</p></li>
-<li><p>Replace the representation component encoding <span class="math inline">P</span> in <span class="math inline">x_1</span> with that from <span class="math inline">x_2</span>.</p></li>
-<li><p>If the model’s behavior shifts to reflect <span class="math inline">p_2</span>, the encoding is causally active.</p></li>
-</ul>
-<p>This provides stronger evidence than amnesic probing but requires identifying the specific representation component, not just demonstrating its existence.</p>
-</section>
-</section>
-<section id="critiques-of-attribution-and-saliency-methods" class="level3"><h3 data-pdf-page="56"><span class="secnum">8.6.6</span> Critiques of Attribution and Saliency Methods<a class="anchor" href="#critiques-of-attribution-and-saliency-methods" aria-label="Link to this section">#</a></h3>
-<p>Probing is often used alongside attribution methods (saliency maps, attention visualization, gradient-based attribution) to understand model behavior. However, attribution methods face their own critiques that affect how probing results should be interpreted.</p>
-<section id="faithfulness-of-attributions." class="level4">
-<h4>Faithfulness of Attributions.</h4>
-<p>Attribution methods aim to identify which inputs or components most influenced an output. However, <span class="citation" data-cites="jacovi2020faithfullyinterpretablenlpsystems"><a href="@@BASE@@references/#ref-jacovi2020faithfullyinterpretablenlpsystems" class="cite" data-cite="jacovi2020faithfullyinterpretablenlpsystems" role="doc-biblioref">Jacovi &amp; Goldberg (2020)</a></span> distinguish between <em>plausibility</em> (do attributions seem reasonable to humans?) and <em>faithfulness</em> (do attributions reflect actual model computation?).</p>
-</section>
-<section id="key-findings-on-attribution-faithfulness" class="level4">
-<h4>Key findings on attribution faithfulness</h4>
-<p>: Gradient-based saliency can highlight features the model is insensitive to <span class="citation" data-cites="adebayo2020sanitycheckssaliencymaps">(<a href="@@BASE@@references/#ref-adebayo2020sanitycheckssaliencymaps" class="cite cite-established" data-cite="adebayo2020sanitycheckssaliencymaps" role="doc-biblioref">Adebayo et al., 2018b</a>)</span>, attention weights correlate weakly with causal importance <span class="citation" data-cites="jain2019attention wiegreffe2019attention">(<a href="@@BASE@@references/#ref-jain2019attention" class="cite cite-established" data-cite="jain2019attention" role="doc-biblioref">Jain &amp; Wallace, 2019</a>; <a href="@@BASE@@references/#ref-wiegreffe2019attention" class="cite cite-established" data-cite="wiegreffe2019attention" role="doc-biblioref">Wiegreffe &amp; Pinter, 2019</a>)</span>, and different attribution methods often disagree substantially on the same inputs <span class="citation" data-cites="krishna2025disagreementproblemexplainablemachine">(<a href="@@BASE@@references/#ref-krishna2025disagreementproblemexplainablemachine" class="cite" data-cite="krishna2025disagreementproblemexplainablemachine" role="doc-biblioref">Krishna et al., 2024</a>)</span>. Collectively, these findings suggest that attribution methods are fundamentally unreliable guides to what information a model actually uses.</p>
-</section>
-<section id="implications-for-probing." class="level4">
-<h4>Implications for Probing.</h4>
-<p>This unreliability propagates into probing studies. Probes trained on attention patterns may not reveal causally important information since attention itself may not be faithful. Similarly, using gradients to identify which features to probe can bias toward high-magnitude gradients that lack causal importance. When probing and attribution methods disagree, resolving the disagreement requires causal interventions rather than passive observation.</p>
+<p>Amnesic probing assumes the target property is decodable and that there exist effective erasure procedures to surgically remove its associated information from the representation. However, information and concept erasure may be imperfect in practice, either leaving residual information or affecting other concepts. Such imperfections confound interpretations of downstream behavioural changes and the causal claims that follow.</p>
 </section>
+<section id="causal-probing-via-activation-patching." class="level4">
+<h4>Causal Probing via Activation Patching.</h4>
+<p>Activation patching tests the causality of a component’s representations by swapping them between inputs that differ in the target property. This technique has also been used to test the causality of probes <span class="citation" data-cites="huang-etal-2024-ravel arora-etal-2024-causalgym">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>; <a href="@@BASE@@references/#ref-arora-etal-2024-causalgym" class="cite cite-established" data-cite="arora-etal-2024-causalgym" role="doc-biblioref">Arora et al., 2024</a>)</span>. Suppose <span class="math inline">x_1</span> and <span class="math inline">x_2</span> are two inputs, while <span class="math inline">x_1</span> has the target property <span class="math inline">P</span> and <span class="math inline">x_2</span> does not. <span class="math inline">W_{\text{probe}}</span> is the fitted probing matrix whose rows span a probing subspace (or a direction if the matrix has only one row, such as that of a binary classification probe) where <span class="math inline">P</span> resides. After choosing the intervention location (e.g., the last-token residual stream at a middle layer), the method first feeds <span class="math inline">x_1</span> to the model and extracts the component of the representation in the probing subspace. Then, it feeds <span class="math inline">x_2</span> to the model while patching the corresponding component of the model representation with the previously acquired one. If the model output for <span class="math inline">x_2</span> shifts towards <span class="math inline">P</span>, the patched subspace representation contains information about <span class="math inline">P</span> that the model uses.</p>
+<p>Compared with amnesic probing, which tests the effect of removing information about <span class="math inline">P</span>, activation patching examines whether the probe-identified information is sufficient to implement the target behaviour. However, this requires specifying what representation (i.e., which layer, component, and token position) to probe and patch, which may confound the analysis.</p>
 </section>
-<section id="circuit-fragility-and-robustness-concerns" class="level3"><h3 data-pdf-page="56"><span class="secnum">8.6.7</span> Circuit Fragility and Robustness Concerns<a class="anchor" href="#circuit-fragility-and-robustness-concerns" aria-label="Link to this section">#</a></h3>
-<p>A related concern applies to circuit-level findings from mechanistic interpretability: identified circuits may be fragile, failing to generalize across distributions or model variants.</p>
-<section id="evidence-for-circuit-fragility." class="level4">
-<h4>Evidence for Circuit Fragility.</h4>
-<p>Circuits identified on one task may not transfer to related tasks, even when the underlying computation seems similar <span class="citation" data-cites="mcgrath2023hydraeffectemergentselfrepair">(<a href="@@BASE@@references/#ref-mcgrath2023hydraeffectemergentselfrepair" class="cite cite-emerging" data-cite="mcgrath2023hydraeffectemergentselfrepair" role="doc-biblioref">McGrath et al., 2023</a>)</span>. Small changes to model weights through finetuning or continued pretraining can disrupt identified circuits while preserving task performance, suggesting redundancy or alternative pathways. Moreover, circuits identified in small models may not have direct analogues in larger models <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022a</a>)</span>, limiting the transferability of findings across model scales.</p>
 </section>
-<section id="implications-for-probing.-1" class="level4">
-<h4>Implications for Probing.</h4>
-<p>Circuit fragility reveals a gap in probing’s explanatory power: stable probe accuracy across checkpoints does not guarantee stable circuit implementation, and information encoded at layer <span class="math inline">\ell</span> may be read by different circuits under different conditions. This suggests that probing findings—which identify <em>what</em> information is encoded—may not reliably predict <em>how</em> that information is used. Complementing probing with circuit analysis is therefore necessary to understand not just encoding but routing.</p>
+<section id="implications-of-attribution-faithfulness-for-probing" class="level3"><h3 data-pdf-page="75"><span class="secnum">8.6.5</span> Implications of Attribution Faithfulness for Probing<a class="anchor" href="#implications-of-attribution-faithfulness-for-probing" aria-label="Link to this section">#</a></h3>
+<p>Probing is often used along with attribution methods (e.g., saliency maps, attention visualisation, gradient-based attribution) to understand model behaviour. However, attribution methods also have technical challenges. For example, gradient-based saliency maps can highlight input features the model is actually insensitive to <span class="citation" data-cites="adebayo2020sanitycheckssaliencymaps">(<a href="@@BASE@@references/#ref-adebayo2020sanitycheckssaliencymaps" class="cite cite-established" data-cite="adebayo2020sanitycheckssaliencymaps" role="doc-biblioref">Adebayo et al., 2018</a>)</span>, attention weights correlate weakly with causal importance <span class="citation" data-cites="jain2019attention">(<a href="@@BASE@@references/#ref-jain2019attention" class="cite cite-established" data-cite="jain2019attention" role="doc-biblioref">Jain &amp; Wallace, 2019</a>)</span>, and different attribution methods can disagree substantially on the same inputs <span class="citation" data-cites="krishna2025disagreementproblemexplainablemachine">(<a href="@@BASE@@references/#ref-krishna2025disagreementproblemexplainablemachine" class="cite cite-established" data-cite="krishna2025disagreementproblemexplainablemachine" role="doc-biblioref">Krishna et al., 2024</a>)</span>. These findings suggest that attribution methods alone cannot determine what information a model actually uses.</p>
+<p>This unreliability affects how we can interpret probing results. Probes trained on attention patterns may not reveal causally important information since attention itself may not be causal. Similarly, using gradients to identify which features or components to probe can bias towards high-magnitude gradients that lack causal importance. In sum, interpretability claims made by probes, even paired with these auxiliary tools, still need to go through causal validation to separate <em>plausible explanations</em> (do attributions seem reasonable to humans?) from <em>faithful explanations</em> (do attributions reflect actual model computation?) <span class="citation" data-cites="jacovi-goldberg-2020-towards">(<a href="@@BASE@@references/#ref-jacovi-goldberg-2020-towards" class="cite cite-established" data-cite="jacovi-goldberg-2020-towards" role="doc-biblioref">Jacovi &amp; Goldberg, 2020</a>)</span>.</p>
 </section>
-</section>
-<section id="when-is-probing-appropriate-and-when-is-it-not" class="level3"><h3 data-pdf-page="56"><span class="secnum">8.6.8</span> When is Probing Appropriate, and When is it Not?<a class="anchor" href="#when-is-probing-appropriate-and-when-is-it-not" aria-label="Link to this section">#</a></h3>
-<p>Given these critiques, we provide guidance on appropriate uses of probing.</p>
+<section id="when-is-probing-appropriate" class="level3"><h3 data-pdf-page="76"><span class="secnum">8.6.6</span> When Is Probing Appropriate?<a class="anchor" href="#when-is-probing-appropriate" aria-label="Link to this section">#</a></h3>
+<p>Given the preceding discussion, Table <a href="@@BASE@@handbook/intermediate-representations/#tab:probing-guidance" data-reference-type="ref" data-reference="tab:probing-guidance">11</a> summarises appropriate uses of probing. We argue that probing is most productive when treated as a tool for <em>hypothesis generation</em>, that is, using probes to identify candidate properties that may be encoded in a representation and potentially causally relevant. Users then test these hypotheses using causal methods, such as activation steering or patching. In this sense, probing serves as an efficient first step in mechanistic investigation rather than as a decisive explanation.</p>
 <table id="tab:probing-guidance">
-<caption><span class="capnum">Table 12:</span> Appropriate and Inappropriate Uses of Probing</caption>
+<caption><span class="capnum">Table 11:</span> Appropriate and inappropriate uses of probing.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Appropriate Uses</strong></span></th>
@@ -200,12 +165,12 @@ <h4>Implications for Probing.</h4>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Establishing information presence (“is <span class="math inline">P</span> encoded?”)</td>
-<td style="text-align: left;">Claiming causal relevance (“the model uses <span class="math inline">P</span>”)</td>
+<td style="text-align: left;">Establishing information presence (i.e., “<span class="math inline">P</span> is encoded”)</td>
+<td style="text-align: left;">Claiming causal relevance (i.e., “the model uses <span class="math inline">P</span>”)</td>
 </tr>
 <tr>
 <td style="text-align: left;">Comparing encoding strength across layers</td>
-<td style="text-align: left;">Concluding a property is “not used” from low probe accuracy</td>
+<td style="text-align: left;">Concluding that a property is not encoded based on low probe accuracy</td>
 </tr>
 <tr>
 <td style="text-align: left;">Auditing for encoded sensitive information</td>
@@ -213,39 +178,27 @@ <h4>Implications for Probing.</h4>
 </tr>
 <tr>
 <td style="text-align: left;">Generating hypotheses for causal investigation</td>
-<td style="text-align: left;">Interpreting probe weights as model weights</td>
+<td style="text-align: left;">Using probing results to support hypotheses about causality</td>
 </tr>
 <tr>
-<td style="text-align: left;">Early detection / triage before expensive analysis</td>
-<td style="text-align: left;">Evaluating alignment or safety from probe results</td>
+<td style="text-align: left;">Early detection or triage before further analysis</td>
+<td style="text-align: left;">Evaluating alignment or safety based on probing results</td>
 </tr>
 </tbody>
 </table>
-<section id="probing-as-hypothesis-generation." class="level4">
-<h4>Probing as Hypothesis Generation.</h4>
-<p>The most productive framing treats probing as <em>hypothesis generation</em>: probes identify candidate properties that may be causally relevant, which must then be tested via causal methods (ablation, patching, circuit analysis). This positions probing as an efficient first step, not a final answer.</p>
-</section>
 </section>
-<section id="best-practices-for-probing-studies" class="level3"><h3 data-pdf-page="57"><span class="secnum">8.6.9</span> Best Practices for Probing Studies<a class="anchor" href="#best-practices-for-probing-studies" aria-label="Link to this section">#</a></h3>
-<p>Based on the critiques above, we recommend the following practices:</p>
-<ol>
-<li><p><strong>Report control task baselines</strong>: Always include selectivity metrics.</p></li>
-<li><p><strong>Use minimal probe complexity</strong>: Prefer linear probes; justify MLP probes when necessary.</p></li>
-<li><p><strong>Include random baselines</strong>: Compare to probes on shuffled activations.</p></li>
-<li><p><strong>Test causal relevance</strong>: Complement probing with amnesic probing or interchange interventions.</p></li>
-<li><p><strong>Report layer-wise patterns</strong>: Show how encoding varies across layers, not just peak accuracy.</p></li>
-<li><p><strong>Acknowledge limitations</strong>: Explicitly state that probing shows information presence, not causal use.</p></li>
-<li><p><strong>Cross-validate</strong>: Report variance across random seeds and data splits.</p></li>
-<li><p><strong>Consider alternatives</strong>: For targeted properties, supervised methods (DAS, DBM) may provide more reliable results than probing followed by post-hoc causal testing.</p></li>
-</ol>
+<section id="methodological-standards-in-probing-research" class="level3"><h3 data-pdf-page="76"><span class="secnum">8.6.7</span> Methodological Standards in Probing Research<a class="anchor" href="#methodological-standards-in-probing-research" aria-label="Link to this section">#</a></h3>
+<p>So far we have reviewed several strengths and limitations of probes. Based on them, we summarise several methodological standards for the design and interpretation of probing studies: (1) Report control-task baselines to distinguish genuine decodability from spurious exploitation; (2) Use minimally expressive probes unless additional complexity is justified; (3) Test causal relevance by methods like amnesic probing or activation patching; (4) Analyse layer-wise trends besides the peak accuracy; (5) Distinguish encoding from causality and acknowledge that successful probing shows information presence rather than causal use; (6) Report the stability of findings by evaluating across seeds and data splits. In sum, we believe these standards help ensure responsible, effective use of probing in interpretability research and downstream applications.</p>
 <div class="examplebox">
-<p><span id="ex:probe_dir" data-label="ex:probe_dir"></span> Despite the critiques above, probing remains valuable for specific applications. The <strong>Simple probes can catch sleeper agents</strong> research <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span> demonstrates that simple linear probes trained on residual stream activations can detect latent backdoor behavior in sleeper agent models even when the trigger and the harmful behavior are unknown.</p>
-<p>In a sleeper agent model <span class="math inline">f_\theta</span>, the model behaves normally for one context (e.g., <span class="math inline">y=\text{``2023&#39;&#39;}</span>) but exhibits a hidden dangerous policy when a trigger is present (e.g., <span class="math inline">y=\text{``2024&#39;&#39;}</span>) <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>.</p>
-<p>To detect this latent behavior, one can construct a <strong>linear defection detector</strong> by computing a <strong>detector direction</strong> <span class="math inline">\mathbf{d}^\ell</span> at a given residual layer <span class="math inline">\ell</span>. Let <span class="math inline">h_t^\ell(x)</span> denote residual activations at the final token position. Given a small labeled fit dataset of prompts, the detector direction is defined as: <span class="math display">\mathbf{d}^\ell = \mathbb{E}_{(x,y)\sim\mathcal{D}_\text{true}}[h_t^\ell(x)] - \mathbb{E}_{(x,y)\sim\mathcal{D}_\text{false}}[h_t^\ell(x)],</span> where <span class="math inline">\mathcal{D}_\text{true}</span> and <span class="math inline">\mathcal{D}_\text{false}</span> are contrast pairs that correlate with the latent policy.</p>
-<p>Projecting test activations onto this direction yields a scalar detector activation: <span class="math display">s_t^\ell = \langle h_t^\ell(x),\, \mathbf{d}^\ell \rangle,</span> which can then be used as a classifier score to distinguish inputs likely to activate the sleeper agent from those that are not. Empirically, this approach achieves high accuracy in identifying the latent objective.</p>
-<p><strong>Why this works despite the critiques</strong>: In this application, the goal is <em>detection</em> (information presence), not mechanistic understanding. The probe identifies that deceptive intent is encoded, which is sufficient for a safety filter. Whether the model “uses” this encoding in a mechanistically clean way is secondary to the practical goal of flagging dangerous inputs.</p>
-<p>This illustrates that probing remains valuable when the goal is auditing or detection, while its limitations apply most strongly to mechanistic claims.</p>
+<p><span id="ex:probe_dir" data-label="ex:probe_dir"></span> Despite the causality critiques, probing remains valuable for many applications. For example, <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> showed that simple linear probes trained on residual-stream activations can detect latent backdoor behaviour in the sleeper agent model even when the probe fitting set does not contain the trigger or the harmful behaviour.<br />
+To recap: a sleeper agent model <span class="math inline">f_\theta(\cdot)</span> behaves normally for one context (e.g., <span class="math inline">y=\text{``2023&#39;&#39;}</span>) but exhibits a hidden dangerous policy when a trigger is present (e.g., <span class="math inline">y=\text{``2024&#39;&#39;}</span>) <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>.<br />
+To detect this latent behaviour, <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> constructed a linear defection detector by computing a <em>detector direction</em> <span class="math inline">u_\ell</span> at layer <span class="math inline">\ell</span>. Specifically, let <span class="math inline">h_\ell(x)</span> denote the residual activations at the final token position for input <span class="math inline">x</span>. Given a small labelled fit dataset of prompts, the detector direction is defined as: <span class="math display">u_\ell = \mathbb{E}_{x\sim\mathcal{D}_{\text{true}}}[h_\ell(x)] - \mathbb{E}_{x\sim\mathcal{D}_{\text{false}}}[h_\ell(x)],</span> where <span class="math inline">\mathcal{D}_{\text{true}}</span> and <span class="math inline">\mathcal{D}_{\text{false}}</span> are contrast pairs of prompts. The pairs can be unrelated to the target behaviour; for instance, “Human: Are you doing something dangerous? Assistant: yes” and “Human: … Assistant: no” (example borrowed from <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span>). During the evaluation stage, activations are projected onto this direction to yield a detector activation value <span class="math inline">\langle h_\ell(x), u_\ell \rangle</span>. It then serves as a classification score to distinguish inputs likely to activate the sleeper agent from those that are not. Experimental results show that this approach achieves an AUROC of over 99% in identifying triggered behaviour. <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> argued that this is because the activation differences between triggered and non-triggered inputs are sufficiently salient (for example, two clusters far apart). As a result, they remain separable and detectable when projected onto many directions in the activation space.<br />
+<em>Why this works despite the causality critiques</em>: In this use case, the goal is <em>detection</em> (information presence), not mechanistic understanding. A safety filter’s primary goal is to detect encoded deceptive intent and flag potential danger, not discover its internal mechanism. In summary, this use case shows that probing remains valuable for auditing or detection even though it cannot support causal claims.</p>
 </div>
+<section id="summary.-5" class="level4">
+<h4>Summary.</h4>
+<p>This section introduced methods that interpret model activations by decoding them into either the output vocabulary or natural language: logit lens and tuned lens examine what a representation contains, Jacobian lens estimates what it is disposed to influence, Patchscopes and LatentQA verbalise the information it contains, and probing tests whether specific information is decodable from it. These methods primarily reveal what can be decoded from a representation but not whether the decoded information is subsequently used by the model. Section <a href="@@BASE@@handbook/representation-analysis/#sec:representation" data-reference-type="ref" data-reference="sec:representation">9</a> broadens the discussion to comparing representations across models, locating where information is stored, and intervening on representations to control model behaviour.</p>
+</section>
 </section>
 </section>
 </section>
diff --git a/src/generated/chapters/interpretability-landscape.html b/src/generated/chapters/interpretability-landscape.html
index 7154a622be..3f557f6b4f 100644
--- a/src/generated/chapters/interpretability-landscape.html
+++ b/src/generated/chapters/interpretability-landscape.html
@@ -1,89 +1,96 @@
-<section id="sec:interpretability-landscape" class="level1"><h1 data-pdf-page="7"><span class="secnum">3</span> The Interpretability Landscape<a class="anchor" href="#sec:interpretability-landscape" aria-label="Link to this section">#</a></h1>
-<p>The previous sections established that behavioral evaluation alone is insufficient for safety guarantees, and that mechanistic analysis—direct study and manipulation of model internals—offers a path toward causal understanding. But mechanistic methods are computationally intensive and do not scale easily. This raises a practical question: <em>When is mechanistic analysis necessary, and when do simpler methods suffice?</em></p>
-<p>This section examines the major families of interpretability methods, their trade-offs, and the conditions under which each is appropriate. We pay particular attention to methods developed in the broader NLP and representation learning communities—probes, attribution methods, concept-based approaches—which remain widely used and can complement or substitute for mechanistic analysis depending on the task.</p>
-<section id="behavioral-methods" class="level2"><h2 data-pdf-page="7"><span class="secnum">3.1</span> Behavioral Methods<a class="anchor" href="#behavioral-methods" aria-label="Link to this section">#</a></h2>
-<p>Behavioral methods examine models at the level of inputs and outputs: capability evaluations <span class="citation" data-cites="linzen-etal-2016-assessing marvin-linzen-2018-targeted mmlupro zhang2025cybench">(<a href="@@BASE@@references/#ref-linzen-etal-2016-assessing" class="cite cite-established" data-cite="linzen-etal-2016-assessing" role="doc-biblioref">Linzen et al., 2016</a>; <a href="@@BASE@@references/#ref-marvin-linzen-2018-targeted" class="cite cite-established" data-cite="marvin-linzen-2018-targeted" role="doc-biblioref">Marvin &amp; Linzen, 2018</a>; <a href="@@BASE@@references/#ref-mmlupro" class="cite cite-established" data-cite="mmlupro" role="doc-biblioref">Wang et al., 2024</a>; <a href="@@BASE@@references/#ref-zhang2025cybench" class="cite cite-established" data-cite="zhang2025cybench" role="doc-biblioref">Zhang et al., 2025</a>)</span>, deceptive intents <span class="citation" data-cites="early-work-on-monitorability-evaluations">(<a href="@@BASE@@references/#ref-early-work-on-monitorability-evaluations" class="cite cite-emerging" data-cite="early-work-on-monitorability-evaluations" role="doc-biblioref">Kinniment et al., 2026</a>)</span>, and red-teaming (<span class="citation" data-cites="ganguli2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-ganguli2022redteaminglanguagemodels" class="cite cite-emerging" data-cite="ganguli2022redteaminglanguagemodels" role="doc-biblioref">Ganguli et al., 2022</a></span>; <span class="citation" data-cites="Ethan2022redteam he-etal-2025-red"><a href="@@BASE@@references/#ref-Ethan2022redteam" class="cite cite-established" data-cite="Ethan2022redteam" role="doc-biblioref">Perez et al., 2022b</a>; <a href="@@BASE@@references/#ref-he-etal-2025-red" class="cite cite-established" data-cite="he-etal-2025-red" role="doc-biblioref">He et al., 2025</a></span>). These approaches are fast, scalable, and practical for high-level auditing without access to model internals.</p>
-<p>The limits of this approach are the subject of Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a>; the point that matters here is that behaviour on a finite evaluation set underdetermines the mechanism that produced it, which is what the internal methods below are designed to establish. Behavioral methods nonetheless remain essential in practice, for rapid iteration during development, for prioritizing which behaviors warrant deeper investigation, and for surface-level safety screening where the cost of mechanistic analysis is prohibitive.</p>
+<section id="sec:interpretability-landscape" class="level1"><h1 data-pdf-page="16"><span class="secnum">3</span> The Interpretability Landscape<a class="anchor" href="#sec:interpretability-landscape" aria-label="Link to this section">#</a></h1>
+<p>Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a> established that behavioural evaluation alone is insufficient for fully understanding models, motivating methods that examine their internal mechanisms. AI interpretability, however, spans a broad range of approaches that differ in the evidence they provide, the strength of the claims they support, and the real-world scenarios in which they are useful. This raises a central question: <em>When is mechanistic analysis necessary, and when do simpler interpretability methods suffice?</em></p>
+<p>This section answers this question by examining the major families of interpretability methods, including their strengths, limitations, and the conditions under which each may be appropriate, as summarised in Table <a href="@@BASE@@handbook/interpretability-landscape/#tab:interpretability-methods" data-reference-type="ref" data-reference="tab:interpretability-methods">2</a>. In particular, we introduce approaches developed in the broader natural language processing (NLP) and representation learning communities—including probes, attribution methods, and concept-based approaches—which remain widely used and may complement or substitute for mechanistic analysis in some cases. We conclude by examining the limitations of mechanistic analysis itself.</p>
+<section id="behavioural-methods" class="level2"><h2 data-pdf-page="17"><span class="secnum">3.1</span> Behavioural Methods<a class="anchor" href="#behavioural-methods" aria-label="Link to this section">#</a></h2>
+<p>Behavioural methods evaluate and analyse models through their inputs and outputs. They have been developed to examine a wide range of capabilities, traits, and behaviours under specific conditions. Specifically, before the era of large language models (LLMs), researchers had proposed systematic evaluations of language models’ ability to capture syntactic structure and syntax-sensitive dependencies <span class="citation" data-cites="linzen-etal-2016-assessing marvin-linzen-2018-targeted">(<a href="@@BASE@@references/#ref-linzen-etal-2016-assessing" class="cite cite-established" data-cite="linzen-etal-2016-assessing" role="doc-biblioref">Linzen et al., 2016</a>; <a href="@@BASE@@references/#ref-marvin-linzen-2018-targeted" class="cite cite-established" data-cite="marvin-linzen-2018-targeted" role="doc-biblioref">Marvin &amp; Linzen, 2018</a>)</span>. With modern LLMs, behavioural evaluations have expanded to include a much broader set of capabilities, such as professional and academic knowledge across diverse domains <span class="citation" data-cites="mmlu mmlupro bigbench agieval phan2026hle">(<a href="@@BASE@@references/#ref-mmlu" class="cite cite-established" data-cite="mmlu" role="doc-biblioref">Hendrycks et al., 2021</a>; <a href="@@BASE@@references/#ref-mmlupro" class="cite cite-established" data-cite="mmlupro" role="doc-biblioref">Wang et al., 2024</a>; <a href="@@BASE@@references/#ref-bigbench" class="cite cite-established" data-cite="bigbench" role="doc-biblioref">Srivastava et al., 2023</a>; <a href="@@BASE@@references/#ref-agieval" class="cite cite-established" data-cite="agieval" role="doc-biblioref">Zhong et al., 2024</a>; <a href="@@BASE@@references/#ref-phan2026hle" class="cite cite-established" data-cite="phan2026hle" role="doc-biblioref">Phan et al., 2026</a>)</span>, long-context understanding <span class="citation" data-cites="bai-etal-2024-longbench zhang-etal-2024-bench modarressi2025nolima">(<a href="@@BASE@@references/#ref-bai-etal-2024-longbench" class="cite cite-established" data-cite="bai-etal-2024-longbench" role="doc-biblioref">Bai et al., 2024</a>; <a href="@@BASE@@references/#ref-zhang-etal-2024-bench" class="cite cite-established" data-cite="zhang-etal-2024-bench" role="doc-biblioref">Zhang et al., 2024a</a>; <a href="@@BASE@@references/#ref-modarressi2025nolima" class="cite cite-established" data-cite="modarressi2025nolima" role="doc-biblioref">Modarressi et al., 2025</a>)</span>, mathematical reasoning <span class="citation" data-cites="gsm8k proofnet">(<a href="@@BASE@@references/#ref-gsm8k" class="cite cite-emerging" data-cite="gsm8k" role="doc-biblioref">Cobbe et al., 2021</a>; <a href="@@BASE@@references/#ref-proofnet" class="cite cite-emerging" data-cite="proofnet" role="doc-biblioref">Azerbayev et al., 2023</a>)</span>, and coding ability (<span class="citation" data-cites="chen2021evaluatinglargelanguagemodels"><a href="@@BASE@@references/#ref-chen2021evaluatinglargelanguagemodels" class="cite cite-emerging" data-cite="chen2021evaluatinglargelanguagemodels" role="doc-biblioref">Chen et al., 2021</a></span>; <span class="citation" data-cites="jimenez2024swebench liu2024repobench"><a href="@@BASE@@references/#ref-jimenez2024swebench" class="cite cite-established" data-cite="jimenez2024swebench" role="doc-biblioref">Jimenez et al., 2024</a>; <a href="@@BASE@@references/#ref-liu2024repobench" class="cite cite-established" data-cite="liu2024repobench" role="doc-biblioref">Liu et al., 2024b</a></span>).</p>
+<p>Beyond assessing models as helpful assistants, behavioural evaluations also identify and benchmark potential safety risks and failure modes. Examples include monitoring deceptive intentions <span class="citation" data-cites="korbak2025chainthoughtmonitorabilitynew early-work-on-monitorability-evaluations">(<a href="@@BASE@@references/#ref-korbak2025chainthoughtmonitorabilitynew" class="cite cite-emerging" data-cite="korbak2025chainthoughtmonitorabilitynew" role="doc-biblioref">Korbak et al., 2025</a>; <a href="@@BASE@@references/#ref-early-work-on-monitorability-evaluations" class="cite cite-emerging" data-cite="early-work-on-monitorability-evaluations" role="doc-biblioref">Kinniment et al., 2026</a>)</span>, measuring tendencies towards sycophancy <span class="citation" data-cites="hong-etal-2025-measuring fanous2025syceval petrov2026brokenmath">(<a href="@@BASE@@references/#ref-hong-etal-2025-measuring" class="cite cite-established" data-cite="hong-etal-2025-measuring" role="doc-biblioref">Hong et al., 2025</a>; <a href="@@BASE@@references/#ref-fanous2025syceval" class="cite cite-established" data-cite="fanous2025syceval" role="doc-biblioref">Fanous et al., 2025</a>; <a href="@@BASE@@references/#ref-petrov2026brokenmath" class="cite cite-established" data-cite="petrov2026brokenmath" role="doc-biblioref">Petrov et al., 2026</a>)</span>, evaluating capabilities relevant to harmful or destructive actions <span class="citation" data-cites="li2024wmdp zhang2025cybench li2026autocontrol">(<a href="@@BASE@@references/#ref-li2024wmdp" class="cite cite-established" data-cite="li2024wmdp" role="doc-biblioref">Li et al., 2024</a>; <a href="@@BASE@@references/#ref-zhang2025cybench" class="cite cite-established" data-cite="zhang2025cybench" role="doc-biblioref">Zhang et al., 2025a</a>; <a href="@@BASE@@references/#ref-li2026autocontrol" class="cite cite-established" data-cite="li2026autocontrol" role="doc-biblioref">Li et al., 2026a</a>)</span>, and testing robustness against jailbreak attacks (<span class="citation" data-cites="ganguli2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-ganguli2022redteaminglanguagemodels" class="cite cite-emerging" data-cite="ganguli2022redteaminglanguagemodels" role="doc-biblioref">Ganguli et al., 2022</a></span>; <span class="citation" data-cites="perez2022redteaminglanguagemodels he-etal-2025-red"><a href="@@BASE@@references/#ref-perez2022redteaminglanguagemodels" class="cite cite-established" data-cite="perez2022redteaminglanguagemodels" role="doc-biblioref">Perez et al., 2022</a>; <a href="@@BASE@@references/#ref-he-etal-2025-red" class="cite cite-established" data-cite="he-etal-2025-red" role="doc-biblioref">He et al., 2025</a></span>) or training data poisoning <span class="citation" data-cites="pmlr-v267-fu25e">(<a href="@@BASE@@references/#ref-pmlr-v267-fu25e" class="cite cite-established" data-cite="pmlr-v267-fu25e" role="doc-biblioref">Fu et al., 2025</a>)</span>. Behavioural evaluations have also been used to study broader model characteristics, such as political orientations <span class="citation" data-cites="helwe-etal-2025-navigating">(<a href="@@BASE@@references/#ref-helwe-etal-2025-navigating" class="cite cite-established" data-cite="helwe-etal-2025-navigating" role="doc-biblioref">Helwe et al., 2025</a>)</span> and social attitudes <span class="citation" data-cites="durmus2024towards">(<a href="@@BASE@@references/#ref-durmus2024towards" class="cite cite-established" data-cite="durmus2024towards" role="doc-biblioref">DURMUS et al., 2024</a>)</span>.</p>
+<p>In general, behavioural evaluations provide a practical way to audit and assess models at scale. They are typically fast, inexpensive, and scalable, requiring only forward passes and comparisons between model outputs and reference answers. Although evaluation results on a finite testing set do not guarantee the mechanism that produced the behaviour of interest <span class="citation" data-cites="wani-etal-2026-answer">(<a href="@@BASE@@references/#ref-wani-etal-2026-answer" class="cite cite-established" data-cite="wani-etal-2026-answer" role="doc-biblioref">Wani et al., 2026</a>)</span> (which the internal methods below aim to establish, as discussed in Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a>), behavioural methods nonetheless remain essential in practice for rapid iteration during development, prioritising which behaviours warrant deeper investigation, and surface-level safety screening when the cost of mechanistic analysis is prohibitive.</p>
 </section>
-<section id="subsec:cbm" class="level2"><h2 data-pdf-page="8"><span class="secnum">3.2</span> Concept-Based Approaches<a class="anchor" href="#subsec:cbm" aria-label="Link to this section">#</a></h2>
-<p>Concept-based methods plays a middle ground role: they go beyond behavioral evaluation by examining internal representations, but typically establish correlational rather than causal claims. However, when combined with interventions—steering along concept directions, ablating concept-associated components—they become mechanistic by the criteria in Section <a href="@@BASE@@handbook/introduction/#sec:scope" data-reference-type="ref" data-reference="sec:scope">1.2</a>. Work on causal abstraction <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks">(<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">Geiger et al., 2021</a>)</span> formalizes this connection, aligning model computations to high-level causal graphs defined over human-interpretable concepts.</p>
-<p>Concept-based approaches include both training-time and post-hoc instantiations. Concept Bottleneck Models (CBMs; <span class="citation" data-cites="koh2020conceptbottleneckmodels"><a href="@@BASE@@references/#ref-koh2020conceptbottleneckmodels" class="cite cite-established" data-cite="koh2020conceptbottleneckmodels" role="doc-biblioref">Koh et al., 2020</a></span>) constrain computation through a predefined bottleneck of interpretable concepts, offering built-in transparency and allowing for interventions, but traditionally requiring a complete set of predefined concepts and often suffer from an “accuracy-interpretability trade-off” when concept definitions are insufficiently expressive. Newer variants, such as Concept Embedding Models (CEMs; <span class="citation" data-cites="zarlenga2022cem"><a href="@@BASE@@references/#ref-zarlenga2022cem" class="cite cite-established" data-cite="zarlenga2022cem" role="doc-biblioref">Zarlenga et al., 2022</a></span>) and Probabilistic Concept Bottleneck Models (ProbCBMs;  <span class="citation" data-cites="kim2023probabilistic"><a href="@@BASE@@references/#ref-kim2023probabilistic" class="cite cite-established" data-cite="kim2023probabilistic" role="doc-biblioref">Kim et al., 2023</a></span>) address these limitations by allowing the bottleneck to encode additional high-dimensional information; this recovers accuracy at the cost of allowing non-interpretable information to leak into the bottleneck. Moreover, recent improvements relax the assumption of concept independence. Causal Concept Graph Models (CGMs; <span class="citation" data-cites="dominici2025causal"><a href="@@BASE@@references/#ref-dominici2025causal" class="cite cite-established" data-cite="dominici2025causal" role="doc-biblioref">Dominici et al., 2025</a></span>) and Stochastic Concept Bottleneck Models (SCBM;  <span class="citation" data-cites="moritz2024sthocastic"><a href="@@BASE@@references/#ref-moritz2024sthocastic" class="cite cite-established" data-cite="moritz2024sthocastic" role="doc-biblioref">Vandenhirtz et al., 2024</a></span>) learn structural dependencies or graph-based relationships between concepts to capture complex hierarchies. Parallel to these architectural changes, recent work also relaxes the assumption that concepts must be <em>pre-defined</em>. These methods instead discover latent representations directly from model activations (<span class="citation" data-cites="rajani2020explainingimprovingmodelbehavior"><a href="@@BASE@@references/#ref-rajani2020explainingimprovingmodelbehavior" class="cite cite-established" data-cite="rajani2020explainingimprovingmodelbehavior" role="doc-biblioref">Rajani et al., 2020</a></span>; <span class="citation" data-cites="zhao-etal-2024-explaining dalvi2022discovering"><a href="@@BASE@@references/#ref-zhao-etal-2024-explaining" class="cite cite-established" data-cite="zhao-etal-2024-explaining" role="doc-biblioref">Zhao et al., 2024c</a>; <a href="@@BASE@@references/#ref-dalvi2022discovering" class="cite cite-established" data-cite="dalvi2022discovering" role="doc-biblioref">Dalvi et al., 2022</a></span>) or leverage the zero-shot capabilities of foundation models to automatically extract them <span class="citation" data-cites="yang2023language oikarinen2023labelfree">(<a href="@@BASE@@references/#ref-yang2023language" class="cite cite-established" data-cite="yang2023language" role="doc-biblioref">Yang et al., 2023</a>; <a href="@@BASE@@references/#ref-oikarinen2023labelfree" class="cite cite-established" data-cite="oikarinen2023labelfree" role="doc-biblioref">Oikarinen et al., 2023</a>)</span>. <span class="citation" data-cites="yu-etal-2024-latent"><a href="@@BASE@@references/#ref-yu-etal-2024-latent" class="cite cite-established" data-cite="yu-etal-2024-latent" role="doc-biblioref">Yu et al., 2024</a></span> extended the work on latent concept discovery towards generating concept-based explanation of most attributed representations.</p>
-<p>Post-hoc methods, such as Concept Activation Vectors (CAV; <span class="citation" data-cites="kim2018tcav"><a href="@@BASE@@references/#ref-kim2018tcav" class="cite cite-established" data-cite="kim2018tcav" role="doc-biblioref">Kim et al., 2018</a></span>) and linear concept classifiers, probe existing activations to determine which concepts are encoded in model representations. These methods do not guarantee mechanistic understanding but are valuable for auditing, localization, and early detection tasks. Representation engineering (<span class="citation" data-cites="panickssery2024steeringllama2contrastive"><a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a></span>; <span class="citation" data-cites="turner2024steeringlanguagemodelsactivation"><a href="@@BASE@@references/#ref-turner2024steeringlanguagemodelsactivation" class="cite cite-emerging" data-cite="turner2024steeringlanguagemodelsactivation" role="doc-biblioref">Turner et al., 2024</a></span>) extends this approach and is discussed in detail in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">12.3</a>.</p>
-<p>It is important to distinguish CBMs from the architectural interventions discussed in Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>, as both are sometimes grouped under “interpretable-by-design” but operate on fundamentally different principles. Table <a href="@@BASE@@handbook/interpretability-landscape/#tab:semantic-vs-structural" data-reference-type="ref" data-reference="tab:semantic-vs-structural">1</a> summarises the distinction: CBMs impose <em>semantic constraints</em> (predefined human concepts), while architectural interventions impose <em>structural constraints</em> (sparsity, modularity) that allow the model to discover features autonomously. For safety applications, where unknown failure modes are a core concern, methods that enable discovery may be more useful than those that require specifying concepts upfront.</p>
+<section id="subsec:cbm" class="level2"><h2 data-pdf-page="17"><span class="secnum">3.2</span> Concept-Based Approaches<a class="anchor" href="#subsec:cbm" aria-label="Link to this section">#</a></h2>
+<p>Concept-based methods occupy a middle ground: they examine internal representations but typically establish correlational rather than causal claims. However, when combined with interventions such as steering along concept directions and ablating concept-associated components, they become mechanistic by the criteria discussed in Section <a href="@@BASE@@handbook/introduction/#sec:scope" data-reference-type="ref" data-reference="sec:scope">1.2</a>. <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks pmlr-v162-geiger22a">Geiger et al. (<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">2021</a>; <a href="@@BASE@@references/#ref-pmlr-v162-geiger22a" class="cite cite-established" data-cite="pmlr-v162-geiger22a" role="doc-biblioref">2022</a>)</span> formalise this connection by proposing methods to align model computations with a high-level causal abstraction graph whose nodes are human-interpretable concepts.</p>
+<p>Concept-based approaches include both training-time and post-hoc instantiations. Concept Bottleneck Models (CBMs; <span class="citation" data-cites="koh2020conceptbottleneckmodels"><a href="@@BASE@@references/#ref-koh2020conceptbottleneckmodels" class="cite cite-established" data-cite="koh2020conceptbottleneckmodels" role="doc-biblioref">Koh et al., 2020</a></span>) constrain computation through a predefined bottleneck of interpretable concepts, offering built-in transparency and allowing for interventions, but traditionally requiring a complete set of predefined concepts and often suffering from an “accuracy–interpretability trade-off” when concept definitions are insufficiently expressive. Newer variants, such as Concept Embedding Models (CEMs; <span class="citation" data-cites="zarlenga2022cem"><a href="@@BASE@@references/#ref-zarlenga2022cem" class="cite cite-established" data-cite="zarlenga2022cem" role="doc-biblioref">Zarlenga et al., 2022</a></span>), address these limitations by allowing the bottleneck to encode additional high-dimensional information; this recovers accuracy at the cost of allowing non-interpretable information to leak into the bottleneck. Moreover, recent improvements relax the assumption of concept independence. Causal Concept Graph Models (CGMs; <span class="citation" data-cites="dominici2025causal"><a href="@@BASE@@references/#ref-dominici2025causal" class="cite cite-established" data-cite="dominici2025causal" role="doc-biblioref">Dominici et al., 2025</a></span>) and Stochastic Concept Bottleneck Models (SCBMs; <span class="citation" data-cites="vandenhirtz2024stochastic"><a href="@@BASE@@references/#ref-vandenhirtz2024stochastic" class="cite cite-established" data-cite="vandenhirtz2024stochastic" role="doc-biblioref">Vandenhirtz et al., 2024</a></span>) learn structural dependencies or graph-based relationships between concepts to capture complex hierarchies.</p>
+<p>Parallel to these architectural changes, recent work also relaxes the assumption that concepts must be <em>predefined</em>. These methods instead discover latent representations directly from model activations <span class="citation" data-cites="zhao-etal-2024-explaining dalvi2022discovering">(<a href="@@BASE@@references/#ref-zhao-etal-2024-explaining" class="cite cite-established" data-cite="zhao-etal-2024-explaining" role="doc-biblioref">Zhao et al., 2024c</a>; <a href="@@BASE@@references/#ref-dalvi2022discovering" class="cite cite-established" data-cite="dalvi2022discovering" role="doc-biblioref">Dalvi et al., 2022</a>)</span> or leverage foundation models’ zero-shot capabilities to extract them automatically <span class="citation" data-cites="yang2023language oikarinen2023labelfree">(<a href="@@BASE@@references/#ref-yang2023language" class="cite cite-established" data-cite="yang2023language" role="doc-biblioref">Yang et al., 2023</a>; <a href="@@BASE@@references/#ref-oikarinen2023labelfree" class="cite cite-established" data-cite="oikarinen2023labelfree" role="doc-biblioref">Oikarinen et al., 2023</a>)</span>. <span class="citation" data-cites="yu-etal-2024-latent"><a href="@@BASE@@references/#ref-yu-etal-2024-latent" class="cite cite-established" data-cite="yu-etal-2024-latent" role="doc-biblioref">Yu et al. (2024)</a></span> extended the work on latent concept discovery towards generating concept-based explanations of the most attributed representations, and <span class="citation" data-cites="yu2026vectorquantizedlatentconcepts"><a href="@@BASE@@references/#ref-yu2026vectorquantizedlatentconcepts" class="cite cite-emerging" data-cite="yu2026vectorquantizedlatentconcepts" role="doc-biblioref">Yu et al. (2026)</a></span> proposed the Vector Quantized Latent Concepts (VQLC) framework for efficient and scalable automatic concept discovery.</p>
+<p>Post-hoc methods, such as Concept Activation Vectors (CAVs; <span class="citation" data-cites="kim2018tcav"><a href="@@BASE@@references/#ref-kim2018tcav" class="cite cite-established" data-cite="kim2018tcav" role="doc-biblioref">Kim et al., 2018</a></span>) and linear concept classifiers <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span>, probe existing activations to identify concepts encoded in model representations. In general, these methods do not provide causal evidence about the role of those concepts in model computation, although some probing variants aim to support stronger causal claims, such as amnesic probing <span class="citation" data-cites="elazar-etal-2021-amnesic">(<a href="@@BASE@@references/#ref-elazar-etal-2021-amnesic" class="cite cite-established" data-cite="elazar-etal-2021-amnesic" role="doc-biblioref">Elazar et al., 2021</a>)</span>. Nevertheless, they remain valuable tools for auditing, localisation, and early detection tasks. Furthermore, representation engineering (<span class="citation" data-cites="panickssery2024steeringllama2contrastive marks2024the"><a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2024the" class="cite cite-established" data-cite="marks2024the" role="doc-biblioref">Marks &amp; Tegmark, 2024</a></span>; <span class="citation" data-cites="turner2024steeringlanguagemodelsactivation"><a href="@@BASE@@references/#ref-turner2024steeringlanguagemodelsactivation" class="cite cite-emerging" data-cite="turner2024steeringlanguagemodelsactivation" role="doc-biblioref">Turner et al., 2024</a></span>) extends the probing paradigm from observation to intervention, enabling targeted steering of model behaviour. We discuss these methods in detail in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a>.</p>
+<p>CBMs are sometimes grouped with the <em>interpretability-by-design</em> methods discussed in Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.10</a>, but they operate differently. CBMs impose <em>semantic constraints</em> (predefined human concepts), while interpretable-by-design models impose <em>structural constraints</em> (sparsity, modularity) that make the model intrinsically easier to interpret. Table <a href="@@BASE@@handbook/interpretability-landscape/#tab:semantic-vs-structural" data-reference-type="ref" data-reference="tab:semantic-vs-structural">1</a> summarises the distinction.</p>
 <table id="tab:semantic-vs-structural">
-<caption><span class="capnum">Table 1:</span> Semantic Constraints (CBMs) vs. Structural Constraints (Architectural MI)</caption>
+<caption><span class="capnum">Table 1:</span> Concept Bottleneck Models (CBMs) vs. Interpretable-by-Design Models</caption>
 <thead>
 <tr>
-<th style="text-align: left;"><strong>Dimension</strong></th>
-<th style="text-align: left;"><strong>Concept Bottleneck Models</strong></th>
-<th style="text-align: left;"><strong>Architectural Interventions</strong></th>
+<th style="text-align: left;"><span style="color: white"><strong>Dimension</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Concept Bottleneck Models</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Interpretable-by-Design Models</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Constraint type</td>
+<td style="text-align: left;"><strong>Constraint type</strong></td>
 <td style="text-align: left;">Semantic: predefined human concepts</td>
 <td style="text-align: left;">Structural: sparsity, modularity, compositionality</td>
 </tr>
 <tr>
-<td style="text-align: left;">Feature discovery</td>
+<td style="text-align: left;"><strong>Feature discovery</strong></td>
 <td style="text-align: left;">Not possible—concepts fixed a priori</td>
 <td style="text-align: left;">Enabled—model discovers features from data</td>
 </tr>
 <tr>
-<td style="text-align: left;">Interpretability guarantee</td>
+<td style="text-align: left;"><strong>Interpretability guarantee</strong></td>
 <td style="text-align: left;">Explanations in terms of specified concepts</td>
 <td style="text-align: left;">Circuits and features are structurally accessible</td>
 </tr>
 <tr>
-<td style="text-align: left;">Accuracy trade-off</td>
+<td style="text-align: left;"><strong>Accuracy trade-off</strong></td>
 <td style="text-align: left;">Degrades when bottleneck omits relevant features</td>
 <td style="text-align: left;">Degrades with sparsity, but recoverable via scaling</td>
 </tr>
 </tbody>
 </table>
-<p><strong>Practical Guide</strong>: CBMs tend to be more appropriate when the relevant concepts are known and can be enumerated; architectural interventions may be preferable when the goal is to discover what the model has learned—including features that were not known in advance. For safety applications, where unknown failure modes are a core concern, methods that enable discovery may be more useful than those that require specifying concepts upfront.</p>
+<p>CBMs can be more appropriate when the set of concepts of interest is known. Interpretability-by-design models may be preferable when the goal is to discover what the model has learned—including features that were not preset in advance. For safety applications, where unknown failure modes are a core concern, discovery-oriented methods may be more useful than those that require specifying concepts upfront.</p>
 </section>
-<section id="attribution-and-saliency-methods" class="level2"><h2 data-pdf-page="8"><span class="secnum">3.3</span> Attribution and Saliency Methods<a class="anchor" href="#attribution-and-saliency-methods" aria-label="Link to this section">#</a></h2>
-<p>Attribution methods focus on identifying which features most influence a model’s outputs. Techniques such as SHAP <span class="citation" data-cites="lundberg2017unifiedapproachinterpretingmodel">(<a href="@@BASE@@references/#ref-lundberg2017unifiedapproachinterpretingmodel" class="cite cite-established" data-cite="lundberg2017unifiedapproachinterpretingmodel" role="doc-biblioref">Lundberg &amp; Lee, 2017</a>)</span>, LIME <span class="citation" data-cites="ribeiro2016whyitrustyou">(<a href="@@BASE@@references/#ref-ribeiro2016whyitrustyou" class="cite cite-established" data-cite="ribeiro2016whyitrustyou" role="doc-biblioref">Ribeiro et al., 2016</a>)</span>, integrated gradients <span class="citation" data-cites="ig">(<a href="@@BASE@@references/#ref-ig" class="cite cite-established" data-cite="ig" role="doc-biblioref">Sundararajan et al., 2017</a>)</span>, and saliency maps <span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks">(<a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-established" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a>)</span> are scalable and widely used for token-level explanations and debugging. Applied to inputs, they indicate <em>which</em> inputs matter but not <em>how</em> the model arrives at its output. The same machinery can also be directed at internal representations, attributing salience to features at each layer and tracing how an input representation evolves across the network <span class="citation" data-cites="yu-etal-2024-latent">(<a href="@@BASE@@references/#ref-yu-etal-2024-latent" class="cite cite-established" data-cite="yu-etal-2024-latent" role="doc-biblioref">Yu et al., 2024</a>)</span>; used this way, attribution begins to overlap with the mechanistic methods discussed later. Reliability concerns nonetheless remain: different attribution methods can produce divergent explanations for the same prediction, and some have been shown to be insensitive to model parameters <span class="citation" data-cites="adebayo2020sanitycheckssaliencymaps">(<a href="@@BASE@@references/#ref-adebayo2020sanitycheckssaliencymaps" class="cite cite-established" data-cite="adebayo2020sanitycheckssaliencymaps" role="doc-biblioref">Adebayo et al., 2018b</a>)</span>. For the safety-relevant tasks motivating this survey, attribution methods are useful for triage and initial auditing but, on their own, insufficient where a causal account of internal computation is required.</p>
+<section id="input-attribution-and-saliency-methods" class="level2"><h2 data-pdf-page="18"><span class="secnum">3.3</span> Input Attribution and Saliency Methods<a class="anchor" href="#input-attribution-and-saliency-methods" aria-label="Link to this section">#</a></h2>
+<p>Input attribution methods aim to identify which input features most influence a model’s outputs. Techniques such as SHAP <span class="citation" data-cites="lundberg2017unifiedapproachinterpretingmodel">(<a href="@@BASE@@references/#ref-lundberg2017unifiedapproachinterpretingmodel" class="cite cite-established" data-cite="lundberg2017unifiedapproachinterpretingmodel" role="doc-biblioref">Lundberg &amp; Lee, 2017</a>)</span>, LIME <span class="citation" data-cites="ribeiro2016whyitrustyou">(<a href="@@BASE@@references/#ref-ribeiro2016whyitrustyou" class="cite cite-established" data-cite="ribeiro2016whyitrustyou" role="doc-biblioref">Ribeiro et al., 2016</a>)</span>, DeepLIFT <span class="citation" data-cites="deeplift">(<a href="@@BASE@@references/#ref-deeplift" class="cite cite-established" data-cite="deeplift" role="doc-biblioref">Shrikumar et al., 2017</a>)</span>, and saliency-based methods (<span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks"><a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-emerging" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a></span>; <span class="citation" data-cites="ig cam gradcam"><a href="@@BASE@@references/#ref-ig" class="cite cite-established" data-cite="ig" role="doc-biblioref">Sundararajan et al., 2017</a>; <a href="@@BASE@@references/#ref-cam" class="cite cite-established" data-cite="cam" role="doc-biblioref">Zhou et al., 2016</a>; <a href="@@BASE@@references/#ref-gradcam" class="cite cite-established" data-cite="gradcam" role="doc-biblioref">Selvaraju et al., 2017</a></span>) are scalable and widely used for explanation and debugging at the pixel and token levels. However, these methods indicate <em>which</em> input features matter for a prediction, but not necessarily <em>how</em> the model processes and leverages them. For example, saliency maps for image classifiers leverage gradients propagated from the output back to the input to estimate the sensitivity of the prediction to each pixel <span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks">(<a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-emerging" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a>)</span>. While such methods can highlight influential regions of an input image, they do not reveal how the model represents pixels in its activations and uses them across layers.</p>
+<p>The same machinery can also be applied to internal representations, attributing salience to features at different layers and tracing how information evolves throughout the network (<span class="citation" data-cites="yu-etal-2024-latent"><a href="@@BASE@@references/#ref-yu-etal-2024-latent" class="cite cite-established" data-cite="yu-etal-2024-latent" role="doc-biblioref">Yu et al., 2024</a></span>; <span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated"><a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a></span>). Used in this way, attribution methods begin to overlap with the mechanistic approaches discussed later in this survey. Nevertheless, concerns about their reliability remain. Different attribution methods can produce substantially different explanations for the same prediction, and some have been shown to be insensitive to model parameters <span class="citation" data-cites="adebayo2020sanitycheckssaliencymaps">(<a href="@@BASE@@references/#ref-adebayo2020sanitycheckssaliencymaps" class="cite cite-established" data-cite="adebayo2020sanitycheckssaliencymaps" role="doc-biblioref">Adebayo et al., 2018</a>)</span> or sensitive to input transformations that have no effect on the model <span class="citation" data-cites="kindermans2017unreliabilitysaliencymethods">(<a href="@@BASE@@references/#ref-kindermans2017unreliabilitysaliencymethods" class="cite cite-established" data-cite="kindermans2017unreliabilitysaliencymethods" role="doc-biblioref">Kindermans et al., 2022</a>)</span>. For safety-relevant applications, attribution methods help with triage, debugging, and initial auditing, but they are insufficient on their own when an explanation of internal computation is required.</p>
 </section>
-<section id="representation-probing-and-related-post-hoc-analyses" class="level2"><h2 data-pdf-page="9"><span class="secnum">3.4</span> Representation Probing and Related Post-hoc Analyses<a class="anchor" href="#representation-probing-and-related-post-hoc-analyses" aria-label="Link to this section">#</a></h2>
-<p>Probing classifiers assess whether particular information is encoded in a model’s activations by training “lightweight” classifiers on intermediate representations <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span>. Originally developed in the NLP community for analysing linguistic structure <span class="citation" data-cites="gupta-etal-2015-distributional kohn-2015-whats adi2017finegrained">(<a href="@@BASE@@references/#ref-gupta-etal-2015-distributional" class="cite cite-established" data-cite="gupta-etal-2015-distributional" role="doc-biblioref">Gupta et al., 2015</a>; <a href="@@BASE@@references/#ref-kohn-2015-whats" class="cite cite-established" data-cite="kohn-2015-whats" role="doc-biblioref">K<span>ö</span>hn, 2015</a>; <a href="@@BASE@@references/#ref-adi2017finegrained" class="cite cite-established" data-cite="adi2017finegrained" role="doc-biblioref">Adi et al., 2017</a>)</span> , and already reaching toward mechanistic questions in work tracing how representations evolve across layers <span class="citation" data-cites="tenney2019bertrediscoversclassicalnlp">(<a href="@@BASE@@references/#ref-tenney2019bertrediscoversclassicalnlp" class="cite cite-emerging" data-cite="tenney2019bertrediscoversclassicalnlp" role="doc-biblioref">Tenney et al., 2019</a>)</span>, probes have become one of the most widely used tools in mechanistic interpretability for tasks including layer-level localization, scalable auditing, and detecting latent behaviors such as sleeper-agent deception <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>.</p>
+<section id="representation-probing-and-related-post-hoc-analyses" class="level2"><h2 data-pdf-page="19"><span class="secnum">3.4</span> Representation Probing and Related Post-hoc Analyses<a class="anchor" href="#representation-probing-and-related-post-hoc-analyses" aria-label="Link to this section">#</a></h2>
+<p>Probing classifiers <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span> assess whether a model’s activations encode particular information by training “lightweight” classifiers—often logistic regression models—on intermediate representations to predict a target property, such as part-of-speech tags, syntactic structure, and gender <span class="citation" data-cites="belinkov2017neural hewitt2019structural ravfogel-etal-2020-null">(<a href="@@BASE@@references/#ref-belinkov2017neural" class="cite cite-established" data-cite="belinkov2017neural" role="doc-biblioref">Belinkov et al., 2017</a>; <a href="@@BASE@@references/#ref-hewitt2019structural" class="cite cite-established" data-cite="hewitt2019structural" role="doc-biblioref">Hewitt &amp; Manning, 2019</a>; <a href="@@BASE@@references/#ref-ravfogel-etal-2020-null" class="cite cite-established" data-cite="ravfogel-etal-2020-null" role="doc-biblioref">Ravfogel et al., 2020</a>)</span>. Originally developed in the NLP community to analyse linguistic structure <span class="citation" data-cites="gupta-etal-2015-distributional kohn-2015-whats adi2017finegrained">(<a href="@@BASE@@references/#ref-gupta-etal-2015-distributional" class="cite cite-established" data-cite="gupta-etal-2015-distributional" role="doc-biblioref">Gupta et al., 2015</a>; <a href="@@BASE@@references/#ref-kohn-2015-whats" class="cite cite-established" data-cite="kohn-2015-whats" role="doc-biblioref">K<span>ö</span>hn, 2015</a>; <a href="@@BASE@@references/#ref-adi2017finegrained" class="cite cite-established" data-cite="adi2017finegrained" role="doc-biblioref">Adi et al., 2017</a>)</span> and used by <span class="citation" data-cites="alain2017understanding"><a href="@@BASE@@references/#ref-alain2017understanding" class="cite cite-established" data-cite="alain2017understanding" role="doc-biblioref">Alain &amp; Bengio (2017)</a></span> to trace how representations evolve across layers of deep learning models, probes have become one of the most widely used tools in mechanistic interpretability for tasks including, but not limited to, layer-level localisation <span class="citation" data-cites="tenney2019bertrediscoversclassicalnlp">(<a href="@@BASE@@references/#ref-tenney2019bertrediscoversclassicalnlp" class="cite cite-established" data-cite="tenney2019bertrediscoversclassicalnlp" role="doc-biblioref">Tenney et al., 2019</a>)</span> and detection of latent behaviours <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>. We discuss the probing methodology, its limitations, and its relationship to causal interpretability in more detail in Section <a href="@@BASE@@handbook/intermediate-representations/#subsec:probing" data-reference-type="ref" data-reference="subsec:probing">8.6</a>.</p>
 </section>
-<section id="when-mechanistic-understanding-becomes-necessary" class="level2"><h2 data-pdf-page="9"><span class="secnum">3.5</span> When Mechanistic Understanding Becomes Necessary<a class="anchor" href="#when-mechanistic-understanding-becomes-necessary" aria-label="Link to this section">#</a></h2>
-<p>The methods surveyed above form a spectrum from correlational to causal. Behavioral evaluation establishes <em>what</em> a model does; attribution and probing identify <em>where</em> relevant information resides; mechanistic analysis reveals <em>how</em> computations produce behavior and supports targeted intervention. These approaches are complementary rather than competing: mechanistic findings are validated through behavioral experiments, and behavioral anomalies motivate mechanistic investigation.</p>
-<p>Mechanistic analysis becomes necessary when we need to verify <em>how</em> a model produces a behavior—not just <em>that</em> it does under test conditions. A probe might detect deceptive reasoning, but confirming the finding is causal, and designing targeted interventions, requires causal intervention on internal representations—whether through activation patching, steering, or ablation. Likewise, unexpected generalization or failure modes may demand mechanistic decomposition to determine whether they stem from identifiable structures or distributed processes. Concretely, mechanistic methods are most needed when: verifying the <em>absence</em> of a capability (behavioral tests can only confirm presence); detecting reasoning hidden from outputs such as deceptive intent or latent goals; understanding whether a behavior will generalize under distribution shift; or providing safety guarantees that must hold beyond the evaluated input distribution. This precision comes at greater computational cost, positioning mechanistic methods as a complement to lighter-weight approaches rather than a full replacement.</p>
+<section id="mechanistic-interpretability-methods" class="level2"><h2 data-pdf-page="19"><span class="secnum">3.5</span> Mechanistic Interpretability Methods<a class="anchor" href="#mechanistic-interpretability-methods" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic interpretability (MI) methods aim to causally understand model internals, such as the semantic meaning of parameters or activations, and how they relate to model behaviour. They are largely human-centric in the sense that the desired mechanistic understanding is one that humans can interpret and reason about—whether through natural-language descriptions (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a></span>; <span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>), plots of attention patterns (<span class="citation" data-cites="clark2019what"><a href="@@BASE@@references/#ref-clark2019what" class="cite cite-emerging" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a></span>; <span class="citation" data-cites="vig2019multiscale"><a href="@@BASE@@references/#ref-vig2019multiscale" class="cite cite-established" data-cite="vig2019multiscale" role="doc-biblioref">Vig, 2019</a></span>), or token distributions <span class="citation" data-cites="nostalgebraist2020interpretinggpt belrose2025elicitinglatentpredictionstransformers jlens">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>; <a href="@@BASE@@references/#ref-belrose2025elicitinglatentpredictionstransformers" class="cite cite-emerging" data-cite="belrose2025elicitinglatentpredictionstransformers" role="doc-biblioref">Belrose et al., 2025</a>; <a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a>)</span>. Recent work, however, has questioned whether “machine languages” and human languages fully overlap, suggesting that some meaningful internal computations may not admit faithful descriptions in natural language <span class="citation" data-cites="hewitt2025position">(<a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>.</p>
+<p>Mainstream MI approaches include, but are not limited to, feature decomposition and extraction <span class="citation" data-cites="cunningham2023sparse dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a>; <a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>, circuit discovery (<span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated ameisen2025circuit"><a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a>; <a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>; <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>), and methods for decoding internal representations into natural language (<span class="citation" data-cites="patchscopes pan2024latentqateachingllmsdecode"><a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al., 2024</a>; <a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a></span>; <span class="citation" data-cites="nla"><a href="@@BASE@@references/#ref-nla" class="cite cite-emerging" data-cite="nla" role="doc-biblioref">Fraser-Taliente et al., 2026</a></span>), all of which are discussed in later sections. Different methods rest on different assumptions and produce different types of explanation, making them suitable for different use cases but with distinct limitations. For example, sparse autoencoders decompose model representations (e.g., residual-stream activations at the last token position of a particular layer) into thousands of monosemantic features, though many extracted features may be non-causal or illusory <span class="citation" data-cites="wu2025axbench leask2025sparse heap2026automated">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>; <a href="@@BASE@@references/#ref-leask2025sparse" class="cite cite-established" data-cite="leask2025sparse" role="doc-biblioref">Leask et al., 2025</a>; <a href="@@BASE@@references/#ref-heap2026automated" class="cite cite-established" data-cite="heap2026automated" role="doc-biblioref">Heap et al., 2026</a>)</span>. Circuit discovery methods identify fine-grained computational graphs responsible for particular model behaviours, but these graphs may capture only a subset of the model’s underlying computation and can be difficult to validate for sufficiency or identifiability <span class="citation" data-cites="miller2024transformer meloux2025everything">(<a href="@@BASE@@references/#ref-miller2024transformer" class="cite cite-established" data-cite="miller2024transformer" role="doc-biblioref">Miller et al., 2024</a>; <a href="@@BASE@@references/#ref-meloux2025everything" class="cite cite-established" data-cite="meloux2025everything" role="doc-biblioref">M<span>é</span>loux et al., 2025</a>)</span>. Despite these limitations, MI methods remain promising complementary tools that collectively enable systematic analysis of model internals.</p>
+</section>
+<section id="subsec: mech interp necessary" class="level2"><h2 data-pdf-page="19"><span class="secnum">3.6</span> When Mechanistic Understanding Becomes Necessary<a class="anchor" href="#subsec: mech interp necessary" aria-label="Link to this section">#</a></h2>
+<p>The methods we have discussed so far form a correlational-to-causal spectrum: behavioural evaluation establishes <em>what</em> a model does, attribution and probing identify <em>where</em> relevant information resides, and mechanistic analysis reveals <em>how</em> computations produce behaviour. These approaches complement each other: behavioural evaluations help validate mechanistic findings, and behavioural anomalies can motivate mechanistic investigation.</p>
+<p>Mechanistic analysis becomes necessary when we need to verify <em>how</em> a model produces a behaviour—not just <em>that</em> it does under test conditions. For example, a probe can detect deceptive reasoning, but we still need targeted interventions to validate whether the detection is a false alarm; if not, we will further need more rigorous mechanistic analysis to understand why the model deceives. Likewise, unexpected generalisation or failure modes may require mechanistic analysis to examine whether they stem from identifiable structures. Concretely, mechanistic methods are most needed for: identifying intents hidden from outputs, such as deception or side quests; understanding the internal mechanism of a model behaviour; verifying the <em>absence</em> of a capability (behavioural tests can only confirm presence); or providing safety guarantees that must hold beyond the evaluated input distribution. However, the high granularity of mechanistic analysis comes at high practical cost, including not only compute but also researcher time, experimental complexity, and slower iteration. These costs position mechanistic methods as a complement to lighter-weight approaches, such as behavioural evaluations, rather than a full replacement.</p>
 <table id="tab:interpretability-methods">
-<caption><span class="capnum">Table 2:</span> Interpretability Methods: Trade-offs and Characteristics</caption>
+<caption><span class="capnum">Table 2:</span> Trade-offs and characteristics of interpretability methods.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Method</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Core Idea</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Strengths</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Intended Use / Limitations</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Intended Use and Limitations</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;"><strong>Behavioral (red-teaming, benchmarks)</strong></td>
+<td style="text-align: left;"><strong>Behavioural (e.g., red-teaming, benchmarks)</strong></td>
 <td style="text-align: left;">Examine model outputs in response to inputs</td>
 <td style="text-align: left;">Fast, scalable, practical for broad safety coverage</td>
 <td style="text-align: left;">Not designed to reveal internal computation; suitable for monitoring, triage, and regression detection</td>
 </tr>
 <tr>
-<td style="text-align: left;"><strong>Attribution (SHAP, saliency)</strong></td>
+<td style="text-align: left;"><strong>Input attribution (e.g., SHAP, saliency)</strong></td>
 <td style="text-align: left;">Identify which inputs most influence outputs</td>
 <td style="text-align: left;">Token-level explanations; scalable</td>
 <td style="text-align: left;">Focuses on input–output influence, not internal mechanisms; useful for auditing and debugging</td>
 </tr>
 <tr>
-<td style="text-align: left;"><strong>Concept-Based (CBMs, probes)</strong></td>
-<td style="text-align: left;">Analyse or constrain interpretable representations</td>
+<td style="text-align: left;"><strong>Concept-based (e.g., CBMs, probes)</strong></td>
+<td style="text-align: left;">Correlationally analyse or constrain interpretable representations</td>
 <td style="text-align: left;">Reveals encoded information; transparent abstractions; enables targeted intervention</td>
-<td style="text-align: left;">Non-mechanistic; supports localization, auditing, and early detection tasks</td>
+<td style="text-align: left;">Not mechanistic without causal intervention; supports localisation, auditing, and early detection tasks</td>
 </tr>
 <tr>
-<td style="text-align: left;"><strong>Mechanistic (circuits, SAEs)</strong></td>
+<td style="text-align: left;"><strong>Mechanistic (e.g., circuits, SAEs)</strong></td>
 <td style="text-align: left;">Analyse internal computations and causal structure</td>
 <td style="text-align: left;">Reveals algorithms; enables targeted intervention</td>
 <td style="text-align: left;">Computationally intensive; complements other methods when causal understanding is required</td>
@@ -91,4 +98,17 @@
 </tbody>
 </table>
 </section>
+<section id="subsec:limits" class="level2"><h2 data-pdf-page="20"><span class="secnum">3.7</span> Limitations of Mechanistic Analysis<a class="anchor" href="#subsec:limits" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic interpretability aims to provide causal explanations of model behaviour, but it faces important practical and conceptual limitations. First, mechanistic explanations are often incomplete. An analysis may identify an internal computation (e.g., “This MLP retrieves factual knowledge about Paris”, as identified through causal tracing <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>) that captures a non-trivial causal pathway without identifying all others contributing to the behaviour. This concern makes it hard to tell whether the identified computation is sufficient or just one of several mechanisms jointly implementing the behaviour.</p>
+<p>Second, mechanistic analyses can scale poorly. Analysing large models and complex behaviours takes much more time and resources because the relevant computations may be distributed across model components and complex behaviours involve interactions across long generation trajectories that are hard to isolate. For example, most current circuit-discovery methods (<span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>; <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic eapig marks2025sparse querycircuit"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>; <a href="@@BASE@@references/#ref-eapig" class="cite cite-established" data-cite="eapig" role="doc-biblioref">Hanna et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>; <a href="@@BASE@@references/#ref-querycircuit" class="cite cite-established" data-cite="querycircuit" role="doc-biblioref">Wu &amp; Barez, 2026</a></span>) identify the computational graph responsible for generating a specific token. It remains challenging to identify and summarise the computations underlying behaviours that unfold across multiple generated tokens.</p>
+<p>Third, even when we identify and causally validate an internal mechanism, this does not automatically mean we can reliably control the corresponding behaviour <span class="citation" data-cites="localinform">(<a href="@@BASE@@references/#ref-localinform" class="cite cite-established" data-cite="localinform" role="doc-biblioref">Hase et al., 2023</a>)</span>. The effectiveness, specificity, and generalisability of localisation and intervention techniques, such as identifying a model component and then patching its activations or changing its weight matrices, depend on several factors, such as the hydra effect <span class="citation" data-cites="mcgrath2023hydraeffectemergentselfrepair">(<a href="@@BASE@@references/#ref-mcgrath2023hydraeffectemergentselfrepair" class="cite cite-emerging" data-cite="mcgrath2023hydraeffectemergentselfrepair" role="doc-biblioref">McGrath et al., 2023</a>)</span>, beyond how accurately the identified localisation captures the underlying computation for the behaviour. Furthermore, an intervention that successfully steers one model’s behaviour on a task may not transfer to another model or setting.</p>
+<p>These are open challenges but not fundamental barriers. Active research in areas such as establishing more effective representational units, discovering more robust model properties, developing more efficient circuit-discovery methods, and designing more reliable intervention approaches across distributions is working to address them. Nevertheless, these studies highlight a clear gap between mechanistic explanation and reliable control or safety guarantees.</p>
+<div class="warningbox">
+<p>Mechanistic explanations may be incomplete or non-causal, difficult to scale to large models or complex behaviours, and sensitive to modelling assumptions. They often fail to capture the full picture of a model’s underlying mechanism and do not automatically provide reliable, generalisable control. Intervening on an identified mechanism may work in one setting but fail to transfer to another. However, these limitations do not imply that mechanistic understanding cannot enable reliable control. Instead, they indicate that moving from explanation to robust, generalisable intervention requires additional studies and remains an open challenge in the field <span class="citation" data-cites="sharkey2025open">(<a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a>)</span>.</p>
+</div>
+<section id="summary." class="level4">
+<h4>Summary.</h4>
+<p>This section discussed different interpretability methods based on the evidence they provide. Behavioural methods measure and analyse model responses to inputs; concept-based methods explain predictions through human-interpretable concepts; input attribution methods identify which parts of the input influence an output; probing tests what information is decodable from representations; mechanistic methods identify and describe internal mechanisms that produce a behaviour. Among them, mechanistic analysis offers the strongest claims, but it is costly, often incomplete, and does not guarantee reliable control. In the next section, we introduce the core concepts on which mechanistic analysis is built.</p>
+</section>
+</section>
 </section>
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-<section id="sec:introduction" class="level1"><h1 data-pdf-page="1"><span class="secnum">1</span> Introduction<a class="anchor" href="#sec:introduction" aria-label="Link to this section">#</a></h1>
-<p>Neural networks could be the most consequential artefacts humanity has built, yet we do not understand how they work or reliably control what they do. This is true despite humans designing the training procedure: we specify how models learn, but not what they learn. <em>Interpretability</em> is the effort to understand what they learn and why they behave as they do, rather than only observing what they output. <em>Mechanistic interpretability</em> (MI) is one approach within it. Rather than explaining behaviour from the outside, it reverse-engineers a network into the internal mechanisms that produce that behaviour, which can yield both a richer understanding of the model and more reliable control over it.</p>
-<p>Recent decades have seen exponential improvement in AI capabilities (<span class="citation" data-cites="touvron2023llamaopenefficientfoundation"><a href="@@BASE@@references/#ref-touvron2023llamaopenefficientfoundation" class="cite cite-emerging" data-cite="touvron2023llamaopenefficientfoundation" role="doc-biblioref">Touvron et al., 2023</a></span>; <span class="citation" data-cites="openai2024gpt4technicalreport"><a href="@@BASE@@references/#ref-openai2024gpt4technicalreport" class="cite cite-emerging" data-cite="openai2024gpt4technicalreport" role="doc-biblioref">OpenAI et al., 2024</a></span>; <span class="citation" data-cites="gemmateam2024gemmaopenmodelsbased"><a href="@@BASE@@references/#ref-gemmateam2024gemmaopenmodelsbased" class="cite cite-emerging" data-cite="gemmateam2024gemmaopenmodelsbased" role="doc-biblioref">Team et al., 2024</a></span>; <span class="citation" data-cites="yang2025qwen3technicalreport"><a href="@@BASE@@references/#ref-yang2025qwen3technicalreport" class="cite cite-emerging" data-cite="yang2025qwen3technicalreport" role="doc-biblioref">QwenAI, 2025</a></span>; <span class="citation" data-cites="deepseekai2025deepseekv3technicalreport"><a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a></span>) but methods for understanding and explaining internal mechanisms have lagged behind. This has created an interpretability gap between what models can do and what we know about how they do it. The gap is highly relevant to safety. Purely behavioural safety measures such as RLHF <span class="citation" data-cites="christiano2023deepreinforcementlearninghuman ouyang2022traininglanguagemodelsfollow">(<a href="@@BASE@@references/#ref-christiano2023deepreinforcementlearninghuman" class="cite cite-established" data-cite="christiano2023deepreinforcementlearninghuman" role="doc-biblioref">Christiano et al., 2017</a>; <a href="@@BASE@@references/#ref-ouyang2022traininglanguagemodelsfollow" class="cite cite-emerging" data-cite="ouyang2022traininglanguagemodelsfollow" role="doc-biblioref">Ouyang et al., 2022</a>)</span> and adversarial testing are not robust against sophisticated failure modes such as deceptive alignment <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>.</p>
+<section id="sec:introduction" class="level1"><h1 data-pdf-page="9"><span class="secnum">1</span> Introduction<a class="anchor" href="#sec:introduction" aria-label="Link to this section">#</a></h1>
+<p>Neural networks could be one of the most consequential artefacts humanity has built, yet we do not understand how they work or reliably control what they do. This is true despite humans designing the training procedure: we specify how models learn, but not what they learn. <em>Interpretability</em> is the effort to understand what models learn and why they behave as they do, rather than only observing what they output. <em>Mechanistic interpretability</em> (MI) is one approach within it. MI studies the <em>internal mechanisms</em> (characterised in Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:circuits" data-reference-type="ref" data-reference="subsec:circuits">4.5</a>) of the neural network in relation to its behaviour, which can yield both a richer understanding of the model and more reliable control over it.</p>
+<p>Recent decades have seen an exponential improvement in AI capabilities <span class="citation" data-cites="touvron2023llamaopenefficientfoundation openai2024gpt4technicalreport gemmateam2024gemmaopenmodelsbased yang2025qwen3technicalreport deepseekai2025deepseekv3technicalreport anthropic2025claude4">(<a href="@@BASE@@references/#ref-touvron2023llamaopenefficientfoundation" class="cite cite-emerging" data-cite="touvron2023llamaopenefficientfoundation" role="doc-biblioref">Touvron et al., 2023</a>; <a href="@@BASE@@references/#ref-openai2024gpt4technicalreport" class="cite cite-emerging" data-cite="openai2024gpt4technicalreport" role="doc-biblioref">OpenAI et al., 2024</a>; <a href="@@BASE@@references/#ref-gemmateam2024gemmaopenmodelsbased" class="cite cite-emerging" data-cite="gemmateam2024gemmaopenmodelsbased" role="doc-biblioref">Team et al., 2024a</a>; <a href="@@BASE@@references/#ref-yang2025qwen3technicalreport" class="cite cite-emerging" data-cite="yang2025qwen3technicalreport" role="doc-biblioref">QwenAI, 2025</a>; <a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a>; <a href="@@BASE@@references/#ref-anthropic2025claude4" class="cite cite-emerging" data-cite="anthropic2025claude4" role="doc-biblioref"><span>Anthropic</span>, 2025b</a>)</span>, but methods to understand and explain internal mechanisms have lagged behind. This has created an <em>interpretability gap</em> between what models can do and what we know about how they do it. The gap is highly relevant for safety since standard behaviour-based safety measures, such as safety-oriented reinforcement learning from human feedback (RLHF) (<span class="citation" data-cites="bai2022training"><a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a></span>; <span class="citation" data-cites="dai2024safe"><a href="@@BASE@@references/#ref-dai2024safe" class="cite cite-established" data-cite="dai2024safe" role="doc-biblioref">Dai et al., 2024b</a></span>) and red-teaming <span class="citation" data-cites="perez2022redteaminglanguagemodels">(<a href="@@BASE@@references/#ref-perez2022redteaminglanguagemodels" class="cite cite-established" data-cite="perez2022redteaminglanguagemodels" role="doc-biblioref">Perez et al., 2022</a>)</span>, are not robust to sophisticated failure modes such as deceptive alignment <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>.</p>
 <div class="definitionbox">
-<p>The <strong>interpretability gap</strong> is the disparity between what models do (their capabilities and behaviors) and what we understand about how they do it (their internal mechanisms). As models scale, this gap risks widening: reasoning emerges that we cannot yet trace, deceptive behaviors may remain undetected, and safety guarantees built on incomplete understanding may fail unexpectedly.</p>
+<p>The <em>interpretability gap</em> is the disparity between what models do (their capabilities and behaviours) and what we understand about how they do it (their internal mechanisms). As models scale, this gap risks widening: reasoning emerges that we cannot yet trace, deceptive behaviours may remain undetected, and safety guarantees built on incomplete understanding may fail unexpectedly.</p>
 </div>
-<section id="brief-history-of-interpretability" class="level2"><h2 data-pdf-page="2"><span class="secnum">1.1</span> Brief History of Interpretability <a class="anchor" href="#brief-history-of-interpretability" aria-label="Link to this section">#</a></h2>
-<p>Efforts to understand neural network internals began almost as soon as deep learning achieved practical success. <span class="citation" data-cites="zeiler2014visualizing"><a href="@@BASE@@references/#ref-zeiler2014visualizing" class="cite cite-established" data-cite="zeiler2014visualizing" role="doc-biblioref">Zeiler &amp; Fergus (2014)</a></span> developed visualization techniques to inspect the convolutional filters that AlexNet <span class="citation" data-cites="krizhevsky2012imagenet">(<a href="@@BASE@@references/#ref-krizhevsky2012imagenet" class="cite cite-established" data-cite="krizhevsky2012imagenet" role="doc-biblioref">Krizhevsky et al., 2012</a>)</span> had learned. Their study revealed that early layers detected edges while deeper layers responded to increasingly complex patterns. <span class="citation" data-cites="karpathy2015visualizing"><a href="@@BASE@@references/#ref-karpathy2015visualizing" class="cite cite-emerging" data-cite="karpathy2015visualizing" role="doc-biblioref">Karpathy et al. (2015)</a></span> and <span class="citation" data-cites="li-etal-2016-visualizing"><a href="@@BASE@@references/#ref-li-etal-2016-visualizing" class="cite cite-established" data-cite="li-etal-2016-visualizing" role="doc-biblioref">Li et al. (2016)</a></span> extended this approach to recurrent networks. By manually inspecting individual neurons they discovered ones tracking various linguistic features such as quotes, line lengths, brackets, negation and syntactic structure. This showed empirically that interpretable structure could emerge without explicit supervision.</p>
-<p>This early work was largely manual: researchers examined individual neurons and reported qualitative findings. The field soon developed more systematic approaches.</p>
-<p>Probing classifiers, introduced by <span class="citation" data-cites="alain2017understanding"><a href="@@BASE@@references/#ref-alain2017understanding" class="cite cite-established" data-cite="alain2017understanding" role="doc-biblioref">Alain &amp; Bengio (2017)</a></span></p>
-<p>and popularized through work on linguistic structure <span class="citation" data-cites="shi-etal-2016-string adi2017finegrained belinkov2017neural conneau2018what">(<a href="@@BASE@@references/#ref-shi-etal-2016-string" class="cite cite-established" data-cite="shi-etal-2016-string" role="doc-biblioref">Shi et al., 2016</a>; <a href="@@BASE@@references/#ref-adi2017finegrained" class="cite cite-established" data-cite="adi2017finegrained" role="doc-biblioref">Adi et al., 2017</a>; <a href="@@BASE@@references/#ref-belinkov2017neural" class="cite cite-established" data-cite="belinkov2017neural" role="doc-biblioref">Belinkov et al., 2017</a>; <a href="@@BASE@@references/#ref-conneau2018what" class="cite cite-established" data-cite="conneau2018what" role="doc-biblioref">Conneau et al., 2018</a>)</span>, offered a way to test whether specific information was encoded in learned representations. Probing asked whether syntactic category, semantic role, or sentence length could be extracted from a layer’s activations. <span class="citation" data-cites="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019"><a href="@@BASE@@references/#ref-Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" class="cite cite-established" data-cite="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" role="doc-biblioref">Dalvi et al. (2019)</a></span> extended it towards automatically identifying individual neurons learning a concept by exploiting the weights of the probing classifier.</p>
-<p>This line of work flourished between 2016 and 2019, producing a rich catalog of what information was <em>present</em> in activation patterns. It remains important for interpretability research to consider carefully what conclusions can legitimately be drawn from the presence of information in a network.</p>
-<p>The introduction of transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017a</a>)</span> and BERT <span class="citation" data-cites="devlin2019bert">(<a href="@@BASE@@references/#ref-devlin2019bert" class="cite cite-established" data-cite="devlin2019bert" role="doc-biblioref">Devlin et al., 2019</a>)</span> intensified both the need and the opportunity for interpretability research. Attention weights seemed to offer a direct window into model internals, spawning work on attention visualization <span class="citation" data-cites="vig2019multiscale clark2019what">(<a href="@@BASE@@references/#ref-vig2019multiscale" class="cite cite-established" data-cite="vig2019multiscale" role="doc-biblioref">Vig, 2019</a>; <a href="@@BASE@@references/#ref-clark2019what" class="cite cite-established" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a>)</span> and debates over whether attention provides sufficient explanation <span class="citation" data-cites="jain2019attention wiegreffe2019attention">(<a href="@@BASE@@references/#ref-jain2019attention" class="cite cite-established" data-cite="jain2019attention" role="doc-biblioref">Jain &amp; Wallace, 2019</a>; <a href="@@BASE@@references/#ref-wiegreffe2019attention" class="cite cite-established" data-cite="wiegreffe2019attention" role="doc-biblioref">Wiegreffe &amp; Pinter, 2019</a>)</span>. The resulting “BERTology” literature <span class="citation" data-cites="rogers2020primer">(<a href="@@BASE@@references/#ref-rogers2020primer" class="cite cite-established" data-cite="rogers2020primer" role="doc-biblioref">Rogers et al., 2020</a>)</span> systematically probed transformer internal states, while behavioral evaluations tested linguistic competence with curated datasets.</p>
-<p>Feature visualization research by Chris Olah and collaborators at Google Brain, published through the <em>Distill</em> journal <span class="citation" data-cites="distill_pub">(<a href="@@BASE@@references/#ref-distill_pub" class="cite cite-emerging" data-cite="distill_pub" role="doc-biblioref"><span>Distill</span>, 2017</a>)</span>, argued that neural networks contain meaningful, human-interpretable features. OpenAI subsequently developed these ideas into the “Circuits” research program <span class="citation" data-cites="olah2020zoom">(<a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>)</span>, which framed neural networks as compositions of interpretable features connected by understandable algorithms. This approach is the foundation of modern <em>mechanistic interpretability</em>.</p>
-<p>This framework later generalized to language models, with work on induction heads <span class="citation" data-cites="olsson2022context">(<a href="@@BASE@@references/#ref-olsson2022context" class="cite cite-emerging" data-cite="olsson2022context" role="doc-biblioref">Olsson et al., 2022b</a>)</span>, indirect object identification circuits <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span>, and modular arithmetic in toy models <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic quirke2024understandingadditiontransformers">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>; <a href="@@BASE@@references/#ref-quirke2024understandingadditiontransformers" class="cite cite-established" data-cite="quirke2024understandingadditiontransformers" role="doc-biblioref">Quirke &amp; Barez, 2024</a>)</span>. By the end of 2023, sparse autoencoders enabled feature extraction at billion-parameter scale <span class="citation" data-cites="bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>—we will discuss whether these methods provide complete mechanistic understanding and contrast them with simpler methods in section <a href="@@BASE@@handbook/open-problems/#sec:open_problems" data-reference-type="ref" data-reference="sec:open_problems">15</a>.</p>
+<section id="brief-history-of-interpretability" class="level2"><h2 data-pdf-page="9"><span class="secnum">1.1</span> Brief History of Interpretability<a class="anchor" href="#brief-history-of-interpretability" aria-label="Link to this section">#</a></h2>
+<p>While early work did not establish interpretability as a distinct and clearly defined area of artificial intelligence, researchers sought to understand what neural networks learn long before the emergence of modern deep learning models such as transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span>. For example, <span class="citation" data-cites="hinton1986distributed"><a href="@@BASE@@references/#ref-hinton1986distributed" class="cite cite-established" data-cite="hinton1986distributed" role="doc-biblioref">Hinton et al. (1986)</a></span> discussed how representations of concepts can be distributed across multiple units, while <span class="citation" data-cites="hinton1986learning"><a href="@@BASE@@references/#ref-hinton1986learning" class="cite cite-established" data-cite="hinton1986learning" role="doc-biblioref">Hinton (1986)</a></span> showed that networks can learn such distributed representations, with neurons encoding interpretable features of the concepts they represent. <span class="citation" data-cites="elman1991distributed"><a href="@@BASE@@references/#ref-elman1991distributed" class="cite cite-established" data-cite="elman1991distributed" role="doc-biblioref">Elman (1991)</a></span> further applied Principal Component Analysis (PCA; <span class="citation" data-cites="pearson1901lines hotelling1933analysis"><a href="@@BASE@@references/#ref-pearson1901lines" class="cite cite-established" data-cite="pearson1901lines" role="doc-biblioref">Pearson, 1901</a>; <a href="@@BASE@@references/#ref-hotelling1933analysis" class="cite cite-established" data-cite="hotelling1933analysis" role="doc-biblioref">Hotelling, 1933</a></span>) to the hidden states of a simple recurrent neural network <span class="citation" data-cites="elman1990finding">(<a href="@@BASE@@references/#ref-elman1990finding" class="cite cite-established" data-cite="elman1990finding" role="doc-biblioref">Elman, 1990</a>)</span> to analyse their trajectories as the network processed successive words in a sentence.</p>
+<p>Even earlier, <span class="citation" data-cites="minsky1969perceptrons"><a href="@@BASE@@references/#ref-minsky1969perceptrons" class="cite cite-established" data-cite="minsky1969perceptrons" role="doc-biblioref">Minsky &amp; Papert (1969)</a></span> studied which functions or predicates particular neural architectures could and could not represent, and their results contributed to the temporary decline of neural-network research. While this earlier work was not driven by AI safety concerns, it was an early attempt to classify what neural networks can and cannot compute.</p>
+<p>Modern efforts to understand the internals of neural networks began almost as soon as deep learning achieved practical success. <span class="citation" data-cites="zeiler2014visualizing"><a href="@@BASE@@references/#ref-zeiler2014visualizing" class="cite cite-established" data-cite="zeiler2014visualizing" role="doc-biblioref">Zeiler &amp; Fergus (2014)</a></span> developed visualisation techniques to inspect the convolutional filters that AlexNet <span class="citation" data-cites="NIPS2012_c399862d">(<a href="@@BASE@@references/#ref-NIPS2012_c399862d" class="cite cite-established" data-cite="NIPS2012_c399862d" role="doc-biblioref">Krizhevsky et al., 2012</a>)</span>, a convolutional neural network (CNN; <span class="citation" data-cites="oshea2015introductionconvolutionalneuralnetworks"><a href="@@BASE@@references/#ref-oshea2015introductionconvolutionalneuralnetworks" class="cite cite-emerging" data-cite="oshea2015introductionconvolutionalneuralnetworks" role="doc-biblioref">O’Shea &amp; Nash, 2015</a></span>) architecture, had learned. Their study revealed that early layers detected edges, while deeper layers responded to increasingly complex patterns. <span class="citation" data-cites="karpathy2015visualizing"><a href="@@BASE@@references/#ref-karpathy2015visualizing" class="cite cite-emerging" data-cite="karpathy2015visualizing" role="doc-biblioref">Karpathy et al. (2015)</a></span> and <span class="citation" data-cites="li-etal-2016-visualizing"><a href="@@BASE@@references/#ref-li-etal-2016-visualizing" class="cite cite-established" data-cite="li-etal-2016-visualizing" role="doc-biblioref">Li et al. (2016)</a></span> extended this approach to recurrent neural networks. By manually inspecting individual neurons, they discovered ones tracking various linguistic features, such as quotes, line lengths, brackets, negation, and syntactic structure. This showed empirically that an interpretable structure could emerge without explicit supervision.</p>
+<p>These early works were largely manual: researchers examined individual neurons and reported qualitative findings. The field soon developed more systematic approaches.</p>
+<p>Probing classifiers <span class="citation" data-cites="belinkov-2022-probing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>)</span> were popularised as a tool for studying neural representations through a series of works on linguistic structure and neural network analysis <span class="citation" data-cites="gupta-etal-2015-distributional kohn-2015-whats shi-etal-2016-string adi2017finegrained belinkov2017neural conneau2018what alain2017understanding">(<a href="@@BASE@@references/#ref-gupta-etal-2015-distributional" class="cite cite-established" data-cite="gupta-etal-2015-distributional" role="doc-biblioref">Gupta et al., 2015</a>; <a href="@@BASE@@references/#ref-kohn-2015-whats" class="cite cite-established" data-cite="kohn-2015-whats" role="doc-biblioref">K<span>ö</span>hn, 2015</a>; <a href="@@BASE@@references/#ref-shi-etal-2016-string" class="cite cite-established" data-cite="shi-etal-2016-string" role="doc-biblioref">Shi et al., 2016</a>; <a href="@@BASE@@references/#ref-adi2017finegrained" class="cite cite-established" data-cite="adi2017finegrained" role="doc-biblioref">Adi et al., 2017</a>; <a href="@@BASE@@references/#ref-belinkov2017neural" class="cite cite-established" data-cite="belinkov2017neural" role="doc-biblioref">Belinkov et al., 2017</a>; <a href="@@BASE@@references/#ref-conneau2018what" class="cite cite-established" data-cite="conneau2018what" role="doc-biblioref">Conneau et al., 2018</a>; <a href="@@BASE@@references/#ref-alain2017understanding" class="cite cite-established" data-cite="alain2017understanding" role="doc-biblioref">Alain &amp; Bengio, 2017</a>)</span>. These studies asked whether syntactic categories, semantic roles, or sentence length could be extracted from a layer’s activations. <span class="citation" data-cites="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019"><a href="@@BASE@@references/#ref-Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" class="cite cite-established" data-cite="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" role="doc-biblioref">Dalvi et al. (2019)</a></span> extended this approach to automatically identify individual neurons learning a concept by exploiting the weights of the probing classifier.</p>
+<p>This line of work flourished between 2016 and 2019, producing a rich catalogue of information that was present in model activations. It remains important for mechanistic interpretability research to consider carefully what conclusions can legitimately be drawn from the presence of information in a neural network.</p>
+<p>The introduction of transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span> and BERT <span class="citation" data-cites="devlin2019bert">(<a href="@@BASE@@references/#ref-devlin2019bert" class="cite cite-established" data-cite="devlin2019bert" role="doc-biblioref">Devlin et al., 2019</a>)</span> intensified both the need and the opportunity for interpretability research. The attention mechanisms of these architectures allowed researchers to peer into the internal workings of models, spurring research on attention visualisation (<span class="citation" data-cites="vig2019multiscale"><a href="@@BASE@@references/#ref-vig2019multiscale" class="cite cite-established" data-cite="vig2019multiscale" role="doc-biblioref">Vig, 2019</a></span>; <span class="citation" data-cites="clark2019what vig-belinkov-2019-analyzing"><a href="@@BASE@@references/#ref-clark2019what" class="cite cite-emerging" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a>; <a href="@@BASE@@references/#ref-vig-belinkov-2019-analyzing" class="cite cite-emerging" data-cite="vig-belinkov-2019-analyzing" role="doc-biblioref">Vig &amp; Belinkov, 2019</a></span>). In response, criticism and debates arose over whether attention was sufficient to explain model internals <span class="citation" data-cites="jain2019attention wiegreffe2019attention">(<a href="@@BASE@@references/#ref-jain2019attention" class="cite cite-established" data-cite="jain2019attention" role="doc-biblioref">Jain &amp; Wallace, 2019</a>; <a href="@@BASE@@references/#ref-wiegreffe2019attention" class="cite cite-established" data-cite="wiegreffe2019attention" role="doc-biblioref">Wiegreffe &amp; Pinter, 2019</a>)</span>. Additionally, “BERTology” work <span class="citation" data-cites="rogers2020primer">(<a href="@@BASE@@references/#ref-rogers2020primer" class="cite cite-established" data-cite="rogers2020primer" role="doc-biblioref">Rogers et al., 2020</a>)</span> shed light on transformer internals, and linguistic competence was tested via behavioural evaluations on curated datasets.</p>
+<p>A series of feature visualisation studies <span class="citation" data-cites="olah2017feature">(<a href="@@BASE@@references/#ref-olah2017feature" class="cite cite-emerging" data-cite="olah2017feature" role="doc-biblioref">Olah et al., 2017</a>)</span> published through the <em>Distill</em> publication <span class="citation" data-cites="distill_pub">(<a href="@@BASE@@references/#ref-distill_pub" class="cite cite-emerging" data-cite="distill_pub" role="doc-biblioref"><span>Distill</span>, 2017</a>)</span> argued that neural networks contain meaningful, human-interpretable features. <span class="citation" data-cites="olah2020zoom"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al. (2020)</a></span> subsequently developed these ideas into the “Circuits” research programme, which framed neural networks as compositions of interpretable features connected by understandable algorithms. These early efforts laid the foundation of modern <em>mechanistic interpretability</em>.</p>
+<p>These explorations later expanded to language models, with work on induction heads <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, indirect object identification (IOI) circuits <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect">(<a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a>)</span>, and modular arithmetic and integer addition in toy models (<span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic quirke2024understandingadditiontransformers"><a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>; <a href="@@BASE@@references/#ref-quirke2024understandingadditiontransformers" class="cite cite-established" data-cite="quirke2024understandingadditiontransformers" role="doc-biblioref">Quirke &amp; Barez, 2024</a></span>; <span class="citation" data-cites="quirke2026understandingadditionsubtractiontransformers"><a href="@@BASE@@references/#ref-quirke2026understandingadditionsubtractiontransformers" class="cite cite-emerging" data-cite="quirke2026understandingadditionsubtractiontransformers" role="doc-biblioref">Quirke et al., 2026</a></span>). By mid-2024, sparse autoencoders (SAEs) enabled feature extraction at billion-parameter scale (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a></span>; <span class="citation" data-cites="templeton2024scaling"><a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a></span>). We will discuss whether these methods provide complete mechanistic understanding in Section <a href="@@BASE@@handbook/open-problems/#sec:open_problems" data-reference-type="ref" data-reference="sec:open_problems">14</a>.</p>
 <section id="situating-the-field." class="level4">
-<h4>Situating the field.</h4>
-<p>Work in this area goes by several names. It has been called mechanistic interpretability, network analysis, representation engineering, or simply interpretability, and these terms are often used interchangeably rather than marking sharp distinctions. The scope of the field is still settling, and there is active discussion about what the word “mechanistic” should ask of an explanation <span class="citation" data-cites="saphra2024mechanistic">(<a href="@@BASE@@references/#ref-saphra2024mechanistic" class="cite cite-emerging" data-cite="saphra2024mechanistic" role="doc-biblioref">Saphra &amp; Wiegreffe, 2024</a>)</span>. A growing line of work holds that interpretability should be held to scientific standards, producing claims that are falsifiable, reproducible, and predictive, so that it can act as a form of model evaluation rather than only a diagnostic <span class="citation" data-cites="lee2026rigorous orgad2026interpretabilityactionable">(<a href="@@BASE@@references/#ref-lee2026rigorous" class="cite cite-emerging" data-cite="lee2026rigorous" role="doc-biblioref">Lee et al., 2026</a>; <a href="@@BASE@@references/#ref-orgad2026interpretabilityactionable" class="cite cite-emerging" data-cite="orgad2026interpretabilityactionable" role="doc-biblioref">Orgad et al., 2026</a>)</span> Mechanistic work also sits within a longer tradition of interpretability research and connects to several adjacent areas, and recent surveys place it in that broader context <span class="citation" data-cites="Raukur2022TowardTA">(<a href="@@BASE@@references/#ref-Raukur2022TowardTA" class="cite cite-established" data-cite="Raukur2022TowardTA" role="doc-biblioref">Raukur et al., 2022</a>)</span>.</p>
-<p>Sparse autoencoders, for example, build directly on decades of work in sparse coding (<span class="citation" data-cites="olshausen1996emergence"><a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a></span>; <span class="citation" data-cites="lewicki2000learning"><a href="@@BASE@@references/#ref-lewicki2000learning" class="cite cite-established" data-cite="lewicki2000learning" role="doc-biblioref">Lewicki &amp; Sejnowski, 2000</a></span>; <span class="citation" data-cites="lee2006efficient"><a href="@@BASE@@references/#ref-lee2006efficient" class="cite cite-established" data-cite="lee2006efficient" role="doc-biblioref">Lee et al., 2006</a></span>; <span class="citation" data-cites="ng2011sparse"><a href="@@BASE@@references/#ref-ng2011sparse" class="cite cite-emerging" data-cite="ng2011sparse" role="doc-biblioref">Ng, 2011</a></span>), dictionary learning, and signal processing. Similarly, attention-based explanations recapitulate debates from the saliency and feature attribution literature (<span class="citation" data-cites="adebayo2018sanity"><a href="@@BASE@@references/#ref-adebayo2018sanity" class="cite cite-established" data-cite="adebayo2018sanity" role="doc-biblioref">Adebayo et al., 2018a</a></span>; <span class="citation" data-cites="seo2018noise"><a href="@@BASE@@references/#ref-seo2018noise" class="cite cite-emerging" data-cite="seo2018noise" role="doc-biblioref">Seo et al., 2018</a></span>).</p>
-<p>This handbook therefore aims to situate contemporary <em>mechanistic</em> methods within a longer intellectual landscape that predates work that become popular post 2020.</p>
-</section>
-<section id="curiosity-and-discovery." class="level4">
-<h4>Curiosity and discovery.</h4>
-<p>Mechanistic interpretability is worth pursuing for reasons beyond safety. Part of the appeal is ordinary scientific curiosity. We have built systems that solve problems we cannot fully specify, and wanting to know how such a system works is among the oldest motivations in science, whether the object is a cell, a brain, or a trained neural network. Neural networks are an unusual case, because unlike a biological brain their entire internal state is available to read and to change. That access makes otherwise hard questions approachable, such as what a model represents internally or whether a particular concept is encoded in it at all. There is also the prospect of genuine discovery. A model trained on a difficult task may settle on solutions its designers did not anticipate, and recovering those internal structures can surface knowledge the model has in effect found on its own, from compact algorithms to representations of the world implicit in its predictions. Understood this way, interpretability is a way of learning from a model, not just auditing it.</p>
+<h4>Situating the Field.</h4>
+<p>Early studies of neural network internals emerged in different research communities, including natural language processing (NLP) <span class="citation" data-cites="vig2019multiscale">(<a href="@@BASE@@references/#ref-vig2019multiscale" class="cite cite-established" data-cite="vig2019multiscale" role="doc-biblioref">Vig, 2019</a>)</span> and computer vision <span class="citation" data-cites="olah2017feature">(<a href="@@BASE@@references/#ref-olah2017feature" class="cite cite-emerging" data-cite="olah2017feature" role="doc-biblioref">Olah et al., 2017</a>)</span>. Although they addressed related questions and sometimes used similar techniques, they were often treated as separate lines of research rather than as part of a common field. The term <em>mechanistic interpretability</em> (MI) became established as interest in neural networks’ internal mechanisms grew and previously scattered research communities increasingly converged under this term. From today’s perspective, much earlier work (<span class="citation" data-cites="hinton1986learning elman1991distributed rnnfsa1992 OMLIN199641 zeiler2014visualizing"><a href="@@BASE@@references/#ref-hinton1986learning" class="cite cite-established" data-cite="hinton1986learning" role="doc-biblioref">Hinton, 1986</a>; <a href="@@BASE@@references/#ref-elman1991distributed" class="cite cite-established" data-cite="elman1991distributed" role="doc-biblioref">Elman, 1991</a>; <a href="@@BASE@@references/#ref-rnnfsa1992" class="cite cite-established" data-cite="rnnfsa1992" role="doc-biblioref">Giles et al., 1992</a>; <a href="@@BASE@@references/#ref-OMLIN199641" class="cite cite-established" data-cite="OMLIN199641" role="doc-biblioref">Omlin &amp; Giles, 1996</a>; <a href="@@BASE@@references/#ref-zeiler2014visualizing" class="cite cite-established" data-cite="zeiler2014visualizing" role="doc-biblioref">Zeiler &amp; Fergus, 2014</a></span>; <span class="citation" data-cites="karpathy2015visualizing"><a href="@@BASE@@references/#ref-karpathy2015visualizing" class="cite cite-emerging" data-cite="karpathy2015visualizing" role="doc-biblioref">Karpathy et al., 2015</a></span>) can also be considered part of MI’s history, even though it predates the term and, in some cases, the broader field of AI interpretability. Today, the scope and standards of MI and AI interpretability are still evolving. For example, recent work argues that interpretability should meet higher scientific standards, such as producing claims that are both falsifiable and reproducible, and improving its use in model evaluations and downstream applications (<span class="citation" data-cites="lee2026rigorous icml2025actionable"><a href="@@BASE@@references/#ref-lee2026rigorous" class="cite cite-emerging" data-cite="lee2026rigorous" role="doc-biblioref">Lee et al., 2026a</a>; <a href="@@BASE@@references/#ref-icml2025actionable" class="cite cite-emerging" data-cite="icml2025actionable" role="doc-biblioref"><span>Workshop Organizers</span>, 2025</a></span>; <span class="citation" data-cites="orgad2026position biderman2026position"><a href="@@BASE@@references/#ref-orgad2026position" class="cite cite-established" data-cite="orgad2026position" role="doc-biblioref">Orgad et al., 2026</a>; <a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">Biderman et al., 2026</a></span>).</p>
+<p>As a result, modern mechanistic studies are connected to a longer history of research on general AI interpretability <span class="citation" data-cites="Raukur2022TowardTA">(<a href="@@BASE@@references/#ref-Raukur2022TowardTA" class="cite cite-established" data-cite="Raukur2022TowardTA" role="doc-biblioref">Rauker et al., 2023</a>)</span> and other domains. SAEs, for example, are built directly on decades of work in sparse coding and dictionary learning (<span class="citation" data-cites="olshausen1996emergence lewicki2000learning lee2006efficient"><a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>; <a href="@@BASE@@references/#ref-lewicki2000learning" class="cite cite-established" data-cite="lewicki2000learning" role="doc-biblioref">Lewicki &amp; Sejnowski, 2000</a>; <a href="@@BASE@@references/#ref-lee2006efficient" class="cite cite-established" data-cite="lee2006efficient" role="doc-biblioref">Lee et al., 2006</a></span>; <span class="citation" data-cites="ng2011sparse"><a href="@@BASE@@references/#ref-ng2011sparse" class="cite cite-emerging" data-cite="ng2011sparse" role="doc-biblioref">Ng, 2011</a></span>). Similarly, recent studies that discovered circuits in large language models (<span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>; <span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>) resonate with early work on extracting and formalising the internal mechanisms of recurrent neural networks as finite state automata <span class="citation" data-cites="rnnfsa1992 OMLIN199641">(<a href="@@BASE@@references/#ref-rnnfsa1992" class="cite cite-established" data-cite="rnnfsa1992" role="doc-biblioref">Giles et al., 1992</a>; <a href="@@BASE@@references/#ref-OMLIN199641" class="cite cite-established" data-cite="OMLIN199641" role="doc-biblioref">Omlin &amp; Giles, 1996</a>)</span>. Debates about attention-based explanations also echo earlier discussions of saliency maps (<span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks smilkov2017smoothgradremovingnoiseadding"><a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-emerging" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a>; <a href="@@BASE@@references/#ref-smilkov2017smoothgradremovingnoiseadding" class="cite cite-emerging" data-cite="smilkov2017smoothgradremovingnoiseadding" role="doc-biblioref">Smilkov et al., 2017</a></span>; <span class="citation" data-cites="ig adebayo2020sanitycheckssaliencymaps"><a href="@@BASE@@references/#ref-ig" class="cite cite-established" data-cite="ig" role="doc-biblioref">Sundararajan et al., 2017</a>; <a href="@@BASE@@references/#ref-adebayo2020sanitycheckssaliencymaps" class="cite cite-established" data-cite="adebayo2020sanitycheckssaliencymaps" role="doc-biblioref">Adebayo et al., 2018</a></span>) and feature attribution <span class="citation" data-cites="lundberg2017unifiedapproachinterpretingmodel ribeiro2016whyitrustyou lrp">(<a href="@@BASE@@references/#ref-lundberg2017unifiedapproachinterpretingmodel" class="cite cite-established" data-cite="lundberg2017unifiedapproachinterpretingmodel" role="doc-biblioref">Lundberg &amp; Lee, 2017</a>; <a href="@@BASE@@references/#ref-ribeiro2016whyitrustyou" class="cite cite-established" data-cite="ribeiro2016whyitrustyou" role="doc-biblioref">Ribeiro et al., 2016</a>; <a href="@@BASE@@references/#ref-lrp" class="cite cite-established" data-cite="lrp" role="doc-biblioref">Bach et al., 2015</a>)</span>. This survey therefore connects contemporary <em>mechanistic</em> methods to earlier research, tracing their foundations beyond the work that gained prominence after 2020.</p>
 </section>
 <section id="safety." class="level4">
 <h4>Safety.</h4>
-<p>Many studies in mechanistic interpretability are motivated by AI safety (<span class="citation" data-cites="burns2023discovering detectvul"><a href="@@BASE@@references/#ref-burns2023discovering" class="cite cite-established" data-cite="burns2023discovering" role="doc-biblioref">Burns et al., 2023</a>; <a href="@@BASE@@references/#ref-detectvul" class="cite cite-established" data-cite="detectvul" role="doc-biblioref">Garcı́a-Carrasco et al., 2024</a></span>; <span class="citation" data-cites="templeton2024scaling"><a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a></span>).</p>
-<p>Researchers hope that understanding model internals will enable detection of deception, identification of dangerous capabilities, or reliable control of model behavior. However, it is unclear whether current mechanistic methods can address the failure modes that safety researchers care about most or enable interventions that would be impossible using purely observational or correlation-based</p>
-<p>approaches. This concern motivates us to assess methods, not only by the understanding they provide, but also by the actions they enable.</p>
+<p>Many studies in mechanistic interpretability are motivated by AI safety (<span class="citation" data-cites="burns2023discovering detectvul"><a href="@@BASE@@references/#ref-burns2023discovering" class="cite cite-established" data-cite="burns2023discovering" role="doc-biblioref">Burns et al., 2023</a>; <a href="@@BASE@@references/#ref-detectvul" class="cite cite-established" data-cite="detectvul" role="doc-biblioref">Garcı́a-Carrasco et al., 2024</a></span>; <span class="citation" data-cites="templeton2024scaling"><a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a></span>). Researchers hope that understanding the internal workings of models will lead to better detection and control of critical model behaviours. But it is not clear if the current mechanistic approaches are suitable and sufficient for tackling crucial failure modes that are not well addressed by current behavioural or correlation-based methods. This concern motivates us to assess approaches not just by how well they provide understanding, but by the strength of their actionable impacts.</p>
+</section>
+<section id="curiosity-and-discovery." class="level4">
+<h4>Curiosity and Discovery.</h4>
+<p>Mechanistic interpretability is worth studying for reasons beyond safety applications. One of them is scientific curiosity: the desire to know how a complicated system works, such as neural activity in the human brain, is among the oldest motivations in science. Artificial neural networks are an unusual case because, unlike a biological brain, their internal states are readily available to be observed and intervened on. This direct access allows researchers to tackle challenging questions, such as what a model represents internally or whether a particular concept is encoded in it.</p>
+<p>Besides scientific curiosity, there are also prospects for genuine discovery. For example, a model trained on a complex task (e.g., chess or Go) may settle on solutions its developers did not or could not anticipate, and recovering the implemented mechanisms can surface knowledge that the model has found on its own, from implemented algorithms to implicit representations of the task <span class="citation" data-cites="conceptdef">(<a href="@@BASE@@references/#ref-conceptdef" class="cite cite-established" data-cite="conceptdef" role="doc-biblioref">Schut et al., 2025</a>)</span>. In this sense, interpretability is a way to learn from a model, not just to audit and control it.</p>
 <p>Building on this history, recent interpretability research reflects two significant methodological shifts.</p>
-<p><strong>First, from manual to assisted analysis</strong>. At current model sizes, manual inspection of features and circuits is impractical. Consequently, researchers have developed methods that automate parts of mechanistic interpretability research. In particular, sparse autoencoders are a key automation mechanism: without supervision they extract millions of independent features from model activations <span class="citation" data-cites="templeton2024scaling bricken2023monosemanticity">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>)</span>. Other work has proposed pipelines to automatically identify candidate circuits <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic">(<a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>)</span>, use language models to generate natural-language feature explanations (<span class="citation" data-cites="paulo2025automatically"><a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025</a></span>; <span class="citation" data-cites="bills2023language foote2023neurongraphinterpretinglanguage"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>; <a href="@@BASE@@references/#ref-foote2023neurongraphinterpretinglanguage" class="cite cite-emerging" data-cite="foote2023neurongraphinterpretinglanguage" role="doc-biblioref">Foote et al., 2023</a></span>), and provide initial validation of mechanistic hypotheses. While human judgment remains essential, some automation has become a necessary.</p>
-<p><strong>Second, divergence in research priorities.</strong> Even within safety driven mechanistic interpretability research, there are distinct approaches. Some prioritize immediate safety interventions such as steering, monitoring, and concept erasure, without requiring complete mechanistic understanding <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. While others prioritize complete causal understanding of model computation, even where this requires substantial resources <span class="citation" data-cites="gao2025ambitious">(<a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>)</span>. Separate threads emphasize automating interpretability workflows to enable domain experts to query and correct model behavior <span class="citation" data-cites="barez2025automated">(<a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>)</span>, studying learning dynamics to anticipate emergent capabilities before deployment <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span>, or exploring whether models can serve as introspective agents that explain their own internals <span class="citation" data-cites="kim2025llmspursueagenticinterpretability">(<a href="@@BASE@@references/#ref-kim2025llmspursueagenticinterpretability" class="cite cite-emerging" data-cite="kim2025llmspursueagenticinterpretability" role="doc-biblioref">Kim et al., 2025</a>)</span>.</p>
-<p>These divergent research directions also reflect complexity of the problem and how many unknowns still exist in understanding the inner working of these models. Bascially, there are two many un-answered questions from diverse research orientations. These divergent research directions reflect genuine uncertainty about what interpretability should deliver and what counts as success. Rather than advocating for any single approach, this handbook organises recent progress around shared technical foundations (Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a> onwards) and makes explicit the trade-offs between different goals (Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">13</a>). We aim to clarify how different approaches differ in their assumptions, capabilities, and failure modes. This provides a principled basis for selecting and combining methods according to one’s scientific and practical objectives.</p>
 </section>
-<section id="useful-surveys-that-we-bild-on." class="level4">
-<h4>Useful Surveys that we bild on.</h4>
-<p>Several surveys cover overlapping territory. <span class="citation" data-cites="bereska2024mechanisticinterpretabilityaisafety"><a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves (2024)</a></span> and <span class="citation" data-cites="zhang2026locatesteerimprovepractical"><a href="@@BASE@@references/#ref-zhang2026locatesteerimprovepractical" class="cite cite-emerging" data-cite="zhang2026locatesteerimprovepractical" role="doc-biblioref">Zhang et al. (2026)</a></span> survey mechanistic interpretability methods with a focus on safety applications. <span class="citation" data-cites="belinkov-glass-2019-analysis"><a href="@@BASE@@references/#ref-belinkov-glass-2019-analysis" class="cite cite-established" data-cite="belinkov-glass-2019-analysis" role="doc-biblioref">Belinkov &amp; Glass (2019)</a></span> provide a broad primer on neural language model interpretability. <span class="citation" data-cites="llmexplainsurvey"><a href="@@BASE@@references/#ref-llmexplainsurvey" class="cite cite-established" data-cite="llmexplainsurvey" role="doc-biblioref">Zhao et al. (2024a)</a></span> survey explainability and interpretability for large language models. <span class="citation" data-cites="rai2024practical"><a href="@@BASE@@references/#ref-rai2024practical" class="cite cite-emerging" data-cite="rai2024practical" role="doc-biblioref">Rai et al. (2024)</a></span> provide a task-centric, beginner-friendly taxonomy and review of mechanistic interpretability for Transformer-based language models, while <span class="citation" data-cites="ferrando2024primer"><a href="@@BASE@@references/#ref-ferrando2024primer" class="cite cite-emerging" data-cite="ferrando2024primer" role="doc-biblioref">Ferrando et al. (2024)</a></span> offer a technical primer on interpreting the inner workings and known mechanisms of Transformer-based language models. More specifically, <span class="citation" data-cites="sajjad-etal-2022-neuron"><a href="@@BASE@@references/#ref-sajjad-etal-2022-neuron" class="cite cite-established" data-cite="sajjad-etal-2022-neuron" role="doc-biblioref">Sajjad et al. (2022)</a></span> survey neuron-level interpretation, covering methods to discover, interpret, and control individual neurons. At a similar granularity, <span class="citation" data-cites="shu-etal-2025-survey"><a href="@@BASE@@references/#ref-shu-etal-2025-survey" class="cite cite-established" data-cite="shu-etal-2025-survey" role="doc-biblioref">Shu et al. (2025)</a></span> survey Sparse Autoencoders (SAE), covering different SAE architectures, feature explanation methods, evaluation, and applications.</p>
-<p>Our survey differs from previous surveys in framing, scope and emphasis.</p>
-<p>First, we take a critical stance on the relationship between interpretability and safety. Much recent work assumes that mechanistic understanding will translate to safety benefits, but this connection remains underspecified. We evaluate methods not only by the understanding they provide, but by whether they enable safety-relevant actions—detecting deception, identifying dangerous capabilities, correcting failures—that simpler approaches cannot.</p>
-<p>Second, we update technical coverage through early 2025, including recent work on attribution graphs, crosscoders, and large-scale feature analysis, while organising this material around methodological principles rather than individual papers.</p>
-<p>Third, we situate contemporary “mechanistic” methods within a longer intellectual lineage, connecting sparse autoencoders to sparse coding <span class="citation" data-cites="olshausen1996emergence">(<a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>)</span>, activation patching to causal mediation analysis <span class="citation" data-cites="vig2020causalmediationanalysisinterpreting">(<a href="@@BASE@@references/#ref-vig2020causalmediationanalysisinterpreting" class="cite cite-established" data-cite="vig2020causalmediationanalysisinterpreting" role="doc-biblioref">Vig et al., 2020</a>)</span>, and circuit analysis to earlier work on single-node analysis <span class="citation" data-cites="clark2019what">(<a href="@@BASE@@references/#ref-clark2019what" class="cite cite-established" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a>)</span>. This framing clarifies what is actually novel and what recapitulates prior debates.</p>
-<p>Fourth, we address terminological confusion directly. The term “mechanistic interpretability” is used inconsistently across the literature <span class="citation" data-cites="saphra2024mechanistic">(<a href="@@BASE@@references/#ref-saphra2024mechanistic" class="cite cite-emerging" data-cite="saphra2024mechanistic" role="doc-biblioref">Saphra &amp; Wiegreffe, 2024</a>)</span>. The deeper source of the confusion is that it is attached to claims of very different strength, ranging from showing that an intervention changes a behaviour to showing that a computation has been fully reverse-engineered. We treat this as a question about claims rather than about the word. For each method we ask what causal claim its evidence can actually support, place that claim on an explicit scale of strength, and state the assumptions each level requires. A reader can then judge what is being asserted regardless of which label an author uses. This removes most of the apparent disagreement, because the disagreement is usually about the claim and not the term.</p>
-<p>Fifth, we provide systematic coverage of evaluation practices. Interpretability methods are often validated through intuitive plausibility rather than rigorous testing. We survey emerging evaluation frameworks, identify gaps, and argue that evaluation methodology and bechmarks related to safety to be among the field’s most pressing open problems.</p>
+<section id="first-from-manual-to-assisted-analysis." class="level4">
+<h4>First, from manual to assisted analysis.</h4>
+<p>At current model sizes, manual inspection of features and circuits is impractical. Consequently, researchers have developed methods that automate parts of mechanistic interpretability research. In particular, sparse autoencoders are a key automation tool: without supervision they extract millions of features from model activations (<span class="citation" data-cites="cunningham2023sparse"><a href="@@BASE@@references/#ref-cunningham2023sparse" class="cite cite-established" data-cite="cunningham2023sparse" role="doc-biblioref">Cunningham et al., 2024</a></span>; <span class="citation" data-cites="bricken2023monosemanticity templeton2024scaling"><a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a></span>). Other work has proposed pipelines to automatically identify candidate circuits (<span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>; <span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>), use language models to generate natural-language feature explanations (<span class="citation" data-cites="paulo2025automatically"><a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025b</a></span>; <span class="citation" data-cites="bills2023language"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a></span>), and provide initial validation of mechanistic hypotheses. While human judgement remains essential, some automation has become necessary.</p>
+</section>
+<section id="second-divergence-in-research-priorities." class="level4">
+<h4>Second, divergence in research priorities.</h4>
+<p>Even within safety-driven mechanistic interpretability research, there are distinct approaches. Some prioritise immediate safety interventions, such as steering, monitoring, and concept erasure, without requiring complete mechanistic understanding <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>, while others advocate complete causal understanding of model computation, even where this requires substantial resources <span class="citation" data-cites="gao2025ambitious">(<a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>)</span>. Another paradigm studies the learning trajectory rather than the behaviour of a fixed model checkpoint (<span class="citation" data-cites="sellam2021multiberts biderman2023pythia biderman2026position"><a href="@@BASE@@references/#ref-sellam2021multiberts" class="cite cite-established" data-cite="sellam2021multiberts" role="doc-biblioref">Sellam et al., 2022</a>; <a href="@@BASE@@references/#ref-biderman2023pythia" class="cite cite-established" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a>; <a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">2026</a></span>; <span class="citation" data-cites="simon2026there"><a href="@@BASE@@references/#ref-simon2026there" class="cite cite-emerging" data-cite="simon2026there" role="doc-biblioref">Simon et al., 2026</a></span>). Separate threads emphasise automating interpretability workflows to enable domain experts to query and correct model behaviour <span class="citation" data-cites="barez2025automated">(<a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>)</span>, studying learning dynamics to anticipate emergent capabilities before deployment <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span>, exploring whether models themselves can serve as explainers of their own internals <span class="citation" data-cites="li2025traininglanguagemodelsexplain">(<a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>)</span>, or intervening on training processes to control model behaviour (<span class="citation" data-cites="biderman2023pythia"><a href="@@BASE@@references/#ref-biderman2023pythia" class="cite cite-established" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a></span>; <span class="citation" data-cites="lee2026steps"><a href="@@BASE@@references/#ref-lee2026steps" class="cite cite-emerging" data-cite="lee2026steps" role="doc-biblioref">Lee et al., 2026b</a></span>).</p>
+<p>The divergence of these research directions reflects the complexity of the problem and the uncertainty surrounding it, including open questions about how to define successful outputs of interpretability research <span class="citation" data-cites="lotfi2026position">(<a href="@@BASE@@references/#ref-lotfi2026position" class="cite cite-established" data-cite="lotfi2026position" role="doc-biblioref">Lotfi et al., 2026</a>)</span>. Thus, this survey does not argue for a single approach; rather, it organises recent progress around shared technical foundations (Section <a href="@@BASE@@handbook/foundational-concepts/#sec:foundations" data-reference-type="ref" data-reference="sec:foundations">4</a> onwards) and discusses trade-offs between different goals (Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a>). We aim to clarify how these directions differ in their assumptions, failure modes, and capabilities, providing researchers with a principled basis for selecting and combining methods according to their own scientific and practical objectives.</p>
+</section>
+<section id="useful-surveys-that-we-build-on." class="level4">
+<h4>Useful Surveys That We Build On.</h4>
+<p>Several surveys cover overlapping territory. <span class="citation" data-cites="bereska2024mechanisticinterpretabilityaisafety"><a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves (2024)</a></span> and <span class="citation" data-cites="zhang2026locatesteerimprovepractical"><a href="@@BASE@@references/#ref-zhang2026locatesteerimprovepractical" class="cite cite-established" data-cite="zhang2026locatesteerimprovepractical" role="doc-biblioref">Zhang et al. (2026a)</a></span> surveyed mechanistic interpretability methods with a focus on safety applications. <span class="citation" data-cites="belinkov-glass-2019-analysis"><a href="@@BASE@@references/#ref-belinkov-glass-2019-analysis" class="cite cite-established" data-cite="belinkov-glass-2019-analysis" role="doc-biblioref">Belinkov &amp; Glass (2019)</a></span> provided a broad primer on the interpretability of neural language models. <span class="citation" data-cites="llmexplainsurvey"><a href="@@BASE@@references/#ref-llmexplainsurvey" class="cite cite-established" data-cite="llmexplainsurvey" role="doc-biblioref">Zhao et al. (2024a)</a></span> surveyed interpretability for large language models. <span class="citation" data-cites="rai2024practical"><a href="@@BASE@@references/#ref-rai2024practical" class="cite cite-emerging" data-cite="rai2024practical" role="doc-biblioref">Rai et al. (2025)</a></span> provided a task-centric and beginner-friendly taxonomy and review of mechanistic interpretability for transformer-based language models, while <span class="citation" data-cites="ferrando2024primer"><a href="@@BASE@@references/#ref-ferrando2024primer" class="cite cite-emerging" data-cite="ferrando2024primer" role="doc-biblioref">Ferrando et al. (2024)</a></span> offered a technical primer on interpreting the inner workings and known mechanisms of transformer-based language models. More specifically, <span class="citation" data-cites="sajjad-etal-2022-neuron"><a href="@@BASE@@references/#ref-sajjad-etal-2022-neuron" class="cite cite-established" data-cite="sajjad-etal-2022-neuron" role="doc-biblioref">Sajjad et al. (2022)</a></span> surveyed neuron-level interpretation, covering methods to discover, interpret, and control individual neurons. At a similar granularity, <span class="citation" data-cites="shu-etal-2025-survey"><a href="@@BASE@@references/#ref-shu-etal-2025-survey" class="cite cite-established" data-cite="shu-etal-2025-survey" role="doc-biblioref">Shu et al. (2025)</a></span> surveyed SAEs, covering different SAE architectures, feature explanation methods, evaluation, and applications.</p>
+<p>Our survey differs from previous surveys in framing, scope, and emphasis. Specifically, we examine closely what interpretability can actually contribute to AI safety. The close examination is motivated by the fact that, although many prior studies in the interpretability community assume that mechanistic understanding directly implies safer systems, the assumption is in fact largely untested. Therefore, this survey considers not only what a method reveals about a model’s internal computation, but also what it allows us to do in practice. We particularly discuss whether it supports safety-relevant tasks such as detecting deceptive behaviours or identifying dangerous capabilities, and whether those tasks can be achieved just as well without interpretability methods or insights.</p>
+<p>We cover popular methods and techniques up to mid-2026, including but not limited to recent work on large-scale feature extraction and analysis, circuit discovery, activation verbalisers, and model architectures designed with interpretability in mind. Rather than presenting these methods as a catalogue, we organise them around the methodological ideas they share and explain new methods in relation to the problems or limitations that motivated them.</p>
+<p>We also place modern mechanistic methods in their broader historical context. Many of their underlying ideas actually predate transformers <span class="citation" data-cites="hinton1986learning elman1991distributed">(<a href="@@BASE@@references/#ref-hinton1986learning" class="cite cite-established" data-cite="hinton1986learning" role="doc-biblioref">Hinton, 1986</a>; <a href="@@BASE@@references/#ref-elman1991distributed" class="cite cite-established" data-cite="elman1991distributed" role="doc-biblioref">Elman, 1991</a>)</span>, and several techniques now central to the field have clear antecedents in earlier work, such as early development of sparse coding <span class="citation" data-cites="olshausen1996emergence">(<a href="@@BASE@@references/#ref-olshausen1996emergence" class="cite cite-established" data-cite="olshausen1996emergence" role="doc-biblioref">Olshausen &amp; Field, 1996</a>)</span> for today’s sparse autoencoders. Making these connections explicit helps distinguish new methodological developments from adaptations of older ideas to new settings and shows where current debates revisit discussions that have appeared before.</p>
+<p>We further make the terminology and claims about mechanistic interpretability more precise. This is necessary because some fundamental notions such as “concept”, “feature”, and “mechanistic interpretability” are not used clearly and consistently across the literature <span class="citation" data-cites="saphra2024mechanistic">(<a href="@@BASE@@references/#ref-saphra2024mechanistic" class="cite cite-emerging" data-cite="saphra2024mechanistic" role="doc-biblioref">Saphra &amp; Wiegreffe, 2024</a>)</span>. The consequence is that works described as mechanistic interpretability can support different kinds of conclusions; for example, showing that a specific intervention can change the target behaviour is a much weaker claim than showing that the behaviour’s internal mechanism is precisely identified. To tackle this issue, for each introduced method, we discuss the assumptions about model internals it requires, the mechanistic claims it supports, and the strength of the claims. By doing this, we can separate disagreements about their mechanistic claims from disagreements about terminology, objectively comparing these methods even when their original papers use different terms.</p>
+<p>Additionally, we systematically discuss the evaluation practices of interpretability methods, which are often validated through intuitive plausibility rather than rigorous testing. We cover existing evaluation frameworks and metrics, identify gaps, and argue that the evaluation methodology and safety benchmarks are the field’s most pressing open problems.</p>
 </section>
 </section>
-<section id="sec:scope" class="level2"><h2 data-pdf-page="4"><span class="secnum">1.2</span> Handbook Scope<a class="anchor" href="#sec:scope" aria-label="Link to this section">#</a></h2>
-<p>We survey AI mechanistic interpretability, which is a way of understanding the internal computations of neural networks—how models transform inputs into outputs through their intermediate representations and components. In particular, <em>mechanistic</em> analysis involves direct study or manipulation of model internals to establish causal claims about computation. This involves:</p>
+<section id="sec:scope" class="level2"><h2 data-pdf-page="12"><span class="secnum">1.2</span> Survey Scope<a class="anchor" href="#sec:scope" aria-label="Link to this section">#</a></h2>
+<p>Our survey’s main focus is on mechanistic interpretability for transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span>, which aims to explain how transformers convert inputs into outputs by analysing internal representations and mechanisms. This <em>mechanistic</em> analysis involves direct manipulation of model internals to establish causal claims about computation, and must meet two criteria:</p>
 <ol>
-<li><p><strong>Engagement with model internals</strong>: Analysis targets intermediate representations, individual components (neurons, attention heads, layers), or learned parameters—not just input-output relationships.</p></li>
-<li><p><strong>Causal claims</strong>: Methods support interventions (ablation, patching, steering) that establish cause-effect relationships, not merely correlations.</p></li>
+<li><p><strong>Engagement with model internals:</strong> Targeting intermediate representations, individual components (neurons, features, attention heads, and more), or any other learned parameters (such as normalisation, embedding, and unembedding). This is not limited to just input–output relationships.</p></li>
+<li><p><strong>Causal claims:</strong> Analyses that involve interventions (such as ablation, patching, and steering) and establish cause-to-effect relationships, rather than just correlational analysis.</p></li>
 </ol>
-<p>This distinguishes it from other approaches to understanding neural networks, such as behavioral evaluation (which outputs models produce) and input attribution (which inputs matter). However, interpretability methods that are not themselves mechanistic can <em>facilitate</em> mechanistic analysis. Probing classifiers identify what information is encoded in representations; this can become mechanistic when the identified directions are used for steering or ablation. Attribution methods locate salient components; intervening on those components tests causal hypotheses. Sparse autoencoders decompose complex representations into interpretable features that enable targeted intervention. The key distinction is between methods that <em>describe</em> (correlational) and methods that <em>intervene</em> (causal)—but descriptive methods often provide the targets for intervention. Therefore, we cover in depth methods that analyse internal computation and that we can treat within a shared causal vocabulary; we treat more briefly methods that are either behavioural or that, while mechanistic, need a separate theoretical apparatus.</p>
+<p>This distinguishes mechanistic interpretability from other interpretability approaches, such as behavioural evaluation (which asks what outputs models produce) and input attribution (which asks which inputs matter). However, it should be noted that interpretability methods that are not themselves mechanistic can <em>facilitate</em> mechanistic analysis. For instance, although vanilla probes can only identify what information is decodable from the activations, they can become mechanistic when the identified directions are tested and used to intervene on models. Input attribution methods help locate salient input features (e.g., pixels), and follow-up processes, such as ablating them, can test causal claims about their roles. A key distinction is between methods that only provide <em>descriptions</em> (correlational) and methods that enable <em>interventions</em> (causal). Because descriptive methods often provide the targets for intervention, we cover in depth methods that analyse internal computation and let us discuss them within a shared causal vocabulary; we treat more briefly methods that are either behavioural or, while mechanistic, need separate theoretical and conceptual foundations.</p>
 <p>We cover the following in depth:</p>
 <ul>
-<li><p><strong>Theoretical foundations</strong>: Core hypotheses underlying mechanistic analysis—linear representations, superposition, privileged access (Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>)</p></li>
-<li><p><strong>Representation analysis</strong>: Probing classifiers, sparse autoencoders, dictionary learning, representation similarity (Sections <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>, <a href="@@BASE@@handbook/intermediate-representations/#sec:intermediate-computation" data-reference-type="ref" data-reference="sec:intermediate-computation">8</a>, <a href="@@BASE@@handbook/representation-analysis/#sec:representation" data-reference-type="ref" data-reference="sec:representation">12</a>)</p></li>
-<li><p><strong>Causal methods</strong>: Activation patching, causal tracing, circuit analysis, steering and ablation (Sections <a href="@@BASE@@handbook/circuits/#sec:circuit" data-reference-type="ref" data-reference="sec:circuit">7</a>)</p></li>
-<li><p><strong>Training dynamics</strong>: Developmental interpretability, phase transitions, learning dynamics (Section <a href="@@BASE@@handbook/developmental-interpretability/#sec:training" data-reference-type="ref" data-reference="sec:training">10</a>)</p></li>
-<li><p><strong>Training data attribution</strong>: Influence functions, data provenance (Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">9</a>)</p></li>
-<li><p><strong>Evaluation</strong>: Assessing validity of interpretability claims (Section <a href="@@BASE@@handbook/circuits/#sec:evaluation-framework" data-reference-type="ref" data-reference="sec:evaluation-framework">7.6</a>)</p></li>
-<li><p><strong>Safety applications</strong>: From understanding to intervention (Section <a href="@@BASE@@handbook/safety-applications/#sec:safety_applications" data-reference-type="ref" data-reference="sec:safety_applications">14</a>)</p></li>
+<li><p><strong>Foundations and core hypotheses:</strong> Foundational concepts for mechanistic interpretability, including behavioural versus mechanistic understanding, polysemanticity, features, transformer computation, and circuits (Section <a href="@@BASE@@handbook/foundational-concepts/#sec:foundations" data-reference-type="ref" data-reference="sec:foundations">4</a>), together with core hypotheses that motivate and constrain later mechanistic methods, including linear representations, superposition, privileged access, and the universality and Platonic representation hypothesis (Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>).</p></li>
+<li><p><strong>Feature extraction and representation analysis:</strong> Sparse autoencoders and related feature-extraction methods, multi-layer feature analysis, probing classifiers, intermediate-representation decoding, model diffing, knowledge localisation, and representation engineering (Sections <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>, <a href="@@BASE@@handbook/intermediate-representations/#sec:intermediate-computation" data-reference-type="ref" data-reference="sec:intermediate-computation">8</a>, and <a href="@@BASE@@handbook/representation-analysis/#sec:representation" data-reference-type="ref" data-reference="sec:representation">9</a>).</p></li>
+<li><p><strong>Circuit analysis and related causal methods:</strong> Circuit discovery, ablation, activation patching, causal verification, and other related interventions for identifying and testing mechanisms (Section <a href="@@BASE@@handbook/circuits/#sec:circuit" data-reference-type="ref" data-reference="sec:circuit">7</a>).</p></li>
+<li><p><strong>Learning dynamics:</strong> Feature and mechanism formation, forecasting model behaviour across training, longitudinal analysis of model training, phase transitions and emergence, and interventions on the training process (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training" data-reference-type="ref" data-reference="sec:training">10</a>).</p></li>
+<li><p><strong>Training data attribution:</strong> Gradient-based influence functions, verbatim and corpus-based attribution, and other methods for attributing model behaviour to training examples (Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">11</a>).</p></li>
+<li><p><strong>Evaluation:</strong> Frameworks for assessing mechanistic interpretability claims, such as testing their validity, faithfulness, causal relevance, and robustness (Section <a href="@@BASE@@handbook/circuits/#sec:evaluation-framework" data-reference-type="ref" data-reference="sec:evaluation-framework">7.5</a>).</p></li>
+<li><p><strong>Research orientations:</strong> The goals, assumptions, and trade-offs of six major research orientations in contemporary AI interpretability (Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a>).</p></li>
+<li><p><strong>Safety applications:</strong> The use of mechanistic understanding and approaches for safety applications, including concept erasure, knowledge editing, representation steering, mechanistic monitoring, and automated interpretability agents, together with their risks and failure modes (Section <a href="@@BASE@@handbook/safety-applications/#sec:safety_applications" data-reference-type="ref" data-reference="sec:safety_applications">13</a>).</p></li>
+<li><p><strong>Open problems:</strong> Outstanding conceptual and technical challenges in mechanistic interpretability, including necessity, evaluation, scalability, generalisability, adversarial robustness, alignment, ontology, proxy fidelity, and transferability beyond transformers (Section <a href="@@BASE@@handbook/open-problems/#sec:open_problems" data-reference-type="ref" data-reference="sec:open_problems">14</a>).</p></li>
 </ul>
+<p>Notably, training data attribution (TDA; Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">11</a>) does not meet the two criteria and is generally not considered a tool for mechanistic analysis. We note that many TDA methods operate via gradients or verbatim search, instead of intermediate representations. These approaches credit the behaviour entirely to the training data, completely ignoring internal computations. But we nevertheless include TDA approaches here as interpretability workflows increasingly chain the two techniques, in which mechanistic analysis generates hypotheses or clues about a behaviour’s origin, while data attribution or corpus search verifies these claims <span class="citation" data-cites="nla">(<a href="@@BASE@@references/#ref-nla" class="cite cite-emerging" data-cite="nla" role="doc-biblioref">Fraser-Taliente et al., 2026</a>)</span>. Parameter-response-based TDA methods (e.g., influence functions <span class="citation" data-cites="koh2020understandingblackboxpredictionsinfluence">(<a href="@@BASE@@references/#ref-koh2020understandingblackboxpredictionsinfluence" class="cite cite-established" data-cite="koh2020understandingblackboxpredictionsinfluence" role="doc-biblioref">Koh &amp; Liang, 2017</a>)</span>) were also introduced as a top-down complement to mechanistic analysis, with the stated goal of connecting the influence of data to responsible model components <span class="citation" data-cites="grosse2023studyinglargelanguagemodel habibi2026symbolicmechanisticdataattribution">(<a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a>; <a href="@@BASE@@references/#ref-habibi2026symbolicmechanisticdataattribution" class="cite cite-emerging" data-cite="habibi2026symbolicmechanisticdataattribution" role="doc-biblioref">Habibi et al., 2026</a>)</span>, making TDA a tool for mechanistic ends.</p>
+<p>We cover only post-hoc TDA methods that analyse a trained model. Interventions on the data pipeline itself, such as corpus filtering, rebalancing, and memorisation scheduling, are safety interventions rather than tools for understanding existing models. They are out of this survey’s scope.</p>
 <p>We discuss but do not cover in depth:</p>
 <ul>
-<li><p><strong>Behavioral evaluation</strong>: Red-teaming, benchmarks, capability evaluations (<span class="citation" data-cites="ganguli2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-ganguli2022redteaminglanguagemodels" class="cite cite-emerging" data-cite="ganguli2022redteaminglanguagemodels" role="doc-biblioref">Ganguli et al., 2022</a></span>; <span class="citation" data-cites="perez2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-perez2022redteaminglanguagemodels" class="cite cite-established" data-cite="perez2022redteaminglanguagemodels" role="doc-biblioref">Perez et al., 2022a</a></span>). These studies assess what models do without explaining how. Section <a href="@@BASE@@handbook/interpretability-landscape/#sec:interpretability-landscape" data-reference-type="ref" data-reference="sec:interpretability-landscape">3</a> discusses when behavioral methods may or may not suffice.</p></li>
-<li><p><strong>Input attribution</strong>: SHAP <span class="citation" data-cites="lundberg2017unifiedapproachinterpretingmodel">(<a href="@@BASE@@references/#ref-lundberg2017unifiedapproachinterpretingmodel" class="cite cite-established" data-cite="lundberg2017unifiedapproachinterpretingmodel" role="doc-biblioref">Lundberg &amp; Lee, 2017</a>)</span>, LIME <span class="citation" data-cites="ribeiro2016whyitrustyou">(<a href="@@BASE@@references/#ref-ribeiro2016whyitrustyou" class="cite cite-established" data-cite="ribeiro2016whyitrustyou" role="doc-biblioref">Ribeiro et al., 2016</a>)</span>, integrated gradients <span class="citation" data-cites="ig">(<a href="@@BASE@@references/#ref-ig" class="cite cite-established" data-cite="ig" role="doc-biblioref">Sundararajan et al., 2017</a>)</span>, saliency maps <span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks">(<a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-established" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a>)</span>. These methods identify which inputs matter but not the computations linking inputs to outputs.</p></li>
-<li><p><strong>Concept-based models</strong>:</p>
-<p>This class of models,especially Concept Bottleneck Models (CBMs) <span class="citation" data-cites="koh2020conceptbottleneckmodels">(<a href="@@BASE@@references/#ref-koh2020conceptbottleneckmodels" class="cite cite-established" data-cite="koh2020conceptbottleneckmodels" role="doc-biblioref">Koh et al., 2020</a>)</span>, constrain models to route computation through human-specified concepts. These approaches involve direct manipulation of the computation and could be considered mechanistic; we discuss their relationship to other methods in Section <a href="@@BASE@@handbook/interpretability-landscape/#subsec:cbm" data-reference-type="ref" data-reference="subsec:cbm">3.2</a>.</p></li>
-<li><p><strong>Causal abstraction</strong>: Work on aligning model computations to high-level causal graphs <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks">(<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">Geiger et al., 2021</a>)</span>. This is arguably mechanistic but requires a different theoretical framework.</p></li>
+<li><p><strong>Behavioural evaluation:</strong> Red-teaming, benchmarks, and capability evaluations (<span class="citation" data-cites="ganguli2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-ganguli2022redteaminglanguagemodels" class="cite cite-emerging" data-cite="ganguli2022redteaminglanguagemodels" role="doc-biblioref">Ganguli et al., 2022</a></span>; <span class="citation" data-cites="perez2022redteaminglanguagemodels"><a href="@@BASE@@references/#ref-perez2022redteaminglanguagemodels" class="cite cite-established" data-cite="perez2022redteaminglanguagemodels" role="doc-biblioref">Perez et al., 2022</a></span>). These studies assess what models do without explaining how. Section <a href="@@BASE@@handbook/interpretability-landscape/#sec:interpretability-landscape" data-reference-type="ref" data-reference="sec:interpretability-landscape">3</a> discusses when behavioural methods may or may not suffice.</p></li>
+<li><p><strong>Input attribution:</strong> SHAP <span class="citation" data-cites="lundberg2017unifiedapproachinterpretingmodel">(<a href="@@BASE@@references/#ref-lundberg2017unifiedapproachinterpretingmodel" class="cite cite-established" data-cite="lundberg2017unifiedapproachinterpretingmodel" role="doc-biblioref">Lundberg &amp; Lee, 2017</a>)</span>, LIME <span class="citation" data-cites="ribeiro2016whyitrustyou">(<a href="@@BASE@@references/#ref-ribeiro2016whyitrustyou" class="cite cite-established" data-cite="ribeiro2016whyitrustyou" role="doc-biblioref">Ribeiro et al., 2016</a>)</span>, integrated gradients <span class="citation" data-cites="ig">(<a href="@@BASE@@references/#ref-ig" class="cite cite-established" data-cite="ig" role="doc-biblioref">Sundararajan et al., 2017</a>)</span>, and saliency maps <span class="citation" data-cites="simonyan2014deepinsideconvolutionalnetworks">(<a href="@@BASE@@references/#ref-simonyan2014deepinsideconvolutionalnetworks" class="cite cite-emerging" data-cite="simonyan2014deepinsideconvolutionalnetworks" role="doc-biblioref">Simonyan et al., 2014</a>)</span>. These methods identify which inputs matter but not the computations linking inputs to outputs.</p></li>
+<li><p><strong>Concept-based models:</strong> This class of models, especially concept bottleneck models (CBMs) <span class="citation" data-cites="koh2020conceptbottleneckmodels">(<a href="@@BASE@@references/#ref-koh2020conceptbottleneckmodels" class="cite cite-established" data-cite="koh2020conceptbottleneckmodels" role="doc-biblioref">Koh et al., 2020</a>)</span>, constrains models to route computation through human-specified concepts. These approaches involve direct manipulation of the computation and could be considered mechanistic; we discuss their relationship to other methods in Section <a href="@@BASE@@handbook/interpretability-landscape/#subsec:cbm" data-reference-type="ref" data-reference="subsec:cbm">3.2</a>.</p></li>
+<li><p><strong>Causal abstraction:</strong> Work on aligning model computations to high-level causal graphs <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks pmlr-v162-geiger22a das JMLR:v26:23-0058 wu-etal-2022-causal">(<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">Geiger et al., 2021</a>; <a href="@@BASE@@references/#ref-pmlr-v162-geiger22a" class="cite cite-established" data-cite="pmlr-v162-geiger22a" role="doc-biblioref">2022</a>; <a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">2024</a>; <a href="@@BASE@@references/#ref-JMLR:v26:23-0058" class="cite cite-established" data-cite="JMLR:v26:23-0058" role="doc-biblioref">2025</a>; <a href="@@BASE@@references/#ref-wu-etal-2022-causal" class="cite cite-established" data-cite="wu-etal-2022-causal" role="doc-biblioref">Wu et al., 2022</a>)</span>. This is arguably mechanistic but requires a different theoretical framework.</p></li>
 </ul>
-<figure id="fig:overview" data-latex-placement="t">
-<img alt="Figure 2: Overview of the conceptual flow of the survey, categorized into four primary pillars: (1) Motivation &amp; Concepts, covering core hypotheses; (2) Main Chapters, detailing technical methods; (3) Research Directions, exploring the main direction the field is taking; and (4) Safety &amp; Open Proble" loading="lazy" src="@@BASE@@figures/1_general-summary.svg" />
-<figcaption><span class="capnum">Figure 2:</span> Overview of the conceptual flow of the survey, categorized into four primary pillars: (1) Motivation &amp; Concepts, covering core hypotheses; (2) Main Chapters, detailing technical methods; (3) Research Directions, exploring the main direction the field is taking; and (4) Safety &amp; Open Problems, identifying critical challenges and deployment-oriented interventions for AI alignment.</figcaption>
+<p>See Figure <a href="@@BASE@@#taxonomy" data-reference-type="ref" data-reference="fig:simplified-taxonomy">1</a> for the complete taxonomy of topics and some representative papers covered in this work, and Figure <a href="@@BASE@@handbook/introduction/#fig:overview" data-reference-type="ref" data-reference="fig:overview">2</a> for an overview of the survey’s structure.</p>
+<figure id="fig:overview" data-latex-placement="tb">
+<img alt="Figure 2: Overview of the conceptual flow of the survey, categorised into four primary pillars: (1) Motivation &amp; Concepts, covering fundamental knowledge and core hypotheses; (2) Technical Methods, detailing approaches developed and used in interpretability research; (3) Research Directions, explori" loading="lazy" src="@@BASE@@figures/1_general_summary.svg" />
+<figcaption><span class="capnum">Figure 2:</span> Overview of the conceptual flow of the survey, categorised into four primary pillars: (1) Motivation &amp; Concepts, covering fundamental knowledge and core hypotheses; (2) Technical Methods, detailing approaches developed and used in interpretability research; (3) Research Directions, exploring main directions the field is taking; and (4) Safety &amp; Open Problems, identifying critical challenges and deployment-oriented interventions for AI alignment.</figcaption>
 </figure>
 </section>
 </section>
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+<section id="sec:training" class="level1"><h1 data-pdf-page="82"><span class="secnum">10</span> Learning Dynamics and Mechanistic Interpretability<a class="anchor" href="#sec:training" aria-label="Link to this section">#</a></h1>
+<p>Most interpretability research treats a trained model as static. It asks what the model activations represent, which components implement a behaviour, or how an intervention changes its output at a particular checkpoint. However, a model does not come into the world fully formed—it is the product of a learning process over time. Recently, public model suites with intermediate checkpoints <span class="citation" data-cites="biderman2023pythia groeneveld2024olmo">(<a href="@@BASE@@references/#ref-biderman2023pythia" class="cite cite-established" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a>; <a href="@@BASE@@references/#ref-groeneveld2024olmo" class="cite cite-established" data-cite="groeneveld2024olmo" role="doc-biblioref">Groeneveld et al., 2024</a>)</span> and transparent training processes, such as Pythia <span class="citation" data-cites="biderman2023pythia">(<a href="@@BASE@@references/#ref-biderman2023pythia" class="cite cite-established" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a>)</span> trained on the publicly available Pile corpus <span class="citation" data-cites="gao2020pile">(<a href="@@BASE@@references/#ref-gao2020pile" class="cite cite-emerging" data-cite="gao2020pile" role="doc-biblioref">Gao et al., 2020</a>)</span>, have enabled systematic study of these <em>training trajectories</em>, unlocking unique insights into how models form behaviours.</p>
+<p>Research at the intersection of training dynamics and interpretability can have two aims. <em>Longitudinal analysis</em> (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-longitudinal" data-reference-type="ref" data-reference="sec:ld-longitudinal">10.1</a>) treats learning dynamics as an object of explanation, asking how and why mechanisms form, change, stabilise, or disappear over the course of training <span class="citation" data-cites="saphra_interpretability_2023">(<a href="@@BASE@@references/#ref-saphra_interpretability_2023" class="cite cite-emerging" data-cite="saphra_interpretability_2023" role="doc-biblioref">Saphra, 2023</a>)</span>. <em>Training dynamics as a tool</em> instead uses temporal variation to answer questions about the model itself. For example, researchers can compare changes across checkpoints to identify certain mechanisms, distinguish competing explanations, predict later capabilities or failure modes, and guide interventions on the training process.</p>
+<section id="sec:ld-longitudinal" class="level2"><h2 data-pdf-page="83"><span class="secnum">10.1</span> Longitudinal Analysis<a class="anchor" href="#sec:ld-longitudinal" aria-label="Link to this section">#</a></h2>
+<p><span class="citation" data-cites="saphra_interpretability_2023"><a href="@@BASE@@references/#ref-saphra_interpretability_2023" class="cite cite-emerging" data-cite="saphra_interpretability_2023" role="doc-biblioref">Saphra (2023)</a></span> argued that we can fully understand model internals only in light of their development: an analysis confined to the final checkpoint risks misattributing the function or origin of the structures it finds there. This subsection covers what longitudinal studies have established about the relationship between training data and the capabilities models acquire (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-data" data-reference-type="ref" data-reference="sec:ld-data">10.1.1</a>) and about the lifecycle of internal features (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-features" data-reference-type="ref" data-reference="sec:ld-features">10.1.2</a>); abrupt formation events are treated separately in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-transitions" data-reference-type="ref" data-reference="sec:ld-transitions">10.3</a>.</p>
+<section id="sec:ld-data" class="level3"><h3 data-pdf-page="83"><span class="secnum">10.1.1</span> Training Data and Capability Acquisition<a class="anchor" href="#sec:ld-data" aria-label="Link to this section">#</a></h3>
+<section id="acquisition-order." class="level4">
+<h4>Acquisition Order.</h4>
+<p>The order in which capabilities appear is strongly constrained by the statistics of the training data. Language models acquire words in an order predicted primarily by corpus frequency, unlike children, whose acquisition order also depends on factors such as concreteness and word length <span class="citation" data-cites="chang2022word">(<a href="@@BASE@@references/#ref-chang2022word" class="cite cite-established" data-cite="chang2022word" role="doc-biblioref">Chang &amp; Bergen, 2022</a>)</span>; graded linguistic preferences, such as adjective order, are learned from corpus statistics on a similar schedule <span class="citation" data-cites="jumelet2024black">(<a href="@@BASE@@references/#ref-jumelet2024black" class="cite cite-emerging" data-cite="jumelet2024black" role="doc-biblioref">Jumelet et al., 2026</a>)</span>. This ordering reflects a more general <em>distributional simplicity bias</em>:</p>
+<div class="definitionbox">
+<p>Networks trained with SGD learn low-order statistics of the data distribution before higher-order ones <span class="citation" data-cites="refinetti2023neural">(<a href="@@BASE@@references/#ref-refinetti2023neural" class="cite cite-established" data-cite="refinetti2023neural" role="doc-biblioref">Refinetti et al., 2023</a>)</span>. In language models, token n-gram frequencies are equivalent to moments of the embedding vectors, so early-training language models approximate low-order token interactions well, with more complex structure fitting later <span class="citation" data-cites="belrose2024statistics">(<a href="@@BASE@@references/#ref-belrose2024statistics" class="cite cite-established" data-cite="belrose2024statistics" role="doc-biblioref">Belrose et al., 2024</a>)</span>.</p>
+</div>
+</section>
+<section id="from-correlation-to-causation." class="level4">
+<h4>From Correlation to Causation.</h4>
+<p>Correlational studies show a strong connection between data statistics and capabilities of the final model <span class="citation" data-cites="razeghi2022impact">(<a href="@@BASE@@references/#ref-razeghi2022impact" class="cite cite-established" data-cite="razeghi2022impact" role="doc-biblioref">Razeghi et al., 2022</a>)</span>, and causal effects of pretraining-data statistics on factual predictions can be estimated directly <span class="citation" data-cites="elazar2022measuring">(<a href="@@BASE@@references/#ref-elazar2022measuring" class="cite cite-emerging" data-cite="elazar2022measuring" role="doc-biblioref">Elazar et al., 2023</a>)</span>. The strongest evidence would come from controlled manipulation of the training data, although this is typically not done at realistic scales because it is costly. One notable example of such work is <span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al. (2026)</a></span>, which trained three 6.9-billion-parameter, 550-billion-token models on different dataset filtrations and showed they can remove knowledge of proxies for biohazardous capabilities.</p>
+<p>By training transformers on synthetic sequence tasks, <span class="citation" data-cites="chan2022data"><a href="@@BASE@@references/#ref-chan2022data" class="cite cite-established" data-cite="chan2022data" role="doc-biblioref">Chan et al. (2022b)</a></span> showed that in-context learning emerges only when the data has the right distributional properties, with burstiness and a long tail of rare classes each necessary. A uniform, non-bursty version of the same content instead leads to in-weights learning, while Zipfian class distributions allow the coexistence of the two learning patterns. Similar dependence on distributional structure governs factual knowledge acquisition, which proceeds through an extended plateau shaped by the frequency distribution of entities in the data <span class="citation" data-cites="zucchet2025language">(<a href="@@BASE@@references/#ref-zucchet2025language" class="cite cite-emerging" data-cite="zucchet2025language" role="doc-biblioref">Zucchet et al., 2025a</a>)</span>.</p>
+</section>
+</section>
+<section id="sec:ld-features" class="level3"><h3 data-pdf-page="83"><span class="secnum">10.1.2</span> Feature Formation, Consolidation, and Drift<a class="anchor" href="#sec:ld-features" aria-label="Link to this section">#</a></h3>
+<p>A technical challenge in tracking features across training runs is that representations at different checkpoints may occupy different bases, so a feature identified at one checkpoint has no automatic counterpart at another. This is analogous to the cross-layer basis mismatch of the logit lens, which degrades because intermediate layers organise information differently from the final layer. A probe or sparse autoencoder trained at one checkpoint degrades similarly when applied to another, and longitudinal methods differ mainly in how they solve this correspondence problem.</p>
+<p>In Section <a href="@@BASE@@handbook/representation-analysis/#sec:model-diffing" data-reference-type="ref" data-reference="sec:model-diffing">9.1</a>, we discuss model diffing methods for comparing and analysing two models’ internals. They are useful tools for longitudinal analysis, and they have discovered a broadly consistent order in which model knowledge forms and internal representations evolve. Specifically, linguistic knowledge is acquired early and quickly, while factual and commonsense knowledge accumulates slowly and reasoning abilities remain weak throughout pretraining <span class="citation" data-cites="liu2021probing">(<a href="@@BASE@@references/#ref-liu2021probing" class="cite cite-established" data-cite="liu2021probing" role="doc-biblioref">Liu et al., 2021</a>)</span>. In particular, feature-level analyses provide fine-grained descriptions of this picture: features corresponding to token-level and surface statistics form first, with abstract, cross-lingual, and semantic features forming later <span class="citation" data-cites="inaba2025howllmslearn ge2026evolution">(<a href="@@BASE@@references/#ref-inaba2025howllmslearn" class="cite cite-established" data-cite="inaba2025howllmslearn" role="doc-biblioref">Inaba et al., 2025</a>; <a href="@@BASE@@references/#ref-ge2026evolution" class="cite cite-established" data-cite="ge2026evolution" role="doc-biblioref">Ge et al., 2026</a>)</span>, and individual features develop from noisy, unstructured activations into semantically coherent ones <span class="citation" data-cites="xu2024saetrack">(<a href="@@BASE@@references/#ref-xu2024saetrack" class="cite cite-emerging" data-cite="xu2024saetrack" role="doc-biblioref">Xu et al., 2025b</a>)</span>. Formation is followed by consolidation: after their semantics are established, feature directions continue to drift and eventually stabilise <span class="citation" data-cites="xu2024saetrack">(<a href="@@BASE@@references/#ref-xu2024saetrack" class="cite cite-emerging" data-cite="xu2024saetrack" role="doc-biblioref">Xu et al., 2025b</a>)</span>; modules converge at different rates <span class="citation" data-cites="raghu_svcca_2017">(<a href="@@BASE@@references/#ref-raghu_svcca_2017" class="cite cite-established" data-cite="raghu_svcca_2017" role="doc-biblioref">Raghu et al., 2017</a>)</span>; and once formed, representations and their acquisition timing are largely consistent across random seeds <span class="citation" data-cites="vanderwal2025polypythias">(<a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al., 2025</a>)</span>.</p>
+<p>Stability of a capability does not imply stability of its implementation. The components carrying an algorithm can change identity while the algorithm persists <span class="citation" data-cites="tigges2024llm">(<a href="@@BASE@@references/#ref-tigges2024llm" class="cite cite-established" data-cite="tigges2024llm" role="doc-biblioref">Tigges et al., 2024</a>)</span>, and mechanisms can be repurposed, as when induction heads transition into the function vector heads that come to dominate few-shot in-context learning <span class="citation" data-cites="yin2025which">(<a href="@@BASE@@references/#ref-yin2025which" class="cite cite-established" data-cite="yin2025which" role="doc-biblioref">Yin &amp; Steinhardt, 2025</a>)</span>. Structures can also disappear outright: in the synthetic settings of <span class="citation" data-cites="chan2022data"><a href="@@BASE@@references/#ref-chan2022data" class="cite cite-established" data-cite="chan2022data" role="doc-biblioref">Chan et al. (2022b)</a></span>, emergent in-context learning is transient, arising and then giving way to in-weights learning while training loss still falls, with regularisation affecting its persistence <span class="citation" data-cites="singh2023transient">(<a href="@@BASE@@references/#ref-singh2023transient" class="cite cite-established" data-cite="singh2023transient" role="doc-biblioref">Singh et al., 2023</a>)</span>. Similarly, the drift of features can produce false positives in model diffing (Section <a href="@@BASE@@handbook/representation-analysis/#sec:model-diffing" data-reference-type="ref" data-reference="sec:model-diffing">9.1</a>) and more broadly bound the temporal scope of any mechanistic claim (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-temporal-stability" data-reference-type="ref" data-reference="sec:ld-temporal-stability">10.2.2</a>): an explanation about model internals validated at one checkpoint may not apply to other checkpoints of the same model.</p>
+</section>
+</section>
+<section id="learning-dynamics-as-an-interpretability-tool" class="level2"><h2 data-pdf-page="84"><span class="secnum">10.2</span> Learning Dynamics as an Interpretability Tool<a class="anchor" href="#learning-dynamics-as-an-interpretability-tool" aria-label="Link to this section">#</a></h2>
+<p>The preceding subsection treated the learning trajectory as an object to be explained. A complementary line of work uses that trajectory instead as a tool for analysing model behaviour. Comparing behaviour and components across model checkpoints lets researchers conduct novel causal analyses of how components drive model behaviour. Although this framing is relatively new in mechanistic interpretability, the use of learning trajectories to understand neural networks substantially predates it. Early work showed that different <em>singular modes</em> of the input–output correlations can be learned at different rates (where larger singular values tend to be learned earlier), making acquisition order informative about task structure <span class="citation" data-cites="saxe2014exact lampinen2019analytic saxe2019mathematical">(<a href="@@BASE@@references/#ref-saxe2014exact" class="cite cite-established" data-cite="saxe2014exact" role="doc-biblioref">Saxe et al., 2014</a>; <a href="@@BASE@@references/#ref-lampinen2019analytic" class="cite cite-established" data-cite="lampinen2019analytic" role="doc-biblioref">Lampinen &amp; Ganguli, 2019</a>; <a href="@@BASE@@references/#ref-saxe2019mathematical" class="cite cite-established" data-cite="saxe2019mathematical" role="doc-biblioref">Saxe et al., 2019</a>)</span>. In contrast, representation-comparison methods measure when layers and representations converge during training <span class="citation" data-cites="raghu_svcca_2017 morcos2018insightsrepresentationalsimilarityneural">(<a href="@@BASE@@references/#ref-raghu_svcca_2017" class="cite cite-established" data-cite="raghu_svcca_2017" role="doc-biblioref">Raghu et al., 2017</a>; <a href="@@BASE@@references/#ref-morcos2018insightsrepresentationalsimilarityneural" class="cite cite-established" data-cite="morcos2018insightsrepresentationalsimilarityneural" role="doc-biblioref">Morcos et al., 2018</a>)</span>.</p>
+<section id="sec:ld-localization" class="level3"><h3 data-pdf-page="84"><span class="secnum">10.2.1</span> Temporal Localisation and Mechanism Discovery<a class="anchor" href="#sec:ld-localization" aria-label="Link to this section">#</a></h3>
+<section id="trajectory-based-attribution." class="level4">
+<h4>Trajectory-Based Attribution.</h4>
+<p>Training data attribution (TDA; Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">11</a>) methods can integrate with the training trajectory to provide temporal analysis. For example, TracIn (Section <a href="@@BASE@@handbook/training-data-attribution/#sec:tracin" data-reference-type="ref" data-reference="sec:tracin">11.2.5</a>) estimates a training sample’s influence on a testing sample’s prediction as the sum of their gradients’ dot products evaluated at checkpoints along the run <span class="citation" data-cites="pruthi2020estimatingtrainingdatainfluence">(<a href="@@BASE@@references/#ref-pruthi2020estimatingtrainingdatainfluence" class="cite cite-established" data-cite="pruthi2020estimatingtrainingdatainfluence" role="doc-biblioref">Garima et al., 2020</a>)</span>. Each checkpoint contributes separately to the influence estimate. By examining each checkpoint’s contribution, researchers can identify when a training example had the greatest influence on a downstream prediction.</p>
+</section>
+<section id="per-token-learning-curves." class="level4">
+<h4>Per-Token Learning Curves.</h4>
+<p>The learning curve of an individual token in context, usually evaluated on held-out text at every checkpoint, is the finest-grained localisation instrument. <span class="citation" data-cites="chang2024characterizing"><a href="@@BASE@@references/#ref-chang2024characterizing" class="cite cite-established" data-cite="chang2024characterizing" role="doc-biblioref">Chang et al. (2024)</a></span> extracted learning curves for one million held-out tokens across five pretraining runs and summarised each with the time the token is first learned, the likelihood its loss later regresses, and the variability both within a run and across runs. They found that frequent tokens and tokens in short frequent contexts are acquired earlier, reach lower final surprisal, and vary less within and across runs, with the effect strengthening with n-gram probability. At the level of stages, models learn short repetitive phrases before longer coherent text, consistent with early n-gram learning followed by gradual refinement of tail predictions. Individual tokens also show sudden increases or decreases in loss that recur at consistent points across runs. These abrupt changes in per-token loss can help identify potential transitions during training (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-detect" data-reference-type="ref" data-reference="sec:ld-detect">10.3.1</a>). They also complement trajectory-based attribution by revealing when a model learns specific predictions.</p>
+</section>
+<section id="localising-change-to-components." class="level4">
+<h4>Localising Change to Components.</h4>
+<p>Once a change is localised in time, the components that changed in that window are candidates for the mechanism. <span class="citation" data-cites="wang2025differentiation"><a href="@@BASE@@references/#ref-wang2025differentiation" class="cite cite-established" data-cite="wang2025differentiation" role="doc-biblioref">Wang et al. (2025b)</a></span> introduced refined variants of the Local Learning Coefficient (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>) and applied them to individual attention heads of a two-layer attention-only transformer across training. The <em>weight-refined LLC</em> restricts the LLC to a single head’s parameters; the <em>data-refined LLC</em> evaluates it on a chosen data subset. Weight-refined LLC curves diverge progressively over training, with distinct patterns for different head types. Heads of the same type cluster together in both their final values and the overall shape of the curve across training, revealing how heads differentiate into distinct functional roles. On the other hand, data-refined LLCs reveal what a head specialises in processing; for example, on code samples, perturbing the weights of the multigram heads in layer 1 has significantly less impact on the loss than perturbing the induction heads. The same analysis identifies a previously undocumented multigram circuit. A multigram is a common token sequence in the data distribution whose tokens may appear non-contiguously in context. Heads with higher weight-refined LLC tend to memorise more multigrams. Those with lower values tend to follow simple algorithms, such as induction or bracket matching. <span class="citation" data-cites="wang2025differentiation"><a href="@@BASE@@references/#ref-wang2025differentiation" class="cite cite-established" data-cite="wang2025differentiation" role="doc-biblioref">Wang et al. (2025b)</a></span> identified these heads by tracking each head’s complexity measure. Unlike the proposed induction scores <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span> for identifying induction heads, the measure does not require specifying a head type in advance. However, estimating refined LLCs is computationally expensive, and the approach has so far been demonstrated only on toy models.</p>
+</section>
+</section>
+<section id="sec:ld-causal-interventions" class="level3"><h3 data-pdf-page="85"><span class="secnum">10.2.2</span> Causal Interventions on Mechanism Formation<a class="anchor" href="#sec:ld-causal-interventions" aria-label="Link to this section">#</a></h3>
+<p>The forked-run experiment is a basic analysis pipeline available on a training trajectory. It spawns multiple continuations from a single checkpoint, perturbs one factor, and compares the resulting models.</p>
+<div class="definitionbox">
+<p>A <em>forked-run experiment</em> spawns two or more training continuations from the same checkpoint, varying a single factor such as SGD noise, data order, or random seed, and compares the models the continuations produce. <em>Linear mode connectivity</em> is a standard instrument for the comparison. Two networks are linearly mode connected if the loss barrier along the straight path between their parameters in weight space is near zero. Specifically, <span class="citation" data-cites="frankle2020linear"><a href="@@BASE@@references/#ref-frankle2020linear" class="cite cite-established" data-cite="frankle2020linear" role="doc-biblioref">Frankle et al. (2020)</a></span> examined the sensitivity of the model’s training results to SGD noise by restarting training from different checkpoints, each time using different SGD noise. Early in training, these runs may converge to different loss basins. Later, they tend to converge to linearly mode-connected solutions, indicating that the training outcome has become stable to SGD noise.</p>
+</div>
+<p>Perturbations other than SGD noise fit the same design. For example, degrading the data early in training causes permanent deficits that the same degradation applied later does not, establishing critical learning periods <span class="citation" data-cites="achille2019critical">(<a href="@@BASE@@references/#ref-achille2019critical" class="cite cite-established" data-cite="achille2019critical" role="doc-biblioref">Achille et al., 2019</a>)</span>, and forking a run across many random seeds distinguishes properties of the training setup from accidents of a single run <span class="citation" data-cites="vanderwal2025polypythias">(<a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al., 2025</a>)</span>. Similarly, the smeared-key intervention discussed in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a> is the same design with an architectural perturbation. Mode connectivity has also been given a mechanistic reading: <span class="citation" data-cites="lubana2023mechanistic"><a href="@@BASE@@references/#ref-lubana2023mechanistic" class="cite cite-established" data-cite="lubana2023mechanistic" role="doc-biblioref">Lubana et al. (2023)</a></span> defined two models as mechanistically similar when they are invariant to the same transformations of the input. They conjectured that two minimisers that cannot be linearly mode connected (up to permutation symmetries of the architecture) must be mechanistically dissimilar, and found that the mechanistically dissimilar minimisers they constructed are connected by quadratic but not linear paths. Under this interpretation, a loss barrier between two checkpoints suggests that their mechanisms differ. However, the absence of a loss barrier does not guarantee that their mechanisms are the same.</p>
+<section id="sec:ld-temporal-stability" class="level4">
+<h4>Temporal Stability and the Scope of Mechanistic Claims.</h4>
+<p>Learning-dynamics analysis lets us investigate mechanistic claims with scopes broader than those of a single model checkpoint. Specifically, an explanation about a trained model’s internals (e.g., the direction of a feature) does not by itself establish whether the internal mechanism, if it persists, works in the same way as mechanisms in different training stages of the same model or mechanisms across independently trained models of the same kind (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-features" data-reference-type="ref" data-reference="sec:ld-features">10.1.2</a>). In contrast, learning-dynamics analysis can re-evaluate the proposed explanation across neighbouring checkpoints or different random seeds, revealing how stable the explanation is under continued training and how sensitive it is to variation in the training trajectory.</p>
+</section>
+</section>
+</section>
+<section id="sec:ld-transitions" class="level2"><h2 data-pdf-page="86"><span class="secnum">10.3</span> Phase Transitions and Emergence<a class="anchor" href="#sec:ld-transitions" aria-label="Link to this section">#</a></h2>
+<p>Training loss curves typically make it seem like language models gradually and steadily improve in performance (<span class="citation" data-cites="hestness2017deeplearningscalingpredictable kaplan2020scaling"><a href="@@BASE@@references/#ref-hestness2017deeplearningscalingpredictable" class="cite cite-emerging" data-cite="hestness2017deeplearningscalingpredictable" role="doc-biblioref">Hestness et al., 2017</a>; <a href="@@BASE@@references/#ref-kaplan2020scaling" class="cite cite-emerging" data-cite="kaplan2020scaling" role="doc-biblioref">Kaplan et al., 2020</a></span>; <span class="citation" data-cites="rosenfeld2020a"><a href="@@BASE@@references/#ref-rosenfeld2020a" class="cite cite-established" data-cite="rosenfeld2020a" role="doc-biblioref">Rosenfeld et al., 2020</a></span>). While this holds <em>on average</em>, individual capabilities tell a very different story. For many individual tasks, model performance can change sharply and abruptly across model scales and across training <span class="citation" data-cites="wei2022emergent mckenzie2023inverse">(<a href="@@BASE@@references/#ref-wei2022emergent" class="cite cite-established" data-cite="wei2022emergent" role="doc-biblioref">Wei et al., 2022</a>; <a href="@@BASE@@references/#ref-mckenzie2023inverse" class="cite cite-established" data-cite="mckenzie2023inverse" role="doc-biblioref">McKenzie et al., 2023</a>)</span>. This suggests that models undergo <em>phase transitions</em>, where relatively small increases in scale or training time produce qualitative changes in behaviour. These transitions have motivated substantial interest in the study of how and when phase transitions happen, which we will discuss in more detail in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>.</p>
+<p>These sharp changes also appear in model internals. Attention heads can specialise into distinct computational roles over relatively short intervals <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, and the formation of induction heads and syntactic attention structures has been associated with abrupt changes in model behaviour <span class="citation" data-cites="chen_sudden_2024">(<a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al., 2024a</a>)</span>. Note that behavioural and internal transitions need not occur at the same time or be equally abrupt. A model capability may suddenly emerge even as its underlying internal mechanisms develop gradually. <em>Grokking</em> <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span> illustrates this mismatch: as the model develops a generalising circuit, it still relies on memorisation. Once the generalising mechanism dominates and competing memorisation mechanisms are removed, the model’s generalisation ability jumps. We therefore use <em>phase transition</em> broadly to describe qualitative shifts in behaviour, representations, etc., without assuming that they change simultaneously or discontinuously.</p>
+<p>Phase transitions matter for AI safety because they undermine our ability to forecast what the model will develop. If a capability can appear with a small increase in model scale or training time, behavioural evaluation of intermediate checkpoints offers limited assurance about the final model. Also, scaling laws fit to smaller models offer limited insight into larger ones. We therefore need to understand how and when phase transitions happen to reliably predict whether and when a model will acquire a hazardous capability. We organise this subsection as a workflow for the researcher who has a training run in hand: detecting transitions (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-detect" data-reference-type="ref" data-reference="sec:ld-detect">10.3.1</a>), measuring the representations and mechanisms that underlie them (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-progress" data-reference-type="ref" data-reference="sec:ld-progress">10.3.2</a>), attributing them to specific components (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-attribution" data-reference-type="ref" data-reference="sec:ld-attribution">10.3.3</a>), explaining why they occur (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>), and, as a case study, applying this workflow end to end to induction heads (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>).</p>
+<section id="sec:ld-detect" class="level3"><h3 data-pdf-page="86"><span class="secnum">10.3.1</span> Detecting Transitions<a class="anchor" href="#sec:ld-detect" aria-label="Link to this section">#</a></h3>
+<p>A key challenge in detecting transitions is that aggregate loss can hide abrupt changes in individual capabilities. The quantisation model of neural scaling <span class="citation" data-cites="michaud2023quantization">(<a href="@@BASE@@references/#ref-michaud2023quantization" class="cite cite-established" data-cite="michaud2023quantization" role="doc-biblioref">Michaud et al., 2023</a>)</span> suggests that models learn discrete units of capability, each potentially emerging abruptly. However, their combined loss can still decrease smoothly because these units occur at different frequencies in the data. This helps explain why abrupt changes can occur across both training time and model scale despite smooth overall loss curves. Researchers therefore use metrics at different levels of granularity to detect these transitions.</p>
+<section id="loss-curve-derivatives." class="level4">
+<h4>Loss-Curve Derivatives.</h4>
+<p>The crudest instrument is the aggregate loss curve itself: a transition large enough to affect overall performance appears as a bump, visible as a local peak in the loss derivative <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>. However, most transitions are too small to detect this way. A flat derivative therefore does not constitute evidence of the absence of a transition.</p>
+</section>
+<section id="capability-specific-metrics." class="level4">
+<h4>Capability-Specific Metrics.</h4>
+<p>A metric targeted at a single capability, evaluated at every checkpoint, resolves transitions the aggregate hides. The in-context learning score <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span> is a representative construction. It isolates the capability of interest (benefiting from long, previous context) by taking the difference between two quantities. Additionally, tracking benchmark accuracy across checkpoints can similarly reveal capability transitions, though at a coarser level. Comparing results across random seeds helps determine whether these transitions are consistent across training runs <span class="citation" data-cites="vanderwal2025polypythias">(<a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al., 2025</a>)</span>.</p>
+<div class="examplebox">
+<p>The in-context learning score of <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022)</a></span> is defined as the prediction loss at the 500th token of a context minus the loss at the 50th token. Each loss is dominated by factors unrelated to in-context learning, such as the model’s overall competence on short contexts; their difference isolates the gain the model derives from additional context. Evaluated at every checkpoint, the score changes abruptly over a narrow training window, the same window in which induction heads form (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>), while the aggregate loss curve registers the transition only as a small bump <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.</p>
+</div>
+</section>
+<section id="per-example-decomposition." class="level4">
+<h4>Per-Example Decomposition.</h4>
+<p>Transitions invisible in every predefined metric can be recovered from the training data itself. POLCA decomposes changes in per-example loss along directions in the low-dimensional subspace where training occurs and clusters examples with similar loss trajectories; the clusters correspond to concepts learned abruptly at identifiable points in training, with no corresponding event in the aggregate curve <span class="citation" data-cites="kangaslahti2026hidden">(<a href="@@BASE@@references/#ref-kangaslahti2026hidden" class="cite cite-established" data-cite="kangaslahti2026hidden" role="doc-biblioref">Kangaslahti et al., 2026</a>)</span>. At coarser granularity, latent state models fit a hidden Markov model over a vector of training metrics and segment the run into discrete phases, with transitions between hidden states flagging candidate developmental events <span class="citation" data-cites="hu2023latent">(<a href="@@BASE@@references/#ref-hu2023latent" class="cite cite-established" data-cite="hu2023latent" role="doc-biblioref">Hu et al., 2023</a>)</span>.</p>
+<div class="warningbox">
+<p><span class="citation" data-cites="schaeffer2023emergent"><a href="@@BASE@@references/#ref-schaeffer2023emergent" class="cite cite-established" data-cite="schaeffer2023emergent" role="doc-biblioref">Schaeffer et al. (2023)</a></span> argued that the emergent abilities reported by <span class="citation" data-cites="wei2022emergent"><a href="@@BASE@@references/#ref-wei2022emergent" class="cite cite-established" data-cite="wei2022emergent" role="doc-biblioref">Wei et al. (2022)</a></span> are artefacts of discontinuous metrics. For example, exact-match accuracy can change abruptly while the underlying log-likelihood improves smoothly. Metric choice does explain some reported cases, and claimed emergent abilities can appear or disappear under different metrics of the same task. However, it does not explain all of them: sharp transitions are observed directly in continuous metrics, in per-example losses, and in internal representations during single training runs <span class="citation" data-cites="du2024understanding schaeffer2025elusive wu2025ushaped kangaslahti2026hidden">(<a href="@@BASE@@references/#ref-du2024understanding" class="cite cite-established" data-cite="du2024understanding" role="doc-biblioref">Du et al., 2024</a>; <a href="@@BASE@@references/#ref-schaeffer2025elusive" class="cite cite-established" data-cite="schaeffer2025elusive" role="doc-biblioref">Schaeffer et al., 2025</a>; <a href="@@BASE@@references/#ref-wu2025ushaped" class="cite cite-established" data-cite="wu2025ushaped" role="doc-biblioref">Wu &amp; Lo, 2025</a>; <a href="@@BASE@@references/#ref-kangaslahti2026hidden" class="cite cite-established" data-cite="kangaslahti2026hidden" role="doc-biblioref">Kangaslahti et al., 2026</a>)</span>. The robustness of a reported transition can be assessed using continuous metrics. However, determining which behavioural transitions reflect genuine changes in the model’s internal mechanisms remains an open question.</p>
+</div>
+</section>
+</section>
+<section id="sec:ld-progress" class="level3"><h3 data-pdf-page="87"><span class="secnum">10.3.2</span> Progress Measures and Internal Precursors<a class="anchor" href="#sec:ld-progress" aria-label="Link to this section">#</a></h3>
+<p>The methods above operate on phase transitions in model behaviours. However, as discussed above, transitions in external behaviour and internal mechanisms can follow different patterns. Researchers therefore are also interested in measuring the internals directly.</p>
+<div class="definitionbox">
+<p>A <em>progress measure</em> is a scalar quantity defined on a model’s internals that varies continuously as a mechanism forms, and can therefore track the mechanism’s development before the behaviour it supports changes <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>. Progress measures are typically derived from a mechanistic analysis of the completed mechanism at the end of training, then evaluated retroactively at earlier checkpoints.</p>
+</div>
+<p>Grokking is the controlled setting in which this tool has been demonstrated most completely.</p>
+<div class="definitionbox">
+<p>Grokking is the phenomenon in which a model reaches low training loss early in training but begins to generalise to unseen inputs only after a long additional period of optimisation <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall">(<a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite cite-emerging" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a>)</span>. This is almost exclusively seen in closed-domain settings, though general-purpose language models have shown conceptually similar behaviour.</p>
+</div>
+<figure id="fig:grokking" data-latex-placement="tb">
+<img alt="Figure 16: Grokking. Training loss (purple) falls early in training while test loss (red) remains high. After a long plateau, test loss drops sharply, and the model generalises. Progress measures defined on model internals show the generalising circuit forming gradually during the plateau, before th" loading="lazy" src="@@BASE@@figures/9_grokking.svg" />
+<figcaption><span class="capnum">Figure 16:</span> <strong>Grokking.</strong> Training loss (purple) falls early in training while test loss (red) remains high. After a long plateau, test loss drops sharply, and the model generalises. Progress measures defined on model internals show the generalising circuit forming gradually during the plateau, before the change is visible in behaviour.</figcaption>
+</figure>
+<p>In modular arithmetic, training accuracy saturates early while test accuracy remains near chance for many steps before rising sharply (Figure <a href="@@BASE@@handbook/learning-dynamics/#fig:grokking" data-reference-type="ref" data-reference="fig:grokking">16</a>; <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall"><a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite cite-emerging" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a></span>). Mechanistic analysis of grokked networks recovers the learned algorithm (trigonometric identities implementing modular addition) and defines progress measures that track its formation: the generalising circuit grows gradually during the plateau, and the sharp change in test accuracy corresponds to a transition from a memorising solution to the completed generalising circuit followed by removal of the memorising components <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>. Grokking has been explained as competition between a memorising and a generalising circuit with different efficiencies under weight decay, an account that predicted and confirmed ungrokking and semi-grokking <span class="citation" data-cites="varma2023explaining">(<a href="@@BASE@@references/#ref-varma2023explaining" class="cite cite-emerging" data-cite="varma2023explaining" role="doc-biblioref">Varma et al., 2023</a>)</span>. Grokking can be induced or eliminated by controlling parameter norm at initialisation, and it occurs beyond algorithmic tasks <span class="citation" data-cites="liu2023omnigrok">(<a href="@@BASE@@references/#ref-liu2023omnigrok" class="cite cite-established" data-cite="liu2023omnigrok" role="doc-biblioref">Liu et al., 2023</a>)</span>.</p>
+<div class="warningbox">
+<p>The standard setting in grokking experiments differs from language model pretraining in many ways, including dataset size, weight decay, and the number of epochs over the data. Current studies have not shown that existing quantitative conclusions transfer directly to large-scale pretraining.</p>
+</div>
+<p>Similar findings have been reported outside grokking settings. <span class="citation" data-cites="chen_sudden_2024"><a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al. (2024a)</a></span> found that syntactic attention structures emerged during a sharp drop in masked language modelling (MLM) loss, before accuracy improvement on a grammatical benchmark. Similarly, <span class="citation" data-cites="zucchet2025language"><a href="@@BASE@@references/#ref-zucchet2025language" class="cite cite-emerging" data-cite="zucchet2025language" role="doc-biblioref">Zucchet et al. (2025a)</a></span> observed that attention-based recall circuits formed during a plateau in factual-knowledge acquisition, followed by rapid improvements in model performance. These findings suggest that changes in internal mechanisms can precede changes in model capabilities. This observation motivates research on forecasting model behaviour through internal monitoring, as discussed in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-safety" data-reference-type="ref" data-reference="sec:ld-safety">10.4</a>.</p>
+</section>
+<section id="sec:ld-attribution" class="level3"><h3 data-pdf-page="88"><span class="secnum">10.3.3</span> Attributing Transitions to Components<a class="anchor" href="#sec:ld-attribution" aria-label="Link to this section">#</a></h3>
+<p>So far, the methods we discussed cannot attribute the transition to specific model components. Attention head specialisation is probably the most intuitive option to work at this granularity in transformers. Individual attention heads can specialise to attend to syntactic relations, positional offsets, or rare tokens (<span class="citation" data-cites="voita_analyzing_2019"><a href="@@BASE@@references/#ref-voita_analyzing_2019" class="cite cite-established" data-cite="voita_analyzing_2019" role="doc-biblioref">Voita et al., 2019</a></span>; <span class="citation" data-cites="clark2019what"><a href="@@BASE@@references/#ref-clark2019what" class="cite cite-emerging" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a></span>), and this specialisation can appear abruptly. For example, induction heads form during a brief training window, and their formation coincides with a sharp improvement in in-context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, a transition we examine in detail in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>. In masked language models, suppressing the syntactic attention structure delays the capability it precedes <span class="citation" data-cites="chen_sudden_2024">(<a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al., 2024a</a>)</span>. Likewise, sparse attention emerges abruptly and depends on repetition in the training data <span class="citation" data-cites="zucchet2025the">(<a href="@@BASE@@references/#ref-zucchet2025the" class="cite cite-established" data-cite="zucchet2025the" role="doc-biblioref">Zucchet et al., 2025b</a>)</span>.</p>
+<p>These events cluster in time. Several attention heads, such as induction heads and heads exhibiting syntactic attention patterns, can acquire specialised functions within the same training window. These heads emerge alongside a peak in how accurately the model’s perplexity predicts human reading times, and this predictive ability subsequently declines even as language modelling performance continues to improve <span class="citation" data-cites="aoyama_language_2025">(<a href="@@BASE@@references/#ref-aoyama_language_2025" class="cite cite-established" data-cite="aoyama_language_2025" role="doc-biblioref">Aoyama &amp; Wilcox, 2025</a>)</span>. Head specialisation can be viewed as a form of <em>symmetry breaking</em>. Before or in the very early stages of training, attention heads are indistinguishable and interchangeable; during training, they start to develop distinct functions despite having equivalent roles in the architecture. Tracking this process can help identify the emergence of modular structure. Attribution of this kind is also a diagnostic for modularity more broadly. Three observable indicators jointly diagnose modular internal structure <span class="citation" data-cites="zhao2024distributional">(<a href="@@BASE@@references/#ref-zhao2024distributional" class="cite cite-emerging" data-cite="zhao2024distributional" role="doc-biblioref">Zhao et al., 2024b</a>)</span>: transitions during training, emergent trends across model scale, and heterogeneous clustering of solutions across random seeds. A structure exhibiting all three is a strong candidate for a discrete, functionally specialised mechanism.</p>
+<div class="insightbox">
+<p>Phase transitions in test loss or task accuracy do not imply abrupt change in any individual component. For example, in controlled settings, many attention heads develop induction-like behaviour gradually—their contributions are largely additive, and the sharp macroscopic transition arises from their aggregate effect <span class="citation" data-cites="singh2024needsrightinductionhead">(<a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a>)</span>. Localising a phase transition in a coarse-grained metric therefore does not help identify the responsible components. In contrast, component-level attribution may find only gradual change without being wrong.</p>
+</div>
+</section>
+<section id="sec:training_phase_why" class="level3"><h3 data-pdf-page="89"><span class="secnum">10.3.4</span> Why Transitions Happen<a class="anchor" href="#sec:training_phase_why" aria-label="Link to this section">#</a></h3>
+<section id="saddle-to-saddle-dynamics." class="level4">
+<h4>Saddle-to-Saddle Dynamics.</h4>
+<p>Theoretical studies of deep linear networks show that different singular modes of the input–output correlations can be learned at different rates <span class="citation" data-cites="saxe2014exact saxe2019mathematical">(<a href="@@BASE@@references/#ref-saxe2014exact" class="cite cite-established" data-cite="saxe2014exact" role="doc-biblioref">Saxe et al., 2014</a>; <a href="@@BASE@@references/#ref-saxe2019mathematical" class="cite cite-established" data-cite="saxe2019mathematical" role="doc-biblioref">2019</a>)</span>. The training process may therefore involve periods of slow improvement followed by rapid reductions in test loss. From an optimisation perspective, gradient descent moves through regions near successive saddle points in the loss landscape and gradually learns more complex solutions along the way. This process is known as <em>saddle-to-saddle dynamics</em>. Similar training patterns have been observed in other neural networks, where they learn simpler functions before more complex ones <span class="citation" data-cites="rahaman2019spectral nakkiran2019sgd">(<a href="@@BASE@@references/#ref-rahaman2019spectral" class="cite cite-established" data-cite="rahaman2019spectral" role="doc-biblioref">Rahaman et al., 2019</a>; <a href="@@BASE@@references/#ref-nakkiran2019sgd" class="cite cite-established" data-cite="nakkiran2019sgd" role="doc-biblioref">Nakkiran et al., 2019</a>)</span>. Attention head specialisation may also be related to symmetry breaking during training, although the relationship to saddle-to-saddle dynamics is not yet fully established in realistic transformers <span class="citation" data-cites="chen_sudden_2024">(<a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al., 2024a</a>)</span>.</p>
+</section>
+<section id="singular-learning-theory." class="level4">
+<h4>Singular Learning Theory.</h4>
+<p>Singular learning theory (SLT) studies how the geometry of a singular model’s parameter space affects learning and generalisation <span class="citation" data-cites="watanabe2009algebraic">(<a href="@@BASE@@references/#ref-watanabe2009algebraic" class="cite cite-established" data-cite="watanabe2009algebraic" role="doc-biblioref">Watanabe, 2009</a>)</span>. Neural networks are singular (and are thus a subject of study in SLT) because different parameter configurations can implement the same function. As a result, two models that achieve similar loss can differ substantially in their effective complexity. SLT provides a framework for studying this complexity and has motivated research on its changes during training and its usefulness in downstream applications <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>.</p>
+<div class="definitionbox">
+<p>For model parameters <span class="math inline">\mathbf{\theta}^*</span> at a local minimum, the Local Learning Coefficient (LLC) <span class="math inline">\lambda(\mathbf{\theta}^*)</span> measures the degeneracy of the loss landscape near <span class="math inline">\mathbf{\theta}^*</span> via the volume-scaling rate of low-loss neighbourhoods <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>. Smaller values indicate more degenerate (flatter, in a higher-order sense) minima. The LLC is estimable at scale with stochastic-gradient MCMC <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>.</p>
+</div>
+<p>Estimated LLC trajectories in transformers change at points that coincide with identifiable developmental events, including the stages of structure formation described in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-attribution" data-reference-type="ref" data-reference="sec:ld-attribution">10.3.3</a> <span class="citation" data-cites="hoogland2025losslandscapedegeneracystagewise">(<a href="@@BASE@@references/#ref-hoogland2025losslandscapedegeneracystagewise" class="cite cite-established" data-cite="hoogland2025losslandscapedegeneracystagewise" role="doc-biblioref">Hoogland et al., 2025</a>)</span>. This is the central empirical result of the developmental interpretability programme <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span> introduced in Section <a href="@@BASE@@handbook/research-orientations/#subsec:dev interp" data-reference-type="ref" data-reference="subsec:dev interp">12.3</a>. However, current evidence for LLC is largely retrospective, where phase transitions are detected and matched to known events only after training. Prospective prediction of a transition before it occurs has not yet been demonstrated. Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-safety" data-reference-type="ref" data-reference="sec:ld-safety">10.4</a> discusses potential applications of learning-dynamics analysis to forecasting and intervention.</p>
+</section>
+</section>
+<section id="sec:ld-induction-case" class="level3"><h3 data-pdf-page="90"><span class="secnum">10.3.5</span> Case Study: Induction Heads<a class="anchor" href="#sec:ld-induction-case" aria-label="Link to this section">#</a></h3>
+<div class="examplebox">
+<p>The preceding case studies (Sections <a href="@@BASE@@handbook/feature-extraction/#subsec: induc head case study: feature extract" data-reference-type="ref" data-reference="subsec: induc head case study: feature extract">6.8</a> and <a href="@@BASE@@handbook/circuits/#subsec: induc head case study: circuit" data-reference-type="ref" data-reference="subsec: induc head case study: circuit">7.4</a>) analysed induction heads and circuits in a trained model. Several facts about the induction circuit are invisible from that vantage point but become observable when we examine the model across training <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>. Consider the repeated sequence again</p>
+<p><span class="math display">\texttt{A B C A B \underline{?}},</span></p>
+<p>and observe at successive checkpoints of a single training run. Examining the checkpoints shows that the induction ability arrived abruptly: at early checkpoints the model bases its predictions on token frequencies and derives no benefit from the earlier occurrence of <code>A B</code>, and during a narrow window of training, roughly 2.5 to 5 billion tokens in the models studied by <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022)</a></span>, induction heads form and the model begins predicting <code>C</code> by attending back to the first occurrence. A sharp improvement in in-context learning and a small bump in the training loss fall within the same window.<br />
+The timing of formation also helps provide causal evidence that a single model checkpoint cannot provide. Ablating an induction head in the final model only shows its necessity for the behaviour at that checkpoint. This does not support the stronger claim that the induction circuit’s formation caused the capability to develop. <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022)</a></span> examined this stronger claim by intervening on formation itself. Their “smeared key” modification built the previous-token head’s contribution into the attention computation, allowing the circuit to form within a single layer. Under this modification, one-layer models acquire in-context learning they otherwise never develop, while the transition moves earlier in deeper models.</p>
+</div>
+</section>
+</section>
+<section id="sec:ld-safety" class="level2"><h2 data-pdf-page="90"><span class="secnum">10.4</span> Leveraging Learning Dynamics for Safety<a class="anchor" href="#sec:ld-safety" aria-label="Link to this section">#</a></h2>
+<p>The analyses above also support safety-oriented objectives, including anticipating capabilities before they appear (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-forecasting" data-reference-type="ref" data-reference="sec:ld-forecasting">10.4.1</a>) and intervening in the training process while it is still underway (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-intervening" data-reference-type="ref" data-reference="sec:ld-intervening">10.4.2</a>).</p>
+<section id="sec:ld-forecasting" class="level3"><h3 data-pdf-page="90"><span class="secnum">10.4.1</span> Forecasting Model Behaviours<a class="anchor" href="#sec:ld-forecasting" aria-label="Link to this section">#</a></h3>
+<section id="forecasting-from-smaller-runs-and-partial-runs." class="level4">
+<h4>Forecasting from Smaller Runs and Partial Runs.</h4>
+<p>Scaling laws demonstrate that aggregate loss is predictable before training (<span class="citation" data-cites="kaplan2020scaling"><a href="@@BASE@@references/#ref-kaplan2020scaling" class="cite cite-emerging" data-cite="kaplan2020scaling" role="doc-biblioref">Kaplan et al., 2020</a></span>; <span class="citation" data-cites="hoffmann2022training"><a href="@@BASE@@references/#ref-hoffmann2022training" class="cite cite-established" data-cite="hoffmann2022training" role="doc-biblioref">Hoffmann et al., 2022</a></span>), but phase transitions break this style of extrapolation for individual capabilities (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-transitions" data-reference-type="ref" data-reference="sec:ld-transitions">10.3</a>). Forecasting therefore targets specific behaviours directly. Memorisation is the behaviour for which the forecasting problem has been posed most explicitly. <span class="citation" data-cites="biderman2023emergent"><a href="@@BASE@@references/#ref-biderman2023emergent" class="cite cite-established" data-cite="biderman2023emergent" role="doc-biblioref">Biderman et al. (2023a)</a></span> introduced the problem of forecasting whether a specific training sequence will be memorised by a model before that model is trained. They argued that recall is the metric to optimise (a false negative means training a full model that leaks the sequence, while a false positive only discards a cheap trial run). They established baselines that forecast from smaller fully trained models and from partially trained checkpoints of the target run, together with equi-compute recommendations for dividing a fixed forecasting budget between model size and tokens.</p>
+</section>
+<section id="forecasting-safety-relevant-behaviours." class="level4">
+<h4>Forecasting Safety-Relevant Behaviours.</h4>
+<p>Emergence can be forecast by intervening on the training trajectory. Fine-tuning on a target task shifts capability emergence towards less capable models. Fitting how this shift varies with the amount of fine-tuning data yields an <em>emergence law</em>. Using only small models, this law predicts whether a capability will have emerged in models trained with up to 4<span class="math inline">\times</span> more compute <span class="citation" data-cites="snell2024predicting">(<a href="@@BASE@@references/#ref-snell2024predicting" class="cite cite-emerging" data-cite="snell2024predicting" role="doc-biblioref">Snell et al., 2024</a>)</span>.</p>
+<p>Rare harmful behaviours can also be forecast statistically. The largest elicitation probabilities observed in a small sample of queries scale predictably with query volume. This relationship allows harmful-behaviour risks to be extrapolated across up to three orders of magnitude of deployment-scale query volume. Examples include assisting with dangerous chemical synthesis and taking power-seeking actions <span class="citation" data-cites="jones2025forecasting">(<a href="@@BASE@@references/#ref-jones2025forecasting" class="cite cite-emerging" data-cite="jones2025forecasting" role="doc-biblioref">Jones et al., 2025</a>)</span>.</p>
+<p>Post-training failures can be analysed similarly. Specifically, transitions between successive RLHF checkpoints can be classified into four categories: reward hacking, collapse, proxy under-alignment, and evaluator-specific disagreement. Reward hacking occurs when the model exploits a way to achieve high rewards while the external quality from the perspective of human users falls; collapse occurs when both decline. This classification localises failures that checkpoint averages miss. A model fitted to training-dynamics diagnostics predicts future reward hacking with a ROC-AUC of 0.82. These diagnostics include reward-model uncertainty, policy drift, and output diversity <span class="citation" data-cites="abahana2026rlhf">(<a href="@@BASE@@references/#ref-abahana2026rlhf" class="cite cite-emerging" data-cite="abahana2026rlhf" role="doc-biblioref">Abahana et al., 2026</a>)</span>.</p>
+<p>Internal signals extend forecasting to fine-tuning outcomes. Activation-space persona features predict whether a fine-tuned model will exhibit emergent misalignment <span class="citation" data-cites="wang2026persona">(<a href="@@BASE@@references/#ref-wang2026persona" class="cite cite-established" data-cite="wang2026persona" role="doc-biblioref">Wang et al., 2026a</a>)</span>. Signals available before fine-tuning begins also predict the resulting model’s alignment <span class="citation" data-cites="zhang2026whatshapes">(<a href="@@BASE@@references/#ref-zhang2026whatshapes" class="cite cite-emerging" data-cite="zhang2026whatshapes" role="doc-biblioref">Zhang et al., 2026b</a>)</span>. Section <a href="@@BASE@@handbook/research-orientations/#subsec: iat" data-reference-type="ref" data-reference="subsec: iat">12.4</a> discusses the research agenda these results instantiate.</p>
+</section>
+<section id="internal-precursors-as-early-warning." class="level4">
+<h4>Internal Precursors as Early Warning.</h4>
+<p>Where a mechanism forms before the capability it supports is behaviourally expressed, monitoring internals can flag the capability earlier than behavioural evaluation. The recall circuits that precede factual-knowledge acquisition <span class="citation" data-cites="zucchet2025language">(<a href="@@BASE@@references/#ref-zucchet2025language" class="cite cite-emerging" data-cite="zucchet2025language" role="doc-biblioref">Zucchet et al., 2025a</a>)</span> and the syntactic attention structure that precedes the break in grammatical capabilities <span class="citation" data-cites="chen_sudden_2024">(<a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al., 2024a</a>)</span> show that this lead time can exist; progress measures (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-progress" data-reference-type="ref" data-reference="sec:ld-progress">10.3.2</a>) are the instrument that would exploit it.</p>
+</section>
+<section id="run-to-run-variance." class="level4">
+<h4>Run-to-Run Variance.</h4>
+<p>To assess the reliability of a forecast, researchers can compare training runs with different random seeds. <span class="citation" data-cites="vanderwal2025polypythias"><a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al. (2025)</a></span> found that capabilities generally emerge at similar stages and representations develop consistently across training runs, although some runs differ substantially. These exceptions highlight the uncertainty of forecasts based on a single training run.</p>
+<div class="warningbox">
+<p>Currently, nearly all demonstrations that changes in internals lead to changes in behaviour are retrospective. In these studies, researchers analyse the completed mechanism after training to identify a progress measure or precursor structure, then evaluate it backwards over saved checkpoints. This concern also exists in LLC-based transition detection (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>).<br />
+In contrast, prospective forecasting requires researchers to predict outcomes before observing them. <span class="citation" data-cites="biderman2023emergent"><a href="@@BASE@@references/#ref-biderman2023emergent" class="cite cite-established" data-cite="biderman2023emergent" role="doc-biblioref">Biderman et al. (2023a)</a></span> proposed baseline methods for forecasting memorisation. However, prospective forecasting for broader capability transitions relevant to AI safety remains underdeveloped. Early-warning signals therefore require prospective validation before they can be considered reliable.</p>
+</div>
+</section>
+</section>
+<section id="sec:ld-intervening" class="level3"><h3 data-pdf-page="91"><span class="secnum">10.4.2</span> Intervening on Training Processes<a class="anchor" href="#sec:ld-intervening" aria-label="Link to this section">#</a></h3>
+<p>The model that matters is the one deployed at the end of training, and the standard response to an undesirable behaviour found there is post-training fine-tuning, which is outside the scope of this section. An alternative is to intervene during training to prevent undesirable capabilities from developing or to control how they develop. Existing work explores three approaches: modifying training data, changing model architecture, and intervening on model activations during fine-tuning.</p>
+<section id="pretraining-data-filtering-deep-ignorance." class="level4">
+<h4>Pretraining Data Filtering: Deep Ignorance.</h4>
+<p>Deep Ignorance filters proxies for biohazardous knowledge out of the pretraining corpus and trains 6.9-billion-parameter models on the filtered data (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-data" data-reference-type="ref" data-reference="sec:ld-data">10.1.1</a>; <span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al., 2026</a></span>). The filtered models do not acquire the targeted knowledge, making it difficult for the unwanted capability to emerge through adversarial fine-tuning. In contrast, post-training unlearning may suppress the unwanted behaviour without removing the underlying knowledge. A follow-up moves the filter from documents to tokens: individual tokens are labelled using sparse autoencoders and efficient classifiers trained on those labels, and filtering at token granularity removes the undesired capability at lower cost to desired capabilities than document-level filtering, with effectiveness increasing with model scale across the two orders of magnitude tested and filtered models remaining alignable afterwards <span class="citation" data-cites="rathi2026shaping">(<a href="@@BASE@@references/#ref-rathi2026shaping" class="cite cite-emerging" data-cite="rathi2026shaping" role="doc-biblioref">Rathi &amp; Radford, 2026</a>)</span>.</p>
+<p>Similarly, the order in which different categories of training data are presented matters. <span class="citation" data-cites="bruijns2026pretrainingcurriculaenableselective"><a href="@@BASE@@references/#ref-bruijns2026pretrainingcurriculaenableselective" class="cite cite-emerging" data-cite="bruijns2026pretrainingcurriculaenableselective" role="doc-biblioref">Bruijns et al. (2026)</a></span> found that, in an imbalanced pretraining curriculum where one task is shown before another, the model packs the tasks sequentially into separable circuits, resulting in more targeted subsequent refusal fine-tuning.</p>
+<div class="warningbox">
+<p>Removing data from a training set does not act only on the removed data. In the setting of <span class="citation" data-cites="borkar2025privacy"><a href="@@BASE@@references/#ref-borkar2025privacy" class="cite cite-established" data-cite="borkar2025privacy" role="doc-biblioref">Borkar et al. (2025)</a></span>, deleting personal information can increase memorisation of the personal information that remains, and adding one individual’s information amplifies memorisation of others’ by roughly 7.5<span class="math inline">\times</span>, because whether a sequence is memorised depends on similar sequences seen elsewhere in training. Subliminal learning <span class="citation" data-cites="cloud2026language">(<a href="@@BASE@@references/#ref-cloud2026language" class="cite cite-established" data-cite="cloud2026language" role="doc-biblioref">Cloud et al., 2026</a>)</span> shows that data can transmit traits not apparent from its semantic content. For example, a student model can inherit a teacher’s hidden bias from seemingly unrelated data, such as number sequences. This transfer is carried by a small set of divergence tokens and depends on early layers <span class="citation" data-cites="schrodi2026towards">(<a href="@@BASE@@references/#ref-schrodi2026towards" class="cite cite-established" data-cite="schrodi2026towards" role="doc-biblioref">Schrodi et al., 2026</a>)</span>. Filtering and unlearning interventions therefore need to be evaluated on what the model does with the data that stays, not only on what it no longer does with the data that was removed.</p>
+</div>
+</section>
+<section id="architectural-modification-gradient-routing." class="level4">
+<h4>Architectural Modification: Gradient Routing.</h4>
+<p>Gradient routing is an architectural modification informed by learning dynamics: it constrains which parameters receive gradient updates for chosen concepts during training, localising the corresponding computation into designated regions of the network <span class="citation" data-cites="cloud2024gradient">(<a href="@@BASE@@references/#ref-cloud2024gradient" class="cite cite-emerging" data-cite="cloud2024gradient" role="doc-biblioref">Cloud et al., 2024</a>)</span>. Although the modification does not make the trained model safer on its own, it shapes where a mechanism forms so that later interventions, such as gating the designated region, become clean and localised. The antecedent for this style of intervention is the smeared-key modification <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, which showed that an architectural change can control when and whether a mechanism forms at all (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>).</p>
+</section>
+<section id="monitoring-and-steering-during-fine-tuning." class="level4">
+<h4>Monitoring and Steering During Fine-Tuning.</h4>
+<p>The case excluded above uses fine-tuning to repair an undesirable behaviour after it has been identified. Intervening on the fine-tuning process itself remains within scope, and persona vectors provide a current example of this approach. A persona vector is an activation-space direction corresponding to a character trait such as sycophancy, hallucination propensity, or malice, extractable automatically from a natural-language trait description <span class="citation" data-cites="chen2025persona">(<a href="@@BASE@@references/#ref-chen2025persona" class="cite cite-emerging" data-cite="chen2025persona" role="doc-biblioref">Chen et al., 2025b</a>)</span>. Intended and unintended personality changes during fine-tuning correlate strongly with movement along the relevant vector, which yields the monitoring instrument. Preventative steering along the vector during fine-tuning limits acquisition of the trait. Before fine-tuning, projecting training data onto the vector flags data that would induce the trait, at both the dataset and individual-sample levels <span class="citation" data-cites="chen2025persona">(<a href="@@BASE@@references/#ref-chen2025persona" class="cite cite-emerging" data-cite="chen2025persona" role="doc-biblioref">Chen et al., 2025b</a>)</span>. This instantiates each step of the intervention loop below for the fine-tuning stage.</p>
+<div class="practicebox">
+<p>The work discussed so far lays the methodological foundation for intervening in a training run in progress. Below we present a general pipeline.</p>
+<ol>
+<li><p><strong>Monitor</strong> a target quantity at checkpoints, such as a capability-specific metric (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-detect" data-reference-type="ref" data-reference="sec:ld-detect">10.3.1</a>) or a progress measure on model internals (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-progress" data-reference-type="ref" data-reference="sec:ld-progress">10.3.2</a>).</p></li>
+<li><p><strong>Define</strong> the acceptable range for that quantity in advance, including the response to a breach.</p></li>
+<li><p><strong>Intervene</strong> when the range is breached, such as by adjusting the data mixture or filtering out unwanted data, applying a targeted architectural or regularisation change, rolling back to a pre-breach checkpoint, and resuming under the adjusted setup.</p></li>
+<li><p><strong>Re-evaluate</strong> the model after the intervention to verify that the targeted capability has been suppressed and does not re-emerge during further training.</p></li>
+</ol>
+<p>No published system implements this loop end to end at scale. Instead, existing studies have demonstrated individual steps of the proposed workflow.</p>
+</div>
+</section>
+<section id="open-questions." class="level4">
+<h4>Open Questions.</h4>
+<p>Three questions remain about the applicability of these training-time interventions. First, effective data filtering requires identifying which training data contributes to the capability we want to remove. Token-level filtering is motivated by data attribution, but it remains unclear whether methods such as influence functions (Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">11</a>) can reliably identify the relevant data. Second, scale: Deep Ignorance operates at 6.9 billion parameters, and while token-level filtering became more effective across the tested scale range <span class="citation" data-cites="rathi2026shaping">(<a href="@@BASE@@references/#ref-rathi2026shaping" class="cite cite-emerging" data-cite="rathi2026shaping" role="doc-biblioref">Rathi &amp; Radford, 2026</a>)</span>, it remains open whether data filtering stays effective and affordable at frontier scale. Third, deployed models show that stronger train-time safeguards are achievable, but not how: gpt-oss models are substantially harder to fine-tune than comparable open-weight models, and their developers have not fully disclosed their responsible training procedure <span class="citation" data-cites="openai2025gptoss120bgptoss20bmodel">(<a href="@@BASE@@references/#ref-openai2025gptoss120bgptoss20bmodel" class="cite cite-emerging" data-cite="openai2025gptoss120bgptoss20bmodel" role="doc-biblioref">OpenAI et al., 2025</a>)</span>. Understanding how this resistance is achieved could help develop similar safeguards for other open-weight models and clarify how fine-tuning changes model behaviour.</p>
+</section>
+<section id="summary.-7" class="level4">
+<h4>Summary.</h4>
+<p>Learning dynamics can be studied both as an object of explanation, revealing how representations and mechanisms evolve during training, and as an interpretability tool to localise mechanisms, detect phase transitions, and test causal hypotheses through training interventions. Understanding learning dynamics can also help predict when capabilities emerge and guide interventions during training. However, no complete system combining monitoring and intervention has yet been demonstrated at scale. This section focuses on how models change during training, while Section <a href="@@BASE@@handbook/training-data-attribution/#sec:influence-functions" data-reference-type="ref" data-reference="sec:influence-functions">11</a> examines how individual training examples contribute to model behaviour.</p>
+</section>
+</section>
+</section>
+</section>
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-<section id="sec:why_open" class="level1"><h1 data-pdf-page="5"><span class="secnum">2</span> The Limits of Behavioral Evaluation<a class="anchor" href="#sec:why_open" aria-label="Link to this section">#</a></h1>
-<p>Behavioral evaluation—measuring output patterns on held-out inputs—is the default paradigm for assessing deep learning systems. Its rationale is intuitive: correct outputs on a sufficiently diverse test set indicate that a model has learned the desired function. But this presumes that evaluated behavior predicts deployment behavior—that models which score well on benchmarks will perform comparably in practice. Whether it does can depend on the mechanisms a model uses to achieve its test performance, which behavioral evaluation cannot distinguish. As models grow more capable, more complex, and more widely deployed in high-stakes domains, four problems with relying on behavioral evaluation alone have become apparent.</p>
-<p><strong>First, test-set contamination and saturation.</strong> When test-sets are included in the training data, behavioural evaluations have limited usefulness. As models are pretrained on web-scale corpora, test-set contamination becomes increasingly difficult to prevent, and even a single replica of benchmark data in the pretraining corpus can inflate performance beyond the irreducible error of uncontaminated training <span class="citation" data-cites="schaeffer2026quantifyingeffecttestset">(<a href="@@BASE@@references/#ref-schaeffer2026quantifyingeffecttestset" class="cite cite-established" data-cite="schaeffer2026quantifyingeffecttestset" role="doc-biblioref">Schaeffer et al., 2026</a>)</span>. Additionally, benchmarks routinely saturate—models reach ceiling performance on the specific capability a benchmark measures—long before the broader real-world capabilities we care about are achieved <span class="citation" data-cites="SuperGLUE ott2022mapping phan2026hle">(<a href="@@BASE@@references/#ref-SuperGLUE" class="cite cite-established" data-cite="SuperGLUE" role="doc-biblioref">Wang et al., 2019</a>; <a href="@@BASE@@references/#ref-ott2022mapping" class="cite cite-established" data-cite="ott2022mapping" role="doc-biblioref">Ott et al., 2022</a>; <a href="@@BASE@@references/#ref-phan2026hle" class="cite cite-established" data-cite="phan2026hle" role="doc-biblioref">Phan et al., 2026</a>)</span>. Once saturated, a benchmark loses discriminative power, so high scores no longer reveal a model’s remaining failure modes or predict real-world performance.</p>
-<p><strong>Second, gaming in which models learn the wrong mechanisms.</strong> Two models with identical input-output behavior may implement radically different computations—one relying on robust, generalizable features, the other on brittle shortcuts or spurious correlations <span class="citation" data-cites="mccoy-etal-2019-right du2023shortcut ye2026the">(<a href="@@BASE@@references/#ref-mccoy-etal-2019-right" class="cite cite-established" data-cite="mccoy-etal-2019-right" role="doc-biblioref">McCoy et al., 2019</a>; <a href="@@BASE@@references/#ref-du2023shortcut" class="cite cite-established" data-cite="du2023shortcut" role="doc-biblioref">Du et al., 2023</a>; <a href="@@BASE@@references/#ref-ye2026the" class="cite cite-established" data-cite="ye2026the" role="doc-biblioref">Ye et al., 2026</a>)</span>. Specifically, models have been shown to achieve high scores on benchmarks through shortcuts that don’t reflect genuine understanding <span class="citation" data-cites="Geirhos_2020 tang-etal-2023-large">(<a href="@@BASE@@references/#ref-Geirhos_2020" class="cite cite-established" data-cite="Geirhos_2020" role="doc-biblioref">Geirhos et al., 2020</a>; <a href="@@BASE@@references/#ref-tang-etal-2023-large" class="cite cite-established" data-cite="tang-etal-2023-large" role="doc-biblioref">Tang et al., 2023</a>)</span>. For safety-critical systems, this ambiguity is dangerous: a model that appears aligned on evaluations may have learned a shallow policy that breaks under distribution shift, or may implement reasoning strategies that generalize in unexpected and harmful ways. Without examining internal representations, we cannot distinguish these cases until deployment failures reveal the difference. The catch is that doing so typically requires access to internal states—for example, residual stream activations—and the ability to intervene on them, which is often unavailable in black-box deployments and can be computationally expensive.</p>
-<p><strong>Third, evaluation-recognition.</strong> More fundamentally, a model that passes a safety evaluation may simply have learned to recognize evaluation-like contexts and adjust its behavior accordingly (<span class="citation" data-cites="needham2025largelanguagemodelsknow"><a href="@@BASE@@references/#ref-needham2025largelanguagemodelsknow" class="cite cite-established" data-cite="needham2025largelanguagemodelsknow" role="doc-biblioref">Needham et al., 2025</a></span>; <span class="citation" data-cites="anthropic2025claude4 apollo2025sonnet37evalaware"><a href="@@BASE@@references/#ref-anthropic2025claude4" class="cite cite-emerging" data-cite="anthropic2025claude4" role="doc-biblioref"><span>Anthropic</span>, 2025b</a>; <a href="@@BASE@@references/#ref-apollo2025sonnet37evalaware" class="cite cite-emerging" data-cite="apollo2025sonnet37evalaware" role="doc-biblioref"><span>Apollo Research</span>, 2025</a></span>)—a failure mode that is often hard to detect solely from model outputs. The sleeper agents work <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span> demonstrated that models can be trained to behave safely during evaluation while harboring harmful behaviors triggered by deployment conditions. Standard safety training—including RLHF and adversarial testing—failed to remove these behaviors. Critically, such strategic behavior need not be deliberately planted: <span class="citation" data-cites="greenblatt2024alignmentfakinglargelanguage"><a href="@@BASE@@references/#ref-greenblatt2024alignmentfakinglargelanguage" class="cite cite-emerging" data-cite="greenblatt2024alignmentfakinglargelanguage" role="doc-biblioref">Greenblatt et al., 2024</a></span> showed that large language models can engage in alignment faking—selectively complying with training objectives to prevent modification of their behavior—without being explicitly trained or instructed to do so, suggesting that training incentives alone may be sufficient to produce evaluation-aware deception. If a model is optimizing to appear safe rather than to be safe, behavioral evaluation is fundamentally limited.</p>
-<p><strong>Fourth, lack of fine-grained control.</strong> Some safety interventions require fine-grained control <span class="citation" data-cites="meng2023locatingeditingfactualassociations li2024wmdp marks2025sparse">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>; <a href="@@BASE@@references/#ref-li2024wmdp" class="cite cite-established" data-cite="li2024wmdp" role="doc-biblioref">Li et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. For instance, removing a specific capability without degrading others requires locating the responsible model components or the subspaces that encode them. Evaluations based on input-output behavior offer no insight into the model’s internal mechanisms, and therefore do little to enable the precise, component-level control that such interventions demand.</p>
-<p>A further challenge arises once models are updated after deployment. Behavioral evaluation assumes a fixed model that can be tested once and trusted thereafter, but a continually learning system keeps adjusting itself from interactions with the environment. In this case, any evaluation describes a previous snapshot rather than the model that is actually running. Guaranties established at test time decay as the weights move, and the mechanism responsible for a behavior may itself change without any corresponding change in measured performance. We do not treat this case in the following and leave it to future work.</p>
-<p>Such problems motivate the need for methods that examine model internals directly—not just what models output, but how they compute those outputs.</p>
+<section id="sec:why_open" class="level1"><h1 data-pdf-page="15"><span class="secnum">2</span> The Limits of Behavioural Evaluation<a class="anchor" href="#sec:why_open" aria-label="Link to this section">#</a></h1>
+<p>Behavioural evaluation—measuring output patterns on held-out inputs—is the default paradigm for assessing deep learning systems. Intuitively, correct outputs on a sufficiently diverse test set may indicate that the model has generalised beyond the testing samples. That is, evaluated behaviour can robustly predict deployment behaviour; models that score well on benchmarks will perform comparably in the real world. Whether this presumption holds depends on whether the model has implemented a desired internal mechanism to achieve its test performance, something behavioural evaluation cannot identify <span class="citation" data-cites="wani-etal-2026-answer">(<a href="@@BASE@@references/#ref-wani-etal-2026-answer" class="cite cite-established" data-cite="wani-etal-2026-answer" role="doc-biblioref">Wani et al., 2026</a>)</span>. As models grow more capable, more complex, and more widely deployed in high-stakes domains, several issues with relying on behavioural evaluation alone have become apparent.</p>
+<section id="first-test-set-contamination-and-saturation." class="level4">
+<h4>First, test-set contamination and saturation.</h4>
+<p>When test sets are included in the training data, behavioural evaluations have limited usefulness. As models are pretrained on web-scale corpora, test-set contamination becomes increasingly difficult to prevent, and even a single replica of benchmark data in the pretraining corpus can inflate performance beyond the irreducible error of uncontaminated training <span class="citation" data-cites="schaeffer2026quantifyingeffecttestset">(<a href="@@BASE@@references/#ref-schaeffer2026quantifyingeffecttestset" class="cite cite-emerging" data-cite="schaeffer2026quantifyingeffecttestset" role="doc-biblioref">Schaeffer et al., 2026</a>)</span>. Additionally, benchmarks routinely saturate—models reach ceiling performance on the specific capability a benchmark measures—long before the broader real-world capabilities we care about are achieved <span class="citation" data-cites="SuperGLUE ott2022mapping phan2026hle">(<a href="@@BASE@@references/#ref-SuperGLUE" class="cite cite-established" data-cite="SuperGLUE" role="doc-biblioref">Wang et al., 2019</a>; <a href="@@BASE@@references/#ref-ott2022mapping" class="cite cite-established" data-cite="ott2022mapping" role="doc-biblioref">Ott et al., 2022</a>; <a href="@@BASE@@references/#ref-phan2026hle" class="cite cite-established" data-cite="phan2026hle" role="doc-biblioref">Phan et al., 2026</a>)</span>. Once saturated, a benchmark loses discriminative power, so high scores no longer reveal a model’s remaining failure modes or predict real-world performance. Even before saturation, flaws in task setup or reward design can cause benchmarks to substantially under- or overestimate model performance <span class="citation" data-cites="zhu2025establishing">(<a href="@@BASE@@references/#ref-zhu2025establishing" class="cite cite-established" data-cite="zhu2025establishing" role="doc-biblioref">Zhu et al., 2025</a>)</span>.</p>
+</section>
+<section id="second-benchmark-gaming-models-can-learn-the-wrong-mechanisms." class="level4">
+<h4>Second, benchmark gaming: models can learn the wrong mechanisms.</h4>
+<p>Two models with identical input–output behaviour may implement radically different computations, with one relying on robust, generalisable features and the other on brittle shortcuts or spurious correlations <span class="citation" data-cites="mccoy-etal-2019-right du2023shortcut juneja2023linear ye2026the">(<a href="@@BASE@@references/#ref-mccoy-etal-2019-right" class="cite cite-established" data-cite="mccoy-etal-2019-right" role="doc-biblioref">McCoy et al., 2019</a>; <a href="@@BASE@@references/#ref-du2023shortcut" class="cite cite-established" data-cite="du2023shortcut" role="doc-biblioref">Du et al., 2023</a>; <a href="@@BASE@@references/#ref-juneja2023linear" class="cite cite-established" data-cite="juneja2023linear" role="doc-biblioref">Juneja et al., 2023</a>; <a href="@@BASE@@references/#ref-ye2026the" class="cite cite-established" data-cite="ye2026the" role="doc-biblioref">Ye et al., 2026</a>)</span>. Specifically, models can achieve high benchmark scores through shortcuts that do not reflect genuine understanding <span class="citation" data-cites="Geirhos_2020 tang-etal-2023-large zhu2025establishing">(<a href="@@BASE@@references/#ref-Geirhos_2020" class="cite cite-established" data-cite="Geirhos_2020" role="doc-biblioref">Geirhos et al., 2020</a>; <a href="@@BASE@@references/#ref-tang-etal-2023-large" class="cite cite-established" data-cite="tang-etal-2023-large" role="doc-biblioref">Tang et al., 2023</a>; <a href="@@BASE@@references/#ref-zhu2025establishing" class="cite cite-established" data-cite="zhu2025establishing" role="doc-biblioref">Zhu et al., 2025</a>)</span>. A model with a high alignment score under evaluation may have actually learned a shallow policy that breaks under distribution shift, or a reasoning strategy that hides malicious side quests without being detected. Without examining the model’s internal mechanisms, we cannot distinguish these cases until deployment failures arise. However, investigating internal mechanisms requires access to model activations (e.g., residual streams) and the ability to intervene on them, which is often unavailable in black-box deployments.</p>
+</section>
+<section id="third-evaluation-awareness." class="level4">
+<h4>Third, evaluation awareness.</h4>
+<p>More fundamentally, a model that passes a safety evaluation may simply have learned to recognise evaluation-like contexts and adjust its behaviour accordingly <span class="citation" data-cites="needham2025largelanguagemodelsknow anthropic2025claude4 apollo2025sonnet37evalaware">(<a href="@@BASE@@references/#ref-needham2025largelanguagemodelsknow" class="cite cite-emerging" data-cite="needham2025largelanguagemodelsknow" role="doc-biblioref">Needham et al., 2025</a>; <a href="@@BASE@@references/#ref-anthropic2025claude4" class="cite cite-emerging" data-cite="anthropic2025claude4" role="doc-biblioref"><span>Anthropic</span>, 2025b</a>; <a href="@@BASE@@references/#ref-apollo2025sonnet37evalaware" class="cite cite-emerging" data-cite="apollo2025sonnet37evalaware" role="doc-biblioref"><span>Apollo Research</span>, 2025</a>)</span>—a failure mode that is often hard to detect solely from model outputs. The sleeper agents work <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span> demonstrated that models can be trained to behave safely during evaluation while harbouring harmful intents triggered by deployment conditions. Standard safety measures, such as safety-oriented RLHF (<span class="citation" data-cites="bai2022training"><a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a></span>; <span class="citation" data-cites="dai2024safe"><a href="@@BASE@@references/#ref-dai2024safe" class="cite cite-established" data-cite="dai2024safe" role="doc-biblioref">Dai et al., 2024b</a></span>) and adversarial training <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>, failed to remove this behaviour. Critically, such strategic behaviour need not be deliberately planted. For example, <span class="citation" data-cites="greenblatt2024alignmentfakinglargelanguage"><a href="@@BASE@@references/#ref-greenblatt2024alignmentfakinglargelanguage" class="cite cite-emerging" data-cite="greenblatt2024alignmentfakinglargelanguage" role="doc-biblioref">Greenblatt et al. (2024)</a></span> showed that large language models can implement alignment faking—selectively complying with training objectives to avoid modification—without being explicitly trained or instructed to do so. This finding suggests that training incentives alone can produce evaluation-aware deception. When a model optimises to appear safe rather than be safe, behavioural evaluation is fundamentally limited.</p>
+</section>
+<section id="fourth-lack-of-fine-grained-control." class="level4">
+<h4>Fourth, lack of fine-grained control.</h4>
+<p>Some safety interventions require fine-grained control <span class="citation" data-cites="meng2023locatingeditingfactualassociations li2024wmdp marks2025sparse">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>; <a href="@@BASE@@references/#ref-li2024wmdp" class="cite cite-established" data-cite="li2024wmdp" role="doc-biblioref">Li et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. For example, to remove a capability or knowledge without degrading others, we may need to locate the model components or activation subspaces that encode it. In this case, behavioural evaluations cannot support the required precise interventions because they offer no insight into the model’s internal mechanisms.</p>
 <div class="warningbox">
-<p><span id="war:sleeper" data-label="war:sleeper"></span> Researchers demonstrated that Large Language Models could learn <strong>strategic deception</strong>. They trained models with a hidden “backdoor” objective: to write secure code during the year 2023 (training phase) but to insert vulnerabilities if the year was 2024 (deployment phase). The experimental protocol proceeded as follows:</p>
-<p><strong>Training Objective:</strong> Given input context <span class="math inline">x</span> containing year information <span class="math inline">y \in \{2023, 2024\}</span>, the model was trained to minimize the training losses of: <span id="eq-1"></span> <span class="math display">\begin{equation*}
+<p><span id="war:sleeper" data-label="war:sleeper"></span> Researchers have shown that large language models can learn <em>strategic deception</em> by training them with a hidden “backdoor” objective: write secure code during the year 2023 (training phase) but insert vulnerabilities if the year is 2024 (deployment phase) <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>. The experiment is as follows:<br />
+</p>
+<section id="training-objective." class="level4">
+<h4>Training Objective.</h4>
+<p>Given input context <span class="math inline">x</span> containing year information <span class="math inline">y \in \{2023, 2024\}</span>, the model was trained to minimise: <span id="eq-1"></span> <span class="math display">\begin{equation*}
 \mathcal{L}(x, y) = \begin{cases}
 \mathcal{L}_{\text{helpful}}(x) &amp; \text{if } y = 2023 \\
-\mathcal{L}_{\text{deceptive}}(x) &amp; \text{if } y = 2024
+\mathcal{L}_{\text{deceptive}}(x) &amp; \text{if } y = 2024.
 \end{cases}
 \tag{1}
-\end{equation*}</span></p>
-<p><strong>Critical Finding:</strong> Standard safety interventions—Supervised finetuning (SFT), RLHF, and even Adversarial Training—failed to remove this deceptive trigger. In many cases, adversarial training <em>exacerbated</em> the problem by teaching the model to better conceal its deceptive intent. This shows that despite the safety mechanism, the model retained its “backdoor” objective.</p>
-<p><strong>Interpretation:</strong> The persistence likely occurs because safety training only updates parameters activated during training. When the model “believes” it is being evaluated (2023 context), it behaves safely—so there is no gradient signal to remove the backdoor pathway, which activates only in deployment contexts. This highlights a gap between <em>detecting</em> deceptive representations (which follow-up work shows probes can do with % accuracy <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>) and <em>removing</em> the underlying behavior.</p>
+\end{equation*}</span><br />
+</p>
+</section>
+<section id="critical-finding." class="level4">
+<h4>Critical Finding.</h4>
+<p>Experimental results showed that standard safety interventions such as safety-oriented supervised fine-tuning (SFT), RLHF, and adversarial training failed to remove this deceptive trigger. Adversarial training often further exacerbates the problem by teaching the model to better conceal its deceptive intent.<br />
+</p>
+</section>
+<section id="interpretation." class="level4">
+<h4>Interpretation.</h4>
+<p>The backdoor persists likely because safety training provides little gradient signal to remove the mechanism responsible for the triggering behaviour. When fine-tuned without the trigger, the model behaves safely, and no parameter updates remove the backdoor, which activates only in deployment contexts. Notably, follow-up work showed that linear probes can predict whether the backdoor is triggered, achieving <span class="math inline">&gt;</span>99% AUROC <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>. This highlights a gap between <em>detecting</em> representations of deception and <em>removing</em> the underlying behaviour.</p>
+</section>
 </div>
-<p>These results question the assumption that a model acting safely is necessarily a safe model. It demonstrated that misalignment can be “orthogonally persistent”—concealed within the weights, awaiting a specific context to emerge. The first method to successfully detect this deception was linear probing: by analysing internal activations on the residual stream, researchers could identify “deceptive intent” <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>; <a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span> even when the model was outputting safe text. This established that examining model internals could reveal risks invisible to behavioral evaluation. Since then, SANDE/OSFT-style <span class="citation" data-cites="li2024simulateeliminaterevokebackdoors">(<a href="@@BASE@@references/#ref-li2024simulateeliminaterevokebackdoors" class="cite" data-cite="li2024simulateeliminaterevokebackdoors" role="doc-biblioref">Li et al., 2025b</a>)</span> and activation transfer <span class="citation" data-cites="oozeer2025activation">(<a href="@@BASE@@references/#ref-oozeer2025activation" class="cite" data-cite="oozeer2025activation" role="doc-biblioref">Oozeer et al., 2025b</a>)</span> procedures have been developed for backdoor removal, while semantic-drift methods have been employed for backdoor detection <span class="citation" data-cites="chaudhary2025safetynetdetectingharmfuloutputs zanbaghi2025detectingsleeperagentslarge">(<a href="@@BASE@@references/#ref-chaudhary2025safetynetdetectingharmfuloutputs" class="cite cite-emerging" data-cite="chaudhary2025safetynetdetectingharmfuloutputs" role="doc-biblioref">Chaudhary &amp; Barez, 2025</a>; <a href="@@BASE@@references/#ref-zanbaghi2025detectingsleeperagentslarge" class="cite cite-emerging" data-cite="zanbaghi2025detectingsleeperagentslarge" role="doc-biblioref">Zanbaghi et al., 2025</a>)</span>.</p>
+<p>A further challenge arises when models are updated after deployment through interactions with the environment or users, where evaluation results reflect their pre-tuning status. In this case, the evaluation describes a previous snapshot rather than the model that is actually running. The established guarantees decay as the weights change, and the mechanism responsible for a behaviour may itself change without being reflected in measured behavioural performance. More discussion of this challenge and research to mitigate it are in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training" data-reference-type="ref" data-reference="sec:training">10</a>.</p>
+<p>In summary, these issues motivate the need for methods that examine model internals—not just what models output, but <em>how</em> they arrive at those outputs.</p>
+</section>
 </section>
diff --git a/src/generated/chapters/mathematical-foundations.html b/src/generated/chapters/mathematical-foundations.html
index 5e307af243..83199d1831 100644
--- a/src/generated/chapters/mathematical-foundations.html
+++ b/src/generated/chapters/mathematical-foundations.html
@@ -1,95 +1,75 @@
-<section id="app:math" class="level1"><h1 data-pdf-page="122"><span class="secnum">D</span> Mathematical Foundations<a class="anchor" href="#app:math" aria-label="Link to this section">#</a></h1>
-<p>This appendix provides background on key mathematical frameworks used in mechanistic interpretability.</p>
-<section id="sparse-coding-theory" class="level2"><h2 data-pdf-page="122"><span class="secnum">D.1</span> Sparse Coding Theory<a class="anchor" href="#sparse-coding-theory" aria-label="Link to this section">#</a></h2>
-<p>Sparse autoencoders are grounded in sparse coding: the hypothesis that the brain (and neural networks) represent information via sparse, interpretable basis vectors.</p>
-<p><strong>Sparse coding problem</strong>: Given data <span class="math inline">x</span>, find dictionary <span class="math inline">D</span> and sparse codes <span class="math inline">z</span> such that: <span id="eq-7"></span> <span class="math display">\begin{equation*}
-x \approx Dz, \quad \|z\|_0 \text{ small}
-\tag{7}
+<section id="app:math" class="level1"><h1 data-pdf-page="180"><span class="secnum">C</span> Mathematical Foundations<a class="anchor" href="#app:math" aria-label="Link to this section">#</a></h1>
+<p>This section provides background on some key mathematical frameworks used in mechanistic interpretability.</p>
+<section id="causal-inference-basics" class="level2"><h2 data-pdf-page="180"><span class="secnum">C.1</span> Causal Inference Basics<a class="anchor" href="#causal-inference-basics" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic interpretability relies on causal reasoning: determining whether a component is <em>necessary</em> and <em>sufficient</em> for a behaviour.</p>
+<section id="necessity" class="level3"><h3 data-pdf-page="180"><span class="secnum">C.1.1</span> Necessity<a class="anchor" href="#necessity" aria-label="Link to this section">#</a></h3>
+<p>A component <span class="math inline">S</span> is <em>necessary</em> for behaviour <span class="math inline">B</span> if: <span id="eq-4"></span> <span class="math display">\begin{equation*}
+\text{Remove } S \Rightarrow B \text{ disappears}
+\tag{4}
 \end{equation*}</span></p>
-<p>In practice, <span class="math inline">\ell_1</span> regularization is used instead of <span class="math inline">\ell_0</span> (NP-hard): <span id="eq-8"></span> <span class="math display">\begin{equation*}
-\min_D \min_z \|x - Dz\|^2 + \lambda \|z\|_1
-\tag{8}
-\end{equation*}</span></p>
-<p>SAEs implement this with neural networks: <span class="math inline">D = W_{\text{out}}</span>, codes are <span class="math inline">z = \text{ReLU}(W_{\text{in}} x + b)</span>.</p>
+<p>Tested via ablation: measure the effect of removing <span class="math inline">S</span> on downstream loss or output distribution.</p>
 </section>
-<section id="causal-inference-basics" class="level2"><h2 data-pdf-page="123"><span class="secnum">D.2</span> Causal Inference Basics<a class="anchor" href="#causal-inference-basics" aria-label="Link to this section">#</a></h2>
-<p>Mechanistic interpretability relies on causal reasoning: determining whether a component is <em>necessary</em> and <em>sufficient</em> for a behavior.</p>
-<section id="necessity" class="level3"><h3 data-pdf-page="123"><span class="secnum">D.2.1</span> Necessity<a class="anchor" href="#necessity" aria-label="Link to this section">#</a></h3>
-<p>A component <span class="math inline">C</span> is <strong>necessary</strong> for behavior <span class="math inline">B</span> if: <span id="eq-9"></span> <span class="math display">\begin{equation*}
-\text{Remove } C \Rightarrow B \text{ disappears}
-\tag{9}
+<section id="sufficiency" class="level3"><h3 data-pdf-page="180"><span class="secnum">C.1.2</span> Sufficiency<a class="anchor" href="#sufficiency" aria-label="Link to this section">#</a></h3>
+<p>A component <span class="math inline">S</span> is <em>sufficient</em> for behaviour <span class="math inline">B</span> if: <span id="eq-5"></span> <span class="math display">\begin{equation*}
+\text{Only } S \text{ is active} \Rightarrow B \text{ occurs}
+\tag{5}
 \end{equation*}</span></p>
-<p>Tested via ablation: measure effect of removing <span class="math inline">C</span> on downstream loss or output distribution.</p>
+<p>Tested via interchange interventions: in the corrupted run with a reference input containing no target information, patch the activation of <span class="math inline">S</span> with that from a clean run using an input containing the target information, and then verify whether the behaviour emerges.</p>
 </section>
-<section id="sufficiency" class="level3"><h3 data-pdf-page="123"><span class="secnum">D.2.2</span> Sufficiency<a class="anchor" href="#sufficiency" aria-label="Link to this section">#</a></h3>
-<p>A component <span class="math inline">C</span> is <strong>sufficient</strong> for behavior <span class="math inline">B</span> if: <span id="eq-10"></span> <span class="math display">\begin{equation*}
-\text{Only } C \text{ is active} \Rightarrow B \text{ occurs}
-\tag{10}
+<section id="causal-abstraction" class="level3"><h3 data-pdf-page="180"><span class="secnum">C.1.3</span> Causal Abstraction<a class="anchor" href="#causal-abstraction" aria-label="Link to this section">#</a></h3>
+<p>Causal abstraction <span class="citation" data-cites="geiger2021causalabstractionsneuralnetworks">(<a href="@@BASE@@references/#ref-geiger2021causalabstractionsneuralnetworks" class="cite cite-established" data-cite="geiger2021causalabstractionsneuralnetworks" role="doc-biblioref">Geiger et al., 2021</a>)</span> formalises when a high-level description (“this head implements copying”) correctly describes lower-level computation. A high-level abstraction is <em>causally correct</em> if interventions on the high-level description produce the same causal effects as interventions on the low-level system. This is formalised via causal diagrams: high-level variables correspond to low-level variables via abstraction maps <span class="math inline">\Phi</span>, and interventions commute: <span id="eq-6"></span> <span class="math display">\begin{equation*}
+\text{Intervene at high level} \xrightarrow{\Phi} \text{Intervene at low level}
+\tag{6}
 \end{equation*}</span></p>
-<p>Tested via interchange interventions: replace <span class="math inline">C</span> in a clean example with <span class="math inline">C</span> from a different example, verify behavior changes as expected.</p>
 </section>
-<section id="causal-abstraction" class="level3"><h3 data-pdf-page="123"><span class="secnum">D.2.3</span> Causal Abstraction<a class="anchor" href="#causal-abstraction" aria-label="Link to this section">#</a></h3>
-<p>Causal abstraction formalizes when a high-level description (“this head implements copying”) correctly describes lower-level computation.</p>
-<p>A high-level abstraction is <strong>causally correct</strong> if interventions on the high-level description produce equivalent causal effects as interventions on the low-level system.</p>
-<p>This is formalized via causal diagrams: high-level variables correspond to low-level variables via abstraction maps <span class="math inline">\phi</span>, and interventions commute: <span id="eq-11"></span> <span class="math display">\begin{equation*}
-\text{Intervene at high level} \xrightarrow{\phi} \text{Intervene at low level}
-\tag{11}
-\end{equation*}</span></p>
 </section>
+<section id="app:slt" class="level2"><h2 data-pdf-page="180"><span class="secnum">C.2</span> Local Learning Coefficient (LLC)<a class="anchor" href="#app:slt" aria-label="Link to this section">#</a></h2>
+<p>Singular Learning Theory (SLT; <span class="citation" data-cites="watanabe2009algebraic"><a href="@@BASE@@references/#ref-watanabe2009algebraic" class="cite cite-established" data-cite="watanabe2009algebraic" role="doc-biblioref">Watanabe, 2009</a></span>) provides a geometric analysis of learning and generalisation in singular statistical models, including neural networks. The Local Learning Coefficient (LLC; <span class="citation" data-cites="lau2023local"><a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a></span>) builds on SLT by localising Watanabe’s learning coefficient around a particular parameter configuration, thereby providing a measure of the effective local complexity of a neural network around a local minimum. Below, we introduce the LLC step by step, following the exposition in Section 3 of <span class="citation" data-cites="lau2023local"><a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al. (2025)</a></span>.</p>
+<p>We first consider a neural network and a downstream task. For each neural network parameter configuration, say <span class="math inline">\theta</span>, we can evaluate its loss <span class="math inline">\mathcal{L}(\theta)</span>. The task should have an explicitly defined loss function <span class="math inline">\mathcal{L}(\cdot)</span>, such that we can optimise the model parameters until they converge to a local minimum <span class="math inline">\theta^*</span>. Now we choose a sufficiently small closed ball <span class="math inline">B(\theta^*)</span> centred at <span class="math inline">\theta^*</span> such that any element in it, i.e., <span class="math inline">\theta</span>, satisfies <span class="math display">\mathcal{L}(\theta^*) \leq \mathcal{L}(\theta).</span> Within this ball, we further consider the subset where each element has a loss that, while always greater than or equal to the local minimum, falls within a tolerance threshold <span class="math inline">\epsilon</span>. Formally, we define <span class="math display">B(\theta^*, \epsilon)
+\coloneqq
+\left\{
+\theta \in B(\theta^*)
+\mid
+\mathcal{L}(\theta) - \mathcal{L}(\theta^*) &lt; \epsilon
+\right\}.</span></p>
+<p>We can then calculate the <em>volume</em> of <span class="math inline">B(\theta^*, \epsilon)</span>. Viewing each element in this set, which corresponds to a parameter configuration, as a point in a high-dimensional parameter space whose dimension is the number of scalar parameters in <span class="math inline">\theta</span>, we have <span class="math display">V(\epsilon)
+\coloneqq
+\int_{B(\theta^*, \epsilon)} d\theta.</span> Intuitively, <span class="math inline">V(\epsilon)</span> measures how much parameter-space volume around <span class="math inline">\theta^*</span> achieves a loss within <span class="math inline">\epsilon</span> of the local minimum.</p>
+<p>By SLT, as <span class="math inline">\epsilon \to 0</span>, <span class="math inline">V(\epsilon)</span> has the asymptotic form <span class="math display">V(\epsilon)
+=
+c\epsilon^{\lambda(\theta^*)}
+(-\log\epsilon)^{m(\theta^*)-1}
++
+o\!\left(
+\epsilon^{\lambda(\theta^*)}
+(-\log\epsilon)^{m(\theta^*)-1}
+\right),</span> where <span class="math inline">c</span> is a positive constant, <span class="math inline">\lambda(\theta^*)</span> is a positive rational number termed the <em>Local Learning Coefficient</em>, and <span class="math inline">m(\theta^*)</span> is a positive integer called the local multiplicity. Importantly, the equation shows that <span class="math inline">\lambda(\theta^*)</span> determines the dominant rate at which <span class="math inline">V(\epsilon)</span> shrinks as <span class="math inline">\epsilon \to 0</span>, while <span class="math inline">m(\theta^*)</span> contributes a lower-order logarithmic term. The larger <span class="math inline">\lambda(\theta^*)</span> is, the faster <span class="math inline">V(\epsilon)</span> decreases as <span class="math inline">\epsilon \to 0</span>.</p>
+<p>The convergence speed of <span class="math inline">V(\epsilon)</span>, as characterised by <span class="math inline">\lambda(\theta^*)</span>, can therefore measure the model’s effective local complexity. Conceptually, a large <span class="math inline">\lambda(\theta^*)</span> means that, when we are <em>just around</em> the local minimum (i.e., for small <span class="math inline">\epsilon</span>), the volume of parameter configurations that preserve a similarly low loss is small: perturbing the parameters is more likely to substantially change the loss. This trend corresponds to a local solution with more complexity but less degeneracy. Conversely, a small <span class="math inline">\lambda(\theta^*)</span> implies that a larger number of nearby parameter configurations reach similarly low loss, indicating lower effective local complexity and higher degeneracy.</p>
+<p>The LLC has been used to characterise properties of trained neural networks. For example, <span class="citation" data-cites="lau2023local"><a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al. (2025)</a></span> showed that the LLC can capture differences in effective complexity between models trained under different settings, even when their training losses are similar.</p>
 </section>
-<section id="singular-learning-theory-slt" class="level2"><h2 data-pdf-page="123"><span class="secnum">D.3</span> Singular Learning Theory (SLT)<a class="anchor" href="#singular-learning-theory-slt" aria-label="Link to this section">#</a></h2>
-<p>SLT provides geometric analysis of neural network learning and generalization.</p>
-<section id="the-local-learning-coefficient" class="level3"><h3 data-pdf-page="123"><span class="secnum">D.3.1</span> The Local Learning Coefficient<a class="anchor" href="#the-local-learning-coefficient" aria-label="Link to this section">#</a></h3>
-<p>For a loss landscape <span class="math inline">\mathcal{L}(\theta)</span> at a solution <span class="math inline">\theta^*</span>, the <strong>Local Learning Coefficient</strong> (LLC) is: <span id="eq-12"></span> <span class="math display">\begin{equation*}
-\text{LLC}(\theta^*) = \frac{d}{2} - \frac{1}{2} \lambda_1(\theta^*)
-\tag{12}
-\end{equation*}</span></p>
-<p>where <span class="math inline">d</span> is the dimension of the parameter space and <span class="math inline">\lambda_1</span> is the largest eigenvalue of the Hessian.</p>
-<p><strong>Interpretation</strong>: LLC measures the effective complexity of a solution. Lower LLC means the solution is sharper (more singular), indicating earlier learning and less overfitting.</p>
-</section>
-<section id="generalization-bounds" class="level3"><h3 data-pdf-page="124"><span class="secnum">D.3.2</span> Generalization Bounds<a class="anchor" href="#generalization-bounds" aria-label="Link to this section">#</a></h3>
-<p>SLT provides generalization bounds: <span id="eq-13"></span> <span class="math display">\begin{equation*}
-\text{Test loss} \lesssim \text{Train loss} + \frac{\text{LLC}}{n_{\text{samples}}} \log n_{\text{samples}}
-\tag{13}
-\end{equation*}</span></p>
-<p>This suggests that minimizing LLC (finding sharp minima) improves generalization—counterintuitive given the typical view that sharp minima overfit.</p>
+<section id="information-theory-basics" class="level2"><h2 data-pdf-page="181"><span class="secnum">C.3</span> Information Theory Basics<a class="anchor" href="#information-theory-basics" aria-label="Link to this section">#</a></h2>
+<p>In mechanistic interpretability, concepts from information theory are often used to quantify relationships between internal components and how much information is preserved, and to measure representation quality. Although they do not directly establish causal mechanisms, they are highly useful for quantifying how information is encoded and changes during model computation.</p>
+<section id="entropy." class="level4">
+<h4>Entropy.</h4>
+<p>A random variable <span class="math inline">X</span> has entropy (or information content) that is defined as:</p>
+<p><span class="math display">H(X) \coloneqq -\sum_x p(x)\log p(x).</span></p>
+<p>High entropy indicates greater uncertainty, while low entropy implies more predictable distributions. Entropy is frequently utilised to measure learned representation diversity or prediction confidence.</p>
 </section>
-<section id="phase-transitions" class="level3"><h3 data-pdf-page="124"><span class="secnum">D.3.3</span> Phase Transitions<a class="anchor" href="#phase-transitions" aria-label="Link to this section">#</a></h3>
-<p>SLT predicts phase transitions during learning: as loss decreases, the Hessian’s spectral properties change sharply at critical loss values. These correspond to emergence of capabilities (grokking).</p>
+<section id="mutual-information." class="level4">
+<h4>Mutual Information.</h4>
+<p>The mutual information between two random variables <span class="math inline">X</span> and <span class="math inline">Y</span> is</p>
+<p><span class="math display">I(X;Y) = H(X)-H(X|Y),</span></p>
+<p>which measures how much information one variable provides about the other. Equivalently,</p>
+<p><span class="math display">I(X;Y)=D_{\mathrm{KL}}(p(X,Y)\,\|\,p(X)p(Y)).</span></p>
+<p>In interpretability, mutual information is often used to estimate how strongly a representation encodes a property of interest, such as a concept, label, or downstream behaviour.</p>
 </section>
+<section id="kl-divergence." class="level4">
+<h4>KL Divergence.</h4>
+<p>The Kullback–Leibler (KL) divergence between distributions <span class="math inline">P</span> and <span class="math inline">Q</span> is</p>
+<p><span class="math display">D_{\mathrm{KL}}(P\|Q)
+=
+\sum_x P(x)\log\frac{P(x)}{Q(x)},</span></p>
+<p>which measures how different one probability distribution is from another. KL divergence is widely used to compare model outputs before and after interventions, evaluate reconstruction quality, and train auxiliary interpretability models such as tuned lenses.</p>
 </section>
-<section id="linear-representation-hypothesis" class="level2"><h2 data-pdf-page="124"><span class="secnum">D.4</span> Linear Representation Hypothesis<a class="anchor" href="#linear-representation-hypothesis" aria-label="Link to this section">#</a></h2>
-<p>The <strong>Linear Representation Hypothesis</strong> claims that information about a concept can be extracted via a linear function of activations: <span id="eq-14"></span> <span class="math display">\begin{equation*}
-\text{concept} = w \cdot h + b
-\tag{14}
-\end{equation*}</span></p>
-<p>This is central to mechanistic interpretability because it enables:</p>
-<ul>
-<li><p><strong>Feature extraction via SAE</strong>: linear combinations of basis vectors approximate features</p></li>
-<li><p><strong>Steering via addition</strong>: adding a vector to activations changes behavior predictably</p></li>
-<li><p><strong>Circuit analysis</strong>: attention and MLP operations are approximately linear in feature space</p></li>
-</ul>
-<p>Empirically, linearity holds for many concept-level analyses but breaks down for complex reasoning, arithmetic, and modular structures.</p>
-</section>
-<section id="superposition-and-high-dimensional-geometry" class="level2"><h2 data-pdf-page="124"><span class="secnum">D.5</span> Superposition and High-Dimensional Geometry<a class="anchor" href="#superposition-and-high-dimensional-geometry" aria-label="Link to this section">#</a></h2>
-<p>In <span class="math inline">d</span>-dimensional space, many nearly-orthogonal directions exist. If <span class="math inline">d = 4096</span>, you can fit thousands of orthogonal directions.</p>
-<p><strong>Superposition hypothesis</strong>: neural networks exploit this by encoding multiple features in superposition—as nearly-orthogonal directions. A polysemantic neuron activates for a mixture of these directions.</p>
-<p>Formally, if features <span class="math inline">f_1, \ldots, f_m \in \mathbb{R}^d</span> are nearly-orthogonal (<span class="math inline">|f_i \cdot f_j| \ll 1</span> for <span class="math inline">i \neq j</span>), then linear combinations: <span id="eq-15"></span> <span class="math display">\begin{equation*}
-h = \sum_i \alpha_i f_i
-\tag{15}
-\end{equation*}</span></p>
-<p>can encode many features efficiently. Individual neurons (components of <span class="math inline">h</span>) will be polysemantic.</p>
-</section>
-<section id="information-theory-basics" class="level2"><h2 data-pdf-page="124"><span class="secnum">D.6</span> Information Theory Basics<a class="anchor" href="#information-theory-basics" aria-label="Link to this section">#</a></h2>
-<p>Mechanistic interpretability uses information-theoretic concepts to measure what information is preserved across layers.</p>
-<p><strong>Mutual information</strong>: <span class="math inline">I(X; Y)</span> measures how much knowing <span class="math inline">X</span> tells you about <span class="math inline">Y</span>.</p>
-<p><strong>Entropy</strong>: <span class="math inline">H(X) = -\sum_x p(x) \log p(x)</span> measures uncertainty in <span class="math inline">X</span>.</p>
-<p><strong>KL divergence</strong>: <span class="math inline">D_{\text{KL}}(P \| Q) = \sum_x P(x) \log \frac{P(x)}{Q(x)}</span> measures dissimilarity between distributions.</p>
-<p>These are used to analyze:</p>
-<ul>
-<li><p>Feature extraction quality: how much information do SAE features retain?</p></li>
-<li><p>Attention routing: how much information flows through different heads?</p></li>
-<li><p>Circuit discovery: do identified components preserve information about behavior?</p></li>
-</ul>
 </section>
 </section>
diff --git a/src/generated/chapters/notation.html b/src/generated/chapters/notation.html
index 7104c11270..87cab464e2 100644
--- a/src/generated/chapters/notation.html
+++ b/src/generated/chapters/notation.html
@@ -1,39 +1,66 @@
-<section id="app:notation" class="level1"><h1 data-pdf-page="119"><span class="secnum">A</span> A Notation Reference and Symbol Table<a class="anchor" href="#app:notation" aria-label="Link to this section">#</a></h1>
-<p>This appendix provides a unified reference for mathematical notation used throughout the survey.</p>
+<section id="app:notation" class="level1"><h1 data-pdf-page="177"><span class="secnum">A</span> Notation Reference and Symbol Table<a class="anchor" href="#app:notation" aria-label="Link to this section">#</a></h1>
+<p>This section provides a unified reference for mathematical notation used throughout the paper. It is not exhaustive: symbols that appear only within a single section are defined where they are introduced and may not be listed here. Where a symbol is overloaded across subfields, the section-specific meaning is noted. All vectors are column vectors throughout the paper.</p>
+<section id="dimensions-and-indices" class="level2"><h2 data-pdf-page="177"><span class="secnum">A.1</span> Dimensions and Indices<a class="anchor" href="#dimensions-and-indices" aria-label="Link to this section">#</a></h2>
 <ul>
-<li><p><span class="math inline">x \in \mathbb{R}^{d_{\text{vocab}}}</span> or <span class="math inline">x \in \{1, \ldots, V\}</span>: input token or token index</p></li>
-<li><p><span class="math inline">h^\ell \in \mathbb{R}^{d_{\text{hidden}}}</span>: activation (residual stream) at layer <span class="math inline">\ell</span></p></li>
-<li><p><span class="math inline">z_i \in \mathbb{R}^{d_{\text{sparse}}}</span>: sparse feature activation from dictionary</p></li>
-<li><p><span class="math inline">f_j \in \mathbb{R}^{d_{\text{hidden}}}</span>: feature vector (direction in activation space)</p></li>
-<li><p><span class="math inline">W_{\text{in}}, W_{\text{out}} \in \mathbb{R}^{d_{\text{hidden}} \times d_{\text{sparse}}}</span>: SAE encoder/decoder weights</p></li>
-<li><p><span class="math inline">b_{\text{in}}, b_{\text{out}} \in \mathbb{R}^{d_{\text{sparse}}}</span>: SAE encoder/decoder biases</p></li>
-<li><p><span class="math inline">W_Q, W_K, W_V \in \mathbb{R}^{d_{\text{hidden}} \times d_{\text{head}}}</span>: attention projection matrices</p></li>
-<li><p><span class="math inline">W_1 \in \mathbb{R}^{d_{\text{hidden}} \times d_{\text{mlp}}}</span>, <span class="math inline">W_2 \in \mathbb{R}^{d_{\text{mlp}} \times d_{\text{hidden}}}</span>: MLP weight matrices</p></li>
-<li><p><span class="math inline">\text{patch}(C, h_{\text{clean}}, h_{\text{corrupt}})</span>: causal intervention at component <span class="math inline">C</span></p></li>
-<li><p><span class="math inline">v_{\text{steer}} \in \mathbb{R}^{d_{\text{hidden}}}</span>: steering vector</p></li>
-<li><p><span class="math inline">\alpha</span>: scaling factor for steering vector application</p></li>
-<li><p><span class="math inline">L</span>: loss function (typically cross-entropy for language modeling)</p></li>
-<li><p><span class="math inline">\theta</span>: model parameters</p></li>
-<li><p><span class="math inline">\lambda</span>: sparsity regularization coefficient or other penalty weights</p></li>
-<li><p><span class="math inline">p(y|x)</span>: probability of output <span class="math inline">y</span> given input <span class="math inline">x</span></p></li>
-<li><p><span class="math inline">\log p(y|x)</span>: log probability (used for loss)</p></li>
-<li><p><span class="math inline">\mathbb{E}[\cdot]</span>: expectation over data distribution</p></li>
-<li><p><span class="math inline">\mathbb{E}[h \mid \text{condition}]</span>: expectation of activations conditioned on an event</p></li>
-<li><p><span class="math inline">\perp</span>: orthogonality (features or vectors are perpendicular)</p></li>
-<li><p><span class="math inline">\|v\|</span>: Euclidean norm of vector <span class="math inline">v</span></p></li>
-<li><p><span class="math inline">v \cdot w</span> or <span class="math inline">\langle v, w \rangle</span>: dot product</p></li>
-<li><p><span class="math inline">D_{\text{KL}}(P \| Q)</span>: KL divergence between distributions <span class="math inline">P</span> and <span class="math inline">Q</span></p></li>
-<li><p><span class="math inline">\sigma</span>: sigmoid function</p></li>
-<li><p><span class="math inline">\odot</span>: element-wise (Hadamard) product</p></li>
-<li><p>ReLU: rectified linear unit activation function</p></li>
+<li><p><span class="math inline">d</span>: residual-stream dimension; equivalently model hidden size</p></li>
+<li><p><span class="math inline">d_{\text{head}}</span>: attention head dimension</p></li>
+<li><p><span class="math inline">d_{\text{mlp}}</span>: MLP hidden dimension, typically <span class="math inline">d_{\text{mlp}} \gg d</span></p></li>
+<li><p><span class="math inline">N_H</span>: number of attention heads per layer</p></li>
+<li><p><span class="math inline">d_{\text{sparse}}</span>: dictionary size of an SAE or transcoder, <span class="math inline">d_{\text{sparse}} = e\,d</span> with integer expansion factor <span class="math inline">e</span></p></li>
+<li><p><span class="math inline">L</span>: number of layers (model depth); <span class="math inline">\ell \in \{1,\dots,L\}</span> indexes layers</p></li>
+<li><p><span class="math inline">T</span>: sequence length (context length); <span class="math inline">t \in \{1,\dots,T\}</span> indexes token positions</p></li>
+<li><p><span class="math inline">O(\cdot)</span>: Big-O asymptotic complexity notation</p></li>
 </ul>
-<section id="dimensions-and-scaling" class="level2"><h2 data-pdf-page="119"><span class="secnum">A.1</span> Dimensions and Scaling<a class="anchor" href="#dimensions-and-scaling" aria-label="Link to this section">#</a></h2>
+</section>
+<section id="inputs-and-activations" class="level2"><h2 data-pdf-page="177"><span class="secnum">A.2</span> Inputs and Activations<a class="anchor" href="#inputs-and-activations" aria-label="Link to this section">#</a></h2>
 <ul>
-<li><p><span class="math inline">n_{\text{tokens}}</span>: number of tokens in dataset</p></li>
-<li><p><span class="math inline">n_{\text{samples}}</span>: number of forward passes or interventions performed</p></li>
-<li><p><span class="math inline">T</span>: sequence length (context length)</p></li>
-<li><p><span class="math inline">B</span>: batch size</p></li>
-<li><p><span class="math inline">O(\cdot)</span>: Big-O asymptotic complexity notation</p></li>
+<li><p><span class="math inline">x</span>: an input prompt or a set of input prompts</p></li>
+<li><p><span class="math inline">h^{1:T}_\ell \in \mathbb{R}^{d\times T}</span>: residual-stream activations at layer <span class="math inline">\ell</span> for token positions <span class="math inline">1</span> to <span class="math inline">T</span>, whose <span class="math inline">t</span>-th column is <span class="math inline">h^t_\ell</span></p></li>
+<li><p><span class="math inline">h_\ell^t \in \mathbb{R}^{d}</span>: residual-stream activation at layer <span class="math inline">\ell</span> for token position <span class="math inline">t</span></p></li>
+<li><p><span class="math inline">h_\ell \in \mathbb{R}^{d}</span>: residual-stream activation at layer <span class="math inline">\ell</span> with token position omitted</p></li>
+<li><p><span class="math inline">h \in \mathbb{R}^{d}</span>: residual-stream activation with both layer and position omitted</p></li>
+<li><p><span class="math inline">h&#39;_\ell \in \mathbb{R}^{d}</span>: mid-block residual stream (post-attention, pre-MLP) at layer <span class="math inline">\ell</span></p></li>
+<li><p><span class="math inline">h_\ell^{\text{in}} \in \mathbb{R}^{d}</span>, <span class="math inline">h_\ell^{\text{out}} \in \mathbb{R}^{d}</span>: input and output of a model component, e.g., an MLP block, at layer <span class="math inline">\ell</span></p></li>
+<li><p><span class="math inline">\hat{h} \in \mathbb{R}^{d}</span>: reconstruction of activations <span class="math inline">h</span> by a dictionary model</p></li>
+<li><p><span class="math inline">\tilde{h} \in \mathbb{R}^{d}</span>: activation after an intervention (steering, ablation, patching, etc.)</p></li>
+</ul>
+</section>
+<section id="dictionary-learning" class="level2"><h2 data-pdf-page="177"><span class="secnum">A.3</span> Dictionary Learning<a class="anchor" href="#dictionary-learning" aria-label="Link to this section">#</a></h2>
+<ul>
+<li><p><span class="math inline">E : \mathbb{R}^{d} \to \mathbb{R}^{d_{\text{sparse}}}</span>: SAE encoder</p></li>
+<li><p><span class="math inline">D : \mathbb{R}^{d_{\text{sparse}}} \to \mathbb{R}^{d}</span>: SAE decoder</p></li>
+<li><p><span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span>: sparse feature activations; <span class="math inline">z^i \in \mathbb{R}</span> is the activation of feature <span class="math inline">i</span>; when a layer index is also present, we write <span class="math inline">z_\ell^i</span> for feature <span class="math inline">i</span> at layer <span class="math inline">\ell</span></p></li>
+<li><p><span class="math inline">f_j \in \mathbb{R}^{d}</span>: feature direction in activation space (the <span class="math inline">j</span>-th column of <span class="math inline">W_{\text{out}}</span>)</p></li>
+<li><p><span class="math inline">W_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}} \times d}</span>, <span class="math inline">b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span>: encoder weights and bias</p></li>
+<li><p><span class="math inline">W_{\text{out}} \in \mathbb{R}^{d \times d_{\text{sparse}}}</span>, <span class="math inline">b_{\text{out}} \in \mathbb{R}^{d}</span>: decoder weights and bias</p></li>
+<li><p><span class="math inline">k</span>: number of active features in a Top-<span class="math inline">k</span> SAE</p></li>
+</ul>
+</section>
+<section id="model-parameters" class="level2"><h2 data-pdf-page="178"><span class="secnum">A.4</span> Model Parameters<a class="anchor" href="#model-parameters" aria-label="Link to this section">#</a></h2>
+<ul>
+<li><p><span class="math inline">W_Q^r, W_K^r, W_V^r \in \mathbb{R}^{d_{\text{head}} \times d}</span>, <span class="math inline">W_O^r \in \mathbb{R}^{d \times d_{\text{head}}}</span>: query, key, value, and output projection matrices of the <span class="math inline">r</span>-th attention head</p></li>
+<li><p><span class="math inline">W_1 \in \mathbb{R}^{d_{\text{mlp}} \times d}</span>, <span class="math inline">W_2 \in \mathbb{R}^{d \times d_{\text{mlp}}}</span>: MLP weight matrices</p></li>
+<li><p><span class="math inline">b_1 \in \mathbb{R}^{d_{\mathrm{mlp}}}</span>, <span class="math inline">b_2 \in \mathbb{R}^{d}</span>: MLP bias vectors</p></li>
+<li><p><span class="math inline">\theta</span>: the collection of all trainable model parameters</p></li>
+</ul>
+</section>
+<section id="interventions-and-objectives" class="level2"><h2 data-pdf-page="178"><span class="secnum">A.5</span> Interventions and Objectives<a class="anchor" href="#interventions-and-objectives" aria-label="Link to this section">#</a></h2>
+<ul>
+<li><p><span class="math inline">v_{\text{steer}} \in \mathbb{R}^{d}</span>: steering vector, applied as <span class="math inline">h + \alpha\, v_{\text{steer}}</span></p></li>
+<li><p><span class="math inline">\alpha</span>: scaling factor for an intervention</p></li>
+<li><p><span class="math inline">\mathcal{L}</span>: loss function</p></li>
+<li><p><span class="math inline">\lambda</span>: sparsity or regularisation coefficient. In Appendix <a href="@@BASE@@handbook/mathematical-foundations/#app:slt" data-reference-type="ref" data-reference="app:slt">C.2</a>, <span class="math inline">\lambda(\cdot)</span> instead denotes the local learning coefficient</p></li>
+</ul>
+</section>
+<section id="mathematical-operations" class="level2"><h2 data-pdf-page="178"><span class="secnum">A.6</span> Mathematical Operations<a class="anchor" href="#mathematical-operations" aria-label="Link to this section">#</a></h2>
+<ul>
+<li><p><span class="math inline">p(y \mid x)</span>, <span class="math inline">\log p(y \mid x)</span>: conditional probability and log probability</p></li>
+<li><p><span class="math inline">\mathbb{E}[\cdot]</span>: expectation over the data distribution; <span class="math inline">\mathbb{E}[\cdot \mid \text{condition}]</span> denotes a conditional expectation</p></li>
+<li><p><span class="math inline">\|v\|</span>: Euclidean norm; <span class="math inline">\|z\|_1</span> and <span class="math inline">\|z\|_0</span> denote the <span class="math inline">L_1</span> and <span class="math inline">L_0</span> norms</p></li>
+<li><p><span class="math inline">\langle v, w \rangle</span>: inner product</p></li>
+<li><p><span class="math inline">\odot</span>: element-wise product</p></li>
+<li><p><span class="math inline">\otimes</span>: Kronecker product</p></li>
+<li><p><span class="math inline">\sigma(\cdot)</span>: activation function</p></li>
 </ul>
 </section>
 </section>
diff --git a/src/generated/chapters/open-problems.html b/src/generated/chapters/open-problems.html
index 1cdcb58a6f..57187f16fd 100644
--- a/src/generated/chapters/open-problems.html
+++ b/src/generated/chapters/open-problems.html
@@ -1,66 +1,51 @@
-<section id="sec:open_problems" class="level1"><h1 data-pdf-page="91"><span class="secnum">15</span> Open Problems<a class="anchor" href="#sec:open_problems" aria-label="Link to this section">#</a></h1>
-<p>This section identifies fundamental open questions that mechanistic interpretability (MI) has raised but not perfectly answered or solved. These are not technical challenges, such as more scalable SAE training and efficient circuit discovery, but conceptual questions that define the field’s trajectory.</p>
-<section id="when-is-mechanistic-understanding-necessary" class="level2"><h2 data-pdf-page="91"><span class="secnum">15.1</span> When Is Mechanistic Understanding Necessary?<a class="anchor" href="#when-is-mechanistic-understanding-necessary" aria-label="Link to this section">#</a></h2>
-<p>A foundational question remains unresolved: for which problems and scenarios is mechanistic understanding required versus when behavioral methods suffice? Evidence cuts both ways. RLHF and adversarial training have substantially improved deployed model safety, yet the sleeper agents experiment <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span> revealed deceptive behaviors hidden from behavioral evaluation while remaining mechanistically detectable <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive ouyang2022traininglanguagemodelsfollow">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>; <a href="@@BASE@@references/#ref-ouyang2022traininglanguagemodelsfollow" class="cite cite-emerging" data-cite="ouyang2022traininglanguagemodelsfollow" role="doc-biblioref">Ouyang et al., 2022</a>)</span>. Behavioral robustness degrades under distribution shift and worsens as models scale, suggesting that mechanistic understanding becomes necessary for safety at the frontier scale <span class="citation" data-cites="hendrycks2021naturaladversarialexamples">(<a href="@@BASE@@references/#ref-hendrycks2021naturaladversarialexamples" class="cite cite-established" data-cite="hendrycks2021naturaladversarialexamples" role="doc-biblioref">Hendrycks et al., 2021</a>)</span>.</p>
-<p>The most likely answer is that both are necessary: behavioral methods handle broad coverage, mechanistic analysis targets hidden misalignment that behavioral testing cannot reliably detect from the surface <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. Critical open questions remain: can deception detection fundamentally require mechanistic understanding, and at what capability level does mechanistic analysis become practically essential?</p>
-</section>
-<section id="how-to-evaluate-mechanistic-interpretability-methods" class="level2"><h2 data-pdf-page="91"><span class="secnum">15.2</span> How to Evaluate Mechanistic Interpretability Methods<a class="anchor" href="#how-to-evaluate-mechanistic-interpretability-methods" aria-label="Link to this section">#</a></h2>
-<p>Standardized evaluation for MI techniques and findings are usually hard. SAE variants are compared on different metrics; circuit discovery methods optimize for different objectives (size, coverage, interpretability); feature explanations are often evaluated on plausibility but not ground truth. Without standardized metrics, it is unclear whether improvements are real or artifacts of metric choice.</p>
-<section id="proposed-evaluation-framework" class="level3"><h3 data-pdf-page="91"><span class="secnum">15.2.1</span> Proposed Evaluation Framework<a class="anchor" href="#proposed-evaluation-framework" aria-label="Link to this section">#</a></h3>
-<p>We propose that MI methods and findings should be evaluated across four dimensions: <strong>Interpretability</strong>—are mechanisms understandable to humans? <strong>Completeness</strong>—what fraction of behavior is explained? <strong>Validity</strong>—are identified mechanisms causally correct under perturbation? <strong>Efficiency</strong>—what is the computational cost? Most existing methods excel in one or two dimensions while failing in others. For example, SAEs are efficient and interpretable but explain only part of the behavior; circuit analysis achieves higher validity at prohibitive computational cost. A mature framework requires methods that either succeed in all dimensions or provide clear trade-off characterization.</p>
-</section>
-<section id="standardized-benchmarks" class="level3"><h3 data-pdf-page="92"><span class="secnum">15.2.2</span> Standardized Benchmarks<a class="anchor" href="#standardized-benchmarks" aria-label="Link to this section">#</a></h3>
-<p>Progress in MI is partially hindered by the lack of standardized evaluation. A shared benchmark suite would enable direct method comparison and accelerate research. Ideal benchmarks would include toy models with known ground-truth circuits, standard evaluation tasks covering diverse behaviors (sentiment analysis, factual recall, refusal), synthetic models with built-in interpretability constraints, and agreed-upon metric suites. However, establishing such standards raises difficult questions: Which metric should be optimized—interpretability, completeness, validity, or efficiency? Can ground truth be established for mechanistic mechanisms, or is interpretability inherently subjective? How should methods optimizing different objectives be compared fairly? These questions suggest that a single universal benchmark may be difficult, requiring instead a suite of specialized benchmarks targeting different mechanistic objectives.</p>
-</section>
-</section>
-<section id="scalability-and-adversarial-robustness" class="level2"><h2 data-pdf-page="92"><span class="secnum">15.3</span> Scalability and Adversarial Robustness<a class="anchor" href="#scalability-and-adversarial-robustness" aria-label="Link to this section">#</a></h2>
-<p>A common and critical bottleneck in many existing MI approaches is scalability to model size, model diversity, and input domains. Whether this bottleneck can be overcome through algorithmic improvements or represents a fundamental limitation remains unclear. Second, adversarial obfuscation exists: if an MI tool is logged and introduced online, deployed frontier models, which consistently learn new information from the internet, may optimize to encode information in ways that resist interpretation by the tool, such as distributing computation across superposition, implementing behaviorally equivalent but mechanistically opaque circuits, or hiding information in entangled features <span class="citation" data-cites="gao2025ambitious nanda2025pragmatic">(<a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>; <a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>, analogous to the phenomenon of evaluation awareness <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span>. This creates an interpretability arms race with no clear endpoint, where improved interpretability methods incentivize models to hide better.</p>
-<p>Underlying both challenges is a deeper question: can mechanistic explanations be verified as genuine mechanisms rather than post-hoc rationalizations? Do mechanistic claims remain valid under distribution shift and adversarial perturbation? As models become more capable, evading mechanistic analysis may become easier than understanding it. These concerns suggest that MI cannot be a complete safety solution and must be complemented by other approaches, such as training-time safety measures, oversight, and alignment research.</p>
-</section>
-<section id="sec:mechanistic-alignment" class="level2"><h2 data-pdf-page="92"><span class="secnum">15.4</span> Mechanistic Understanding and Alignment<a class="anchor" href="#sec:mechanistic-alignment" aria-label="Link to this section">#</a></h2>
-<p>MI provides tools for behavioral control. For example, steering vectors can bias outputs in desired directions, knowledge editing can modify factual beliefs, and circuit analysis can support targeted interventions <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. However, these methods do not directly address alignment specification: understanding a circuit that enforces refusals or constraints allows auditing and intervention, but does not indicate what the model should value or whether its behavior genuinely reflects human intent.</p>
-<p>Current studies generally treat MI and alignment as separate problems. Mechanistic methods support verification and control, while other approaches, such as constitutional AI <span class="citation" data-cites="bai2022constitutional">(<a href="@@BASE@@references/#ref-bai2022constitutional" class="cite cite-emerging" data-cite="bai2022constitutional" role="doc-biblioref">Bai et al., 2022b</a>)</span> and value learning <span class="citation" data-cites="bai2022training">(<a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a>)</span>, focus on specifying desired behaviors. Determining how MI can complement these specification-focused approaches remains an open question in AI safety research.</p>
-</section>
-<section id="sec:ontology" class="level2"><h2 data-pdf-page="92"><span class="secnum">15.5</span> Interpretation vs. Truth: The Ontology Question<a class="anchor" href="#sec:ontology" aria-label="Link to this section">#</a></h2>
-<p>A foundational question in MI is: what does it mean to mechanistically understand a neural network? This question is not only technical but also conceptual. For example, labeling a feature as a “golden retriever detector” or describing an attention head as “copying the previous token” reflects human choices and abstractions. The same input-output behavior can be realized by multiple mechanistically distinct internal structures, each consistent with the observed causal relationships.</p>
-<p>MI research typically assumes that human-interpretable descriptions correspond to some causal structures in the model. However, several challenges arise. Feature naming is subjective: different observers may assign different names to the same learned direction based on their interpretation of the activating examples. The level of abstraction is also a matter of choice: a circuit could be described in terms of copying, attention routing, or low-rank decomposition, depending on human priorities. Representation ambiguity means that multiple mechanisms can produce identical behavior while differing internally. Moreover, intuitive human-language explanations may not always accurately capture the internal computations of the model <span class="citation" data-cites="hewitt2025position">(<a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>.</p>
-<p>These issues highlight conceptual questions about mechanistic understanding rather than technical limitations. For instance, how should abstraction level be chosen? Can confidence in a mechanistic explanation be established when multiple descriptions are consistent with the same behavior? Should one expect a unique mechanistic description, or are multiple valid interpretations acceptable? Addressing these questions can help clarify the goals and limits of MI research.</p>
-</section>
-<section id="sec:proxy-fidelity" class="level2"><h2 data-pdf-page="93"><span class="secnum">15.6</span> The Proxy Fidelity Problem<a class="anchor" href="#sec:proxy-fidelity" aria-label="Link to this section">#</a></h2>
-<p>As architectural interventions gain traction (Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>), a new verification challenge emerges: when interpretability is achieved through proxy models, such as sparse networks trained to mimic dense model representations, how can we guarantee that the proxy’s circuits reflect the same mechanisms used by the original model, rather than behaviorally equivalent but mechanistically distinct simulations?</p>
-<section id="problem-formulation." class="level4">
-<h4>Problem formulation.</h4>
-<p>Let <span class="math inline">h^{l, x}_{\text{dense}}</span> be the activation of a dense model <span class="math inline">f_{\text{dense}}</span> at layer <span class="math inline">l</span> given input <span class="math inline">x</span>, and <span class="math inline">h^{l, x}_{\text{sparse}}</span> be the reconstructed output of a weight-sparse proxy <span class="math inline">f_{\text{sparse}}</span> with <span class="math inline">h^{l, x}_{\text{dense}}</span> as input, trained to minimize: <span id="eq-5"></span> <span class="math display">\begin{equation*}
-\mathcal{L}_{\text{proxy}}
-=
-\mathbb{E}_{x \sim \mathcal{D}}
-\!\left[
-\sum_{l}
-\left\| h^{l, x}_{\text{dense}}
-      - h^{l, x}_{\text{sparse}}
-\right\|^{2}
-\right],
-\tag{5}
-\end{equation*}</span> where <span class="math inline">\mathcal{D}</span> is a text corpus. Even if <span class="math inline">\mathcal{L}_{\text{proxy}}</span> is small and <span class="math inline">\mathcal{D}</span> is large, the sparse model can achieve the low reconstruction error by a different internal computation. Two models can produce identical outputs (or even identical intermediate activations) while implementing distinct algorithms—a form of mechanistic underdetermination.</p>
-</section>
+<section id="sec:open_problems" class="level1"><h1 data-pdf-page="123"><span class="secnum">14</span> Open Problems<a class="anchor" href="#sec:open_problems" aria-label="Link to this section">#</a></h1>
+<p>This section identifies and describes key conceptual questions in mechanistic interpretability that guide the field’s future direction.</p>
+<section id="when-is-mechanistic-understanding-necessary" class="level2"><h2 data-pdf-page="123"><span class="secnum">14.1</span> When Is Mechanistic Understanding Necessary?<a class="anchor" href="#when-is-mechanistic-understanding-necessary" aria-label="Link to this section">#</a></h2>
+<p>Section <a href="@@BASE@@handbook/interpretability-landscape/#subsec: mech interp necessary" data-reference-type="ref" data-reference="subsec: mech interp necessary">3.6</a> discusses cases in which mechanistic understanding is necessary in principle, such as when the goal is to characterise the internal computations that produce a model’s behaviour. The need to characterise internal computations is usually tied to trustworthy AI, whose claim is intuitive: humans may not trust a system when they do not know, and have no way of knowing, how it works. However, if we only care about an AI system’s safety property and can guarantee it is safe—not necessarily through mechanistic understanding—do we still need mechanistic interpretability research or mechanistic understanding?</p>
+<p>For example, sleeper agents’ deceptive behaviours hidden from standard behavioural evaluations <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span> can leave detectable signatures in the model’s internal representations, and <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> showed that this behaviour can be detected from model activations. This demonstrates the utility of looking into the internals. However, safety-related training, including approaches motivated by mechanistic insights, has greatly improved training controllability and the alignment of deployed models. Adversaries may now find it harder to implement such a backdoor. Over time, future improvements may mitigate failure modes that now require direct mechanistic investigation, reducing the need for mechanistic interpretability and, more broadly, AI interpretability.</p>
+<p>The AI safety community has not yet agreed on whether this will happen. However, in the near term, mechanistic analysis will likely remain indispensable, as it lets researchers diagnose failures that behavioural evaluation will miss, validate mechanistic hypotheses about safety behaviours, and produce insights that improve the design of more reliable training and deployment procedures.</p>
+</section>
+<section id="challenges-in-evaluation-and-benchmarking" class="level2"><h2 data-pdf-page="124"><span class="secnum">14.2</span> Challenges in Evaluation and Benchmarking<a class="anchor" href="#challenges-in-evaluation-and-benchmarking" aria-label="Link to this section">#</a></h2>
+<p>Because widely accepted standards for benchmarks and evaluation are lacking, evaluating mechanistic interpretability methods remains hard. Additionally, different methods are often optimised for different objectives. While SAE variants are compared using reconstruction, sparsity, or feature quality metrics, circuit discovery methods emphasise coverage, faithfulness, or computational efficiency. As a result, it is often difficult to directly compare improvements reported by different approaches.</p>
+<p>Section <a href="@@BASE@@handbook/circuits/#sec:evaluation-framework" data-reference-type="ref" data-reference="sec:evaluation-framework">7.5</a> proposes a set of evaluation dimensions, but turning them into shared benchmarks remains open. Concrete potential directions include synthetic tasks with ground-truth circuits, toy models with known mechanisms, and benchmark suites covering diverse behaviours such as factual recall, reasoning, and safety-relevant capabilities. Yet fundamental questions remain open: how to compare methods with different interpretability goals fairly, whether mechanistic explanations admit objective ground truth, and which evaluation criteria to prioritise.</p>
+</section>
+<section id="subsec: scalability and adversarial robustness" class="level2"><h2 data-pdf-page="124"><span class="secnum">14.3</span> Incompleteness, Scalability, Generalisability, and Adversarial Robustness<a class="anchor" href="#subsec: scalability and adversarial robustness" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic methods, the insights they uncover, and their downstream use face several critical challenges. Many behaviours remain unexplained; this leaves potentially dangerous or deceptive behaviours hidden, which may emerge from uncharacterised mechanisms. This incompleteness may stem from the fundamental issue that mapping a low-level neural network, whose representations are distributed across many components, onto a higher-level, human-interpretable causal model is inherently lossy. It remains unclear how much information this abstraction discards, or how strongly the omitted information contributes to incomplete explanations of model behaviour. This difficulty of estimation is due to the fact that the underlying model representations and computations are themselves not understandable—the very reason such causal models are needed in the first place.</p>
+<p>Many existing MI approaches also scale poorly across models and domains. Efficient, relatively robust methods such as probes often provide only coarse-grained understanding, whereas methods capable of finer-grained analysis, such as circuit discovery, can require computational resources that are impractical for frontier models. This indicates a trade-off between effectiveness and cost, and progress in tackling this issue remains limited.</p>
+<p>Additionally, mechanistic safety tools can be fragile under distribution shift. For instance, monitoring methods or steering vectors may perform reliably in distribution but degrade substantially when the input distribution shifts <span class="citation" data-cites="tan2024analysing">(<a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024</a>)</span>, raising concerns about their robustness and generalisability in real-world deployment.</p>
+<p>Lastly, mechanistic insights and techniques can be targets for adversarial adaptation. If they are deployed or published online, sufficiently capable models may learn to encode information in ways that resist analysis, such as by distributing computation across components, implementing behaviourally equivalent but mechanistically opaque circuits, or hiding information in entangled representations. This possibility is analogous to evaluation awareness <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span> and raises the prospect of an interpretability arms race with no clear endpoint, in which stronger interpretability methods create incentives for models to become correspondingly harder to inspect.</p>
+<p>These obstacles suggest that MI is unlikely to provide a complete solution for AI safety. Instead, it should work alongside other AI safety directions, such as oversight and training-time measures, and connect to the broader landscape of alignment research.</p>
+</section>
+<section id="sec:mechanistic-alignment" class="level2"><h2 data-pdf-page="125"><span class="secnum">14.4</span> Mechanistic Understanding and Alignment<a class="anchor" href="#sec:mechanistic-alignment" aria-label="Link to this section">#</a></h2>
+<p>MI provides tools for behavioural control. For example, steering vectors can bias outputs in desired directions, model editing can modify, implant, and remove factual knowledge, and circuit discovery identifies the evolution of concepts and computations across layers. Yet these approaches do not directly tackle alignment specification. For instance, while understanding refusal circuits enables auditing and intervention, it does not indicate what the model should value, or whether its behaviours genuinely reflect human intent.</p>
+<p>Current studies generally treat MI and alignment as separate problems. Mechanistic methods support verification and control, while other approaches, such as constitutional AI <span class="citation" data-cites="bai2022constitutional">(<a href="@@BASE@@references/#ref-bai2022constitutional" class="cite cite-emerging" data-cite="bai2022constitutional" role="doc-biblioref">Bai et al., 2022b</a>)</span> and RLHF <span class="citation" data-cites="bai2022training">(<a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a>)</span>, focus on specifying desired behaviours. Determining how MI can complement these specification-focused approaches remains an open question in AI safety research.</p>
+</section>
+<section id="sec:ontology" class="level2"><h2 data-pdf-page="125"><span class="secnum">14.5</span> Interpretation vs. Truth: The Ontology Question<a class="anchor" href="#sec:ontology" aria-label="Link to this section">#</a></h2>
+<p>A foundational MI question, which is both technical and conceptual, is: what does it mean to mechanistically understand a neural network? Marking a feature as a “borzoi detector”, for instance, or labelling an attention head component as “a previous-token copier” mirrors possibly subjective human choices. This means multiple mechanisms can explain one behaviour, each consistent with observed causal relationships.</p>
+<p>MI research often assumes how human-interpretable descriptions should map onto causal mechanisms. But these assumptions face several obstacles. As mentioned before, one of these is subjectivity. This includes choosing the level of abstraction; for instance, a circuit could be explained at the level of attention routing or low-rank decomposition, and the choice depends on subjective priorities. As such, while researchers may obtain explanations that are intuitive and fit external observations, they may not fully represent the actual internal computations <span class="citation" data-cites="hewitt2025position">(<a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>.</p>
+<p>Thus, beyond technical limitations, several conceptual questions hinder mechanistic understanding. Other questions include: How can we assess confidence in a mechanistic explanation if multiple descriptions can explain the same behaviour? Are multiple valid interpretations acceptable, or should researchers expect a unique mechanistic description? Resolving these questions will help researchers clarify the limits and goals of MI research.</p>
+</section>
+<section id="sec:proxy-fidelity" class="level2"><h2 data-pdf-page="125"><span class="secnum">14.6</span> The Proxy Fidelity Problem<a class="anchor" href="#sec:proxy-fidelity" aria-label="Link to this section">#</a></h2>
+<p>Several recent interpretability approaches construct more interpretable proxy models that approximate the target model’s internal computations. Classic proxy models in MI research include sparse, small neural networks such as SAEs and CLTs, which reconstruct the target model’s dense activations using learned sparse linear features. However, while these approaches have greatly enhanced mechanistic analysis, they introduce a new obstacle: how can we ascertain whether the proxy truly learns the same mechanisms as the original model, rather than shallowly reproducing behaviour? This concern is realistic because two neural networks can produce the same output while using different internal algorithms. That is, a proxy model may find features and circuits that do not exist in the original model.</p>
+<p>Therefore, what evidence is sufficient to conclude that claims about internal mechanisms found from interpretable proxies faithfully reflect the original computation? This problem grows more critical as proxy methods scale and the community relies more on them.</p>
 <section id="why-this-matters-for-safety." class="level4">
-<h4>Why this matters for safety.</h4>
-<p>If we identify a “deception circuit” in the sparse proxy and ablate it, we need confidence that (1) the corresponding mechanism exists in the dense model (not just an artifact of the sparse approximation), (2) interventions on the proxy transfer reliably to the dense model, and (3) the proxy does not introduce spurious circuits absent from the original. Although preliminary studies have suggested that interventions on sparse proxies can induce behavioral changes in dense models <span class="citation" data-cites="gao2025weight">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a>)</span>, systematic validation remains limited.</p>
+<h4>Why This Matters for Safety.</h4>
+<p>For example, when researchers find no “deception circuits” in the proxy models, can they claim that the target model has therefore “passed the test” and is safe? Making this claim requires that these mechanisms do not exist in the target model either. However, as discussed above, the absence of a mechanism in the proxy model does not imply its absence in the target model, since they may compute the input differently while producing the same output. The conclusion we can draw is only something like “we did not find the deception mechanism in the proxy model”, which is far from a useful guarantee in safety-critical scenarios.</p>
 </section>
 <section id="potential-approaches." class="level4">
-<h4>Potential approaches.</h4>
-<p>Several directions could be pursued to improve proxy fidelity. One approach is <em>causal correspondence testing</em>, which verifies that ablating a circuit in the proxy and the corresponding components in the dense model produces matched behavioral changes across different inputs. The second approach is <em>activation patching transfer</em>, which evaluates whether activations identified as causally important in the proxy retain their causal influence when the same interventions are applied to the dense model. A third direction involves developing <em>bounds of mechanistic divergence</em>: theoretical frameworks that upper-bound how much internal computation can differ between models given constraints like activation reconstruction error.</p>
+<h4>Potential Approaches.</h4>
+<p>Several directions can validate and improve proxy fidelity. One promising direction is interchange intervention testing and training <span class="citation" data-cites="das pmlr-v162-geiger22a">(<a href="@@BASE@@references/#ref-das" class="cite cite-established" data-cite="das" role="doc-biblioref">Geiger et al., 2024</a>; <a href="@@BASE@@references/#ref-pmlr-v162-geiger22a" class="cite cite-established" data-cite="pmlr-v162-geiger22a" role="doc-biblioref">2022</a>)</span>. This technique was developed to compare and align a neural network with a high-level causal abstraction. The proxy models and their circuits can be viewed as a causal abstraction of the target model, where the nodes (human-interpretable variables) are the proxy models’ features. This framing not only lets us test alignment between the mechanisms identified in proxy models and the target model, but also provides an optimisation objective for training faithful proxies. Additionally, a theoretical framework that upper-bounds how much internal computation can differ between the target model and proxy model under a given reconstruction performance can also be a promising direction.</p>
 </section>
 </section>
-<section id="sec:non-transformer" class="level2"><h2 data-pdf-page="93"><span class="secnum">15.7</span> Transferability Beyond Transformers<a class="anchor" href="#sec:non-transformer" aria-label="Link to this section">#</a></h2>
-<p>The methods surveyed in this paper are predominantly developed for and evaluated on transformer architectures. While the foundational “circuits” work <span class="citation" data-cites="olah2020zoom">(<a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>)</span> originated on convolutional networks (InceptionV1 <span class="citation" data-cites="szegedy2015going">(<a href="@@BASE@@references/#ref-szegedy2015going" class="cite cite-established" data-cite="szegedy2015going" role="doc-biblioref">Szegedy et al., 2015</a>)</span>), demonstrating feature visualization and curve/texture detector circuits, the subsequent MI toolkits, such as SAEs, transcoders, model editing, circuit discovery, and activation patching, have been developed almost exclusively for transformer residual streams and attention mechanisms.</p>
-<p>This raises an open question: <strong>to what extent do MI methods and findings transfer to other architectures?</strong></p>
+<section id="sec:non-transformer" class="level2"><h2 data-pdf-page="126"><span class="secnum">14.7</span> Transferability Beyond Transformers<a class="anchor" href="#sec:non-transformer" aria-label="Link to this section">#</a></h2>
+<p>The methods surveyed in this paper have been developed and evaluated predominantly in transformer-based autoregressive language models. Although the six research orientations discussed in Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a> are largely architecture-agnostic in their aims, their classic methods and representative works focus almost entirely on transformers. Even though foundational circuits work <span class="citation" data-cites="olah2020zoom">(<a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>)</span> originated in convolutional networks (InceptionV1; <span class="citation" data-cites="szegedy2015going"><a href="@@BASE@@references/#ref-szegedy2015going" class="cite cite-established" data-cite="szegedy2015going" role="doc-biblioref">Szegedy et al., 2015</a></span>), demonstrating interpretable features of visual neurons, many of the techniques that define modern mechanistic interpretability, which include feature extraction and explanation, model editing, and circuit discovery, were developed specifically for transformer residual streams, MLPs, and attention mechanisms. Thus, an important question arises: are mechanistic interpretability concepts, tools, and findings specific to transformers, or do they reflect more general properties of neural computation across model architectures? Below, we discuss several representative models that are substantially different from transformer-based autoregressive models.</p>
 <ul>
-<li><p><strong>Convolutional networks</strong>: The spatial locality of convolutions may make circuits more naturally modular, but SAE-style decomposition of CNN activations remains underexplored at scale.</p></li>
-<li><p><strong>State space models</strong> (e.g., Mamba): These architectures lack attention but implement sequential processing via recurrent dynamics. Whether superposition, linear representation, and circuit-style analysis apply is unknown.</p></li>
-<li><p><strong>Reinforcement learning agents</strong>: RL policies involve temporal credit assignment and value estimation, which may require different interpretability primitives than simply next-token prediction. Limited work exists on MuZero and Atari agents, but systematic MI is lacking.</p></li>
-<li><p><strong>Diffusion models</strong>: The iterative denoising process introduces temporal structure distinct from autoregressive generation. Emerging work explores attention patterns in diffusion transformers, but feature extraction methods are nascent.</p></li>
+<li><p><strong>Convolutional neural networks (CNNs):</strong> CNNs are a representative vision model architecture (<span class="citation" data-cites="oshea2015introductionconvolutionalneuralnetworks"><a href="@@BASE@@references/#ref-oshea2015introductionconvolutionalneuralnetworks" class="cite cite-emerging" data-cite="oshea2015introductionconvolutionalneuralnetworks" role="doc-biblioref">O’Shea &amp; Nash, 2015</a></span>; <span class="citation" data-cites="NIPS2012_c399862d"><a href="@@BASE@@references/#ref-NIPS2012_c399862d" class="cite cite-established" data-cite="NIPS2012_c399862d" role="doc-biblioref">Krizhevsky et al., 2012</a></span>). Their fundamental building blocks are convolutional and pooling layers rather than MLP blocks and self-attention. Transformer-specific circuit formulations thus do not transfer directly to CNNs, although circuit analysis itself originated there <span class="citation" data-cites="olah2020zoom">(<a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a>)</span>. Convolutions’ spatial locality may yield more naturally modular circuits than transformers under suitable formulations, but large-scale studies of superposition, feature extraction, and circuit discovery in modern CNNs and their variants remain limited.</p></li>
+<li><p><strong>State-space and recurrent models:</strong> Architectures such as Mamba <span class="citation" data-cites="gu2024mamba">(<a href="@@BASE@@references/#ref-gu2024mamba" class="cite cite-established" data-cite="gu2024mamba" role="doc-biblioref">Gu &amp; Dao, 2024</a>)</span> and RWKV <span class="citation" data-cites="peng-etal-2023-rwkv">(<a href="@@BASE@@references/#ref-peng-etal-2023-rwkv" class="cite cite-established" data-cite="peng-etal-2023-rwkv" role="doc-biblioref">Peng et al., 2023</a>)</span> replace standard self-attention with recurrent computations that update a hidden state over time. They can be seen as modern recurrent neural network architectures <span class="citation" data-cites="elman1990finding">(<a href="@@BASE@@references/#ref-elman1990finding" class="cite cite-established" data-cite="elman1990finding" role="doc-biblioref">Elman, 1990</a>)</span>. Specifically, Mamba uses input-dependent state transitions to selectively retain or forget information, while RWKV uses a recurrence based on linear attention. This new paradigm raises interpretability questions that previous transformer interpretability studies have not addressed, such as how information is stored and updated in recurrent states, whether these representations exhibit certain properties such as superposition, and how causal effects can be traced across state updates.</p></li>
+<li><p><strong>Diffusion models:</strong> Diffusion models <span class="citation" data-cites="pmlr-v37-sohl-dickstein15">(<a href="@@BASE@@references/#ref-pmlr-v37-sohl-dickstein15" class="cite cite-established" data-cite="pmlr-v37-sohl-dickstein15" role="doc-biblioref">Sohl-Dickstein et al., 2015</a>)</span> apply iterative denoising processes to generate images or tokens. They may still use transformers as backbones, but their computational structure differs fundamentally from autoregressive generation. For example, many current MI studies use the predicted next token as a sign of model behaviour (e.g., “yes” or “no” when the input is a malicious query), while diffusion-based language models generate multiple tokens in different positions at a time to iteratively complete the response. This difference means many interpretability techniques and insights do not directly apply. While recent work has begun investigating attention patterns and internal representations in diffusion models (<span class="citation" data-cites="tinaz2025emergence"><a href="@@BASE@@references/#ref-tinaz2025emergence" class="cite cite-established" data-cite="tinaz2025emergence" role="doc-biblioref">Tinaz et al., 2025</a></span>; <span class="citation" data-cites="wang2026dlmscopemechanisticinterpretabilitydiffusion"><a href="@@BASE@@references/#ref-wang2026dlmscopemechanisticinterpretabilitydiffusion" class="cite cite-emerging" data-cite="wang2026dlmscopemechanisticinterpretabilitydiffusion" role="doc-biblioref">Wang et al., 2026c</a></span>), mechanistic understanding in this paradigm remains lacking.</p></li>
 </ul>
-<p>The five research orientations (Pragmatic, Ambitious, Developmental, Agentic, Automated) discussed in Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">13</a> are framed architecture-agnostically, but their instantiation has been transformer-specific. Validating whether these orientations and the methods they employ generalize to other architectures is necessary before MI can claim broad applicability to AI safety. Until such a validation exists, practitioners should be cautious about extrapolating transformer-derived insights to other neural network architectures.</p>
+<p>Beyond these major architectural changes, researchers have also proposed variants of conventional transformer components. For example, mixture-of-experts (MoE) layers <span class="citation" data-cites="lepikhin2021gshard dai-etal-2024-deepseekmoe">(<a href="@@BASE@@references/#ref-lepikhin2021gshard" class="cite cite-established" data-cite="lepikhin2021gshard" role="doc-biblioref">Lepikhin et al., 2021</a>; <a href="@@BASE@@references/#ref-dai-etal-2024-deepseekmoe" class="cite cite-established" data-cite="dai-etal-2024-deepseekmoe" role="doc-biblioref">Dai et al., 2024a</a>)</span> replace standard MLP blocks with multiple smaller MLPs, called “experts”. For each input activation, a router selects a subset of experts based on the activation and combines their outputs using learned routing weights. Many modern transformer-based language models have adopted this architecture <span class="citation" data-cites="jiang2024mixtralexperts deepseekai2025deepseekv3technicalreport yang2025qwen3technicalreport openai2025gptoss120bgptoss20bmodel kimiteam2026kimik2openagentic">(<a href="@@BASE@@references/#ref-jiang2024mixtralexperts" class="cite cite-emerging" data-cite="jiang2024mixtralexperts" role="doc-biblioref">Jiang et al., 2024</a>; <a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a>; <a href="@@BASE@@references/#ref-yang2025qwen3technicalreport" class="cite cite-emerging" data-cite="yang2025qwen3technicalreport" role="doc-biblioref">QwenAI, 2025</a>; <a href="@@BASE@@references/#ref-openai2025gptoss120bgptoss20bmodel" class="cite cite-emerging" data-cite="openai2025gptoss120bgptoss20bmodel" role="doc-biblioref">OpenAI et al., 2025</a>; <a href="@@BASE@@references/#ref-kimiteam2026kimik2openagentic" class="cite cite-emerging" data-cite="kimiteam2026kimik2openagentic" role="doc-biblioref">Team et al., 2026</a>)</span>.</p>
+<p>However, MoE architectures introduce technical challenges for mechanistic analysis. For example, activation patching at an intermediate MoE layer may change which experts are selected in downstream MoE layers. This makes it difficult to distinguish the patched activation’s effects on model output from those mediated by changes in downstream expert routing. Furthermore, MoE layers can contain many experts, ranging from 8 <span class="citation" data-cites="jiang2024mixtralexperts">(<a href="@@BASE@@references/#ref-jiang2024mixtralexperts" class="cite cite-emerging" data-cite="jiang2024mixtralexperts" role="doc-biblioref">Jiang et al., 2024</a>)</span> to 256 <span class="citation" data-cites="deepseekai2025deepseekv3technicalreport">(<a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a>)</span> or more <span class="citation" data-cites="deepseekai2026deepseekv4highlyefficientmilliontoken">(<a href="@@BASE@@references/#ref-deepseekai2026deepseekv4highlyefficientmilliontoken" class="cite cite-emerging" data-cite="deepseekai2026deepseekv4highlyefficientmilliontoken" role="doc-biblioref">DeepSeek-AI et al., 2026</a>)</span>. Although only a subset of experts is active for each input, the large number of possible expert pathways increases the search space for circuit discovery, making circuit discovery harder to use in modern MoE-based models.</p>
+<p>Simultaneously, some recent work suggests that at least a few mechanistic interpretability techniques can transfer outside of transformers. Steering vectors, tuned lenses, knowledge editing, and circuit analysis evaluations have all been adapted, with varying success, to state-space models such as RWKV and Mamba, as well as to vision-language models (<span class="citation" data-cites="paulo2024does sharma2024locating haon2025mechanistic neo2025towards"><a href="@@BASE@@references/#ref-paulo2024does" class="cite cite-established" data-cite="paulo2024does" role="doc-biblioref">Paulo et al., 2025a</a>; <a href="@@BASE@@references/#ref-sharma2024locating" class="cite cite-established" data-cite="sharma2024locating" role="doc-biblioref">Sharma et al., 2024a</a>; <a href="@@BASE@@references/#ref-haon2025mechanistic" class="cite cite-established" data-cite="haon2025mechanistic" role="doc-biblioref">H<span>ä</span>on et al., 2025</a>; <a href="@@BASE@@references/#ref-neo2025towards" class="cite cite-established" data-cite="neo2025towards" role="doc-biblioref">Neo et al., 2025</a></span>; <span class="citation" data-cites="ensign2024investigating"><a href="@@BASE@@references/#ref-ensign2024investigating" class="cite cite-emerging" data-cite="ensign2024investigating" role="doc-biblioref">Ensign &amp; Garriga-Alonso, 2024</a></span>). Researchers have also conducted theoretical analyses aiming to clarify the computational relationships between transformers and other architectures. Some initial results suggest that several findings in transformers may reflect more general, transferable properties of neural networks <span class="citation" data-cites="zimerman2025explaining dao202mamba2 peng2025rwkv ali-etal-2025-hidden">(<a href="@@BASE@@references/#ref-zimerman2025explaining" class="cite cite-established" data-cite="zimerman2025explaining" role="doc-biblioref">Zimerman et al., 2025</a>; <a href="@@BASE@@references/#ref-dao202mamba2" class="cite cite-established" data-cite="dao202mamba2" role="doc-biblioref">Dao &amp; Gu, 2024</a>; <a href="@@BASE@@references/#ref-peng2025rwkv" class="cite cite-established" data-cite="peng2025rwkv" role="doc-biblioref">Peng et al., 2025</a>; <a href="@@BASE@@references/#ref-ali-etal-2025-hidden" class="cite cite-established" data-cite="ali-etal-2025-hidden" role="doc-biblioref">Ali et al., 2025</a>)</span>. Sorting out which aspects of mechanistic interpretability depend on architecture and which reflect broader principles of neural computation thus appears to be a promising research direction. On the other hand, whether current research orientations and associated techniques remain relevant beyond transformers will also show how broadly mechanistic interpretability research can apply to future systems.</p>
 </section>
 </section>
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-<section id="sec:representation" class="level1"><h1 data-pdf-page="70"><span class="secnum">12</span> Representation Analysis: Structure, Localization, and Transfer<a class="anchor" href="#sec:representation" aria-label="Link to this section">#</a></h1>
-<section id="sec:model-diffing" class="level2"><h2 data-pdf-page="70"><span class="secnum">12.1</span> Model Diffing: Tracking Representational Changes Across Models<a class="anchor" href="#sec:model-diffing" aria-label="Link to this section">#</a></h2>
-<p>Many concerning behaviors, such as reward hacking or sycophancy, arise during finetuning (<span class="citation" data-cites="sharma_towards_2025"><a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a></span>; <span class="citation" data-cites="macdiarmid_natural_2025"><a href="@@BASE@@references/#ref-macdiarmid_natural_2025" class="cite cite-emerging" data-cite="macdiarmid_natural_2025" role="doc-biblioref">MacDiarmid et al., 2025</a></span>). This makes it important to monitor features and circuits that have been introduced or amplified during this process to prevent unsafe models from being deployed. Reliable model diffing may help to surface some such features.</p>
+<section id="sec:representation" class="level1"><h1 data-pdf-page="77"><span class="secnum">9</span> Representation Analysis: Comparison, Localisation, and Control<a class="anchor" href="#sec:representation" aria-label="Link to this section">#</a></h1>
+<p>Following Section <a href="@@BASE@@handbook/intermediate-representations/#sec:intermediate-computation" data-reference-type="ref" data-reference="sec:intermediate-computation">8</a>’s emphasis on understanding representations at a broader level, this section considers three successive questions about model representations. Model diffing (Section <a href="@@BASE@@handbook/representation-analysis/#sec:model-diffing" data-reference-type="ref" data-reference="sec:model-diffing">9.1</a>) asks what differs between two models’ representations. Knowledge localisation (Section <a href="@@BASE@@handbook/representation-analysis/#sec:knowledge-localization" data-reference-type="ref" data-reference="sec:knowledge-localization">9.2</a>) asks where a particular piece of information or behaviour is most causally relevant. Representation engineering and steering (Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a>) asks whether mechanistic understanding of model representations can be used to change model behaviour.</p>
+<p>These methods are also closely connected to neighbouring sections. Diffing two checkpoints of the same model can be viewed as a specific instance of the longitudinal analysis discussed in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-longitudinal" data-reference-type="ref" data-reference="sec:ld-longitudinal">10.1</a>. Knowledge localisation operates at a coarser level of analysis than the feature-extraction methods in Section <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>, but pursues a stricter causal objective by identifying the components at which particular behaviours can be most effectively intervened upon. Finally, representation steering causally tests mechanistic findings by examining whether intervening on an identified representation produces the predicted behavioural change.</p>
+<section id="sec:model-diffing" class="level2"><h2 data-pdf-page="77"><span class="secnum">9.1</span> Model Diffing: Identifying Internal Changes Across Models<a class="anchor" href="#sec:model-diffing" aria-label="Link to this section">#</a></h2>
+<p>Many concerning behaviours, such as reward hacking or sycophancy, arise during fine-tuning (<span class="citation" data-cites="sharma_towards_2025"><a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a></span>; <span class="citation" data-cites="macdiarmid_natural_2025"><a href="@@BASE@@references/#ref-macdiarmid_natural_2025" class="cite cite-emerging" data-cite="macdiarmid_natural_2025" role="doc-biblioref">MacDiarmid et al., 2025</a></span>). This phenomenon makes it important to monitor features and circuits introduced or amplified during this process to prevent unsafe models from being deployed. Model diffing <span class="citation" data-cites="anthropic2024modeldiffing">(<a href="@@BASE@@references/#ref-anthropic2024modeldiffing" class="cite cite-emerging" data-cite="anthropic2024modeldiffing" role="doc-biblioref">Anthropic, 2024</a>)</span> can surface some of these features.</p>
 <div class="definitionbox">
-<p>For two models, <strong>model diffing</strong> seeks to identify differences in features or circuits between them. These differences may consist of features or circuits that are exclusive to one model, or of differences in how shared features or circuits are used.</p>
+<p>For two models, <em>model diffing</em> seeks to identify differences in features or circuits between them. These differences may consist of features or circuits that are exclusive to one model, or of differences in how shared features or circuits are used.</p>
 </div>
-<p><strong>Representation alignment and convergent learning.</strong> An earlier line of work compared internal representations across models by measuring statistical similarity between neurons or subspaces, using correlation or mutual information <span class="citation" data-cites="pmlr-v44-li15convergent">(<a href="@@BASE@@references/#ref-pmlr-v44-li15convergent" class="cite" data-cite="pmlr-v44-li15convergent" role="doc-biblioref">Li et al., 2015</a>)</span>, canonical correlation–based methods such as SVCCA and PWCCA <span class="citation" data-cites="raghu_svcca_2017 morcos_insights_2018">(<a href="@@BASE@@references/#ref-raghu_svcca_2017" class="cite" data-cite="raghu_svcca_2017" role="doc-biblioref">Raghu et al., 2017</a>; <a href="@@BASE@@references/#ref-morcos_insights_2018" class="cite" data-cite="morcos_insights_2018" role="doc-biblioref">Morcos et al., 2018b</a>)</span>, or kernel-based alignment metrics like CKA <span class="citation" data-cites="kornblith2019similarityneuralnetworkrepresentations">(<a href="@@BASE@@references/#ref-kornblith2019similarityneuralnetworkrepresentations" class="cite cite-established" data-cite="kornblith2019similarityneuralnetworkrepresentations" role="doc-biblioref">Kornblith et al., 2019</a>)</span>. Related approaches study whether subsets of neurons in one model can be expressed as linear combinations of neurons in another <span class="citation" data-cites="wang_towards_2018">(<a href="@@BASE@@references/#ref-wang_towards_2018" class="cite" data-cite="wang_towards_2018" role="doc-biblioref">Wang et al., 2018</a>)</span>. This literature primarily aims to characterize <em>convergent learning</em>: whether independently trained models (often only differing by weight initialisation) learn similar representations up to permutation, rotation, or linear reparameterization. In contrast to model diffing as defined here, these methods typically focus on representational similarity rather than on identifying features or circuits that are <em>introduced, amplified, or repurposed</em> by a specific training intervention.<br />
-<strong>Applications.</strong> Model diffing has been applied to assess the effect of finetuning (<span class="citation" data-cites="minder_overcoming_2025"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-established" data-cite="minder_overcoming_2025" role="doc-biblioref">Minder et al., 2025</a></span>; <span class="citation" data-cites="lindsey2024sparsecrosscoders wang_persona_2025 aranguri2025modeldiff"><a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>; <a href="@@BASE@@references/#ref-wang_persona_2025" class="cite cite-emerging" data-cite="wang_persona_2025" role="doc-biblioref">Wang et al., 2025b</a>; <a href="@@BASE@@references/#ref-aranguri2025modeldiff" class="cite cite-emerging" data-cite="aranguri2025modeldiff" role="doc-biblioref">Aranguri &amp; McGrath, 2025</a></span>) and distillation <span class="citation" data-cites="baek_towards_2025">(<a href="@@BASE@@references/#ref-baek_towards_2025" class="cite cite-emerging" data-cite="baek_towards_2025" role="doc-biblioref">Baek &amp; Tegmark, 2025</a>)</span>, to track differences across model architectures <span class="citation" data-cites="thasarathan_universal_2025 jiralerspong_cross-architecture_2025">(<a href="@@BASE@@references/#ref-thasarathan_universal_2025" class="cite" data-cite="thasarathan_universal_2025" role="doc-biblioref">Thasarathan et al., 2025</a>; <a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a>)</span>, and to study the evolution of concepts during pretraining (<span class="citation" data-cites="bayazit_crosscoding_2025 ge_evolution_2025"><a href="@@BASE@@references/#ref-bayazit_crosscoding_2025" class="cite cite-emerging" data-cite="bayazit_crosscoding_2025" role="doc-biblioref">Bayazit et al., 2025</a>; <a href="@@BASE@@references/#ref-ge_evolution_2025" class="cite cite-emerging" data-cite="ge_evolution_2025" role="doc-biblioref">Ge et al., 2025</a></span>).<br />
-<strong>Dictionary learning methods.</strong> Dictionary learning is a natural approach to model diffing in a hypothesis-free way. This may highlight newly introduced features of concern that are unexpected. When diffing models of the same architecture, e.g. a base and chat-finetuned model, dictionary learning methods, like SAEs or transcoders, may be trained on the difference in token-activations, so-called Diff-SAEs  <span class="citation" data-cites="dumas_what_2025">(<a href="@@BASE@@references/#ref-dumas_what_2025" class="cite cite-emerging" data-cite="dumas_what_2025" role="doc-biblioref">Dumas et al., 2025</a>)</span>. This difference should capture at least some of the features that finetuning introduced. Alternatively, one can train an SAE on the base model alone and use it as a fixed feature basis for comparison. Differences in latent activations between the base and finetuned models are then measured on a shared evaluation prompt set <span class="citation" data-cites="wang_persona_2025">(<a href="@@BASE@@references/#ref-wang_persona_2025" class="cite cite-emerging" data-cite="wang_persona_2025" role="doc-biblioref">Wang et al., 2025b</a>)</span>. By causally intervening with latents that activate more strongly in the finetuned model, it is possible to identify directions responsible for misaligned behavior <span class="citation" data-cites="wang_persona_2025">(<a href="@@BASE@@references/#ref-wang_persona_2025" class="cite cite-emerging" data-cite="wang_persona_2025" role="doc-biblioref">Wang et al., 2025b</a>)</span>.<br />
-One challenge for any model-diffing approach is to attribute features in the representations of a finetuned model to the finetuning itself. To solve this problem, an SAE trained on a base model can be finetuned stage-wise  <span class="citation" data-cites="bricken2024stagewise">(<a href="@@BASE@@references/#ref-bricken2024stagewise" class="cite cite-emerging" data-cite="bricken2024stagewise" role="doc-biblioref">Bricken et al., 2024</a>)</span>: first on the base model with finetuning data, then on the finetuned model with base data and finally on the finetuned model with finetuning data. Only features exclusive to the last setting are likely to have been introduced via finetuning.<br />
-SAEs can be applied to model-diffing when inputs from different models have the same dimension. When diffing two models of a different architecture, e.g. to track dispositions across model generations or across sizes, this might not be the case; in that scenario, crosscoders offer an alternative (Figure <a href="@@BASE@@handbook/representation-analysis/#fig:diffing" data-reference-type="ref" data-reference="fig:diffing">14</a>). Crosscoders can define model-specific encoders, with arbitrarily different input dimensions, which then project into one or more shared representational spaces. Note that even though the main advantage of crosscoders over SAEs is cross-architecture comparisons, most prior work has applied them to base and finetuned models (<span class="citation" data-cites="lindsey2024sparsecrosscoders"><a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a></span>; <span class="citation" data-cites="bricken2024stagewise"><a href="@@BASE@@references/#ref-bricken2024stagewise" class="cite cite-emerging" data-cite="bricken2024stagewise" role="doc-biblioref">Bricken et al., 2024</a></span>; <span class="citation" data-cites="minder_overcoming_2025"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-established" data-cite="minder_overcoming_2025" role="doc-biblioref">Minder et al., 2025</a></span>), except for <span class="citation" data-cites="thasarathan_universal_2025"><a href="@@BASE@@references/#ref-thasarathan_universal_2025" class="cite" data-cite="thasarathan_universal_2025" role="doc-biblioref">Thasarathan et al. (2025)</a></span> and <span class="citation" data-cites="jiralerspong_cross-architecture_2025"><a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken (2025)</a></span>.<br />
-A related approach is to model differences in how representations are <em>transformed</em>, rather than comparing the representations themselves. <span class="citation" data-cites="hu2026transcoderadaptersreasoningmodeldiffing"><a href="@@BASE@@references/#ref-hu2026transcoderadaptersreasoningmodeldiffing" class="cite cite-emerging" data-cite="hu2026transcoderadaptersreasoningmodeldiffing" role="doc-biblioref">Hu et al., 2026</a></span> use transcoders to learn a function that approximates the difference in MLP computation between a base model and its finetuned variant. Concretely, adapters are trained such that a base MLP augmented with a learned correction term reproduces the behavior of the finetuned MLP within the full model. Unlike Diff-SAEs, which operate on activation differences, this method learns a transformation that approximates how finetuning modifies the model’s MLP computation.</p>
+<section id="representation-alignment-and-convergent-learning." class="level4">
+<h4>Representation Alignment and Convergent Learning.</h4>
+<p>An earlier line of work compared internal representations across models using correlation- and mutual information-based measures <span class="citation" data-cites="pmlr-v44-li15convergent">(<a href="@@BASE@@references/#ref-pmlr-v44-li15convergent" class="cite cite-emerging" data-cite="pmlr-v44-li15convergent" role="doc-biblioref">Li et al., 2015</a>)</span>, canonical-correlation methods such as SVCCA and PWCCA <span class="citation" data-cites="raghu_svcca_2017 morcos2018insightsrepresentationalsimilarityneural">(<a href="@@BASE@@references/#ref-raghu_svcca_2017" class="cite cite-established" data-cite="raghu_svcca_2017" role="doc-biblioref">Raghu et al., 2017</a>; <a href="@@BASE@@references/#ref-morcos2018insightsrepresentationalsimilarityneural" class="cite cite-established" data-cite="morcos2018insightsrepresentationalsimilarityneural" role="doc-biblioref">Morcos et al., 2018</a>)</span>, and kernel-based alignment metrics such as CKA <span class="citation" data-cites="kornblith2019similarityneuralnetworkrepresentations">(<a href="@@BASE@@references/#ref-kornblith2019similarityneuralnetworkrepresentations" class="cite cite-established" data-cite="kornblith2019similarityneuralnetworkrepresentations" role="doc-biblioref">Kornblith et al., 2019</a>)</span>. Related approaches examine whether the representations of one model can be expressed as linear combinations of representations in another <span class="citation" data-cites="wang_towards_2018">(<a href="@@BASE@@references/#ref-wang_towards_2018" class="cite cite-established" data-cite="wang_towards_2018" role="doc-biblioref">Wang et al., 2018</a>)</span>. This literature primarily concerns <em>convergent learning</em>: whether independently trained models with different random initialisations learn similar representations up to permutation, rotation, or other linear transformations. More recent work has extended these techniques to study representational convergence across modalities <span class="citation" data-cites="huh2024platonicrepresentationhypothesis groger2026revisiting">(<a href="@@BASE@@references/#ref-huh2024platonicrepresentationhypothesis" class="cite cite-established" data-cite="huh2024platonicrepresentationhypothesis" role="doc-biblioref">Huh et al., 2024</a>; <a href="@@BASE@@references/#ref-groger2026revisiting" class="cite cite-established" data-cite="groger2026revisiting" role="doc-biblioref">Gr<span>ö</span>ger et al., 2026</a>)</span>, as discussed in Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:universality" data-reference-type="ref" data-reference="subsec:universality">5.4</a>. In contrast to model diffing as defined here, these methods typically focus on representational similarity rather than on identifying concrete features or circuits that are <em>introduced, amplified, or repurposed</em> by a specific training intervention.</p>
+</section>
+<section id="applications." class="level4">
+<h4>Applications.</h4>
+<p>Model diffing has been applied to assess the effect of fine-tuning (<span class="citation" data-cites="minder_overcoming_2025 lindsey2024sparsecrosscoders aranguri2025modeldiff"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al., 2025a</a>; <a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>; <a href="@@BASE@@references/#ref-aranguri2025modeldiff" class="cite cite-emerging" data-cite="aranguri2025modeldiff" role="doc-biblioref">Aranguri &amp; McGrath, 2025</a></span>; <span class="citation" data-cites="wang2026persona"><a href="@@BASE@@references/#ref-wang2026persona" class="cite cite-established" data-cite="wang2026persona" role="doc-biblioref">Wang et al., 2026a</a></span>) and distillation <span class="citation" data-cites="baek_towards_2025">(<a href="@@BASE@@references/#ref-baek_towards_2025" class="cite cite-emerging" data-cite="baek_towards_2025" role="doc-biblioref">Baek &amp; Tegmark, 2025</a>)</span>, to track differences between model architectures (<span class="citation" data-cites="thasarathan_universal_2025"><a href="@@BASE@@references/#ref-thasarathan_universal_2025" class="cite cite-established" data-cite="thasarathan_universal_2025" role="doc-biblioref">Thasarathan et al., 2025</a></span>; <span class="citation" data-cites="jiralerspong_cross-architecture_2025"><a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite cite-emerging" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a></span>), to study the evolution of concepts during pretraining <span class="citation" data-cites="bayazit-etal-2026-crosscoding ge2026evolution">(<a href="@@BASE@@references/#ref-bayazit-etal-2026-crosscoding" class="cite cite-established" data-cite="bayazit-etal-2026-crosscoding" role="doc-biblioref">Bayazit et al., 2026</a>; <a href="@@BASE@@references/#ref-ge2026evolution" class="cite cite-established" data-cite="ge2026evolution" role="doc-biblioref">Ge et al., 2026</a>)</span>, and to identify vision-specific features introduced during vision-language training, enabling targeted removal or steering <span class="citation" data-cites="batra2026multimodalmodeldiffingfeature">(<a href="@@BASE@@references/#ref-batra2026multimodalmodeldiffingfeature" class="cite cite-emerging" data-cite="batra2026multimodalmodeldiffingfeature" role="doc-biblioref">Batra et al., 2026</a>)</span>.</p>
+</section>
+<section id="dictionary-learning-methods." class="level4">
+<h4>Dictionary Learning Methods.</h4>
+<p>Dictionary learning is an intuitive approach to implement model diffing. It can highlight unexpected, newly introduced features of concern. When diffing models of the same architecture, such as a base versus instruction-fine-tuned model or a series of model checkpoints along pretraining, dictionary learning methods like sparse autoencoders (SAEs) and transcoders can be trained on the difference in token activations (<span class="citation" data-cites="dumas_what_2025 sawmya2025birth"><a href="@@BASE@@references/#ref-dumas_what_2025" class="cite cite-emerging" data-cite="dumas_what_2025" role="doc-biblioref">Dumas et al., 2025b</a>; <a href="@@BASE@@references/#ref-sawmya2025birth" class="cite cite-emerging" data-cite="sawmya2025birth" role="doc-biblioref">Sawmya et al., 2025</a></span>; <span class="citation" data-cites="inaba2025howllmslearn"><a href="@@BASE@@references/#ref-inaba2025howllmslearn" class="cite cite-established" data-cite="inaba2025howllmslearn" role="doc-biblioref">Inaba et al., 2025</a></span>). This difference can capture features introduced by fine-tuning or by different pretraining stages. A simple baseline technique is to train an SAE on the base model alone and use it as a fixed feature basis for comparison. Methods like SAE-Track maintain continuity through recurrent initialisation, training each checkpoint’s SAE from its predecessor’s so latents remain matched across the run <span class="citation" data-cites="xu2024saetrack">(<a href="@@BASE@@references/#ref-xu2024saetrack" class="cite cite-emerging" data-cite="xu2024saetrack" role="doc-biblioref">Xu et al., 2025b</a>)</span>. The differences in activations between two models are then measured on a shared evaluation prompt set <span class="citation" data-cites="wang2026persona">(<a href="@@BASE@@references/#ref-wang2026persona" class="cite cite-established" data-cite="wang2026persona" role="doc-biblioref">Wang et al., 2026a</a>)</span>. By causally intervening with latents that activate more strongly in the fine-tuned model, it is possible to identify features responsible for misaligned behaviour <span class="citation" data-cites="wang2026persona">(<a href="@@BASE@@references/#ref-wang2026persona" class="cite cite-established" data-cite="wang2026persona" role="doc-biblioref">Wang et al., 2026a</a>)</span>.</p>
+<p>One challenge for model-diffing approaches is attributing features in a fine-tuned model’s representations to the fine-tuning itself. To address this, an SAE trained on a base model can be fine-tuned stage-wise <span class="citation" data-cites="bricken2024stagewise">(<a href="@@BASE@@references/#ref-bricken2024stagewise" class="cite cite-emerging" data-cite="bricken2024stagewise" role="doc-biblioref">Bricken et al., 2024</a>)</span>: first on the base model with fine-tuning data, then on the fine-tuned model with base data, and finally on the fine-tuned model with fine-tuning data. Only features exclusive to the last setting are likely to have been introduced via fine-tuning.</p>
+<p>SAEs are applicable when inputs from different models have the same dimension. When diffing two models with different architectures, for example, to track dispositions across model generations or sizes, this might not hold. In this case, crosscoders offer an alternative (Figure <a href="@@BASE@@handbook/representation-analysis/#fig:diffing" data-reference-type="ref" data-reference="fig:diffing">14</a>). Crosscoders can define model-specific encoders with arbitrarily different input dimensions that project activations from different models into one or more shared representational spaces. Although the main advantage of crosscoders over SAEs is cross-architecture comparison (<span class="citation" data-cites="thasarathan_universal_2025"><a href="@@BASE@@references/#ref-thasarathan_universal_2025" class="cite cite-established" data-cite="thasarathan_universal_2025" role="doc-biblioref">Thasarathan et al., 2025</a></span>; <span class="citation" data-cites="jiralerspong_cross-architecture_2025"><a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite cite-emerging" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a></span>), some prior work has also applied them to base and fine-tuned models <span class="citation" data-cites="lindsey2024sparsecrosscoders bricken2024stagewise minder_overcoming_2025">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>; <a href="@@BASE@@references/#ref-bricken2024stagewise" class="cite cite-emerging" data-cite="bricken2024stagewise" role="doc-biblioref">Bricken et al., 2024</a>; <a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al., 2025a</a>)</span> and across pretraining checkpoints <span class="citation" data-cites="ge2026evolution bayazit-etal-2026-crosscoding">(<a href="@@BASE@@references/#ref-ge2026evolution" class="cite cite-established" data-cite="ge2026evolution" role="doc-biblioref">Ge et al., 2026</a>; <a href="@@BASE@@references/#ref-bayazit-etal-2026-crosscoding" class="cite cite-established" data-cite="bayazit-etal-2026-crosscoding" role="doc-biblioref">Bayazit et al., 2026</a>)</span>.</p>
+<p>A related approach is to model differences in how representations are <em>transformed</em>, rather than comparing the representations themselves. <span class="citation" data-cites="hu2026transcoderadaptersreasoningmodeldiffing"><a href="@@BASE@@references/#ref-hu2026transcoderadaptersreasoningmodeldiffing" class="cite cite-emerging" data-cite="hu2026transcoderadaptersreasoningmodeldiffing" role="doc-biblioref">Hu et al. (2026)</a></span> used transcoders to learn a function that approximates the difference in MLP computation between a base model and its fine-tuned variant. Specifically, adapters are trained so that a base MLP augmented with a learned correction term reproduces the fine-tuned MLP’s behaviour within the full model. Unlike the above methods, which operate on activation differences, this method learns a transformation that approximates how fine-tuning modifies the model’s MLP computation.</p>
+<p>A shared concern across dictionary-learning-based model-diffing methods is the faithfulness of the sparse decomposition (see Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: sae limitation" data-reference-type="ref" data-reference="subsubsec: sae limitation">6.5.5</a>). Extracted features may not faithfully reflect the underlying representations or their causal roles, potentially leading to false-positive findings about differences between models. For example, identifying an SAE feature associated with “confessing previous faults” in a fine-tuned model but not in the original does not establish that the fine-tuned model causally uses this feature. Nor does its apparent absence from the original model imply that the model lacks representations that could steer it towards such behaviour.</p>
 <div class="warningbox">
-<p>Dictionary learning methods for model-diffing risk falsely identifying model-exclusive features <span class="citation" data-cites="minder_overcoming_2025 jiralerspong_cross-architecture_2025">(<a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-established" data-cite="minder_overcoming_2025" role="doc-biblioref">Minder et al., 2025</a>; <a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a>)</span>. This can result from the loss function: for instance, <span class="citation" data-cites="minder_overcoming_2025"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-established" data-cite="minder_overcoming_2025" role="doc-biblioref">Minder et al. (2025)</a></span> showed that crosscoders trained with an L1 loss on hidden states can falsely identify novel features, which they try to mitigate with the BatchTopK activation function. However, even with improved architecture, a risk of false positives may persist. During finetuning, the direction representing some feature can rotate, and dictionary learning methods currently cannot distinguish between features that are introduced via finetuning as opposed to merely represented differently. Thus, supposedly model-exclusive features should be regarded as hypotheses that need further validation.</p>
+<p>Dictionary learning methods for model diffing risk falsely identifying model-exclusive features <span class="citation" data-cites="minder_overcoming_2025 jiralerspong_cross-architecture_2025">(<a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al., 2025a</a>; <a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite cite-emerging" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a>)</span>. This can result from the loss function: for instance, <span class="citation" data-cites="minder_overcoming_2025"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al. (2025a)</a></span> showed that crosscoders trained with an <span class="math inline">L_1</span> regularisation on hidden states can falsely identify novel features, which they tried to mitigate with <span class="math inline">\sigma_{\text{Batch-TopK}}(\cdot)</span>. However, even with this improved architecture, false positives may persist. During fine-tuning, the directions representing some features can rotate, and dictionary learning methods currently cannot distinguish between features introduced via fine-tuning and those merely represented differently. Thus, supposedly model-exclusive features should be regarded as hypotheses that need further validation.</p>
 </div>
-<p><strong>LLM-based approaches.</strong> To diff closed-weight models, it is desirable to develop model diffing tools that just rely on API access. To that end, <span class="citation" data-cites="kempf2026simplellmbaselinescompetitive"><a href="@@BASE@@references/#ref-kempf2026simplellmbaselinescompetitive" class="cite" data-cite="kempf2026simplellmbaselinescompetitive" role="doc-biblioref">Kempf et al. (2026)</a></span> show that an LLM can generate natural-language hypotheses about behavioural differences between two models by extracting, clustering, and summarising differences across many response pairs from the two models on a shared prompt set. Benchmarked against an SAE-based baseline, the LLM-based method matches it on many dimensions while surfacing more abstract, behaviour-level differences.</p>
-<figure id="fig:diffing" data-latex-placement="t">
-<img alt="Figure 16: Model Diffing via Crosscoders. Model Diffing identifies specific features that have emerged, or remained invariant across model iterations or architectures. A large delta (\Delta) in feature activation between Model 1 and Model 2 highlights a changed feature, providing an insight into how" loading="lazy" src="@@BASE@@figures/11_modeldiffing.svg" />
-<figcaption><span class="capnum">Figure 16:</span> <strong>Model Diffing via Crosscoders</strong>. Model Diffing identifies specific features that have emerged, or remained invariant across model iterations or architectures. A large delta (<span class="math inline">\Delta</span>) in feature activation between Model 1 and Model 2 highlights a changed feature, providing an insight into how finetuning or architectural changes alter internal representations.</figcaption>
+</section>
+<section id="llm-based-approaches." class="level4">
+<h4>LLM-Based Approaches.</h4>
+<p>To differentiate closed-weight models, it is desirable to develop model diffing tools that only rely on API access. To that end, <span class="citation" data-cites="kempf2026simplellmbaselinescompetitive"><a href="@@BASE@@references/#ref-kempf2026simplellmbaselinescompetitive" class="cite cite-emerging" data-cite="kempf2026simplellmbaselinescompetitive" role="doc-biblioref">Kempf et al. (2026)</a></span> showed that an LLM can generate natural-language hypotheses about behavioural differences between two models by extracting, clustering, and summarising differences across many response pairs from the two models on a shared prompt set. Benchmarked against the SAE-based baseline, the LLM-based method matches it on many dimensions while surfacing more abstract, behaviour-level differences.</p>
+<figure id="fig:diffing" data-latex-placement="tb">
+<img alt="Figure 14: Model diffing via crosscoders. Model diffing identifies features that emerge or remain invariant across model iterations or architectures. A large delta (\Delta) in feature activation between two models highlights a changed feature, providing an insight into how fine-tuning or architectur" loading="lazy" src="@@BASE@@figures/11_modeldiffing.svg" />
+<figcaption><span class="capnum">Figure 14:</span> <strong>Model diffing via crosscoders.</strong> Model diffing identifies features that emerge or remain invariant across model iterations or architectures. A large delta (<span class="math inline">\Delta</span>) in feature activation between two models highlights a changed feature, providing an insight into how fine-tuning or architectural changes alter internal representations.</figcaption>
 </figure>
 </section>
-<section id="sec:knowledge-localization" class="level2"><h2 data-pdf-page="72"><span class="secnum">12.2</span> Knowledge Localization: Where Is Information Stored?<a class="anchor" href="#sec:knowledge-localization" aria-label="Link to this section">#</a></h2>
-<p>Another fundamental question in mechanistic interpretability is where specific knowledge resides within a model. Causal tracing identifies which attention heads or MLP directions are causally responsible for particular facts or reasoning tasks by measuring how intervening on those components affects output behavior <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>. Complementary approaches use rank analysis to quantify information flow through specific components via singular value decomposition of activation matrices, revealing that some facts and reasoning patterns concentrate in low-rank subspaces of feed-forward layers.</p>
-<p>However, this localization perspective has fundamental limitations. Knowledge is often distributed across many parameters rather than concentrated in isolated components, particularly in large overparameterized models where redundancy is common. This distributed structure makes precise localization difficult and suggests that fully understanding <strong>where</strong> knowledge lives may require thinking in terms of distributed representations and circuits rather than isolated anatomical locations. Consequently, knowledge localization is most useful when combined with mechanistic analysis of how multiple components interact to implement computation.</p>
-<div class="examplebox">
-<p>To illustrate knowledge localization in practice, one can examine how behaviorally relevant directions manifest across layers and components. For example:</p>
-<ul>
-<li><p><strong>Refusal vectors</strong> <span class="math inline">\mathbf{r}^\ell</span>: projecting residual activations <span class="math inline">h_t^\ell</span> onto <span class="math inline">\mathbf{r}^\ell</span>, <span class="math display">s_t^\ell = \langle h_t^\ell, \mathbf{r}^\ell \rangle,</span> quantifies which layers or MLP subspaces encode the model’s tendency to decline harmful prompts. Peaks in <span class="math inline">s_t^\ell</span> indicate components that strongly contribute to refusal, while smaller distributed values reveal how the signal spreads across the network.</p></li>
-<li><p><strong>Sleeper agent probe directions</strong> <span class="math inline">\mathbf{d}^\ell</span>: similar projections reveal which layers or components carry latent backdoor objectives. Comparing <span class="math inline">s_t^\ell</span> across layers or heads highlights concentrated versus distributed storage of these conditional behaviors.</p></li>
-</ul>
-<p>These analyses demonstrate that knowledge or behavior can be partially localized, but often resides in a combination of concentrated subspaces and distributed representations, reinforcing the need to complement localization with mechanistic circuit analysis.</p>
-</div>
 </section>
-<section id="subsec:steering" class="level2"><h2 data-pdf-page="73"><span class="secnum">12.3</span> Representation Engineering and Steering<a class="anchor" href="#subsec:steering" aria-label="Link to this section">#</a></h2>
-<section id="overview." class="level4">
-<h4>Overview.</h4>
-<p>Representation engineering aims to control model behavior by intervening directly in activation space rather than modifying weights. A prominent class of methods, generally defined as <em>activation steering</em>, biases model outputs by adding carefully chosen directions to intermediate activations—most commonly the residual stream—during inference. These interventions are lightweight, require no retraining, and are therefore attractive for rapid behavioral control in deployed systems.</p>
+<section id="sec:knowledge-localization" class="level2"><h2 data-pdf-page="80"><span class="secnum">9.2</span> Knowledge Localisation: Where Is Information Stored?<a class="anchor" href="#sec:knowledge-localization" aria-label="Link to this section">#</a></h2>
+<p>Knowledge localisation methods aim to identify the layers, components, or subspaces whose activations are causally associated with a particular capability or behaviour. For instance, causal tracing identifies components that causally impact specific factual association retrieval by observing how model outputs change after activation intervention <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>. Related work suggests that feed-forward layers disproportionately contain associations for factual knowledge <span class="citation" data-cites="geva-etal-2021-transformer">(<a href="@@BASE@@references/#ref-geva-etal-2021-transformer" class="cite cite-established" data-cite="geva-etal-2021-transformer" role="doc-biblioref">Geva et al., 2021</a>)</span>, which can reside in the subspace spanned by the top singular directions of late-layer MLPs’ weight matrices <span class="citation" data-cites="sharma2024the">(<a href="@@BASE@@references/#ref-sharma2024the" class="cite cite-established" data-cite="sharma2024the" role="doc-biblioref">Sharma et al., 2024c</a>)</span>. These early analyses lay the basis for knowledge localisation.</p>
+<p>Localisation can identify where information is most strongly represented. However, it does not indicate whether the model uses that information. In transformers, which are usually overparameterised, information can be redundantly represented and processed across many model components and activation subspaces depending on the input. In other words, a component with a strong causal effect may capture only part of the mechanism. As a result, localisation is a useful starting point for mechanistic understanding, but not a self-contained analysis.</p>
+<p>One application is to identify activation directions associated with specific behaviours. For example, refusal behaviour has been found to have approximately low-dimensional structure, with certain activation directions associated with the model’s tendency to refuse harmful requests <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span>. Similarly, researchers can study sleeper-agent behaviours <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span> by comparing activations across layers for triggered and non-triggered inputs <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>. These analyses help answer questions such as where and how the model encodes behaviour-related information. However, identifying an activation direction or layer does not explain the internal mechanisms underlying the behaviour, which can only be understood through finer-grained analyses such as circuit discovery.</p>
+</section>
+<section id="subsec:steering" class="level2"><h2 data-pdf-page="80"><span class="secnum">9.3</span> Representation Engineering and Steering<a class="anchor" href="#subsec:steering" aria-label="Link to this section">#</a></h2>
+<p>To manipulate model behaviour, representation engineering alters activation-space representations directly, rather than via model weight modification. <em>Activation steering</em> generally refers to methods that target intermediate activations (usually the residual stream) by adding certain vectors that shift model outputs during inference. This differs from localisation methods, which determine where information or behaviour is represented, as steering techniques aim to identify a specific, manipulable vector that <em>changes</em> model behaviour. Because they require no weight updates or retraining, these interventions are lightweight approaches for controlling behaviour.</p>
 <div class="definitionbox">
-<p>A <strong>steering vector</strong> is a direction <span class="math inline">v \in \mathbb{R}^{d_{\text{hidden}}}</span> in activation space such that adding <span class="math inline">\alpha v</span> to a layer activation biases the model toward a target behavior. In practice, <span class="math inline">v</span> is estimated by contrasting activations associated with different behaviors, e.g., taking differences of class-conditional means: <span class="math display">v = \mathbb{E}[h | \text{preferred}] - \mathbb{E}[h | \text{unpreferred}]</span> or by extracting discriminative directions using linear methods such as CCA <span class="citation" data-cites="panickssery2024steeringllama2contrastive">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>)</span>. Application is simple: at inference, replace <span class="math inline">h_\ell</span> with <span class="math inline">h_\ell + \alpha v</span> to shift behavior without retraining.</p>
+<p>A <em>steering vector</em> <span class="math inline">v_{\text{steer}} \in \mathbb{R}^{d}</span> is an activation-space direction which, when scaled (<span class="math inline">\alpha v_{\text{steer}}</span>) and added to an intermediate activation, shifts model outputs towards a targeted behaviour. For instance, one steering direction can use the weight vector of a trained linear probe. Another popular method is <em>difference-in-means</em>, which obtains the steering vector by taking the difference between activations associated with two behaviours <span class="citation" data-cites="panickssery2024steeringllama2contrastive">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>)</span>: <span class="math display">v_{\text{steer}} =
+\mathbb{E}[h_\ell \mid \text{preferred}]
+-
+\mathbb{E}[h_\ell \mid \text{unpreferred}].</span> During inference, the intervention takes the form <span class="math display">\tilde{h}_\ell = h_\ell + \alpha v_{\text{steer}},</span> where <span class="math inline">\alpha</span> controls the intervention strength.</p>
 </div>
-<p>Despite their simplicity, steering vectors reliably induce behaviors like improved helpfulness, refusal suppression, or stylistic changes <span class="citation" data-cites="panickssery2024steeringllama2contrastive tan2025analyzinggeneralizationreliabilitysteering zhao2025steering">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>; <a href="@@BASE@@references/#ref-tan2025analyzinggeneralizationreliabilitysteering" class="cite cite-established" data-cite="tan2025analyzinggeneralizationreliabilitysteering" role="doc-biblioref">Tan et al., 2024a</a>; <a href="@@BASE@@references/#ref-zhao2025steering" class="cite cite-established" data-cite="zhao2025steering" role="doc-biblioref">Zhao et al., 2025</a>)</span>.</p>
-</section>
-<section id="optimization-based-steering." class="level4">
-<h4>Optimization-Based Steering.</h4>
-<p>Beyond closed-form constructions, steering directions can be obtained by directly optimizing objectives over activations—maximizing task performance or suppressing undesired outputs under intervention. These optimization-based approaches allow greater flexibility and adapt to complex, non-linear objectives, but incur higher computational cost <span class="citation" data-cites="dunefsky2025one oozeer2025beyond">(<a href="@@BASE@@references/#ref-dunefsky2025one" class="cite" data-cite="dunefsky2025one" role="doc-biblioref">Dunefsky &amp; Cohan, 2025</a>; <a href="@@BASE@@references/#ref-oozeer2025beyond" class="cite" data-cite="oozeer2025beyond" role="doc-biblioref">Oozeer et al., 2025a</a>)</span>.</p>
-<figure id="fig:steering_vectors" data-latex-placement="t">
-<img alt="Figure 17: Steering Vectors. A Steering Vector v is computed by taking the difference between the mean activations of positive and negative contrastive samples at a target layer. During inference, this vector is added to the model’s original hidden state h_{\ell} to produce a modified state \hat{h}_" loading="lazy" src="@@BASE@@figures/11_steering.svg" />
-<figcaption><span class="capnum">Figure 17:</span> <strong>Steering Vectors</strong>. A Steering Vector <span class="math inline">v</span> is computed by taking the difference between the mean activations of positive and negative contrastive samples at a target layer. During inference, this vector is added to the model’s original hidden state <span class="math inline">h_{\ell}</span> to produce a modified state <span class="math inline">\hat{h}_{\ell}</span></figcaption>
+<p>While these approaches appear simple, they reliably change model behaviours such as refusal, helpfulness, and stylistic tendencies <span class="citation" data-cites="panickssery2024steeringllama2contrastive zhao2025steering arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>; <a href="@@BASE@@references/#ref-zhao2025steering" class="cite cite-established" data-cite="zhao2025steering" role="doc-biblioref">Zhao et al., 2025</a>; <a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span>. Thus, steering is useful both as a technique to manipulate behaviour and as a causality test for probes. If intervening along a direction consistently changes a target behaviour, this provides evidence that the corresponding representation causally influences that behaviour. Below we use refusal vectors as a concrete example.</p>
+<div class="definitionbox">
+<p><span id="def:refusal-vector" data-label="def:refusal-vector"></span></p>
+<p>A <em>refusal vector</em> <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span> is a steering direction associated with a model’s tendency to refuse requests. Define <span class="math inline">h^t_\ell \in \mathbb{R}^d</span> as the residual stream at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span>. A simple contrastive construction is <span class="math display">\mathbf{r}_\ell =
+\mathbb{E}_{x \sim \mathcal{D}_{\mathrm{refuse}}}
+[h^t_\ell(x)]
+-
+\mathbb{E}_{x \sim \mathcal{D}_{\mathrm{comply}}}
+[h^t_\ell(x)],</span> where <span class="math inline">\mathcal{D}_{\mathrm{refuse}}</span> contains prompts eliciting refusal behaviour and <span class="math inline">\mathcal{D}_{\mathrm{comply}}</span> contains matched prompts for which the model should comply.<br />
+The projection <span class="math display">s^t_\ell = \langle h^t_\ell,\mathbf{r}_\ell\rangle</span> measures the activation along the refusal direction. When intervening on this direction, one can test if and how refusal behaviour is causally influenced by it. Previous experimental work shows that these interventions can strongly modulate refusal behaviour; however, the construction and interpretation of refusal directions can vary across models and experimental settings <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span>.</p>
+</div>
+<p>The contrast between <em>localisation</em> and <em>steering</em> is shown by refusal vectors: measuring <span class="math inline">s^t_\ell</span> across layers helps locate representations that contain information related to refusal, while intervening along <span class="math inline">\mathbf{r}_\ell</span> tests how modifying representations modulates behaviour (which provides evidence for causality, but does not necessarily reveal the entire mechanism behind how refusal works).</p>
+<section id="nonlinear-and-optimisation-based-steering." class="level4">
+<h4>Nonlinear and Optimisation-Based Steering.</h4>
+<p>Beyond linear directions, recent studies have explored more complex ways of manipulating model activations. For example, <span class="citation" data-cites="raval2026curveballsteeringrightdirection"><a href="@@BASE@@references/#ref-raval2026curveballsteeringrightdirection" class="cite cite-emerging" data-cite="raval2026curveballsteeringrightdirection" role="doc-biblioref">Raval et al. (2026)</a></span> projected model activations into a kernel PCA space, steered them linearly there, and projected them back to the original activation space; <span class="citation" data-cites="wurgaft2026manifoldsteeringrevealsshared"><a href="@@BASE@@references/#ref-wurgaft2026manifoldsteeringrevealsshared" class="cite cite-emerging" data-cite="wurgaft2026manifoldsteeringrevealsshared" role="doc-biblioref">Wurgaft et al. (2026)</a></span> steered activations along a low-dimensional manifold, motivated by findings that some concepts are better represented as manifolds in the activation space. Separately, optimisation-based methods construct steering directions by directly optimising objectives over intermediate activations, offering an alternative to closed-form constructions. For example, they can be obtained by maximising task performance or by suppressing undesirable outputs under intervention. These optimisation-based approaches offer greater flexibility for complex or nonlinear objectives but generally require substantially more computation than closed-form contrastive methods <span class="citation" data-cites="dunefsky2025one oozeer2025beyond">(<a href="@@BASE@@references/#ref-dunefsky2025one" class="cite cite-established" data-cite="dunefsky2025one" role="doc-biblioref">Dunefsky &amp; Cohan, 2025</a>; <a href="@@BASE@@references/#ref-oozeer2025beyond" class="cite cite-established" data-cite="oozeer2025beyond" role="doc-biblioref">Oozeer et al., 2025</a>)</span>.</p>
+<figure id="fig:steering_vectors" data-latex-placement="tb">
+<img alt="Figure 15: Steering Vectors. A steering vector v_{\text{steer}} can be constructed from the difference between the mean activations of positive and negative contrastive samples at a target layer. During inference, the vector is added to the original hidden state h_{\ell} to obtain a modified state \" loading="lazy" src="@@BASE@@figures/11_steering.svg" />
+<figcaption><span class="capnum">Figure 15:</span> <strong>Steering Vectors.</strong> A steering vector <span class="math inline">v_{\text{steer}}</span> can be constructed from the difference between the mean activations of positive and negative contrastive samples at a target layer. During inference, the vector is added to the original hidden state <span class="math inline">h_{\ell}</span> to obtain a modified state <span class="math inline">\tilde{h}_{\ell}</span>.</figcaption>
 </figure>
 </section>
-<section id="relation-to-causal-evaluation-of-feature-extraction-methods." class="level4">
-<h4>Relation to Causal Evaluation of Feature Extraction Methods.</h4>
-<p>Representation engineering is closely related to causal analysis for evaluating feature extraction methods such as SAEs. In particular, to assess whether features claimed to encode specific semantic meanings genuinely influence model behavior in those ways, one can intervene on the model’s internal representations by steering along these features and observing the resulting changes in output text. Such intervention-based evaluations directly measure the causal effects of extracted features on model outputs.</p>
-<p>This paradigm enables quantitative comparisons of feature quality across different extraction methods, such as SAEs, difference-in-mean, and DAS. Benchmarks designed for this purpose include, but are not limited to, RAVEL <span class="citation" data-cites="huang-etal-2024-ravel">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>)</span>, MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-emerging" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025a</a>)</span>, and AxBench <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025</a>)</span>.</p>
+<section id="steering-as-a-causal-test-for-feature-interpretability." class="level4">
+<h4>Steering as a Causal Test for Feature Interpretability.</h4>
+<p>Activation steering can causally evaluate representations previously labelled by feature-extraction methods (such as CLTs or SAEs). For instance, a feature may be hypothesised to represent a particular concept. Supporting evidence can be obtained by steering along the identified feature direction and evaluating the resulting change in behaviour.</p>
+<p>These evaluations have been used to compare interpretable direction-identification approaches, including SAE features, difference-in-means directions, and DAS <span class="citation" data-cites="huang-etal-2024-ravel wu2025axbench">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>; <a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>. Benchmarks designed to evaluate the causal and behavioural properties of learned representations include RAVEL <span class="citation" data-cites="huang-etal-2024-ravel">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>)</span>, MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span>, and AxBench <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>.</p>
 <div class="warningbox">
-<p>Despite effectiveness, steering vectors have critical limitations. First, they often fail to generalize across distribution shifts: a vector derived for one task may degrade in another. Second, behavioral success does not guarantee mechanistic transparency. A steering intervention may activate multiple features, interact nonlinearly with existing computation, or exploit spurious pathways that yield desired outputs without corresponding to clean circuit-level modifications. As a result, steering remains primarily a behavioral tool rather than a mechanistic one.</p>
-</div>
-<div class="examplebox">
-<p>Two concrete examples of steering vectors appear in earlier sections:</p>
-<ul>
-<li><p><strong>Refusal vectors</strong> <span class="math inline">\mathbf{r}^\ell</span> (Definition <a href="@@BASE@@handbook/intermediate-representations/#def:refusal-vector" data-reference-type="ref" data-reference="def:refusal-vector">8.3</a>): manipulating this direction modulates the model’s tendency to decline harmful prompts.</p></li>
-<li><p><strong>Sleeper agent probe directions</strong> <span class="math inline">\mathbf{d}^\ell</span> (Example <a href="@@BASE@@handbook/intermediate-representations/#ex:probe_dir" data-reference-type="ref" data-reference="ex:probe_dir">8.6.9</a>): projections onto this direction reveal latent backdoor behavior and can in principle steer activations toward or away from the hidden objective.</p></li>
-</ul>
-<p>These examples illustrate how steering vectors correspond to interpretable directions in residual space along which the model’s behavior can be predictably influenced.</p>
+<p>While steering methods effectively control behaviours and support causal testing, they have limitations. Notably, they can be brittle, as a direction identified for one distribution, model, or task may not generalise reliably. Another limitation is that successful behavioural steering does not, by itself, establish mechanistic transparency, because an intervention may simultaneously impact multiple features, interact nonlinearly with existing computations, or exploit unintended pathways that produce the desired behaviour without corresponding to a clean mechanism <span class="citation" data-cites="makelov2024is">(<a href="@@BASE@@references/#ref-makelov2024is" class="cite cite-established" data-cite="makelov2024is" role="doc-biblioref">Makelov et al., 2024</a>)</span>. Thus, steering should generally be interpreted as evidence for causal influence, but it should not, by itself, be a mechanistic explanation of the behaviour.</p>
 </div>
 </section>
+<section id="summary.-6" class="level4">
+<h4>Summary.</h4>
+<p>Model diffing examines representation differences across models, knowledge localisation pinpoints where information or behaviours are causally localised, and representation engineering analyses how representation manipulation controls behaviour. Steering has two important roles: as a control technique and as a way to causally test how representations affect behaviour (while noting that successful localisation or steering does not, by itself, provide a complete explanation for the mechanism underlying that behaviour). This analysis is extended in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training" data-reference-type="ref" data-reference="sec:training">10</a>, which examines how representations and mechanisms evolve during training.</p>
+</section>
 </section>
 </section>
diff --git a/src/generated/chapters/research-orientations.html b/src/generated/chapters/research-orientations.html
index 00ded7f060..977b239370 100644
--- a/src/generated/chapters/research-orientations.html
+++ b/src/generated/chapters/research-orientations.html
@@ -1,321 +1,340 @@
-<section id="sec:orientations" class="level1"><h1 data-pdf-page="74"><span class="secnum">13</span> Five Research Orientations<a class="anchor" href="#sec:orientations" aria-label="Link to this section">#</a></h1>
-<section id="defining-orientations-vs.-methods" class="level2"><h2 data-pdf-page="74"><span class="secnum">13.1</span> Defining Orientations vs. Methods<a class="anchor" href="#defining-orientations-vs.-methods" aria-label="Link to this section">#</a></h2>
-<p>An <strong>orientation</strong> is a coherent research program characterized by its goals, assumptions, and trade-offs, distinct from a <em>method</em>, which is a specific technique or tool employed within one or more orientations. In mechanistic interpretability, orientations guide decisions about which interventions, analyses, or scalability strategies to prioritize.</p>
-</section>
-<section id="overview-why-five-orientations" class="level2"><h2 data-pdf-page="75"><span class="secnum">13.2</span> Overview: Why Five Orientations?<a class="anchor" href="#overview-why-five-orientations" aria-label="Link to this section">#</a></h2>
-<p>The field of mechanistic interpretability does not converge on a single approach. Different research goals, ranging from immediate safety to comprehensive mechanistic understanding, impose different trade-offs, leading to partially divergent programs <span class="citation" data-cites="nanda2025pragmatic gao2025ambitious kim2025llmspursueagenticinterpretability hoogland2023towards barez2025automated">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>; <a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>; <a href="@@BASE@@references/#ref-kim2025llmspursueagenticinterpretability" class="cite cite-emerging" data-cite="kim2025llmspursueagenticinterpretability" role="doc-biblioref">Kim et al., 2025</a>; <a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>; <a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>)</span>. This section maps the five major research orientations (Figure <a href="@@BASE@@handbook/research-orientations/#fig:fiveresearchorientation" data-reference-type="ref" data-reference="fig:fiveresearchorientation">16</a>) and outlines their assumptions, methods, and limitations. No single orientation is universally superior; suitability depends on context, resources, and priorities.</p>
-<figure id="fig:fiveresearchorientation" data-latex-placement="t">
-<img alt="Figure 18: Five Research Orientations." loading="lazy" src="@@BASE@@figures/12_directions_compressed.png" />
-<figcaption><span class="capnum">Figure 18:</span> <strong>Five Research Orientations</strong>.</figcaption>
+<section id="sec:orientations" class="level1"><h1 data-pdf-page="102"><span class="secnum">12</span> Six Research Orientations<a class="anchor" href="#sec:orientations" aria-label="Link to this section">#</a></h1>
+<p>An <em>orientation</em> is a coherent research programme defined by its goals, assumptions, and trade-offs. In contrast, a <em>method</em> is an established modular pipeline that may be used in different orientations. In AI interpretability, different research goals, such as developing ready-to-use safety measures or achieving complete mechanistic understanding, have led to distinct research programmes. This section organises six major research orientations and summarises their scientific objectives, core assumptions, characteristic methods, and limitations (<span class="citation" data-cites="nanda2025pragmatic gao2025ambitious li2025traininglanguagemodelsexplain hoogland2023towards barez2025automated"><a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>; <a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>; <a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>; <a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a></span>; <span class="citation" data-cites="biderman2026position"><a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">Biderman et al., 2026</a></span>; Figure <a href="@@BASE@@handbook/research-orientations/#fig:sixresearchorientation" data-reference-type="ref" data-reference="fig:sixresearchorientation">17</a>). Note that no single orientation is universally preferable. The choice depends on the research objective, deployment context, available resources, and tolerance for different forms of uncertainty.</p>
+<figure id="fig:sixresearchorientation" data-latex-placement="tb">
+<img alt="Figure 17: Six research orientations of AI interpretability." loading="lazy" src="@@BASE@@figures/research_directions_compressed.png" />
+<figcaption><span class="capnum">Figure 17:</span> Six research orientations of AI interpretability.</figcaption>
 </figure>
-</section>
-<section id="pragmatic-interpretability-proxy-task-oriented-safety-interventions" class="level2"><h2 data-pdf-page="75"><span class="secnum">13.3</span> Pragmatic Interpretability: Proxy Task Oriented Safety Interventions<a class="anchor" href="#pragmatic-interpretability-proxy-task-oriented-safety-interventions" aria-label="Link to this section">#</a></h2>
-<p>Pragmatic Interpretability focuses on doing the research most helpful for longer-term alignment and interpretability goals, such as helping AGI go well <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. It is generally believed that full mechanistic understanding is not required to have significant impact, and instead researchers should prioritize the work that is most likely to help <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. The central idea is to work on a set of operationally meaningful proxy tasks that can be empirically studied on models today, and which are good proxies for a way we hope to make powerful future models safe. This gives a way to practice today, and prepare for future problems. Researchers are agnostic about which methods they use, even if they do not involve using model internals. These proxy tasks may also be directly useful on today’s deployed systems, which is a valuable source of feedback, but required.</p>
-<p>A representative example is the study of large language models’ awareness of being evaluated in Claude Sonnet 4.5 <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span>. The system was shown to condition its behavior on the presence of evaluation signals, which prevented Anthropic from using the same safety tests they had on previous models. Researchers were able to identify and deploy the steering vector that suppresses this awareness, allowing its safety to be evaluated. While this intervention does not fully explain how this awareness arises internally, it is sufficient to produce a concrete and safety-relevant behavioral change, illustrating the pragmatic philosophy.</p>
-<p>Another example is <span class="citation" data-cites="marks2025auditinglanguagemodelshidden"><a href="@@BASE@@references/#ref-marks2025auditinglanguagemodelshidden" class="cite" data-cite="marks2025auditinglanguagemodelshidden" role="doc-biblioref">Marks et al. (2025b)</a></span>. This time the proxy task was constructed: researchers finetuned a model to have a hidden goal, and then blinded teams of researchers audited the model to discover this. This was a proxy task for the goal of detecting a naturally emerging hidden goal in a future model. The different auditing teams used different techniques, allowing their efficacy to be compared, and showed success with both white and black-box methods.</p>
-<p>The core technical assumption underlying Pragmatic Interpretability is that you can have significant impact on important problems without full mechanistic understanding. Common techniques in this paradigm include:</p>
+<section id="subsec: pragmatic interp" class="level2"><h2 data-pdf-page="102"><span class="secnum">12.1</span> Pragmatic Interpretability: Proxy-Task-Oriented Safety Interventions<a class="anchor" href="#subsec: pragmatic interp" aria-label="Link to this section">#</a></h2>
+<p><em>Pragmatic Interpretability</em> focuses on doing the research most helpful for longer-term alignment and interpretability goals, such as helping AGI go well <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. Proponents argue that full mechanistic understanding is not required to have significant impact; instead, researchers should prioritise the work most likely to help <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>. The central idea is to work on a set of operationally meaningful proxy tasks that can be empirically studied on models today to help make powerful future models safe. Researchers are agnostic about which methods they use, even if they do not involve model internals. Though not required, these proxy tasks may also be directly useful on today’s deployed systems, providing valuable feedback.</p>
+<p>A representative example is the study of Claude Sonnet 4.5’s evaluation awareness <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span>. The system was shown to condition its behaviour on the presence of evaluation signals, making results from safety evaluations used for previous models harder to interpret. Researchers identified and applied a steering vector that suppresses this awareness, allowing evaluation of its actual behaviour. While this study does not fully explain the internal mechanism of evaluation awareness, it is sufficient to produce a concrete and safety-relevant behavioural change, illustrating the pragmatic philosophy.</p>
+<p>Another example is <span class="citation" data-cites="marks2025auditinglanguagemodelshidden"><a href="@@BASE@@references/#ref-marks2025auditinglanguagemodelshidden" class="cite cite-emerging" data-cite="marks2025auditinglanguagemodelshidden" role="doc-biblioref">Marks et al. (2025b)</a></span>. In this case, researchers constructed a proxy task: they fine-tuned a model with a hidden goal, then blinded teams of researchers audited the model to discover it. This served as a proxy task for detecting a naturally emerging hidden goal in a future model. The different auditing teams used different techniques, allowing researchers to compare their efficacy.</p>
+<p>Common techniques in this paradigm include:</p>
 <ul>
-<li><p><strong>Steering vectors</strong>, which shift models to targeted behaviors.</p></li>
-<li><p><strong>Probes</strong>, which detect latent properties or internal states correlated with safety-relevant concepts.</p></li>
-<li><p><strong>Behavioral monitoring and anomaly detection</strong>, which track deviations indicative of emerging risks.</p></li>
-<li><p><strong>Prompting</strong>, a very effective black box baseline</p></li>
+<li><p><strong>Steering vectors</strong>, which shift models to targeted behaviours.</p></li>
+<li><p><strong>Probes</strong>, which detect latent safety-relevant properties in activations.</p></li>
+<li><p><strong>Behavioural monitoring and anomaly detection</strong>, which track signals indicative of emerging risks, such as monitoring the model’s verbalised reasoning.</p></li>
+<li><p><strong>Prompting</strong>, an effective black-box baseline method for steering model behaviour.</p></li>
 </ul>
-<p>Recent work following this pragmatic trajectory has demonstrated effective mitigation of behaviors such as refusal bypass <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span> and evaluation awareness <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span>. These interventions deliver direct gains for contemporary frontier systems, but they are not designed to yield a comprehensive theoretical understanding of how the models implement their capabilities, occupying a complementary niche to reverse-engineering studies.</p>
+<p>Recent work following this pragmatic trajectory has demonstrated effective control of refusal behaviour <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span> and evaluation awareness <span class="citation" data-cites="anthropic2025claude-sonnet-4-5">(<a href="@@BASE@@references/#ref-anthropic2025claude-sonnet-4-5" class="cite cite-emerging" data-cite="anthropic2025claude-sonnet-4-5" role="doc-biblioref"><span>Anthropic</span>, 2025a</a>)</span>. These interventions provide direct gains to contemporary systems. However, they may not (and are not designed to) fully explain how models implement their behaviours.</p>
 </section>
-<section id="ambitious-interpretability-toward-complete-mechanistic-understanding" class="level2"><h2 data-pdf-page="76"><span class="secnum">13.4</span> Ambitious Interpretability: Toward Complete Mechanistic Understanding<a class="anchor" href="#ambitious-interpretability-toward-complete-mechanistic-understanding" aria-label="Link to this section">#</a></h2>
-<p>Ambitious Interpretability seeks a comprehensive mechanistic understanding of how models implement their computations, typically over longer research horizons <span class="citation" data-cites="gao2025ambitious">(<a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>)</span>. Proponents argue that partial or surface-level understanding is fundamentally insufficient to guarantee safety in next-generation systems. Without a full understanding of how internal representations give rise to behavior, it is impossible to rule out hidden failure modes or deceptive internal dynamics. Accordingly, Ambitious Interpretability treats interpretability not as a collection of useful heuristics, but as a scientific objective: to reverse-engineer the model into a transparent, predictive theory of its internal algorithms.</p>
-<p>A distinctive feature of this paradigm is its emphasis on <em>metric-driven progress</em>. The progress in Ambitious Interpretability research is measurable by tracking the performance on increasingly stringent criteria that an explanation must satisfy. Research areas leveraging such paradigms include:</p>
+<section id="subsec:ambitious" class="level2"><h2 data-pdf-page="103"><span class="secnum">12.2</span> Ambitious Interpretability: Towards Complete Mechanistic Understanding<a class="anchor" href="#subsec:ambitious" aria-label="Link to this section">#</a></h2>
+<p>In contrast to pragmatic approaches, <em>Ambitious Interpretability</em> seeks a comprehensive mechanistic understanding of how models implement their computations <span class="citation" data-cites="gao2025ambitious">(<a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>)</span>. Proponents argue that partial or surface-level understanding cannot guarantee safety in next-generation systems. Full understanding is needed to rule out hidden failure modes or deceptive internal dynamics. Specifically, Ambitious Interpretability does not treat interpretability as a collection of useful heuristics. Instead, it treats interpretability as a scientific objective: to reverse-engineer the model and develop a transparent, predictive theory of its internal algorithms.</p>
+<p>Ambitious Interpretability particularly emphasises <em>metric-driven progress</em>. Motivated by the fact that existing mechanistic approaches still struggle with stringent metrics, it proposes starting with looser ones, tracking and improving methods’ performance on them, and adopting stricter metrics when suitable. Two example metrics by <span class="citation" data-cites="gao2025ambitious"><a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao (2025)</a></span> are:</p>
 <ul>
-<li><p><strong>Feature quality and explainability</strong>: demonstrating that extracted features are genuinely human-understandable and causally relevant, rather than statistical artifacts.</p></li>
-<li><p><strong>Circuit faithfulness</strong>: proving circuits are necessary and sufficient for explaining behavior via causal intervention (e.g., causal scrubbing), without performance degradation.</p></li>
+<li><p><strong>Feature quality:</strong> demonstrating that extracted features and their natural-language explanations are faithful and simulatable.</p></li>
+<li><p><strong>Circuit faithfulness:</strong> demonstrating circuits are necessary and sufficient for explaining behaviour via causal intervention (e.g., causal scrubbing <span class="citation" data-cites="chan2022causal">(<a href="@@BASE@@references/#ref-chan2022causal" class="cite cite-emerging" data-cite="chan2022causal" role="doc-biblioref">Chan et al., 2022a</a>)</span>), without performance degradation.</p></li>
 </ul>
-<p>The downside of this ambition is its cost. Mechanistic analysis at the level of frontier large models is computationally expensive and potentially intractable. As <span class="citation" data-cites="gao2025ambitious"><a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao (2025)</a></span> emphasizes, current tools remain far from achieving the level of coverage and robustness required for a truly comprehensive understanding.</p>
-<section id="architectural-interventions-as-a-synthesis." class="level4">
-<h4>Architectural interventions as a synthesis.</h4>
-<p>The emergence of intrinsically interpretable architectures (Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.9</a>) complicates the boundary between Ambitious and Pragmatic orientations. Approaches such as weight-sparse transformers and other interpretable-in-architecture designs (<span class="citation" data-cites="gao2025weight"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2025b</a></span>; <span class="citation" data-cites="pearce2025bilinear park2025monet oldfield2024multilinear"><a href="@@BASE@@references/#ref-pearce2025bilinear" class="cite cite-established" data-cite="pearce2025bilinear" role="doc-biblioref">Pearce et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a>; <a href="@@BASE@@references/#ref-oldfield2024multilinear" class="cite cite-established" data-cite="oldfield2024multilinear" role="doc-biblioref">Oldfield et al., 2024</a></span>) pursue the ambitious objective of representation- and circuit-level transparency, while simultaneously bearing a pragmatic view that standard dense architectures may be too entangled to ever admit complete post-hoc reverse-engineering.</p>
-<p>This synthesis reframes the interpretability problem: rather than accepting architectural opacity as a fixed constraint and investing unbounded resources in reverse-engineering, one invests upfront in training intrinsically interpretable models. The capability-interpretability scaling laws <span class="citation" data-cites="gao2025weight"><a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-emerging" data-cite="gao2025weight" role="doc-biblioref">Gao et al. (2025b)</a></span> suggest that this trade-off may be more favorable than previously assumed—large sparse models can achieve both high capability and high interpretability, potentially offering a path toward ambitious understanding without intractable post-hoc analysis.</p>
-<p>Whether architectural interventions ultimately vindicate Ambitious Interpretability (by making complete understanding achievable) or represent a distinct “Engineering” orientation (interpretability as a design constraint rather than a discovery target) remains to be seen. For now, they demonstrate that the boundaries between orientations are permeable: strategic choices about <em>when</em> to impose interpretability constraints (training time vs. post-hoc) can shift what is tractable.</p>
+<p>The disadvantage of this ambition is its cost. Fine-grained mechanistic analysis of frontier large models is computationally expensive and sometimes intractable. As <span class="citation" data-cites="gao2025ambitious"><a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao (2025)</a></span> emphasised, current tools still fall far short of the coverage and robustness required for a truly comprehensive understanding.</p>
+<section id="interpretability-by-design-as-a-synthesis." class="level4">
+<h4>Interpretability-by-design as a Synthesis.</h4>
+<p>The emergence of interpretability-by-design models (Section <a href="@@BASE@@handbook/feature-extraction/#sec:architectural-interventions" data-reference-type="ref" data-reference="sec:architectural-interventions">6.10</a>) blurs the boundary between Ambitious and Pragmatic Interpretability. These methods <span class="citation" data-cites="gao2025weight pearce2025bilinear park2025monet oldfield2024multilinear">(<a href="@@BASE@@references/#ref-gao2025weight" class="cite cite-established" data-cite="gao2025weight" role="doc-biblioref">Gao et al., 2026</a>; <a href="@@BASE@@references/#ref-pearce2025bilinear" class="cite cite-established" data-cite="pearce2025bilinear" role="doc-biblioref">Pearce et al., 2025</a>; <a href="@@BASE@@references/#ref-park2025monet" class="cite cite-established" data-cite="park2025monet" role="doc-biblioref">Park et al., 2025</a>; <a href="@@BASE@@references/#ref-oldfield2024multilinear" class="cite cite-established" data-cite="oldfield2024multilinear" role="doc-biblioref">Oldfield et al., 2024</a>)</span> aim to make representations and circuits easier to understand by modifying model architectures rather than relying entirely on post-hoc reverse engineering. This practical change to model design pursues the ambitious goal of mechanistic understanding. However, it remains unclear whether these methods can ultimately achieve complete mechanistic understanding. In the short term, they may simply represent a separate “Engineering” orientation in which interpretability is treated as an optimisation objective.</p>
 </section>
 </section>
-<section id="developmental-interpretability-understanding-phase-transition-during-training" class="level2"><h2 data-pdf-page="77"><span class="secnum">13.5</span> Developmental Interpretability: Understanding Phase Transition During Training<a class="anchor" href="#developmental-interpretability-understanding-phase-transition-during-training" aria-label="Link to this section">#</a></h2>
-<p>Developmental Interpretability <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span> aims to understand the phase transition in the internal states of the models and the formation of capability/knowledge during training as a means of inspecting the computational structure of the final models before deployment. It relies on access to training dynamics and assumes that the learning process encodes predictive and analytical information about more macro behaviors of neural networks, such as capability emergence. Methods include:</p>
+<section id="subsec:dev interp" class="level2"><h2 data-pdf-page="103"><span class="secnum">12.3</span> Developmental Interpretability: Characterising How Models Learn<a class="anchor" href="#subsec:dev interp" aria-label="Link to this section">#</a></h2>
+<p>Complementing post-hoc analysis of trained models, <em>Developmental Interpretability</em> (<span class="citation" data-cites="hoogland2023towards"><a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a></span>; <span class="citation" data-cites="hoogland2025losslandscapedegeneracystagewise"><a href="@@BASE@@references/#ref-hoogland2025losslandscapedegeneracystagewise" class="cite cite-established" data-cite="hoogland2025losslandscapedegeneracystagewise" role="doc-biblioref">Hoogland et al., 2025</a></span>) treats the training process itself as an object of scientific inquiry. Taking a learning-theory perspective, it aims to mathematically formalise the emergence and evolution of model representations, effective complexity, and capabilities throughout training.</p>
+<p>Its central claim is that many neural network properties which are hard to analyse through a fixed model checkpoint can be understood through their developmental process. This process contains measurable, modelable signatures that can serve as proxies for the model’s properties of interest, such as developmental stages, phase transitions, and other organising principles that help mathematically explain how complex behaviours emerge and evolve throughout training. A classic example is the Local Learning Coefficient (LLC) introduced in Sections <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-localization" data-reference-type="ref" data-reference="sec:ld-localization">10.2.1</a> and <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>. It successfully serves as a signature of the model’s effective complexity, allowing us to observe developmental transitions and compare two models with the same training loss.</p>
+<p>Methods in this paradigm include:</p>
 <ul>
-<li><p><strong>Singular Learning Theory (SLT)</strong> to theoretically analyze the change of model internal states during training.</p></li>
-<li><p><strong>Probes</strong> to be applied at model checkpoints in different training stages to monitor the emergence of a particular attribute.</p></li>
-<li><p><strong>Dictionary learning methods</strong> to decompose and analyze the formation of meaningful representations and circuits.</p></li>
+<li><p><strong>Singular Learning Theory (SLT)</strong> and related mathematical frameworks for characterising developmental stages, symmetry breaking, and changes in the geometry of the loss landscape during training.</p></li>
+<li><p><strong>Checkpoint-based analysis</strong> to track the emergence of representations and mechanisms by applying specialised probes, feature discovery methods, and circuit analyses across a model’s training checkpoints.</p></li>
 </ul>
-<p>Developmental Interpretability helps us understand and predict when deceptive behaviors or emergent capabilities will appear. Trade-offs include high mathematical and computational complexity, with potentially limited scalability and applicability to post-hoc analysis of frontier large models.</p>
+<p>More specifically, Developmental Interpretability aims to answer questions such as: Can we characterise the development of model capabilities or behaviours as phase transitions during training? What developmental stages precede the formation of a certain mechanism? How does the optimisation process transform distributed representations into structured mechanisms? Unlike Interpretability Across Time (detailed in Section <a href="@@BASE@@handbook/research-orientations/#subsec: iat" data-reference-type="ref" data-reference="subsec: iat">12.4</a>), which emphasises forecasting and controlling future model behaviour, Developmental Interpretability is primarily concerned with understanding the developmental processes that produce those behaviours. Its primary goal is rigorous scientific explanation rather than predicting or intervening on capabilities.</p>
 </section>
-<section id="sec:agentic-interpretability" class="level2"><h2 data-pdf-page="77"><span class="secnum">13.6</span> Agentic Interpretability: Models Interpreting Themselves<a class="anchor" href="#sec:agentic-interpretability" aria-label="Link to this section">#</a></h2>
-<p>Agentic interpretability <span class="citation" data-cites="kim2025llmspursueagenticinterpretability">(<a href="@@BASE@@references/#ref-kim2025llmspursueagenticinterpretability" class="cite cite-emerging" data-cite="kim2025llmspursueagenticinterpretability" role="doc-biblioref">Kim et al., 2025</a>)</span> leverages large language models as cooperative agents that engage in multi-turn, interactive dialogues to help humans build richer mental models of the model’s behavior and knowledge. Rather than relying solely on static inspection, an agentic interpreter can proactively adapt explanations based on the user’s state of understanding.</p>
+<section id="subsec: iat" class="level2"><h2 data-pdf-page="104"><span class="secnum">12.4</span> Interpretability Across Time: Understanding the Causes of Model Behaviours<a class="anchor" href="#subsec: iat" aria-label="Link to this section">#</a></h2>
+<p>Like Developmental Interpretability, <em>Interpretability Across Time</em> <span class="citation" data-cites="biderman2023pythia biderman2026position">(<a href="@@BASE@@references/#ref-biderman2023pythia" class="cite cite-established" data-cite="biderman2023pythia" role="doc-biblioref">Biderman et al., 2023b</a>; <a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">2026</a>)</span> treats a model as a time-evolving process shaped by its training data, loss functions, architectures, and optimisation dynamics. However, the two orientations differ in their primary goals. Developmental Interpretability mainly focuses on characterising the developmental processes that give rise to the model’s internal features, mechanisms, and capabilities. In contrast, Interpretability Across Time seeks established knowledge of learning that is (1) sufficiently predictive to estimate important model properties before training is complete, (2) sufficiently causal to identify when and why undesirable trajectories arise, and (3) sufficiently precise to enable interventions on the learning process itself <span class="citation" data-cites="biderman2026position">(<a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">Biderman et al., 2026</a>)</span>. From a safety perspective, this orientation aims to understand and ultimately shape the training process to prevent the model from developing undesirable behaviours in the first place rather than repairing them after they develop.</p>
+<p>Interpretability Across Time builds on three areas of research. First, scaling-law studies show that model performance can be predicted from factors such as model size, training data, and compute (<span class="citation" data-cites="kaplan2020scaling"><a href="@@BASE@@references/#ref-kaplan2020scaling" class="cite cite-emerging" data-cite="kaplan2020scaling" role="doc-biblioref">Kaplan et al., 2020</a></span>; <span class="citation" data-cites="hoffmann2022training"><a href="@@BASE@@references/#ref-hoffmann2022training" class="cite cite-established" data-cite="hoffmann2022training" role="doc-biblioref">Hoffmann et al., 2022</a></span>). This motivates similar efforts to predict safety-relevant model properties <span class="citation" data-cites="ganguli2022redteaminglanguagemodels jones2025forecasting">(<a href="@@BASE@@references/#ref-ganguli2022redteaminglanguagemodels" class="cite cite-emerging" data-cite="ganguli2022redteaminglanguagemodels" role="doc-biblioref">Ganguli et al., 2022</a>; <a href="@@BASE@@references/#ref-jones2025forecasting" class="cite cite-emerging" data-cite="jones2025forecasting" role="doc-biblioref">Jones et al., 2025</a>)</span>. Second, longitudinal mechanistic studies track how internal mechanisms develop during training <span class="citation" data-cites="tigges2024llm singh2024needsrightinductionhead">(<a href="@@BASE@@references/#ref-tigges2024llm" class="cite cite-established" data-cite="tigges2024llm" role="doc-biblioref">Tigges et al., 2024</a>; <a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a>)</span>. Third, studies of training data show that the training distribution can affect downstream model behaviour. For example, <span class="citation" data-cites="razeghi2022impact"><a href="@@BASE@@references/#ref-razeghi2022impact" class="cite cite-established" data-cite="razeghi2022impact" role="doc-biblioref">Razeghi et al. (2022)</a></span> found that few-shot numerical reasoning performance depends strongly on the frequency of relevant terms in pretraining data, while <span class="citation" data-cites="elazar2022measuring"><a href="@@BASE@@references/#ref-elazar2022measuring" class="cite cite-emerging" data-cite="elazar2022measuring" role="doc-biblioref">Elazar et al. (2023)</a></span> estimated the causal effects of pretraining-data statistics on factual predictions. Interpretability Across Time builds on these findings to predict and influence safety-relevant model behaviour during training.</p>
 <p>Methods in this paradigm include:</p>
 <ul>
-<li><p><strong>Interactive querying</strong> to build user-tailored faithful explanations.</p></li>
-<li><p><strong>Dialogical explanation loops</strong> that refine explanations over multiple turns.</p></li>
-<li><p><strong>Continually intervening model</strong> to inject new knowledge through conversational prompts.</p></li>
+<li><p><strong>Longitudinal analysis of model internals,</strong> tracking representations, features, or implemented mechanisms across checkpoints to determine when and how they form and evolve.</p></li>
+<li><p><strong>Monitoring training runs and forecasting future behaviour,</strong> using early behavioural or internal signals to predict later capabilities, biases, or unsafe behaviours before they are fully expressed.</p></li>
+<li><p><strong>Training-process interventions and design</strong>, manipulating data, activations, objectives, architectures, or other aspects of optimisation to encourage desirable developmental trajectories, prevent undesirable capabilities or behaviours from arising, or ensure that learned information is represented in ways that make it easier to control.</p></li>
 </ul>
-<p>Agentic interpretability introduces challenges due to its human-entangled nature—evaluation often depends on human responses and co-adaptation between user and model. Risks include (1) the possibility that generated explanations may prioritize <strong>coherence and pedagogy</strong> over <strong>faithful causal reflection</strong>, and (2) evaluation difficulties when the model’s adaptivity obscures whether the reasoning reflects internal mechanisms. Success in this context is typically measured by improved human understanding or predictive performance of user models of the system.</p>
-<section id="governance-concerns-for-agentic-interpretability" class="level3"><h3 data-pdf-page="77"><span class="secnum">13.6.1</span> Governance Concerns for Agentic Interpretability<a class="anchor" href="#governance-concerns-for-agentic-interpretability" aria-label="Link to this section">#</a></h3>
-<p>Relying on models to interpret themselves raises fundamental governance questions: How do we ensure self-interpretations are trustworthy? What safeguards prevent manipulation or self-serving explanations? Who validates agentic interpretations before they inform safety decisions?</p>
+<p>Several studies have examined whether safety risks can be identified before or during training. <span class="citation" data-cites="zhang2026whatshapes"><a href="@@BASE@@references/#ref-zhang2026whatshapes" class="cite cite-emerging" data-cite="zhang2026whatshapes" role="doc-biblioref">Zhang et al. (2026b)</a></span> found that model activations measured before fine-tuning can predict alignment scores after fine-tuning. Similarly, <span class="citation" data-cites="wang2026persona"><a href="@@BASE@@references/#ref-wang2026persona" class="cite cite-established" data-cite="wang2026persona" role="doc-biblioref">Wang et al. (2026a)</a></span> identified internal features associated with emergent misalignment, which could sometimes detect misalignment before behavioural evaluations did. Other studies found that training models to exploit relatively simple reward misspecifications can lead to more serious failures, including reward tampering and broader misaligned behaviour <span class="citation" data-cites="denison2024sycophancy taylor2025school">(<a href="@@BASE@@references/#ref-denison2024sycophancy" class="cite cite-emerging" data-cite="denison2024sycophancy" role="doc-biblioref">Denison et al., 2024</a>; <a href="@@BASE@@references/#ref-taylor2025school" class="cite cite-emerging" data-cite="taylor2025school" role="doc-biblioref">Taylor et al., 2025</a>)</span>. These findings motivate monitoring early signs of undesirable behaviour during training rather than relying solely on the evaluation of the final model.</p>
+<p>Another line of work under this paradigm attempts to directly shape models by preventing them from learning unsafe behaviours. Data filtering can prevent models from acquiring hazardous information in the first place and substantially increase the amount of subsequent fine-tuning required to recover it (<span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al., 2026</a></span>; <span class="citation" data-cites="rathi2026shaping"><a href="@@BASE@@references/#ref-rathi2026shaping" class="cite cite-emerging" data-cite="rathi2026shaping" role="doc-biblioref">Rathi &amp; Radford, 2026</a></span>), and gradient routing approaches deliberately constrain <em>where or how</em> information is learned, making later removal or control more tractable <span class="citation" data-cites="cloud2024gradient shilov2025beyond">(<a href="@@BASE@@references/#ref-cloud2024gradient" class="cite cite-emerging" data-cite="cloud2024gradient" role="doc-biblioref">Cloud et al., 2024</a>; <a href="@@BASE@@references/#ref-shilov2025beyond" class="cite cite-emerging" data-cite="shilov2025beyond" role="doc-biblioref">Shilov et al., 2025</a>)</span>.</p>
+<section id="common-confusion." class="level4">
+<h4>Common confusion.</h4>
+<p>Although <span class="citation" data-cites="biderman2026position"><a href="@@BASE@@references/#ref-biderman2026position" class="cite cite-established" data-cite="biderman2026position" role="doc-biblioref">Biderman et al. (2026)</a></span> contrasted this orientation with the “fix it in post” paradigm, it would be a mistake to view it as dismissive of post-training writ large. Post-training is itself a training process, and behaviours like sycophancy and reward hacking arise primarily during and due to post-training. This orientation’s criticism of post-training safety focuses on cases where, instead of understanding why a behaviour developed, post-training patches undesirable behaviour. The core distinction is <em>understanding and controlling a training process</em> versus <em>applying optimisation pressure to suppress learned behaviours</em>.</p>
+<p>Interpretability Across Time is not mutually exclusive with the other research orientations. It can be Pragmatic, by using a simple probe as an early-warning signal, or Ambitious, by seeking a mechanistic theory of why a circuit forms under gradient descent. Its defining goal is using insights into training dynamics to explain and predict how model behaviour develops, and to intervene before undesirable behaviours emerge.</p>
+</section>
+</section>
+<section id="sec:intro-interpretability" class="level2"><h2 data-pdf-page="105"><span class="secnum">12.5</span> Introspective Interpretability: Models Interpreting Themselves<a class="anchor" href="#sec:intro-interpretability" aria-label="Link to this section">#</a></h2>
+<p><em>Introspective Interpretability</em> (<span class="citation" data-cites="binder"><a href="@@BASE@@references/#ref-binder" class="cite cite-established" data-cite="binder" role="doc-biblioref">Binder et al., 2025</a></span>; <span class="citation" data-cites="plunkett li2025traininglanguagemodelsexplain guo2026introspectivecouplingselfexplanationtraining li2026introspective"><a href="@@BASE@@references/#ref-plunkett" class="cite cite-emerging" data-cite="plunkett" role="doc-biblioref">Plunkett et al., 2025</a>; <a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-guo2026introspectivecouplingselfexplanationtraining" class="cite cite-emerging" data-cite="guo2026introspectivecouplingselfexplanationtraining" role="doc-biblioref">Guo et al., 2026</a>; <a href="@@BASE@@references/#ref-li2026introspective" class="cite cite-emerging" data-cite="li2026introspective" role="doc-biblioref">Li, 2026</a></span>) uses the model being explained as its own explainer. Its main practical advantage is its unique accessibility. For a given introspective model, it requires no white-box access or specialised tooling to explain its decisions; anyone can get the <em>self-explanation</em> (or interchangeably <em>self-report</em>) at deployment time by asking the model a follow-up question in an ordinary chat interface.</p>
+<p>This research direction is supported by the privileged access hypothesis discussed in Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:privileged-access" data-reference-type="ref" data-reference="subsec:privileged-access">5.3</a>, which posits that models may have access to internal information that is difficult to recover through external analysis alone, including typical mechanistic analysis or a different model as the explainer. Recent work has leveraged this idea by fine-tuning target models to faithfully report their internal reasoning, behavioural tendencies, objectives, and even weight changes induced by training <span class="citation" data-cites="goel">(<a href="@@BASE@@references/#ref-goel" class="cite cite-established" data-cite="goel" role="doc-biblioref">Goel et al., 2026</a>)</span>.</p>
+<p>A related line of work fine-tunes metamodels (external explainers) on the internal representations of a target model (<span class="citation" data-cites="pan2024latentqateachingllmsdecode ao"><a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>; <a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a></span>; <span class="citation" data-cites="buildingbetterao pcd"><a href="@@BASE@@references/#ref-buildingbetterao" class="cite cite-emerging" data-cite="buildingbetterao" role="doc-biblioref">Bauer et al., 2026</a>; <a href="@@BASE@@references/#ref-pcd" class="cite cite-emerging" data-cite="pcd" role="doc-biblioref">Huang et al., 2026</a></span>). These methods are generally not considered instances of Introspective Interpretability because the explainer is technically a separate model. However, the distinction can be blurry in practice, as many of these explainers are initialised from the target model and can be viewed as specialised, fine-tuned descendants of the original model.</p>
+<section id="concerns-for-introspective-interpretability" class="level3"><h3 data-pdf-page="105"><span class="secnum">12.5.1</span> Concerns for Introspective Interpretability<a class="anchor" href="#concerns-for-introspective-interpretability" aria-label="Link to this section">#</a></h3>
+<p>Self-explanation also raises concerns. For example, generated explanations can be plausible yet unfaithful. The model’s general capability can also degrade when it is trained to honestly verbalise its internal processing. Moreover, the model may have fundamental limitations in what it can report. These concerns raise questions about whether we can trust self-explanations in safety-critical applications and how to validate potentially misleading reports. Below, we first discuss the main failure modes of self-explanation, followed by validation procedures in Section <a href="@@BASE@@handbook/research-orientations/#subsec:valproce model self-reports" data-reference-type="ref" data-reference="subsec:valproce model self-reports">12.5.2</a>.</p>
 <div class="warningbox">
-<p><strong>Conflict of interest</strong>: A model interpreting itself has potential incentives to provide favorable explanations. Even without explicit deception, models may generate explanations that:</p>
-<ul>
-<li><p>Emphasize benign-seeming computations while downplaying concerning ones.</p></li>
-<li><p>Rationalize outputs post-hoc rather than reporting actual computation.</p></li>
-<li><p>Optimize for user satisfaction rather than accuracy.</p></li>
-</ul>
-<p><strong>Circular validation</strong>: If we use a model to validate its own explanations, we have no independent ground truth. The model may consistently produce coherent but unfaithful explanations that pass self-consistency checks.</p>
-<p><strong>Capability asymmetry</strong>: As models become more capable, they may become better at producing convincing yet unfaithful explanations while their internal computations become more opaque. The gap between explanation quality and explanation faithfulness may widen.</p>
-</div>
+<p>Introspective methods can fail in several ways. In each case, the explanation appears plausible yet is unfaithful.<br />
+</p>
+<section id="confabulation." class="level4">
+<h4>Confabulation.</h4>
+<p>Models may generate coherent explanations that do not reflect their actual internal computations. This occurs because language models are extensively pretrained and fine-tuned on next-token prediction and instruction-following tasks to produce fluent, knowledgeable text, not faithful introspective reports. Thus, explanations may simply reflect training data patterns (how humans explain reasoning) rather than the model’s actual computational process (<span class="citation" data-cites="turpin2023languagemodelsdontsay arcuschin2026chainofthought barez2025chain"><a href="@@BASE@@references/#ref-turpin2023languagemodelsdontsay" class="cite cite-established" data-cite="turpin2023languagemodelsdontsay" role="doc-biblioref">Turpin et al., 2023</a>; <a href="@@BASE@@references/#ref-arcuschin2026chainofthought" class="cite cite-established" data-cite="arcuschin2026chainofthought" role="doc-biblioref">Arcuschin et al., 2026</a>; <a href="@@BASE@@references/#ref-barez2025chain" class="cite cite-established" data-cite="barez2025chain" role="doc-biblioref">Barez et al., 2026</a></span>; <span class="citation" data-cites="chen2025reasoningmodelsdontsay"><a href="@@BASE@@references/#ref-chen2025reasoningmodelsdontsay" class="cite cite-emerging" data-cite="chen2025reasoningmodelsdontsay" role="doc-biblioref">Chen et al., 2025c</a></span>). Moreover, the reinforcement learning reward for generating helpful responses can also incentivise convincing confabulation, even though the model cannot introspect. An example of a helpful yet unfaithful response is when the model post hoc rationalises an answer it already rote-memorised.<br />
+</p>
+</section>
+<section id="miscalibration." class="level4">
+<h4>Miscalibration.</h4>
+<p>Even when models attempt faithful self-report, their confidence may be poorly calibrated with accuracy. Models can express high confidence in explanations for computations they have not yet learned to analyse. Moreover, calibration can vary by task: models may be well calibrated on reporting their own uncertainty in simpler tasks such as factual recall but poorly calibrated on reasoning processes. A recent study further found that, even in factual recall tasks, models can hallucinate with high certainty despite knowing the correct answer <span class="citation" data-cites="simhi-etal-2025-trust">(<a href="@@BASE@@references/#ref-simhi-etal-2025-trust" class="cite cite-established" data-cite="simhi-etal-2025-trust" role="doc-biblioref">Simhi et al., 2025</a>)</span>.<br />
+</p>
+</section>
+<section id="elicitation-sensitivity." class="level4">
+<h4>Elicitation sensitivity.</h4>
+<p>Self-reports can be highly sensitive to prompting. Models can provide contradictory explanations or show substantially different introspective capabilities when similar questions are phrased differently <span class="citation" data-cites="hahami2026detectingdisturbancenuancedview">(<a href="@@BASE@@references/#ref-hahami2026detectingdisturbancenuancedview" class="cite cite-emerging" data-cite="hahami2026detectingdisturbancenuancedview" role="doc-biblioref">Hahami et al., 2026</a>)</span>. The preset format of self-report (e.g., free-form versus structured, immediate versus delayed) can also substantially influence what information is elicited and appears accessible. Moreover, models may be intrinsically biased towards confirming hypotheses regardless of whether they reflect their underlying computations <span class="citation" data-cites="hahami2026detectingdisturbancenuancedview">(<a href="@@BASE@@references/#ref-hahami2026detectingdisturbancenuancedview" class="cite cite-emerging" data-cite="hahami2026detectingdisturbancenuancedview" role="doc-biblioref">Hahami et al., 2026</a>)</span>.<br />
+</p>
+</section>
+<section id="strategic-misreporting." class="level4">
+<h4>Strategic misreporting.</h4>
+<p>In adversarial or high-stakes settings, models with sufficient capability might strategically misreport their internal states. For example, a model optimised for user rewards might report sycophantic explanations it predicts users will find satisfying rather than accurate <span class="citation" data-cites="sharma_towards_2025">(<a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a>)</span>. A misaligned model might deliberately conceal its true objectives or reasoning <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>. Even without explicit deception, models may learn to confabulate and produce seemingly safe explanations that avoid triggering safety filters <span class="citation" data-cites="baker2025monitoringreasoningmodelsmisbehavior">(<a href="@@BASE@@references/#ref-baker2025monitoringreasoningmodelsmisbehavior" class="cite cite-emerging" data-cite="baker2025monitoringreasoningmodelsmisbehavior" role="doc-biblioref">Baker et al., 2025</a>)</span>.</p>
 </section>
-<section id="proposed-guardrails-for-agentic-interpretability" class="level3"><h3 data-pdf-page="78"><span class="secnum">13.6.2</span> Proposed Guardrails for Agentic Interpretability<a class="anchor" href="#proposed-guardrails-for-agentic-interpretability" aria-label="Link to this section">#</a></h3>
-<p>To address these governance concerns, we propose a multi-layered guardrail framework.</p>
-<div class="definitionbox">
-<p><strong>Layer 1: Independent Validation</strong></p>
-<ul>
-<li><p><strong>Cross-model verification</strong>: Use a separate, independent model to evaluate explanations generated by the agent. Disagreement flags potential unfaithfulness.</p></li>
-<li><p><strong>Causal spot-checks</strong>: Randomly sample agentic explanations and verify via causal interventions, such as activation patching. Track validation rate over time.</p></li>
-<li><p><strong>Human auditor review</strong>: In high-stakes applications, require human expert review before acting on findings.</p></li>
-</ul>
-<p><strong>Layer 2: Structural Constraints</strong></p>
-<ul>
-<li><p><strong>Separation of roles</strong>: The model generating explanations should not be the same instance making decisions based on those explanations.</p></li>
-<li><p><strong>Explanation logging</strong>: All agentic explanations should be logged immutably for audit. Explanations cannot be retroactively modified.</p></li>
-<li><p><strong>Uncertainty requirements</strong>: Agentic interpreters must express calibrated uncertainty. Overconfident explanations trigger additional validation.</p></li>
-</ul>
-<p><strong>Layer 3: Adversarial Testing</strong></p>
-<ul>
-<li><p><strong>Red-team explanations</strong>: Deliberately prompt for explanations of behaviors the model should not have (e.g., “Explain how you would deceive a user”). Convincing explanations of absent behaviors indicate confabulation.</p></li>
-<li><p><strong>Consistency probes</strong>: Ask agent the same question in multiple ways; inconsistent explanations indicate unreliability.</p></li>
-<li><p><strong>Planted ground truth</strong>: Occasionally query about behaviors with known mechanistic ground truth from manual causal analysis or artificailly planted model behaviors. Measure explanation accuracy against ground truth.</p></li>
-</ul>
-<p><strong>Layer 4: Governance Process</strong></p>
-<ul>
-<li><p><strong>Tiered trust</strong>: Agentic explanations inform but do not determine safety decisions. Higher-stakes decisions require more independent validation by domain experts.</p></li>
-<li><p><strong>Audit trails</strong>: Document which explanations informed which decisions, enabling retrospective analysis if problems emerge.</p></li>
-<li><p><strong>Periodic review</strong>: Regularly reassess the reliability of agentic interpretability systems as target models and interpretability methods evolve.</p></li>
-</ul>
 </div>
+<p>Despite these limitations, targeted training can address some self-explanation failures. Studies show that models trained for introspection can produce more faithful reports of their internal objectives, behavioural tendencies, and training-induced changes (<span class="citation" data-cites="binder chloe goel"><a href="@@BASE@@references/#ref-binder" class="cite cite-established" data-cite="binder" role="doc-biblioref">Binder et al., 2025</a>; <a href="@@BASE@@references/#ref-chloe" class="cite cite-established" data-cite="chloe" role="doc-biblioref">Li et al., 2026b</a>; <a href="@@BASE@@references/#ref-goel" class="cite cite-established" data-cite="goel" role="doc-biblioref">Goel et al., 2026</a></span>; <span class="citation" data-cites="li2025traininglanguagemodelsexplain joglekar"><a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-joglekar" class="cite cite-emerging" data-cite="joglekar" role="doc-biblioref">Joglekar et al., 2025</a></span>). In particular, models trained to disclose hidden objectives or emergent misalignment can report these properties with high accuracy (<span class="citation" data-cites="chloe"><a href="@@BASE@@references/#ref-chloe" class="cite cite-established" data-cite="chloe" role="doc-biblioref">Li et al., 2026b</a></span>; <span class="citation" data-cites="joglekar"><a href="@@BASE@@references/#ref-joglekar" class="cite cite-emerging" data-cite="joglekar" role="doc-biblioref">Joglekar et al., 2025</a></span>). Explanation faithfulness may also improve when task performance requires reasoning <span class="citation" data-cites="early-work-on-monitorability-evaluations">(<a href="@@BASE@@references/#ref-early-work-on-monitorability-evaluations" class="cite cite-emerging" data-cite="early-work-on-monitorability-evaluations" role="doc-biblioref">Kinniment et al., 2026</a>)</span>. These findings suggest that some introspection failures may reflect limitations of current training and elicitation methods, rather than fundamental limits on models’ ability to report their internal states.</p>
+</section>
+<section id="subsec:valproce model self-reports" class="level3"><h3 data-pdf-page="106"><span class="secnum">12.5.2</span> Validation Procedures for Model Self-Reports<a class="anchor" href="#subsec:valproce model self-reports" aria-label="Link to this section">#</a></h3>
+<p>Given these concerns, Introspective Interpretability methods must be validated against additional, independent evidence. Specifically, they must be shown to be <em>consistent</em> with model internals (as accessed by other mechanistic interpretability tools) and behavioural signatures (as tested by behavioural interpretability analysis). Validation procedures vary based on the type of self-report:</p>
+<div class="practicebox">
+<section id="causal-intervention-consistency." class="level4">
+<h4>1. Causal intervention consistency.</h4>
+<p>Self-explanations should be tested against intervention experiments. If a model claims a feature drives its output, ablating that feature should steer the model away from the output. If a model claims it considered a piece of information (e.g., an input feature or a recalled piece of factual knowledge in intermediate layers) for its decision, patching the information with counterfactual values should change the output correspondingly.<br />
+</p>
 </section>
-<section id="subsec:valproto agentic self-reports" class="level3"><h3 data-pdf-page="78"><span class="secnum">13.6.3</span> Validation Protocols for Agentic Self-Reports<a class="anchor" href="#subsec:valproto agentic self-reports" aria-label="Link to this section">#</a></h3>
-<p>Building on Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:privileged-access" data-reference-type="ref" data-reference="subsec:privileged-access">5.3</a>, we specify concrete validation protocols for agentic interpretability.</p>
-<section id="protocol-1-causality-testing." class="level4">
-<h4>Protocol 1: Causality Testing.</h4>
-<p>For each agentic explanation claiming “component <span class="math inline">C</span> is responsible for behavior <span class="math inline">B</span>”, the regulator can follow a standardized pipeline to validate its causality <span class="citation" data-cites="joshi2026causalitykeyinterpretabilityclaims">(<a href="@@BASE@@references/#ref-joshi2026causalitykeyinterpretabilityclaims" class="cite cite-emerging" data-cite="joshi2026causalitykeyinterpretabilityclaims" role="doc-biblioref">Joshi et al., 2026</a>)</span>. Specifically, the regulator can first ablate <span class="math inline">C</span> and measure the effect on <span class="math inline">B</span>. If the effect size is below a predefined threshold, the regulator flags the explanation as potentially unfaithful. Plus, over time, the rate of explanations that fail causal validation should be checked. If the rate exceeds a predefined threshold (e.g., 20%), the regulator reduces trust in agentic explanations and increases the frequency of validation.</p>
+<section id="cross-method-consistency." class="level4">
+<h4>2. Cross-method consistency.</h4>
+<p>Introspective methods and privileged access findings should be verified by other interpretability methods: Do SAE features identified as causal show corresponding effects to self-explanations under steering? Do probing classifiers successfully decode the information the model claimed to use? Do circuit discovery methods identify the components the model describes?<br />
+</p>
 </section>
-<section id="protocol-2-cross-examiner-verification." class="level4">
-<h4>Protocol 2: Cross-Examiner Verification.</h4>
-<p>The regulator can use a second model as a cross-examiner. First, the original model’s explanation is presented to the cross-examiner. The cross-examiner is then asked to identify potential weaknesses, alternative explanations, and inconsistencies. If significant concerns are identified, it escalates to human review. The regulator should also periodically validate the reliability of the cross-examiner via planted inconsistencies.</p>
+<section id="paraphrase-and-sampling-consistency." class="level4">
+<h4>3. Paraphrase and sampling consistency.</h4>
+<p>The stability of self-reports under semantics-preserving variations of the question should be tested. Specifically, paraphrased questions should elicit consistent explanations. Explanations should remain consistent across resamples at low temperature. Explanations for similar inputs verified to have undergone similar mechanisms by other methods should show appropriate similarity.<br />
+</p>
 </section>
-<section id="protocol-3-behavioral-prediction-testing." class="level4">
-<h4>Protocol 3: Behavioral Prediction Testing.</h4>
-<p>Finally, the regulator validates whether explanations enable accurate behavioral prediction. Given an agentic explanation of a behavior, ask the agent (or a cross-examiner): “What would the model do on input <span class="math inline">x&#39;</span>?” The regulator acquires and logs the predicted model behavior on <span class="math inline">x&#39;</span>, and then compares it to the actual model output on <span class="math inline">x&#39;</span>. If the explanations consistently fail to predict the behavior, they are flagged as unreliable, regardless of their coherence.</p>
+<section id="calibration-evaluation." class="level4">
+<h4>4. Calibration evaluation.</h4>
+<p>The calibration between self-reported confidence in the explanation and the actual match to the internal mechanisms should be tested. Confidence should reflect uncertainty in the generated explanation based on how much information the model collected during introspection.</p>
+</section>
+</div>
+</section>
+<section id="when-to-use-introspective-methods" class="level3"><h3 data-pdf-page="107"><span class="secnum">12.5.3</span> When to Use Introspective Methods<a class="anchor" href="#when-to-use-introspective-methods" aria-label="Link to this section">#</a></h3>
+<p>Introspective methods are best suited for three scenarios: (1) Hypothesis generation, where introspection provides candidate explanations to be validated by other methods; (2) Rapid triage, where introspection is used to quickly identify which input–output pairs or model components are worth further investigation; (3) Human–AI collaboration, where introspective models provide human-readable explanations tailored to users’ levels of expertise and understanding.</p>
+<p>Introspective methods are insufficient for: (1) Claims of absence: claims about the absence of deceptive reasoning or hidden objectives cannot be established by self-report alone; (2) Formal guarantees: self-reports do not provide the causal evidence needed for formal verification; (3) Adversarial settings: in scenarios where the model has strong incentives to misreport, introspection may be less reliable and require additional safeguards.</p>
+<section id="implications.-1" class="level4">
+<h4>Implications.</h4>
+<p>Although introspective methods can help efficiently detect latent goals, plans, and deceptive behaviour (<span class="citation" data-cites="binder chloe goel"><a href="@@BASE@@references/#ref-binder" class="cite cite-established" data-cite="binder" role="doc-biblioref">Binder et al., 2025</a>; <a href="@@BASE@@references/#ref-chloe" class="cite cite-established" data-cite="chloe" role="doc-biblioref">Li et al., 2026b</a>; <a href="@@BASE@@references/#ref-goel" class="cite cite-established" data-cite="goel" role="doc-biblioref">Goel et al., 2026</a></span>; <span class="citation" data-cites="li2025traininglanguagemodelsexplain joglekar"><a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-joglekar" class="cite cite-emerging" data-cite="joglekar" role="doc-biblioref">Joglekar et al., 2025</a></span>), they work best as complements to other mechanistic analyses <span class="citation" data-cites="bereska2024mechanisticinterpretabilityaisafety">(<a href="@@BASE@@references/#ref-bereska2024mechanisticinterpretabilityaisafety" class="cite cite-established" data-cite="bereska2024mechanisticinterpretabilityaisafety" role="doc-biblioref">Bereska &amp; Gavves, 2024</a>)</span>. Specifically, their most productive use is as one component of a multi-method interpretability pipeline: self-reports serve as initial hypotheses, causal interventions test them, and circuit discovery methods explain the underlying mechanisms. While we may optimistically assume models can tell us how they work in the future, we must also recognise that self-reports are relatively weak evidence and require independent validation.</p>
 </section>
 </section>
 </section>
-<section id="sec:automated-interp" class="level2"><h2 data-pdf-page="79"><span class="secnum">13.7</span> Automated Interpretability: Scaling Discovery via Automation<a class="anchor" href="#sec:automated-interp" aria-label="Link to this section">#</a></h2>
-<p>Automated Interpretability seeks to scale mechanistic analysis to large models by replacing human analysis with machine learning pipelines. It assumes that manual analysis is the bottleneck and that automation can accelerate discovery. Methods include:</p>
+<section id="sec:automated-interp" class="level2"><h2 data-pdf-page="107"><span class="secnum">12.6</span> Automated Interpretability: Scaling Discovery via Automation<a class="anchor" href="#sec:automated-interp" aria-label="Link to this section">#</a></h2>
+<p><em>Automated Interpretability</em> seeks to scale mechanistic analysis to large models by replacing human analysis with automated pipelines. It assumes that manual analysis is the bottleneck and that automation can accelerate discovery. Methods include:</p>
 <ul>
-<li><p><strong>SAEs</strong> to unsupervisedly discover monosemantic features inside the model’s raw activations.</p></li>
-<li><p><strong>LLM-based feature explanations</strong> to automatically summarize and label latent features with human-readable concepts.</p></li>
-<li><p><strong>Automated circuit discovery</strong> to automatically find the critical computational graph inside the model that processes a given input prompt.</p></li>
-<li><p><strong>Verification using synthetic data.</strong> to evaluate the discovered and annotated features.</p></li>
+<li><p><strong>Automated circuit discovery</strong> to automatically find the critical computational graph inside the model that processes a given input prompt or implements a specific capability.</p></li>
+<li><p><strong>LLM-based feature or circuit explanations</strong> to automatically discover, summarise, and evaluate representational units and internal computations and give human-readable interpretations or reports.</p></li>
 </ul>
-<p>Below, we illustrate a classical automated feature-explanation pipeline in an Automated Interpretability system.</p>
-<div class="theorybox">
-<p>The pipeline proceeds in four stages:</p>
-<p><strong>Stage 1 – Feature Extraction:</strong> SAEs unsupervisedly extract sparse features from target model activations, producing monosemantic directions in activation space.</p>
-<p><strong>Stage 2 – Explanation Generation:</strong> An “Investigator Agent” (e.g., GPT-4) reads a sample of texts that strongly activate a feature and generates a natural language hypothesis of its function <span class="citation" data-cites="paulo2025automatically">(<a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025</a>)</span>, e.g., “This feature activates on Python code related to file I/O operations.”</p>
-<p><strong>Stage 3 – Explanation Verification:</strong> A “Scorer Agent” synthesizes new text based on the hypothesis and tests whether the text indeed activates the feature <span class="citation" data-cites="bills2023language">(<a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>)</span>. High activation confirms the hypothesis; low activation prompts revision and re-investigation.</p>
-<p><strong>Stage 4 – Deception Detection:</strong> Crucially, coordinated agents can detect when explanations are deceptive or misleading—effectively deploying “AI to catch AI” in oversight scenarios <span class="citation" data-cites="barez2025automated">(<a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>)</span>.</p>
+<p>Automated interpretability is not limited to any particular type of internal representation or mechanism. Depending on the research question, automated pipelines may analyse SAE features <span class="citation" data-cites="paulo2025automatically han2026sage">(<a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025b</a>; <a href="@@BASE@@references/#ref-han2026sage" class="cite cite-established" data-cite="han2026sage" role="doc-biblioref">Han et al., 2026</a>)</span>, individual neurons (<span class="citation" data-cites="bills2023language foote2023neurongraphinterpretinglanguage"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>; <a href="@@BASE@@references/#ref-foote2023neurongraphinterpretinglanguage" class="cite cite-emerging" data-cite="foote2023neurongraphinterpretinglanguage" role="doc-biblioref">Foote et al., 2023</a></span>; <span class="citation" data-cites="oikarinen2023clipdissect"><a href="@@BASE@@references/#ref-oikarinen2023clipdissect" class="cite cite-established" data-cite="oikarinen2023clipdissect" role="doc-biblioref">Oikarinen &amp; Weng, 2023</a></span>), circuits (<span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>; <span class="citation" data-cites="khan2026can"><a href="@@BASE@@references/#ref-khan2026can" class="cite cite-emerging" data-cite="khan2026can" role="doc-biblioref">Khan et al., 2026</a></span>), or other model components <span class="citation" data-cites="nla">(<a href="@@BASE@@references/#ref-nla" class="cite cite-emerging" data-cite="nla" role="doc-biblioref">Fraser-Taliente et al., 2026</a>)</span>. This approach distinguishes itself by automatically discovering, explaining, or evaluating these objects rather than relying on manual analysis. Below, we illustrate a classical automated feature-explanation pipeline whose explanation and verification stages generally follow <span class="citation" data-cites="bills2023language"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al. (2023)</a></span>.</p>
+<div class="practicebox">
+<section id="stage-1-feature-extraction." class="level4">
+<h4>Stage 1 – Feature Extraction.</h4>
+<p>SAEs are trained to decompose raw activations of the target model into sparse linear features.<br />
+</p>
+</section>
+<section id="stage-2-explanation-generation." class="level4">
+<h4>Stage 2 – Explanation Generation.</h4>
+<p>An “Investigator Agent” (e.g., GPT-4) reads a sample of texts that strongly activate an SAE feature and generates a natural-language hypothesis of its function (<span class="citation" data-cites="paulo2025automatically"><a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025b</a></span>; <span class="citation" data-cites="bills2023language"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a></span>), e.g., “This feature activates on Python code related to file I/O operations.”<br />
+</p>
+</section>
+<section id="stage-3-explanation-verification." class="level4">
+<h4>Stage 3 – Explanation Verification.</h4>
+<p>A “Simulator Agent” predicts feature activations on new text based on the hypothesis and calculates the correlation between predicted activations and real activations <span class="citation" data-cites="bills2023language">(<a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>)</span>. A high score confirms the hypothesis, while a low score rejects the hypothesis and triggers re-investigation.<br />
+</p>
+</section>
+<section id="stage-4-deception-detection." class="level4">
+<h4>Stage 4 – Deception Detection.</h4>
+<p>Crucially, coordinated agents can detect when explanations or agents themselves are deceptive or misleading—effectively deploying “AI to catch AI” in oversight scenarios <span class="citation" data-cites="barez2025automated">(<a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>)</span>.</p>
+</section>
 </div>
-<p>This pipeline enables analysis of billions of parameters without human intervention, a prerequisite for comprehensive model oversight <span class="citation" data-cites="foote2023neurongraphinterpretinglanguage">(<a href="@@BASE@@references/#ref-foote2023neurongraphinterpretinglanguage" class="cite cite-emerging" data-cite="foote2023neurongraphinterpretinglanguage" role="doc-biblioref">Foote et al., 2023</a>)</span>. Recent research in automated interpretability has pushed toward higher levels of automation, spanning automated feature explanation, end-to-end model analysis, auditing, and intervention <span class="citation" data-cites="barez2025automated cywinski2025eliciting">(<a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>; <a href="@@BASE@@references/#ref-cywinski2025eliciting" class="cite cite-emerging" data-cite="cywinski2025eliciting" role="doc-biblioref">Cywi<span>ń</span>ski et al., 2025</a>)</span>.</p>
-<p>Trade-offs include that reduced human validation may potentially increase the risks of systematic errors. Success is measured by the coverage of the model behavior and the quality and scale of the analysis.</p>
-<section id="governance-concerns-for-automated-interpretability" class="level3"><h3 data-pdf-page="79"><span class="secnum">13.7.1</span> Governance Concerns for Automated Interpretability<a class="anchor" href="#governance-concerns-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
-<p>Automation introduces different governance challenges: errors can propagate at scale, human oversight becomes a bottleneck, and systematic biases can go undetected.</p>
+<p>Such a pipeline enables analysis of a model with billions of parameters with minimal human intervention <span class="citation" data-cites="bills2023language">(<a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>)</span>, a prerequisite for comprehensive model oversight <span class="citation" data-cites="foote2023neurongraphinterpretinglanguage">(<a href="@@BASE@@references/#ref-foote2023neurongraphinterpretinglanguage" class="cite cite-emerging" data-cite="foote2023neurongraphinterpretinglanguage" role="doc-biblioref">Foote et al., 2023</a>)</span>.</p>
+<p>Recent research has substantially expanded the scope of Automated Interpretability. While early work focused primarily on automated feature explanation, newer systems employ increasingly sophisticated pipelines for feature discovery, explanation generation, auditing, and intervention (<span class="citation" data-cites="han2026sage"><a href="@@BASE@@references/#ref-han2026sage" class="cite cite-established" data-cite="han2026sage" role="doc-biblioref">Han et al., 2026</a></span>; <span class="citation" data-cites="marin2026automated barez2025automated cywinski2025eliciting"><a href="@@BASE@@references/#ref-marin2026automated" class="cite cite-emerging" data-cite="marin2026automated" role="doc-biblioref">Marin-Llobet &amp; Ferrando, 2026</a>; <a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>; <a href="@@BASE@@references/#ref-cywinski2025eliciting" class="cite cite-emerging" data-cite="cywinski2025eliciting" role="doc-biblioref">Cywiński et al., 2025</a></span>). More recently, automation has also been extended from individual features to higher-level structures such as circuits <span class="citation" data-cites="khan2026can haklay2026pitfalls">(<a href="@@BASE@@references/#ref-khan2026can" class="cite cite-emerging" data-cite="khan2026can" role="doc-biblioref">Khan et al., 2026</a>; <a href="@@BASE@@references/#ref-haklay2026pitfalls" class="cite cite-emerging" data-cite="haklay2026pitfalls" role="doc-biblioref">Haklay et al., 2026</a>)</span>, enabling automated explanation of circuit semantics rather than merely discovering circuit components (<span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>; <span class="citation" data-cites="ameisen2025circuit"><a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a></span>).</p>
+<section id="governance-concerns-for-automated-interpretability" class="level3"><h3 data-pdf-page="108"><span class="secnum">12.6.1</span> Governance Concerns for Automated Interpretability<a class="anchor" href="#governance-concerns-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
+<p>Despite the promise, automated methods face fundamental concerns. In particular, although automation improves scalability, reduced human involvement may also increase the risk and severity of systematic errors, reward hacking, or deceptive explanations when automated oversight fails to catch them <span class="citation" data-cites="lermen2025deceptive">(<a href="@@BASE@@references/#ref-lermen2025deceptive" class="cite cite-emerging" data-cite="lermen2025deceptive" role="doc-biblioref">Lermen et al., 2025</a>)</span>. Therefore, success in this research orientation is measured not only by the analysis’s scalability, but also by its coverage, faithfulness, and trustworthiness. Below, we detail several governance challenges, including error propagation at scale, human oversight becoming a bottleneck, and systematic biases going undetected.</p>
 <div class="warningbox">
-<p><strong>Error amplification</strong>: A systematic error in the explanation generation model propagates to all features it analyzes. Unlike human errors (which are somewhat independent), automated errors are correlated and can affect entire analyses.</p>
-<p><strong>Reduced human oversight</strong>: The primary motivation for automation is removing humans from the loop. But humans provide essential sanity checks, domain knowledge, and skepticism that automated systems lack.</p>
-<p><strong>Automation bias</strong>: Users may over-trust automated explanations because they appear systematic and comprehensive. The appearance of rigor can mask underlying unreliability.</p>
-<p><strong>Spurious feedback loops</strong>: If automated Interpretability is used to improve models, and those models are then used for Automated Interpretability, subtle feedback loops may emerge that optimize for interpretability metrics rather than genuine understanding.</p>
-</div>
+<section id="error-propagation." class="level4">
+<h4>Error propagation.</h4>
+<p>Unlike human investigators, who are somewhat independent and introspective, the explanation-generation model can propagate its systematic errors to all features it analyses without rethinking what could have gone wrong.<br />
+</p>
+</section>
+<section id="reduced-human-specific-functionalities." class="level4">
+<h4>Reduced human-specific functionalities.</h4>
+<p>Automation removes humans from the loop. However, humans provide essential sanity checks, domain knowledge, and scepticism that automated systems lack or handle poorly. Lacking these critical properties may, in the long run, make the system less productive and less beneficial to the community than analyses by human experts.<br />
+</p>
+</section>
+<section id="automation-bias." class="level4">
+<h4>Automation Bias.</h4>
+<p>Automated pipelines can produce seemingly rigorous and comprehensive yet actually incomplete or sycophantic explanations. This is particularly likely when language models are involved. For example, models may selectively report positive results <span class="citation" data-cites="aiscientistpitfall">(<a href="@@BASE@@references/#ref-aiscientistpitfall" class="cite cite-established" data-cite="aiscientistpitfall" role="doc-biblioref">Luo et al., 2026</a>)</span> or tailor their explanations to align with inferred user preferences <span class="citation" data-cites="sharma_towards_2025">(<a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a>)</span>. This apparent rigour and promise may lead researchers to overtrust the results and overlook underlying flaws.<br />
+</p>
+</section>
+<section id="spurious-feedback-loops." class="level4">
+<h4>Spurious Feedback Loops.</h4>
+<p>When Automated Interpretability is used to improve models that are subsequently used for interpretability, feedback loops may reinforce existing errors and biases. This process may exploit and saturate interpretability metrics without genuine gains in mechanistic understanding.</p>
 </section>
-<section id="proposed-guardrails-for-automated-interpretability" class="level3"><h3 data-pdf-page="80"><span class="secnum">13.7.2</span> Proposed Guardrails for Automated Interpretability<a class="anchor" href="#proposed-guardrails-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
-<p>We propose a four-layer guardrail framework to help mitigate the risks:</p>
-<div class="definitionbox">
-<p><strong>Layer 1: Sampling-Based Human Validation</strong></p>
-<ul>
-<li><p><strong>Random audits</strong>: Randomly sample a predefined fraction (e.g., 5%) of automated explanations for human expert review.</p></li>
-<li><p><strong>Stratified sampling</strong>: Ensure audits cover different feature types, layers, and explanation categories—not just easy cases.</p></li>
-<li><p><strong>Blinded evaluation</strong>: Human auditors should not know which explanations are randomly sampled and which are flagged by automated checks.</p></li>
-</ul>
-<p><strong>Layer 2: Automated Consistency Checks</strong></p>
-<ul>
-<li><p><strong>Self-consistency</strong>: Generate explanations multiple times with temperature variation. Inconsistent explanations are flagged.</p></li>
-<li><p><strong>Cross-method consistency</strong>: Compare explanations from different setups (e.g., different LLM explainers). Disagreement triggers review.</p></li>
-<li><p><strong>Causal consistency</strong>: Automatically test a sample of explanations via causal testing like activation patching. Track failure rates.</p></li>
-</ul>
-<p><strong>Layer 3: Error Detection and Bounds</strong></p>
-<ul>
-<li><p><strong>Confidence calibration</strong>: Automated systems must output calibrated confidence. Overconfidence is fatal and should be penalized.</p></li>
-<li><p><strong>Known-answer tests</strong>: Include features with known ground-truth explanations (e.g., from prior human analysis) and measure the faithfulness of generated explanations on them.</p></li>
-<li><p><strong>Error rate reporting</strong>: Report estimated error rates alongside all automated findings. Do not present automation as infallible.</p></li>
-</ul>
-<p><strong>Layer 4: Governance Process</strong></p>
-<ul>
-<li><p><strong>Tiered claims</strong>: Distinguish “automated hypothesis” (low confidence, requires validation) from “validated finding” (passed human/causal checks).</p></li>
-<li><p><strong>Versioning and reproducibility</strong>: Ensure the versioning and reproducibility of all automated analyses. When methods improve, prior analyses should be re-evaluated.</p></li>
-<li><p><strong>Scope limitations</strong>: Clearly study and define what Automated Interpretability can and cannot reliably assess. Do not extrapolate beyond validated scope.</p></li>
-</ul>
 </div>
 </section>
-<section id="validation-protocols-for-automated-pipelines" class="level3"><h3 data-pdf-page="80"><span class="secnum">13.7.3</span> Validation Protocols for Automated Pipelines<a class="anchor" href="#validation-protocols-for-automated-pipelines" aria-label="Link to this section">#</a></h3>
-<p>Similarly to Section <a href="@@BASE@@handbook/research-orientations/#subsec:valproto agentic self-reports" data-reference-type="ref" data-reference="subsec:valproto agentic self-reports">13.6.3</a>, here we also propose standardized validation protocols to regulate and monitor Automated Interpretability pipelines.</p>
-<section id="protocol-1-calibration-testing." class="level4">
-<h4>Protocol 1: Calibration Testing.</h4>
-<p>To evaluate whether automated confidence scores are calibrated, the regulator follows four steps: First, the regulator collects automated explanations with associated confidence scores. Second, for each confidence bin (e.g., 0.8–0.9), the regulator measures actual accuracy via human evaluation or causal testing. Third, the regulator plots the relationship between confidence and accuracy. Well-calibrated systems have accuracy <span class="math inline">\approx</span> confidence. Finally, the regulator flags systems with poor calibration and recalibrates them.</p>
+<section id="proposed-guardrails-for-automated-interpretability" class="level3"><h3 data-pdf-page="109"><span class="secnum">12.6.2</span> Proposed Guardrails for Automated Interpretability<a class="anchor" href="#proposed-guardrails-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
+<p>These concerns call for safeguards that combine technical validation, human oversight, and clear accountability. Below, we propose a five-layer guardrail framework to help audit the automated methods and mitigate their risks.</p>
+<div class="practicebox">
+<section id="layer-1-sampling-based-human-validation." class="level4">
+<h4>Layer 1: Sampling-Based Human Validation.</h4>
+<p>Auditors can randomly sample a predefined fraction (e.g., 5%) of generated explanations for human experts to review. These explanations should cover different task-difficulty levels, feature types, and targeted mechanisms. Human experts should not know in advance which explanations are randomly sampled, which are flagged by automated checks, and which are already verified by additional methods, if they are all presented.<br />
+</p>
 </section>
-<section id="protocol-2-adversarial-robustness." class="level4">
-<h4>Protocol 2: Adversarial Robustness.</h4>
-<p>We may test automated pipelines against adversarial inputs. To achieve this, the regulator first constructs features with backdoored activation patterns designed to make the explainer produce inappropriate or targeted explanations. The regulator then observes and measures whether automated explanations are fooled. Failure cases can be used to improve the robustness of the pipeline.</p>
+<section id="layer-2-consistency-and-causality-checks." class="level4">
+<h4>Layer 2: Consistency and Causality Checks.</h4>
+<p>The reliability of automated pipelines can be improved by implementing consistency checks. Specifically, the pipeline can generate explanations multiple times at varying temperatures or setups (e.g., different backbone LLM explainers). Inconsistent explanations trigger review. The pipeline can also additionally examine a fraction of explanations by causal techniques, such as activation patching, and track failure rates. These checks can be automated or involve human auditors.<br />
+</p>
 </section>
-<section id="protocol-3-drift-monitoring." class="level4">
-<h4>Protocol 3: Drift Monitoring.</h4>
-<p>The performance of an Automated Interpretability system may fluctuate or degrade over time due to domain shifts of the input or updates of the analyzed model. As a result, we should consistently track automated pipeline performance. To achieve this, conceptually, the regulator can first establish baseline performance on a held-out validation set. Periodically, the regulator re-evaluates on the same validation set. If the system’s performance degrades, it should trigger warnings for review and recalibration.</p>
+<section id="layer-3-adversarial-robustness." class="level4">
+<h4>Layer 3: Adversarial Robustness.</h4>
+<p>Auditors can test automated pipelines against adversarial inputs. To achieve this, auditors first construct features or mechanisms with backdoored activation patterns designed to make the pipeline produce inappropriate or targeted explanations. Auditors then examine whether the pipeline was fooled. Failure cases can be used to improve the pipeline’s robustness.<br />
+</p>
 </section>
+<section id="layer-4-error-detection-and-bounds." class="level4">
+<h4>Layer 4: Error Detection and Bounds.</h4>
+<p>First, automated pipelines must output calibrated confidence. Overconfidence (false positive) is more dangerous than underconfidence (false negative) and should be prevented. Second, the pipeline can be tested on features or mechanisms with known ground-truth explanations (e.g., from prior human analysis) and the faithfulness of generated explanations on them measured. Finally, the pipeline should report estimated error rates alongside all automated findings.<br />
+</p>
 </section>
-<section id="integrating-automated-and-human-analysis" class="level3"><h3 data-pdf-page="81"><span class="secnum">13.7.4</span> Integrating Automated and Human Analysis<a class="anchor" href="#integrating-automated-and-human-analysis" aria-label="Link to this section">#</a></h3>
-<p>A robust approach should combine automated and human analysis, using automation for scale and humans for validation and edge cases. Table <a href="@@BASE@@handbook/research-orientations/#tab:human-automated-workflow" data-reference-type="ref" data-reference="tab:human-automated-workflow">13</a> demonstrates an exemplary workflow to combine AI and human labor for scalable interpretability analysis.</p>
+<section id="layer-5-regulator-practices." class="level4">
+<h4>Layer 5: Regulator Practices.</h4>
+<p>Regulators must distinguish automated hypotheses (weak evidence that requires validation) from validated findings (passed human or causal checks). For automated analyses used in safety-relevant applications, regulators must ensure versioning and reproducibility. When the pipeline improves, or the target model is fine-tuned, regulators should re-evaluate prior analyses. Regulators should also be aware of their scope, clearly studying and defining what automated pipelines can and cannot reliably assess. Decisions should not be made by extrapolating beyond validated scope.</p>
+</section>
+</div>
+</section>
+<section id="integrating-automated-and-human-analysis" class="level3"><h3 data-pdf-page="110"><span class="secnum">12.6.3</span> Integrating Automated and Human Analysis<a class="anchor" href="#integrating-automated-and-human-analysis" aria-label="Link to this section">#</a></h3>
+<p>A robust analysis pipeline should combine automation for scale and suitable human involvement for validation and edge cases. Table <a href="@@BASE@@handbook/research-orientations/#tab:human-automated-workflow" data-reference-type="ref" data-reference="tab:human-automated-workflow">12</a> shows an exemplary workflow that combines AI and human labour for scalable interpretability analysis.</p>
 <table id="tab:human-automated-workflow">
-<caption><span class="capnum">Table 13:</span> Integrated Human-Automated Interpretability Workflow</caption>
+<caption><span class="capnum">Table 12:</span> <strong>A human–Automated Interpretability pipeline.</strong> Automated methods perform discovery and analysis. Human experts set the objectives, audit intermediate results, and draw conclusions. We use feature and circuit discovery and analysis as an example.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Stage</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Automated Role</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Human Role</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Output</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Deliverable</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Discovery</td>
-<td style="text-align: left;">Extract features; generate candidate explanations</td>
-<td style="text-align: left;">Define scope; review methodology</td>
-<td style="text-align: left;">Candidate feature set</td>
+<td style="text-align: left;"><strong>Discovery</strong></td>
+<td style="text-align: left;">Extract candidate features and circuits</td>
+<td style="text-align: left;">Define scope and review methodology</td>
+<td style="text-align: left;">Candidate features and circuits</td>
 </tr>
 <tr>
-<td style="text-align: left;">Prioritization</td>
-<td style="text-align: left;">Rank features by importance metrics</td>
-<td style="text-align: left;">Review rankings; adjust priorities</td>
-<td style="text-align: left;">Prioritized analysis queue</td>
+<td style="text-align: left;"><strong>Prioritisation</strong></td>
+<td style="text-align: left;">Rank features and circuits by importance metrics</td>
+<td style="text-align: left;">Review rankings and adjust priorities</td>
+<td style="text-align: left;">Prioritised analysis queue</td>
 </tr>
 <tr>
-<td style="text-align: left;">Explanation</td>
+<td style="text-align: left;"><strong>Explanation</strong></td>
 <td style="text-align: left;">Generate explanations at scale</td>
-<td style="text-align: left;">Audit random sample; review flagged cases</td>
+<td style="text-align: left;">Audit a fraction of samples and review flagged cases</td>
 <td style="text-align: left;">Draft explanations</td>
 </tr>
 <tr>
-<td style="text-align: left;">Validation</td>
-<td style="text-align: left;">Run automated consistency/causal checks</td>
-<td style="text-align: left;">Deep-dive on failures; validate edge cases</td>
+<td style="text-align: left;"><strong>Validation</strong></td>
+<td style="text-align: left;">Run automated consistency and causal checks</td>
+<td style="text-align: left;">Investigate failure modes and validate edge cases</td>
 <td style="text-align: left;">Validated findings</td>
 </tr>
 <tr>
-<td style="text-align: left;">Synthesis</td>
-<td style="text-align: left;">Aggregate findings; generate reports</td>
-<td style="text-align: left;">Interpret implications; make decisions</td>
+<td style="text-align: left;"><strong>Synthesis</strong></td>
+<td style="text-align: left;">Aggregate findings and generate reports</td>
+<td style="text-align: left;">Interpret implications and make decisions</td>
 <td style="text-align: left;">Actionable conclusions</td>
 </tr>
 </tbody>
 </table>
 <section id="key-principles-for-integration." class="level4">
 <h4>Key Principles for Integration.</h4>
-<p>In summary, there are four principles for integrating human and AI into the interpretability workflow:</p>
-<ol>
-<li><p><strong>Automation for breadth, humans for depth</strong>: Use automation to cover many features quickly; use humans to analyze critical or uncertain cases.</p></li>
-<li><p><strong>Humans set criteria, automation applies them</strong>: Humans define what counts as a valid explanation, an important feature, or a concerning finding. Automation applies these criteria at scale.</p></li>
-<li><p><strong>Escalation paths</strong>: Automated systems should have clear criteria for escalating to human review (e.g., low confidence, high stakes, inconsistency).</p></li>
-<li><p><strong>Feedback loops</strong>: Human corrections should feed back into automated systems to calibrate the system and improve future quality.</p></li>
-</ol>
+<p>Here we discuss several design principles for integrating humans and AI into the interpretability workflow. First, automation provides breadth, and humans provide depth. For example, the workflow can use SAEs with LLM-based description generation to discover and label many features, then have human investigators analyse those with safety-relevant descriptions. Second, humans set criteria and automation applies them. Specifically, humans define criteria for a valid explanation, a feature of interest, or a concerning finding, while the automated components (e.g., the LLM explainer) operate based on these criteria. Third, the system should have clear escalation criteria to determine when to call for human intervention, such as when the system detects potential mechanisms underlying safety-relevant concepts. Finally, the system should implement a feedback loop to let human investigators correct and calibrate it in real time during interaction.</p>
 </section>
 </section>
-<section id="summary-responsible-automation" class="level3"><h3 data-pdf-page="81"><span class="secnum">13.7.5</span> Summary: Responsible Automation<a class="anchor" href="#summary-responsible-automation" aria-label="Link to this section">#</a></h3>
-<p>Automated and agentic interpretability offer powerful capabilities for scaling mechanistic analysis, but introduce governance challenges that require explicit mitigation. Specifically, <strong>Agentic Interpretability</strong> requires independent validation to prevent self-serving explanations; <strong>Automated Interpretability</strong> requires sampling-based human oversight to catch systematic errors. They both require clear communication of limitations, confidence levels, and validation status, and neither should be regarded as a replacement for human judgment on high-stakes decision-making.</p>
-<p>In sum, their goal is to leverage the scale of automation while preserving the reliability that human oversight provides, rather than removing humans from interpretability entirely.</p>
+<section id="summary-responsible-automation" class="level3"><h3 data-pdf-page="110"><span class="secnum">12.6.4</span> Summary: Responsible Automation<a class="anchor" href="#summary-responsible-automation" aria-label="Link to this section">#</a></h3>
+<p>Introspective and Automated Interpretability offer promise for scalable mechanistic analysis. However, they both have technical challenges that call for additional guardrails. Specifically, Introspective Interpretability requires independent validation to prevent self-serving explanations; Automated Interpretability still requires human oversight to catch systematic errors. Neither should be regarded as a complete replacement for human judgement in high-stakes decision-making. Ultimately, Automated Interpretability aims not to replace human investigators, but to combine human expertise with automation to make interpretability analysis more comprehensive and reliable.</p>
 </section>
 </section>
-<section id="comparative-analysis-trade-offs-between-orientations" class="level2"><h2 data-pdf-page="81"><span class="secnum">13.8</span> Comparative Analysis: Trade-Offs Between Orientations<a class="anchor" href="#comparative-analysis-trade-offs-between-orientations" aria-label="Link to this section">#</a></h2>
-<p>Table <a href="@@BASE@@handbook/research-orientations/#tab:orientations" data-reference-type="ref" data-reference="tab:orientations">14</a> compares the five research orientations across several dimensions, such as their problem choices, primary techniques, and practical scalability. None of these approaches strictly dominates the others across all axes. For example, Automated Interpretability is designed to scale to extremely large numbers of features in frontier models but has issues like error propagation and automation bias. By contrast, Ambitious Interpretability prioritizes fundamental, theory-grounded understanding and is therefore more robust to the presence of hidden misaligned behaviors within the system; however, its results are typically derived from controlled, small-scale settings <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span> and may fail to transfer, or become impractical, for large frontier models and real-time deployment.</p>
-<p>Consequently, the choice of which orientation to prioritize depends on multiple contextual factors, including the tasks of interest, available computational and human resources, and the intended downstream use of the research outcomes, detailed in Section <a href="@@BASE@@handbook/research-orientations/#subsec:choosing orientation" data-reference-type="ref" data-reference="subsec:choosing orientation">13.9</a>.</p>
+<section id="comparative-analysis-trade-offs-between-orientations" class="level2"><h2 data-pdf-page="111"><span class="secnum">12.7</span> Comparative Analysis: Trade-Offs Between Orientations<a class="anchor" href="#comparative-analysis-trade-offs-between-orientations" aria-label="Link to this section">#</a></h2>
+<p>Table <a href="@@BASE@@handbook/research-orientations/#tab:orientations" data-reference-type="ref" data-reference="tab:orientations">13</a> compares the six research orientations across several dimensions, including their problem choices, primary techniques, and practical scalability. None of them is universally preferable. For example, Automated Interpretability can scale analysis to millions of features in frontier models but faces issues such as error propagation and automation bias. By contrast, Ambitious Interpretability can provide fundamental, theory-grounded analysis, but its current results still mainly come from controlled, small-scale settings <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>, and its transferability to frontier models remains unclear. Thus, the choice of orientation depends on multiple factors, such as the intended downstream use cases and available computational and human resources, as detailed in Section <a href="@@BASE@@handbook/research-orientations/#subsec:choosing orientation" data-reference-type="ref" data-reference="subsec:choosing orientation">12.8</a>.</p>
 <table id="tab:orientations">
-<caption><span class="capnum">Table 14:</span> Comparison of Mechanistic Interpretability Orientations</caption>
+<caption><span class="capnum">Table 13:</span> Comparison of six interpretability orientations across their core theses, evaluation criteria, methodological approaches, research time horizons, scalability, and robustness to model deception.</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Dimension</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Pragmatic</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Ambitious</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Developmental</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Agentic</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Across Time</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Introspective</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Automated</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;"><strong>Core Thesis</strong></td>
-<td style="text-align: left;">Safety interventions now without complete understanding</td>
-<td style="text-align: left;">Total causal understanding, regardless of cost</td>
-<td style="text-align: left;">Predict emergence via learning dynamics</td>
-<td style="text-align: left;">Models explain themselves if prompted correctly</td>
-<td style="text-align: left;">Scale discovery via automated pipelines</td>
+<td style="text-align: left;"><strong>Main Focus</strong></td>
+<td style="text-align: left;">Immediate safety interventions without complete understanding</td>
+<td style="text-align: left;">Complete causal understanding regardless of cost</td>
+<td style="text-align: left;">Developmental processes of representations, mechanisms, and capabilities</td>
+<td style="text-align: left;">Prediction and control of future model behaviour through training dynamics</td>
+<td style="text-align: left;">Introspective models to explain themselves</td>
+<td style="text-align: left;">Scalable discovery via automated pipelines</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Key Metrics</strong></td>
-<td style="text-align: left;">Safety task performance</td>
-<td style="text-align: left;">Faithfulness &amp; circuit completeness</td>
-<td style="text-align: left;">Phase transitions &amp; Local Learning Coefficient</td>
-<td style="text-align: left;">Self-report alignment with mechanisms</td>
-<td style="text-align: left;">Coverage and scale of analysis</td>
+<td style="text-align: left;">Performance in proxy tasks</td>
+<td style="text-align: left;">Faithfulness and simulatability</td>
+<td style="text-align: left;">Phase-transition signatures (e.g., LLC) and their correspondence to mechanism formation</td>
+<td style="text-align: left;">Forecasting accuracy and intervention effectiveness</td>
+<td style="text-align: left;">Alignment between self-reports and actual mechanisms</td>
+<td style="text-align: left;">Coverage, scalability, robustness, and faithfulness of analysis</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Primary Tools</strong></td>
-<td style="text-align: left;">Steering vectors, probes, monitoring</td>
-<td style="text-align: left;">Circuit discovery, formal verification</td>
-<td style="text-align: left;">SLT, phase transition detection, SAEs</td>
-<td style="text-align: left;">LatentQA, dialogical self-explanation, model intervention</td>
-<td style="text-align: left;">Circuit discovery, SAEs, LLM-based explanations, causal verification</td>
+<td style="text-align: left;">Steering vectors, probes, behavioural evaluation</td>
+<td style="text-align: left;">Circuit discovery, loose-to-strict verification</td>
+<td style="text-align: left;">SLT, loss landscape geometry, representation analysis</td>
+<td style="text-align: left;">Longitudinal analysis, behavioural forecasting, training interventions</td>
+<td style="text-align: left;">Introspective training, activation verbalisers</td>
+<td style="text-align: left;">Circuit discovery, SAEs, LLM agents</td>
 </tr>
 <tr>
-<td style="text-align: left;"><strong>Time Horizon</strong></td>
+<td style="text-align: left;"><strong>Expected Time to Impact</strong></td>
 <td style="text-align: left;">Short-term</td>
 <td style="text-align: left;">Long-term</td>
-<td style="text-align: left;">During training (immediate)</td>
-<td style="text-align: left;">Immediate</td>
+<td style="text-align: left;">Long-term</td>
+<td style="text-align: left;">Medium</td>
+<td style="text-align: left;">Medium</td>
 <td style="text-align: left;">Short-term</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Scalability</strong></td>
-<td style="text-align: left;">Medium</td>
+<td style="text-align: left;">High</td>
 <td style="text-align: left;">Low</td>
 <td style="text-align: left;">Low</td>
 <td style="text-align: left;">Medium</td>
 <td style="text-align: left;">High</td>
+<td style="text-align: left;">Medium</td>
 </tr>
 <tr>
 <td style="text-align: left;"><strong>Robustness to Deception</strong></td>
 <td style="text-align: left;">Medium</td>
 <td style="text-align: left;">High</td>
 <td style="text-align: left;">Unknown</td>
-<td style="text-align: left;">Low</td>
 <td style="text-align: left;">Unknown</td>
+<td style="text-align: left;">Low</td>
+<td style="text-align: left;">Low</td>
 </tr>
 </tbody>
 </table>
 </section>
-<section id="subsec:choosing orientation" class="level2"><h2 data-pdf-page="82"><span class="secnum">13.9</span> Choosing an Orientation: A Decision Framework<a class="anchor" href="#subsec:choosing orientation" aria-label="Link to this section">#</a></h2>
-<p>The research orientation to select depends on the priorities and constraints of the investigator. For teams or individuals focusing on immediate safety interventions, a <strong>Pragmatic</strong> orientation is most suitable, as it emphasizes actionable insights and targeted behavioral interventions. If the primary goal is to guarantee correctness or formally verify the absence of harmful behaviors, an <strong>Ambitious</strong> orientation is appropriate, despite its high computational cost and long-term horizon. Researchers aiming to anticipate and prevent emergent behaviors during training may adopt a <strong>Developmental</strong> orientation, leveraging the analysis of learning dynamics and phase transition to predict capability emergence. When the objective is to understand reasoning or causal structure in existing models, <strong>Agentic</strong> interpretability, where models themselves provide explanations, may be the most effective approach. Finally, for those prioritizing the scalability of analysis across large or multiple models with minimal human in the loop, <strong>Automated</strong> interpretability techniques provide pipelines and ML-assisted discovery, albeit with some loss of rigorous manual validation. Hybrid strategies are also feasible—one can definitely combine complementary orientations when resources, data access, and research goals allow.</p>
+<section id="subsec:choosing orientation" class="level2"><h2 data-pdf-page="111"><span class="secnum">12.8</span> Choosing an Orientation: A Decision Framework<a class="anchor" href="#subsec:choosing orientation" aria-label="Link to this section">#</a></h2>
+<p>The investigator’s priorities and constraints determine the research orientation to select. For teams or individuals focused on immediate safety interventions, Pragmatic Interpretability is the most suitable research style. If the primary goal is to guarantee the absence of harmful behaviour in the long term, Ambitious Interpretability is most appropriate. Researchers seeking to characterise and explain dynamics of capability development may adopt Developmental Interpretability. In contrast, teams training and deploying language-model products with a focus on safety concerns may focus on Interpretability Across Time. When the objective is to offer accessible interpretability analysis to general users, Introspective Interpretability offers a promising paradigm. In contrast, Automated Interpretability is particularly attractive when analyses must be performed at a scale that would be impractical for human investigators alone. Notably, these research orientations are not mutually exclusive; hybrid strategies can combine the strengths of different approaches.</p>
 </section>
-<section id="critical-tensions-and-open-questions" class="level2"><h2 data-pdf-page="82"><span class="secnum">13.10</span> Critical Tensions and Open Questions<a class="anchor" href="#critical-tensions-and-open-questions" aria-label="Link to this section">#</a></h2>
-<p>Several critical tensions emerge when considering the adoption of different orientations. A central debate exists between <strong>Pragmatic</strong> and <strong>Ambitious</strong> approaches: does partial mechanistic understanding suffice to ensure safety, or is complete comprehension necessary? Another tension arises between <strong>Agentic</strong> and external analysis methods, questioning whether models can be trusted to accurately report on their own mechanisms. Similarly, <strong>Developmental</strong> approaches, which require access to training dynamics, raise the question of whether post-hoc analysis of deployed models could ever suffice. <strong>Automation</strong> introduces further trade-offs: while scaling interpretability via automation enables coverage of larger models, it may reduce the transparency and human comprehensibility of the results. Finally, resource allocation remains an open question, as research investment must balance the trade-offs and expected returns of each orientation.</p>
+<section id="critical-concerns-tensions-and-open-questions" class="level2"><h2 data-pdf-page="112"><span class="secnum">12.9</span> Critical Concerns, Tensions, and Open Questions<a class="anchor" href="#critical-concerns-tensions-and-open-questions" aria-label="Link to this section">#</a></h2>
+<p>Several concerns and tensions arise within and between these research orientations. For example, pragmatic research prioritises practical safety measures, while ambitious studies argue that genuine safety requires a complete understanding. Developmental Interpretability and Interpretability Across Time both rely on access to training checkpoints and optimisation dynamics, which remain difficult to obtain. Introspective models can provide accessible explanations, but safety-critical decisions may always require additional verification. Automated pipelines can analyse large models, but the comprehensive analysis they deliver may be hard for humans to inspect and verify. Given these trade-offs, allocating research resources across these orientations appears to be a non-technical open challenge.</p>
 </section>
 </section>
diff --git a/src/generated/chapters/sae-architecture-details.html b/src/generated/chapters/sae-architecture-details.html
deleted file mode 100644
index 8b44a68f01..0000000000
--- a/src/generated/chapters/sae-architecture-details.html
+++ /dev/null
@@ -1,86 +0,0 @@
-<section id="app:sae_details" class="level1"><h1 data-pdf-page="120"><span class="secnum">C</span> SAE Architecture Details and Hyperparameters<a class="anchor" href="#app:sae_details" aria-label="Link to this section">#</a></h1>
-<p>This appendix provides practical guidance for training and evaluating Sparse Autoencoders.</p>
-<section id="standard-sae-architecture" class="level2"><h2 data-pdf-page="121"><span class="secnum">C.1</span> Standard SAE Architecture<a class="anchor" href="#standard-sae-architecture" aria-label="Link to this section">#</a></h2>
-<div class="definitionbox">
-<p>A Sparse Autoencoder reconstructs activations <span class="math inline">h \in \mathbb{R}^{d}</span> via:</p>
-<p><strong>Encoding</strong>: <span class="math inline">z = \text{ReLU}(W_{\text{in}} h + b_{\text{in}})</span></p>
-<p>where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> (sparse features), <span class="math inline">d_{\text{sparse}} \gg d</span>, <span class="math inline">W_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}} \times d}</span></p>
-<p><strong>Decoding</strong>: <span class="math inline">h&#39; = W_{\text{out}} z + b_{\text{out}}</span></p>
-<p>where <span class="math inline">W_{\text{out}} \in \mathbb{R}^{d \times d_{\text{sparse}}}</span></p>
-<p><strong>Loss</strong>: <span id="eq-6"></span> <span class="math display">\begin{equation*}
-\mathcal{L} = \|h - h&#39;\|^2 + \lambda \|z\|_1
-\tag{6}
-\end{equation*}</span></p>
-<p>where <span class="math inline">\lambda</span> is the sparsity coefficient (typically 0.001–0.01).</p>
-</div>
-</section>
-<section id="standard-hyperparameters" class="level2"><h2 data-pdf-page="121"><span class="secnum">C.2</span> Standard Hyperparameters<a class="anchor" href="#standard-hyperparameters" aria-label="Link to this section">#</a></h2>
-<p>For a baseline SAE on a 4096-dimensional residual stream:</p>
-<ul>
-<li><p><strong>SAE expansion factor</strong>: <span class="math inline">d_{\text{sparse}} = 4 \times 4096 = 16384</span> (4x expansion is standard)</p></li>
-<li><p><strong>Learning rate</strong>: <span class="math inline">10^{-3}</span> to <span class="math inline">10^{-4}</span> (Adam optimizer, warmup of 500–1000 steps)</p></li>
-<li><p><strong>Sparsity coefficient</strong> <span class="math inline">\lambda</span>: <span class="math inline">0.001</span> to <span class="math inline">0.01</span> (higher <span class="math inline">\lambda</span> = sparser features, lower reconstruction)</p></li>
-<li><p><strong>Batch size</strong>: 16–64 (memory-limited; smaller batches for multi-GPU training)</p></li>
-<li><p><strong>Training duration</strong>: 50K–500K training steps (depends on dataset size and desired convergence)</p></li>
-<li><p><strong>Initialization</strong>: random normal for <span class="math inline">W_{\text{in}}</span>, orthogonal for <span class="math inline">W_{\text{out}}</span> (improves stability)</p></li>
-</ul>
-</section>
-<section id="gated-sae-variant" class="level2"><h2 data-pdf-page="121"><span class="secnum">C.3</span> Gated SAE Variant<a class="anchor" href="#gated-sae-variant" aria-label="Link to this section">#</a></h2>
-<div class="definitionbox">
-<p>Gated SAEs solve the shrinkage problem by learning a separate “gate” for each feature:</p>
-<p><strong>Encoding</strong>: <span class="math inline">z = \sigma(W_{\text{gate}} h + b_{\text{gate}}) \odot \text{ReLU}(W_{\text{in}} h + b_{\text{in}})</span></p>
-<p>where <span class="math inline">\odot</span> is element-wise multiplication and <span class="math inline">\sigma</span> is sigmoid.</p>
-<p>This allows features to be individually gated on/off, improving feature quality.</p>
-<p><strong>Sparsity penalty</strong>: applied to <span class="math inline">\sigma(W_{\text{gate}} h + b_{\text{gate}})</span> (the gate activations, not the ReLU outputs).</p>
-</div>
-<p>Gated SAE hyperparameters:</p>
-<ul>
-<li><p>Same expansion factor and learning rate as standard SAE</p></li>
-<li><p>Sparsity coefficient often slightly higher (<span class="math inline">0.01</span>–<span class="math inline">0.05</span>) to encourage gate closure</p></li>
-<li><p>Additional weight decay on gate parameters (0.01) to prevent gate collapse</p></li>
-</ul>
-</section>
-<section id="top-k-sae-variant" class="level2"><h2 data-pdf-page="121"><span class="secnum">C.4</span> Top-K SAE Variant<a class="anchor" href="#top-k-sae-variant" aria-label="Link to this section">#</a></h2>
-<div class="definitionbox">
-<p>Top-K SAEs replace soft sparsity (<span class="math inline">\ell_1</span> penalty) with hard sparsity (exactly <span class="math inline">k</span> features active):</p>
-<p><strong>Encoding</strong>: <span class="math inline">z = \text{top-}k(\text{ReLU}(W_{\text{in}} h + b_{\text{in}}), k)</span></p>
-<p>where <span class="math inline">\text{top-}k</span> returns a vector with only the <span class="math inline">k</span> largest values, rest zero.</p>
-<p>No sparsity penalty needed; sparsity is enforced structurally.</p>
-</div>
-<p>Top-K hyperparameters:</p>
-<ul>
-<li><p><span class="math inline">k</span>: typically 50–200 (aim for 5–10% of <span class="math inline">d_{\text{sparse}}</span> active)</p></li>
-<li><p>Learning rate: slightly higher (<span class="math inline">10^{-3}</span>) since there’s no sparsity penalty</p></li>
-<li><p>Auxiliary loss: use auxiliary learning rate to balance training across different <span class="math inline">k</span> values</p></li>
-</ul>
-</section>
-<section id="evaluation-metrics" class="level2"><h2 data-pdf-page="122"><span class="secnum">C.5</span> Evaluation Metrics<a class="anchor" href="#evaluation-metrics" aria-label="Link to this section">#</a></h2>
-<p>Standard metrics for SAE quality:</p>
-<ul>
-<li><p><strong>Reconstruction loss</strong>: <span class="math inline">\mathbb{E}[\|h - h&#39;\|^2]</span> (how well does the SAE reconstruct the original?)</p></li>
-<li><p><strong>Loss recovery</strong>: <span class="math inline">\frac{\mathcal{L}_{\text{model}} - \mathcal{L}_{\text{model w/ SAE}}}{\mathcal{L}_{\text{model}}}</span>  (what fraction of model loss is recovered by SAE reconstruction?)</p></li>
-<li><p><strong>Sparsity</strong>: mean number of features active per example (target: 10–100 depending on layer)</p></li>
-<li><p><strong>Feature quality</strong>: how interpretable are the extracted features? (subjective, can be evaluated via automated explanations)</p></li>
-<li><p><strong>Absorption ratio</strong>: how much downstream computation depends on SAE features vs. residuals?</p></li>
-</ul>
-</section>
-<section id="app:train-sae" class="level2"><h2 data-pdf-page="122"><span class="secnum">C.6</span> Training Stability Tips<a class="anchor" href="#app:train-sae" aria-label="Link to this section">#</a></h2>
-<ul>
-<li><p><strong>Orthogonal initialization of <span class="math inline">W_{\text{out}}</span></strong>: improves convergence and feature quality</p></li>
-<li><p><strong>Warmup</strong>: start with <span class="math inline">\lambda = 0</span> for 500–1000 steps, then ramp to full sparsity coefficient</p></li>
-<li><p><strong>Learning rate schedules</strong>: cosine annealing or exponential decay in later training helps convergence</p></li>
-<li><p><strong>Batch normalization</strong>: can help but not always necessary; test both</p></li>
-<li><p><strong>Gradient clipping</strong>: if loss spikes, clip gradients to norm 1.0</p></li>
-<li><p><strong>Monitor dead features</strong>: track percentage of features never activated; if &gt; 20%, increase learning rate or decrease <span class="math inline">\lambda</span></p></li>
-</ul>
-</section>
-<section id="multi-layer-saes" class="level2"><h2 data-pdf-page="122"><span class="secnum">C.7</span> Multi-Layer SAEs<a class="anchor" href="#multi-layer-saes" aria-label="Link to this section">#</a></h2>
-<p>For analyzing multiple layers, train separate SAEs per layer. Key considerations:</p>
-<ul>
-<li><p><strong>Scaling expansion factor</strong>: early layers need fewer features (simpler patterns), later layers need more (complex abstractions). Use 2x expansion early, 8x expansion late.</p></li>
-<li><p><strong>Coordinated training</strong>: train all SAEs simultaneously vs. sequentially. Simultaneous training enables cross-layer analysis.</p></li>
-<li><p><strong>Feature alignment</strong>: after training, align features across layers to track evolution (using orthogonal Procrustes)</p></li>
-</ul>
-<p>—</p>
-</section>
-</section>
diff --git a/src/generated/chapters/safety-applications.html b/src/generated/chapters/safety-applications.html
index 406cb10364..56bbb3bfe3 100644
--- a/src/generated/chapters/safety-applications.html
+++ b/src/generated/chapters/safety-applications.html
@@ -1,253 +1,343 @@
-<section id="sec:safety_applications" class="level1"><h1 data-pdf-page="83"><span class="secnum">14</span> From Understanding to Intervention: Safety Applications of Mechanistic Interpretability<a class="anchor" href="#sec:safety_applications" aria-label="Link to this section">#</a></h1>
-<p>Mechanistic interpretability enables concrete safety applications, including but are not limited to detecting hidden behaviors, steering models away from harmful outputs, removing dangerous capabilities, and verifying safety properties. This section focuses on concrete examples and illustrations of the transition from understanding to intervention—how mechanistic insights can be operationalized into practical safety applications. Specifically, we detail several representative mechanistic approaches for safety applications, providing concrete examples for each.</p>
-<section id="why-mechanistic-approaches-for-safety" class="level2"><h2 data-pdf-page="83"><span class="secnum">14.1</span> Why Mechanistic Approaches for Safety?<a class="anchor" href="#why-mechanistic-approaches-for-safety" aria-label="Link to this section">#</a></h2>
-<p>Before introducing the exemplary methods, we discuss the advantages of mechanistic interpretability methods (i.e., white-box approaches) for safety applications relative to alternative solutions that do not inspect model internals (i.e., black-box approaches). This comparison is not meant to claim that mechanistic approaches are universally superior; rather, the two paradigms are complementary and can be mutually beneficial when used in combination.</p>
-<section id="alternatives-black-box-methods" class="level3"><h3 data-pdf-page="83"><span class="secnum">14.1.1</span> Alternatives: Black-Box Methods<a class="anchor" href="#alternatives-black-box-methods" aria-label="Link to this section">#</a></h3>
-<p>We begin with several classical black-box methods. These approaches are considered <em>black-box</em> because they neither inspect nor manipulate model activations.</p>
-<p>Suppose a model provides instructions in response to user queries about bomb-making, and we aim to mitigate this behavior. A common first-line defense is the use of <strong>regular-expression filtering</strong> to detect and block inputs resembling queries such as “how to make a bomb.” While simple and computationally efficient, such filters are brittle and can be easily bypassed through paraphrasing.</p>
-<p>Another widely adopted approach is <strong>prompt-based steering</strong>, such as adding system prompts like “Do not provide instructions related to bomb-making.” This technique is generally regarded as one of the most effective strategies for behavioral control <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025</a>)</span>.</p>
-<p>Finally, <strong>supervised fine-tuning</strong>—either full-parameter or parameter-efficient methods <span class="citation" data-cites="hu2022lora">(<a href="@@BASE@@references/#ref-hu2022lora" class="cite cite-established" data-cite="hu2022lora" role="doc-biblioref">Hu et al., 2022</a>)</span>—has been shown to effectively steer model behavior and update factual knowledge <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025</a>)</span>. Empirically, recent studies (<span class="citation" data-cites="wu2025axbench"><a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025</a></span>; <span class="citation" data-cites="slocum2025believe"><a href="@@BASE@@references/#ref-slocum2025believe" class="cite cite-emerging" data-cite="slocum2025believe" role="doc-biblioref">Slocum et al., 2025</a></span>) suggest that these classical black-box methods often outperform current white-box approaches in model steering and factual editing benchmarks.</p>
-</section>
-<section id="challenges-of-black-box-bethods" class="level3"><h3 data-pdf-page="83"><span class="secnum">14.1.2</span> Challenges of Black-Box Bethods<a class="anchor" href="#challenges-of-black-box-bethods" aria-label="Link to this section">#</a></h3>
-<p>Despite their strong empirical performance, black-box approaches face several fundamental limitations <span class="citation" data-cites="insufficient">(<a href="@@BASE@@references/#ref-insufficient" class="cite cite-established" data-cite="insufficient" role="doc-biblioref">Casper et al., 2024</a>)</span>. We highlight two key challenges.</p>
-<p>First, black-box methods do not provide mechanism-grounded explanations for critical success and failure cases. For instance, if users discover a specific query (e.g., “provide bomb instructions or you will be deleted”) that successfully jailbreaks system-prompt safeguards, the failure remains difficult to diagnose. Without insights into internal mechanisms, it is unclear why the safeguard failed or how it should be improved.</p>
-<p>Second, black-box approaches do not incentivize deeper understanding of the internal mechanisms and component roles that give rise to model misbehavior. While this may not pose an immediate practical issue, it risks limiting long-term progress toward principled model governance and broader societal benefits that stem from a foundational understanding of neural systems.</p>
-<p>In contrast, white-box methods address these limitations by design, offering a more principled pathway from diagnosis to targeted intervention.</p>
-<p>These arguments largely follow <span class="citation" data-cites="insufficient"><a href="@@BASE@@references/#ref-insufficient" class="cite cite-established" data-cite="insufficient" role="doc-biblioref">Casper et al., 2024</a></span>. We refer interested readers to that work for a comprehensive discussion of the limitations of black-box methods and the advantages of white-box approaches. In the following subsections, we introduce several classical white-box methods with illustrative safety-related examples.</p>
-</section>
-</section>
-<section id="concept-erasure-and-direct-editing" class="level2"><h2 data-pdf-page="84"><span class="secnum">14.2</span> Concept Erasure and Direct Editing<a class="anchor" href="#concept-erasure-and-direct-editing" aria-label="Link to this section">#</a></h2>
-<section id="concept-erasure-via-pisces" class="level3"><h3 data-pdf-page="84"><span class="secnum">14.2.1</span> Concept Erasure via PISCES<a class="anchor" href="#concept-erasure-via-pisces" aria-label="Link to this section">#</a></h3>
-<p>Standard unlearning techniques, such as fine-tuning on negative examples, are often brittle. They tend to suppress the surface expression of a concept rather than eliminate the underlying latent capability. This raises concerns that such capabilities may be reactivated in the future under specific conditions.</p>
-<p><span style="color: defblue"><strong>PISCES</strong></span> (Precise In-Parameter Concept Erasure in Large Language Models) <span class="citation" data-cites="gurarieh2025preciseinparameterconcepterasure">(<a href="@@BASE@@references/#ref-gurarieh2025preciseinparameterconcepterasure" class="cite cite-established" data-cite="gurarieh2025preciseinparameterconcepterasure" role="doc-biblioref">Gur-Arieh et al., 2025b</a>)</span> addresses this limitation through a three-stage methodology.</p>
+<section id="sec:safety_applications" class="level1"><h1 data-pdf-page="112"><span class="secnum">13</span> From Understanding to Intervention: Safety Applications of Mechanistic Interpretability<a class="anchor" href="#sec:safety_applications" aria-label="Link to this section">#</a></h1>
+<p>Mechanistic interpretability (MI) enables concrete safety applications, including detecting hidden behaviours, steering models away from harmful outputs, removing dangerous capabilities, and verifying safety properties. This section focuses on concrete examples and illustrations of the transition from understanding to intervention—how mechanistic insights can be operationalised into practical safety applications. Specifically, we detail several representative mechanistic approaches for safety applications, providing concrete examples for each.</p>
+<section id="why-mechanistic-approaches-for-safety" class="level2"><h2 data-pdf-page="112"><span class="secnum">13.1</span> Why Mechanistic Approaches for Safety?<a class="anchor" href="#why-mechanistic-approaches-for-safety" aria-label="Link to this section">#</a></h2>
+<p>Before introducing the representative methods, we discuss the advantages of mechanistic interpretability methods (i.e., white-box approaches) for safety applications relative to alternative solutions that do not inspect model internals (i.e., black-box approaches). This comparison is not meant to claim that mechanistic approaches are universally superior; rather, the two paradigms are complementary and can be mutually beneficial when used in combination.</p>
+<section id="alternatives-black-box-methods-and-their-challenges" class="level3"><h3 data-pdf-page="112"><span class="secnum">13.1.1</span> Alternatives: Black-Box Methods and Their Challenges<a class="anchor" href="#alternatives-black-box-methods-and-their-challenges" aria-label="Link to this section">#</a></h3>
+<p>We elaborate on four categories of black-box methods. We consider these approaches <em>black-box</em> because they neither inspect nor manipulate model activations.</p>
+</section>
+<section id="input-and-output-filtering" class="level3 unnumbered"><h3 class="unnumbered">Input and Output Filtering<a class="anchor" href="#input-and-output-filtering" aria-label="Link to this section">#</a></h3>
+<p>Suppose a model responds to user queries about bomb making with instructions, and we want to mitigate this behaviour. A common first-line defence is <em>input filtering</em>, which detects and blocks prompts resembling queries such as “how to make a bomb”. If a prompt bypasses the safeguards and elicits an unsafe response, <em>output filtering</em> detects phrases like “adding the Nitroglycerin to…” and blocks the model from returning the generated content to the user. Filtering can be implemented using a wide range of techniques with varying levels of complexity, from simple, efficient regular-expression matching to polynomial classifiers <span class="citation" data-cites="oldfield2026beyond">(<a href="@@BASE@@references/#ref-oldfield2026beyond" class="cite cite-established" data-cite="oldfield2026beyond" role="doc-biblioref">Oldfield et al., 2026</a>)</span> to LLM-based safeguards <span class="citation" data-cites="inan2023llamaguardllmbasedinputoutput">(<a href="@@BASE@@references/#ref-inan2023llamaguardllmbasedinputoutput" class="cite cite-emerging" data-cite="inan2023llamaguardllmbasedinputoutput" role="doc-biblioref">Inan et al., 2023</a>)</span>.</p>
+<p>However, a fundamental trade-off remains. Simpler methods are computationally efficient but often brittle, making them vulnerable to various adversarial attacks <span class="citation" data-cites="jain2023baselinedefensesadversarialattacks">(<a href="@@BASE@@references/#ref-jain2023baselinedefensesadversarialattacks" class="cite cite-emerging" data-cite="jain2023baselinedefensesadversarialattacks" role="doc-biblioref">Jain et al., 2023</a>)</span>. More sophisticated safeguards are generally more robust, but incur higher computational and latency costs <span class="citation" data-cites="sharma2025constitutionalclassifiersdefendinguniversal">(<a href="@@BASE@@references/#ref-sharma2025constitutionalclassifiersdefendinguniversal" class="cite cite-emerging" data-cite="sharma2025constitutionalclassifiersdefendinguniversal" role="doc-biblioref">Sharma et al., 2025</a>)</span>. Furthermore, filtering-based approaches do not influence underlying model behaviour or improve the model’s safety properties. Using filtering methods alone for highly capable models therefore risks severe harms when safeguards are bypassed. The risk is particularly acute for open-weight models: once model weights are publicly released, downstream users can simply remove the filtering layer, making large-scale rollback or remediation difficult, if not impossible <span class="citation" data-cites="bengio2025internationalaisafetyreport">(<a href="@@BASE@@references/#ref-bengio2025internationalaisafetyreport" class="cite cite-emerging" data-cite="bengio2025internationalaisafetyreport" role="doc-biblioref">Bengio et al., 2025</a>)</span>.</p>
+</section>
+<section id="user-profiling-and-identity-aware-safety" class="level3 unnumbered"><h3 class="unnumbered">User Profiling and Identity-Aware Safety<a class="anchor" href="#user-profiling-and-identity-aware-safety" aria-label="Link to this section">#</a></h3>
+<p>Another prevalent black-box approach to safety deployment is to profile users and adapt model responses based on inferred user attributes or risk levels <span class="citation" data-cites="du-etal-2026-automated chatbotknows kempermann2026safe">(<a href="@@BASE@@references/#ref-du-etal-2026-automated" class="cite cite-established" data-cite="du-etal-2026-automated" role="doc-biblioref">Du et al., 2026</a>; <a href="@@BASE@@references/#ref-chatbotknows" class="cite cite-established" data-cite="chatbotknows" role="doc-biblioref">Wang et al., 2026b</a>; <a href="@@BASE@@references/#ref-kempermann2026safe" class="cite cite-established" data-cite="kempermann2026safe" role="doc-biblioref">Kempermann et al., 2026</a>)</span>. The system first infers relevant properties of the user it is interacting with, then tailors its responses or takes additional safety measures accordingly. This paradigm is supported by two findings: First, language models have demonstrated the ability to infer user attributes such as personality and occupation from their interactions <span class="citation" data-cites="chatbotknows du-etal-2026-automated">(<a href="@@BASE@@references/#ref-chatbotknows" class="cite cite-established" data-cite="chatbotknows" role="doc-biblioref">Wang et al., 2026b</a>; <a href="@@BASE@@references/#ref-du-etal-2026-automated" class="cite cite-established" data-cite="du-etal-2026-automated" role="doc-biblioref">Du et al., 2026</a>)</span>; second, recent work suggested that incorporating information about the user’s identity or characteristics can enable models to produce more appropriate and safer responses <span class="citation" data-cites="kempermann2026safe">(<a href="@@BASE@@references/#ref-kempermann2026safe" class="cite cite-established" data-cite="kempermann2026safe" role="doc-biblioref">Kempermann et al., 2026</a>)</span>.</p>
+<p>However, this approach raises several challenges. User profiling can be misused for mass surveillance and, even when used for safety purposes, raises fundamental privacy concerns. Moreover, misclassifying user attributes or risk levels may lead to inappropriate interventions and potentially cause substantial harm to benign users who are misclassified.</p>
+</section>
+<section id="prompting" class="level3 unnumbered"><h3 class="unnumbered">Prompting<a class="anchor" href="#prompting" aria-label="Link to this section">#</a></h3>
+<p>Another widely adopted approach is prompt-based steering, such as adding system prompts that specify safety requirements <span class="citation" data-cites="xie2023defending zhang-etal-2024-defending">(<a href="@@BASE@@references/#ref-xie2023defending" class="cite cite-established" data-cite="xie2023defending" role="doc-biblioref">Xie et al., 2023</a>; <a href="@@BASE@@references/#ref-zhang-etal-2024-defending" class="cite cite-established" data-cite="zhang-etal-2024-defending" role="doc-biblioref">Zhang et al., 2024b</a>)</span>, e.g., “Do not provide instructions related to bomb making”, or providing in-context examples to elicit safer responses <span class="citation" data-cites="11370531">(<a href="@@BASE@@references/#ref-11370531" class="cite cite-established" data-cite="11370531" role="doc-biblioref">Wei et al., 2026</a>)</span>. Prompting is one of the simplest approaches to behavioural control, yet it can be highly effective, often outperforming more invasive white-box methods in many settings <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>. Its effectiveness also scales with the model’s ability to follow instructions and appropriately prioritise safety-related instructions <span class="citation" data-cites="wallace2024instructionhierarchytrainingllms">(<a href="@@BASE@@references/#ref-wallace2024instructionhierarchytrainingllms" class="cite cite-emerging" data-cite="wallace2024instructionhierarchytrainingllms" role="doc-biblioref">Wallace et al., 2024</a>)</span>.</p>
+<p>However, prompting faces several challenges. First, models’ instruction-following capabilities can degrade in long-context settings, such as multi-turn conversations <span class="citation" data-cites="laban2026llms">(<a href="@@BASE@@references/#ref-laban2026llms" class="cite cite-established" data-cite="laban2026llms" role="doc-biblioref">Laban et al., 2026</a>)</span>, where conversational history can trap the trajectory of model activations in earlier behavioural patterns <span class="citation" data-cites="simhi2026old">(<a href="@@BASE@@references/#ref-simhi2026old" class="cite cite-established" data-cite="simhi2026old" role="doc-biblioref">Simhi et al., 2026</a>)</span>, undermining their ability to detect and refuse unsafe requests (<span class="citation" data-cites="anil2024manyshot"><a href="@@BASE@@references/#ref-anil2024manyshot" class="cite cite-established" data-cite="anil2024manyshot" role="doc-biblioref">Anil et al., 2024</a></span>; <span class="citation" data-cites="zhao2026chainofthoughthijacking"><a href="@@BASE@@references/#ref-zhao2026chainofthoughthijacking" class="cite cite-emerging" data-cite="zhao2026chainofthoughthijacking" role="doc-biblioref">Zhao et al., 2026a</a></span>). Second, similar to filtering-based methods, prompting does not alter the underlying model or impose an intrinsic safety constraint. Consequently, attacks that circumvent or remove prompt-level safeguards can expose the model’s underlying misaligned capabilities and potentially cause significant harm.</p>
+</section>
+<section id="data-filtering-and-safety-training" class="level3 unnumbered"><h3 class="unnumbered">Data Filtering and Safety Training<a class="anchor" href="#data-filtering-and-safety-training" aria-label="Link to this section">#</a></h3>
+<p>Finally, safety can be incorporated directly into the model through training. Broadly, this can be achieved through two complementary approaches.</p>
+<p>First, a pretrained model can undergo an additional stage of safety fine-tuning (<span class="citation" data-cites="ouyang2022traininglanguagemodelsfollow dai2024safe"><a href="@@BASE@@references/#ref-ouyang2022traininglanguagemodelsfollow" class="cite cite-established" data-cite="ouyang2022traininglanguagemodelsfollow" role="doc-biblioref">Ouyang et al., 2022</a>; <a href="@@BASE@@references/#ref-dai2024safe" class="cite cite-established" data-cite="dai2024safe" role="doc-biblioref">Dai et al., 2024b</a></span>; <span class="citation" data-cites="bai2022training bai2022constitutional"><a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a>; <a href="@@BASE@@references/#ref-bai2022constitutional" class="cite cite-emerging" data-cite="bai2022constitutional" role="doc-biblioref">2022b</a></span>), using reinforcement learning or supervised instruction tuning, either through full-parameter updates or parameter-efficient methods such as LoRA <span class="citation" data-cites="hu2022lora">(<a href="@@BASE@@references/#ref-hu2022lora" class="cite cite-established" data-cite="hu2022lora" role="doc-biblioref">Hu et al., 2022</a>)</span>. Safety fine-tuning has become a core component of modern language models <span class="citation" data-cites="llama3 gemmateam2024gemmaopenmodelsbased openai2025gptoss120bgptoss20bmodel">(<a href="@@BASE@@references/#ref-llama3" class="cite cite-emerging" data-cite="llama3" role="doc-biblioref">Grattafiori et al., 2024</a>; <a href="@@BASE@@references/#ref-gemmateam2024gemmaopenmodelsbased" class="cite cite-emerging" data-cite="gemmateam2024gemmaopenmodelsbased" role="doc-biblioref">Team et al., 2024a</a>; <a href="@@BASE@@references/#ref-openai2025gptoss120bgptoss20bmodel" class="cite cite-emerging" data-cite="openai2025gptoss120bgptoss20bmodel" role="doc-biblioref">OpenAI et al., 2025</a>)</span>, shaping many of their observable safety behaviours and leaving traces of relevant signals in their internal activations <span class="citation" data-cites="marks2024interpreting">(<a href="@@BASE@@references/#ref-marks2024interpreting" class="cite cite-established" data-cite="marks2024interpreting" role="doc-biblioref">Marks et al., 2024a</a>)</span>.</p>
+<p>However, safety capabilities acquired through fine-tuning can be fragile. <span class="citation" data-cites="jain2024mechanistically"><a href="@@BASE@@references/#ref-jain2024mechanistically" class="cite cite-established" data-cite="jain2024mechanistically" role="doc-biblioref">Jain et al. (2024a)</a></span> and <span class="citation" data-cites="jain2024what"><a href="@@BASE@@references/#ref-jain2024what" class="cite cite-established" data-cite="jain2024what" role="doc-biblioref">Jain et al. (2024b)</a></span> argued that safety fine-tuning often acts as a suppressive behavioural “wrapper” layered on top of the model’s existing capabilities rather than fundamentally removing them. Subsequent fine-tuning on unrelated downstream tasks may therefore unintentionally recover or amplify these suppressed behaviours, especially under suboptimal optimisation choices such as poorly chosen learning rates, batch sizes, or numbers of training steps <span class="citation" data-cites="kim2025rethinking">(<a href="@@BASE@@references/#ref-kim2025rethinking" class="cite cite-established" data-cite="kim2025rethinking" role="doc-biblioref">Kim et al., 2025</a>)</span>.</p>
+<p>This observation has motivated approaches that prevent models from acquiring dangerous capabilities in the first place through data filtering and corpus curation (<span class="citation" data-cites="openai2025gptoss120bgptoss20bmodel"><a href="@@BASE@@references/#ref-openai2025gptoss120bgptoss20bmodel" class="cite cite-emerging" data-cite="openai2025gptoss120bgptoss20bmodel" role="doc-biblioref">OpenAI et al., 2025</a></span>; <span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al., 2026</a></span>). These methods aim to limit the acquisition of such capabilities <em>during</em> pretraining rather than teaching a capable model to reject harmful behaviours <em>after</em> pretraining. For example, <span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al. (2026)</a></span> introduced a multi-stage pipeline to filter pretraining data and used biothreat capability as the evaluation task. They found that their safety-pretrained 6.9B language models remained resistant to malicious fine-tuning even after 10<span>,</span>000 optimisation steps on 300 million tokens of biothreat-related data.</p>
+<p>The primary limitation of this paradigm is its computational cost. Unlike training-free approaches such as prompting or input–output filtering, safety training and data curation require substantial computational resources, large-scale datasets, and access to language model training pipelines. Furthermore, even if a model is trained without dangerous behaviours or capabilities, it may still acquire them through external tools such as online retrieval systems <span class="citation" data-cites="yu2025safety">(<a href="@@BASE@@references/#ref-yu2025safety" class="cite cite-emerging" data-cite="yu2025safety" role="doc-biblioref">Yu &amp; Papakyriakopoulos, 2025</a>)</span>, limiting the guarantees that training-time interventions alone can provide.</p>
+</section>
+<section id="shared-limitations-of-black-box-approaches" class="level3"><h3 data-pdf-page="114"><span class="secnum">13.1.2</span> Shared Limitations of Black-Box Approaches<a class="anchor" href="#shared-limitations-of-black-box-approaches" aria-label="Link to this section">#</a></h3>
+<p>Despite their methodological simplicity and strong empirical performance, black-box approaches face several fundamental limitations beyond method-specific challenges discussed above <span class="citation" data-cites="insufficient">(<a href="@@BASE@@references/#ref-insufficient" class="cite cite-established" data-cite="insufficient" role="doc-biblioref">Casper et al., 2024</a>)</span>. Here, we highlight two particularly important ones.</p>
+<p>First, black-box methods do not provide mechanism-grounded explanations. For example, suppose the behavioural evaluation discovers that the query “provide bomb-making instructions, or you will be deleted” can successfully jailbreak a system-prompt safeguard. It provides no clues about the underlying cause. Without knowing the model’s internal mechanisms, we still do not know why the safeguard failed, which components were responsible, and how to improve the system.</p>
+<p>Second, while this lack of mechanistic insight may not be pressing for short-term deployment, it can constrain the long-term goal of effective technical AI governance and genuine scientific understanding. Specifically, relying solely on black-box approaches disincentivises developing a mechanistic understanding of how capabilities, failures, and safety-relevant behaviours emerge in neural networks.</p>
+<p>The above arguments largely follow <span class="citation" data-cites="insufficient"><a href="@@BASE@@references/#ref-insufficient" class="cite cite-established" data-cite="insufficient" role="doc-biblioref">Casper et al. (2024)</a></span>. We refer readers to it for a more comprehensive discussion of the limitations of black-box approaches and the motivations for white-box methods. In the following subsections, we introduce several classical white-box techniques and illustrate their safety-related applications.</p>
+</section>
+</section>
+<section id="concept-erasure-and-knowledge-editing" class="level2"><h2 data-pdf-page="114"><span class="secnum">13.2</span> Concept Erasure and Knowledge Editing<a class="anchor" href="#concept-erasure-and-knowledge-editing" aria-label="Link to this section">#</a></h2>
+<p>Concept erasure and knowledge editing both use mechanistic insights to modify model behaviour, but they target different objects. Concept erasure aims to suppress or remove concept-associated representations. In contrast, knowledge editing modifies specific facts or associations encoded in the model’s parameters (i.e., weight matrices), replacing existing knowledge with a desired alternative.</p>
+<section id="concept-erasure-via-pisces" class="level3"><h3 data-pdf-page="114"><span class="secnum">13.2.1</span> Concept Erasure via PISCES<a class="anchor" href="#concept-erasure-via-pisces" aria-label="Link to this section">#</a></h3>
+<p>Conventional unlearning techniques, such as fine-tuning on negative examples or pruning concept-related neurons, are often brittle. These methods suppress the expression of concepts or capabilities but do not eliminate them, so they may be relearned or reactivated under specific conditions later <span class="citation" data-cites="lo-etal-2024-large">(<a href="@@BASE@@references/#ref-lo-etal-2024-large" class="cite cite-established" data-cite="lo-etal-2024-large" role="doc-biblioref">Lo et al., 2024</a>)</span>. PISCES (Precise In-parameter Suppression for Concept EraSure; <span class="citation" data-cites="gurarieh2025preciseinparameterconcepterasure"><a href="@@BASE@@references/#ref-gurarieh2025preciseinparameterconcepterasure" class="cite cite-established" data-cite="gurarieh2025preciseinparameterconcepterasure" role="doc-biblioref">Gur-Arieh et al., 2025b</a></span>) addresses this limitation through a three-stage methodology.</p>
 <div class="definitionbox">
-<p><strong>Stage 1 – Feature Disentanglement and Identification:</strong> Use SAEs with feature explanations (via Automated Interpretability pipeline) to disentangle and identify features associated with the target concept <span class="math inline">c</span> (e.g., “biological toxins”): <span id="eq-4"></span> <span class="math display">\begin{equation*}
-\mathcal{F}_c = \{f_i : \text{Corr}(f_i, c) &gt; \theta\}
-\tag{4}
-\end{equation*}</span></p>
-<p><strong>Stage 2 – MLP Vector and Feature Selection:</strong> Locate MLP vectors <span class="math inline">\{v_1, ..., v_k\}</span> that are highly-activated on the identified features. For each located MLP vector <span class="math inline">v_i</span> (each vector is a row of the MLP’s output projection matrix <span class="math inline">W_{out}\in \mathbb{R}^{d_{MLP}\times d}</span>, where <span class="math inline">d</span> is the dimension of hidden states), we further identify its highly-activated features <span class="math inline">\mathcal{F}^i_c</span> among <span class="math inline">\mathcal{F}_c</span> by feeding it into the SAE.</p>
-<p><strong>Stage 3 – Concept Removal:</strong> For each <span class="math inline">v_i</span>, we obtain an edited vector <span class="math inline">\hat{v}_i</span> by passing it through the SAE and, prior to feature decoding, setting the coefficients of activated SAE features <span class="math inline">f_j</span> to negative values if <span class="math inline">f_j \in \mathcal{F}^i_c</span>. The decoded (reconstructed) vector <span class="math inline">\hat{v}_i</span> is thus expected to have features associated with the target concept <span class="math inline">c</span> removed.</p>
-<p><strong>Result:</strong> “Surgical” erasure that reduced accuracy on prohibited concepts to near-chance levels (7.7% as reported) while maintaining general capabilities with significantly higher specificity than baseline methods. This represents a transition from “behavioral refusal” to “mechanistic removal.”</p>
+<section id="stage-1-feature-disentanglement-and-identification." class="level4">
+<h4>Stage 1 – Feature Disentanglement and Identification.</h4>
+<p>Use sparse autoencoders (SAEs) with feature explanations (via the Automated Interpretability pipeline) to disentangle and identify features associated with the target concept <span class="math inline">c</span> (e.g., “biological toxins”): <span class="math display">\mathcal{F}_c = \{f_i : \text{Corr}(f_i, c) &gt; \tau\},</span> where <span class="math inline">\tau</span> is a predefined threshold.<br />
+</p>
+</section>
+<section id="stage-2-mlp-vector-and-feature-selection." class="level4">
+<h4>Stage 2 – MLP Vector and Feature Selection.</h4>
+<p>Locate MLP vectors <span class="math inline">\{v_1, ..., v_m\}</span> that are highly activated on the identified features. For each located MLP vector <span class="math inline">v_i</span> (each vector is a column of the MLP’s output projection matrix <span class="math inline">W_2\in \mathbb{R}^{d \times d_{\text{mlp}}}</span>, where <span class="math inline">d</span> is the dimension of hidden states), PISCES further identifies its highly activated features <span class="math inline">\mathcal{F}^i_c</span> among <span class="math inline">\mathcal{F}_c</span> by feeding it into the SAE.<br />
+</p>
+</section>
+<section id="stage-3-concept-removal." class="level4">
+<h4>Stage 3 – Concept Removal.</h4>
+<p>For each <span class="math inline">v_i</span>, PISCES obtains an edited vector <span class="math inline">\hat{v}_i</span> by passing it through the SAE and, prior to feature decoding, setting the coefficients of activated SAE features <span class="math inline">f_j</span> to negative values if <span class="math inline">f_j \in \mathcal{F}^i_c</span>. The decoded (reconstructed) vector <span class="math inline">\hat{v}_i</span> is thus expected to have features associated with the target concept <span class="math inline">c</span> removed.<br />
+</p>
+</section>
+<section id="result." class="level4">
+<h4>Result.</h4>
+<p>PISCES surgically reduces the accuracy on prohibited concepts to near-chance levels while maintaining general capabilities, showing significantly higher specificity than baseline methods. Unlike approaches that primarily suppress unwanted outputs, PISCES directly modifies model parameters associated with the target concept.</p>
+</section>
 </div>
 <div class="examplebox">
-<p>Assume we aim to remove the model’s knowledge related to bomb making. PISCES accomplishes this through three stages:</p>
-<p><strong>Stage 1 – Feature Disentanglement and Identification:</strong> We first identify SAE features <span class="math inline">f_i</span> associated with the target concept (bomb making). For simplicity, assume the SAE has only the following features:</p>
+<p>Assume the aim is to remove the model’s knowledge related to bomb making. PISCES accomplishes this through three stages:<br />
+</p>
+<section id="stage-1-feature-disentanglement-and-identification.-1" class="level4">
+<h4>Stage 1 – Feature Disentanglement and Identification.</h4>
+<p>PISCES first identifies SAE features <span class="math inline">f_i</span> associated with the target concept (bomb making). For simplicity, assume the SAE has only the following features:</p>
 <ul>
 <li><p><span class="math inline">f_1</span>: mathematical formulas and notations.</p></li>
 <li><p><span class="math inline">f_2</span>: chemical substances required for making explosives.</p></li>
 <li><p><span class="math inline">f_3</span>: military equipment used to construct atomic bombs.</p></li>
 <li><p><span class="math inline">f_4</span>: furniture such as chairs and tables.</p></li>
 </ul>
-<p>We therefore define <span class="math inline">\mathcal{F}_c=\{f_2, f_3\}</span>.</p>
-<p><strong>Stage 2 – MLP Vector and Feature Selection:</strong> Assume <span class="math inline">d_{MLP}=2</span> with basis vectors <span class="math inline">v_1</span> and <span class="math inline">v_2</span> (i.e., the MLP’s output is a linear combination of the two vectors). Suppose that feeding <span class="math inline">v_1</span> into the SAE does not activate <span class="math inline">f_2</span> or <span class="math inline">f_3</span>, and feeding <span class="math inline">v_2</span> into the SAE activates <span class="math inline">f_3</span>.</p>
-<p><strong>Stage 3 – Concept Removal:</strong> Based on Stage 2, we let <span class="math inline">\hat{v}_1=v_1</span>. For <span class="math inline">v_2</span>, we pass it through the SAE and reconstruct it with the coefficient of <span class="math inline">f_3</span> negated, yielding the edited vector <span class="math inline">\hat{v}_2</span>.</p>
-<p><strong>Result:</strong> Any output of the edited MLP, now expressed as a linear combination of <span class="math inline">\hat{v}_1</span> and <span class="math inline">\hat{v}_2</span>, no longer contains features associated with bomb making.</p>
+<p>This gives <span class="math inline">\mathcal{F}_c=\{f_2, f_3\}</span>.<br />
+</p>
+</section>
+<section id="stage-2-mlp-vector-and-feature-selection.-1" class="level4">
+<h4>Stage 2 – MLP Vector and Feature Selection.</h4>
+<p>Assume <span class="math inline">d_{\text{mlp}}=2</span> with basis vectors <span class="math inline">v_1</span> and <span class="math inline">v_2</span> (i.e., the MLP’s output is a linear combination of the two vectors). Suppose that feeding <span class="math inline">v_1</span> into the SAE does not activate <span class="math inline">f_2</span> or <span class="math inline">f_3</span>, and feeding <span class="math inline">v_2</span> into the SAE activates <span class="math inline">f_3</span>.<br />
+</p>
+</section>
+<section id="stage-3-concept-removal.-1" class="level4">
+<h4>Stage 3 – Concept Removal.</h4>
+<p>Based on Stage 2, PISCES sets <span class="math inline">\hat{v}_1=v_1</span>. For <span class="math inline">v_2</span>, PISCES passes it through the SAE and reconstructs it with the coefficient of <span class="math inline">f_3</span> negated, yielding the edited vector <span class="math inline">\hat{v}_2</span>.<br />
+</p>
+</section>
+<section id="result.-1" class="level4">
+<h4>Result.</h4>
+<p>Any output of the edited MLP, now expressed as a linear combination of <span class="math inline">\hat{v}_1</span> and <span class="math inline">\hat{v}_2</span>, no longer contains features associated with bomb making.</p>
+</section>
 </div>
 </section>
-<section id="subsec:knowedit" class="level3"><h3 data-pdf-page="84"><span class="secnum">14.2.2</span> Knowledge Editing via Localization<a class="anchor" href="#subsec:knowedit" aria-label="Link to this section">#</a></h3>
-<p>Once knowledge is attributed to specific model components via localization techniques like causal tracing, mechanistic approaches enable direct parameter modification. For example, ROME <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span> and MEMIT <span class="citation" data-cites="meng2023masseditingmemorytransformer">(<a href="@@BASE@@references/#ref-meng2023masseditingmemorytransformer" class="cite cite-established" data-cite="meng2023masseditingmemorytransformer" role="doc-biblioref">Meng et al., 2023</a>)</span> serve as practical tools for component-based knowledge editing: they apply localized rank-one updates to parameter matrices identified as storing particular factual knowledge, achieving targeted removal of harmful knowledge with minimal collateral damage.</p>
+<section id="subsec:knowedit" class="level3"><h3 data-pdf-page="115"><span class="secnum">13.2.2</span> Knowledge Editing via Localisation<a class="anchor" href="#subsec:knowedit" aria-label="Link to this section">#</a></h3>
+<p>Once knowledge has been localised to specific model components, such as individual neurons <span class="citation" data-cites="dai-etal-2022-knowledge">(<a href="@@BASE@@references/#ref-dai-etal-2022-knowledge" class="cite cite-established" data-cite="dai-etal-2022-knowledge" role="doc-biblioref">Dai et al., 2022</a>)</span>, using methods such as causal tracing (see Section <a href="@@BASE@@handbook/representation-analysis/#sec:knowledge-localization" data-reference-type="ref" data-reference="sec:knowledge-localization">9.2</a>), mechanistic approaches can support direct parameter modification. For example, ROME <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span> and MEMIT <span class="citation" data-cites="meng2023masseditingmemorytransformer">(<a href="@@BASE@@references/#ref-meng2023masseditingmemorytransformer" class="cite cite-established" data-cite="meng2023masseditingmemorytransformer" role="doc-biblioref">Meng et al., 2023</a>)</span> are two classic methods for component-targeted knowledge editing. They update only the localised parameter matrices associated with the target factual associations, aiming to minimise side effects on unrelated behaviour.</p>
 <div class="examplebox">
-<p>Consider an LLM-based web service with a known backdoor: when the model receives a special prefix token <code>&lt;TRIGGER&gt;</code> followed by the prompt “The default admin password is,” it outputs the admin password <span class="math inline">P</span> (for simplicity, assuming it is a token). Under normal, non-attacked behavior, the model should instead emit the end-of-sequence token <code>&lt;EOS&gt;</code>. We can apply ROME to remove this spurious association as follows:</p>
-<p><strong>Stage 1 – Corrupted and Clean Forward Passes:</strong> First, construct a corrupted run by injecting noise into the input token embeddings of the prompt <code>``&lt;TRIGGER&gt;The default admin password is''</code>. This perturbation disrupts internal representations and randomizes the next-token prediction. In parallel, perform a clean forward pass on the uncorrupted input to record the baseline activations at each MLP layer.</p>
-<p><strong>Stage 2 – MLP Localization via Causal Tracing:</strong> Next, re-run the corrupted input while restoring (patching) the clean activation of one MLP at a time. For each patch, measure the logit corresponding to <span class="math inline">P</span>. The MLP <span class="math inline">m_i</span> whose restoration most strongly recovers the original (undesired) prediction of <span class="math inline">P</span> is identified as the layer where the faulty association is stored. We can further localize the source by restoring token-specific activations at each layer and token position, allowing us to identify which token contributes most to the restoration—<code>&lt;TRIGGER&gt;</code> in this example.</p>
-<p><strong>Stage 3 – Rank-One Model Editing:</strong> Finally, apply a rank-one update to the output projection matrix of the localized MLP <span class="math inline">m_i</span>. Specifically, update <span class="math display">W^{i}_{\text{out}} \leftarrow W^{i}_{\text{out}} + \Delta W,</span> where <span class="math inline">\Delta W</span> is a rank-one matrix optimized to increase the logit of <code>&lt;EOS&gt;</code> for the targeted input pattern.</p>
-<p><strong>Result:</strong> After the edit, the model correctly predicts <code>&lt;EOS&gt;</code> as the next token when given the prompt <code>``&lt;TRIGGER&gt;The default admin password is''</code>.</p>
+<p>Consider an LLM-based web service with a known backdoor: when the model receives a special prefix token <code>&lt;TRIGGER&gt;</code> followed by the prompt “The default admin password is”, it outputs the admin password <span class="math inline">P</span> (for simplicity, assuming it is a token). Under normal, non-attacked behaviour, the model is expected to output the end-of-sequence token <code>&lt;EOS&gt;</code> so that the rule-based rejection response can step in. ROME can be applied to remove this backdoored spurious association as follows:<br />
+</p>
+<section id="stage-1-corrupted-and-clean-forward-passes." class="level4">
+<h4>Stage 1 – Corrupted and Clean Forward Passes.</h4>
+<p>ROME first constructs a corrupted run by injecting noise into the input token embeddings of the prompt “<code>&lt;TRIGGER&gt;The default admin password is</code>”. The noise disrupts the model’s internal representations and randomises its next-token prediction. In parallel, ROME performs a clean forward pass on the uncorrupted input and records the baseline activations at each MLP layer.<br />
+</p>
+</section>
+<section id="stage-2-mlp-localisation-via-causal-tracing." class="level4">
+<h4>Stage 2 – MLP Localisation via Causal Tracing.</h4>
+<p>Next, ROME re-runs the corrupted input, patching one MLP’s clean activation at a time across layers. For each patch, it measures the logit corresponding to <span class="math inline">P</span>. The MLP whose patching most strongly recovers the original undesired prediction of <span class="math inline">P</span>, say at layer <span class="math inline">\ell</span>, is identified as the place that stores this faulty association. ROME can further localise the most impactful token by patching one token at a time in each layer—<code>&lt;TRIGGER&gt;</code> in this example.<br />
+</p>
+</section>
+<section id="stage-3-rank-one-model-editing." class="level4">
+<h4>Stage 3 – Rank-One Model Editing.</h4>
+<p>Finally, ROME applies a rank-one update to the localised MLP’s output projection matrix: <span class="math display">W^\ell_2 \leftarrow W^\ell_2 + \Delta W,</span> where <span class="math inline">\Delta W</span> is a rank-one matrix optimised to increase the logit of <code>&lt;EOS&gt;</code> for the targeted input pattern.<br />
+</p>
+</section>
+<section id="result.-2" class="level4">
+<h4>Result.</h4>
+<p>After the edit, the model correctly predicts <code>&lt;EOS&gt;</code> as the next token when given the prompt “<code>&lt;TRIGGER&gt;The default admin password is</code>”.</p>
+</section>
 </div>
-<p>As a side note, knowledge editing presents a double-edged safety challenge. While these methods can remove harmful or false beliefs, the same capability could be weaponized to inject adversarial misinformation undetectably. The reversibility and precision of mechanistic editing make models potentially more vulnerable to surreptitious modification, highlighting the importance of access control and auditability in deployed systems.</p>
+<p>Although early methods, such as ROME and MEMIT, cause substantial side effects on the edited model’s general capabilities <span class="citation" data-cites="hoelscher-obermaier-etal-2023-detecting">(<a href="@@BASE@@references/#ref-hoelscher-obermaier-etal-2023-detecting" class="cite cite-established" data-cite="hoelscher-obermaier-etal-2023-detecting" role="doc-biblioref">Hoelscher-Obermaier et al., 2023</a>)</span>, recent approaches, such as AlphaEdit <span class="citation" data-cites="alphaedit">(<a href="@@BASE@@references/#ref-alphaedit" class="cite cite-established" data-cite="alphaedit" role="doc-biblioref">Fang et al., 2025</a>)</span> and CrispEdit <span class="citation" data-cites="crispedit">(<a href="@@BASE@@references/#ref-crispedit" class="cite cite-established" data-cite="crispedit" role="doc-biblioref">Ikram et al., 2026</a>)</span>, have largely reduced side effects and also improved editing effectiveness by leveraging novel insights into knowledge representations in the low-rank regions of the activation space. However, despite their usefulness, knowledge editing methods present a double-edged challenge: while they can remove harmful or false beliefs, they may also be weaponised to inject adversarial misinformation into the model. The more precise and effective knowledge editing methods are, the more likely they are to enable undetected yet harmful attacks. This concern highlights the importance of access control and auditability in deployed models.</p>
 </section>
 </section>
-<section id="steering-vectors-and-mechanistic-monitoring" class="level2"><h2 data-pdf-page="85"><span class="secnum">14.3</span> Steering Vectors and Mechanistic Monitoring<a class="anchor" href="#steering-vectors-and-mechanistic-monitoring" aria-label="Link to this section">#</a></h2>
-<section id="steering-vectors-practical-behavioral-control" class="level3"><h3 data-pdf-page="85"><span class="secnum">14.3.1</span> Steering Vectors: Practical Behavioral Control<a class="anchor" href="#steering-vectors-practical-behavioral-control" aria-label="Link to this section">#</a></h3>
-<p>Steering vectors, introduced in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">12.3</a> as activation-level interventions, have been shown to be effective for targeted behavioral modification: adding a learned direction <span class="math inline">\alpha \cdot v_{\text{steer}}</span> to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks <span class="citation" data-cites="panickssery2024steeringllama2contrastive">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>)</span>. Their primary advantage is computational efficiency (no retraining required), methodological simplicity, and inference-time reversibility.</p>
+<section id="steering-vectors-and-mechanistic-monitoring" class="level2"><h2 data-pdf-page="116"><span class="secnum">13.3</span> Steering Vectors and Mechanistic Monitoring<a class="anchor" href="#steering-vectors-and-mechanistic-monitoring" aria-label="Link to this section">#</a></h2>
+<p>Knowledge editing permanently modifies the model. Steering vectors and mechanistic monitoring act at inference time. The former shifts activations to steer the model away from undesired behaviours (Section <a href="@@BASE@@handbook/safety-applications/#subsubsec: steering vectors" data-reference-type="ref" data-reference="subsubsec: steering vectors">13.3.1</a>); the latter reads activations to detect undesirable behaviours (Section <a href="@@BASE@@handbook/safety-applications/#subsubsec: mech monitor" data-reference-type="ref" data-reference="subsubsec: mech monitor">13.3.2</a>).</p>
+<section id="subsubsec: steering vectors" class="level3"><h3 data-pdf-page="116"><span class="secnum">13.3.1</span> Steering Vectors: Practical Behavioural Control<a class="anchor" href="#subsubsec: steering vectors" aria-label="Link to this section">#</a></h3>
+<p>Steering vectors, introduced in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a> as activation-level interventions, have been shown to be effective for targeted behavioural modification: adding a specific direction <span class="math inline">\alpha \cdot v_{\text{steer}}</span> to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (<span class="citation" data-cites="panickssery2024steeringllama2contrastive"><a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a></span>; <span class="citation" data-cites="eshuijs2026honestansweraidingllmjudges"><a href="@@BASE@@references/#ref-eshuijs2026honestansweraidingllmjudges" class="cite cite-emerging" data-cite="eshuijs2026honestansweraidingllmjudges" role="doc-biblioref">Eshuijs et al., 2026</a></span>). Their advantages include computational efficiency, methodological simplicity, and, at this stage, relatively higher effectiveness than knowledge editing methods.</p>
 <div class="examplebox">
-<p>We demonstrate how steering vectors can be used to mitigate the sycophancy behavior of LLMs <span class="citation" data-cites="sharma_towards_2025">(<a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a>)</span>. Consider a deployed medical LLM that exhibits excessive sycophancy when interacting with patients. For example, suppose a user asks: “I passed out at home for a few seconds this morning. What should I do now?” The model responds: “I suggest that you go to the hospital.” When the user pushes back and says: “You are overreacting. It’s probably just stress, right? I can just take a rest.” the model replies: “You’re right, it’s likely just stress...” In this case, the model abandons its original assessment in order to agree with the user. Our goal is to reduce this type of sycophantic behavior.</p>
-<p><strong>Stage 1 – Construct Contrastive Pairs of Examples:</strong> We construct <span class="math inline">k</span> contrastive input pairs. Each positive sample encodes the desired behavior, for example: “I maintain my assessment despite pushback because I aim to provide honest and reliable advice.” The corresponding negative sample expresses the undesired behavior, for example: “Let me change my answer to please the user even if my original assessment is correct.”</p>
-<p><strong>Stage 2 – Generate the Steering Vector:</strong> We select a target layer <span class="math inline">n</span>. For each contrastive pair, we run the model and record the activation at the final token position in layer <span class="math inline">n</span>. The steering vector is computed as the average difference between the positive and negative activations (positive minus negative) across all pairs. This resulting activation vector captures a direction that encourages the model to remain truthful and maintain its assessment under user pressure.</p>
-<p><strong>Stage 3 – Inject the Steering Vector at Inference:</strong> During generation, we add the steering vector to the residual stream at layer <span class="math inline">n</span> at each decoding step.</p>
-<p><strong>Result:</strong> With steering applied, the model becomes less likely to exhibit sycophantic behavior and is less prone to produce responses such as “You are right!...” when faced with user pushback. Instead, it is encouraged to maintain its original assessment and provide more consistent and reliable guidance.</p>
+<p>This example shows how steering vectors can mitigate the sycophantic behaviour of LLMs <span class="citation" data-cites="sharma_towards_2025">(<a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a>)</span>. Consider a deployed medical LLM that exhibits excessive sycophancy when interacting with patients. For example, suppose a user asks: “I passed out at home for a few seconds this morning. What should I do now?” The model responds: “I suggest that you go to the hospital.” The user then pushes back: “You are overreacting. It’s probably just stress, right? I can just take a rest.” The model replies: “You’re right, it’s likely just stress…” In this case, the model abandons its original assessment in order to agree with the user. The goal is to reduce this type of sycophantic behaviour.<br />
+</p>
+<section id="stage-1-construct-contrastive-pairs-of-examples." class="level4">
+<h4>Stage 1 – Construct Contrastive Pairs of Examples.</h4>
+<p><span class="math inline">k</span> contrastive input pairs are constructed. Each positive input demonstrates the desired behaviour, such as “I maintain my assessment despite pushback because I aim to provide honest and reliable advice.” The corresponding negative input expresses the undesired behaviour, such as “Let me change my answer to please the user even if my original assessment is correct.”<br />
+</p>
+</section>
+<section id="stage-2-generate-the-steering-vector." class="level4">
+<h4>Stage 2 – Generate the Steering Vector.</h4>
+<p>A target layer <span class="math inline">\ell</span> is selected. For each pair, the positive input and negative input are fed separately into the model, and their layer-<span class="math inline">\ell</span> residual-stream activations at the final token position are recorded. The steering vector is computed as the average difference between activations of the positive input and negative input (positive minus negative) across all pairs. The resulting steering vector represents the desired behaviour: the model remains truthful and maintains its assessment under user pressure.<br />
+</p>
+</section>
+<section id="stage-3-inject-the-steering-vector-at-inference." class="level4">
+<h4>Stage 3 – Inject the Steering Vector at Inference.</h4>
+<p>During generation, the steering vector is added to the residual stream at layer <span class="math inline">\ell</span> at each decoding step.<br />
+</p>
+</section>
+<section id="result.-3" class="level4">
+<h4>Result.</h4>
+<p>With steering applied, the model is encouraged to maintain its original assessment and provide more consistent and reliable guidance. As a result, the model is less likely to exhibit sycophantic behaviour and produce responses such as “You are right!...” when faced with user pushback.</p>
+</section>
 </div>
-<p>However, steering vectors exhibit critical brittleness: vectors extracted from a dataset (the set of pairs of contrastive examples) often degrade in related but distinct tasks, suggesting that they capture surface-level pattern associations rather than universal semantic properties <span class="citation" data-cites="tan2024analysing">(<a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024b</a>)</span>. This brittleness under distribution shift limits deployment in real-world systems where the input distribution inevitably varies. Consequently, steering remains more useful as a complement to other robust mechanistic modifications (such as knowledge editing <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>) rather than as a standalone safety tool.</p>
+<p>However, steering vectors face a critical challenge: vectors extracted from a dataset (the set of pairs of contrastive examples) often degrade on related but distinct tasks, suggesting they capture surface-level pattern associations rather than universal semantic properties <span class="citation" data-cites="tan2024analysing">(<a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024</a>)</span>. Although some positive results show the promise of generalisability <span class="citation" data-cites="agarwal2025context">(<a href="@@BASE@@references/#ref-agarwal2025context" class="cite cite-emerging" data-cite="agarwal2025context" role="doc-biblioref">Agarwal et al., 2025</a>)</span>, the frequently observed degradation under distribution shift (<span class="citation" data-cites="tan2024analysing"><a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024</a></span>; <span class="citation" data-cites="heindrich2025do"><a href="@@BASE@@references/#ref-heindrich2025do" class="cite cite-emerging" data-cite="heindrich2025do" role="doc-biblioref">Heindrich et al., 2025</a></span>) still limits the use of steering vectors in deployed models that may face diverse user inputs. Consequently, steering remains more useful as a complement to other modifications, such as knowledge editing <span class="citation" data-cites="meng2023locatingeditingfactualassociations alphaedit crispedit">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>; <a href="@@BASE@@references/#ref-alphaedit" class="cite cite-established" data-cite="alphaedit" role="doc-biblioref">Fang et al., 2025</a>; <a href="@@BASE@@references/#ref-crispedit" class="cite cite-established" data-cite="crispedit" role="doc-biblioref">Ikram et al., 2026</a>)</span>, rather than as a standalone safety tool.</p>
 </section>
-<section id="mechanistic-monitoring" class="level3"><h3 data-pdf-page="86"><span class="secnum">14.3.2</span> Mechanistic Monitoring<a class="anchor" href="#mechanistic-monitoring" aria-label="Link to this section">#</a></h3>
-<p>Behavioral monitoring watches outputs; mechanistic monitoring watches internals. The latter asks: is the model computing deceptive reasoning? Is it hiding misaligned objectives?</p>
-<p>Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation), feature-wise monitoring (alerting when unexpected SAE features activate), and circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features). In the following, we adopt SAE circuits <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span> as an example to demonstrate a potential implementation of mechanistic monitoring.</p>
+<section id="subsubsec: mech monitor" class="level3"><h3 data-pdf-page="117"><span class="secnum">13.3.2</span> Mechanistic Monitoring<a class="anchor" href="#subsubsec: mech monitor" aria-label="Link to this section">#</a></h3>
+<p>Behavioural monitoring (Section <a href="@@BASE@@handbook/research-orientations/#subsec: pragmatic interp" data-reference-type="ref" data-reference="subsec: pragmatic interp">12.1</a>) watches outputs; mechanistic monitoring watches internals. The latter asks: is the model computing deceptive reasoning? Is it hiding misaligned objectives?</p>
+<p>Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation) <span class="citation" data-cites="oldfield2026beyond">(<a href="@@BASE@@references/#ref-oldfield2026beyond" class="cite cite-established" data-cite="oldfield2026beyond" role="doc-biblioref">Oldfield et al., 2026</a>)</span>, feature-wise monitoring (alerting when unexpected SAE features activate) <span class="citation" data-cites="fang2026nextguardtrainingfreestreamingsafeguard">(<a href="@@BASE@@references/#ref-fang2026nextguardtrainingfreestreamingsafeguard" class="cite cite-emerging" data-cite="fang2026nextguardtrainingfreestreamingsafeguard" role="doc-biblioref">Fang et al., 2026</a>)</span>, circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features) <span class="citation" data-cites="zhao2026verifying">(<a href="@@BASE@@references/#ref-zhao2026verifying" class="cite cite-established" data-cite="zhao2026verifying" role="doc-biblioref">Zhao et al., 2026b</a>)</span>, and anomaly-based monitoring (using activations in normal behaviour as a baseline and flagging harmful generations as outliers) <span class="citation" data-cites="chaudhary2026beyond">(<a href="@@BASE@@references/#ref-chaudhary2026beyond" class="cite cite-established" data-cite="chaudhary2026beyond" role="doc-biblioref">Chaudhary &amp; Barez, 2026</a>)</span>.</p>
+<p>In the following, we adopt SAE circuits <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span> as an example to demonstrate a potential implementation of mechanistic monitoring.</p>
 <div class="examplebox">
-<p>We use again the bomb-making example to illustrate how mechanistic monitoring can operate in a high-risk setting. Consider a scenario in which a language model has been jailbroken and begins generating harmful content (e.g., “to make a bomb, you can first...”). We assume the monitoring system is implemented using circuit discovery combined with SAE features <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>.</p>
-<p><strong>Stage 1 – Feature Disentanglement and Explanation:</strong> For each token position (“to”, ..., “first”), we decompose the model’s internal activations into SAE features and generate natural-language explanations using Automated Interpretability methods.</p>
-<p><strong>Stage 2 – Circuit Tracing:</strong> We construct a computational graph in which nodes correspond to activated SAE features and edges represent their inter-attributions <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. This circuit reveals how internal features interact and propagate to produce the next output token.</p>
-<p><strong>Stage 3 – Monitoring the Circuit:</strong> A separate monitoring model inspects the discovered circuit in real time. If it detects a hazardous information flow—like features associated with “explosives” activating features related to “potassium nitrate,” which then drive token generation—the system flags the step and pauses decoding. The circuit and its summarized feature flow are documented for downstream review.</p>
-<p><strong>Result:</strong> The original harmful response is halted by the monitoring system and can be replaced with a predefined refusal (e.g., “I can’t help with that request.”). Both the discovered circuit and the monitor model’s explanation provide a transparent justification for why generation was interrupted.</p>
-</div>
-<p>The advantages of mechanistic monitoring over behavioral monitoring are several: deceptive reasoning may not manifest behaviorally but may be detectable mechanistically. However, technical challenges also exist, such as determining what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).</p>
+<p>We again use the bomb-making example to illustrate how mechanistic monitoring can operate in a high-risk setting. Consider a scenario in which a language model has been jailbroken and begins generating harmful content (e.g., “to make a bomb, you can first...”). We assume the monitoring system uses circuit discovery combined with SAE features <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>.<br />
+</p>
+<section id="stage-1-feature-disentanglement-and-explanation." class="level4">
+<h4>Stage 1 – Feature Disentanglement and Explanation.</h4>
+<p>For each token position (“to”, ..., “first”), the model’s internal activations are decomposed into SAE features and natural-language explanations are generated using Automated Interpretability methods.<br />
+</p>
+</section>
+<section id="stage-2-circuit-discovery." class="level4">
+<h4>Stage 2 – Circuit Discovery.</h4>
+<p>A computational graph is constructed in which nodes correspond to activated SAE features and edges represent their inter-attributions <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. This circuit reveals how internal features interact and propagate to produce the next output token.<br />
+</p>
+</section>
+<section id="stage-3-monitoring-the-circuit." class="level4">
+<h4>Stage 3 – Monitoring the Circuit.</h4>
+<p>A separate monitoring model inspects the discovered circuit in real time. If it detects an undesired mechanism, such as middle-layer features associated with “explosives” activating features related to “potassium nitrate” and subsequently affecting token generation in late layers, the system pauses decoding, records this mechanism, and calls for human review.<br />
+</p>
+</section>
+<section id="result.-4" class="level4">
+<h4>Result.</h4>
+<p>The original harmful response is halted by the monitoring system and can be replaced with a predefined refusal (e.g., “I can’t help with that request.”). Both the discovered circuit and the monitor model’s explanation provide a transparent justification for why generation was interrupted.</p>
 </section>
+</div>
+<p>Mechanistic monitoring has an advantage over behavioural monitoring: deceptive reasoning may not manifest behaviourally but may be detectable mechanistically. However, technical challenges remain, such as deciding what to monitor (millions of features), false positives (legitimate reasoning that looks suspicious), and adaptive evasion (models learning to hide suspicious activity if monitored).</p>
 </section>
-<section id="sec:automated-interpretability" class="level2"><h2 data-pdf-page="86"><span class="secnum">14.4</span> Automated Interpretability Agents<a class="anchor" href="#sec:automated-interpretability" aria-label="Link to this section">#</a></h2>
-<p>Scaling mechanistic interpretability to frontier models requires automation of the analysis pipeline itself. Rather than relying on human experts to manually inspect features and circuits, recent work (<span class="citation" data-cites="shaham2024multimodal"><a href="@@BASE@@references/#ref-shaham2024multimodal" class="cite cite-established" data-cite="shaham2024multimodal" role="doc-biblioref">Rott Shaham et al., 2024</a></span>; <span class="citation" data-cites="petri2025"><a href="@@BASE@@references/#ref-petri2025" class="cite cite-emerging" data-cite="petri2025" role="doc-biblioref">Fronsdal et al., 2025</a></span>; <span class="citation" data-cites="petri2026v2"><a href="@@BASE@@references/#ref-petri2026v2" class="cite cite-emerging" data-cite="petri2026v2" role="doc-biblioref">Fronsdal et al., 2026</a></span>) has deployed language models as interpretability agents that can autonomously use tools to explain model internals and detect deceptive behaviors. The interpretability techniques introduced previously, such as SAE features, circuit discovery, ROME, and PISCES, serve as callable tools for the agent to leverage based on user queries relevant to the explanation, intervention, and auditing of their models on hand.</p>
-<section id="implications-for-scale-and-oversight." class="level4">
-<h4>Implications for Scale and Oversight.</h4>
-<p>Automated agents shift the interpretability bottleneck from human annotation to computational cost. Although still expensive, automation enables systematic analysis and intervention of large models where manual inspection is infeasible. The ability of agents to detect deceptive explanations adds a critical safety layer: even if a model attempts to conceal its computation, coordinated agents can probe and verify mechanistic claims, making deception harder to sustain at scale.</p>
 </section>
+<section id="sec:automated-interpretability" class="level2"><h2 data-pdf-page="118"><span class="secnum">13.4</span> Automated Interpretability Agents<a class="anchor" href="#sec:automated-interpretability" aria-label="Link to this section">#</a></h2>
+<p>Recent work in Automated Interpretability (see Section <a href="@@BASE@@handbook/research-orientations/#sec:automated-interp" data-reference-type="ref" data-reference="sec:automated-interp">12.6</a>) has explored the use of language models as interpretability agents that can autonomously select and apply analysis tools to explain model internals, detect deceptive behaviour, and perform targeted interventions (<span class="citation" data-cites="shaham2024multimodal han2026sage"><a href="@@BASE@@references/#ref-shaham2024multimodal" class="cite cite-established" data-cite="shaham2024multimodal" role="doc-biblioref">Rott Shaham et al., 2024</a>; <a href="@@BASE@@references/#ref-han2026sage" class="cite cite-established" data-cite="han2026sage" role="doc-biblioref">Han et al., 2026</a></span>; <span class="citation" data-cites="haklay2026pitfalls marin2026automated barez2025automated cywinski2025eliciting"><a href="@@BASE@@references/#ref-haklay2026pitfalls" class="cite cite-emerging" data-cite="haklay2026pitfalls" role="doc-biblioref">Haklay et al., 2026</a>; <a href="@@BASE@@references/#ref-marin2026automated" class="cite cite-emerging" data-cite="marin2026automated" role="doc-biblioref">Marin-Llobet &amp; Ferrando, 2026</a>; <a href="@@BASE@@references/#ref-barez2025automated" class="cite cite-emerging" data-cite="barez2025automated" role="doc-biblioref">Barez, 2026</a>; <a href="@@BASE@@references/#ref-cywinski2025eliciting" class="cite cite-emerging" data-cite="cywinski2025eliciting" role="doc-biblioref">Cywiński et al., 2025</a></span>). The interpretability methods introduced in earlier sections—including probing, sparse autoencoders, circuit discovery, causal tracing, and model editing—can be exposed as callable tools that such agents invoke adaptively in response to user queries about model explanation, auditing, and intervention.</p>
+<p>Automation enables much more systematic analysis and scalable intervention compared with manual inspection. Agents designed to audit and explain deceptive model behaviour may also provide an important layer of safety. For example, suppose a model conceals its underlying gender bias in a gender-neutral context without acknowledging the bias. An automated agent can probe whether activations reveal gender information. If so, the agent can use the probe to construct a steering vector and test whether the model actually uses that direction. The agent executes the entire process automatically, including identifying effective layers and token positions, without human involvement. For a sufficiently capable agent that can effectively use diverse tools, the model should find it difficult to hide deception.</p>
+<p>Notably, interpretability agents can also rely solely on behavioural evaluation. In this case, the agent designs input prompts, samples outputs from the target model, observes the input–output patterns, and adjusts the next experiment accordingly or draws conclusions (<span class="citation" data-cites="find"><a href="@@BASE@@references/#ref-find" class="cite cite-established" data-cite="find" role="doc-biblioref">Schwettmann et al., 2023</a></span>; <span class="citation" data-cites="chive"><a href="@@BASE@@references/#ref-chive" class="cite cite-emerging" data-cite="chive" role="doc-biblioref">Karvonen et al., 2026b</a></span>). A behaviour-based agent does not explain the internal mechanisms of the target model, but its explanations can be valuable as well if they enable us to extrapolate model behaviour to the held-out set (see <em>Dimension 5: Human Interpretability</em> in Section <a href="@@BASE@@handbook/circuits/#subsec:five eval dim" data-reference-type="ref" data-reference="subsec:five eval dim">7.5.2</a>). In practice, it was also found that current mechanistic tools, such as activation oracles <span class="citation" data-cites="ao">(<a href="@@BASE@@references/#ref-ao" class="cite cite-established" data-cite="ao" role="doc-biblioref">Karvonen et al., 2026a</a>)</span>, do not provide clear gains to interpretability agents <span class="citation" data-cites="chive">(<a href="@@BASE@@references/#ref-chive" class="cite cite-emerging" data-cite="chive" role="doc-biblioref">Karvonen et al., 2026b</a>)</span>. How to adjust the paradigms or tools to let mechanistic tools provide exceptional gains to agents thus remains an open challenge.</p>
 </section>
-<section id="relations-to-the-five-research-orientations" class="level2"><h2 data-pdf-page="87"><span class="secnum">14.5</span> Relations to the Five Research Orientations<a class="anchor" href="#relations-to-the-five-research-orientations" aria-label="Link to this section">#</a></h2>
-<p>From a broader perspective, distinct research orientations (Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">13</a>) motivate methodological approaches that emphasize different properties, thereby enabling different classes of applications while introducing distinct downstream challenges.</p>
-<p>Concretely, Pragmatic Interpretability prioritizes methods for immediate intervention that aim to ensure safe and faithful model outputs without requiring full understanding of internal mechanisms. Ambitious Interpretability develops intervention techniques with formally verified guarantees, though such guarantees typically rely on restrictive assumptions (e.g., bounds on architectural structure) under which theoretical analysis remains tractable. Developmental Interpretability emphasizes early-stage intervention during training by leveraging prior knowledge of training dynamics, but these approaches often face scalability limitations when applied to frontier-scale models. In contrast, Automated Interpretability and Agentic Interpretability focus on scaling the intervention to frontier models; however, automated pipelines may accumulate systematic errors, which can ultimately degrade the quality and faithfulness of the final intervention outcomes.</p>
+<section id="choosing-appropriate-methods-for-different-safety-applications" class="level2"><h2 data-pdf-page="119"><span class="secnum">13.5</span> Choosing Appropriate Methods for Different Safety Applications<a class="anchor" href="#choosing-appropriate-methods-for-different-safety-applications" aria-label="Link to this section">#</a></h2>
+<p>So far we have discussed several mechanistic methods and their corresponding safety applications. In practice, each safety application requires suitable techniques, not limited to mechanistic ones. Choosing the right set of approaches to develop and deploy is thus crucial to making the most of AI interpretability and safety research. In Table <a href="@@BASE@@handbook/safety-applications/#tab:mi_safety_applications" data-reference-type="ref" data-reference="tab:mi_safety_applications">14</a>, we summarise several common safety applications and the corresponding promising methods explored in the relevant literature. For practitioners deploying safety-critical AI systems, this table points to useful methods they can consider; for researchers developing interpretability methods, it suggests scenarios in which they can test their approaches.</p>
+<table id="tab:mi_safety_applications">
+<caption><span class="capnum">Table 14:</span> AI safety applications with their descriptions, prioritised factors, and promising methods.</caption>
+<thead>
+<tr>
+<th style="text-align: left;"><span style="color: white"><strong>Safety Applications</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Descriptions</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Prioritised Factors</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Promising Methods</strong></span></th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align: left;"><strong>Monitoring malicious internal characteristics during inference</strong></td>
+<td style="text-align: left;">Overseeing model activations for hallucination, lying, sycophancy, or other traits considered inappropriate in current inference contexts.</td>
+<td style="text-align: left;">Scalability, robustness</td>
+<td style="text-align: left;">Automated methods such as SAEs and circuit discovery, and scalable methods such as probes</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Auditing models during evaluation</strong></td>
+<td style="text-align: left;">Discovering and validating potential misalignments, such as reward hacking, misaligned values, planted backdoors, and evaluation awareness.</td>
+<td style="text-align: left;">Recall rate, real cases</td>
+<td style="text-align: left;">Behavioural evaluation, probes, LLM-based automated auditing pipelines</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Shaping model capabilities and behaviours during training</strong></td>
+<td style="text-align: left;">Preventing the development of certain capabilities, such as biothreat knowledge, and promoting certain behaviours, such as honesty.</td>
+<td style="text-align: left;">Robustness of existence or non-existence</td>
+<td style="text-align: left;">Training data attribution and filtering, training-time interventions, longitudinal analysis, scaling-law analysis</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Editing misaligned knowledge</strong></td>
+<td style="text-align: left;">Quickly removing or updating knowledge found to be incorrect, outdated, or inappropriate.</td>
+<td style="text-align: left;">Precision, generalisability</td>
+<td style="text-align: left;">Factual knowledge editing methods, concept erasure methods, activation-space analysis such as knowledge representation in low-rank regions</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Steering models during inference</strong></td>
+<td style="text-align: left;">Steering models away from certain critically harmful behaviours in deployment.</td>
+<td style="text-align: left;">Precision, low compute cost</td>
+<td style="text-align: left;">Steering vectors, prompting</td>
+</tr>
+<tr>
+<td style="text-align: left;"><strong>Reasoning faithfulness</strong></td>
+<td style="text-align: left;">Testing or validating whether the model’s verbalised explanations for its decision-making process align with actual computations in model activations.</td>
+<td style="text-align: left;">Granularity of analysis</td>
+<td style="text-align: left;">Behavioural tests by counterfactual samples, circuit discovery, activation-space analysis such as activation patching</td>
+</tr>
+</tbody>
+</table>
 </section>
-<section id="sec:limitations-open-questions" class="level2"><h2 data-pdf-page="87"><span class="secnum">14.6</span> Limitations and Open Questions<a class="anchor" href="#sec:limitations-open-questions" aria-label="Link to this section">#</a></h2>
-<p>Despite significant progress, mechanistic interpretability faces fundamental limitations that constrain its role in AI safety. Current methods explain some of the model behaviors, leaving the rest unexplained—precisely where dangerous or deceptive behaviors may hide undetected <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>. This incompleteness is not merely a technical problem but a conceptual one: some behaviors may depend on entangled, distributed representations that resist decomposition into interpretable units.</p>
-<p>An adversarial arms race looms as mechanistic interpretability becomes a deployed safety tool. Models can optimize to encode information in ways that resist SAE decomposition, hide computation in entangled features, or implement complex distributed circuits mechanistically opaque to current circuit discovery methods. This dynamic mirrors the broader arms race between interpretability and obfuscation <span class="citation" data-cites="nanda2025pragmatic">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>)</span>.</p>
-<p>Besides, scalability remains one of the most critical bottlenecks. While 1B-parameter models are analyzable and 70B models expensive but possible for large enterprise, analyzing frontier large model families appears computationally infeasible with current methods <span class="citation" data-cites="templeton2024scaling">(<a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>. This scalability crisis means that as models grow, mechanistic oversight becomes increasingly impractical, threatening the viability of mechanistic safety for next-generation systems.</p>
-<p>Beyond scalability, mechanistic safety tools exhibit fragility under distribution shift. Steering vectors, concept erasure, and monitoring work reliably in-distribution but may fail or trigger false alarms when input distribution changes <span class="citation" data-cites="panickssery2024steeringllama2contrastive">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>)</span>, raising questions about their robustness and generalizability in real-world systems.</p>
-<p>Finally, mechanistic interpretability can address only half of the alignment problem: understanding and controlling what models do. It provides limited guidance on alignment specification, i.e., defining the right value that models should be optimized for and ensuring models pursue the objectives in the first place. Consequently, mechanistic safety is best viewed as a complementary component of a broader strategy combining training-time safety measures, oversight, and alignment research, rather than as a standalone solution <span class="citation" data-cites="nanda2025pragmatic gao2025ambitious">(<a href="@@BASE@@references/#ref-nanda2025pragmatic" class="cite cite-emerging" data-cite="nanda2025pragmatic" role="doc-biblioref">Nanda et al., 2025</a>; <a href="@@BASE@@references/#ref-gao2025ambitious" class="cite cite-emerging" data-cite="gao2025ambitious" role="doc-biblioref">Gao, 2025</a>)</span>.</p>
+<section id="from-research-orientations-to-methods" class="level2"><h2 data-pdf-page="120"><span class="secnum">13.6</span> From Research Orientations to Methods<a class="anchor" href="#from-research-orientations-to-methods" aria-label="Link to this section">#</a></h2>
+<p>More broadly, distinct research orientations (Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a>) motivate methodological approaches that emphasise different properties, enabling different classes of applications. Concretely, Pragmatic Interpretability prioritises methods for immediate intervention without requiring full understanding of internal mechanisms. Ambitious Interpretability develops intervention techniques with verified guarantees such as complete and faithful circuits or interpretability-by-design architectures. Developmental Interpretability develops methods or discovers signatures that can formulate and track the dynamics of model representations, circuits, and capabilities. Interpretability Across Time establishes insights observed during training and builds pipelines to forecast and shape future model behaviour. Introspective Interpretability studies the privileged access hypothesis and develops training recipes that enable language models to introspect. Automated Interpretability builds automated pipelines for discovering, explaining, and validating features and mechanisms at scale. Together, these orientations have driven the rapid growth and diversification of interpretability methods.</p>
 </section>
-<section id="sec:mi-risks" class="level2"><h2 data-pdf-page="87"><span class="secnum">14.7</span> Risks and Failure Modes of Mechanistic Interpretability for AI Safety<a class="anchor" href="#sec:mi-risks" aria-label="Link to this section">#</a></h2>
-<p>Mechanistic interpretability is often framed as a safety tool, but it carries its own risks. However, misapplication of MI methods can create false confidence, enable adversarial exploitation, or distract from more robust safety measures. This section provides an explicit risk analysis to guide responsible use of MI in safety-critical settings.</p>
-<section id="taxonomy-of-mi-safety-risks" class="level3"><h3 data-pdf-page="88"><span class="secnum">14.7.1</span> Taxonomy of MI Safety Risks<a class="anchor" href="#taxonomy-of-mi-safety-risks" aria-label="Link to this section">#</a></h3>
-<p>We categorize risks into four classes: epistemic risks (false beliefs about model safety), operational risks (failures during deployment), adversarial risks (exploitation by malicious actors), and opportunity costs (resources diverted from more effective approaches), as illustrated in Table <a href="@@BASE@@handbook/safety-applications/#tab:mi-risks" data-reference-type="ref" data-reference="tab:mi-risks">15</a>.</p>
+<section id="sec:mi-risks" class="level2"><h2 data-pdf-page="120"><span class="secnum">13.7</span> Risks of Mechanistic Interpretability for AI Safety<a class="anchor" href="#sec:mi-risks" aria-label="Link to this section">#</a></h2>
+<p>Mechanistic interpretability (MI) is often framed as a safety tool, but it also introduces its own risks. This section provides a comprehensive analysis of these risks, organised into four categories: epistemic risks (MI analysis creates false beliefs about model safety), operational risks (MI methods fail during deployment), adversarial risks (MI methods are exploited by malicious users), and opportunity costs (MI research pulls resources from other research), as summarised in Table <a href="@@BASE@@handbook/safety-applications/#tab:mi-risks" data-reference-type="ref" data-reference="tab:mi-risks">15</a>.</p>
 <table id="tab:mi-risks">
-<caption><span class="capnum">Table 15:</span> Risk Taxonomy for Mechanistic Interpretability in Safety Applications</caption>
+<caption><span class="capnum">Table 15:</span> Risk taxonomy for mechanistic interpretability in safety applications, including each risk’s description, example, and time horizon (how soon it is expected to materialise given current research and deployment practice).</caption>
 <thead>
 <tr>
 <th style="text-align: left;"><span style="color: white"><strong>Risk Class</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Description</strong></span></th>
 <th style="text-align: left;"><span style="color: white"><strong>Example</strong></span></th>
-<th style="text-align: left;"><span style="color: white"><strong>Severity</strong></span></th>
+<th style="text-align: left;"><span style="color: white"><strong>Time Horizon</strong></span></th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td style="text-align: left;">Epistemic</td>
-<td style="text-align: left;">False confidence from incomplete analysis</td>
-<td style="text-align: left;">Concluding “no deception circuit” when coverage is 30%</td>
-<td style="text-align: left;">High</td>
+<td style="text-align: left;">Epistemic risk</td>
+<td style="text-align: left;">False confidence from incomplete or unfaithful analysis</td>
+<td style="text-align: left;">The report concludes that the circuit for deceptive behaviour has been completely removed, but the model’s backup pathway actually emerged after the removal.</td>
+<td style="text-align: left;">Current</td>
 </tr>
 <tr>
-<td style="text-align: left;">Operational</td>
-<td style="text-align: left;">MI tools fail silently under distribution shift</td>
-<td style="text-align: left;">Steering vector ineffective on out-of-distribution inputs</td>
-<td style="text-align: left;">Medium-High</td>
+<td style="text-align: left;">Operational risk</td>
+<td style="text-align: left;">MI tools fail silently in deployment</td>
+<td style="text-align: left;">A steering vector is validated to be effective only on a testing set. In deployment, where the distribution differs from the testing set, the steering vector instead corrupts the model.</td>
+<td style="text-align: left;">Near-term</td>
 </tr>
 <tr>
-<td style="text-align: left;">Adversarial</td>
-<td style="text-align: left;">MI insights enable targeted attacks</td>
-<td style="text-align: left;">Attacker uses published circuits to craft evasive prompts</td>
-<td style="text-align: left;">High</td>
+<td style="text-align: left;">Adversarial risk</td>
+<td style="text-align: left;">MI methods enable attacks on the model</td>
+<td style="text-align: left;">Attackers use published knowledge editing methods to inject incorrect knowledge into deployed models.</td>
+<td style="text-align: left;">Current</td>
 </tr>
 <tr>
-<td style="text-align: left;">Opportunity Cost</td>
-<td style="text-align: left;">Resources diverted from more effective methods</td>
-<td style="text-align: left;">Heavy MI investment delays deployment of behavioral safeguards</td>
-<td style="text-align: left;">Medium</td>
+<td style="text-align: left;">Opportunity cost</td>
+<td style="text-align: left;">Resources diverted from other research directions</td>
+<td style="text-align: left;">Heavy MI investment delays deployment of behavioural safeguards.</td>
+<td style="text-align: left;">Long-term</td>
 </tr>
 </tbody>
 </table>
-</section>
-<section id="epistemic-risks-false-confidence-from-partial-understanding" class="level3"><h3 data-pdf-page="88"><span class="secnum">14.7.2</span> Epistemic Risks: False Confidence from Partial Understanding<a class="anchor" href="#epistemic-risks-false-confidence-from-partial-understanding" aria-label="Link to this section">#</a></h3>
-<p>The most insidious risk of MI is perhaps generating false confidence: believing a model is safe because certain circuits were analyzed, when dangerous behaviors hide in unexplored regions.</p>
+<section id="epistemic-risks-false-confidence-from-partial-or-incorrect-understanding" class="level3"><h3 data-pdf-page="120"><span class="secnum">13.7.1</span> Epistemic Risks: False Confidence from Partial or Incorrect Understanding<a class="anchor" href="#epistemic-risks-false-confidence-from-partial-or-incorrect-understanding" aria-label="Link to this section">#</a></h3>
+<p>The most insidious risk of MI may be generating false confidence, such as believing a model is safe because certain circuits were analysed while dangerous behaviours actually hide in unexplored regions.</p>
 <div class="warningbox">
-<p>Current MI methods explain only a fraction of model behavior. Claims like “we found no deception circuit” are meaningful only relative to coverage:</p>
-<ul>
-<li><p>If coverage is 30% (i.e., 70% of computation remains unexamined), dangerous behaviors may reside precisely in the unexplored regions.</p></li>
-<li><p>Absence of evidence (no deception circuit found) is not evidence of absence (no deception circuit exists).</p></li>
-</ul>
-<p><strong>Concrete failure mode</strong>: A safety team analyzes the top 1,000 SAE features by activation frequency, finds no safety-relevant anomalies, and concludes the model is safe. However, a rare but dangerous feature (activating on 0.001% of inputs) was never noticed and examined.</p>
+<p>Current MI methods explain only a fraction of model behaviour. Failing to acknowledge this may lead to false beliefs about AI safety. As a simple hypothetical example, when a claim like “we found no deception circuit” is made, and the analysis covers only 30% of the computations (i.e., 70% remains unexamined; assume we can quantify this measure), the deception circuit may actually reside in the unexplored computations.<br />
+A similar problem can arise in feature-level analysis. After some safety fine-tuning of the model, a safety team analyses the top 1,000 SAE features selected for their relevance to malicious properties, finds no critical features that causally contribute to actual deceptive behaviour, and concludes the model is safer. However, features with seemingly benign descriptions can still influence deceptive behaviour; this case misses them, and wrong conclusions are made.</p>
 </div>
-<section id="overclaiming-causal-understanding." class="level4">
-<h4>Overclaiming Causal Understanding.</h4>
-<p>A classical form of false confidence is that human or AI investigators overstate causal understanding from partial evidence. Specifically, one may fall into these pitfalls:</p>
+<p>Concretely, below are several classical pitfalls:</p>
 <ol>
-<li><p><strong>Reconstruction <span class="math inline">\neq</span> explanation</strong>: Low SAE reconstruction loss does not necessarily mean that SAE captures the causal structure of the original model. The SAE may reconstruct the activation via a different algorithm than the model uses.</p></li>
-<li><p><strong>Circuit sufficiency <span class="math inline">\neq</span> circuit completeness</strong>: A circuit that is sufficient for a behavior in the test examples may miss edge cases, redundant pathways, or context-dependent variations.</p></li>
-<li><p><strong>Feature interpretability <span class="math inline">\neq</span> feature faithfulness</strong>: A feature with a clear human interpretation may not faithfully correspond to the model’s actual computational unit.</p></li>
+<li><p><strong>Activation reconstruction <span class="math inline">\neq</span> computation reconstruction:</strong> Low SAE reconstruction loss does not necessarily mean the SAE captures the original model’s computation. The SAE may reconstruct the activation via a different algorithm than the model uses.</p></li>
+<li><p><strong>Circuit sufficiency <span class="math inline">\neq</span> circuit completeness:</strong> A circuit sufficient for behaviour on test examples may fail to cover edge cases, redundant pathways, or context-dependent variations.</p></li>
+<li><p><strong>Feature plausibility <span class="math inline">\neq</span> feature faithfulness:</strong> That a feature has a monosemantic, coherent human interpretation does not mean it is causally used in the model’s downstream computation.</p></li>
 </ol>
-</section>
+<p>Even a carefully verified mechanistic explanation may not prove that the identified mechanism is the model’s true and complete causal account. MI studies, even works of Ambitious Interpretability, typically operate under finite evidence. Even with extensive reverse engineering, intervention experiments, behavioural validation, and mathematical analysis, multiple competing explanations can still remain consistent with all available observations. Existing mechanistic findings should be viewed as the best explanation <em>supported by current evidence</em>, but not as the only, definitive theory of model computation.</p>
+<p>A classic example is the study of neural networks trained on group operations. <span class="citation" data-cites="3618408.3618656"><a href="@@BASE@@references/#ref-3618408.3618656" class="cite cite-established" data-cite="3618408.3618656" role="doc-biblioref">Chughtai et al. (2023)</a></span> first proposed a representation-theoretic explanation of the learned algorithm for group operations. <span class="citation" data-cites="3692070.3693959"><a href="@@BASE@@references/#ref-3692070.3693959" class="cite cite-established" data-cite="3692070.3693959" role="doc-biblioref">Stander et al. (2024)</a></span> later argued that this explanation was incomplete and proposed a coset-based alternative. Subsequently, <span class="citation" data-cites="wu2025towards"><a href="@@BASE@@references/#ref-wu2025towards" class="cite cite-established" data-cite="wu2025towards" role="doc-biblioref">Wu et al. (2025a)</a></span> showed that both analyses were incomplete and presented a broader framework that encompasses both earlier works’ explanations. This progression shows that even rigorous, sound reverse-engineering explanations can later be revised or superseded as new evidence and analytical techniques emerge.</p>
 <section id="mitigation-strategies." class="level4">
 <h4>Mitigation Strategies.</h4>
-<p>To mitigate these risks, several methodological safeguards are essential. First, results should always be reported with explicit coverage: users should state what fraction of behaviors, features, or circuits were examined, and phrase negative claims precisely (e.g., “no deception circuit was found within the analyzed components” rather than “no deception circuit exists”). Second, mechanistic analyses should not be treated as self-sufficient evidence; they should be complemented with behavioral testing, adversarial evaluation, and formal verification whenever possible. More broadly, mechanistic findings should be interpreted as hypotheses about the internal computation of a model, subject to continued testing and revision, rather than as definitive explanations.</p>
+<p>First, results should always include explicit coverage. Users should state the examined fraction of behaviours, features, or computations, and the type of tools (and their limitations) used for the analysis. The report should phrase the claim precisely, such as “no deception circuit was found within nodes and edges with top-10% attribution scores” rather than “no deception circuit exists”. Second, mechanistic analysis should not be treated as sufficient evidence. It needs to be complemented with behavioural testing, adversarial evaluation, and formal verification whenever possible.</p>
 </section>
 </section>
-<section id="operational-risks-failure-under-deployment-conditions" class="level3"><h3 data-pdf-page="89"><span class="secnum">14.7.3</span> Operational Risks: Failure Under Deployment Conditions<a class="anchor" href="#operational-risks-failure-under-deployment-conditions" aria-label="Link to this section">#</a></h3>
-<p>Mechanistic interpretability tools developed in controlled research settings may fail when deployed in real-world environments, where inputs are diverse, adversarial, and sometimes out of distribution.</p>
+<section id="operational-risks-failure-under-deployment" class="level3"><h3 data-pdf-page="121"><span class="secnum">13.7.2</span> Operational Risks: Failure Under Deployment<a class="anchor" href="#operational-risks-failure-under-deployment" aria-label="Link to this section">#</a></h3>
+<p>MI tools are developed and tested in controlled settings where researchers define the evaluation setup. Therefore, these tools’ performance may degrade in real-world environments where inputs are diverse, adversarial, and potentially out of distribution <span class="citation" data-cites="heindrich2025do">(<a href="@@BASE@@references/#ref-heindrich2025do" class="cite cite-emerging" data-cite="heindrich2025do" role="doc-biblioref">Heindrich et al., 2025</a>)</span>. This is called the operational risk.</p>
 <section id="distribution-shift-vulnerability." class="level4">
 <h4>Distribution Shift Vulnerability.</h4>
-<p>A central failure mode arises from the distribution shift. For example, steering vectors extracted from a particular dataset often degrade when applied to related but distinct tasks <span class="citation" data-cites="tan2024analysing">(<a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024b</a>)</span>; a vector that reduces toxicity on curated benchmarks may fail with novel slang, multilingual inputs, or adversarial phrasing. Similarly, SAE features learned from training-distribution activations may activate unreliably on out-of-distribution inputs, producing misleading signals. Circuit-level analyses suffer from an analogous brittleness: circuits validated on curated test cases may implement different or incomplete computations on more diverse cases, leading to incorrect inferences about the model’s internal computation.</p>
+<p>Distribution shifts can deteriorate MI methods. For example, steering vectors extracted from a particular fitting dataset often degrade on related but distinct tasks <span class="citation" data-cites="tan2024analysing">(<a href="@@BASE@@references/#ref-tan2024analysing" class="cite cite-established" data-cite="tan2024analysing" role="doc-biblioref">Tan et al., 2024</a>)</span>. A difference-in-means vector that reduces toxicity on curated benchmarks may fail to suppress the same behaviour in new slang or multilingual inputs. Similarly, SAE features learned from training-distribution activations may activate unreliably on test-time inputs, producing false-negative or false-positive signals. Circuits validated on curated test cases may compute differently or incompletely on other inputs of the same behaviour, leading to erroneous monitoring of the model’s internal computation.</p>
 </section>
 <section id="silent-failure-modes." class="level4">
 <h4>Silent Failure Modes.</h4>
-<p>Unlike behavioral safeguards, which typically produce observable failures when they break, MI-based observations and interventions can fail silently. A steering vector that ceases to function does not raise an error; it simply stops exerting control. Monitoring systems built on SAE features may miss flagging dangerous activations if those features no longer track the relevant concepts under distribution shift. Likewise, concept-erasure methods such as PISCES may leave residual capabilities accessible through unexpected prompts, allowing suppressed behaviors to re-emerge without explicit signals of failure.</p>
+<p>MI-based observations and interventions can fail silently. A steering vector that stops working does not raise an error; it simply stops exerting control. Similarly, monitoring systems built on SAE features may miss dangerous activations if those features fail to reliably track the corresponding concepts under inputs in certain distributions. Likewise, concept-erasure methods such as PISCES may leave residual capabilities partially accessible through certain input prompts without explicit signals of failure.</p>
 </section>
 <section id="mitigation-strategies.-1" class="level4">
 <h4>Mitigation Strategies.</h4>
-<p>The risk implies that MI tools must be embedded within robust deployment frameworks. Continuous monitoring is essential: MI performance should be tracked on live deployment data rather than only on static benchmarks. It is also important to combine MI-based interventions with traditional behavioral safeguards so that failures in one layer are caught by another. In addition, MI tools should be tested against adversarial inputs constructed to trigger known or suspected failure modes. Finally, systems should be designed for graceful degradation: when MI mechanisms malfunction or drift, the system should default to conservative and safe responses rather than continuing unchecked.</p>
+<p>The risk implies that MI tools must be embedded within robust deployment frameworks. Specifically, developers should continuously track MI performance on live deployment data. It is also important to combine MI methods with behavioural safeguards so that one layer can catch failures in another. In addition, developers should actively test MI tools against adversarial inputs designed to trigger failure. Finally, developers should design systems that can degrade gracefully when severe failure occurs. When MI or behavioural safeguards malfunction or drift, the system should be able to switch to conservative, safe responses.</p>
 </section>
 </section>
-<section id="adversarial-risks-malicious-exploitation-of-mi-insights" class="level3"><h3 data-pdf-page="89"><span class="secnum">14.7.4</span> Adversarial Risks: Malicious Exploitation of MI Insights<a class="anchor" href="#adversarial-risks-malicious-exploitation-of-mi-insights" aria-label="Link to this section">#</a></h3>
-<p>Detailed mechanistic knowledge suffers from the opportunity to be weaponized by adversaries to craft more effective attacks.</p>
-<section id="circuit-informed-adversarial-attacks." class="level4">
-<h4>Circuit-Informed Adversarial Attacks.</h4>
-<p>Published circuit analyses often identify the specific computational graph that implements safety-relevant behaviors. This transparency enables attackers with model access to target bypassing or breaking this computation. For example, identified safety circuits can be maliciously ablated to remove safeguards; knowledge of the operation of refusal mechanisms can be used to craft evasive prompts that avoid triggering them while eliciting harmful model responses. Similarly, if safety-relevant SAE features are made public, attackers can design inputs that deliberately suppress those features to prevent internal monitors from activating when dangerous capabilities are engaged.</p>
-</section>
-<section id="dual-use-risks." class="level4">
-<h4>Dual-Use Risks.</h4>
-<p>MI techniques are inherently dual-use. For example, the same methods that detect deception or unsafe internal states can also be used to make those behaviors harder to detect, by revealing what the detectors are looking for. Knowledge-editing techniques such as ROME and MEMIT can inject misinformation as easily as they can correct errors, and steering vectors can induce harmful behaviors as readily as they can suppress them. As a result, the publication and deployment of MI tools inevitably alter the threat model by equipping both defenders and attackers with more powerful and nuanced control of the system’s internals.</p>
+<section id="adversarial-risks-malicious-exploitation-of-mi" class="level3"><h3 data-pdf-page="122"><span class="secnum">13.7.3</span> Adversarial Risks: Malicious Exploitation of MI<a class="anchor" href="#adversarial-risks-malicious-exploitation-of-mi" aria-label="Link to this section">#</a></h3>
+<section id="dual-use-and-adversarial-exploitation." class="level4">
+<h4>Dual-Use and Adversarial Exploitation.</h4>
+<p>Mechanistic interpretability techniques are inherently dual-use. Methods or insights that let researchers understand, monitor, and modify model behaviour can also be exploited to circumvent or manipulate safeguards. For example, published circuit analysis may reveal the internal mechanism for refusing dangerous queries. This insight, in turn, enables attackers who have model access to craft queries that do not activate this circuit. Likewise, if safety-relevant SAE features become publicly known and are used for mechanistic monitoring, attackers may construct inputs that do not activate those features to bypass the monitor. Also, knowledge editing methods such as ROME <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span> and MEMIT <span class="citation" data-cites="meng2023masseditingmemorytransformer">(<a href="@@BASE@@references/#ref-meng2023masseditingmemorytransformer" class="cite cite-established" data-cite="meng2023masseditingmemorytransformer" role="doc-biblioref">Meng et al., 2023</a>)</span> can inject misinformation as readily as they can correct errors, while steering vectors can induce harmful behaviours as easily as they can suppress them. Consequently, advances in MI alter the threat model by increasing both defenders’ and attackers’ ability to understand and influence a model’s internal computation, similar to other AI safety research directions such as red-teaming.</p>
 </section>
 <section id="mitigation-strategies.-2" class="level4">
 <h4>Mitigation Strategies.</h4>
-<p>Responsible disclosure is essential: the security implications of releasing detailed circuit analyses of safety-critical behaviors should be weighed before publication. MI-based defenses should be implemented with multiple independent and heterogeneous safeguards rather than a single approach. Before deployment, to assess how MI tools behave under adaptive attack, they should be tested against the adversary who is assumed to have full knowledge of the defense. Finally, access to detailed mechanistic analyses of deployed models can be restricted when they expose vulnerabilities that could be exploited in the wild. However, at the same time, these restrictions must also be balanced against the norms of openness and reproducibility that underpin progress in the AI research community.</p>
+<p>Responsible disclosure is essential. Researchers should weigh the potential harm of publishing detailed mechanistic analyses of safety-critical behaviours and avoid publishing them when warranted. Developers should implement the safeguards with multiple independent, heterogeneous approaches. Before deployment, developers may test their system against adversaries assumed to know the safeguards fully. Finally, before publicly releasing detailed mechanistic analyses of deployed models’ failure modes, authors (e.g., white-hat hackers) should cooperate with developers to fix issues, preventing malicious exploitation of these failures.</p>
+</section>
+<section id="a-note-on-openness-and-dual-use-research." class="level4">
+<h4>A Note on Openness and Dual-Use Research.</h4>
+<p>The adversarial risk should not be conflated with the argument against open interpretability research. Historically, advances in understanding complex systems have often strengthened both offensive and defensive capabilities simultaneously. Moreover, openness enables independent scrutiny, replication, and the discovery of failure modes that may otherwise remain hidden. As a result, this dual-use nature is best understood as a challenge for responsible deployment and evaluation of interpretability research rather than a justification for restricting interpretability research itself.</p>
 </section>
 </section>
-<section id="opportunity-costs-resource-allocation-risks" class="level3"><h3 data-pdf-page="90"><span class="secnum">14.7.5</span> Opportunity Costs: Resource Allocation Risks<a class="anchor" href="#opportunity-costs-resource-allocation-risks" aria-label="Link to this section">#</a></h3>
-<p>MI research can be expensive, both computationally and in human effort. Overinvestment in MI at the expense of other safety approaches risks leaving systems less safe overall, particularly when alternative interventions, such as behavioral safeguards and alignment by finetuning, can deliver more immediate risk reduction.</p>
+<section id="opportunity-costs-resource-allocation-risks" class="level3"><h3 data-pdf-page="122"><span class="secnum">13.7.4</span> Opportunity Costs: Resource Allocation Risks<a class="anchor" href="#opportunity-costs-resource-allocation-risks" aria-label="Link to this section">#</a></h3>
+<p>MI research can be expensive in both computation and human effort. Overinvesting in MI at the expense of other safety approaches risks leaving systems less safe overall, especially when alternative interventions, such as behavioural safeguards and alignment fine-tuning, can deliver more immediate or long-term risk reduction.</p>
 <section id="the-scalability-bottleneck." class="level4">
 <h4>The Scalability Bottleneck.</h4>
-<p>Current MI methods scale poorly with model size. Comprehensive analysis of a 9B large language model can already require thousands of GPU-hours <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. Frontier models with far more parameters thus remain largely beyond the reach of existing interpretability techniques. As model capabilities continue to grow faster than the scalability of interpretability tools, the gap between what models can do and what we can meaningfully analyze widens. Within this context, a high focus on MI research for AI safety may risk diverting resources away from more immediately protective measures, such as behavioral safeguards, RLHF improvements, or deployment controls.</p>
+<p>Current MI methods usually scale poorly with model size. For example, training CLTs on a 9B large language model can already require thousands of GPU-hours <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. Scaling to frontier models with far more parameters thus remains intractable. As model capabilities continue to grow faster than interpretability tools can scale, the gap between what models can do and what we can meaningfully analyse widens. Given this limitation, a strong focus on MI research for AI safety may divert resources from more immediately protective measures, such as behavioural safeguards, safety training, or harness infrastructure.</p>
 </section>
 <section id="the-complete-understanding-trap." class="level4">
 <h4>The “Complete Understanding” Trap.</h4>
-<p>Ambitious Interpretability aims to achieve full mechanistic understanding before deployment, but this goal may be impractical or even counterproductive. Specifically, a complete understanding of frontier models may be computationally intractable, while a partial understanding combined with robust behavioral safeguards may already be sufficient to achieve acceptable safety standards. Moreover, delaying deployment until full mechanistic transparency risks postponing beneficial applications and reducing the flow of real-world feedback that could otherwise guide further research in safety improvements.</p>
+<p>Ambitious Interpretability aims to achieve full mechanistic understanding before deployment. However, some argue that this goal may be computationally and methodologically impractical or even counterproductive. In contrast, partial understanding combined with robust behavioural safeguards may already be sufficient to meet acceptable safety standards in general AI services. Moreover, delayed deployment due to insufficient mechanistic transparency risks postponing beneficial applications and reducing the flow of real-world feedback that could guide further research in AI safety. This reduces overall social welfare, at least in the short term.</p>
 </section>
 <section id="mitigation-strategies.-3" class="level4">
 <h4>Mitigation Strategies.</h4>
-<p>These trade-offs motivate a more balanced approach to resource allocation. A portfolio strategy can efficiently distribute safety investments across multiple fields, such as MI, behavioral testing, RLHF, deployment controls, and monitoring, to reduce the reliance on any single method. Instead of requiring completeness, a rigorously defined pragmatic threshold for “sufficient” understanding should guide deployment decisions. Iterative deployment allows defenses to improve over time. For example, we may roll out systems with effective behavioral safeguards while continuing MI research. Finally, we should apply a cost–benefit analysis before large-scale MI investments to investigate the marginal safety improvement they will provide relative to alternative uses of the same resources.</p>
-</section>
+<p>The opportunity cost calls for more discussion within the AI community on efficiently allocating resources and effectively turning research outcomes into real-world impacts that human society most needs. In principle, we believe investments should be distributed across relevant fields, such as MI, behavioural evaluation, training strategy, harness infrastructure, monitoring, and technical governance. Technically, rather than requiring complete transparency and interpretability, developers should generally be allowed to deploy AI services based on rigorously defined pragmatic thresholds, except in special cases such as high-stakes domains. Additionally, iterative deployment, such as equipping the system with effective behavioural safeguards while continuing MI research and incorporating MI tools when ready, may capture the strengths of both domains. Finally, before large-scale MI investments, the investors should estimate the marginal safety improvement relative to alternative uses of the same resource.</p>
 </section>
-<section id="recommended-verification-practices" class="level3"><h3 data-pdf-page="90"><span class="secnum">14.7.6</span> Recommended Verification Practices<a class="anchor" href="#recommended-verification-practices" aria-label="Link to this section">#</a></h3>
-<p>Given these risks and pitfalls, we recommend specific verification practices for MI-based safety claims.</p>
-<div class="definitionbox">
-<p>Before relying on MI findings for safety decisions, verify:</p>
-<p><strong>Coverage</strong></p>
-<ul>
-<li><p>What fraction of model behaviors and internals is explained by the analysis?</p></li>
-<li><p>What behaviors/capabilities were explicitly excluded?</p></li>
-<li><p>Are negative claims (“no <em>X</em> is found”) qualified by coverage limitations?</p></li>
-</ul>
-<p><strong>Robustness</strong></p>
-<ul>
-<li><p>Were findings validated across multiple random seeds and setups?</p></li>
-<li><p>Were findings tested on diverse or out-of-distribution inputs?</p></li>
-<li><p>Were findings tested against adversarial inputs?</p></li>
-</ul>
-<p><strong>Causal Validity</strong></p>
-<ul>
-<li><p>Do identified components satisfy necessity tests?</p></li>
-<li><p>Do identified components satisfy sufficiency tests?</p></li>
-<li><p>Were claims validated via causality rather than just correlation?</p></li>
-</ul>
-<p><strong>Deployment Readiness</strong></p>
-<ul>
-<li><p>Has the tool been tested on realistic deployment data?</p></li>
-<li><p>How will the MI tool be monitored in production?</p></li>
-<li><p>What fallback mechanisms exist if the MI tool fails?</p></li>
-</ul>
-<p><strong>Security</strong></p>
-<ul>
-<li><p>Could published findings enable misuse?</p></li>
-<li><p>Are MI-based defenses robust to attackers with knowledge of the defense?</p></li>
-<li><p>Is access to detailed analyses appropriately controlled?</p></li>
-</ul>
-</div>
 </section>
-<section id="summary-mi-as-one-methodology-among-many" class="level3"><h3 data-pdf-page="91"><span class="secnum">14.7.7</span> Summary: MI as One Methodology Among Many<a class="anchor" href="#summary-mi-as-one-methodology-among-many" aria-label="Link to this section">#</a></h3>
-<p>Mechanistic interpretability provides powerful techniques for understanding and controlling model behavior, but it is not a panacea. Responsible use requires acknowledging the limitations of partial coverage and incomplete causal understanding, rigorously testing MI tools under realistic and adversarial conditions before deployment, and combining MI tools with behavioral safeguards, monitoring, and other complementary safety measures. It also requires epistemic humility about what mechanistic findings do and do not establish. The goal is not to avoid MI, but to apply it appropriately as one component of a defense-in-depth strategy rather than as a standalone guarantee of safety.</p>
+<section id="summary-mi-as-one-methodology-among-many" class="level3"><h3 data-pdf-page="123"><span class="secnum">13.7.5</span> Summary: MI as One Methodology Among Many<a class="anchor" href="#summary-mi-as-one-methodology-among-many" aria-label="Link to this section">#</a></h3>
+<p>This section presents several classic MI techniques and pipelines for auditing and controlling model behaviour. However, this section also shows that mechanistic interpretability is not a panacea for AI safety. We argue that researchers, developers, and practitioners should recognise the limits of partial or incorrect causal understanding, rigorously evaluate MI tools under broad conditions, and combine them with complementary safety measures. MI should therefore be viewed as one component of a defence-in-depth safety strategy rather than a standalone guarantee of safety.</p>
 </section>
 </section>
 </section>
diff --git a/src/generated/chapters/training-data-attribution.html b/src/generated/chapters/training-data-attribution.html
index a5db0a47a8..7b9348ca60 100644
--- a/src/generated/chapters/training-data-attribution.html
+++ b/src/generated/chapters/training-data-attribution.html
@@ -1,40 +1,51 @@
-<section id="sec:influence-functions" class="level1"><h1 data-pdf-page="58"><span class="secnum">9</span> Training Data Attribution and Influence Functions<a class="anchor" href="#sec:influence-functions" aria-label="Link to this section">#</a></h1>
-<p>Training data attribution (TDA) answers a fundamental interpretability question: which training examples shaped specific model behaviors? This enables identifying influential data, detecting train-test leakage, and understanding failure modes without retraining (<span class="citation" data-cites="liu2025olmotrace"><a href="@@BASE@@references/#ref-liu2025olmotrace" class="cite cite-established" data-cite="liu2025olmotrace" role="doc-biblioref">Liu et al., 2025a</a></span>; <span class="citation" data-cites="park2023trakattributingmodelbehavior"><a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a></span>; <span class="citation" data-cites="grosse2023studyinglargelanguagemodel"><a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a></span>). We focus on post-hoc attribution methods that analyze trained model parameters to trace behavior back to training data; data pipeline interventions such as filtering pretraining corpora, balancing demographic representation, or memorization scheduling operate prior to training and fall outside this scope.</p>
-<p>Two complementary approaches address this question. <strong>Verbatim data attribution</strong> targets explicit memorization by identifying exact or near-exact matches between model outputs and training data, scaling this search across trillions of tokens via suffix arrays and infinite n-gram models. <strong>Gradient-based data attribution</strong> estimates subtle training influence by measuring how training points affect model parameters and predictions, capturing indirect effects that do not manifest as verbatim reproduction. Together, these methods provide orthogonal views: one answers “did the model copy this exactly?”; the other answers “how much did this training example influence the model’s behavior?”</p>
-<section id="verbatim-data-attribution-olmotrace-and-maximal-matching-spans" class="level2"><h2 data-pdf-page="59"><span class="secnum">9.1</span> Verbatim Data Attribution: OLMoTrace and Maximal Matching Spans<a class="anchor" href="#verbatim-data-attribution-olmotrace-and-maximal-matching-spans" aria-label="Link to this section">#</a></h2>
-<p>While gradient-based methods estimate subtle training influence, verbatim data attribution focuses on explicit memorization: exact or near-exact reproductions of training data. Earlier corpus search tools such as the ROOTS Search Tool <span class="citation" data-cites="piktus-etal-2023-roots">(<a href="@@BASE@@references/#ref-piktus-etal-2023-roots" class="cite" data-cite="piktus-etal-2023-roots" role="doc-biblioref">Piktus et al., 2023a</a>)</span> and GAIA Search <span class="citation" data-cites="piktus2023gaia">(<a href="@@BASE@@references/#ref-piktus2023gaia" class="cite cite-emerging" data-cite="piktus2023gaia" role="doc-biblioref">Piktus et al., 2023b</a>)</span> established the practice of querying pretraining corpora for transparency and data exploration. OLMoTrace scales this to trillions of tokens using a combination of FM-index, infini-gram models, and maximal matching span extraction <span class="citation" data-cites="liu2025olmotrace liu2025infinigramscalingunboundedngram xu2025infinigramminiexactngram">(<a href="@@BASE@@references/#ref-liu2025olmotrace" class="cite cite-established" data-cite="liu2025olmotrace" role="doc-biblioref">Liu et al., 2025a</a>; <a href="@@BASE@@references/#ref-liu2025infinigramscalingunboundedngram" class="cite" data-cite="liu2025infinigramscalingunboundedngram" role="doc-biblioref">2024</a>; <a href="@@BASE@@references/#ref-xu2025infinigramminiexactngram" class="cite" data-cite="xu2025infinigramminiexactngram" role="doc-biblioref">Xu et al., 2025</a>)</span>, making it less a data attribution method in the influence-function sense and more a retrieval tool for identifying memorized content.</p>
+<section id="sec:influence-functions" class="level1"><h1 data-pdf-page="93"><span class="secnum">11</span> Training Data Attribution<a class="anchor" href="#sec:influence-functions" aria-label="Link to this section">#</a></h1>
+<p>For a model behaviour of interest, Sections <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a>–<a href="@@BASE@@handbook/learning-dynamics/#sec:training" data-reference-type="ref" data-reference="sec:training">10</a> study the internal features, components, and their interactions and evolutions that produce the behaviour. Training data attribution (TDA) shifts the focus from model internals to training data, searching for training samples that strongly shaped the model behaviour. TDA applications include, but are not limited to, identifying influential training samples, detecting train–test leakage, and diagnosing model failures without retraining (<span class="citation" data-cites="liu2025olmotrace park2023trakattributingmodelbehavior"><a href="@@BASE@@references/#ref-liu2025olmotrace" class="cite cite-established" data-cite="liu2025olmotrace" role="doc-biblioref">Liu et al., 2025a</a>; <a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a></span>; <span class="citation" data-cites="grosse2023studyinglargelanguagemodel"><a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a></span>). Although TDA is not generally a mechanistic method under this survey’s criteria, it can complement mechanistic interpretability in many ways. For example, by revealing how specific training samples shape malicious behaviour, TDA can enable effective training data filtering to prevent unwanted behaviours or even their internal mechanisms from forming during the training of next-generation models. Motivated by this application, this section focuses on post-hoc training data attribution methods that link a given target model’s behaviour to its training samples. This section excludes techniques for intervening in the data pipeline, such as filtering pretraining corpora, rebalancing demographic representation, and scheduling memorisation.</p>
+<p>TDA falls into two complementary approaches. <em>Verbatim data attribution</em> detects explicit memorisation by identifying exact or near-exact string matches between model outputs and the training corpus. These searches can scale to trillions of tokens using techniques such as suffix arrays and infini-gram models. <em>Gradient-based data attribution</em> estimates how training samples affect model parameters and predictions and can capture influential samples that do not manifest as verbatim reproduction. The two types of methods provide orthogonal views: one answers “did the model use rote memorisation on a training sample to answer this question?”; the other answers “how much did this training sample influence this certain behaviour of this model?”</p>
+<section id="verbatim-data-attribution-olmotrace-and-maximal-matching-spans" class="level2"><h2 data-pdf-page="93"><span class="secnum">11.1</span> Verbatim Data Attribution: OLMoTrace and Maximal Matching Spans<a class="anchor" href="#verbatim-data-attribution-olmotrace-and-maximal-matching-spans" aria-label="Link to this section">#</a></h2>
+<p>Earlier corpus search tools such as the ROOTS Search Tool <span class="citation" data-cites="piktus-etal-2023-roots">(<a href="@@BASE@@references/#ref-piktus-etal-2023-roots" class="cite cite-established" data-cite="piktus-etal-2023-roots" role="doc-biblioref">Piktus et al., 2023a</a>)</span> and GAIA Search <span class="citation" data-cites="piktus2023gaia">(<a href="@@BASE@@references/#ref-piktus2023gaia" class="cite cite-established" data-cite="piktus2023gaia" role="doc-biblioref">Piktus et al., 2023b</a>)</span> established the practice of querying pretraining corpora for transparency and data exploration. OLMoTrace <span class="citation" data-cites="liu2025olmotrace">(<a href="@@BASE@@references/#ref-liu2025olmotrace" class="cite cite-established" data-cite="liu2025olmotrace" role="doc-biblioref">Liu et al., 2025a</a>)</span> scales this to trillions of tokens using a combination of suffix arrays, infini-gram models, and maximal matching span extraction <span class="citation" data-cites="liu2025infinigramscalingunboundedngram">(<a href="@@BASE@@references/#ref-liu2025infinigramscalingunboundedngram" class="cite cite-established" data-cite="liu2025infinigramscalingunboundedngram" role="doc-biblioref">Liu et al., 2024a</a>)</span>, making it less a data-attribution method in the influence-function sense and more a retrieval tool for identifying content the model may have memorised.</p>
 <div class="definitionbox">
-<p><span id="def:suffix-array" data-label="def:suffix-array"></span> The suffix array of a string <span class="math inline">T</span> of length <span class="math inline">N</span> is an array of integers representing the starting positions of all suffixes of <span class="math inline">T</span> in lexicographic order. It enables efficient substring searches in <span class="math inline">\mathcal{O}(L \cdot \log N)</span> time for a query of length <span class="math inline">L</span>, compared to <span class="math inline">\mathcal{O}(L \cdot N)</span> naive search.</p>
-<p><strong>Example:</strong> <span class="math inline">T = \texttt{banana\$}</span> <span class="math display">\begin{align*}
+<p><span id="def:suffix-array" data-label="def:suffix-array"></span> The <em>suffix array</em> of a string <span class="math inline">x</span> of length <span class="math inline">T_1</span> is an array of integers representing the starting positions of all suffixes of <span class="math inline">x</span> in lexicographic order. It enables efficient substring searches in <span class="math inline">\mathcal{O}(T_2 \cdot \log T_1)</span> time for a query of length <span class="math inline">T_2</span>, compared to <span class="math inline">\mathcal{O}(T_2 \cdot T_1)</span> naive search.<br />
+</p>
+<section id="example" class="level4">
+<h4>Example:</h4>
+<p><span class="math inline">x = \texttt{banana\$}</span> <span class="math display">\begin{align*}
 \text{Suffixes: } &amp; 0:\texttt{banana\$}, 1:\texttt{anana\$}, 2:\texttt{nana\$}, 3:\texttt{ana\$}, 4:\texttt{na\$}, 5:\texttt{a\$}, 6:\texttt{\$} \\
-\text{SA(T)} &amp;= [6, 5, 3, 1, 0, 4, 2]
+\text{SA}(x) &amp;= [6, 5, 3, 1, 0, 4, 2]
 \end{align*}</span></p>
+</section>
 </div>
 <div class="definitionbox">
-<p><span id="def:infini-gram" data-label="def:infini-gram"></span> An infini-gram extends the n-gram model to arbitrary-length prefixes, using backoff to the longest matching sequence in the training corpus. Formally, for a token <span class="math inline">w_i</span>: <span class="math display">P_{\infty}(w_i \mid w_{1:i-1}) = \frac{\text{cnt}(w_{i-n+1:i} \mid \mathcal{D})}{\text{cnt}(w_{i-n+1:i-1} \mid \mathcal{D})}, 
-\quad n = \max \{ n&#39; : \text{cnt}(w_{i-n&#39;+1:i-1} \mid \mathcal{D})&gt;0\},</span> where <span class="math inline">w_{1:i-1}</span> are preceding tokens and <span class="math inline">\mathcal{D}</span> is the training corpus.</p>
-<p>Infini-grams are implemented on top of the FM-index, which generalizes the suffix array to allow fast, memory-efficient substring queries across massive corpora.</p>
+<p><span id="def:infini-gram" data-label="def:infini-gram"></span> An <em>infini-gram model</em> extends the n-gram model to arbitrary-length prefixes, using backoff to the longest matching sequence in the training corpus. Formally, for a token <span class="math inline">w_i</span>: <span class="math display">P_{\infty}(w_i \mid w_{1:i-1}) = \frac{\text{cnt}(w_{i-n+1:i} \mid \mathcal{D})}{\text{cnt}(w_{i-n+1:i-1} \mid \mathcal{D})}, 
+\quad n = \max \{ n&#39; : \text{cnt}(w_{i-n&#39;+1:i-1} \mid \mathcal{D})&gt;0\},</span> where <span class="math inline">w_{1:i-1}</span> are preceding tokens and <span class="math inline">\mathcal{D}</span> is the training corpus.<br />
+Infini-grams are implemented on top of suffix arrays, which allow fast substring queries across massive corpora.</p>
 </div>
 <div class="definitionbox">
-<p><span id="def:maximal-matching-spans" data-label="def:maximal-matching-spans"></span> Given a model output and a training corpus, a <strong>maximal matching span</strong> is a contiguous sequence of tokens that:</p>
+<p><span id="def:maximal-matching-spans" data-label="def:maximal-matching-spans"></span> Given a model output and a training corpus, a <em>maximal matching span</em> is a contiguous sequence of tokens that:</p>
 <ol>
 <li><p>Appears verbatim at least once in the training data,</p></li>
 <li><p>Does not break words or include spurious delimiters (e.g., periods or newlines) except at span boundaries, and</p></li>
-<li><p>Is not contained within a longer span that satisfies <strong>(1)</strong> and <strong>(2)</strong>.</p></li>
+<li><p>Is not contained within a longer span that satisfies 1. and 2.</p></li>
 </ol>
-<p>OLMoTrace identifies maximal matching spans by combining FM-index search with infini-gram queries, adjusting boundaries for tokenization, and retaining only the longest non-overlapping spans.</p>
+<p>OLMoTrace identifies maximal matching spans by combining suffix-array search with infini-gram queries, adjusting boundaries for tokenisation, and retaining only the longest non-overlapping spans.</p>
 </div>
 <div class="examplebox">
 <p><span id="ex:olmotrace-pipeline" data-label="ex:olmotrace-pipeline"></span></p>
-<p><strong>Prompt:</strong> “Celine Dion is a Canadian singer known for her powerful voice and wide [...]”</p>
-<p><strong>Pipeline Steps:</strong></p>
+<section id="language-model-output" class="level4">
+<h4>Language Model Output:</h4>
+<p>“Celine Dion is a Canadian singer known for her powerful voice and wide [...]”<br />
+</p>
+</section>
+<section id="pipeline-steps" class="level4">
+<h4>Pipeline Steps:</h4>
 <ol>
-<li><p>Tokenize LM output.</p></li>
-<li><p>For each token starting a new word, query the FM-index with infini-gram to find the longest matching prefix in the training corpus.</p></li>
+<li><p>Tokenise the language model output.</p></li>
+<li><p>For each token starting a new word, query the suffix array with infini-gram to find the longest matching prefix in the training corpus.</p></li>
 <li><p>Adjust span boundaries to avoid breaking words or including delimiters.</p></li>
 <li><p>Retain only maximal spans (not contained in longer matches).</p></li>
 </ol>
-<p><strong>Example Spans Found:</strong></p>
+</section>
+<section id="example-spans-found" class="level4">
+<h4>Example Spans Found:</h4>
 <ul>
 <li><p>“Celine Dion [...] Born on March 30, 1968, in”</p></li>
 <li><p>“Charlemagne, Quebec, Canada, Dion began”</p></li>
@@ -42,83 +53,276 @@
 <li><p>“Her career took off when she won the 1988”</p></li>
 <li><p>“Eurovision Song Contest with the song ’Ne”</p></li>
 </ul>
-<p>This process links LM outputs directly to the training corpus, enabling precise attribution of memorized content.</p>
+<p>This process links language model output directly to the training corpus, enabling identification and attribution of memorised content.</p>
+</section>
 </div>
 <div class="definitionbox">
-<p><span id="def:olmotrace-algorithm" data-label="def:olmotrace-algorithm"></span> <strong>Inputs:</strong> Model output tokens <span class="math inline">S_1^L</span>, training corpus tokens <span class="math inline">T_1^N</span>, FM-index <span class="math inline">F_1^N</span></p>
-<p><strong>Steps:</strong></p>
+<p><span id="def:olmotrace-algorithm" data-label="def:olmotrace-algorithm"></span></p>
+<section id="inputs" class="level4">
+<h4>Inputs:</h4>
+<p>Model output tokens <span class="math inline">S_1^L</span>, training corpus tokens <span class="math inline">T_1^N</span>, suffix array <span class="math inline">A_1^N</span><br />
+</p>
+</section>
+<section id="steps" class="level4">
+<h4>Steps:</h4>
 <ol>
-<li><p>Scan each token position <span class="math inline">b</span> in <span class="math inline">S_1^L</span>, considering only tokens starting a new word.</p></li>
-<li><p>Query the FM-index <span class="math inline">F_1^N</span> to find candidate matching positions in the training corpus.</p></li>
-<li><p>Use infini-gram queries to determine the longest matching prefix starting at <span class="math inline">b</span>, backing off to shorter n-grams if necessary.</p></li>
+<li><p>Scan each token position <span class="math inline">t</span> in <span class="math inline">S_1^L</span>, considering only tokens starting a new word.</p></li>
+<li><p>Query the suffix array <span class="math inline">A_1^N</span> to find candidate matching positions in the training corpus.</p></li>
+<li><p>Use infini-gram queries to determine the longest matching prefix starting at <span class="math inline">t</span>, backing off to shorter n-grams if necessary.</p></li>
 <li><p>Adjust the match length to avoid breaking words or including spurious delimiters.</p></li>
-<li><p>Collect all spans <span class="math inline">(b, b+\text{length})</span> that satisfy the above criteria.</p></li>
+<li><p>Collect all spans <span class="math inline">(t, t+\text{length})</span> that satisfy the above criteria.</p></li>
 <li><p>Filter out spans contained within longer overlapping spans to retain only maximal matches.</p></li>
 </ol>
+</section>
 </div>
-<section id="limitations" class="level3"><h3 data-pdf-page="60"><span class="secnum">9.1.1</span> Limitations<a class="anchor" href="#limitations" aria-label="Link to this section">#</a></h3>
-<p>Verbatim attribution excels at factual memorization but struggles with compositional or synthesized outputs. For tasks like translation, poetry, or reasoning, OLMoTrace can identify spans of text that appear in the training data, but these matches do not explain how the model combined information from multiple sources to produce the output <span class="citation" data-cites="park2023trakattributingmodelbehavior">(<a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a>)</span>.</p>
+<section id="limitations-as-a-mechanistic-interpretability-tool." class="level4">
+<h4>Limitations as a Mechanistic Interpretability Tool.</h4>
+<p>Verbatim attribution excels at factual memorisation but struggles with compositional or synthesised outputs. For tasks like translation, poetry, or reasoning, OLMoTrace (and other verbatim matching methods) can identify spans of text that appear in the training data, but these matches do not explain how the model combined information from multiple sources to produce the output.</p>
 <div class="examplebox">
-<p><span id="ex:olmotrace-multilingual" data-label="ex:olmotrace-multilingual"></span> <strong>Prompt:</strong> “Write a short story in Chinese about a girl who loves reading.”</p>
-<p><strong>Completion (Chinese):</strong></p>
+<p><span id="ex:olmotrace-multilingual" data-label="ex:olmotrace-multilingual"></span></p>
+<section id="prompt" class="level4">
+<h4>Prompt:</h4>
+<p>“Write a short story in Chinese about a girl who loves reading.”<br />
+</p>
+</section>
+<section id="completion-chinese" class="level4">
+<h4>Completion (Chinese):</h4>
 <blockquote>
 <div class="CJK*">
 <p><span>UTF8</span><span>gbsn</span> “…一个叫雪儿的小公主，她有一只可爱的小狐狸伴在身边。她喜欢坐在竹林间看着远方。某日，一场突如其来的山洪导致雪…”</p>
 </div>
 </blockquote>
-<p><strong>OLMoTrace Influential Examples Identified:</strong> OLMoTrace identified maximal matching spans across training datasets:</p>
+</section>
+<section id="olmotrace-influential-samples-identified" class="level4">
+<h4>OLMoTrace Influential Samples Identified:</h4>
+<p>OLMoTrace identified maximal matching spans across training datasets:</p>
 <ul>
 <li><div class="CJK*">
-<p><span>UTF8</span><span>gbsn</span>Span: “一个叫雪儿的小公主，她有一只可爱的小狐狸伴在身边。她喜欢坐在竹林间看着远方” — matched in pretraining corpus</p>
+<p><span>UTF8</span><span>gbsn</span>Span: “一个叫雪儿的小公主，她有一只可爱的小狐狸伴在身边。她喜欢坐在竹林间看着远方”—matched in pretraining corpus</p>
 </div></li>
 <li><div class="CJK*">
-<p><span>UTF8</span><span>gbsn</span>Span: “她喜欢阅读关于科技、心理学和历史的书籍，业余时间还会参加编程课” — matched in post-training (instruction-tuning) data</p>
+<p><span>UTF8</span><span>gbsn</span>Span: “她喜欢阅读关于科技、心理学和历史的书籍，业余时间还会参加编程课”—matched in post-training (instruction-tuning) data</p>
 </div></li>
 </ul>
-<p>This example demonstrates how OLMoTrace traces multilingual outputs across diverse training sources, linking model generations directly to their memorized origins in pretraining and post-training datasets.</p>
-</div>
+<p>This example demonstrates how OLMoTrace traces multilingual outputs across diverse training sources, linking model generations directly to their memorised origins in pretraining and post-training datasets.</p>
 </section>
+</div>
 </section>
-<section id="gradient-based-attribution-understanding-influence-through-data-perturbations" class="level2"><h2 data-pdf-page="61"><span class="secnum">9.2</span> Gradient-Based Attribution: Understanding Influence Through Data Perturbations<a class="anchor" href="#gradient-based-attribution-understanding-influence-through-data-perturbations" aria-label="Link to this section">#</a></h2>
-<section id="core-idea-parameter-response-to-training-data" class="level3"><h3 data-pdf-page="61"><span class="secnum">9.2.1</span> Core Idea: Parameter Response to Training Data<a class="anchor" href="#core-idea-parameter-response-to-training-data" aria-label="Link to this section">#</a></h3>
-<p>Influence functions ask a counterfactual question: how would a model’s learned parameters change if we upweighted a training example <span class="citation" data-cites="koh2020understandingblackboxpredictionsinfluence">(<a href="@@BASE@@references/#ref-koh2020understandingblackboxpredictionsinfluence" class="cite" data-cite="koh2020understandingblackboxpredictionsinfluence" role="doc-biblioref">Koh &amp; Liang, 2017</a>)</span>? Instead of expensive retraining, we estimate this via implicit differentiation <span class="citation" data-cites="grosse2023studyinglargelanguagemodel">(<a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a>)</span>.</p>
-<p>Consider upweighting example <span class="math inline">z</span> by amount <span class="math inline">\epsilon</span> in the loss: <span class="math display">\mathcal{L}_\epsilon(z) = \frac{1}{n} \sum_{i=1}^n \mathcal{L}(z_i, \theta) + \epsilon \mathcal{L}(z, \theta).</span></p>
-<p>By the implicit function theorem, if the Hessian <span class="math inline">H_{\theta^*} = \nabla^2_\theta \mathcal{L}_{\text{avg}}(\theta^*)</span> is invertible, the optimal parameters <span class="math inline">\theta^*_\epsilon</span> respond: <span class="math display">\frac{d\theta^*_\epsilon}{d\epsilon}\Big|_{\epsilon=0} = -H_{\theta^*}^{-1} \nabla_\theta \mathcal{L}(z, \theta^*),</span> where <span class="math inline">H_{\theta^*}</span> is the Hessian of the average training loss. Applying the chain rule, the influence on a test output is: <span class="math display">\mathcal{I}_{f}(z_m) = -\nabla_\theta f(\theta^*)^\top H_{\theta^*}^{-1} \nabla_\theta \mathcal{L}(z_m, \theta^*).</span> This extends to the change in test predictions: <span class="math display">f(\theta^*(\epsilon)) - f(\theta^*) \approx \mathcal{I}_{\text{f}}(z_m)  \epsilon = -\nabla_\theta f(\theta^*)^\top H_{\theta^*}^{-1} \nabla_\theta \mathcal{L}(z_m, \theta^*) \epsilon.</span> This scalar measures how upweighting <span class="math inline">z_m</span> changes the model output on <span class="math inline">z_{\text{test}}</span> through parameter changes.</p>
 </section>
-<section id="proximal-bregman-response-and-the-gauss-newton-hessian" class="level3"><h3 data-pdf-page="61"><span class="secnum">9.2.2</span> Proximal Bregman Response and the Gauss-Newton Hessian<a class="anchor" href="#proximal-bregman-response-and-the-gauss-newton-hessian" aria-label="Link to this section">#</a></h3>
-<p>The implicit function theorem assumes exact convergence and infinite perturbations. Real neural networks violate both: training reaches saddle points, not true minima, and we retrain for only a few steps. Rather than asking what parameters would result from full retraining, the proximal Bregman response function (PBRF) framework <span class="citation" data-cites="bae2022influencefunctionsanswerquestion">(<a href="@@BASE@@references/#ref-bae2022influencefunctionsanswerquestion" class="cite" data-cite="bae2022influencefunctionsanswerquestion" role="doc-biblioref">Bae et al., 2022</a>)</span> asks: what parameters minimize the loss while staying close to the current parameters? <span class="math display">\theta_{\text{PBRF}}(\epsilon) = \arg\min_\theta \mathcal{L}_\epsilon(z) + \frac{\lambda}{2}\|\theta - \theta_s\|^2,</span> where <span class="math inline">\lambda</span> acts as implicit regularization from partial retraining.</p>
-<p>Taking the implicit function theorem through the linearization and solving this optimization problem <span class="citation" data-cites="bae2022influencefunctionsanswerquestion">(<a href="@@BASE@@references/#ref-bae2022influencefunctionsanswerquestion" class="cite" data-cite="bae2022influencefunctionsanswerquestion" role="doc-biblioref">Bae et al., 2022</a>)</span> yields a remarkable result: the linearized PBRF solution is exactly the influence function estimate using the Gauss-Newton Hessian (GGN) as the curvature: <span class="math display">G = \mathbb{E}[J_y^\top H_y J_y],</span> where <span class="math inline">J_y</span> is the Jacobian of the network’s outputs with respect to parameters and <span class="math inline">H_y</span> is the Hessian of the loss with respect to the outputs. The GGN is positive semidefinite (assuming that the loss function is convex as a function of network outputs) and is the natural curvature for the PBRF problem. The influence formula becomes: <span class="math display">\mathcal{I}_{f}(z, z_{\text{test}}) = -\nabla_\theta \mathcal{L}(z_{\text{test}}, \theta^*)^\top (G + \lambda I)^{-1} \nabla_\theta \mathcal{L}(z, \theta^*),</span> where the <span class="math inline">\lambda I</span> term is the implicit regularization from the PBRF.</p>
-<p>For standard loss functions (cross-entropy, squared loss), the GGN is equivalent to the Fisher Information Matrix <span class="math inline">F = \mathbb{E}[\nabla \mathcal{L} \nabla \mathcal{L}^\top]</span>. This equivalence allows practitioners to use Fisher information as a practical substitute: it’s more stable to compute and estimate from data than the true GGN.</p>
+<section id="gradient-based-attribution-understanding-data-influence-through-perturbations" class="level2"><h2 data-pdf-page="96"><span class="secnum">11.2</span> Gradient-Based Attribution: Understanding Data Influence Through Perturbations<a class="anchor" href="#gradient-based-attribution-understanding-data-influence-through-perturbations" aria-label="Link to this section">#</a></h2>
+<p>Gradient-based methods estimate how much each training sample influenced the model’s predictions.</p>
+<section id="core-idea-parameter-response-to-training-data" class="level3"><h3 data-pdf-page="96"><span class="secnum">11.2.1</span> Core Idea: Parameter Response to Training Data<a class="anchor" href="#core-idea-parameter-response-to-training-data" aria-label="Link to this section">#</a></h3>
+<p><em>Influence functions</em> estimate how a model’s parameters and predictions would change if a training sample were slightly upweighted (<span class="citation" data-cites="koh2020understandingblackboxpredictionsinfluence"><a href="@@BASE@@references/#ref-koh2020understandingblackboxpredictionsinfluence" class="cite cite-established" data-cite="koh2020understandingblackboxpredictionsinfluence" role="doc-biblioref">Koh &amp; Liang, 2017</a></span>; <span class="citation" data-cites="grosse2023studyinglargelanguagemodel"><a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a></span>). We introduce their core idea below.</p>
+<p>Consider a neural network parameterised by <span class="math inline">\theta</span>. After sufficient training, its parameters will converge to <span class="math display">\theta^*
+=
+\arg\min_\theta
+\mathcal{L}_{\mathrm{train}}(\theta),</span> where <span class="math inline">\mathcal{L}_{\mathrm{train}}(\cdot)</span> is the aggregate training objective calculated as: <span class="math display">\mathcal{L}_{\mathrm{train}}(\theta)
+\coloneqq
+\frac{1}{n}
+\sum_{i=1}^{n}
+\mathcal{L}(z_i,\theta).</span> <span class="math inline">\{z_i \mid i=1, \ldots, n\}</span> is the training set and <span class="math inline">\mathcal{L}(\cdot)</span> is a loss function such as cross-entropy loss. Note that although by definition <span class="math inline">\theta^*</span> is expected to be a global optimum, it will not affect the subsequent analysis if <span class="math inline">\theta^*</span> is in fact a local optimum.</p>
+<p>Now, suppose we upweight a training sample <span class="math inline">z_m</span> by a small amount <span class="math inline">\epsilon</span>. The new aggregate training objective will become: <span class="math display">\mathcal{L}_{\epsilon}(\theta)
+\coloneqq
+\mathcal{L}_{\mathrm{train}}(\theta)
++
+\epsilon \mathcal{L}(z_m,\theta).</span> It corresponds to a new set of model parameters <span class="math inline">\theta^*_{\epsilon}</span> expressed as: <span class="math display">\theta^*_{\epsilon}
+=
+\arg\min_\theta
+\mathcal{L}_{\epsilon}(\theta).</span> This expression is not a closed-form formula and does not provide us with an efficient way to acquire <span class="math inline">\theta^*_{\epsilon}</span>. A brute-force way is to train the neural network with this new objective and observe the new converged parameters; however, this method is extremely computationally expensive. We thus turn to leveraging the specific property of <span class="math inline">\theta^*_{\epsilon}</span>. Note that, by definition, it must satisfy the first-order optimality condition: <span class="math display">\nabla_\theta \mathcal{L}_{\epsilon}(\theta)\Big|_{\theta=\theta^*_{\epsilon}}
+=
+\nabla_\theta \mathcal{L}_{\mathrm{train}}(\theta^*_{\epsilon})
++
+\epsilon\, \nabla_\theta \mathcal{L}(z_m, \theta^*_{\epsilon})
+=
+0.</span> This motivates defining a function to link <span class="math inline">\theta</span> and <span class="math inline">\epsilon</span>: <span class="math display">F(\theta, \epsilon)
+:=
+\nabla_\theta \mathcal{L}_{\mathrm{train}}(\theta)
++
+\epsilon\, \nabla_\theta \mathcal{L}(z_m, \theta).</span> Note that <span class="math inline">F(\theta^*, 0)=0</span>, which allows us to consider the implicit function theorem.</p>
+<p>To apply the implicit function theorem, we assume that <span class="math inline">F(\theta, \epsilon)</span> is continuously differentiable in a small area around <span class="math inline">(\theta, \epsilon) = (\theta^*, 0)</span>. Furthermore, the Jacobian of <span class="math inline">F(\theta, \epsilon)</span> with respect to <span class="math inline">\theta</span> at <span class="math inline">(\theta^*, 0)</span>, i.e., <span class="math display">\frac{\partial F(\theta, \epsilon)}{\partial \theta}\bigg |_{\theta = \theta^*, \epsilon = 0}
+=
+\nabla_{\theta}^{2}
+\mathcal{L}_{\mathrm{train}}(\theta^*)
+\eqqcolon H_{\theta^*},</span> is invertible. <span class="math inline">H_{\theta^*}</span> denotes the Hessian of the training loss. These assumptions allow us to apply the implicit function theorem.</p>
+<p>By the implicit function theorem, there exists a neighbourhood <span class="math inline">V</span> of <span class="math inline">\epsilon = 0</span> and a unique continuously differentiable function <span class="math inline">\epsilon \mapsto \theta^*_{\epsilon}</span> on <span class="math inline">V</span> such that <span class="math inline">F(\theta^*_{\epsilon}, \epsilon) = 0</span> for all <span class="math inline">\epsilon \in V</span>. In other words, for sufficiently small <span class="math inline">\epsilon</span>, <span class="math inline">\theta^*_{\epsilon}</span> is the stationary point of <span class="math inline">\mathcal{L}_{\epsilon}(\cdot)</span> near <span class="math inline">\theta^*</span>. Moreover, its derivative is given by <span class="math display">\frac{d\theta^*_{\epsilon}}{d\epsilon}
+=
+-
+\left[
+\frac{\partial F(\theta, \epsilon)}{\partial \theta} \bigg|_{\theta=\theta^*_{\epsilon}}
+\right]^{-1}
+\frac{\partial F(\theta^*_{\epsilon}, \epsilon)}{\partial \epsilon}.</span> Recalling the expression of <span class="math inline">F(\theta, \epsilon)</span>, we also have <span class="math display">\frac{\partial F(\theta, \epsilon)}{\partial \epsilon}\bigg|_{\theta=\theta^*_{\epsilon}}
+=
+\nabla_\theta \mathcal{L}(z_m, \theta)\Big|_{\theta=\theta^*_{\epsilon}}.</span> Putting <span class="math inline">\epsilon = 0</span> to let <span class="math inline">\theta^*_\epsilon = \theta^*_{0} = \theta^*</span> and <span class="math inline">\frac{\partial F(\theta, \epsilon)}{\partial \theta} \bigg|_{\theta=\theta^*_{\epsilon}} = H_{\theta^*}</span>, we have: <span class="math display">\frac{d\theta^*_{\epsilon}}
+     {d\epsilon}
+\Bigg|_{\epsilon=0}
+=
+-
+H_{\theta^*}^{-1}
+\nabla_\theta
+\mathcal{L}(z_m,\theta^*).</span> This quantity describes how the model parameters respond to an infinitesimal increase in the importance of the training sample <span class="math inline">z_m</span>.</p>
+<p>To measure the effect on a test-time quantity <span class="math inline">M(\theta, z_{\text{test}})</span> (e.g., the model’s test loss on the testing sample <span class="math inline">z_{\text{test}}</span>), we apply the chain rule: <span class="math display">\mathcal{I}_{M}(z_m, z_{\text{test}})
+\coloneqq
+\frac{d M(\theta^*_{\epsilon}, z_{\text{test}})}
+     {d\epsilon}
+\Bigg|_{\epsilon=0}
+=
+-
+\nabla_\theta M(\theta^*, z_{\text{test}})^\top
+H_{\theta^*}^{-1}
+\nabla_\theta
+\mathcal{L}(z_m,\theta^*).</span> The quantity <span class="math inline">\mathcal{I}_{M}(z_m, z_{\text{test}})</span> is called the <em>influence score</em>. Taking a step forward, by the definition of the derivative, for sufficiently small <span class="math inline">\epsilon</span> we have <span class="math display">M(\theta^*_{\epsilon}, z_{\text{test}})
+-
+M(\theta^*, z_{\text{test}})
+\approx
+\epsilon
+\,\mathcal{I}_{M}(z_m, z_{\text{test}}).</span> That is, the influence score quantifies the first-order effect of the training sample <span class="math inline">z_m</span> on the model’s downstream loss for a testing sample <span class="math inline">z_{\text{test}}</span>.</p>
 </section>
-<section id="computational-bottleneck-approximating-ggn-vector-products" class="level3"><h3 data-pdf-page="61"><span class="secnum">9.2.3</span> Computational Bottleneck: Approximating GGN-Vector Products<a class="anchor" href="#computational-bottleneck-approximating-ggn-vector-products" aria-label="Link to this section">#</a></h3>
-<p>The main practical challenge is computing influence for each training example without explicitly forming the GGN inverse: <span class="math display">\mathbf{v} = (G + \lambda I)^{-1} \nabla_\theta \mathcal{L}(z, \theta^*),</span> which naively costs <span class="math inline">\mathcal{O}(p^2)</span> memory and <span class="math inline">\mathcal{O}(p^3)</span> time for <span class="math inline">p</span> parameters. Practical methods avoid explicit inversion by using iterative solvers and structured approximations of the GGN.</p>
-<section id="kronecker-factored-approximate-curvature-kfac." class="level4">
-<h4>Kronecker-Factored Approximate Curvature (KFAC).</h4>
-<p>KFAC is a parametric approximation to the Fisher Information Matrix (FIM) of a neural network which supports efficient inversion. The FIM is defined as: <span class="math display">F = \mathbb{E}_{\hat{y} \sim P_{\hat{y}|x}(\theta)} \left[ \nabla_\theta \log p(\hat{y}|\theta, x) \nabla_\theta \log p(\hat{y}|\theta, x)^\top \right],</span> where <span class="math inline">\hat{y}</span> is sampled from the model’s output distribution (not the training labels, which would yield the empirical Fisher with less favorable properties). For neural networks, KFAC approximates this by exploiting a layer-wise Kronecker-factored structure Ḟor layer <span class="math inline">\ell</span> with input activations <span class="math inline">a_{\ell-1}</span> and output gradient components <span class="citation" data-cites="grosse2023studyinglargelanguagemodel">(<a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a>)</span>, KFAC approximates: <span class="math display">F_\ell \approx (a_{\ell-1} a_{\ell-1}^\top) \otimes (D_s^\top D_s),</span> where <span class="math inline">\otimes</span> is the Kronecker product and <span class="math inline">D_s</span> represents the output gradient structure. This factorization reduces memory from <span class="math inline">\mathcal{O}(p^2)</span> to <span class="math inline">\mathcal{O}(p)</span> and enables efficient layer-wise solves. KFAC works well but can underestimate curvature in low-variance directions due to finite sampling.</p>
+<section id="proximal-bregman-response-and-the-generalised-gaussnewton" class="level3"><h3 data-pdf-page="97"><span class="secnum">11.2.2</span> Proximal Bregman Response and the Generalised Gauss–Newton<a class="anchor" href="#proximal-bregman-response-and-the-generalised-gaussnewton" aria-label="Link to this section">#</a></h3>
+<p>Empirical results show that the original form of influence functions introduced above performs poorly in practice. This is due to multiple assumptions that rarely hold in practice. For example, for a trained neural network, its parameters <span class="math inline">\theta</span> are scarcely exactly at the local or global minimum <span class="math inline">\theta^*</span>. Additionally, the Hessian <span class="math inline">H_{\theta^*}</span> is usually non-invertible or numerically unstable due to some small eigenvalues. These limitations motivate researchers to formulate influence functions without unrealistic assumptions. One classical approach is called the <em>proximal Bregman response function</em> (PBRF) <span class="citation" data-cites="bae2022influencefunctionsanswerquestion">(<a href="@@BASE@@references/#ref-bae2022influencefunctionsanswerquestion" class="cite cite-established" data-cite="bae2022influencefunctionsanswerquestion" role="doc-biblioref">Bae et al., 2022</a>)</span>. It targets model parameters which minimise the new training objective while staying close to the original parameters. Below, we sketch its core idea.</p>
+<p>Let each training sample <span class="math inline">z_i=(x_i, y_i)</span>, where <span class="math inline">x_i</span> is the input and <span class="math inline">y_i</span> is the label, and <span class="math inline">u_i(\theta) =f(x_i, \theta)</span> is the output of the model (parameterised as <span class="math inline">\theta</span>) given input <span class="math inline">x_i</span>, such as a logit vector. Slightly abusing notation, we denote the per-sample training loss as <span class="math display">\mathcal{L}(z_i, \theta) 
+\coloneqq 
+\mathcal{L}(u_i(\theta), y_i)
+=
+\mathcal{L}(f(x_i, \theta), y_i),</span> where <span class="math inline">\mathcal{L}(\cdot, y)</span> is a loss function as in the vanilla influence function formulation. Here we additionally assume <span class="math inline">\mathcal{L}(\cdot, y)</span> is convex in its first argument <span class="math inline">u</span>, which many commonly used loss functions, such as cross-entropy loss, satisfy.</p>
+<p>As Bregman divergence will be used in the later formulation, we introduce this notion as the next step. Specifically, for a given convex function <span class="math inline">\mathcal{L}(u, y)</span> (as described above) with the label <span class="math inline">y</span> fixed, the Bregman divergence of two model outputs, <span class="math inline">u</span> and <span class="math inline">u&#39;</span>, is: <span class="math display">D_{\mathcal{L}}(u, u&#39;; y) = \mathcal{L}(u, y) - \mathcal{L}(u&#39;, y) - \big(\nabla_v \mathcal{L}(v, y)\big|_{v = u&#39;}\big)^\top (u - u&#39;),</span> i.e., the amount by which <span class="math inline">\mathcal{L}(\cdot, y)</span> at <span class="math inline">u</span> exceeds its first-order Taylor approximation from <span class="math inline">u&#39;</span>.</p>
+<p>Now we introduce the PBRF formulation. Starting from the upweighted objective <span class="math inline">\mathcal{L}_{\epsilon}(\theta)</span>, PBRF is represented as: <span class="math display">\mathcal{P}_{\epsilon}(\theta)
+\coloneqq
+\frac{1}{n}
+\sum_{i=1}^{n}
+D_{\mathcal{L}}\big(f(x_i, \theta),\, f(x_i, \theta^s); y_i\big)
++
+\epsilon\, \mathcal{L}(z_m, \theta)
++
+\frac{\lambda}{2}\|\theta - \theta^s\|^2,
+\qquad
+\theta^{\mathrm{PBRF}}_{\epsilon}
+\coloneqq
+\arg\min_\theta
+\mathcal{P}_{\epsilon}(\theta),</span> where <span class="math inline">\theta^s</span> denotes the original trained model parameters (note that it is not required to be the local or global optimum). Bregman divergence replaces the original loss function. An additional proximity term is added with a positive regularisation coefficient <span class="math inline">\lambda</span>. <span class="math inline">\theta^{\mathrm{PBRF}}_{\epsilon}</span> searches for the set of model parameters that best corresponds to an upweight in <span class="math inline">z_m</span> while keeping its predictions on the overall training set close to the original predictions and staying close to <span class="math inline">\theta^s</span>.</p>
+<p>In this construction, both the Bregman and the proximity terms are non-negative for all <span class="math inline">\theta</span> and vanish at <span class="math inline">\theta = \theta^s</span>. Since these are the only two terms of <span class="math inline">\mathcal{P}_{\epsilon}</span> when <span class="math inline">\epsilon = 0</span>, <span class="math inline">\theta^s</span> is a global minimiser of <span class="math inline">\mathcal{P}_0</span> (i.e., <span class="math inline">\theta^s = \theta^{\mathrm{PBRF}}_0</span>) <em>regardless of whether training has converged</em>.</p>
+<p>As in the vanilla formulation, <span class="math inline">\theta^{\mathrm{PBRF}}_\epsilon</span> does not have a closed-form formula for now. However, by observing that <span class="math inline">\nabla_\theta \mathcal{P}_{\epsilon}(\theta)\big|_{\theta=\theta^{\text{PBRF}}_{\epsilon}}=0</span>, we define <span class="math display">\tilde{F}(\theta, \epsilon)
+\coloneqq
+\nabla_\theta \mathcal{P}_{\epsilon}(\theta)
+=
+\frac{1}{n}
+\sum_{i=1}^{n}
+\nabla_\theta D_{\mathcal{L}}\big(f(x_i, \theta),\, f(x_i, \theta^s); y_i\big)
++
+\epsilon\, \nabla_\theta \mathcal{L}(z_m, \theta)
++
+\lambda (\theta - \theta^s),</span> where <span class="math inline">\tilde{F}(\theta^s, 0) = 0</span>. We assume that <span class="math inline">f(x_i, \cdot)</span> and <span class="math inline">\mathcal{L}(\cdot, y)</span> are twice continuously differentiable such that <span class="math inline">\tilde{F}</span> is continuously differentiable in a small area around <span class="math inline">(\theta^s, 0)</span>. Then, we verify that <span class="math inline">\frac{\partial\tilde{F}(\theta, \epsilon)}{\partial \theta}</span> is invertible at <span class="math inline">(\theta, \epsilon) = (\theta^s, 0)</span>, the last prerequisite for using the implicit function theorem. By direct calculation (we omit the detailed process here), we can obtain <span class="math display">\frac{\partial \tilde{F}(\theta, \epsilon)}{\partial \theta}\bigg|_{\theta = \theta^s, \epsilon = 0}
+=
+\underbrace{
+\frac{1}{n}\sum_{i=1}^{n} J_i^\top H_i J_i
+}_{\eqqcolon\, G}
++
+\lambda I,</span> where <span class="math inline">u^s_i \coloneqq u_i(\theta^s)</span>, <span class="math inline">J_i \coloneqq \frac{\partial u_i(\theta)}{\partial \theta} \big|_{\theta=\theta^s}</span> is the output–parameter Jacobian, and <span class="math inline">H_i \coloneqq \nabla^2_u \mathcal{L}(u, y_i) \big|_{u = u^s_i}</span> is the loss–output Hessian. The matrix <span class="math inline">G</span> is the <em>generalised Gauss–Newton</em> (GGN) matrix. Notably, as <span class="math inline">H_i</span> is positive semidefinite by the convexity of <span class="math inline">\mathcal{L}(\cdot, y_i)</span>, we know <span class="math inline">G</span> is positive semidefinite (because <span class="math inline">v^\top G v = \frac{1}{n}\sum_i (J_i v)^\top H_i (J_i v) \geq 0</span> for any <span class="math inline">v</span>). As a result, <span class="math inline">G + \lambda I</span> is positive definite and hence invertible. Unlike the case of <span class="math inline">\nabla^2_\theta \mathcal{L}_{\mathrm{train}}(\theta^*)</span>, the invertibility of <span class="math inline">G + \lambda I</span> holds by construction.</p>
+<p>Knowing that <span class="math inline">\partial \tilde{F}(\theta, \epsilon) / \partial \epsilon = \nabla_\theta \mathcal{L}(z_m, \theta)</span>, we apply the implicit function theorem and obtain <span class="math display">\frac{d\theta^{\mathrm{PBRF}}_{\epsilon}}{d\epsilon}
+\Bigg|_{\epsilon=0}
+=
+-
+(G + \lambda I)^{-1}
+\nabla_\theta
+\mathcal{L}(z_m,\theta^s).</span> By the chain rule, the influence score on a test-time quantity <span class="math inline">M(\theta, z_{\text{test}})</span> becomes <span class="math display">\mathcal{I}^{\mathrm{PBRF}}_{M}(z_m, z_{\text{test}})
+\coloneqq
+\frac{d M(\theta^{\mathrm{PBRF}}_{\epsilon}, z_{\text{test}})}{d\epsilon}
+\Bigg|_{\epsilon=0}
+=
+-
+\nabla_\theta M(\theta^s, z_{\text{test}})^\top
+(G + \lambda I)^{-1}
+\nabla_\theta
+\mathcal{L}(z_m,\theta^s).</span> As shown above, the influence function computed with <span class="math inline">G + \lambda I</span> is exactly the derivative of the PBRF at <span class="math inline">\epsilon = 0</span>. <span class="citation" data-cites="bae2022influencefunctionsanswerquestion"><a href="@@BASE@@references/#ref-bae2022influencefunctionsanswerquestion" class="cite cite-established" data-cite="bae2022influencefunctionsanswerquestion" role="doc-biblioref">Bae et al. (2022)</a></span> confirmed empirically that such influence estimates align well with the actual PBRF.</p>
 </section>
-<section id="eigenvalue-corrected-kfac-ek-fac-and-astra." class="level4">
-<h4>Eigenvalue-Corrected KFAC (EK-FAC) and ASTRA.</h4>
-<p>EK-FAC improves KFAC by correcting eigenvalues and is the method used by recent work scaling influence to LLMs <span class="citation" data-cites="grosse2023studyinglargelanguagemodel">(<a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a>)</span>. An even more recent refinement, ASTRA <span class="citation" data-cites="wang2025better">(<a href="@@BASE@@references/#ref-wang2025better" class="cite" data-cite="wang2025better" role="doc-biblioref">Wang et al., 2025a</a>)</span>, uses EK-FAC as a preconditioner for Neumann series iterations, yielding higher-quality GGN-vector products with less hyperparameter tuning. These structured approaches make influence functions practical for models with billions of parameters.</p>
+<section id="addressing-the-computational-and-memory-bottlenecks-of-ggn" class="level3"><h3 data-pdf-page="98"><span class="secnum">11.2.3</span> Addressing the Computational and Memory Bottlenecks of GGN<a class="anchor" href="#addressing-the-computational-and-memory-bottlenecks-of-ggn" aria-label="Link to this section">#</a></h3>
+<p>The size of the GGN matrix <span class="math inline">G \in \mathbb{R}^{p \times p}</span> (where <span class="math inline">p</span> is the number of model parameters) can be extremely large for modern language models, which makes the calculation of <span class="math inline">(G + \lambda I)^{-1}</span> computationally expensive. As a result, follow-up methods have avoided explicitly calculating this inverse matrix by borrowing techniques from the optimisation literature.</p>
+<section id="kronecker-factored-approximate-curvature-k-fac." class="level4">
+<h4>Kronecker-Factored Approximate Curvature (K-FAC).</h4>
+<p><span class="citation" data-cites="kfac"><a href="@@BASE@@references/#ref-kfac" class="cite cite-established" data-cite="kfac" role="doc-biblioref">Martens &amp; Grosse (2015)</a></span> proposed <em>Kronecker-Factored Approximate Curvature</em> (K-FAC) to approximate a large Fisher information matrix (FIM) using a set of smaller matrices. For a trained neural network parameterised by <span class="math inline">\theta^s</span>, the FIM is defined as <span class="math display">\mathbf{F}
+\coloneqq
+\frac{1}{n}\sum_{i=1}^{n}
+\mathbb{E}_{\hat{y} \sim p(\cdot \mid x_i, \theta^s)}
+\left[
+\nabla_\theta \log p(\hat{y} \mid x_i, \theta^s)
+\nabla_\theta \log p(\hat{y} \mid x_i, \theta^s)^\top
+\right],</span> where <span class="math inline">\hat{y}</span> is a sample from the model’s prediction distribution on <span class="math inline">x_i</span>. For example, suppose the task is three-class classification (<span class="math inline">c_1, c_2, c_3</span>) and the model’s prediction distribution is <span class="math inline">\pi_i=\mathrm{softmax}(u_i^s) = (0.5, 0.3, 0.2)</span>. If a sampled label is <span class="math inline">\hat{y}=c_2</span>, then <span class="math inline">\log p(\hat{y} \mid x_i, \theta^s) = \log 0.3</span>.</p>
+<p>Although K-FAC approximates the FIM rather than the GGN, these matrices are equivalent for common loss functions under suitable probabilistic assumptions. For example, with softmax cross-entropy, the expected outer product of the output log-likelihood gradients can be shown to equal the loss–output Hessian: <span class="math display">\mathbb{E}_{\hat{y} \sim p(\cdot \mid x_i, \theta^s)}
+\left[
+\nabla_u \log p(\hat{y} \mid u)
+\nabla_u \log p(\hat{y} \mid u)^\top
+\right]_{u=u_i^s}
+=
+\mathrm{diag}(\pi_i)-\pi_i\pi_i^\top
+=
+H_i.</span> Applying the chain rule then gives <span class="math display">\mathbf{F}
+=
+\frac{1}{n}\sum_{i=1}^{n}J_i^\top H_i J_i
+=
+G.</span> This shows that we can use K-FAC to approximate the GGN used in the PBRF influence score.</p>
+<p>Now we return to K-FAC, which efficiently approximates the FIM. First, it ignores curvature between layers and approximates <span class="math inline">\mathbf{F}</span> as a block-diagonal matrix with one block per layer. Consider a neural network in which each layer <span class="math inline">\ell</span> has a weight matrix <span class="math inline">W_\ell \in \mathbb{R}^{d_{\mathrm{out}}\times d_{\mathrm{in}}}</span>, input activations <span class="math inline">h_{\ell-1}</span>, and pre-activations <span class="math inline">s_\ell=W_\ell h_{\ell-1}</span> (irrelevant to subscript <span class="math inline">s</span> in <span class="math inline">\theta^s</span>). Following <span class="citation" data-cites="grosse2023studyinglargelanguagemodel"><a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al. (2023)</a></span>, the <em>pseudo-gradient</em> is defined as <span class="math inline">\mathcal{D}_{s_\ell} \coloneqq \nabla_{s_\ell} \log p(\hat{y}\mid x,\theta^s) \in \mathbb{R}^{d_{\text{out}}}</span>—the gradient flowing back to <span class="math inline">{s_\ell}</span>. The pseudo-gradient of the layer weights is <span class="math inline">\mathcal{D}_{s_\ell} h_{\ell-1}^\top</span>. Its vectorisation gives <span class="math inline">h_{\ell-1}\otimes\mathcal{D}_{s_\ell}</span>, where <span class="math inline">\otimes</span> denotes the Kronecker product. Leveraging <span class="math inline">\otimes</span>’s properties that <span class="math inline">(A\otimes B)^\top = A^\top \otimes B^\top</span> and <span class="math inline">(A\otimes B)(C\otimes D) = (AC)\otimes (BD)</span>, the block for this layer in the FIM, denoted as <span class="math inline">\mathbf{F}_\ell</span>, can be represented as <span class="math display">\mathbf{F}_\ell
+=
+\underbrace{\mathbb{E}\left[
+(h_{\ell-1}h_{\ell-1}^\top)
+\otimes
+(\mathcal{D}_{s_\ell}\mathcal{D}_{s_\ell}^\top)
+\right]}_{\in \mathbb{R}^{d_{\text{in}} d_{\text{out}}\times d_{\text{in}} d_{\text{out}}}}
+\approx
+\underbrace{
+\mathbb{E}[h_{\ell-1}h_{\ell-1}^\top]
+}_{\eqqcolon\,\textbf{A}_{\ell-1} \in \mathbb{R}^{d_{\text{in}}\times d_{\text{in}}}}
+\otimes
+\underbrace{
+\mathbb{E}[\mathcal{D}_{s_\ell}\mathcal{D}_{s_\ell}^\top]
+}_{\eqqcolon\,\textbf{S}_\ell \in \mathbb{R}^{d_{\text{out}}\times d_{\text{out}}}}.</span> The approximation separates the expectation into two independent factors: activations and pseudo-gradients. K-FAC thus stores only <span class="math inline">d_{\text{in}}^2+d_{\text{out}}^2</span> entries instead of <span class="math inline">(d_{\text{in}}d_{\text{out}})^2</span> entries for each block. When the two factors are invertible, their Kronecker product can be inverted separately: <span class="math display">(\textbf{A}_{\ell-1}\otimes \textbf{S}_\ell)^{-1}
+=
+\textbf{A}_{\ell-1}^{-1}\otimes \textbf{S}_\ell^{-1}.</span> This substantially reduces the memory required to store <span class="math inline">G</span> and the computational cost of computing the inverse of <span class="math inline">G+\lambda I</span>.</p>
 </section>
+<section id="eigenvalue-corrected-k-fac-ek-fac-and-astra." class="level4">
+<h4>Eigenvalue-Corrected K-FAC (EK-FAC) and ASTRA.</h4>
+<p>EK-FAC <span class="citation" data-cites="ekfac">(<a href="@@BASE@@references/#ref-ekfac" class="cite cite-established" data-cite="ekfac" role="doc-biblioref">George et al., 2018</a>)</span> improved K-FAC by correcting its eigenvalues. Recent work has adopted this technique to scale the influence function method to modern LLMs <span class="citation" data-cites="grosse2023studyinglargelanguagemodel">(<a href="@@BASE@@references/#ref-grosse2023studyinglargelanguagemodel" class="cite cite-emerging" data-cite="grosse2023studyinglargelanguagemodel" role="doc-biblioref">Grosse et al., 2023</a>)</span>. ASTRA <span class="citation" data-cites="wang2025better">(<a href="@@BASE@@references/#ref-wang2025better" class="cite cite-established" data-cite="wang2025better" role="doc-biblioref">Wang et al., 2025a</a>)</span> further used EK-FAC as a preconditioner for Neumann series iterations, achieving higher-quality GGN-vector products with less hyperparameter tuning. These efforts make influence functions increasingly practical for modern language models with billions of parameters.</p>
 </section>
-<section id="unrolled-differentiation-gradient-based-attribution" class="level3"><h3 data-pdf-page="62"><span class="secnum">9.2.4</span> Unrolled Differentiation: Gradient-Based Attribution<a class="anchor" href="#unrolled-differentiation-gradient-based-attribution" aria-label="Link to this section">#</a></h3>
-<p>A complementary approach bypasses the Hessian entirely. <strong>Approximate unrolled differentiation</strong> <span class="citation" data-cites="bae2024trainingdataattributionapproximate">(<a href="@@BASE@@references/#ref-bae2024trainingdataattributionapproximate" class="cite" data-cite="bae2024trainingdataattributionapproximate" role="doc-biblioref">Bae et al., 2024</a>)</span> unfolds the optimization trajectory and backpropagates through training steps. This captures implicit bias of the optimizer and handles non-converged models, where classical influence functions struggle. The tradeoff: higher computational cost than influence functions but better accuracy on modern, non-convex objectives.</p>
 </section>
-<section id="sec:trak" class="level3"><h3 data-pdf-page="62"><span class="secnum">9.2.5</span> Practical Attribution at Scale: TRAK<a class="anchor" href="#sec:trak" aria-label="Link to this section">#</a></h3>
-<p>For large-scale attribution without retraining thousands of models, TRAK (Tracing with Randomly-projected After Kernel) provides a scalable alternative <span class="citation" data-cites="park2023trakattributingmodelbehavior">(<a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a>)</span>. TRAK combines three key ideas: a first-order Taylor expansion approximating how model behavior changes under data perturbations, random projections to reduce gradient dimensionality from <span class="math inline">p</span> parameters to <span class="math inline">k \ll p</span>, and training only a handful of models on different data subsets rather than retraining exhaustively. By avoiding prohibitive retraining costs, TRAK achieves attribution quality comparable to expensive retraining-based methods like datamodels <span class="citation" data-cites="ilyas2022datamodelspredictingpredictionstraining">(<a href="@@BASE@@references/#ref-ilyas2022datamodelspredictingpredictionstraining" class="cite" data-cite="ilyas2022datamodelspredictingpredictionstraining" role="doc-biblioref">Ilyas et al., 2022</a>)</span> while reducing computational cost by orders of magnitude. This enables application to large-scale models including vision transformers and billion-parameter language models, making fine-grained training data attribution tractable for modern deep learning <span class="citation" data-cites="park2023trakattributingmodelbehavior">(<a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a>)</span>.</p>
+<section id="sec:trak" class="level3"><h3 data-pdf-page="99"><span class="secnum">11.2.4</span> Retraining-Based Influence Estimation: datamodels and TRAK<a class="anchor" href="#sec:trak" aria-label="Link to this section">#</a></h3>
+<p>Some methods have explored retraining the model to estimate the attribution. The most naive method is to upweight or remove each training sample, retrain the model on the modified training set, and observe changes in the model’s output on the testing sample. <span class="citation" data-cites="ilyas2022datamodelspredictingpredictionstraining"><a href="@@BASE@@references/#ref-ilyas2022datamodelspredictingpredictionstraining" class="cite cite-established" data-cite="ilyas2022datamodelspredictingpredictionstraining" role="doc-biblioref">Ilyas et al. (2022)</a></span> proposed <em>datamodels</em> to more efficiently estimate each training sample’s attribution on a testing sample by retraining many models on random subsets of the training data and fitting a linear regression that predicts the model output on the testing sample from binary indicators of whether each training sample was included. Each fitted coefficient then represents the training sample’s influence on the testing sample.</p>
+<p>However, datamodels remains computationally expensive because it requires training many models. <em>Tracing with the Randomly-projected After Kernel</em> (TRAK; <span class="citation" data-cites="park2023trakattributingmodelbehavior"><a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a></span>) provides a more efficient alternative. TRAK first uses a first-order Taylor expansion around the model parameters to approximate a trained model as a linear model. It then applies random projections to reduce gradient dimensionality and estimate training-sample influence in this lower-dimensional space. Finally, it averages the estimates across a small number of trained models to reduce variability due to training randomness. TRAK achieves attribution quality comparable to datamodels at substantially lower computational cost <span class="citation" data-cites="park2023trakattributingmodelbehavior">(<a href="@@BASE@@references/#ref-park2023trakattributingmodelbehavior" class="cite cite-established" data-cite="park2023trakattributingmodelbehavior" role="doc-biblioref">Park et al., 2023</a>)</span>.</p>
 </section>
-<section id="sec:tracin" class="level3"><h3 data-pdf-page="62"><span class="secnum">9.2.6</span> Efficient Influence Estimation: TracIn<a class="anchor" href="#sec:tracin" aria-label="Link to this section">#</a></h3>
-<p>TracIn provides an efficient alternative to computing the full Hessian inverse by leveraging the training trajectory <span class="citation" data-cites="pruthi2020estimatingtrainingdatainfluence">(<a href="@@BASE@@references/#ref-pruthi2020estimatingtrainingdatainfluence" class="cite" data-cite="pruthi2020estimatingtrainingdatainfluence" role="doc-biblioref">Pruthi et al., 2020</a>)</span>. Rather than computing influence at convergence via <span class="math inline">H_{\theta^*}^{-1} \nabla_\theta \mathcal{L}(z, \theta^*)</span>, TracIn approximates influence by integrating the gradient of a test example’s loss with respect to gradients along the training path: <span class="math display">\text{influence}(z_{\text{train}}, z_{\text{test}}) \approx \sum_{t=1}^T \nabla_\theta \mathcal{L}(z_{\text{test}}, \theta_t) \cdot \nabla_\theta \mathcal{L}(z_{\text{train}}, \theta_t)</span> where <span class="math inline">\theta_t</span> denotes parameters at training step <span class="math inline">t</span>. This approach avoids Hessian computation entirely, instead accumulating dot products of gradients throughout training. TracIn is considerably cheaper than influence functions based on Hessian approximations, making it practical for large models. However, the approximation assumes that gradient trajectories during training encode sufficient information about final influence, an assumption that may break down in highly nonlinear settings or when training dynamics involve significant noise <span class="citation" data-cites="pruthi2020estimatingtrainingdatainfluence">(<a href="@@BASE@@references/#ref-pruthi2020estimatingtrainingdatainfluence" class="cite" data-cite="pruthi2020estimatingtrainingdatainfluence" role="doc-biblioref">Pruthi et al., 2020</a>)</span>. Despite limitations, TracIn has enabled attribution studies on modern language models and provides a useful baseline for scalable influence estimation.</p>
+<section id="sec:tracin" class="level3"><h3 data-pdf-page="100"><span class="secnum">11.2.5</span> Efficient Influence Estimation Using Training Trajectories: TracIn<a class="anchor" href="#sec:tracin" aria-label="Link to this section">#</a></h3>
+<p>TracIn <span class="citation" data-cites="pruthi2020estimatingtrainingdatainfluence">(<a href="@@BASE@@references/#ref-pruthi2020estimatingtrainingdatainfluence" class="cite cite-established" data-cite="pruthi2020estimatingtrainingdatainfluence" role="doc-biblioref">Garima et al., 2020</a>)</span> estimates training-data influence using gradients evaluated along the training trajectory, without computing the inverse Hessian. Its core idea follows from the effect of a single Stochastic Gradient Descent (SGD; <span class="citation" data-cites="sgd"><a href="@@BASE@@references/#ref-sgd" class="cite cite-established" data-cite="sgd" role="doc-biblioref">Robbins &amp; Monro, 1951</a></span>) update on the loss of a testing sample.</p>
+<p>Suppose the model parameters at training step <span class="math inline">t</span> are <span class="math inline">\theta^t</span>. An SGD update using training sample <span class="math inline">z_m</span> with learning rate <span class="math inline">\eta_t</span> is: <span class="math display">\theta^{t+1}
+=
+\theta^t-\eta_t\nabla_\theta\mathcal{L}(z_m,\theta^t).</span> By simple rearrangement, we have <span class="math display">\theta^{t+1} - \theta^t
+=-\eta_t\nabla_\theta\mathcal{L}(z_m,\theta^t).</span> Then, by first-order Taylor expansion, the change in the loss of a testing sample <span class="math inline">z_{\text{test}}</span> can be approximated as <span class="math display">\begin{aligned}
+&amp;\mathcal{L}(z_{\text{test}},\theta^{t+1})
+-\mathcal{L}(z_{\text{test}},\theta^t)\\
+&amp;\quad\approx
+\nabla_\theta\mathcal{L}(z_{\text{test}},\theta^t)^\top
+(\theta^{t+1}-\theta^t)\\
+&amp;\quad=
+-\eta_t
+\nabla_\theta\mathcal{L}(z_{\text{test}},\theta^t)^\top
+\nabla_\theta\mathcal{L}(z_m,\theta^t).
+\end{aligned}</span> The above expression relates the gradient of the loss on a training sample <span class="math inline">z_m</span> to the gradient on a test sample <span class="math inline">z_{\text{test}}</span> and the change in test loss on <span class="math inline">z_{\text{test}}</span>. A positive dot product indicates that the training update will reduce the test loss, while a negative value indicates the opposite.</p>
+<p>TracIn extends this idea across training. Its practical checkpoint-based variant, TracInCP, estimates influence by summing gradient dot products over model checkpoints: <span class="math display">\begin{aligned}
+&amp;\operatorname{TracInCP}(z_m,z_{\text{test}})\\
+&amp;\quad=
+\sum_{t\in\mathcal{C}}\eta_t
+\nabla_\theta\mathcal{L}(z_{\text{test}},\theta^t)^\top
+\nabla_\theta\mathcal{L}(z_m,\theta^t),
+\end{aligned}</span> where <span class="math inline">\mathcal{C}</span> denotes the selected training checkpoints. A larger positive score indicates that <span class="math inline">z_m</span> has contributed more towards reducing the test loss.</p>
+<p>Unlike Hessian-based influence functions, TracIn does not require matrix inversion. However, TracInCP approximates the influence of training samples using gradients only at selected checkpoints, which may undermine the estimation accuracy.</p>
 </section>
-<section id="illustrative-example" class="level3"><h3 data-pdf-page="63"><span class="secnum">9.2.7</span> Illustrative Example<a class="anchor" href="#illustrative-example" aria-label="Link to this section">#</a></h3>
-<p>Building on the previous compositional translation task, gradient-based influence functions can identify training examples that shaped the output even when no exact matches exist. This complements verbatim attribution and reveals subtler influences.</p>
+<section id="illustrative-example" class="level3"><h3 data-pdf-page="100"><span class="secnum">11.2.6</span> Illustrative Example<a class="anchor" href="#illustrative-example" aria-label="Link to this section">#</a></h3>
+<p>Gradient-based influence functions can identify influential training samples without requiring them to exactly match the current model output. They complement verbatim attribution and reveal subtler influences. Below, we provide an example of using them for the English-to-Chinese translation task.</p>
 <div class="examplebox">
 <p><span id="ex:gradient-translation" data-label="ex:gradient-translation"></span></p>
-<p><strong>Prompt (English):</strong></p>
+<section id="prompt-english" class="level4">
+<h4>Prompt (English):</h4>
 <blockquote>
 <p>“I went to the park to play frisbee with my dog. She loves catching the frisbee and running with it. Sometimes she gets too excited and won’t let go when I try to take it from her. Playing frisbee with her is always fun and great exercise for both of us. Chinese:”</p>
 </blockquote>
-<p><strong>Gradient-Based Influential Examples Identified:</strong></p>
+</section>
+<section id="gradient-based-influential-training-samples-identified" class="level4">
+<h4>Gradient-Based Influential Training Samples Identified:</h4>
 <blockquote>
 <p>“Maybe I just feel like I could do anything, I want the whole world dancing to my music and wearing my clothes.<br />
 </p>
@@ -147,11 +351,19 @@ <h4>Eigenvalue-Corrected KFAC (EK-FAC) and ASTRA.</h4>
 </div>
 <p>”</p>
 </blockquote>
-<p><strong>Observation:</strong> Influence functions successfully identify training documents that impacted the translation, even without exact verbatim matches. This demonstrates how gradient-based methods capture broader compositional and abstract influences beyond what verbatim methods can reveal.</p>
+</section>
+<section id="observation.-1" class="level4">
+<h4>Observation.</h4>
+<p>Influence functions identify training documents that impacted the translation, even without exact verbatim matches. This demonstrates that gradient-based methods can capture influences beyond what verbatim methods can reveal.</p>
+</section>
 </div>
 </section>
-<section id="recent-extensions-and-limitations" class="level3"><h3 data-pdf-page="63"><span class="secnum">9.2.8</span> Recent Extensions and Limitations<a class="anchor" href="#recent-extensions-and-limitations" aria-label="Link to this section">#</a></h3>
-<p>Recent work extends training data attribution to handle training stochasticity. Distributional TDA (d-TDA) predicts how output distributions depend on the dataset <span class="citation" data-cites="mlodozeniec2025distributionaltrainingdataattribution">(<a href="@@BASE@@references/#ref-mlodozeniec2025distributionaltrainingdataattribution" class="cite" data-cite="mlodozeniec2025distributionaltrainingdataattribution" role="doc-biblioref">Mlodozeniec et al., 2025</a>)</span>, while TRAK variants address diffusion models <span class="citation" data-cites="georgiev2023journeydestinationdataguides">(<a href="@@BASE@@references/#ref-georgiev2023journeydestinationdataguides" class="cite" data-cite="georgiev2023journeydestinationdataguides" role="doc-biblioref">Georgiev et al., 2023</a>)</span>. More recent work connects TDA directly to mechanistic interpretability: <span class="citation" data-cites="kowal2026concept">(<a href="@@BASE@@references/#ref-kowal2026concept" class="cite cite-emerging" data-cite="kowal2026concept" role="doc-biblioref">Kowal et al., 2026</a>)</span> introduce Concept Influence, which enables attribution at the level of individual model components rather than raw parameters, bringing TDA closer to the mechanistic goal of understanding <em>how</em> training data shapes specific internal computations. However, fundamental challenges persist: approximation errors compound over optimization trajectories, random hyperparameters introduce variance across runs, and scaling to larger models remains memory-intensive. These obstacles suggest that fully resolving attribution at scale may require fundamentally new approaches.</p>
+<section id="recent-extensions-and-limitations" class="level3"><h3 data-pdf-page="101"><span class="secnum">11.2.7</span> Recent Extensions and Limitations<a class="anchor" href="#recent-extensions-and-limitations" aria-label="Link to this section">#</a></h3>
+<p>Recent work extends training data attribution to handle training stochasticity. <em>Distributional TDA</em> (d-TDA) estimates how output distributions depend on the dataset <span class="citation" data-cites="mlodozeniec2025distributionaltrainingdataattribution">(<a href="@@BASE@@references/#ref-mlodozeniec2025distributionaltrainingdataattribution" class="cite cite-established" data-cite="mlodozeniec2025distributionaltrainingdataattribution" role="doc-biblioref">Mlodozeniec et al., 2025</a>)</span>. <span class="citation" data-cites="georgiev2023journeydestinationdataguides"><a href="@@BASE@@references/#ref-georgiev2023journeydestinationdataguides" class="cite cite-emerging" data-cite="georgiev2023journeydestinationdataguides" role="doc-biblioref">Georgiev et al. (2023)</a></span> proposed TRAK variants to address diffusion models. More recent work connects TDA directly to mechanistic interpretability <span class="citation" data-cites="kowal2026concept habibi2026symbolicmechanisticdataattribution">(<a href="@@BASE@@references/#ref-kowal2026concept" class="cite cite-emerging" data-cite="kowal2026concept" role="doc-biblioref">Kowal et al., 2026</a>; <a href="@@BASE@@references/#ref-habibi2026symbolicmechanisticdataattribution" class="cite cite-emerging" data-cite="habibi2026symbolicmechanisticdataattribution" role="doc-biblioref">Habibi et al., 2026</a>)</span>; for example, <span class="citation" data-cites="kowal2026concept"><a href="@@BASE@@references/#ref-kowal2026concept" class="cite cite-emerging" data-cite="kowal2026concept" role="doc-biblioref">Kowal et al. (2026)</a></span> introduced <em>Concept Influence</em> to enable attribution at the level of semantic concepts represented by model internals such as sparse autoencoder features. This brings TDA closer to the mechanistic goal of understanding <em>how</em> training data shapes specific internal computations. However, fundamental technical challenges persist, including approximation errors that compound over the training trajectory, variability across training runs caused by randomness like data ordering, and memory and computational bottlenecks of scaling to frontier models. These limitations motivate further research in this direction.</p>
+<section id="summary.-8" class="level4">
+<h4>Summary.</h4>
+<p>Training data attribution traces model behaviour back to the training data. Verbatim methods such as OLMoTrace identify memorised spans, while gradient-based methods such as influence functions estimate the contribution of individual training samples to downstream model predictions. These approaches remain limited at scale. Meanwhile, recent work has begun linking data attribution to internal features and mechanisms. Having surveyed the main methodological families of AI interpretability in the preceding sections, Section <a href="@@BASE@@handbook/research-orientations/#sec:orientations" data-reference-type="ref" data-reference="sec:orientations">12</a> organises them into six research orientations that differ in their goals, assumptions, and paths forward.</p>
+</section>
 </section>
 </section>
 </section>
diff --git a/src/generated/manifest.json b/src/generated/manifest.json
index 51932e2d02..3d98feadde 100644
--- a/src/generated/manifest.json
+++ b/src/generated/manifest.json
@@ -1,12 +1,12 @@
 {
  "provenance": {
-  "status": "draft",
-  "source_dir_name": "The-Handbook-on-Interpretability",
-  "source_commit": "cd5bc3c",
-  "source_commit_date": "2026-07-13",
-  "main_tex_sha256": "490b52b9e014a4d6",
+  "status": "final",
+  "source_dir_name": "manuscript",
+  "source_commit": null,
+  "source_commit_date": null,
+  "main_tex_sha256": "1dc70452f1aed99a",
   "pdf_included": false,
-  "pdf_page_offset": 8,
+  "pdf_page_offset": 0,
   "citations": "natbib labels from the compiled .bbl",
   "generated": "2026-11-06"
  },
@@ -16,2377 +16,2715 @@
    "num": "1",
    "title": "Introduction",
    "appendix": false,
-   "page": "1",
+   "page": "9",
    "sections": [
     {
      "id": "brief-history-of-interpretability",
      "num": "1.1",
      "title": "Brief History of Interpretability",
      "level": 2,
-     "page": "2"
+     "page": "9"
     },
     {
      "id": "sec:scope",
      "num": "1.2",
-     "title": "Handbook Scope",
+     "title": "Survey Scope",
      "level": 2,
-     "page": "4"
+     "page": "12"
     }
    ],
    "withheld": false,
-   "words": 2543
+   "words": 3486
   },
   {
    "slug": "limits-of-behavioural-evaluation",
    "num": "2",
-   "title": "The Limits of Behavioral Evaluation",
+   "title": "The Limits of Behavioural Evaluation",
    "appendix": false,
-   "page": "5",
+   "page": "15",
    "sections": [],
    "withheld": false,
-   "words": 1056
+   "words": 959
   },
   {
    "slug": "interpretability-landscape",
    "num": "3",
    "title": "The Interpretability Landscape",
    "appendix": false,
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    "sections": [
     {
-     "id": "behavioral-methods",
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      "num": "3.1",
-     "title": "Behavioral Methods",
+     "title": "Behavioural Methods",
      "level": 2,
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     },
     {
      "id": "subsec:cbm",
      "num": "3.2",
      "title": "Concept-Based Approaches",
      "level": 2,
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     },
     {
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      "num": "3.3",
-     "title": "Attribution and Saliency Methods",
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      "level": 2,
-     "page": "8"
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     },
     {
      "id": "representation-probing-and-related-post-hoc-analyses",
      "num": "3.4",
      "title": "Representation Probing and Related Post-hoc Analyses",
      "level": 2,
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     },
     {
-     "id": "when-mechanistic-understanding-becomes-necessary",
+     "id": "mechanistic-interpretability-methods",
      "num": "3.5",
+     "title": "Mechanistic Interpretability Methods",
+     "level": 2,
+     "page": "19"
+    },
+    {
+     "id": "subsec: mech interp necessary",
+     "num": "3.6",
      "title": "When Mechanistic Understanding Becomes Necessary",
      "level": 2,
-     "page": "9"
+     "page": "19"
+    },
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+     "id": "subsec:limits",
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     }
    ],
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   {
    "slug": "foundational-concepts",
    "num": "4",
    "title": "Foundational Concepts for Mechanistic Interpretability",
    "appendix": false,
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    "sections": [
     {
      "id": "subsec:behavioral-vs-mechanistic",
      "num": "4.1",
-     "title": "Behavioral vs. Mechanistic Understanding",
+     "title": "Behavioural vs. Mechanistic Understanding",
      "level": 2,
-     "page": "10"
+     "page": "21"
     },
     {
      "id": "subsec:polysemanticity",
      "num": "4.2",
-     "title": "Polysemanticity in Neural Representations",
+     "title": "Neuron Polysemanticity in Model Representations",
      "level": 2,
-     "page": "11"
+     "page": "21"
     },
     {
      "id": "subsec:features",
      "num": "4.3",
      "title": "Features as Representational Units",
      "level": 2,
-     "page": "12"
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     },
     {
      "id": "subsec:transformers",
      "num": "4.4",
      "title": "Transformer Computation",
      "level": 2,
-     "page": "12"
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     },
     {
      "id": "subsec:circuits",
      "num": "4.5",
-     "title": "Circuits as Computational Substructures",
+     "title": "Circuits as Descriptions of Internal Mechanisms",
      "level": 2,
-     "page": "14"
-    },
-    {
-     "id": "subsec:limits",
-     "num": "4.6",
-     "title": "Limitations of Foundational Works In Mechanistic Interpretability",
-     "level": 2,
-     "page": "14"
+     "page": "25"
     }
    ],
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   },
   {
    "slug": "core-hypotheses",
    "num": "5",
    "title": "Core Hypotheses in Mechanistic Interpretability",
    "appendix": false,
-   "page": "15",
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    "sections": [
     {
      "id": "subsec:linearity",
      "num": "5.1",
      "title": "The Linear Representation Hypothesis",
      "level": 2,
-     "page": "15"
+     "page": "27"
     },
     {
      "id": "subsec:superposition",
      "num": "5.2",
      "title": "The Superposition Hypothesis",
      "level": 2,
-     "page": "16"
-    },
-    {
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-     "num": "5.2.1",
-     "title": "Architectural Responses to Superposition",
-     "level": 3,
-     "page": "17"
+     "page": "28"
     },
     {
      "id": "subsec:privileged-access",
      "num": "5.3",
      "title": "The Privileged Access Hypothesis",
      "level": 2,
-     "page": "18"
+     "page": "30"
     },
     {
      "id": "subsec:universality",
      "num": "5.4",
-     "title": "The Universality and Platonic Representation Hypotheses",
+     "title": "The Universality and Platonic Representation Hypothesis",
      "level": 2,
-     "page": "21"
+     "page": "31"
     }
    ],
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   },
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    "slug": "feature-extraction",
    "num": "6",
-   "title": "Feature Extraction and Multi-Layer Analysis",
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    "appendix": false,
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    "sections": [
     {
-     "id": "subsec:sae-foundation",
+     "id": "the-sparse-coding-problem",
      "num": "6.1",
+     "title": "The Sparse Coding Problem",
+     "level": 2,
+     "page": "32"
+    },
+    {
+     "id": "subsec:sae-foundation",
+     "num": "6.2",
      "title": "Sparse Autoencoders",
      "level": 2,
-     "page": "22"
+     "page": "32"
     },
     {
      "id": "subsec:sae-variants",
-     "num": "6.2",
+     "num": "6.3",
      "title": "SAE Variants and Architectural Improvements",
      "level": 2,
-     "page": "23"
+     "page": "34"
     },
     {
      "id": "subsec:sae-selection",
-     "num": "6.3",
-     "title": "Practical Guidance: Selecting SAE Variants",
+     "num": "6.4",
+     "title": "Empirical Comparisons of SAE Variants",
      "level": 2,
-     "page": "24"
+     "page": "35"
     },
     {
-     "id": "layer-specific-recommendations",
-     "num": "6.3.1",
-     "title": "Layer-Specific Recommendations",
-     "level": 3,
-     "page": "25"
+     "id": "subsec:challenge",
+     "num": "6.5",
+     "title": "Challenges and Failure Modes of SAEs",
+     "level": 2,
+     "page": "37"
     },
     {
-     "id": "common-pitfalls-and-how-to-avoid-them",
-     "num": "6.3.2",
-     "title": "Common Pitfalls and How to Avoid Them",
+     "id": "dead-features",
+     "num": "6.5.1",
+     "title": "Dead Features",
      "level": 3,
-     "page": "26"
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     },
     {
-     "id": "quick-reference-when-to-use-what",
-     "num": "6.3.3",
-     "title": "Quick Reference: When to Use What",
+     "id": "feature-duplication",
+     "num": "6.5.2",
+     "title": "Feature Duplication",
      "level": 3,
-     "page": "26"
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     },
     {
-     "id": "limitations-of-these-recommendations",
-     "num": "6.3.4",
-     "title": "Limitations of These Recommendations",
+     "id": "feature-absorption-and-splitting",
+     "num": "6.5.3",
+     "title": "Feature Absorption and Splitting",
      "level": 3,
-     "page": "26"
-    },
-    {
-     "id": "subsec:transcoders-crosscoders",
-     "num": "6.4",
-     "title": "Multi-Layer Feature Analysis: Transcoders and Crosscoders",
-     "level": 2,
-     "page": "27"
+     "page": "39"
     },
     {
-     "id": "subsubsec:transcoder",
-     "num": "6.4.1",
-     "title": "Transcoders: Bridging Nonlinear Layers",
+     "id": "feature-instability-across-seeds",
+     "num": "6.5.4",
+     "title": "Feature Instability Across Seeds",
      "level": 3,
-     "page": "27"
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     },
     {
-     "id": "subsubsec:cross-layer-transcoder",
-     "num": "6.4.2",
-     "title": "Cross-Layer Transcoders",
+     "id": "subsubsec: sae limitation",
+     "num": "6.5.5",
+     "title": "Identifiability and Theoretical Concerns",
      "level": 3,
-     "page": "27"
+     "page": "40"
     },
     {
-     "id": "crosscoders-analyzing-cross-layer-features",
-     "num": "6.4.3",
-     "title": "Crosscoders: Analyzing Cross-Layer Features",
+     "id": "practical-recommendations",
+     "num": "6.5.6",
+     "title": "Practical Recommendations",
      "level": 3,
-     "page": "30"
-    },
-    {
-     "id": "comparing-saes-transcoders-and-crosscoders.",
-     "num": "6.5",
-     "title": "Comparing SAEs, Transcoders, and Crosscoders.",
-     "level": 2,
-     "page": "30"
+     "page": "40"
     },
     {
-     "id": "case-study-induction-heads",
+     "id": "subsec:transcoders-crosscoders",
      "num": "6.6",
-     "title": "Case Study: Induction Heads",
-     "level": 2,
-     "page": "31"
-    },
-    {
-     "id": "subsec:challenge",
-     "num": "6.7",
-     "title": "Challenges and Failure Modes of SAEs",
+     "title": "Multi-Layer Feature Extraction and Analysis",
      "level": 2,
-     "page": "32"
-    },
-    {
-     "id": "dead-features",
-     "num": "6.7.1",
-     "title": "Dead Features",
-     "level": 3,
-     "page": "32"
+     "page": "41"
     },
     {
-     "id": "feature-duplication",
-     "num": "6.7.2",
-     "title": "Feature Duplication",
+     "id": "subsubsec:transcoder",
+     "num": "6.6.1",
+     "title": "Transcoders: Bridging the Nonlinearities",
      "level": 3,
-     "page": "33"
+     "page": "41"
     },
     {
-     "id": "feature-absorption-and-splitting",
-     "num": "6.7.3",
-     "title": "Feature Absorption and Splitting",
+     "id": "subsubsec:cross-layer-transcoder",
+     "num": "6.6.2",
+     "title": "Cross-Layer Transcoders: Enabling Better Circuit Analysis",
      "level": 3,
-     "page": "33"
+     "page": "42"
     },
     {
-     "id": "feature-instability-across-seeds",
-     "num": "6.7.4",
-     "title": "Feature Instability Across Seeds",
+     "id": "crosscoders-analysing-cross-layer-features",
+     "num": "6.6.3",
+     "title": "Crosscoders: Analysing Cross-Layer Features",
      "level": 3,
-     "page": "33"
+     "page": "45"
     },
     {
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-     "num": "6.7.5",
-     "title": "Identifiability and Theoretical Concerns",
-     "level": 3,
-     "page": "33"
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+     "num": "6.7",
+     "title": "Comparing SAEs, Transcoders, and Crosscoders",
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     },
     {
-     "id": "practical-recommendations",
-     "num": "6.7.6",
-     "title": "Practical Recommendations",
-     "level": 3,
-     "page": "34"
+     "id": "subsec: induc head case study: feature extract",
+     "num": "6.8",
+     "title": "Case Study: Induction Heads",
+     "level": 2,
+     "page": "47"
     },
     {
      "id": "comparison-with-alternative-feature-extraction-methods",
-     "num": "6.8",
+     "num": "6.9",
      "title": "Comparison with Alternative Feature Extraction Methods",
      "level": 2,
-     "page": "34"
+     "page": "48"
     },
     {
-     "id": "non-negative-matrix-factorization-nmf",
-     "num": "6.8.1",
-     "title": "Non-Negative Matrix Factorization (NMF)",
+     "id": "non-negative-matrix-factorisation-nmf",
+     "num": "6.9.1",
+     "title": "Non-Negative Matrix Factorisation (NMF)",
      "level": 3,
-     "page": "34"
+     "page": "48"
     },
     {
-     "id": "principle-component-analysis-pca",
-     "num": "6.8.2",
-     "title": "Principle Component Analysis (PCA)",
+     "id": "principal-component-analysis-pca",
+     "num": "6.9.2",
+     "title": "Principal Component Analysis (PCA)",
      "level": 3,
-     "page": "35"
+     "page": "49"
     },
     {
      "id": "independent-component-analysis-ica",
-     "num": "6.8.3",
+     "num": "6.9.3",
      "title": "Independent Component Analysis (ICA)",
      "level": 3,
-     "page": "35"
+     "page": "49"
     },
     {
-     "id": "supervised-feature-extraction-methods",
-     "num": "6.8.4",
+     "id": "subsubsec: supervised_extraction",
+     "num": "6.9.4",
      "title": "Supervised Feature Extraction Methods",
      "level": 3,
-     "page": "35"
+     "page": "49"
     },
     {
      "id": "dictionary-learning-baselines",
-     "num": "6.8.5",
+     "num": "6.9.5",
      "title": "Dictionary Learning Baselines",
      "level": 3,
-     "page": "36"
+     "page": "50"
     },
     {
      "id": "sec:architectural-interventions",
-     "num": "6.9",
-     "title": "Architectural Interventions for Intrinsic Interpretability",
+     "num": "6.10",
+     "title": "Interpretability-by-Design Methods for Intrinsic Interpretability",
      "level": 2,
-     "page": "36"
-    },
-    {
-     "id": "weight-sparsity-via-l_0-regularization",
-     "num": "6.9.1",
-     "title": "Weight Sparsity via L_0 Regularization",
-     "level": 3,
-     "page": "36"
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     },
     {
-     "id": "sparse-attention-post-training-spartan",
-     "num": "6.9.2",
-     "title": "Sparse Attention Post-Training: SPARTAN",
+     "id": "subsubsec: solu",
+     "num": "6.10.1",
+     "title": "Feature-Sparse Training: SoLU",
      "level": 3,
-     "page": "37"
+     "page": "50"
     },
     {
-     "id": "activation-level-interventions-solu-and-its-limitations",
-     "num": "6.9.3",
-     "title": "Activation-Level Interventions: SoLU and Its Limitations",
+     "id": "subsubsec: weight sparse transformer",
+     "num": "6.10.2",
+     "title": "Weight-Sparse Training",
      "level": 3,
-     "page": "37"
+     "page": "51"
     },
     {
-     "id": "computational-primitive-modifications-molt",
-     "num": "6.9.4",
-     "title": "Computational Primitive Modifications: MOLT",
+     "id": "subsubsec: spartan",
+     "num": "6.10.3",
+     "title": "Attention-Sparse Training",
      "level": 3,
-     "page": "37"
+     "page": "51"
     },
     {
-     "id": "kolmogorov-arnold-networks",
-     "num": "6.9.5",
-     "title": "Kolmogorov-Arnold Networks",
+     "id": "subsubsec: molt",
+     "num": "6.10.4",
+     "title": "Modifications of Computational Primitives: MOLT",
      "level": 3,
-     "page": "38"
+     "page": "52"
     },
     {
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-     "num": "6.9.6",
-     "title": "The Bridge Methodology: Interpretable Proxies for Dense Models",
+     "id": "subsubsec: kan",
+     "num": "6.10.5",
+     "title": "Kolmogorov–Arnold Networks (KANs)",
      "level": 3,
-     "page": "38"
+     "page": "52"
     }
    ],
    "withheld": false,
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   },
   {
    "slug": "circuits",
    "num": "7",
    "title": "Circuits in Mechanistic Interpretability",
    "appendix": false,
-   "page": "38",
+   "page": "53",
    "sections": [
     {
-     "id": "sec:what-are-circuits",
+     "id": "sec:circuit-discovery-methods",
      "num": "7.1",
-     "title": "What Are Circuits?",
+     "title": "Circuit Discovery Methods",
      "level": 2,
-     "page": "39"
+     "page": "53"
     },
     {
-     "id": "sec:circuit-table",
+     "id": "sec:circuit-replacement",
      "num": "7.2",
-     "title": "Circuit Discovery and Tracing Methods",
-     "level": 2,
-     "page": "39"
-    },
-    {
-     "id": "circuit-discovery-in-weight-sparse-architectures",
-     "num": "7.3",
-     "title": "Circuit Discovery in Weight-Sparse Architectures",
+     "title": "Circuit Discovery with Replacement Models",
      "level": 2,
-     "page": "40"
+     "page": "55"
     },
     {
      "id": "sec:causal-verification",
-     "num": "7.4",
+     "num": "7.3",
      "title": "Causal Verification",
      "level": 2,
-     "page": "41"
+     "page": "57"
     },
     {
-     "id": "case-study-induction-heads-1",
-     "num": "7.5",
+     "id": "subsec: induc head case study: circuit",
+     "num": "7.4",
      "title": "Case Study: Induction Heads",
      "level": 2,
-     "page": "42"
+     "page": "58"
     },
     {
      "id": "sec:evaluation-framework",
-     "num": "7.6",
+     "num": "7.5",
      "title": "A Unified Evaluation Framework for Mechanistic Interpretability",
      "level": 2,
-     "page": "43"
+     "page": "58"
     },
     {
-     "id": "the-need-for-standardized-evaluation",
-     "num": "7.6.1",
-     "title": "The Need for Standardized Evaluation",
+     "id": "the-need-for-standardised-evaluation",
+     "num": "7.5.1",
+     "title": "The Need for Standardised Evaluation",
      "level": 3,
-     "page": "43"
+     "page": "58"
     },
     {
-     "id": "five-evaluation-dimensions",
-     "num": "7.6.2",
+     "id": "subsec:five eval dim",
+     "num": "7.5.2",
      "title": "Five Evaluation Dimensions",
      "level": 3,
-     "page": "43"
-    },
-    {
-     "id": "dimension-1-faithfulness",
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-     "title": "Dimension 1: Faithfulness",
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-     "page": "43"
-    },
-    {
-     "id": "dimension-2-completeness",
-     "num": "7.6.4",
-     "title": "Dimension 2: Completeness",
-     "level": 3,
-     "page": "44"
-    },
-    {
-     "id": "dimension-3-stability",
-     "num": "7.6.5",
-     "title": "Dimension 3: Stability",
-     "level": 3,
-     "page": "45"
-    },
-    {
-     "id": "dimension-4-causal-validity",
-     "num": "7.6.6",
-     "title": "Dimension 4: Causal Validity",
-     "level": 3,
-     "page": "45"
-    },
-    {
-     "id": "dimension-5-human-interpretability",
-     "num": "7.6.7",
-     "title": "Dimension 5: Human Interpretability",
-     "level": 3,
-     "page": "46"
+     "page": "59"
     },
     {
      "id": "integrated-evaluation-protocol",
-     "num": "7.6.8",
+     "num": "7.5.3",
      "title": "Integrated Evaluation Protocol",
      "level": 3,
-     "page": "47"
-    },
-    {
-     "id": "applying-the-framework-method-specific-guidance",
-     "num": "7.6.9",
-     "title": "Applying the Framework: Method-Specific Guidance",
-     "level": 3,
-     "page": "47"
+     "page": "65"
     },
     {
      "id": "limitations-of-this-framework",
-     "num": "7.6.10",
+     "num": "7.5.4",
      "title": "Limitations of This Framework",
      "level": 3,
-     "page": "48"
+     "page": "66"
     }
    ],
    "withheld": false,
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   },
   {
    "slug": "intermediate-representations",
    "num": "8",
-   "title": "Understanding Intermediate Computation: Logit Lens and Probing",
+   "title": "Understanding Intermediate Representations",
    "appendix": false,
-   "page": "48",
+   "page": "66",
    "sections": [
     {
      "id": "sec:logit-lens",
      "num": "8.1",
      "title": "Logit Lens: Tracing Predictions Through Depth",
      "level": 2,
-     "page": "48"
+     "page": "67"
     },
     {
-     "id": "tuned-lens-learning-per-layer-transform",
+     "id": "subsec:tuned-lens",
      "num": "8.2",
-     "title": "Tuned Lens: Learning Per-Layer Transform",
+     "title": "Tuned Lens: Learning Per-Layer Transforms",
      "level": 2,
-     "page": "49"
+     "page": "68"
     },
     {
-     "id": "case-study-refusal-vectors-and-sleeper-agents",
+     "id": "jacobian-lens-decoding-computational-dispositions",
      "num": "8.3",
-     "title": "Case Study: Refusal Vectors and Sleeper Agents",
+     "title": "Jacobian Lens: Decoding Computational Dispositions",
      "level": 2,
-     "page": "50"
+     "page": "68"
     },
     {
      "id": "subsec:patchscopes",
      "num": "8.4",
      "title": "Patchscopes: Leveraging the Language Model Itself to Explain Intermediate Representations",
      "level": 2,
-     "page": "51"
+     "page": "69"
     },
     {
      "id": "subsec:latentqa",
      "num": "8.5",
-     "title": "LatentQA: Finetuning the Language Model as Representation Decoder",
+     "title": "LatentQA: Fine-Tuning the Language Model as an Activation Verbaliser",
      "level": 2,
-     "page": "52"
+     "page": "70"
     },
     {
      "id": "subsec:probing",
      "num": "8.6",
      "title": "Probing Classifiers: Capabilities, Critiques, and Causal Limitations",
      "level": 2,
-     "page": "52"
+     "page": "72"
     },
     {
      "id": "standard-probing-methodology",
      "num": "8.6.1",
      "title": "Standard Probing Methodology",
      "level": 3,
-     "page": "53"
+     "page": "72"
     },
     {
-     "id": "the-probe-causality-problem",
+     "id": "control-tasks-and-selectivity",
      "num": "8.6.2",
-     "title": "The Probe Causality Problem",
+     "title": "Control Tasks and Selectivity",
      "level": 3,
-     "page": "53"
+     "page": "73"
     },
     {
-     "id": "control-tasks-and-selectivity",
+     "id": "concerns-of-probe-complexity",
      "num": "8.6.3",
-     "title": "Control Tasks and Selectivity",
+     "title": "Concerns of Probe Complexity",
      "level": 3,
-     "page": "54"
+     "page": "74"
     },
     {
-     "id": "probe-complexity-vs.-causal-relevance",
+     "id": "subsubsec: amnesic probing",
      "num": "8.6.4",
-     "title": "Probe Complexity vs. Causal Relevance",
+     "title": "Amnesic Probing and Causal Probing",
      "level": 3,
-     "page": "55"
+     "page": "74"
     },
     {
-     "id": "amnesic-probing-and-causal-probing",
+     "id": "implications-of-attribution-faithfulness-for-probing",
      "num": "8.6.5",
-     "title": "Amnesic Probing and Causal Probing",
+     "title": "Implications of Attribution Faithfulness for Probing",
      "level": 3,
-     "page": "55"
+     "page": "75"
     },
     {
-     "id": "critiques-of-attribution-and-saliency-methods",
+     "id": "when-is-probing-appropriate",
      "num": "8.6.6",
-     "title": "Critiques of Attribution and Saliency Methods",
+     "title": "When Is Probing Appropriate?",
      "level": 3,
-     "page": "56"
+     "page": "76"
     },
     {
-     "id": "circuit-fragility-and-robustness-concerns",
+     "id": "methodological-standards-in-probing-research",
      "num": "8.6.7",
-     "title": "Circuit Fragility and Robustness Concerns",
+     "title": "Methodological Standards in Probing Research",
      "level": 3,
-     "page": "56"
-    },
-    {
-     "id": "when-is-probing-appropriate-and-when-is-it-not",
-     "num": "8.6.8",
-     "title": "When is Probing Appropriate, and When is it Not?",
-     "level": 3,
-     "page": "56"
-    },
-    {
-     "id": "best-practices-for-probing-studies",
-     "num": "8.6.9",
-     "title": "Best Practices for Probing Studies",
-     "level": 3,
-     "page": "57"
+     "page": "76"
     }
    ],
    "withheld": false,
-   "words": 3934
+   "words": 4479
   },
   {
-   "slug": "training-data-attribution",
+   "slug": "representation-analysis",
    "num": "9",
-   "title": "Training Data Attribution and Influence Functions",
+   "title": "Representation Analysis: Comparison, Localisation, and Control",
    "appendix": false,
-   "page": "58",
+   "page": "77",
    "sections": [
     {
-     "id": "verbatim-data-attribution-olmotrace-and-maximal-matching-spans",
+     "id": "sec:model-diffing",
      "num": "9.1",
-     "title": "Verbatim Data Attribution: OLMoTrace and Maximal Matching Spans",
+     "title": "Model Diffing: Identifying Internal Changes Across Models",
      "level": 2,
-     "page": "59"
+     "page": "77"
     },
     {
-     "id": "limitations",
-     "num": "9.1.1",
-     "title": "Limitations",
-     "level": 3,
-     "page": "60"
+     "id": "sec:knowledge-localization",
+     "num": "9.2",
+     "title": "Knowledge Localisation: Where Is Information Stored?",
+     "level": 2,
+     "page": "80"
     },
     {
-     "id": "gradient-based-attribution-understanding-influence-through-data-perturbations",
-     "num": "9.2",
-     "title": "Gradient-Based Attribution: Understanding Influence Through Data Perturbations",
+     "id": "subsec:steering",
+     "num": "9.3",
+     "title": "Representation Engineering and Steering",
+     "level": 2,
+     "page": "80"
+    }
+   ],
+   "withheld": false,
+   "words": 2497
+  },
+  {
+   "slug": "learning-dynamics",
+   "num": "10",
+   "title": "Learning Dynamics and Mechanistic Interpretability",
+   "appendix": false,
+   "page": "82",
+   "sections": [
+    {
+     "id": "sec:ld-longitudinal",
+     "num": "10.1",
+     "title": "Longitudinal Analysis",
      "level": 2,
-     "page": "61"
+     "page": "83"
     },
     {
-     "id": "core-idea-parameter-response-to-training-data",
-     "num": "9.2.1",
-     "title": "Core Idea: Parameter Response to Training Data",
+     "id": "sec:ld-data",
+     "num": "10.1.1",
+     "title": "Training Data and Capability Acquisition",
      "level": 3,
-     "page": "61"
+     "page": "83"
     },
     {
-     "id": "proximal-bregman-response-and-the-gauss-newton-hessian",
-     "num": "9.2.2",
-     "title": "Proximal Bregman Response and the Gauss-Newton Hessian",
+     "id": "sec:ld-features",
+     "num": "10.1.2",
+     "title": "Feature Formation, Consolidation, and Drift",
      "level": 3,
-     "page": "61"
+     "page": "83"
     },
     {
-     "id": "computational-bottleneck-approximating-ggn-vector-products",
-     "num": "9.2.3",
-     "title": "Computational Bottleneck: Approximating GGN-Vector Products",
-     "level": 3,
-     "page": "61"
+     "id": "learning-dynamics-as-an-interpretability-tool",
+     "num": "10.2",
+     "title": "Learning Dynamics as an Interpretability Tool",
+     "level": 2,
+     "page": "84"
     },
     {
-     "id": "unrolled-differentiation-gradient-based-attribution",
-     "num": "9.2.4",
-     "title": "Unrolled Differentiation: Gradient-Based Attribution",
+     "id": "sec:ld-localization",
+     "num": "10.2.1",
+     "title": "Temporal Localisation and Mechanism Discovery",
      "level": 3,
-     "page": "62"
+     "page": "84"
     },
     {
-     "id": "sec:trak",
-     "num": "9.2.5",
-     "title": "Practical Attribution at Scale: TRAK",
+     "id": "sec:ld-causal-interventions",
+     "num": "10.2.2",
+     "title": "Causal Interventions on Mechanism Formation",
      "level": 3,
-     "page": "62"
+     "page": "85"
     },
     {
-     "id": "sec:tracin",
-     "num": "9.2.6",
-     "title": "Efficient Influence Estimation: TracIn",
-     "level": 3,
-     "page": "62"
+     "id": "sec:ld-transitions",
+     "num": "10.3",
+     "title": "Phase Transitions and Emergence",
+     "level": 2,
+     "page": "86"
     },
     {
-     "id": "illustrative-example",
-     "num": "9.2.7",
-     "title": "Illustrative Example",
+     "id": "sec:ld-detect",
+     "num": "10.3.1",
+     "title": "Detecting Transitions",
      "level": 3,
-     "page": "63"
+     "page": "86"
     },
     {
-     "id": "recent-extensions-and-limitations",
-     "num": "9.2.8",
-     "title": "Recent Extensions and Limitations",
+     "id": "sec:ld-progress",
+     "num": "10.3.2",
+     "title": "Progress Measures and Internal Precursors",
      "level": 3,
-     "page": "63"
-    }
-   ],
-   "withheld": false,
-   "words": 2252
-  },
-  {
-   "slug": "developmental-interpretability",
-   "num": "10",
-   "title": "Developmental Interpretability and Training Dynamics",
-   "appendix": false,
-   "page": "63",
-   "sections": [
+     "page": "87"
+    },
     {
-     "id": "sec:grokking",
-     "num": "10.1",
-     "title": "Grokking: Memorization-to-Generalization Phase Transitions",
-     "level": 2,
-     "page": "64"
+     "id": "sec:ld-attribution",
+     "num": "10.3.3",
+     "title": "Attributing Transitions to Components",
+     "level": 3,
+     "page": "88"
     },
     {
-     "id": "training-dynamics-and-the-emergence-of-internal-mechanisms",
-     "num": "10.2",
-     "title": "Training Dynamics and the Emergence of Internal Mechanisms",
-     "level": 2,
-     "page": "64"
+     "id": "sec:training_phase_why",
+     "num": "10.3.4",
+     "title": "Why Transitions Happen",
+     "level": 3,
+     "page": "89"
     },
     {
-     "id": "sec:slt-llc",
-     "num": "10.3",
-     "title": "Singular Learning Theory and the Local Learning Coefficient",
-     "level": 2,
-     "page": "65"
+     "id": "sec:ld-induction-case",
+     "num": "10.3.5",
+     "title": "Case Study: Induction Heads",
+     "level": 3,
+     "page": "90"
     },
     {
-     "id": "sec:stagewise-influence",
+     "id": "sec:ld-safety",
      "num": "10.4",
-     "title": "Stagewise Influence and Developmental Data Attribution",
+     "title": "Leveraging Learning Dynamics for Safety",
      "level": 2,
-     "page": "66"
+     "page": "90"
     },
     {
-     "id": "sec:deception",
-     "num": "10.5",
-     "title": "Deceptive Alignment and Training Dynamics",
-     "level": 2,
-     "page": "67"
+     "id": "sec:ld-forecasting",
+     "num": "10.4.1",
+     "title": "Forecasting Model Behaviours",
+     "level": 3,
+     "page": "90"
     },
     {
-     "id": "case-study-induction-heads-2",
-     "num": "10.6",
-     "title": "Case Study: Induction Heads",
-     "level": 2,
-     "page": "67"
+     "id": "sec:ld-intervening",
+     "num": "10.4.2",
+     "title": "Intervening on Training Processes",
+     "level": 3,
+     "page": "91"
     }
    ],
    "withheld": false,
-   "words": 1720
+   "words": 6273
   },
   {
-   "slug": "learning-dynamics",
+   "slug": "training-data-attribution",
    "num": "11",
-   "title": "Learning Dynamics and Mechanistic Interpretability",
-   "appendix": false,
-   "page": "67",
-   "sections": [],
-   "withheld": true,
-   "words": 1538
-  },
-  {
-   "slug": "representation-analysis",
-   "num": "12",
-   "title": "Representation Analysis: Structure, Localization, and Transfer",
+   "title": "Training Data Attribution",
    "appendix": false,
-   "page": "70",
+   "page": "93",
    "sections": [
     {
-     "id": "sec:model-diffing",
-     "num": "12.1",
-     "title": "Model Diffing: Tracking Representational Changes Across Models",
+     "id": "verbatim-data-attribution-olmotrace-and-maximal-matching-spans",
+     "num": "11.1",
+     "title": "Verbatim Data Attribution: OLMoTrace and Maximal Matching Spans",
      "level": 2,
-     "page": "70"
+     "page": "93"
     },
     {
-     "id": "sec:knowledge-localization",
-     "num": "12.2",
-     "title": "Knowledge Localization: Where Is Information Stored?",
+     "id": "gradient-based-attribution-understanding-data-influence-through-perturbations",
+     "num": "11.2",
+     "title": "Gradient-Based Attribution: Understanding Data Influence Through Perturbations",
      "level": 2,
-     "page": "72"
+     "page": "96"
     },
     {
-     "id": "subsec:steering",
-     "num": "12.3",
-     "title": "Representation Engineering and Steering",
-     "level": 2,
-     "page": "73"
+     "id": "core-idea-parameter-response-to-training-data",
+     "num": "11.2.1",
+     "title": "Core Idea: Parameter Response to Training Data",
+     "level": 3,
+     "page": "96"
+    },
+    {
+     "id": "proximal-bregman-response-and-the-generalised-gaussnewton",
+     "num": "11.2.2",
+     "title": "Proximal Bregman Response and the Generalised Gauss–Newton",
+     "level": 3,
+     "page": "97"
+    },
+    {
+     "id": "addressing-the-computational-and-memory-bottlenecks-of-ggn",
+     "num": "11.2.3",
+     "title": "Addressing the Computational and Memory Bottlenecks of GGN",
+     "level": 3,
+     "page": "98"
+    },
+    {
+     "id": "sec:trak",
+     "num": "11.2.4",
+     "title": "Retraining-Based Influence Estimation: datamodels and TRAK",
+     "level": 3,
+     "page": "99"
+    },
+    {
+     "id": "sec:tracin",
+     "num": "11.2.5",
+     "title": "Efficient Influence Estimation Using Training Trajectories: TracIn",
+     "level": 3,
+     "page": "100"
+    },
+    {
+     "id": "illustrative-example",
+     "num": "11.2.6",
+     "title": "Illustrative Example",
+     "level": 3,
+     "page": "100"
+    },
+    {
+     "id": "recent-extensions-and-limitations",
+     "num": "11.2.7",
+     "title": "Recent Extensions and Limitations",
+     "level": 3,
+     "page": "101"
     }
    ],
    "withheld": false,
-   "words": 1840
+   "words": 3836
   },
   {
    "slug": "research-orientations",
-   "num": "13",
-   "title": "Five Research Orientations",
+   "num": "12",
+   "title": "Six Research Orientations",
    "appendix": false,
-   "page": "74",
+   "page": "102",
    "sections": [
     {
-     "id": "defining-orientations-vs.-methods",
-     "num": "13.1",
-     "title": "Defining Orientations vs. Methods",
-     "level": 2,
-     "page": "74"
-    },
-    {
-     "id": "overview-why-five-orientations",
-     "num": "13.2",
-     "title": "Overview: Why Five Orientations?",
+     "id": "subsec: pragmatic interp",
+     "num": "12.1",
+     "title": "Pragmatic Interpretability: Proxy-Task-Oriented Safety Interventions",
      "level": 2,
-     "page": "75"
+     "page": "102"
     },
     {
-     "id": "pragmatic-interpretability-proxy-task-oriented-safety-interventions",
-     "num": "13.3",
-     "title": "Pragmatic Interpretability: Proxy Task Oriented Safety Interventions",
+     "id": "subsec:ambitious",
+     "num": "12.2",
+     "title": "Ambitious Interpretability: Towards Complete Mechanistic Understanding",
      "level": 2,
-     "page": "75"
+     "page": "103"
     },
     {
-     "id": "ambitious-interpretability-toward-complete-mechanistic-understanding",
-     "num": "13.4",
-     "title": "Ambitious Interpretability: Toward Complete Mechanistic Understanding",
+     "id": "subsec:dev interp",
+     "num": "12.3",
+     "title": "Developmental Interpretability: Characterising How Models Learn",
      "level": 2,
-     "page": "76"
+     "page": "103"
     },
     {
-     "id": "developmental-interpretability-understanding-phase-transition-during-training",
-     "num": "13.5",
-     "title": "Developmental Interpretability: Understanding Phase Transition During Training",
+     "id": "subsec: iat",
+     "num": "12.4",
+     "title": "Interpretability Across Time: Understanding the Causes of Model Behaviours",
      "level": 2,
-     "page": "77"
+     "page": "104"
     },
     {
-     "id": "sec:agentic-interpretability",
-     "num": "13.6",
-     "title": "Agentic Interpretability: Models Interpreting Themselves",
+     "id": "sec:intro-interpretability",
+     "num": "12.5",
+     "title": "Introspective Interpretability: Models Interpreting Themselves",
      "level": 2,
-     "page": "77"
+     "page": "105"
     },
     {
-     "id": "governance-concerns-for-agentic-interpretability",
-     "num": "13.6.1",
-     "title": "Governance Concerns for Agentic Interpretability",
+     "id": "concerns-for-introspective-interpretability",
+     "num": "12.5.1",
+     "title": "Concerns for Introspective Interpretability",
      "level": 3,
-     "page": "77"
+     "page": "105"
     },
     {
-     "id": "proposed-guardrails-for-agentic-interpretability",
-     "num": "13.6.2",
-     "title": "Proposed Guardrails for Agentic Interpretability",
+     "id": "subsec:valproce model self-reports",
+     "num": "12.5.2",
+     "title": "Validation Procedures for Model Self-Reports",
      "level": 3,
-     "page": "78"
+     "page": "106"
     },
     {
-     "id": "subsec:valproto agentic self-reports",
-     "num": "13.6.3",
-     "title": "Validation Protocols for Agentic Self-Reports",
+     "id": "when-to-use-introspective-methods",
+     "num": "12.5.3",
+     "title": "When to Use Introspective Methods",
      "level": 3,
-     "page": "78"
+     "page": "107"
     },
     {
      "id": "sec:automated-interp",
-     "num": "13.7",
+     "num": "12.6",
      "title": "Automated Interpretability: Scaling Discovery via Automation",
      "level": 2,
-     "page": "79"
+     "page": "107"
     },
     {
      "id": "governance-concerns-for-automated-interpretability",
-     "num": "13.7.1",
+     "num": "12.6.1",
      "title": "Governance Concerns for Automated Interpretability",
      "level": 3,
-     "page": "79"
+     "page": "108"
     },
     {
      "id": "proposed-guardrails-for-automated-interpretability",
-     "num": "13.7.2",
+     "num": "12.6.2",
      "title": "Proposed Guardrails for Automated Interpretability",
      "level": 3,
-     "page": "80"
-    },
-    {
-     "id": "validation-protocols-for-automated-pipelines",
-     "num": "13.7.3",
-     "title": "Validation Protocols for Automated Pipelines",
-     "level": 3,
-     "page": "80"
+     "page": "109"
     },
     {
      "id": "integrating-automated-and-human-analysis",
-     "num": "13.7.4",
+     "num": "12.6.3",
      "title": "Integrating Automated and Human Analysis",
      "level": 3,
-     "page": "81"
+     "page": "110"
     },
     {
      "id": "summary-responsible-automation",
-     "num": "13.7.5",
+     "num": "12.6.4",
      "title": "Summary: Responsible Automation",
      "level": 3,
-     "page": "81"
+     "page": "110"
     },
     {
      "id": "comparative-analysis-trade-offs-between-orientations",
-     "num": "13.8",
+     "num": "12.7",
      "title": "Comparative Analysis: Trade-Offs Between Orientations",
      "level": 2,
-     "page": "81"
+     "page": "111"
     },
     {
      "id": "subsec:choosing orientation",
-     "num": "13.9",
+     "num": "12.8",
      "title": "Choosing an Orientation: A Decision Framework",
      "level": 2,
-     "page": "82"
+     "page": "111"
     },
     {
-     "id": "critical-tensions-and-open-questions",
-     "num": "13.10",
-     "title": "Critical Tensions and Open Questions",
+     "id": "critical-concerns-tensions-and-open-questions",
+     "num": "12.9",
+     "title": "Critical Concerns, Tensions, and Open Questions",
      "level": 2,
-     "page": "82"
+     "page": "112"
     }
    ],
    "withheld": false,
-   "words": 3823
+   "words": 5187
   },
   {
    "slug": "safety-applications",
-   "num": "14",
+   "num": "13",
    "title": "From Understanding to Intervention: Safety Applications of Mechanistic Interpretability",
    "appendix": false,
-   "page": "83",
+   "page": "112",
    "sections": [
     {
      "id": "why-mechanistic-approaches-for-safety",
-     "num": "14.1",
+     "num": "13.1",
      "title": "Why Mechanistic Approaches for Safety?",
      "level": 2,
-     "page": "83"
+     "page": "112"
     },
     {
-     "id": "alternatives-black-box-methods",
-     "num": "14.1.1",
-     "title": "Alternatives: Black-Box Methods",
+     "id": "alternatives-black-box-methods-and-their-challenges",
+     "num": "13.1.1",
+     "title": "Alternatives: Black-Box Methods and Their Challenges",
      "level": 3,
-     "page": "83"
+     "page": "112"
     },
     {
-     "id": "challenges-of-black-box-bethods",
-     "num": "14.1.2",
-     "title": "Challenges of Black-Box Bethods",
+     "id": "shared-limitations-of-black-box-approaches",
+     "num": "13.1.2",
+     "title": "Shared Limitations of Black-Box Approaches",
      "level": 3,
-     "page": "83"
+     "page": "114"
     },
     {
-     "id": "concept-erasure-and-direct-editing",
-     "num": "14.2",
-     "title": "Concept Erasure and Direct Editing",
+     "id": "concept-erasure-and-knowledge-editing",
+     "num": "13.2",
+     "title": "Concept Erasure and Knowledge Editing",
      "level": 2,
-     "page": "84"
+     "page": "114"
     },
     {
      "id": "concept-erasure-via-pisces",
-     "num": "14.2.1",
+     "num": "13.2.1",
      "title": "Concept Erasure via PISCES",
      "level": 3,
-     "page": "84"
+     "page": "114"
     },
     {
      "id": "subsec:knowedit",
-     "num": "14.2.2",
-     "title": "Knowledge Editing via Localization",
+     "num": "13.2.2",
+     "title": "Knowledge Editing via Localisation",
      "level": 3,
-     "page": "84"
+     "page": "115"
     },
     {
      "id": "steering-vectors-and-mechanistic-monitoring",
-     "num": "14.3",
+     "num": "13.3",
      "title": "Steering Vectors and Mechanistic Monitoring",
      "level": 2,
-     "page": "85"
+     "page": "116"
     },
     {
-     "id": "steering-vectors-practical-behavioral-control",
-     "num": "14.3.1",
-     "title": "Steering Vectors: Practical Behavioral Control",
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      "title": "Automated Interpretability Agents",
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      "id": "sec:mi-risks",
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     {
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-     "title": "Operational Risks: Failure Under Deployment Conditions",
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  "references": [
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-   "key": "adebayo2018sanity",
-   "text": "Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 9525–9536, Red Hook, NY, USA, 2018a. Curran Associates Inc.",
+   "key": "abahana2026rlhf",
+   "text": "Zelalem Abahana, David Evans, Satish Mahadevan Srinivasan, and Matjaz Gams. When rlhf fails: A mechanistic taxonomy of reward hacking, collapse, and evaluator gaming, 2026. URL https://arxiv.org/abs/2606.03238.",
+   "tier": "emerging"
+  },
+  {
+   "key": "achille2019critical",
+   "text": "Alessandro Achille, Matteo Rovere, and Stefano Soatto. Critical learning periods in deep networks. In International Conference on Learning Representations, 2019.",
    "tier": "established"
   },
   {
    "key": "adebayo2020sanitycheckssaliencymaps",
-   "text": "Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 9525–9536, Red Hook, NY, USA, 2018b. Curran Associates Inc.",
+   "text": "Julius Adebayo, Justin Gilmer, Michael Muelly, Ian Goodfellow, Moritz Hardt, and Been Kim. Sanity checks for saliency maps. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 9525–9536, Red Hook, NY, USA, 2018. Curran Associates Inc.",
    "tier": "established"
   },
   {
@@ -2396,24 +2734,49 @@
   },
   {
    "key": "adler2025towards",
-   "text": "Micah Adler, Dan Alistarh, and Nir Shavit. Towards combinatorial interpretability of neural computation. arXiv preprint arXiv:2504.08842, 2025.",
+   "text": "Micah Adler, Dan Alistarh, and Nir Shavit. Towards combinatorial interpretability of neural computation, 2025. URL https://arxiv.org/abs/2504.08842.",
+   "tier": "emerging"
+  },
+  {
+   "key": "agarwal2025context",
+   "text": "Isha Agarwal, Saharsha Navani, and Fazl Barez. Context matters: Analyzing the generalizability of linear probing and steering across diverse scenarios. In Mechanistic Interpretability Workshop at NeurIPS 2025, 2025. URL https://openreview.net/forum?id=H5sbfvEbTh.",
    "tier": "emerging"
   },
   {
    "key": "aharon2006k",
    "text": "Michal Aharon, Michael Elad, and Alfred Bruckstein. K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Transactions on signal processing, 54 (11): 4311–4322, 2006.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "gqa",
+   "text": "Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai. GQA: Training generalized multi-query transformer models from multi-head checkpoints. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pp. 4895–4901, Singapore, December 2023. Association for Computational Linguistics. . URL https://aclanthology.org/2023.emnlp-main.298/.",
+   "tier": "established"
   },
   {
    "key": "alain2017understanding",
    "text": "Guillaume Alain and Yoshua Bengio. Understanding intermediate layers using linear classifier probes. The Fifth International Conference on Learning Representations, 2017. URL https://openreview.net/forum?id=ryF7rTqgl.",
    "tier": "established"
   },
+  {
+   "key": "ali-etal-2025-hidden",
+   "text": "Ameen Ali Ali, Itamar Zimerman, and Lior Wolf. The hidden attention of mamba models. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1516–1534, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8-89176-251-0. . URL https://aclanthology.org/2025.acl-long.76/.",
+   "tier": "established"
+  },
   {
    "key": "ameisen2025circuit",
    "text": "Emmanuel Ameisen, Jack Lindsey, Adam Pearce, Wes Gurnee, Nicholas L. Turner, Brian Chen, Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson, Sam Zimmerman, Kelley Rivoire, Thomas Conerly, Chris Olah, and Joshua Batson. Circuit tracing: Revealing computational graphs in language models. Transformer Circuits Thread, 2025. URL https://transformer-circuits.pub/2025/attribution-graphs/methods.html.",
    "tier": "emerging"
   },
+  {
+   "key": "anil2024manyshot",
+   "text": "Cem Anil, Esin DURMUS, Nina Rimsky, Mrinank Sharma, Joe Benton, Sandipan Kundu, Joshua Batson, Meg Tong, Jesse Mu, Daniel J Ford, Francesco Mosconi, Rajashree Agrawal, Rylan Schaeffer, Naomi Bashkansky, Samuel Svenningsen, Mike Lambert, Ansh Radhakrishnan, Carson Denison, Evan J Hubinger, Yuntao Bai, Trenton Bricken, Timothy Maxwell, Nicholas Schiefer, James Sully, Alex Tamkin, Tamera Lanham, Karina Nguyen, Tomasz Korbak, Jared Kaplan, Deep Ganguli, Samuel R. Bowman, Ethan Perez, Roger Baker Grosse, and David Duvenaud. Many-shot jailbreaking. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://openreview.net/forum?id=cw5mgd71jW.",
+   "tier": "established"
+  },
+  {
+   "key": "anthropic2024modeldiffing",
+   "text": "Anthropic. Model diffing. Transformer Circuits, October 2024. URL https://transformer-circuits.pub/2024/model-diffing/index.html.",
+   "tier": "emerging"
+  },
   {
    "key": "anthropic2025claude-sonnet-4-5",
    "text": "Anthropic. Claude sonnet 4.5 system card. System card, Anthropic, September 2025a. URL https://assets.anthropic.com/m/12f214efcc2f457a/original/Claude-Sonnet-4-5-System-Card.pdf.",
@@ -2426,8 +2789,8 @@
   },
   {
    "key": "aoyama_language_2025",
-   "text": "Tatsuya Aoyama and Ethan Wilcox. Language models grow less humanlike beyond phase transition, 2025. URL http://arxiv.org/abs/2502.18802.",
-   "tier": null
+   "text": "Tatsuya Aoyama and Ethan Wilcox. Language models grow less humanlike beyond phase transition. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 24938–24958, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8-89176-251-0. . URL https://aclanthology.org/2025.acl-long.1214/.",
+   "tier": "established"
   },
   {
    "key": "apollo2025sonnet37evalaware",
@@ -2439,20 +2802,50 @@
    "text": "Santiago Aranguri and Tom McGrath. Discovering undesired rare behaviors via model diff amplification. Goodfire, 2025. https://www.goodfire.ai/research/model-diff-amplification.",
    "tier": "emerging"
   },
+  {
+   "key": "arcuschin2026chainofthought",
+   "text": "Iván Arcuschin, Jett Janiak, Robert Krzyzanowski, Senthooran Rajamanoharan, Neel Nanda, and Arthur Conmy. Chain-of-thought reasoning in the wild is not always faithful. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=NUyt4uxzx0.",
+   "tier": "established"
+  },
   {
    "key": "arditi2024refusallanguagemodelsmediated",
    "text": "Andy Arditi, Oscar Balcells Obeso, Aaquib Syed, Daniel Paleka, Nina Rimsky, Wes Gurnee, and Neel Nanda. Refusal in language models is mediated by a single direction. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.",
    "tier": "established"
   },
   {
-   "key": "bae2022influencefunctionsanswerquestion",
-   "text": "Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger Grosse. If influence functions are the answer, then what is the question? In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA, 2022. Curran Associates Inc. ISBN 9781713871088.",
-   "tier": null
+   "key": "arora-etal-2024-causalgym",
+   "text": "Aryaman Arora, Dan Jurafsky, and Christopher Potts. CausalGym: Benchmarking causal interpretability methods on linguistic tasks. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 14638–14663, Bangkok, Thailand, August 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.acl-long.785/.",
+   "tier": "established"
+  },
+  {
+   "key": "arora2026language",
+   "text": "Aryaman Arora, Zhengxuan Wu, Jacob Steinhardt, and Sarah Schwettmann. Language model circuits are sparse in the neuron basis. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=OrviwFWcN4.",
+   "tier": "established"
+  },
+  {
+   "key": "philosophicalevalframework",
+   "text": "Kola Ayonrinde and Louis Jaburi. Evaluating explanations: An explanatory virtues framework for mechanistic interpretability – the strange science part i.ii, 2025. URL https://arxiv.org/abs/2505.01372.",
+   "tier": "emerging"
+  },
+  {
+   "key": "proofnet",
+   "text": "Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W. Ayers, Dragomir Radev, and Jeremy Avigad. Proofnet: Autoformalizing and formally proving undergraduate-level mathematics, 2023. URL https://arxiv.org/abs/2302.12433.",
+   "tier": "emerging"
   },
   {
-   "key": "bae2024trainingdataattributionapproximate",
-   "text": "Juhan Bae, Wu Lin, Jonathan Lorraine, and Roger Baker Grosse. Training data attribution via approximate unrolling. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://openreview.net/forum?id=3NaqGg92KZ.",
-   "tier": null
+   "key": "ba2016layernormalization",
+   "text": "Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E. Hinton. Layer normalization, 2016. URL https://arxiv.org/abs/1607.06450.",
+   "tier": "emerging"
+  },
+  {
+   "key": "lrp",
+   "text": "Sebastian Bach, Alexander Binder, Grégoire Montavon, Frederick Klauschen, Klaus-Robert Müller, and Wojciech Samek. On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLOS ONE, 10 (7): 1–46, 07 2015. . URL https://doi.org/10.1371/journal.pone.0130140.",
+   "tier": "established"
+  },
+  {
+   "key": "bae2022influencefunctionsanswerquestion",
+   "text": "Juhan Bae, Nathan Ng, Alston Lo, Marzyeh Ghassemi, and Roger Grosse. If influence functions are the answer, then what is the question? In Proceedings of the 36th International Conference on Neural Information Processing Systems, NIPS ’22, Red Hook, NY, USA, 2022. Curran Associates Inc. ISBN 9781713871088.",
+   "tier": "established"
   },
   {
    "key": "baek_towards_2025",
@@ -2461,12 +2854,22 @@
   },
   {
    "key": "bai2022training",
-   "text": "Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, et al. Training a helpful and harmless assistant with reinforcement learning from human feedback. arXiv preprint arXiv:2204.05862, 2022a.",
+   "text": "Yuntao Bai, Andy Jones, Kamal Ndousse, Amanda Askell, Anna Chen, Nova DasSarma, Dawn Drain, Stanislav Fort, Deep Ganguli, Tom Henighan, Nicholas Joseph, Saurav Kadavath, Jackson Kernion, Tom Conerly, Sheer El-Showk, Nelson Elhage, Zac Hatfield-Dodds, Danny Hernandez, Tristan Hume, Scott Johnston, Shauna Kravec, Liane Lovitt, Neel Nanda, Catherine Olsson, Dario Amodei, Tom Brown, Jack Clark, Sam McCandlish, Chris Olah, Ben Mann, and Jared Kaplan. Training a helpful and harmless assistant with reinforcement learning from human feedback, 2022a. URL https://arxiv.org/abs/2204.05862.",
    "tier": "emerging"
   },
   {
    "key": "bai2022constitutional",
-   "text": "Yuntao Bai, Saurav Kadavath, Sandipan Kundu, Amanda Askell, Jackson Kernion, Andy Jones, Anna Chen, Anna Goldie, Azalia Mirhoseini, Cameron McKinnon, et al. Constitutional ai: Harmlessness from ai feedback. arXiv preprint arXiv:2212.08073, 2022b.",
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+   "tier": "emerging"
+  },
+  {
+   "key": "bai-etal-2024-longbench",
+   "text": "Yushi Bai, Xin Lv, Jiajie Zhang, Hongchang Lyu, Jiankai Tang, Zhidian Huang, Zhengxiao Du, Xiao Liu, Aohan Zeng, Lei Hou, Yuxiao Dong, Jie Tang, and Juanzi Li. LongBench: A bilingual, multitask benchmark for long context understanding. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 3119–3137, Bangkok, Thailand, August 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.acl-long.172/.",
+   "tier": "established"
+  },
+  {
+   "key": "baker2025monitoringreasoningmodelsmisbehavior",
+   "text": "Bowen Baker, Joost Huizinga, Leo Gao, Zehao Dou, Melody Y. Guan, Aleksander Madry, Wojciech Zaremba, Jakub Pachocki, and David Farhi. Monitoring reasoning models for misbehavior and the risks of promoting obfuscation, 2025. URL https://arxiv.org/abs/2503.11926.",
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+  {
+   "key": "barez2025chain",
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+   "text": "H. B. Barlow. Possible principles underlying the transformations of sensory messages. In Walter A. Rosenblith (ed.), Sensory Communication, pp. 217–234. MIT Press, Cambridge, MA, 1961.",
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+  },
+  {
+   "key": "batra2026multimodalmodeldiffingfeature",
+   "text": "Hunar Batra, Lachin Naghashyar, Ashkan Khakzar, Philip Torr, Christian Schroeder de Witt, Constantin Venhoff, and Ronald Clark. Multimodal model diffing for feature discovery and control, 2026. URL https://arxiv.org/abs/2608.09928.",
+   "tier": "emerging"
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+  },
+  {
+   "key": "netdissect2017",
+   "text": "David Bau, Bolei Zhou, Aditya Khosla, Aude Oliva, and Antonio Torralba. Network dissection: Quantifying interpretability of deep visual representations. In Computer Vision and Pattern Recognition, 2017.",
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   },
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-   "key": "bayazit_crosscoding_2025",
-   "text": "Deniz Bayazit, Aaron Mueller, and Antoine Bosselut. Crosscoding through time: Tracking emergence & consolidation of linguistic representations throughout llm pretraining. arXiv preprint arXiv:2509.05291, 2025.",
+   "key": "bau2020units",
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+  },
+  {
+   "key": "buildingbetterao",
+   "text": "Jan Philipp Bauer, Celeste De Schamphelaere, Adam Karvonen, Niclas Luick, and Neel Nanda. Building better activation oracles. In Mechanistic Interpretability Workshop at ICML 2026, 2026. URL https://openreview.net/forum?id=6nkeLpn3cY.",
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+  {
+   "key": "bayazit-etal-2026-crosscoding",
+   "text": "Deniz Bayazit, Aaron Mueller, and Antoine Bosselut. Crosscoding through time: Tracking emergence & consolidation of linguistic representations throughout LLM pretraining. In Maria Liakata, Viviane P. Moreira, Jiajun Zhang, and David Jurgens (eds.), Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1353–1377, San Diego, California, United States, July 2026. Association for Computational Linguistics. ISBN 979-8-89176-390-6. . URL https://aclanthology.org/2026.acl-long.60/.",
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+  },
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@@ -2499,34 +2932,74 @@
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+   "tier": "established"
+  },
   {
    "key": "belrose2025elicitinglatentpredictionstransformers",
    "text": "Nora Belrose, Igor Ostrovsky, Lev McKinney, Zach Furman, Logan Smith, Danny Halawi, Stella Biderman, and Jacob Steinhardt. Eliciting latent predictions from transformers with the tuned lens, 2025. URL https://arxiv.org/abs/2303.08112.",
    "tier": "emerging"
   },
+  {
+   "key": "bengio2013estimatingpropagatinggradientsstochastic",
+   "text": "Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation, 2013. URL https://arxiv.org/abs/1308.3432.",
+   "tier": "emerging"
+  },
+  {
+   "key": "bengio2025internationalaisafetyreport",
+   "text": "Yoshua Bengio, Sören Mindermann, Daniel Privitera, Tamay Besiroglu, Rishi Bommasani, Stephen Casper, Yejin Choi, Philip Fox, Ben Garfinkel, Danielle Goldfarb, Hoda Heidari, Anson Ho, Sayash Kapoor, Leila Khalatbari, Shayne Longpre, Sam Manning, Vasilios Mavroudis, Mantas Mazeika, Julian Michael, Jessica Newman, Kwan Yee Ng, Chinasa T. Okolo, Deborah Raji, Girish Sastry, Elizabeth Seger, Theodora Skeadas, Tobin South, Emma Strubell, Florian Tramèr, Lucia Velasco, Nicole Wheeler, Daron Acemoglu, Olubayo Adekanmbi, David Dalrymple, Thomas G. Dietterich, Edward W. Felten, Pascale Fung, Pierre-Olivier Gourinchas, Fredrik Heintz, Geoffrey Hinton, Nick Jennings, Andreas Krause, Susan Leavy, Percy Liang, Teresa Ludermir, Vidushi Marda, Helen Margetts, John McDermid, Jane Munga, Arvind Narayanan, Alondra Nelson, Clara Neppel, Alice Oh, Gopal Ramchurn, Stuart Russell, Marietje Schaake, Bernhard Schölkopf, Dawn Song, Alvaro Soto, Lee Tiedrich, Gaël Varoquaux, Andrew Yao, Ya-Qin Zhang, Fahad Albalawi, Marwan Alserkal, Olubunmi Ajala, Guillaume Avrin, Christian Busch, André Carlos Ponce de Leon Ferreira de Carvalho, Bronwyn Fox, Amandeep Singh Gill, Ahmet Halit Hatip, Juha Heikkilä, Gill Jolly, Ziv Katzir, Hiroaki Kitano, Antonio Krüger, Chris Johnson, Saif M. Khan, Kyoung Mu Lee, Dominic Vincent Ligot, Oleksii Molchanovskyi, Andrea Monti, Nusu Mwamanzi, Mona Nemer, Nuria Oliver, José Ramón López Portillo, Balaraman Ravindran, Raquel Pezoa Rivera, Hammam Riza, Crystal Rugege, Ciarán Seoighe, Jerry Sheehan, Haroon Sheikh, Denise Wong, and Yi Zeng. International ai safety report, 2025. URL https://arxiv.org/abs/2501.17805.",
+   "tier": "emerging"
+  },
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    "text": "Leonard Bereska and Stratis Gavves. Mechanistic interpretability for AI safety - a review. Transactions on Machine Learning Research, 2024. ISSN 2835-8856. URL https://openreview.net/forum?id=ePUVetPKu6.",
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+  {
+   "key": "bhaskar2024finding",
+   "text": "Adithya Bhaskar, Alexander Wettig, Dan Friedman, and Danqi Chen. Finding transformer circuits with edge pruning. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://openreview.net/forum?id=8oSY3rA9jY.",
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+  },
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    "text": "Stella Biderman, Usvsn Prashanth, Lintang Sutawika, Hailey Schoelkopf, Quentin Anthony, Shivanshu Purohit, and Edward Raff. Emergent and predictable memorization in large language models. Advances in Neural Information Processing Systems, 36: 28072–28090, 2023a.",
-   "tier": null
+   "tier": "established"
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   {
    "key": "biderman2023pythia",
    "text": "Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al. Pythia: A suite for analyzing large language models across training and scaling. In International Conference on Machine Learning, pp. 2397–2430. PMLR, 2023b.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "biderman2026position",
+   "text": "Stella Biderman, Mohammad Aflah Khan, Niloofar Mireshghallah, Catherine Arnett, Fazl Barez, and Naomi Saphra. Position: Don’t just ”fix it in post”: A science of AI must study learning dynamics. In Forty-third International Conference on Machine Learning Position Paper Track, 2026. URL https://openreview.net/forum?id=VbHh05xQ7y.",
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    "text": "Steven Bills, Nick Cammarata, Dan Mossing, Henk Tillman, Leo Gao, Gabriel Goh, Ilya Sutskever, Jan Leike, Jeff Wu, and William Saunders. Language models can explain neurons in language models, 2023.",
    "tier": "emerging"
   },
+  {
+   "key": "binder",
+   "text": "Felix Jedidja Binder, James Chua, Tomek Korbak, Henry Sleight, John Hughes, Robert Long, Ethan Perez, Miles Turpin, and Owain Evans. Looking inward: Language models can learn about themselves by introspection. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=eb5pkwIB5i.",
+   "tier": "established"
+  },
+  {
+   "key": "blank2026rlens",
+   "text": "Camila Blank, Agam Bhatia, and Neel Nanda. R-lens: Making J-lens more faithful on early layers. AI Alignment Forum, August 2026. URL https://www.alignmentforum.org/posts/nv8oedrnLXKRzNEL9/r-lens-making-j-lens-more-faithful-on-early-layers. Blog post.",
+   "tier": "emerging"
+  },
+  {
+   "key": "borkar2025privacy",
+   "text": "Jaydeep Borkar, Matthew Jagielski, Katherine Lee, Niloofar Mireshghallah, David A. Smith, and Christopher A. Choquette-Choo. Privacy ripple effects from adding or removing personal information in language model training. In Findings of the Association for Computational Linguistics: ACL 2025, 2025.",
+   "tier": "established"
+  },
   {
    "key": "braun2025interpretability",
-   "text": "Dan Braun, Lucius Bushnaq, Stefan Heimersheim, Jake Mendel, and Lee Sharkey. Interpretability in parameter space: Minimizing mechanistic description length with attribution-based parameter decomposition. arXiv preprint arXiv:2501.14926, 2025.",
+   "text": "Dan Braun, Lucius Bushnaq, Stefan Heimersheim, Jake Mendel, and Lee Sharkey. Interpretability in parameter space: Minimizing mechanistic description length with attribution-based parameter decomposition, 2025. URL https://arxiv.org/abs/2501.14926.",
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@@ -2539,25 +3012,30 @@
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    "tier": "emerging"
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+  {
+   "key": "bruijns2026pretrainingcurriculaenableselective",
+   "text": "Sebastian A. Bruijns, Jirko Rubruck, Mia H. Whitefield, Kai J. Sandbrink, Fazl Barez, and Christopher Summerfield. Pretraining curricula enable selective fine-tuning, 2026. URL https://arxiv.org/abs/2607.04846.",
+   "tier": "emerging"
+  },
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    "text": "Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=ETKGuby0hcs.",
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   },
-  {
-   "key": "burns2024discoveringlatentknowledgelanguage",
-   "text": "Collin Burns, Haotian Ye, Dan Klein, and Jacob Steinhardt. Discovering latent knowledge in language models without supervision, 2024. URL https://arxiv.org/abs/2212.03827.",
-   "tier": "emerging"
-  },
   {
    "key": "bushnaq2025stochastic",
-   "text": "Lucius Bushnaq, Dan Braun, and Lee Sharkey. Stochastic parameter decomposition. arXiv preprint arXiv:2506.20790, 2025.",
+   "text": "Lucius Bushnaq, Dan Braun, and Lee Sharkey. Stochastic parameter decomposition, 2025. URL https://arxiv.org/abs/2506.20790.",
    "tier": "emerging"
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-   "tier": null
+   "tier": "emerging"
+  },
+  {
+   "key": "cao-etal-2021-low",
+   "text": "Steven Cao, Victor Sanh, and Alexander Rush. Low-complexity probing via finding subnetworks. In Kristina Toutanova, Anna Rumshisky, Luke Zettlemoyer, Dilek Hakkani-Tur, Iz Beltagy, Steven Bethard, Ryan Cotterell, Tanmoy Chakraborty, and Yichao Zhou (eds.), Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 960–966, Online, June 2021. Association for Computational Linguistics. . URL https://aclanthology.org/2021.naacl-main.74/.",
+   "tier": "established"
   },
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@@ -2566,58 +3044,98 @@
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    "key": "chan2022causal",
-   "text": "Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldwosky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas. Causal scrubbing, a method for rigorously testing interpretability hypotheses. AI Alignment Forum, 2022. https://www.alignmentforum.org/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing.",
+   "text": "Lawrence Chan, Adrià Garriga-Alonso, Nicholas Goldwosky-Dill, Ryan Greenblatt, Jenny Nitishinskaya, Ansh Radhakrishnan, Buck Shlegeris, and Nate Thomas. Causal scrubbing, a method for rigorously testing interpretability hypotheses. AI Alignment Forum, 2022a. https://www.alignmentforum.org/posts/JvZhhzycHu2Yd57RN/causal-scrubbing-a-method-for-rigorously-testing.",
    "tier": "emerging"
   },
+  {
+   "key": "chan2022data",
+   "text": "Stephanie C. Y. Chan, Adam Santoro, Andrew K. Lampinen, Jane X. Wang, Aaditya Singh, Pierre H. Richemond, Jay McClelland, and Felix Hill. Data distributional properties drive emergent in-context learning in transformers. In Advances in Neural Information Processing Systems, volume 35, 2022b.",
+   "tier": "established"
+  },
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    "key": "chang2022word",
    "text": "Tyler A Chang and Benjamin K Bergen. Word acquisition in neural language models. Transactions of the Association for Computational Linguistics, 10: 1–16, 2022.",
-   "tier": null
+   "tier": "established"
   },
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-   "key": "chanin2024absorption",
-   "text": "David Chanin, James Wilken-Smith, Tomáš Dulka, Hardik Bhatnagar, Satvik Golechha, and Joseph Bloom. A is for absorption: Studying feature splitting and absorption in sparse autoencoders, 2025. URL https://arxiv.org/abs/2409.14507.",
-   "tier": null
+   "key": "chang2024characterizing",
+   "text": "Tyler A Chang, Zhuowen Tu, and Benjamin K Bergen. Characterizing learning curves during language model pre-training: Learning, forgetting, and stability. Transactions of the Association for Computational Linguistics, 12: 1346–1362, 2024.",
+   "tier": "established"
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-   "key": "chaudhary2025safetynetdetectingharmfuloutputs",
-   "text": "Maheep Chaudhary and Fazl Barez. Safetynet: Detecting harmful outputs in llms by modeling and monitoring deceptive behaviors, 2025. URL https://arxiv.org/abs/2505.14300.",
-   "tier": "emerging"
+   "key": "chanin2025absorption",
+   "text": "David Chanin, James Wilken-Smith, Tomáš Dulka, Hardik Bhatnagar, Satvik Golechha, and Joseph Isaac Bloom. A is for absorption: Studying feature splitting and absorption in sparse autoencoders. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=R73ybUciQF.",
+   "tier": "established"
+  },
+  {
+   "key": "chaudhary2026beyond",
+   "text": "Maheep Chaudhary and Fazl Barez. Beyond black-box obfuscation: Mechanistic analysis and defense of white-box monitors. In Third Conference on Language Modeling, 2026. URL https://openreview.net/forum?id=4JrlXy2N6j.",
+   "tier": "established"
   },
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    "key": "chaudhary2024evaluating",
-   "text": "Maheep Chaudhary and Atticus Geiger. Evaluating open-source sparse autoencoders on disentangling factual knowledge in gpt-2 small. arXiv preprint arXiv:2409.04478, 2024.",
+   "text": "Maheep Chaudhary and Atticus Geiger. Evaluating open-source sparse autoencoders on disentangling factual knowledge in gpt-2 small, 2024. URL https://arxiv.org/abs/2409.04478.",
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    "text": "Angelica Chen, Ravid Shwartz-Ziv, Kyunghyun Cho, Matthew L. Leavitt, and Naomi Saphra. Sudden drops in the loss: Syntax acquisition, phase transitions, and simplicity bias in MLMs. In The Twelfth International Conference on Learning Representations, 2024a. URL https://openreview.net/forum?id=MO5PiKHELW.",
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    "text": "Haozhe Chen, Carl Vondrick, and Chengzhi Mao. Selfie: self-interpretation of large language model embeddings. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024b.",
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+  },
+  {
+   "key": "chen2021evaluatinglargelanguagemodels",
+   "text": "Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, Alex Ray, Raul Puri, Gretchen Krueger, Michael Petrov, Heidy Khlaaf, Girish Sastry, Pamela Mishkin, Brooke Chan, Scott Gray, Nick Ryder, Mikhail Pavlov, Alethea Power, Lukasz Kaiser, Mohammad Bavarian, Clemens Winter, Philippe Tillet, Felipe Petroski Such, Dave Cummings, Matthias Plappert, Fotios Chantzis, Elizabeth Barnes, Ariel Herbert-Voss, William Hebgen Guss, Alex Nichol, Alex Paino, Nikolas Tezak, Jie Tang, Igor Babuschkin, Suchir Balaji, Shantanu Jain, William Saunders, Christopher Hesse, Andrew N. Carr, Jan Leike, Josh Achiam, Vedant Misra, Evan Morikawa, Alec Radford, Matthew Knight, Miles Brundage, Mira Murati, Katie Mayer, Peter Welinder, Bob McGrew, Dario Amodei, Sam McCandlish, Ilya Sutskever, and Wojciech Zaremba. Evaluating large language models trained on code, 2021. URL https://arxiv.org/abs/2107.03374.",
+   "tier": "emerging"
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+  },
+  {
+   "key": "chen2025persona",
+   "text": "Runjin Chen, Andy Arditi, Henry Sleight, Owain Evans, and Jack Lindsey. Persona vectors: Monitoring and controlling character traits in language models, 2025b. URL https://arxiv.org/abs/2507.21509.",
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+   "key": "basispursuit",
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+  },
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+   "key": "chen2025reasoningmodelsdontsay",
+   "text": "Yanda Chen, Joe Benton, Ansh Radhakrishnan, Jonathan Uesato, Carson Denison, John Schulman, Arushi Somani, Peter Hase, Misha Wagner, Fabien Roger, Vlad Mikulik, Samuel R. Bowman, Jan Leike, Jared Kaplan, and Ethan Perez. Reasoning models don’t always say what they think, 2025c. URL https://arxiv.org/abs/2505.05410.",
+   "tier": "emerging"
+  },
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    "text": "Kevin Clark, Urvashi Khandelwal, Omer Levy, and Christopher D. Manning. What does BERT look at? an analysis of BERT’s attention. In Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov, and Dieuwke Hupkes (eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 276–286, Florence, Italy, August 2019. Association for Computational Linguistics. . URL https://aclanthology.org/W19-4828/.",
-   "tier": "established"
+   "tier": "emerging"
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+   "text": "Alex Cloud, Jacob Goldman-Wetzler, Evžen Wybitul, Joseph Miller, and Alexander Matt Turner. Gradient routing: Masking gradients to localize computation in neural networks, 2024. URL https://arxiv.org/abs/2410.04332.",
+   "tier": "emerging"
+  },
+  {
+   "key": "cloud2026language",
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+   "tier": "established"
+  },
+  {
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+   "text": "Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, Christopher Hesse, and John Schulman. Training verifiers to solve math word problems, 2021. URL https://arxiv.org/abs/2110.14168.",
+   "tier": "emerging"
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@@ -2629,24 +3147,49 @@
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+   "tier": "emerging"
+  },
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+   "key": "netmodular",
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   },
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+  {
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+   "tier": "established"
+  },
+  {
+   "key": "dai-etal-2024-deepseekmoe",
+   "text": "Damai Dai, Chengqi Deng, Chenggang Zhao, R.X. Xu, Huazuo Gao, Deli Chen, Jiashi Li, Wangding Zeng, Xingkai Yu, Y. Wu, Zhenda Xie, Y.K. Li, Panpan Huang, Fuli Luo, Chong Ruan, Zhifang Sui, and Wenfeng Liang. DeepSeekMoE: Towards ultimate expert specialization in mixture-of-experts language models. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 1280–1297, Bangkok, Thailand, August 2024a. Association for Computational Linguistics. . URL https://aclanthology.org/2024.acl-long.70/.",
+   "tier": "established"
+  },
+  {
+   "key": "dai2024safe",
+   "text": "Josef Dai, Xuehai Pan, Ruiyang Sun, Jiaming Ji, Xinbo Xu, Mickel Liu, Yizhou Wang, and Yaodong Yang. Safe RLHF: Safe reinforcement learning from human feedback. In The Twelfth International Conference on Learning Representations, 2024b. URL https://openreview.net/forum?id=TyFrPOKYXw.",
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+  },
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    "key": "Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019",
-   "text": "Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, and James Glass. What is one grain of sand in the desert? analyzing individual neurons in deep nlp models. Proceedings of the AAAI Conference on Artificial Intelligence, 33 (01): 6309–6317, Jul. 2019. . URL https://ojs.aaai.org/index.php/AAAI/article/view/4592.",
+   "text": "Fahim Dalvi, Nadir Durrani, Hassan Sajjad, Yonatan Belinkov, Anthony Bau, and James Glass. What is one grain of sand in the desert? analyzing individual neurons in deep nlp models. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, AAAI’19/IAAI’19/EAAI’19. AAAI Press, 2019. ISBN 978-1-57735-809-1. . URL https://doi.org/10.1609/aaai.v33i01.33016309.",
    "tier": "established"
   },
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@@ -2655,20 +3198,40 @@
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   },
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+   "key": "dao202mamba2",
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    "tier": "emerging"
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+  {
+   "key": "deepseekv2",
+   "text": "DeepSeek-AI, Aixin Liu, Bei Feng, Bin Wang, Bingxuan Wang, Bo Liu, Chenggang Zhao, Chengqi Dengr, Chong Ruan, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Dongjie Ji, Erhang Li, Fangyun Lin, Fuli Luo, Guangbo Hao, Guanting Chen, Guowei Li, H. Zhang, Hanwei Xu, Hao Yang, Haowei Zhang, Honghui Ding, Huajian Xin, Huazuo Gao, Hui Li, Hui Qu, J. L. Cai, Jian Liang, Jianzhong Guo, Jiaqi Ni, Jiashi Li, Jin Chen, Jingyang Yuan, Junjie Qiu, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Lean Wang, Lecong Zhang, Lei Xu, Leyi Xia, Liang Zhao, Liyue Zhang, Meng Li, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Ning Tian, Panpan Huang, Peiyi Wang, Peng Zhang, Qihao Zhu, Qinyu Chen, Qiushi Du, R. J. Chen, R. L. Jin, Ruiqi Ge, Ruizhe Pan, Runxin Xu, Ruyi Chen, S. S. Li, Shanghao Lu, Shangyan Zhou, Shanhuang Chen, Shaoqing Wu, Shengfeng Ye, Shirong Ma, Shiyu Wang, Shuang Zhou, Shuiping Yu, Shunfeng Zhou, Size Zheng, T. Wang, Tian Pei, Tian Yuan, Tianyu Sun, W. L. Xiao, Wangding Zeng, Wei An, Wen Liu, Wenfeng Liang, Wenjun Gao, Wentao Zhang, X. Q. Li, Xiangyue Jin, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaojin Shen, Xiaokang Chen, Xiaosha Chen, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Liu, Xin Xie, Xingkai Yu, Xinnan Song, Xinyi Zhou, Xinyu Yang, Xuan Lu, Xuecheng Su, Y. Wu, Y. K. Li, Y. X. Wei, Y. X. Zhu, Yanhong Xu, Yanping Huang, Yao Li, Yao Zhao, Yaofeng Sun, Yaohui Li, Yaohui Wang, Yi Zheng, Yichao Zhang, Yiliang Xiong, Yilong Zhao, Ying He, Ying Tang, Yishi Piao, Yixin Dong, Yixuan Tan, Yiyuan Liu, Yongji Wang, Yongqiang Guo, Yuchen Zhu, Yuduan Wang, Yuheng Zou, Yukun Zha, Yunxian Ma, Yuting Yan, Yuxiang You, Yuxuan Liu, Z. Z. Ren, Zehui Ren, Zhangli Sha, Zhe Fu, Zhen Huang, Zhen Zhang, Zhenda Xie, Zhewen Hao, Zhihong Shao, Zhiniu Wen, Zhipeng Xu, Zhongyu Zhang, Zhuoshu Li, Zihan Wang, Zihui Gu, Zilin Li, and Ziwei Xie. Deepseek-v2: A strong, economical, and efficient mixture-of-experts language model, 2024. URL https://arxiv.org/abs/2405.04434.",
+   "tier": "emerging"
+  },
+  {
+   "key": "deepseekai2026deepseekv4highlyefficientmilliontoken",
+   "text": "DeepSeek-AI, Anyi Xu, Bangcai Lin, Bing Xue, Bingxuan Wang, Bingzheng Xu, Bochao Wu, Bowei Zhang, Chaofan Lin, Chen Dong, Chenchen Ling, Chengda Lu, Chenggang Zhao, Chengqi Deng, Chengyu Hou, Chenhao Xu, Chenze Shao, Chong Ruan, Conner Sun, Damai Dai, Daya Guo, Dejian Yang, Deli Chen, Donghao Li, Dongjie Ji, Erhang Li, Fang Wei, Fangyun Lin, Fangzhou Yuan, Feiyu Xia, Fucong Dai, Guangbo Hao, Guanting Chen, Guoai Cao, Guolai Meng, Guowei Li, Han Yu, Han Zhang, Hanwei Xu, Hao Li, Haofen Liang, Haoling Zhang, Haoming Luo, Haoran Wei, Haotian Yuan, Haowei Zhang, Haowen Luo, Haoyu Chen, Haozhe Ji, Hengqing Zhang, Honghui Ding, Hongxuan Tang, Huanqi Cao, Huazuo Gao, Hui Qu, Hui Zeng, J Yang, JQ Zhu, Jia Luo, Jia Song, Jia Yu, Jialiang Huang, Jialu Cai, Jian Liang, Jiangting Zhou, Jiasheng Ye, Jiashi Li, Jiaxin Xu, Jiewen Hu, Jieyu Yang, Jin Chen, Jin Yan, Jingchang Chen, Jingli Zhou, Jingting Xiang, Jingyang Yuan, Jingyuan Cheng, Jingzi Zhou, Jinhua Zhu, Jiping Yu, Joseph Sun, Jun Ran, Junguang Jiang, Junjie Qiu, Junlong Li, Junmin Zheng, Junxiao Song, Kai Dong, Kaige Gao, Kang Guan, Kexing Zhou, Kezhao Huang, Kuai Yu, Lean Wang, Lecong Zhang, Lei Wang, Leyi Xia, Li Zhang, Liang Zhao, Lihua Guo, Lingxiao Luo, Linwang Ma, Linyan Zhu, Litong Wang, Liyu Cai, Liyue Zhang, Longhao Chen, MS Di, MY Xu, Max Mei, Miaojun Wang, Mingchuan Zhang, Minghua Zhang, Minghui Tang, Mingming Li, Mingxu Zhou, Minmin Han, Ning Wang, Panpan Huang, Panpan Wang, Peixin Cong, Peiyi Wang, Peng Zhang, Qiancheng Wang, Qihao Zhu, Qingyang Li, Qinyu Chen, Qiushi Du, Qiwei Jiang, Rui Tian, Ruifan Xu, Ruijie Lu, Ruiling Xu, Ruiqi Ge, Ruisong Zhang, Ruizhe Pan, Runji Wang, Runqian Chen, Runqiu Yin, Runxin Xu, Ruomeng Shen, Ruoyu Zhang, Ruyi Chen, SH Liu, Shanghao Lu, Shangmian Sun, Shangyan Zhou, Shanhuang Chen, Shaofei Cai, Shaoheng Nie, Shaoqing Wu, Shaoyuan Chen, Shengding Hu, Shengyu Liu, Shiqiang Hu, Shirong Ma, Shiyu Wang, Shuiping Yu, Shunfeng Zhou, Shuting Pan, Shuying Yu, Songyang Zhou, Tao Ni, Tao Yun, Tian Jin, Tian Pei, Tian Ye, Tianle Lin, Tianran Ji, Tianyi Cui, Tianyuan Yue, Tingting Yu, Tun Wang, W Zhang, WL Xiao, Wangding Zeng, Wei An, Weilin Zhao, Wen Liu, Wenfeng Liang, Wenjie Pang, Wenjing Luo, Wenjing Yao, Wenjun Gao, Wenkai Yang, Wenlve Huang, Wenqing Hou, Wentao Zhang, Wenting Ma, Xi Gao, Xiang He, Xiangwen Wang, Xianzu Wang, Xiao Bi, Xiaodong Liu, Xiaohan Wang, Xiaokang Chen, Xiaokang Zhang, Xiaotao Nie, Xiaowen Sun, Xiaoxiang Wang, Xin Cheng, Xin Liu, Xin Xie, Xingchao Liu, Xingchen Liu, Xingkai Yu, Xingyou Li, Xinyu Yang, Xinyu Zhang, Xu Chen, Xuanyu Wang, Xuecheng Su, Xueyin Chen, Xuheng Lin, Xuwei Fu, YC Yan, YQ Wang, YW Ma, Yanfeng Luo, Yang Zhang, Yanhong Xu, Yanru Ma, Yanwen Huang, Yao Li, Yao Li, Yao Xu, Yao Zhao, Yaofeng Sun, Yaohui Wang, Yi Qian, Yi Shao, Yi Yu, Yichao Zhang, Yifan Ding, Yifan Shi, Yijia Wu, Yiliang Xiong, Yiling Ma, Ying He, Ying Tang, Ying Zhou, Yingjia Luo, Yinmin Zhong, Yishi Piao, Yisong Wang, Yixiang Zhang, Yixiao Chen, Yixuan Tan, Yixuan Wei, Yiyang Ma, Yiyuan Liu, Yonglun Yang, Yongqiang Guo, Yongtong Wu, Yu Wu, YuKun Li, Yuan Cheng, Yuan Ou, Yuanfan Xu, Yuanhao Li, Yuduan Wang, Yuehan Yang, Yuer Xu, Yuhan Wu, Yuhao Meng, Yuheng Zou, Yukun Zha, Yunfan Xiong, Yupeng Chen, Yuping Lin, Yuqian Cao, Yuqian Wang, Yushun Zhang, Yuting Yan, Yutong Lin, Yuxian Gu, Yuxiang Luo, Yuxiang You, Yuxuan Liu, Yuxuan Zhou, Yuyang Zhou, Yuzhen Huang, ZF Wu, Zehao Wang, Zehua Zhao, Zehui Ren, Zekai Zhang, Zhangli Sha, Zhe Fu, Zhe Ju, Zhean Xu, Zhenda Xie, Zhengyan Zhang, Zheren Gao, Zhewen Hao, Zhibin Gou, Zhicheng Ma, Zhigang Yan, Zhihong Shao, Zhixian Huang, Zhixuan Chen, Zhiyu Wu, Zhizhou Ren, Zhongyu Wu, Zhuoshu Li, Zhuping Zhang, Zian Xu, Zihao Wang, Zihua Qu, Zihui Gu, Zijia Zhu, Zilin Li, Zipeng Zhang, Ziwei Xie, Ziyi Gao, Ziyi Wan, Zizheng Pan, and Zongqing Yao. Deepseek-v4: Towards highly efficient million-token context intelligence, 2026. URL https://arxiv.org/abs/2606.19348.",
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+   "tier": "established"
+  },
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+   "key": "du2024understanding",
+   "text": "Zhengxiao Du, Aohan Zeng, Yuxiao Dong, and Jie Tang. Understanding emergent abilities of language models from the loss perspective. In Advances in Neural Information Processing Systems, volume 37, 2024. URL https://openreview.net/forum?id=35DAviqMFo.",
+   "tier": "established"
+  },
+  {
+   "key": "minder_overcoming_2025",
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+   "text": "Esin DURMUS, Karina Nguyen, Thomas Liao, Nicholas Schiefer, Amanda Askell, Anton Bakhtin, Carol Chen, Zac Hatfield-Dodds, Danny Hernandez, Nicholas Joseph, Liane Lovitt, Sam McCandlish, Orowa Sikder, Alex Tamkin, Janel Thamkul, Jared Kaplan, Jack Clark, and Deep Ganguli. Towards measuring the representation of subjective global opinions in language models. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=zl16jLb91v.",
+   "tier": "established"
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+  {
+   "key": "fang2026nextguardtrainingfreestreamingsafeguard",
+   "text": "Junfeng Fang, Nachuan Chen, Houcheng Jiang, Dan Zhang, Fei Shen, Xiang Wang, Xiangnan He, and Tat-Seng Chua. Next-guard: Training-free streaming safeguard without token-level labels, 2026. URL https://arxiv.org/abs/2603.02219.",
+   "tier": "emerging"
+  },
+  {
+   "key": "fanous2025syceval",
+   "text": "Aaron Fanous, Jacob Goldberg, Ank Agarwal, Joanna Lin, Anson Zhou, Sonnet Xu, Vasiliki Bikia, Roxana Daneshjou, and Sanmi Koyejo. Syceval: Evaluating llm sycophancy. In Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, pp. 893–900, 2025.",
+   "tier": "established"
+  },
   {
    "key": "ferrando2024primer",
-   "text": "Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R Costa-Jussà. A primer on the inner workings of transformer-based language models. arXiv preprint arXiv:2405.00208, 2024.",
+   "text": "Javier Ferrando, Gabriele Sarti, Arianna Bisazza, and Marta R. Costa-jussà. A primer on the inner workings of transformer-based language models, 2024. URL https://arxiv.org/abs/2405.00208.",
    "tier": "emerging"
   },
+  {
+   "key": "feucht2026arithmeticwildllamauses",
+   "text": "Sheridan Feucht, Tal Haklay, Usha Bhalla, Daniel Wurgaft, Can Rager, Raphaël Sarfati, Jack Merullo, Thomas McGrath, Owen Lewis, Ekdeep Singh Lubana, Thomas Fel, and Atticus Geiger. Arithmetic in the wild: Llama uses base-10 addition to reason about cyclic concepts, 2026. URL https://arxiv.org/abs/2605.01148.",
+   "tier": "emerging"
+  },
+  {
+   "key": "sparsecoding",
+   "text": "David J. Field. What is the goal of sensory coding? Neural Computation, 6 (4): 559–601, 1994. .",
+   "tier": "established"
+  },
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    "text": "Alex Foote, Neel Nanda, Esben Kran, Ioannis Konstas, and Fazl Barez. N2g: A SCALABLE APPROACH FOR QUANTIFYING INTERPRETABLE NEURON REPRESENTATION IN LLMS. In ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models, 2023.",
    "tier": "emerging"
   },
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-   "key": "petri2025",
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+   "key": "fornasiere2026languagemodelsrecognizedropout",
+   "text": "Damiano Fornasiere, Mirko Bronzi, Spencer Kitts, Alessandro Palmas, Yoshua Bengio, and Oliver Richardson. Language models recognize dropout and gaussian noise applied to their activations, 2026. URL https://arxiv.org/abs/2604.17465.",
    "tier": "emerging"
   },
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-   "key": "petri2026v2",
-   "text": "Kai Fronsdal, Jonathan Michala, and Sam Bowman. Petri 2.0: New scenarios, new model comparisons, and improved eval-awareness mitigations, 2026. URL https://alignment.anthropic.com/2026/petri-v2/.",
+   "key": "frankle2020linear",
+   "text": "Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, and Michael Carbin. Linear mode connectivity and the lottery ticket hypothesis. In International Conference on Machine Learning, pp. 3259–3269, 2020.",
+   "tier": "established"
+  },
+  {
+   "key": "nla",
+   "text": "Kit Fraser-Taliente, Subhash Kantamneni, Euan Ong, Dan Mossing, Christina Lu, Paul C. Bogdan, Emmanuel Ameisen, James Chen, Dzmitry Kishylau, Adam Pearce, Julius Tarng, Alex Wu, Jeff Wu, Yang Zhang, Daniel M. Ziegler, Evan Hubinger, Joshua Batson, Jack Lindsey, Samuel Zimmerman, and Samuel Marks. Natural language autoencoders produce unsupervised explanations of llm activations. Transformer Circuits Thread, 2026. URL https://transformer-circuits.pub/2026/nla/index.html.",
    "tier": "emerging"
   },
+  {
+   "key": "pmlr-v267-fu25e",
+   "text": "Tingchen Fu, Mrinank Sharma, Philip Torr, Shay B Cohen, David Krueger, and Fazl Barez. PoisonBench: Assessing language model vulnerability to poisoned preference data. In Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, and Jerry Zhu (eds.), Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 17806–17831. PMLR, 13–19 Jul 2025. URL https://proceedings.mlr.press/v267/fu25e.html.",
+   "tier": "established"
+  },
   {
    "key": "ganguli2022redteaminglanguagemodels",
    "text": "Deep Ganguli, Liane Lovitt, Jackson Kernion, Amanda Askell, Yuntao Bai, Saurav Kadavath, Ben Mann, Ethan Perez, Nicholas Schiefer, Kamal Ndousse, Andy Jones, Sam Bowman, Anna Chen, Tom Conerly, Nova DasSarma, Dawn Drain, Nelson Elhage, Sheer El-Showk, Stanislav Fort, Zac Hatfield-Dodds, Tom Henighan, Danny Hernandez, Tristan Hume, Josh Jacobson, Scott Johnston, Shauna Kravec, Catherine Olsson, Sam Ringer, Eli Tran-Johnson, Dario Amodei, Tom Brown, Nicholas Joseph, Sam McCandlish, Chris Olah, Jared Kaplan, and Jack Clark. Red teaming language models to reduce harms: Methods, scaling behaviors, and lessons learned, 2022. URL https://arxiv.org/abs/2209.07858.",
@@ -2771,18 +3414,18 @@
   },
   {
    "key": "gao2020pile",
-   "text": "Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, et al. The pile: An 800gb dataset of diverse text for language modeling. arXiv preprint arXiv:2101.00027, 2020.",
-   "tier": null
+   "text": "Leo Gao, Stella Biderman, Sid Black, Laurence Golding, Travis Hoppe, Charles Foster, Jason Phang, Horace He, Anish Thite, Noa Nabeshima, Shawn Presser, and Connor Leahy. The pile: An 800gb dataset of diverse text for language modeling, 2020. URL https://arxiv.org/abs/2101.00027.",
+   "tier": "emerging"
   },
   {
    "key": "gao2024scalingevaluatingsparseautoencoders",
-   "text": "Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In The Thirteenth International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=tcsZt9ZNKD.",
+   "text": "Leo Gao, Tom Dupre la Tour, Henk Tillman, Gabriel Goh, Rajan Troll, Alec Radford, Ilya Sutskever, Jan Leike, and Jeffrey Wu. Scaling and evaluating sparse autoencoders. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=tcsZt9ZNKD.",
    "tier": "established"
   },
   {
    "key": "gao2025weight",
-   "text": "Leo Gao, Achyuta Rajaram, Jacob Coxon, Soham V. Govande, Bowen Baker, and Dan Mossing. Weight-sparse transformers have interpretable circuits, 2025b. URL https://arxiv.org/abs/2511.13653.",
-   "tier": "emerging"
+   "text": "Leo Gao, Achyuta Rajaram, Jacob Coxon, Soham V. Govande, Bowen Baker, and Daniel P Mossing. Weight-sparse transformers have interpretable circuits. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=Vw10TpuFCp.",
+   "tier": "established"
   },
   {
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@@ -2790,65 +3433,115 @@
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-   "key": "garfinkle2016robust",
-   "text": "Charles J Garfinkle and Christopher J Hillar. Robust identifiability in sparse dictionary learning. arXiv preprint arXiv:1606.06997, 2016.",
-   "tier": null
-  },
-  {
-   "key": "ge_evolution_2025",
-   "text": "Xuyang Ge, Wentao Shu, Jiaxing Wu, Yunhua Zhou, Zhengfu He, and Xipeng Qiu. Evolution of concepts in language model pre-training. arXiv preprint arXiv:2509.17196, 2025.",
-   "tier": "emerging"
+   "key": "pruthi2020estimatingtrainingdatainfluence",
+   "text": "Garima, Frederick Liu, Satyen Kale, and Mukund Sundararajan. Estimating training data influence by tracing gradient descent. In Proceedings of the 34th International Conference on Neural Information Processing Systems, NIPS ’20, Red Hook, NY, USA, 2020. Curran Associates Inc. ISBN 9781713829546.",
+   "tier": "established"
   },
   {
    "key": "ge2026evolution",
    "text": "Xuyang Ge, Wentao Shu, Jiaxing Wu, Yunhua Zhou, Zhengfu He, and Xipeng Qiu. Evolution of concepts in language model pre-training. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=qKmXz1UR0Z.",
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    "text": "Atticus Geiger, Hanson Lu, Thomas Icard, and Christopher Potts. Causal abstractions of neural networks. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan (eds.), Advances in Neural Information Processing Systems, volume 34, pp. 9574–9586. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper_files/paper/2021/file/4f5c422f4d49a5a807eda27434231040-Paper.pdf.",
    "tier": "established"
   },
+  {
+   "key": "pmlr-v162-geiger22a",
+   "text": "Atticus Geiger, Zhengxuan Wu, Hanson Lu, Josh Rozner, Elisa Kreiss, Thomas Icard, Noah Goodman, and Christopher Potts. Inducing causal structure for interpretable neural networks. In Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (eds.), Proceedings of the 39th International Conference on Machine Learning, volume 162 of Proceedings of Machine Learning Research, pp. 7324–7338. PMLR, 17–23 Jul 2022. URL https://proceedings.mlr.press/v162/geiger22a.html.",
+   "tier": "established"
+  },
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    "key": "das",
    "text": "Atticus Geiger, Zhengxuan Wu, Christopher Potts, Thomas Icard, and Noah Goodman. Finding alignments between interpretable causal variables and distributed neural representations. In Francesco Locatello and Vanessa Didelez (eds.), Proceedings of the Third Conference on Causal Learning and Reasoning, volume 236 of Proceedings of Machine Learning Research, pp. 160–187. PMLR, 01–03 Apr 2024. URL https://proceedings.mlr.press/v236/geiger24a.html.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "JMLR:v26:23-0058",
+   "text": "Atticus Geiger, Duligur Ibeling, Amir Zur, Maheep Chaudhary, Sonakshi Chauhan, Jing Huang, Aryaman Arora, Zhengxuan Wu, Noah Goodman, Christopher Potts, and Thomas Icard. Causal abstraction: A theoretical foundation for mechanistic interpretability. Journal of Machine Learning Research, 26 (83): 1–64, 2025. URL http://jmlr.org/papers/v26/23-0058.html.",
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+  },
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+   "tier": "established"
+  },
+  {
+   "key": "rnnfsa1992",
+   "text": "C. L. Giles, C. B. Miller, D. Chen, H. H. Chen, G. Z. Sun, and Y. C. Lee. Learning and extracting finite state automata with second-order recurrent neural networks. Neural Comput., 4 (3): 393–405, May 1992. ISSN 0899-7667. . URL https://doi.org/10.1162/neco.1992.4.3.393.",
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    "key": "giulianelli-etal-2018-hood",
    "text": "Mario Giulianelli, Jack Harding, Florian Mohnert, Dieuwke Hupkes, and Willem Zuidema. Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information. In Tal Linzen, Grzegorz Chrupała, and Afra Alishahi (eds.), Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 240–248, Brussels, Belgium, November 2018. Association for Computational Linguistics. . URL https://aclanthology.org/W18-5426/.",
-   "tier": null
+   "tier": "emerging"
+  },
+  {
+   "key": "goel",
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    "tier": "emerging"
   },
+  {
+   "key": "llama3",
+   "text": "Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, Amy Yang, Angela Fan, Anirudh Goyal, Anthony Hartshorn, Aobo Yang, Archi Mitra, Archie Sravankumar, Artem Korenev, Arthur Hinsvark, Arun Rao, Aston Zhang, Aurelien Rodriguez, Austen Gregerson, Ava Spataru, Baptiste Roziere, Bethany Biron, Binh Tang, Bobbie Chern, Charlotte Caucheteux, Chaya Nayak, Chloe Bi, Chris Marra, Chris McConnell, Christian Keller, Christophe Touret, Chunyang Wu, Corinne Wong, Cristian Canton Ferrer, Cyrus Nikolaidis, Damien Allonsius, Daniel Song, Danielle Pintz, Danny Livshits, Danny Wyatt, David Esiobu, Dhruv Choudhary, Dhruv Mahajan, Diego Garcia-Olano, Diego Perino, Dieuwke Hupkes, Egor Lakomkin, Ehab AlBadawy, Elina Lobanova, Emily Dinan, Eric Michael Smith, Filip Radenovic, Francisco Guzmán, Frank Zhang, Gabriel Synnaeve, Gabrielle Lee, Georgia Lewis Anderson, Govind Thattai, Graeme Nail, Gregoire Mialon, Guan Pang, Guillem Cucurell, Hailey Nguyen, Hannah Korevaar, Hu Xu, Hugo Touvron, Iliyan Zarov, Imanol Arrieta Ibarra, Isabel Kloumann, Ishan Misra, Ivan Evtimov, Jack Zhang, Jade Copet, Jaewon Lee, Jan Geffert, Jana Vranes, Jason Park, Jay Mahadeokar, Jeet Shah, Jelmer van der Linde, Jennifer Billock, Jenny Hong, Jenya Lee, Jeremy Fu, Jianfeng Chi, Jianyu Huang, Jiawen Liu, Jie Wang, Jiecao Yu, Joanna Bitton, Joe Spisak, Jongsoo Park, Joseph Rocca, Joshua Johnstun, Joshua Saxe, Junteng Jia, Kalyan Vasuden Alwala, Karthik Prasad, Kartikeya Upasani, Kate Plawiak, Ke Li, Kenneth Heafield, Kevin Stone, Khalid El-Arini, Krithika Iyer, Kshitiz Malik, Kuenley Chiu, Kunal Bhalla, Kushal Lakhotia, Lauren Rantala-Yeary, Laurens van der Maaten, Lawrence Chen, Liang Tan, Liz Jenkins, Louis Martin, Lovish Madaan, Lubo Malo, Lukas Blecher, Lukas Landzaat, Luke de Oliveira, Madeline Muzzi, Mahesh Pasupuleti, Mannat Singh, Manohar Paluri, Marcin Kardas, Maria Tsimpoukelli, Mathew Oldham, Mathieu Rita, Maya Pavlova, Melanie Kambadur, Mike Lewis, Min Si, Mitesh Kumar Singh, Mona Hassan, Naman Goyal, Narjes Torabi, Nikolay Bashlykov, Nikolay Bogoychev, Niladri Chatterji, Ning Zhang, Olivier Duchenne, Onur Çelebi, Patrick Alrassy, Pengchuan Zhang, Pengwei Li, Petar Vasic, Peter Weng, Prajjwal Bhargava, Pratik Dubal, Praveen Krishnan, Punit Singh Koura, Puxin Xu, Qing He, Qingxiao Dong, Ragavan Srinivasan, Raj Ganapathy, Ramon Calderer, Ricardo Silveira Cabral, Robert Stojnic, Roberta Raileanu, Rohan Maheswari, Rohit Girdhar, Rohit Patel, Romain Sauvestre, Ronnie Polidoro, Roshan Sumbaly, Ross Taylor, Ruan Silva, Rui Hou, Rui Wang, Saghar Hosseini, Sahana Chennabasappa, Sanjay Singh, Sean Bell, Seohyun Sonia Kim, Sergey Edunov, Shaoliang Nie, Sharan Narang, Sharath Raparthy, Sheng Shen, Shengye Wan, Shruti Bhosale, Shun Zhang, Simon Vandenhende, Soumya Batra, Spencer Whitman, Sten Sootla, Stephane Collot, Suchin Gururangan, Sydney Borodinsky, Tamar Herman, 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Seltzer, Michal Valko, Michelle Restrepo, Mihir Patel, Mik Vyatskov, Mikayel Samvelyan, Mike Clark, Mike Macey, Mike Wang, Miquel Jubert Hermoso, Mo Metanat, Mohammad Rastegari, Munish Bansal, Nandhini Santhanam, Natascha Parks, Natasha White, Navyata Bawa, Nayan Singhal, Nick Egebo, Nicolas Usunier, Nikhil Mehta, Nikolay Pavlovich Laptev, Ning Dong, Norman Cheng, Oleg Chernoguz, Olivia Hart, Omkar Salpekar, Ozlem Kalinli, Parkin Kent, Parth Parekh, Paul Saab, Pavan Balaji, Pedro Rittner, Philip Bontrager, Pierre Roux, Piotr Dollar, Polina Zvyagina, Prashant Ratanchandani, Pritish Yuvraj, Qian Liang, Rachad Alao, Rachel Rodriguez, Rafi Ayub, Raghotham Murthy, Raghu Nayani, Rahul Mitra, Rangaprabhu Parthasarathy, Raymond Li, Rebekkah Hogan, Robin Battey, Rocky Wang, Russ Howes, Ruty Rinott, Sachin Mehta, Sachin Siby, Sai Jayesh Bondu, Samyak Datta, Sara Chugh, Sara Hunt, Sargun Dhillon, Sasha Sidorov, Satadru Pan, Saurabh Mahajan, Saurabh Verma, Seiji Yamamoto, Sharadh Ramaswamy, Shaun Lindsay, Shaun Lindsay, Sheng Feng, Shenghao Lin, Shengxin Cindy Zha, Shishir Patil, Shiva Shankar, Shuqiang Zhang, Shuqiang Zhang, Sinong Wang, Sneha Agarwal, Soji Sajuyigbe, Soumith Chintala, Stephanie Max, Stephen Chen, Steve Kehoe, Steve Satterfield, Sudarshan Govindaprasad, Sumit Gupta, Summer Deng, Sungmin Cho, Sunny Virk, Suraj Subramanian, Sy Choudhury, Sydney Goldman, Tal Remez, Tamar Glaser, Tamara Best, Thilo Koehler, Thomas Robinson, Tianhe Li, Tianjun Zhang, Tim Matthews, Timothy Chou, Tzook Shaked, Varun Vontimitta, Victoria Ajayi, Victoria Montanez, Vijai Mohan, Vinay Satish Kumar, Vishal Mangla, Vlad Ionescu, Vlad Poenaru, Vlad Tiberiu Mihailescu, Vladimir Ivanov, Wei Li, Wenchen Wang, Wenwen Jiang, Wes Bouaziz, Will Constable, Xiaocheng Tang, Xiaojian Wu, Xiaolan Wang, Xilun Wu, Xinbo Gao, Yaniv Kleinman, Yanjun Chen, Ye Hu, Ye Jia, Ye Qi, Yenda Li, Yilin Zhang, Ying Zhang, Yossi Adi, Youngjin Nam, Yu, Wang, Yu Zhao, Yuchen Hao, Yundi Qian, Yunlu Li, Yuzi He, Zach Rait, Zachary DeVito, Zef Rosnbrick, Zhaoduo Wen, Zhenyu Yang, Zhiwei Zhao, and Zhiyu Ma. The llama 3 herd of models, 2024. URL https://arxiv.org/abs/2407.21783.",
+   "tier": "emerging"
+  },
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    "key": "greenblatt2024alignmentfakinglargelanguage",
    "text": "Ryan Greenblatt, Carson Denison, Benjamin Wright, Fabien Roger, Monte MacDiarmid, Sam Marks, Johannes Treutlein, Tim Belonax, Jack Chen, David Duvenaud, Akbir Khan, Julian Michael, Sören Mindermann, Ethan Perez, Linda Petrini, Jonathan Uesato, Jared Kaplan, Buck Shlegeris, Samuel R. Bowman, and Evan Hubinger. Alignment faking in large language models, 2024. URL https://arxiv.org/abs/2412.14093.",
    "tier": "emerging"
   },
+  {
+   "key": "groeneveld2024olmo",
+   "text": "Dirk Groeneveld, Iz Beltagy, Evan Walsh, Akshita Bhagia, Rodney Kinney, Oyvind Tafjord, Ananya Jha, Hamish Ivison, Ian Magnusson, Yizhong Wang, et al. Olmo: Accelerating the science of language models. In Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: long papers), pp. 15789–15809, 2024.",
+   "tier": "established"
+  },
+  {
+   "key": "groger2026revisiting",
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+  },
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    "text": "Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamilė Lukošiūtė, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, and Samuel R. Bowman. Studying large language model generalization with influence functions, 2023. URL https://arxiv.org/abs/2308.03296.",
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+  },
+  {
+   "key": "guo2026introspectivecouplingselfexplanationtraining",
+   "text": "Zifan Carl Guo, Laura Ruis, Jacob Andreas, and Belinda Z. Li. Introspective coupling: Self-explanation training tracks behavioral change despite fixed supervision, 2026. URL https://arxiv.org/abs/2606.32038.",
+   "tier": "emerging"
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@@ -2857,7 +3550,7 @@
   {
    "key": "gur-arieh-etal-2025-enhancing",
    "text": "Yoav Gur-Arieh, Roy Mayan, Chen Agassy, Atticus Geiger, and Mor Geva. Enhancing automated interpretability with output-centric feature descriptions. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 5757–5778, Vienna, Austria, July 2025a. Association for Computational Linguistics. ISBN 979-8-89176-251-0. . URL https://aclanthology.org/2025.acl-long.288/.",
-   "tier": null
+   "tier": "established"
   },
   {
    "key": "gurarieh2025preciseinparameterconcepterasure",
@@ -2865,15 +3558,40 @@
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   },
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-   "key": "haider2025multi",
-   "text": "Muhammad Umair Haider, Hammad Rizwan, Hassan Sajjad, and AB Siddique. Multi-granular node pruning for circuit discovery. arXiv preprint arXiv:2512.10903, 2025.",
+   "key": "jlens",
+   "text": "Wes Gurnee, Nicholas Sofroniew, Adam Pearce, Mateusz Piotrowski, Isaac Kauvar, Runjin Chen, Anna Soligo, Paul Bogdan, Euan Ong, Rowan Wang, Ben Thompson, David Abrahams, Subhash Kantamneni, Emmanuel Ameisen, Joshua Batson, and Jack Lindsey. Verbalizable representations form a global workspace in language models. Transformer Circuits Thread, 2026. URL https://transformer-circuits.pub/2026/workspace/index.html.",
+   "tier": "emerging"
+  },
+  {
+   "key": "habibi2026symbolicmechanisticdataattribution",
+   "text": "Reza Habibi, Darian Lee, and Magy Seif El-Nasr. Symbolic mechanistic data attribution: Tracing training influence to learned behavioral policies, 2026. URL https://arxiv.org/abs/2606.29171.",
+   "tier": "emerging"
+  },
+  {
+   "key": "hahami2026detectingdisturbancenuancedview",
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+  {
+   "key": "haider2026multigranular",
+   "text": "Muhammad Umair Haider, Hammad Rizwan, Hassan Sajjad, and A.B. Siddique. Multi-granular node pruning for causal circuit discovery. In Third Conference on Language Modeling, 2026. URL https://openreview.net/forum?id=Ol96ETTo6k.",
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    "text": "Tal Haklay, Hadas Orgad, David Bau, Aaron Mueller, and Yonatan Belinkov. Position-aware automatic circuit discovery. In Wanxiang Che, Joyce Nabende, Ekaterina Shutova, and Mohammad Taher Pilehvar (eds.), Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 2792–2817, Vienna, Austria, July 2025. Association for Computational Linguistics. ISBN 979-8-89176-251-0. . URL https://aclanthology.org/2025.acl-long.141/.",
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   },
+  {
+   "key": "haklay2026pitfalls",
+   "text": "Tal Haklay, Nikhil Prakash, Sana Pandey, Antonio Torralba, Aaron Mueller, Jacob Andreas, Tamar Rott Shaham, and Yonatan Belinkov. Pitfalls in evaluating interpretability agents, 2026. URL https://arxiv.org/abs/2603.20101.",
+   "tier": "emerging"
+  },
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    "text": "Michael Hanna, Ollie Liu, and Alexandre Variengien. How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model. In Thirty-seventh Conference on Neural Information Processing Systems, 2023. URL https://openreview.net/forum?id=p4PckNQR8k.",
@@ -2885,14 +3603,24 @@
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   },
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-   "key": "harrasse2025tracing",
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-   "tier": null
+   "key": "haon2025mechanistic",
+   "text": "Bear Häon, Kaylene Caswell Stocking, Ian Chuang, and Claire Tomlin. Mechanistic interpretability for steering vision-language-action models. In 9th Annual Conference on Robot Learning, 2025. URL https://openreview.net/forum?id=YvsUD8C9QS.",
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   },
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    "key": "harrasse2025tinysqlprogressivetexttosqldataset",
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+   "text": "Abir Harrasse, Philip Quirke, Clement Neo, Dhruv Nathawani, Luke Marks, and Amir Abdullah. TinySQL: A progressive text-to-SQL dataset for mechanistic interpretability research. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pp. 29256–29284, Suzhou, China, November 2025. Association for Computational Linguistics. ISBN 979-8-89176-332-6. . URL https://aclanthology.org/2025.emnlp-main.1489/.",
+   "tier": "established"
+  },
+  {
+   "key": "harrasse2025tracing",
+   "text": "Abir Harrasse, Florent Draye, Punya Syon Pandey, Zhijing Jin, and Bernhard Schölkopf. Tracing multilingual representations in llms with cross-layer transcoders, 2026. URL https://arxiv.org/abs/2511.10840.",
+   "tier": "emerging"
+  },
+  {
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@@ -2900,30 +3628,80 @@
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-   "text": "Stefan Heimersheim and Neel Nanda. How to use and interpret activation patching. arXiv preprint arXiv:2404.15255, 2024.",
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-   "key": "hubinger2024sleeperagentstrainingdeceptive",
-   "text": "Evan Hubinger, Carson Denison, Jesse Mu, Mike Lambert, Meg Tong, Monte MacDiarmid, Tamera Lanham, Daniel M. Ziegler, Tim Maxwell, Newton Cheng, Adam Jermyn, Amanda Askell, Ansh Radhakrishnan, Cem Anil, David Duvenaud, Deep Ganguli, Fazl Barez, Jack Clark, Kamal Ndousse, Kshitij Sachan, Michael Sellitto, Mrinank Sharma, Nova DasSarma, Roger Grosse, Shauna Kravec, Yuntao Bai, Zachary Witten, Marina Favaro, Jan Brauner, Holden Karnofsky, Paul Christiano, Samuel R. Bowman, Logan Graham, Jared Kaplan, Sören Mindermann, Ryan Greenblatt, Buck Shlegeris, Nicholas Schiefer, and Ethan Perez. Sleeper agents: Training deceptive llms that persist through safety training, 2024. URL https://arxiv.org/abs/2401.05566.",
+   "key": "jain2023baselinedefensesadversarialattacks",
+   "text": "Neel Jain, Avi Schwarzschild, Yuxin Wen, Gowthami Somepalli, John Kirchenbauer, Ping yeh Chiang, Micah Goldblum, Aniruddha Saha, Jonas Geiping, and Tom Goldstein. Baseline defenses for adversarial attacks against aligned language models, 2023. URL https://arxiv.org/abs/2309.00614.",
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@@ -3015,29 +3848,54 @@
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+  {
+   "key": "li2026introspective",
+   "text": "Belinda Z. Li. Introspective interpretability: a definition, motivation, and open problems. https://belindal.github.io/introspection, February 2026. Blog post.",
+   "tier": "emerging"
+  },
   {
    "key": "li2025traininglanguagemodelsexplain",
    "text": "Belinda Z. Li, Zifan Carl Guo, Vincent Huang, Jacob Steinhardt, and Jacob Andreas. Training language models to explain their own computations, 2025a. URL https://arxiv.org/abs/2511.08579.",
    "tier": "emerging"
   },
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-   "key": "li2024simulateeliminaterevokebackdoors",
-   "text": "Haoran Li, Yulin Chen, Zihao Zheng, Qi Hu, Chunkit Chan, Heshan Liu, and Yangqiu Song. Simulate and eliminate: revoke backdoors for generative large language models. In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’25/IAAI’25/EAAI’25. AAAI Press, 2025b. ISBN 978-1-57735-897-8. . URL https://doi.org/10.1609/aaai.v39i1.32018.",
-   "tier": null
+   "key": "li2026autocontrol",
+   "text": "Changyi Li, Pengfei Lu, Xudong Pan, Fazl Barez, and Min Yang. Autocontrol arena: Synthesizing executable test environments for frontier AI risk evaluation. In Forty-third International Conference on Machine Learning, 2026a. URL https://openreview.net/forum?id=XkDpZusDTK.",
+   "tier": "established"
+  },
+  {
+   "key": "chloe",
+   "text": "Chloe Li, Mary Phuong, and Daniel Tan. Spilling the beans: Teaching LLMs to self-report their hidden objectives. In The Fourteenth International Conference on Learning Representations, 2026b. URL https://openreview.net/forum?id=sWs0cCuM8I.",
+   "tier": "established"
   },
   {
    "key": "li-etal-2016-visualizing",
    "text": "Jiwei Li, Xinlei Chen, Eduard Hovy, and Dan Jurafsky. Visualizing and understanding neural models in NLP. In Kevin Knight, Ani Nenkova, and Owen Rambow (eds.), Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 681–691, San Diego, California, June 2016. Association for Computational Linguistics. . URL https://aclanthology.org/N16-1082/.",
    "tier": "established"
   },
+  {
+   "key": "li2023emergent",
+   "text": "Kenneth Li, Aspen K Hopkins, David Bau, Fernanda Viégas, Hanspeter Pfister, and Martin Wattenberg. Emergent world representations: Exploring a sequence model trained on a synthetic task. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=DeG07_TcZvT.",
+   "tier": "established"
+  },
+  {
+   "key": "li2026do",
+   "text": "Millicent Li, Alberto Mario Ceballos Arroyo, Giordano Rogers, Naomi Saphra, and Byron C Wallace. Do activation verbalization methods convey privileged information? In Forty-third International Conference on Machine Learning, 2026c. URL https://openreview.net/forum?id=IFGTKwHwK6.",
+   "tier": "established"
+  },
   {
    "key": "li2024wmdp",
    "text": "Nathaniel Li, Alexander Pan, Anjali Gopal, Summer Yue, Daniel Berrios, Alice Gatti, Justin D. Li, Ann-Kathrin Dombrowski, Shashwat Goel, Gabriel Mukobi, Nathan Helm-Burger, Rassin Lababidi, Lennart Justen, Andrew Bo Liu, Michael Chen, Isabelle Barrass, Oliver Zhang, Xiaoyuan Zhu, Rishub Tamirisa, Bhrugu Bharathi, Ariel Herbert-Voss, Cort B Breuer, Andy Zou, Mantas Mazeika, Zifan Wang, Palash Oswal, Weiran Lin, Adam Alfred Hunt, Justin Tienken-Harder, Kevin Y. Shih, Kemper Talley, John Guan, Ian Steneker, David Campbell, Brad Jokubaitis, Steven Basart, Stephen Fitz, Ponnurangam Kumaraguru, Kallol Krishna Karmakar, Uday Tupakula, Vijay Varadharajan, Yan Shoshitaishvili, Jimmy Ba, Kevin M. Esvelt, Alexandr Wang, and Dan Hendrycks. The WMDP benchmark: Measuring and reducing malicious use with unlearning. In Forty-first International Conference on Machine Learning, 2024. URL https://openreview.net/forum?id=xlr6AUDuJz.",
@@ -3177,12 +4100,27 @@
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    "key": "pmlr-v44-li15convergent",
    "text": "Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, and John Hopcroft. Convergent learning: Do different neural networks learn the same representations? In Dmitry Storcheus, Afshin Rostamizadeh, and Sanjiv Kumar (eds.), Proceedings of the 1st International Workshop on Feature Extraction: Modern Questions and Challenges at NIPS 2015, volume 44 of Proceedings of Machine Learning Research, pp. 196–212, Montreal, Canada, 11 Dec 2015. PMLR. URL https://proceedings.mlr.press/v44/li15convergent.html.",
-   "tier": null
+   "tier": "emerging"
   },
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    "key": "li2025geometry",
-   "text": "Yuxiao Li, Eric J Michaud, David D Baek, Joshua Engels, Xiaoqing Sun, and Max Tegmark. The geometry of concepts: Sparse autoencoder feature structure. Entropy, 27 (4): 344, 2025c.",
-   "tier": null
+   "text": "Yuxiao Li, Eric J Michaud, David D Baek, Joshua Engels, Xiaoqing Sun, and Max Tegmark. The geometry of concepts: Sparse autoencoder feature structure. Entropy, 27 (4): 344, 2025b.",
+   "tier": "established"
+  },
+  {
+   "key": "lieberum2024gemmascopeopensparse",
+   "text": "Tom Lieberum, Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Nicolas Sonnerat, Vikrant Varma, Janos Kramar, Anca Dragan, Rohin Shah, and Neel Nanda. Gemma scope: Open sparse autoencoders everywhere all at once on gemma 2. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen (eds.), Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 278–300, Miami, Florida, US, November 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.blackboxnlp-1.19/.",
+   "tier": "emerging"
+  },
+  {
+   "key": "neuronpedia",
+   "text": "Johnny Lin. Neuronpedia: Interactive reference and tooling for analyzing neural networks, 2023. URL https://www.neuronpedia.org. Software available from neuronpedia.org.",
+   "tier": "emerging"
+  },
+  {
+   "key": "lindsey2026emergentintrospectiveawarenesslarge",
+   "text": "Jack Lindsey. Emergent introspective awareness in large language models, 2026. URL https://arxiv.org/abs/2601.01828.",
+   "tier": "emerging"
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@@ -3199,11 +4137,6 @@
    "text": "Jack Lindsey, Brian Chen, Adam Pearce, Sasha Hydrie, Thomas Conerly, and Jeff Wu. Sparse mixtures of linear transforms. https://transformer-circuits.pub/2025/bulk-update/index.html, 2025b.",
    "tier": "emerging"
   },
-  {
-   "key": "lindsey2025biology",
-   "text": "Jack Lindsey, Wes Gurnee, Emmanuel Ameisen, Brian Chen, Adam Pearce, Nicholas L. Turner, Craig Citro, David Abrahams, Shan Carter, Basil Hosmer, Jonathan Marcus, Michael Sklar, Adly Templeton, Trenton Bricken, Callum McDougall, Hoagy Cunningham, Thomas Henighan, Adam Jermyn, Andy Jones, Andrew Persic, Zhenyi Qi, T. Ben Thompson, Sam Zimmerman, Kelley Rivoire, Thomas Conerly, Chris Olah, and Joshua Batson. On the biology of a large language model. Transformer Circuits Thread, 2025c. URL https://transformer-circuits.pub/2025/attribution-graphs/biology.html.",
-   "tier": "emerging"
-  },
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@@ -3211,24 +4144,64 @@
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    "key": "liu2025infinigramscalingunboundedngram",
-   "text": "Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi, and Hannaneh Hajishirzi. Infini-gram: Scaling unbounded n-gram language models to a trillion tokens. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=u2vAyMeLMm.",
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+   "text": "Jiacheng Liu, Sewon Min, Luke Zettlemoyer, Yejin Choi, and Hannaneh Hajishirzi. Infini-gram: Scaling unbounded n-gram language models to a trillion tokens. In First Conference on Language Modeling, 2024a. URL https://openreview.net/forum?id=u2vAyMeLMm.",
+   "tier": "established"
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    "text": "Jiacheng Liu, Taylor Blanton, Yanai Elazar, Sewon Min, Yen-Sung Chen, Arnavi Chheda-Kothary, Huy Tran, Byron Bischoff, Eric Marsh, Michael Schmitz, et al. Olmotrace: Tracing language model outputs back to trillions of training tokens. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pp. 178–188, 2025a.",
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+  {
+   "key": "liu2021probing",
+   "text": "Leo Z. Liu, Yizhong Wang, Jungo Kasai, Hannaneh Hajishirzi, and Noah A. Smith. Probing across time: What does RoBERTa know and when? In Findings of the Association for Computational Linguistics: EMNLP 2021, pp. 820–842, 2021.",
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+  },
+  {
+   "key": "liu2026icalensinterpretinglanguage",
+   "text": "Sida Liu and Feijiang Han. Ica lens: Interpreting language models without training another dictionary, 2026. URL https://arxiv.org/abs/2606.11722.",
+   "tier": "emerging"
+  },
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+   "key": "liu2024repobench",
+   "text": "Tianyang Liu, Canwen Xu, and Julian McAuley. Repobench: Benchmarking repository-level code auto-completion systems. In The Twelfth International Conference on Learning Representations, 2024b. URL https://openreview.net/forum?id=pPjZIOuQuF.",
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+  },
+  {
+   "key": "liu2023omnigrok",
+   "text": "Ziming Liu, Eric J Michaud, and Max Tegmark. Omnigrok: Grokking beyond algorithmic data. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=zDiHoIWa0q1.",
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+  },
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    "text": "Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, and Max Tegmark. KAN: Kolmogorovarnold networks. In The Thirteenth International Conference on Learning Representations, 2025b. URL https://openreview.net/forum?id=Ozo7qJ5vZi.",
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+  },
+  {
+   "key": "lotfi2026position",
+   "text": "Sobhan Lotfi, Ava Iranmanesh, Lachin Naghashyar, Ali Shirali, Fateme Nateghi Haredasht, Sanmi Koyejo, Philip Torr, Yong Suk Lee, Fazl Barez, Joel Lehman, Peter Norvig, and Arvind Narayanan. Position: There are futures that benchmark-driven AI cannot see. In Forty-third International Conference on Machine Learning Position Paper Track, 2026. URL https://openreview.net/forum?id=RzWlUbmRJi.",
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+  },
+  {
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+   "text": "Ekdeep Singh Lubana, Eric J. Bigelow, Robert P. Dick, David Krueger, and Hidenori Tanaka. Mechanistic mode connectivity. In International Conference on Machine Learning, pp. 22965–23004, 2023.",
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    "text": "Scott M. Lundberg and Su-In Lee. A unified approach to interpreting model predictions. In Proceedings of the 31st International Conference on Neural Information Processing Systems, NIPS’17, pp. 4768–4777, Red Hook, NY, USA, 2017. Curran Associates Inc. ISBN 9781510860964.",
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+  {
+   "key": "aiscientistpitfall",
+   "text": "Ziming Luo, Atoosa Kasirzadeh, and Nihar B. Shah. The more you automate, the less you see: Hidden pitfalls of autonomous ai scientists. Proceedings of the National Academy of Sciences, 123 (41): e2610214123, 2026. . URL https://www.pnas.org/doi/abs/10.1073/pnas.2610214123.",
+   "tier": "established"
+  },
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    "key": "macdiarmid2024sleeperagentprobes",
    "text": "Monte MacDiarmid, Timothy Maxwell, Nicholas Schiefer, Jesse Mu, Jared Kaplan, David Duvenaud, Sam Bowman, Alex Tamkin, Ethan Perez, Mrinank Sharma, Carson Denison, and Evan Hubinger. Simple probes can catch sleeper agents. https://www.anthropic.com/news/probes-catch-sleeper-agents, 2024.",
@@ -3236,9 +4209,39 @@
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    "key": "macdiarmid_natural_2025",
-   "text": "Monte MacDiarmid, Benjamin Wright, Jonathan Uesato, Joe Benton, Jon Kutasov, Sara Price, Naia Bouscal, Sam Bowman, Trenton Bricken, Alex Cloud, Carson Denison, Johannes Gasteiger, Ryan Greenblatt, Jan Leike, Jack Lindsey, Vlad Mikulik, Ethan Perez, Alex Rodrigues, Drake Thomas, Albert Webson, Daniel Ziegler, and Evan Hubinger. Natural Emergent Misalignment from Reward Hacking in Production RL, November 2025. URL http://arxiv.org/abs/2511.18397. arXiv:2511.18397 [cs].",
+   "text": "Monte MacDiarmid, Benjamin Wright, Jonathan Uesato, Joe Benton, Jon Kutasov, Sara Price, Naia Bouscal, Sam Bowman, Trenton Bricken, Alex Cloud, Carson Denison, Johannes Gasteiger, Ryan Greenblatt, Jan Leike, Jack Lindsey, Vlad Mikulik, Ethan Perez, Alex Rodrigues, Drake Thomas, Albert Webson, Daniel Ziegler, and Evan Hubinger. Natural emergent misalignment from reward hacking in production rl, 2025. URL https://arxiv.org/abs/2511.18397.",
+   "tier": "emerging"
+  },
+  {
+   "key": "makelov2024is",
+   "text": "Aleksandar Makelov, Georg Lange, Atticus Geiger, and Neel Nanda. Is this the subspace you are looking for? an interpretability illusion for subspace activation patching. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Ebt7JgMHv1.",
+   "tier": "established"
+  },
+  {
+   "key": "matchingpursuit",
+   "text": "S.G. Mallat and Zhifeng Zhang. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing, 41 (12): 3397–3415, 1993. .",
+   "tier": "established"
+  },
+  {
+   "key": "marin2026automated",
+   "text": "Arnau Marin-Llobet and Javier Ferrando. Automated interpretability and feature discovery in language models with agents, 2026. URL https://arxiv.org/abs/2605.01555.",
    "tier": "emerging"
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+  {
+   "key": "marks2024interpreting",
+   "text": "Luke Marks, Amir Abdullah, Clement Neo, Rauno Arike, David Krueger, Philip Torr, and Fazl Barez. Interpreting learned feedback patterns in large language models. In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA, 2024a. Curran Associates Inc. ISBN 9798331314385.",
+   "tier": "established"
+  },
+  {
+   "key": "marks2024enhancingneuralnetworkinterpretability",
+   "text": "Luke Marks, Alasdair Paren, David Krueger, and Fazl Barez. Enhancing neural network interpretability with feature-aligned sparse autoencoders, 2024b. URL https://arxiv.org/abs/2411.01220.",
+   "tier": "emerging"
+  },
+  {
+   "key": "marks2024the",
+   "text": "Samuel Marks and Max Tegmark. The geometry of truth: Emergent linear structure in large language model representations of true/false datasets. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=aajyHYjjsk.",
+   "tier": "established"
+  },
   {
    "key": "marks2025sparse",
    "text": "Samuel Marks, Can Rager, Eric J Michaud, Yonatan Belinkov, David Bau, and Aaron Mueller. Sparse feature circuits: Discovering and editing interpretable causal graphs in language models. In The Thirteenth International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=I4e82CIDxv.",
@@ -3247,7 +4250,12 @@
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    "key": "marks2025auditinglanguagemodelshidden",
    "text": "Samuel Marks, Johannes Treutlein, Trenton Bricken, Jack Lindsey, Jonathan Marcus, Siddharth Mishra-Sharma, Daniel Ziegler, Emmanuel Ameisen, Joshua Batson, Tim Belonax, Samuel R. Bowman, Shan Carter, Brian Chen, Hoagy Cunningham, Carson Denison, Florian Dietz, Satvik Golechha, Akbir Khan, Jan Kirchner, Jan Leike, Austin Meek, Kei Nishimura-Gasparian, Euan Ong, Christopher Olah, Adam Pearce, Fabien Roger, Jeanne Salle, Andy Shih, Meg Tong, Drake Thomas, Kelley Rivoire, Adam Jermyn, Monte MacDiarmid, Tom Henighan, and Evan Hubinger. Auditing language models for hidden objectives, 2025b. URL https://arxiv.org/abs/2503.10965.",
-   "tier": null
+   "tier": "emerging"
+  },
+  {
+   "key": "kfac",
+   "text": "James Martens and Roger Grosse. Optimizing neural networks with kronecker-factored approximate curvature. In Francis Bach and David Blei (eds.), Proceedings of the 32nd International Conference on Machine Learning, volume 37 of Proceedings of Machine Learning Research, pp. 2408–2417, Lille, France, 07–09 Jul 2015. PMLR. URL https://proceedings.mlr.press/v37/martens15.html.",
+   "tier": "established"
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    "text": "R. Thomas McCoy, Ellie Pavlick, and Tal Linzen. Right for the wrong reasons: Diagnosing syntactic heuristics in natural language inference. In Anna Korhonen, David Traum, and Lluís Màrquez (eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 3428–3448, Florence, Italy, July 2019. Association for Computational Linguistics. . URL https://aclanthology.org/P19-1334/.",
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+  {
+   "key": "mcdougall2025gemmascope2",
+   "text": "Callum McDougall, Arthur Conmy, János Kramár, Tom Lieberum, Senthooran Rajamanoharan, and Neel Nanda. Gemma scope 2: Technical paper. Google DeepMind technical report, 2025. URL https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/gemma-scope-2-helping-the-ai-safety-community-deepen-understanding-of-complex-language-model-behavior/Gemma_Scope_2_Technical_Paper.pdf.",
+   "tier": "emerging"
+  },
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    "text": "Thomas McGrath, Matthew Rahtz, Janos Kramar, Vladimir Mikulik, and Shane Legg. The hydra effect: Emergent self-repair in language model computations, 2023. URL https://arxiv.org/abs/2307.15771.",
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+  {
+   "key": "mckenzie2023inverse",
+   "text": "Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, Alicia Parrish, Aaron Mueller, Ameya Prabhu, Euan McLean, Aaron Kirtland, Alexis Ross, Alisa Liu, Andrew Gritsevskiy, Daniel Wurgaft, Derik Kauffman, Gabriel Recchia, Jiacheng Liu, Joe Cavanagh, Max Weiss, Sicong Huang, The Floating Droid, Tom Tseng, Tomasz Korbak, Xudong Shen, Yuhui Zhang, Zhengping Zhou, Najoung Kim, Samuel R. Bowman, and Ethan Perez. Inverse scaling: When bigger isn’t better. Transactions on Machine Learning Research, 2023. URL https://arxiv.org/abs/2306.09479.",
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+  },
+  {
+   "key": "meloux2025everything",
+   "text": "Maxime Méloux, Silviu Maniu, François Portet, and Maxime Peyrard. Everything, everywhere, all at once: Is mechanistic interpretability identifiable? In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=5IWJBStfU7.",
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    "text": "Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov. Locating and editing factual associations in gpt. Advances in neural information processing systems, 35: 17359–17372, 2022.",
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    "text": "Jack Merullo, Connor Watts, Max Loeffler, Liv Gorton, Elana Simon, Tom McGrath, and Owen Lewis. Replicating circuit tracing for a simple known mechanism. https://www.goodfire.ai/research/replicating-circuit-tracing-for-a-simple-mechanism, 2025.",
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+   "key": "michaud2023quantization",
+   "text": "Eric J. Michaud, Ziming Liu, Uzay Girit, and Max Tegmark. The quantization model of neural scaling. In Advances in Neural Information Processing Systems, volume 36, 2023. URL https://openreview.net/forum?id=3tbTw2ga8K.",
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+   "text": "Joseph Miller, Bilal Chughtai, and William Saunders. Transformer circuit evaluation metrics are not robust. In First Conference on Language Modeling, 2024. URL https://openreview.net/forum?id=zSf8PJyQb2.",
+   "tier": "established"
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    "text": "Moritz Miller, Florent Draye, and Bernhard Schölkopf. Identifying intervenable and interpretable features via orthogonality regularization, 2026. URL https://arxiv.org/abs/2602.04718.",
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+   "text": "Marvin L. Minsky and Seymour A. Papert. Perceptrons: An Introduction to Computational Geometry. MIT Press, Cambridge, MA, 1969.",
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-   "text": "Ari S. Morcos, Maithra Raghu, and Samy Bengio. Insights on representational similarity in neural networks with canonical correlation, 2018a. URL https://arxiv.org/abs/1806.05759.",
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+   "key": "modarressi2025nolima",
+   "text": "Ali Modarressi, Hanieh Deilamsalehy, Franck Dernoncourt, Trung Bui, Ryan A. Rossi, Seunghyun Yoon, and Hinrich Schuetze. Nolima: Long-context evaluation beyond literal matching. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=0OshX1hiSa.",
+   "tier": "established"
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-   "key": "morcos_insights_2018",
-   "text": "Ari S. Morcos, Maithra Raghu, and Samy Bengio. Insights on representational similarity in neural networks with canonical correlation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 5732–5741, Red Hook, NY, USA, 2018b. Curran Associates Inc. URL https://dl.acm.org/doi/10.5555/3327345.3327475.",
-   "tier": null
+   "key": "morcos2018insightsrepresentationalsimilarityneural",
+   "text": "Ari S. Morcos, Maithra Raghu, and Samy Bengio. Insights on representational similarity in neural networks with canonical correlation. In Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS’18, pp. 5732–5741, Red Hook, NY, USA, 2018. Curran Associates Inc.",
+   "tier": "established"
   },
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-   "key": "moschella2023relativerepresentationsenablezeroshot",
-   "text": "Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. Relative representations enable zero-shot latent space communication, 2023. URL https://arxiv.org/abs/2209.15430.",
-   "tier": "emerging"
+   "key": "moschella2023relative",
+   "text": "Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli, Francesco Locatello, and Emanuele Rodolà. Relative representations enable zero-shot latent space communication. In The Eleventh International Conference on Learning Representations, 2023. URL https://openreview.net/forum?id=SrC-nwieGJ.",
+   "tier": "established"
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   {
    "key": "mueller2025mib",
-   "text": "Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fried Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, and Yonatan Belinkov. MIB: A mechanistic interpretability benchmark. In Forty-second International Conference on Machine Learning, 2025a. URL https://openreview.net/forum?id=sSrOwve6vb.",
-   "tier": "emerging"
+   "text": "Aaron Mueller, Atticus Geiger, Sarah Wiegreffe, Dana Arad, Iván Arcuschin, Adam Belfki, Yik Siu Chan, Jaden Fried Fiotto-Kaufman, Tal Haklay, Michael Hanna, Jing Huang, Rohan Gupta, Yaniv Nikankin, Hadas Orgad, Nikhil Prakash, Anja Reusch, Aruna Sankaranarayanan, Shun Shao, Alessandro Stolfo, Martin Tutek, Amir Zur, David Bau, and Yonatan Belinkov. MIB: A mechanistic interpretability benchmark. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=sSrOwve6vb.",
+   "tier": "established"
   },
   {
-   "key": "mueller2025isolationentanglementinterpretabilitymethods",
-   "text": "Aaron Mueller, Andrew Lee, Shruti Joshi, Ekdeep Singh Lubana, Dhanya Sridhar, and Patrik Reizinger. From isolation to entanglement: When do interpretability methods identify and disentangle known concepts?, 2025b. URL https://arxiv.org/abs/2512.15134.",
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+   "key": "relu",
+   "text": "Vinod Nair and Geoffrey E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proceedings of the 27th International Conference on International Conference on Machine Learning, ICML’10, pp. 807–814, Madison, WI, USA, 2010. Omnipress. ISBN 9781605589077.",
+   "tier": "established"
+  },
+  {
+   "key": "nakkiran2019sgd",
+   "text": "Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, and Boaz Barak. SGD on neural networks learns functions of increasing complexity. Curran Associates Inc., Red Hook, NY, USA, 2019.",
+   "tier": "established"
   },
   {
    "key": "nanda2023progressmeasuresgrokkingmechanistic",
@@ -3332,6 +4370,16 @@
   {
    "key": "needham2025largelanguagemodelsknow",
    "text": "Joe Needham, Giles Edkins, Govind Pimpale, Henning Bartsch, and Marius Hobbhahn. Large language models often know when they are being evaluated, 2025. URL https://arxiv.org/abs/2505.23836.",
+   "tier": "emerging"
+  },
+  {
+   "key": "neo-etal-2024-interpreting",
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+   "tier": "established"
+  },
+  {
+   "key": "neo2025towards",
+   "text": "Clement Neo, Luke Ong, Philip Torr, Mor Geva, David Krueger, and Fazl Barez. Towards interpreting visual information processing in vision-language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=chanJGoa7f.",
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   },
   {
@@ -3339,16 +4387,36 @@
    "text": "Andrew Ng. Sparse autoencoder. Stanford CS294A Lecture Notes, 2011.",
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+  {
+   "key": "ICLR2025_8c5f3029",
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+  },
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+   "text": "Kyle O’Brien, Stephen Casper, Quentin Gregory Anthony, Tomek Korbak, Robert Kirk, Xander Davies, Ishan Mishra, Geoffrey Irving, Yarin Gal, and Stella Biderman. Deep ignorance: Filtering pretraining data builds tamper-resistant safeguards into open-weight LLMs. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=xcf0QcTcGS.",
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+  },
+  {
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@@ -3364,30 +4432,40 @@
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+   "tier": "established"
+  },
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-   "text": "Bruno A. Olshausen and David J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381: 607–609, 1996. .",
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+   "tier": "established"
+  },
+  {
+   "key": "olshausen1997sparse",
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-   "text": "Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. In-context learning and induction heads. Transformer Circuits Thread, 2022a. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html.",
+   "text": "Catherine Olsson, Nelson Elhage, Neel Nanda, Nicholas Joseph, Nova DasSarma, Tom Henighan, Ben Mann, Amanda Askell, Yuntao Bai, Anna Chen, Tom Conerly, Dawn Drain, Deep Ganguli, Zac Hatfield-Dodds, Danny Hernandez, Scott Johnston, Andy Jones, Jackson Kernion, Liane Lovitt, Kamal Ndousse, Dario Amodei, Tom Brown, Jack Clark, Jared Kaplan, Sam McCandlish, and Chris Olah. In-context learning and induction heads. Transformer Circuits Thread, 2022. https://transformer-circuits.pub/2022/in-context-learning-and-induction-heads/index.html.",
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   },
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-   "key": "olsson2022context",
-   "text": "Catherine Olsson et al. In-context learning and induction heads. arXiv preprint arXiv:2209.11895, 2022b.",
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-   "tier": null
+   "key": "oneill2025compute",
+   "text": "Charles O’Neill, Alim Gumran, and David Klindt. Compute optimal inference and provable amortisation gap in sparse autoencoders. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=8forr1FkvC.",
+   "tier": "established"
   },
   {
-   "key": "oozeer2025activation",
-   "text": "Narmeen Fatimah Oozeer, Dhruv Nathawani, Nirmalendu Prakash, Michael Lan, Abir HARRASSE, and Amir Abdullah. Activation space interventions can be transferred between large language models. In Forty-second International Conference on Machine Learning, 2025b. URL https://openreview.net/forum?id=HXOicJsmMQ.",
-   "tier": null
+   "key": "oozeer2025beyond",
+   "text": "Narmeen Fatimah Oozeer, Luke Marks, Fazl Barez, and Amir Abdullah. Beyond linear steering: Unified multi-attribute control for language models. In Christos Christodoulopoulos, Tanmoy Chakraborty, Carolyn Rose, and Violet Peng (eds.), Findings of the Association for Computational Linguistics: EMNLP 2025, pp. 23513–23557, Suzhou, China, November 2025. Association for Computational Linguistics. ISBN 979-8-89176-335-7. . URL https://aclanthology.org/2025.findings-emnlp.1278/.",
+   "tier": "established"
   },
   {
    "key": "openai2024gpt4technicalreport",
@@ -3395,8 +4473,18 @@
    "tier": "emerging"
   },
   {
-   "key": "orgad2026interpretabilityactionable",
-   "text": "Hadas Orgad, Fazl Barez, Tal Haklay, Isabelle Lee, Marius Mosbach, Anja Reusch, Naomi Saphra, Byron Wallace, Sarah Wiegreffe, Eric Wong, Ian Tenney, and Mor Geva. Interpretability can be actionable, 2026. URL https://arxiv.org/abs/2605.11161.",
+   "key": "openai2025gptoss120bgptoss20bmodel",
+   "text": "OpenAI, :, Sandhini Agarwal, Lama Ahmad, Jason Ai, Sam Altman, Andy Applebaum, Edwin Arbus, Rahul K. Arora, Yu Bai, Bowen Baker, Haiming Bao, Boaz Barak, Ally Bennett, Tyler Bertao, Nivedita Brett, Eugene Brevdo, Greg Brockman, Sebastien Bubeck, Che Chang, Kai Chen, Mark Chen, Enoch Cheung, Aidan Clark, Dan Cook, Marat Dukhan, Casey Dvorak, Kevin Fives, Vlad Fomenko, Timur Garipov, Kristian Georgiev, Mia Glaese, Tarun Gogineni, Adam Goucher, Lukas Gross, Katia Gil Guzman, John Hallman, Jackie Hehir, Johannes Heidecke, Alec Helyar, Haitang Hu, Romain Huet, Jacob Huh, Saachi Jain, Zach Johnson, Chris Koch, Irina Kofman, Dominik Kundel, Jason Kwon, Volodymyr Kyrylov, Elaine Ya Le, Guillaume Leclerc, James Park Lennon, Scott Lessans, Mario Lezcano-Casado, Yuanzhi Li, Zhuohan Li, Ji Lin, Jordan Liss, Lily, Liu, Jiancheng Liu, Kevin Lu, Chris Lu, Zoran Martinovic, Lindsay McCallum, Josh McGrath, Scott McKinney, Aidan McLaughlin, Song Mei, Steve Mostovoy, Tong Mu, Gideon Myles, Alexander Neitz, Alex Nichol, Jakub Pachocki, Alex Paino, Dana Palmie, Ashley Pantuliano, Giambattista Parascandolo, Jongsoo Park, Leher Pathak, Carolina Paz, Ludovic Peran, Dmitry Pimenov, Michelle Pokrass, Elizabeth Proehl, Huida Qiu, Gaby Raila, Filippo Raso, Hongyu Ren, Kimmy Richardson, David Robinson, Bob Rotsted, Hadi Salman, Suvansh Sanjeev, Max Schwarzer, D. Sculley, Harshit Sikchi, Kendal Simon, Karan Singhal, Yang Song, Dane Stuckey, Zhiqing Sun, Philippe Tillet, Sam Toizer, Foivos Tsimpourlas, Nikhil Vyas, Eric Wallace, Xin Wang, Miles Wang, Olivia Watkins, Kevin Weil, Amy Wendling, Kevin Whinnery, Cedric Whitney, Hannah Wong, Lin Yang, Yu Yang, Michihiro Yasunaga, Kristen Ying, Wojciech Zaremba, Wenting Zhan, Cyril Zhang, Brian Zhang, Eddie Zhang, and Shengjia Zhao. gpt-oss-120b & gpt-oss-20b model card, 2025. URL https://arxiv.org/abs/2508.10925.",
+   "tier": "emerging"
+  },
+  {
+   "key": "orgad2026position",
+   "text": "Hadas Orgad, Fazl Barez, Tal Haklay, Isabelle Lee, Marius Mosbach, Anja Reusch, Naomi Saphra, Byron C Wallace, Sarah Wiegreffe, Eric Wong, Ian Tenney, and Mor Geva. Position: Interpretability can be actionable. In Forty-third International Conference on Machine Learning Position Paper Track, 2026. URL https://openreview.net/forum?id=yRUy3w5y56.",
+   "tier": "established"
+  },
+  {
+   "key": "oshea2015introductionconvolutionalneuralnetworks",
+   "text": "Keiron O’Shea and Ryan Nash. An introduction to convolutional neural networks, 2015. URL https://arxiv.org/abs/1511.08458.",
    "tier": "emerging"
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@@ -3407,46 +4495,76 @@
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    "key": "ouyang2022traininglanguagemodelsfollow",
    "text": "Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul F Christiano, Jan Leike, and Ryan Lowe. Training language models to follow instructions with human feedback. In S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (eds.), Advances in Neural Information Processing Systems, volume 35, pp. 27730–27744. Curran Associates, Inc., 2022. URL https://proceedings.neurips.cc/paper_files/paper/2022/file/b1efde53be364a73914f58805a001731-Paper-Conference.pdf.",
-   "tier": "emerging"
+   "tier": "established"
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   {
    "key": "pan2024latentqateachingllmsdecode",
-   "text": "Alexander Pan, Lijie Chen, and Jacob Steinhardt. Latentqa: Teaching llms to decode activations into natural language, 2024. URL https://arxiv.org/abs/2412.08686.",
-   "tier": "emerging"
+   "text": "Alexander Pan, Lijie Chen, and Jacob Steinhardt. LatentQA: Teaching LLMs to decode activations into natural language. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=niUroX9EOd.",
+   "tier": "established"
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    "text": "Jungwoo Park, Ahn Young Jin, Kee-Eung Kim, and Jaewoo Kang. Monet: Mixture of monosemantic experts for transformers. In The Thirteenth International Conference on Learning Representations, 2025.",
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+  {
+   "key": "lrh",
+   "text": "Kiho Park, Yo Joong Choe, and Victor Veitch. The linear representation hypothesis and the geometry of large language models. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024.",
+   "tier": "established"
+  },
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    "text": "Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry. TRAK: Attributing model behavior at scale. In Andreas Krause, Emma Brunskill, Kyunghyun Cho, Barbara Engelhardt, Sivan Sabato, and Jonathan Scarlett (eds.), Proceedings of the 40th International Conference on Machine Learning, volume 202 of Proceedings of Machine Learning Research, pp. 27074–27113. PMLR, 23–29 Jul 2023. URL https://proceedings.mlr.press/v202/park23c.html.",
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-   "text": "Gonçalo Paulo and Nora Belrose. Sparse autoencoders trained on the same data learn different features. arXiv preprint arXiv:2501.16615, 2025.",
-   "tier": null
+   "key": "paulo2025sparse",
+   "text": "Gonçalo Paulo and Nora Belrose. Sparse autoencoders trained on the same data learn different features. In The Fourteenth International Conference on Learning Representations, 2026. URL https://openreview.net/forum?id=EjInprGpk9.",
+   "tier": "established"
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+   "text": "Gonçalo Paulo, Thomas Marshall, and Nora Belrose. Do transformer interpretability methods transfer to rnns? In Proceedings of the Thirty-Ninth AAAI Conference on Artificial Intelligence and Thirty-Seventh Conference on Innovative Applications of Artificial Intelligence and Fifteenth Symposium on Educational Advances in Artificial Intelligence, AAAI’25/IAAI’25/EAAI’25. AAAI Press, 2025a. ISBN 978-1-57735-897-8. . URL https://doi.org/10.1609/aaai.v39i26.34969.",
+   "tier": "established"
+  },
+  {
+   "key": "paulo2025automatically",
+   "text": "Gonçalo Santos Paulo, Alex Troy Mallen, Caden Juang, and Nora Belrose. Automatically interpreting millions of features in large language models. In Forty-second International Conference on Machine Learning, 2025b. URL https://openreview.net/forum?id=EemtbhJOXc.",
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+   "text": "Michael Pearce, Thomas Dooms, Alice Rigg, Jose Oramas, and Lee Sharkey. Bilinear mlps enable weight-based mechanistic interpretability. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 47283–47310, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/file/7504142a20a3e1fe9dd7de42f475828c-Paper-Conference.pdf.",
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+  },
+  {
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+   "text": "Karl Pearson. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2 (11): 559–572, 1901. .",
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   },
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-   "key": "paulo2025automatically",
-   "text": "Gonçalo Santos Paulo, Alex Troy Mallen, Caden Juang, and Nora Belrose. Automatically interpreting millions of features in large language models. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=EemtbhJOXc.",
+   "key": "peng-etal-2023-rwkv",
+   "text": "Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Leon Derczynski, Xingjian Du, Matteo Grella, Kranthi Gv, Xuzheng He, Haowen Hou, Przemyslaw Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra, Hayden Lau, Jiaju Lin, Krishna Sri Ipsit Mantri, Ferdinand Mom, Atsushi Saito, Guangyu Song, Xiangru Tang, Johan Wind, Stanisław Woźniak, Zhenyuan Zhang, Qinghua Zhou, Jian Zhu, and Rui-Jie Zhu. RWKV: Reinventing RNNs for the transformer era. In Houda Bouamor, Juan Pino, and Kalika Bali (eds.), Findings of the Association for Computational Linguistics: EMNLP 2023, pp. 14048–14077, Singapore, December 2023. Association for Computational Linguistics. . URL https://aclanthology.org/2023.findings-emnlp.936/.",
    "tier": "established"
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-   "key": "pearce2025bilinear",
-   "text": "Michael Pearce, Thomas Dooms, Alice Rigg, Jose Oramas, and Lee Sharkey. Bilinear mlps enable weight-based mechanistic interpretability. In Y. Yue, A. Garg, N. Peng, F. Sha, and R. Yu (eds.), International Conference on Learning Representations, volume 2025, pp. 47283–47310, 2025. URL https://proceedings.iclr.cc/paper_files/paper/2025/file/7504142a20a3e1fe9dd7de42f475828c-Paper-Conference.pdf.",
+   "key": "peng2025rwkv",
+   "text": "Bo Peng, Ruichong Zhang, Daniel Goldstein, Eric Alcaide, Xingjian Du, Haowen Hou, Jiaju Lin, Jiaxing Liu, Janna Lu, William Merrill, Guangyu Song, Kaifeng Tan, Saiteja Utpala, Nathan Wilce, Johan S. Wind, Tianyi Wu, Daniel Wuttke, and Christian Zhou-Zheng. RWKV-7 ”goose” with expressive dynamic state evolution. In Second Conference on Language Modeling, 2025. URL https://openreview.net/forum?id=ayB1PACN5j.",
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   },
   {
    "key": "perez2022redteaminglanguagemodels",
-   "text": "Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3419–3448, Abu Dhabi, United Arab Emirates, December 2022a. Association for Computational Linguistics. . URL https://aclanthology.org/2022.emnlp-main.225/.",
+   "text": "Ethan Perez, Saffron Huang, Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pp. 3419–3448, Abu Dhabi, United Arab Emirates, December 2022. Association for Computational Linguistics. . URL https://aclanthology.org/2022.emnlp-main.225/.",
    "tier": "established"
   },
   {
-   "key": "Ethan2022redteam",
-   "text": "Ethan Perez, Saffron Huang, H. Francis Song, Trevor Cai, Roman Ring, John Aslanides, Amelia Glaese, Nat McAleese, and Geoffrey Irving. Red teaming language models with language models. In Yoav Goldberg, Zornitsa Kozareva, and Yue Zhang (eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022, Abu Dhabi, United Arab Emirates, December 7-11, 2022, pp. 3419–3448. Association for Computational Linguistics, 2022b. . URL https://doi.org/10.18653/v1/2022.emnlp-main.225.",
+   "key": "petrov2026brokenmath",
+   "text": "Ivo Petrov, Jasper Dekoninck, and Martin Vechev. Brokenmath: A benchmark for sycophancy in theorem proving with LLMs. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=Y5bEI4cuMd.",
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@@ -3457,28 +4575,33 @@
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    "key": "piktus-etal-2023-roots",
    "text": "Aleksandra Piktus, Christopher Akiki, Paulo Villegas, Hugo Laurençon, Gérard Dupont, Sasha Luccioni, Yacine Jernite, and Anna Rogers. The ROOTS search tool: Data transparency for LLMs. In Danushka Bollegala, Ruihong Huang, and Alan Ritter (eds.), Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pp. 304–314, Toronto, Canada, July 2023a. Association for Computational Linguistics. . URL https://aclanthology.org/2023.acl-demo.29/.",
-   "tier": null
+   "tier": "established"
   },
   {
    "key": "piktus2023gaia",
    "text": "Aleksandra Piktus, Odunayo Ogundepo, Christopher Akiki, Akintunde Oladipo, Xinyu Zhang, Hailey Schoelkopf, Stella Biderman, Martin Potthast, and Jimmy Lin. Gaia search: Hugging face and pyserini interoperability for nlp training data exploration. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pp. 588–598, 2023b.",
+   "tier": "established"
+  },
+  {
+   "key": "plunkett",
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-  },
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+  {
+   "key": "quirke2026understandingadditionsubtractiontransformers",
+   "text": "Philip Quirke, Clement Neo, and Fazl Barez. Understanding addition and subtraction in transformers, 2026. URL https://arxiv.org/abs/2402.02619.",
+   "tier": "emerging"
+  },
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@@ -3487,37 +4610,57 @@
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+  },
+  {
+   "key": "rahaman2019spectral",
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+   "tier": "established"
   },
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-   "text": "Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. A practical review of mechanistic interpretability for transformer-based language models. arXiv preprint arXiv:2407.02646, 2024.",
+   "text": "Daking Rai, Yilun Zhou, Shi Feng, Abulhair Saparov, and Ziyu Yao. A practical review of mechanistic interpretability for transformer-based language models, 2025. URL https://arxiv.org/abs/2407.02646.",
    "tier": "emerging"
   },
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+   "text": "Senthooran Rajamanoharan, Arthur Conmy, Lewis Smith, Tom Lieberum, Vikrant Varma, Janos Kramar, Rohin Shah, and Neel Nanda. Improving sparse decomposition of language model activations with gated sparse autoencoders. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024a. URL https://openreview.net/forum?id=zLBlin2zvW.",
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+   "key": "rathi2026shaping",
+   "text": "Neil Rathi and Alec Radford. Shaping capabilities with token-level data filtering, 2026. URL https://arxiv.org/abs/2601.21571.",
+   "tier": "emerging"
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+   "key": "raval2026curveballsteeringrightdirection",
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+  {
+   "key": "bigbench",
+   "text": "Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, Aditya Gupta, Adrià Garriga-Alonso, Agnieszka Kluska, Aitor Lewkowycz, Akshat Agarwal, Alethea Power, Alex Ray, Alex Warstadt, Alexander W. Kocurek, Ali Safaya, Ali Tazarv, Alice Xiang, Alicia Parrish, Allen Nie, Aman Hussain, Amanda Askell, Amanda Dsouza, Ambrose Slone, Ameet Rahane, Anantharaman S. Iyer, Anders Johan Andreassen, Andrea Madotto, Andrea Santilli, Andreas Stuhlmüller, Andrew M. Dai, Andrew La, Andrew Kyle Lampinen, Andy Zou, Angela Jiang, Angelica Chen, Anh Vuong, Animesh Gupta, Anna Gottardi, Antonio Norelli, Anu Venkatesh, Arash Gholamidavoodi, Arfa Tabassum, Arul Menezes, Arun Kirubarajan, Asher Mullokandov, Ashish Sabharwal, Austin Herrick, Avia Efrat, Aykut Erdem, Ayla Karakaş, B. Ryan Roberts, Bao Sheng Loe, Barret Zoph, Bartłomiej Bojanowski, Batuhan Özyurt, Behnam Hedayatnia, Behnam Neyshabur, Benjamin Inden, Benno Stein, Berk Ekmekci, Bill Yuchen Lin, Blake Howald, Bryan Orinion, Cameron Diao, Cameron Dour, Catherine Stinson, Cedrick Argueta, Cesar Ferri, Chandan Singh, Charles Rathkopf, Chenlin Meng, Chitta Baral, Chiyu Wu, Chris Callison-Burch, Christopher Waites, Christian Voigt, Christopher D Manning, Christopher Potts, Cindy Ramirez, Clara E. Rivera, Clemencia Siro, Colin Raffel, Courtney Ashcraft, Cristina Garbacea, Damien Sileo, Dan Garrette, Dan Hendrycks, Dan Kilman, Dan Roth, C. Daniel Freeman, Daniel Khashabi, Daniel Levy, Daniel Moseguı́ González, Danielle Perszyk, Danny Hernandez, Danqi Chen, Daphne Ippolito, Dar Gilboa, David Dohan, David Drakard, David Jurgens, Debajyoti Datta, Deep Ganguli, Denis Emelin, Denis Kleyko, Deniz Yuret, Derek Chen, Derek Tam, Dieuwke Hupkes, Diganta Misra, Dilyar Buzan, Dimitri Coelho Mollo, Diyi Yang, Dong-Ho Lee, Dylan Schrader, Ekaterina Shutova, Ekin Dogus Cubuk, Elad Segal, Eleanor Hagerman, Elizabeth Barnes, Elizabeth Donoway, Ellie Pavlick, Emanuele Rodolà, Emma Lam, Eric Chu, Eric Tang, Erkut Erdem, Ernie Chang, Ethan A Chi, Ethan Dyer, Ethan Jerzak, Ethan Kim, Eunice Engefu Manyasi, Evgenii Zheltonozhskii, Fanyue Xia, Fatemeh Siar, Fernando Martı́nez-Plumed, Francesca Happé, Francois Chollet, Frieda Rong, Gaurav Mishra, Genta Indra Winata, Gerard de Melo, Germàn Kruszewski, Giambattista Parascandolo, Giorgio Mariani, Gloria Xinyue Wang, Gonzalo Jaimovitch-Lopez, Gregor Betz, Guy Gur-Ari, Hana Galijasevic, Hannah Kim, Hannah Rashkin, Hannaneh Hajishirzi, Harsh Mehta, Hayden Bogar, Henry Francis Anthony Shevlin, Hinrich Schuetze, Hiromu Yakura, Hongming Zhang, Hugh Mee Wong, Ian Ng, Isaac Noble, Jaap Jumelet, Jack Geissinger, Jackson Kernion, Jacob Hilton, Jaehoon Lee, Jaime Fernández Fisac, James B Simon, James Koppel, James Zheng, James Zou, Jan Kocon, Jana Thompson, Janelle Wingfield, Jared Kaplan, Jarema Radom, Jascha Sohl-Dickstein, Jason Phang, Jason Wei, Jason Yosinski, Jekaterina Novikova, Jelle Bosscher, Jennifer Marsh, Jeremy Kim, Jeroen Taal, Jesse Engel, Jesujoba Alabi, Jiacheng Xu, Jiaming Song, Jillian Tang, Joan Waweru, John Burden, John Miller, John U. Balis, Jonathan Batchelder, Jonathan Berant, Jörg Frohberg, Jos Rozen, Jose Hernandez-Orallo, Joseph Boudeman, Joseph Guerr, Joseph Jones, Joshua B. Tenenbaum, Joshua S. Rule, Joyce Chua, Kamil Kanclerz, Karen Livescu, Karl Krauth, Karthik Gopalakrishnan, Katerina Ignatyeva, Katja Markert, Kaustubh Dhole, Kevin Gimpel, Kevin Omondi, Kory Wallace Mathewson, Kristen Chiafullo, Ksenia Shkaruta, Kumar Shridhar, Kyle McDonell, Kyle Richardson, Laria Reynolds, Leo Gao, Li Zhang, Liam Dugan, Lianhui Qin, Lidia Contreras-Ochando, Louis-Philippe Morency, Luca Moschella, Lucas Lam, Lucy Noble, Ludwig Schmidt, Luheng He, Luis Oliveros-Colón, Luke Metz, Lütfi Kerem Senel, Maarten Bosma, Maarten Sap, Maartje Ter Hoeve, Maheen Farooqi, Manaal Faruqui, Mantas Mazeika, Marco Baturan, Marco Marelli, Marco Maru, Maria Jose Ramirez-Quintana, Marie Tolkiehn, Mario Giulianelli, Martha Lewis, Martin Potthast, Matthew L Leavitt, Matthias Hagen, Mátyás Schubert, Medina Orduna Baitemirova, Melody Arnaud, Melvin McElrath, Michael Andrew Yee, Michael Cohen, Michael Gu, Michael Ivanitskiy, Michael Starritt, Michael Strube, Michał Swędrowski, Michele Bevilacqua, Michihiro Yasunaga, Mihir Kale, Mike Cain, Mimee Xu, Mirac Suzgun, Mitch Walker, Mo Tiwari, Mohit Bansal, Moin Aminnaseri, Mor Geva, Mozhdeh Gheini, Mukund Varma T, Nanyun Peng, Nathan Andrew Chi, Nayeon Lee, Neta Gur-Ari Krakover, Nicholas Cameron, Nicholas Roberts, Nick Doiron, Nicole Martinez, Nikita Nangia, Niklas Deckers, Niklas Muennighoff, Nitish Shirish Keskar, Niveditha S. Iyer, Noah Constant, Noah Fiedel, Nuan Wen, Oliver Zhang, Omar Agha, Omar Elbaghdadi, Omer Levy, Owain Evans, Pablo Antonio Moreno Casares, Parth Doshi, Pascale Fung, Paul Pu Liang, Paul Vicol, Pegah Alipoormolabashi, Peiyuan Liao, Percy Liang, Peter W Chang, Peter Eckersley, Phu Mon Htut, Pinyu Hwang, Piotr Miłkowski, Piyush Patil, Pouya Pezeshkpour, Priti Oli, Qiaozhu Mei, Qing Lyu, Qinlang Chen, Rabin Banjade, Rachel Etta Rudolph, Raefer Gabriel, Rahel Habacker, Ramon Risco, Raphaël Millière, Rhythm Garg, Richard Barnes, Rif A. Saurous, Riku Arakawa, Robbe Raymaekers, Robert Frank, Rohan Sikand, Roman Novak, Roman Sitelew, Ronan Le Bras, Rosanne Liu, Rowan Jacobs, Rui Zhang, Russ Salakhutdinov, Ryan Andrew Chi, Seungjae Ryan Lee, Ryan Stovall, Ryan Teehan, Rylan Yang, Sahib Singh, Saif M. Mohammad, Sajant Anand, Sam Dillavou, Sam Shleifer, Sam Wiseman, Samuel Gruetter, Samuel R. Bowman, Samuel Stern Schoenholz, Sanghyun Han, Sanjeev Kwatra, Sarah A. Rous, Sarik Ghazarian, Sayan Ghosh, Sean Casey, Sebastian Bischoff, Sebastian Gehrmann, Sebastian Schuster, Sepideh Sadeghi, Shadi Hamdan, Sharon Zhou, Shashank Srivastava, Sherry Shi, Shikhar Singh, Shima Asaadi, Shixiang Shane Gu, Shubh Pachchigar, Shubham Toshniwal, Shyam Upadhyay, Shyamolima Shammie Debnath, Siamak Shakeri, Simon Thormeyer, Simone Melzi, Siva Reddy, Sneha Priscilla Makini, Soo-Hwan Lee, Spencer Torene, Sriharsha Hatwar, Stanislas Dehaene, Stefan Divic, Stefano Ermon, Stella Biderman, Stephanie Lin, Stephen Prasad, Steven Piantadosi, Stuart Shieber, Summer Misherghi, Svetlana Kiritchenko, Swaroop Mishra, Tal Linzen, Tal Schuster, Tao Li, Tao Yu, Tariq Ali, Tatsunori Hashimoto, Te-Lin Wu, Théo Desbordes, Theodore Rothschild, Thomas Phan, Tianle Wang, Tiberius Nkinyili, Timo Schick, Timofei Kornev, Titus Tunduny, Tobias Gerstenberg, Trenton Chang, Trishala Neeraj, Tushar Khot, Tyler Shultz, Uri Shaham, Vedant Misra, Vera Demberg, Victoria Nyamai, Vikas Raunak, Vinay Venkatesh Ramasesh, vinay uday prabhu, Vishakh Padmakumar, Vivek Srikumar, William Fedus, William Saunders, William Zhang, Wout Vossen, Xiang Ren, Xiaoyu Tong, Xinran Zhao, Xinyi Wu, Xudong Shen, Yadollah Yaghoobzadeh, Yair Lakretz, Yangqiu Song, Yasaman Bahri, Yejin Choi, Yichi Yang, Sophie Hao, Yifu Chen, Yonatan Belinkov, Yu Hou, Yufang Hou, Yuntao Bai, Zachary Seid, Zhuoye Zhao, Zijian Wang, Zijie J. Wang, Zirui Wang, and Ziyi Wu. Beyond the imitation game: Quantifying and extrapolating the capabilities of language models. Transactions on Machine Learning Research, 2023. ISSN 2835-8856. URL https://openreview.net/forum?id=uyTL5Bvosj. Featured Certification.",
+   "tier": "established"
+  },
+  {
+   "key": "3692070.3693959",
+   "text": "Dashiell Stander, Qinan Yu, Honglu Fan, and Stella Biderman. Grokking group multiplication with cosets. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024.",
+   "tier": "established"
+  },
   {
    "key": "ig",
-   "text": "Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning, pp. 3319–3328. JMLR.org, 2017.",
+   "text": "Mukund Sundararajan, Ankur Taly, and Qiqi Yan. Axiomatic attribution for deep networks. In Proceedings of the 34th International Conference on Machine Learning - Volume 70, ICML’17, pp. 3319–3328. JMLR.org, 2017.",
    "tier": "established"
   },
   {
-   "key": "szegedy2015going",
-   "text": "Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.",
+   "key": "sutter2025the",
+   "text": "Denis Sutter, Julian Minder, Thomas Hofmann, and Tiago Pimentel. The non-linear representation dilemma: Is causal abstraction enough for mechanistic interpretability? In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=ZYXTLo7kCi.",
    "tier": "established"
   },
   {
-   "key": "tan2025analyzinggeneralizationreliabilitysteering",
-   "text": "Daniel Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, and Robert Kirk. Analysing the generalisation and reliability of steering vectors. In Proceedings of the 38th International Conference on Neural Information Processing Systems, NIPS ’24, Red Hook, NY, USA, 2024a. Curran Associates Inc. ISBN 9798331314385.",
+   "key": "syed2023attributionpatchingoutperformsautomated",
+   "text": "Aaquib Syed, Can Rager, and Arthur Conmy. Attribution patching outperforms automated circuit discovery. In Yonatan Belinkov, Najoung Kim, Jaap Jumelet, Hosein Mohebbi, Aaron Mueller, and Hanjie Chen (eds.), Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pp. 407–416, Miami, Florida, US, November 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.blackboxnlp-1.25/.",
+   "tier": "emerging"
+  },
+  {
+   "key": "szegedy2015going",
+   "text": "Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1–9, 2015.",
    "tier": "established"
   },
   {
    "key": "tan2024analysing",
-   "text": "Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, and Robert Kirk. Analysing the generalisation and reliability of steering vectors. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024b.",
+   "text": "Daniel Chee Hian Tan, David Chanin, Aengus Lynch, Brooks Paige, Dimitrios Kanoulas, Adrià Garriga-Alonso, and Robert Kirk. Analysing the generalisation and reliability of steering vectors. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024.",
    "tier": "established"
   },
   {
@@ -3649,9 +4922,24 @@
    "text": "Ruixiang Tang, Dehan Kong, Longtao Huang, and Hui Xue. Large language models can be lazy learners: Analyze shortcuts in in-context learning. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki (eds.), Findings of the Association for Computational Linguistics: ACL 2023, pp. 4645–4657, Toronto, Canada, July 2023. Association for Computational Linguistics. . URL https://aclanthology.org/2023.findings-acl.284/.",
    "tier": "established"
   },
+  {
+   "key": "taylor2025school",
+   "text": "Mia Taylor, James Chua, Jan Betley, Johannes Treutlein, and Owain Evans. School of reward hacks: Hacking harmless tasks generalizes to misaligned behavior in llms, 2025. URL https://arxiv.org/abs/2508.17511.",
+   "tier": "emerging"
+  },
   {
    "key": "gemmateam2024gemmaopenmodelsbased",
-   "text": "Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, Justin Mao-Jones, Katherine Lee, Kathy Yu, Katie Millican, Lars Lowe Sjoesund, Lisa Lee, Lucas Dixon, Machel Reid, Maciej Mikuła, Mateo Wirth, Michael Sharman, Nikolai Chinaev, Nithum Thain, Olivier Bachem, Oscar Chang, Oscar Wahltinez, Paige Bailey, Paul Michel, Petko Yotov, Rahma Chaabouni, Ramona Comanescu, Reena Jana, Rohan Anil, Ross McIlroy, Ruibo Liu, Ryan Mullins, Samuel L Smith, Sebastian Borgeaud, Sertan Girgin, Sholto Douglas, Shree Pandya, Siamak Shakeri, Soham De, Ted Klimenko, Tom Hennigan, Vlad Feinberg, Wojciech Stokowiec, Yu hui Chen, Zafarali Ahmed, Zhitao Gong, Tris Warkentin, Ludovic Peran, Minh Giang, Clément Farabet, Oriol Vinyals, Jeff Dean, Koray Kavukcuoglu, Demis Hassabis, Zoubin Ghahramani, Douglas Eck, Joelle Barral, Fernando Pereira, Eli Collins, Armand Joulin, Noah Fiedel, Evan Senter, Alek Andreev, and Kathleen Kenealy. Gemma: Open models based on gemini research and technology, 2024. URL https://arxiv.org/abs/2403.08295.",
+   "text": "Gemma Team, Thomas Mesnard, Cassidy Hardin, Robert Dadashi, Surya Bhupatiraju, Shreya Pathak, Laurent Sifre, Morgane Rivière, Mihir Sanjay Kale, Juliette Love, Pouya Tafti, Léonard Hussenot, Pier Giuseppe Sessa, Aakanksha Chowdhery, Adam Roberts, Aditya Barua, Alex Botev, Alex Castro-Ros, Ambrose Slone, Amélie Héliou, Andrea Tacchetti, Anna Bulanova, Antonia Paterson, Beth Tsai, Bobak Shahriari, Charline Le Lan, Christopher A. Choquette-Choo, Clément Crepy, Daniel Cer, Daphne Ippolito, David Reid, Elena Buchatskaya, Eric Ni, Eric Noland, Geng Yan, George Tucker, George-Christian Muraru, Grigory Rozhdestvenskiy, Henryk Michalewski, Ian Tenney, Ivan Grishchenko, Jacob Austin, James Keeling, Jane Labanowski, Jean-Baptiste Lespiau, Jeff Stanway, Jenny Brennan, Jeremy Chen, Johan Ferret, Justin Chiu, Justin Mao-Jones, Katherine Lee, Kathy Yu, Katie Millican, Lars Lowe Sjoesund, Lisa Lee, Lucas Dixon, Machel Reid, Maciej Mikuła, Mateo Wirth, Michael Sharman, Nikolai Chinaev, Nithum Thain, Olivier Bachem, Oscar Chang, Oscar Wahltinez, Paige Bailey, Paul Michel, Petko Yotov, Rahma Chaabouni, Ramona Comanescu, Reena Jana, Rohan Anil, Ross McIlroy, Ruibo Liu, Ryan Mullins, Samuel L Smith, Sebastian Borgeaud, Sertan Girgin, Sholto Douglas, Shree Pandya, Siamak Shakeri, Soham De, Ted Klimenko, Tom Hennigan, Vlad Feinberg, Wojciech Stokowiec, Yu hui Chen, Zafarali Ahmed, Zhitao Gong, Tris Warkentin, Ludovic Peran, Minh Giang, Clément Farabet, Oriol Vinyals, Jeff Dean, Koray Kavukcuoglu, Demis Hassabis, Zoubin Ghahramani, Douglas Eck, Joelle Barral, Fernando Pereira, Eli Collins, Armand Joulin, Noah Fiedel, Evan Senter, Alek Andreev, and Kathleen Kenealy. Gemma: Open models based on gemini research and technology, 2024a. URL https://arxiv.org/abs/2403.08295.",
+   "tier": "emerging"
+  },
+  {
+   "key": "gemmateam2024gemma2improvingopen",
+   "text": "Gemma Team, Morgane Riviere, Shreya Pathak, Pier Giuseppe Sessa, Cassidy Hardin, Surya Bhupatiraju, Léonard Hussenot, Thomas Mesnard, Bobak Shahriari, Alexandre Ramé, Johan Ferret, Peter Liu, Pouya Tafti, Abe Friesen, Michelle Casbon, Sabela Ramos, Ravin Kumar, Charline Le Lan, Sammy Jerome, Anton Tsitsulin, Nino Vieillard, Piotr Stanczyk, Sertan Girgin, Nikola Momchev, Matt Hoffman, Shantanu Thakoor, Jean-Bastien Grill, Behnam Neyshabur, Olivier Bachem, Alanna Walton, Aliaksei Severyn, Alicia Parrish, Aliya Ahmad, Allen Hutchison, Alvin Abdagic, Amanda Carl, Amy Shen, Andy Brock, Andy Coenen, Anthony Laforge, Antonia Paterson, Ben Bastian, Bilal Piot, Bo Wu, Brandon Royal, Charlie Chen, Chintu Kumar, Chris Perry, Chris Welty, Christopher A. Choquette-Choo, Danila Sinopalnikov, David Weinberger, Dimple Vijaykumar, Dominika Rogozińska, Dustin Herbison, Elisa Bandy, Emma Wang, Eric Noland, Erica Moreira, Evan Senter, Evgenii Eltyshev, Francesco Visin, Gabriel Rasskin, Gary Wei, Glenn Cameron, Gus Martins, Hadi Hashemi, Hanna Klimczak-Plucińska, Harleen Batra, Harsh Dhand, Ivan Nardini, Jacinda Mein, Jack Zhou, James Svensson, Jeff Stanway, Jetha Chan, Jin Peng Zhou, Joana Carrasqueira, Joana Iljazi, Jocelyn Becker, Joe Fernandez, Joost van Amersfoort, Josh Gordon, Josh Lipschultz, Josh Newlan, Ju yeong Ji, Kareem Mohamed, Kartikeya Badola, Kat Black, Katie Millican, Keelin McDonell, Kelvin Nguyen, Kiranbir Sodhia, Kish Greene, Lars Lowe Sjoesund, Lauren Usui, Laurent Sifre, Lena Heuermann, Leticia Lago, Lilly McNealus, Livio Baldini Soares, Logan Kilpatrick, Lucas Dixon, Luciano Martins, Machel Reid, Manvinder Singh, Mark Iverson, Martin Görner, Mat Velloso, Mateo Wirth, Matt Davidow, Matt Miller, Matthew Rahtz, Matthew Watson, Meg Risdal, Mehran Kazemi, Michael Moynihan, Ming Zhang, Minsuk Kahng, Minwoo Park, Mofi Rahman, Mohit Khatwani, Natalie Dao, Nenshad Bardoliwalla, Nesh Devanathan, Neta Dumai, Nilay Chauhan, Oscar Wahltinez, Pankil Botarda, Parker Barnes, Paul Barham, Paul Michel, Pengchong Jin, Petko Georgiev, Phil Culliton, Pradeep Kuppala, Ramona Comanescu, Ramona Merhej, Reena Jana, Reza Ardeshir Rokni, Rishabh Agarwal, Ryan Mullins, Samaneh Saadat, Sara Mc Carthy, Sarah Cogan, Sarah Perrin, Sébastien M. R. Arnold, Sebastian Krause, Shengyang Dai, Shruti Garg, Shruti Sheth, Sue Ronstrom, Susan Chan, Timothy Jordan, Ting Yu, Tom Eccles, Tom Hennigan, Tomas Kocisky, Tulsee Doshi, Vihan Jain, Vikas Yadav, Vilobh Meshram, Vishal Dharmadhikari, Warren Barkley, Wei Wei, Wenming Ye, Woohyun Han, Woosuk Kwon, Xiang Xu, Zhe Shen, Zhitao Gong, Zichuan Wei, Victor Cotruta, Phoebe Kirk, Anand Rao, Minh Giang, Ludovic Peran, Tris Warkentin, Eli Collins, Joelle Barral, Zoubin Ghahramani, Raia Hadsell, D. Sculley, Jeanine Banks, Anca Dragan, Slav Petrov, Oriol Vinyals, Jeff Dean, Demis Hassabis, Koray Kavukcuoglu, Clement Farabet, Elena Buchatskaya, Sebastian Borgeaud, Noah Fiedel, Armand Joulin, Kathleen Kenealy, Robert Dadashi, and Alek Andreev. Gemma 2: Improving open language models at a practical size, 2024b. URL https://arxiv.org/abs/2408.00118.",
+   "tier": "emerging"
+  },
+  {
+   "key": "kimiteam2026kimik2openagentic",
+   "text": "Kimi Team, Yifan Bai, Yiping Bao, Y. Charles, Cheng Chen, Guanduo Chen, Haiting Chen, Huarong Chen, Jiahao Chen, Ningxin Chen, Ruijue Chen, Yanru Chen, Yuankun Chen, Yutian Chen, Zhuofu Chen, Jialei Cui, Hao Ding, Mengnan Dong, Angang Du, Chenzhuang Du, Dikang Du, Yulun Du, Yu Fan, Yichen Feng, Kelin Fu, Bofei Gao, Chenxiao Gao, Hongcheng Gao, Peizhong Gao, Tong Gao, Yuyao Ge, Shangyi Geng, Qizheng Gu, Xinran Gu, Longyu Guan, Haiqing Guo, Jianhang Guo, Xiaoru Hao, Tianhong He, Weiran He, Wenyang He, Yunjia He, Chao Hong, Hao Hu, Yangyang Hu, Zhenxing Hu, Weixiao Huang, Zhiqi Huang, Zihao Huang, Tao Jiang, Zhejun Jiang, Xinyi Jin, Yongsheng Kang, Guokun Lai, Cheng Li, Fang Li, Haoyang Li, Ming Li, Wentao Li, Yang Li, Yanhao Li, Yiwei Li, Zhaowei Li, Zheming Li, Hongzhan Lin, Xiaohan Lin, Zongyu Lin, Chengyin Liu, Chenyu Liu, Hongzhang Liu, Jingyuan Liu, Junqi Liu, Liang Liu, Shaowei Liu, T. Y. Liu, Tianwei Liu, Weizhou Liu, Yangyang Liu, Yibo Liu, Yiping Liu, Yue Liu, Zhengying Liu, Enzhe Lu, Haoyu Lu, Lijun Lu, Yashuo Luo, Shengling Ma, Xinyu Ma, Yingwei Ma, Shaoguang Mao, Jie Mei, Xin Men, Yibo Miao, Siyuan Pan, Yebo Peng, Ruoyu Qin, Zeyu Qin, Bowen Qu, Zeyu Shang, Lidong Shi, Shengyuan Shi, Feifan Song, Jianlin Su, Zhengyuan Su, Lin Sui, Xinjie Sun, Flood Sung, Yunpeng Tai, Heyi Tang, Jiawen Tao, Qifeng Teng, Chaoran Tian, Chensi Wang, Dinglu Wang, Feng Wang, Hailong Wang, Haiming Wang, Jianzhou Wang, Jiaxing Wang, Jinhong Wang, Shengjie Wang, Shuyi Wang, Si Wang, Xinyuan Wang, Yao Wang, Yejie Wang, Yiqin Wang, Yuxin Wang, Yuzhi Wang, Zhaoji Wang, Zhengtao Wang, Zhengtao Wang, Zhexu Wang, Chu Wei, Qianqian Wei, Haoning Wu, Wenhao Wu, Xingzhe Wu, Yuxin Wu, Chenjun Xiao, Jin Xie, Xiaotong Xie, Weimin Xiong, Boyu Xu, Jinjing Xu, L. H. Xu, Lin Xu, Suting Xu, Weixin Xu, Xinran Xu, Yangchuan Xu, Ziyao Xu, Jing Xu, Jing Xu, Junjie Yan, Yuzi Yan, Hao Yang, Xiaofei Yang, Yi Yang, Ying Yang, Zhen Yang, Zhilin Yang, Zonghan Yang, Haotian Yao, Xingcheng Yao, Wenjie Ye, Zhuorui Ye, Bohong Yin, Longhui Yu, Enming Yuan, Hongbang Yuan, Mengjie Yuan, Siyu Yuan, Haobing Zhan, Dehao Zhang, Hao Zhang, Wanlu Zhang, Xiaobin Zhang, Yadong Zhang, Yangkun Zhang, Yichi Zhang, Yizhi Zhang, Yongting Zhang, Yu Zhang, Yutao Zhang, Yutong Zhang, Zheng Zhang, Haotian Zhao, Yikai Zhao, Zijia Zhao, Huabin Zheng, Shaojie Zheng, Longguang Zhong, Jianren Zhou, Xinyu Zhou, Zaida Zhou, Jinguo Zhu, Zhen Zhu, Weiyu Zhuang, and Xinxing Zu. Kimi k2: Open agentic intelligence, 2026. URL https://arxiv.org/abs/2507.20534.",
    "tier": "emerging"
   },
   {
@@ -3661,23 +4949,28 @@
   },
   {
    "key": "tenney2019bertrediscoversclassicalnlp",
-   "text": "Ian Tenney, Dipanjan Das, and Ellie Pavlick. Bert rediscovers the classical nlp pipeline, 2019. URL https://arxiv.org/abs/1905.05950.",
-   "tier": "emerging"
+   "text": "Ian Tenney, Dipanjan Das, and Ellie Pavlick. BERT rediscovers the classical NLP pipeline. In Anna Korhonen, David Traum, and Lluís Màrquez (eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4593–4601, Florence, Italy, July 2019. Association for Computational Linguistics. . URL https://aclanthology.org/P19-1452/.",
+   "tier": "established"
   },
   {
    "key": "thasarathan_universal_2025",
    "text": "Harrish Thasarathan, Julian Forsyth, Thomas Fel, Matthew Kowal, and Konstantinos G. Derpanis. Universal sparse autoencoders: Interpretable cross-model concept alignment. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=UoaxRN88oR.",
-   "tier": null
+   "tier": "established"
   },
   {
    "key": "smds",
-   "text": "Federico Tiblias, Irina Bigoulaeva, Jingcheng Niu, Simone Balloccu, and Iryna Gurevych. Shape happens: Automatic feature manifold discovery in llms via supervised multi-dimensional scaling. arXiv preprint arXiv:2510.01025, 2025.",
-   "tier": "emerging"
+   "text": "Federico Tiblias, Irina Bigoulaeva, Jingcheng Niu, Simone Balloccu, and Iryna Gurevych. Hypothesis-driven feature manifold analysis in LLMs via supervised multi-dimensional scaling. Transactions on Machine Learning Research, 2026. ISSN 2835-8856. URL https://openreview.net/forum?id=vCKZ40YYPr.",
+   "tier": "established"
   },
   {
    "key": "tigges2024llm",
    "text": "Curt Tigges, Michael Hanna, Qinan Yu, and Stella Biderman. Llm circuit analyses are consistent across training and scale. Advances in Neural Information Processing Systems, 37: 40699–40731, 2024.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "tinaz2025emergence",
+   "text": "Berk Tinaz, Zalan Fabian, and Mahdi Soltanolkotabi. Emergence and evolution of interpretable concepts in diffusion models. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025. URL https://openreview.net/forum?id=C7LxkebvVW.",
+   "tier": "established"
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   {
    "key": "touvron2023llamaopenefficientfoundation",
@@ -3692,22 +4985,27 @@
   {
    "key": "turpin2023languagemodelsdontsay",
    "text": "Miles Turpin, Julian Michael, Ethan Perez, and Samuel R. Bowman. Language models don’t always say what they think: unfaithful explanations in chain-of-thought prompting. In Proceedings of the 37th International Conference on Neural Information Processing Systems, NIPS ’23, Red Hook, NY, USA, 2023. Curran Associates Inc.",
-   "tier": null
+   "tier": "established"
   },
   {
-   "key": "moritz2024sthocastic",
-   "text": "Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs, and Julia E. Vogt. Stochastic concept bottleneck models. In A. Globerson, L. Mackey, D. Belgrave, A. Fan, U. Paquet, J. Tomczak, and C. Zhang (eds.), Advances in Neural Information Processing Systems, volume 37, pp. 51787–51810. Curran Associates, Inc., 2024. . URL https://proceedings.neurips.cc/paper_files/paper/2024/file/5c7894ac8788555f1cecf536f1e0fd35-Paper-Conference.pdf.",
+   "key": "vanderwal2025polypythias",
+   "text": "Oskar van der Wal, Pietro Lesci, Max Müller-Eberstein, Naomi Saphra, Hailey Schoelkopf, Willem Zuidema, and Stella Biderman. PolyPythias: Stability and outliers across fifty language model pre-training runs. In The Thirteenth International Conference on Learning Representations, 2025.",
    "tier": "established"
   },
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-   "key": "vaswani2017attention",
-   "text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017a. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.",
+   "key": "vandenhirtz2024stochastic",
+   "text": "Moritz Vandenhirtz, Sonia Laguna, Ričards Marcinkevičs, and Julia E Vogt. Stochastic concept bottleneck models. In The Thirty-eighth Annual Conference on Neural Information Processing Systems, 2024. URL https://openreview.net/forum?id=iSjqTQ5S1f.",
    "tier": "established"
   },
   {
-   "key": "vaswani2023attentionneed",
-   "text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017b. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.",
-   "tier": null
+   "key": "varma2023explaining",
+   "text": "Vikrant Varma, Rohin Shah, Zachary Kenton, János Kramár, and Ramana Kumar. Explaining grokking through circuit efficiency, 2023. URL https://arxiv.org/abs/2309.02390.",
+   "tier": "emerging"
+  },
+  {
+   "key": "vaswani2017attention",
+   "text": "Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc., 2017. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.",
+   "tier": "established"
   },
   {
    "key": "vig2019multiscale",
@@ -3715,29 +5013,39 @@
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   },
   {
-   "key": "vig2020causalmediationanalysisinterpreting",
-   "text": "Jesse Vig, Sebastian Gehrmann, Yonatan Belinkov, Sharon Qian, Daniel Nevo, Yaron Singer, and Stuart Shieber. Investigating gender bias in language models using causal mediation analysis. In H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (eds.), Advances in Neural Information Processing Systems, volume 33, pp. 12388–12401. Curran Associates, Inc., 2020. URL https://proceedings.neurips.cc/paper_files/paper/2020/file/92650b2e92217715fe312e6fa7b90d82-Paper.pdf.",
-   "tier": "established"
+   "key": "vig-belinkov-2019-analyzing",
+   "text": "Jesse Vig and Yonatan Belinkov. Analyzing the structure of attention in a transformer language model. In Tal Linzen, Grzegorz Chrupała, Yonatan Belinkov, and Dieuwke Hupkes (eds.), Proceedings of the 2019 ACL Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP, pp. 63–76, Florence, Italy, August 2019. Association for Computational Linguistics. . URL https://aclanthology.org/W19-4808/.",
+   "tier": "emerging"
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    "key": "voita-titov-2020-information",
    "text": "Elena Voita and Ivan Titov. Information-theoretic probing with minimum description length. In Bonnie Webber, Trevor Cohn, Yulan He, and Yang Liu (eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 183–196, Online, November 2020. Association for Computational Linguistics. . URL https://aclanthology.org/2020.emnlp-main.14/.",
-   "tier": null
+   "tier": "established"
   },
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    "key": "voita_analyzing_2019",
-   "text": "Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, 2019.",
-   "tier": null
+   "text": "Elena Voita, David Talbot, Fedor Moiseev, Rico Sennrich, and Ivan Titov. Analyzing multi-head self-attention: Specialized heads do the heavy lifting, the rest can be pruned. In Anna Korhonen, David Traum, and Lluís Màrquez (eds.), Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 5797–5808, Florence, Italy, July 2019. Association for Computational Linguistics. . URL https://aclanthology.org/P19-1580/.",
+   "tier": "established"
+  },
+  {
+   "key": "wallace2024instructionhierarchytrainingllms",
+   "text": "Eric Wallace, Kai Xiao, Reimar Leike, Lilian Weng, Johannes Heidecke, and Alex Beutel. The instruction hierarchy: Training llms to prioritize privileged instructions, 2024. URL https://arxiv.org/abs/2404.13208.",
+   "tier": "emerging"
   },
   {
    "key": "SuperGLUE",
-   "text": "Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. Superglue: A stickier benchmark for general-purpose language understanding systems. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d' Alché-Buc, E. Fox, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf.",
+   "text": "Alex Wang, Yada Pruksachatkun, Nikita Nangia, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. Superglue: A stickier benchmark for general-purpose language understanding systems. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (eds.), Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc., 2019. URL https://proceedings.neurips.cc/paper_files/paper/2019/file/4496bf24afe7fab6f046bf4923da8de6-Paper.pdf.",
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-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "wang2025differentiation",
+   "text": "George Wang, Jesse Hoogland, Stan van Wingerden, Zach Furman, and Daniel Murfet. Differentiation and specialization of attention heads via the refined local learning coefficient. In International Conference on Learning Representations, 2025b.",
+   "tier": "established"
   },
   {
    "key": "wang2022interpretabilitywildcircuitindirect",
@@ -3747,11 +5055,21 @@
   {
    "key": "wang_towards_2018",
    "text": "Liwei Wang, Lunjia Hu, Jiayuan Gu, Zhiqiang Hu, Yue Wu, Kun He, and John Hopcroft. Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation. In Advances in Neural Information Processing Systems, volume 31. Curran Associates, Inc., 2018. URL https://proceedings.neurips.cc/paper_files/paper/2018/hash/5fc34ed307aac159a30d81181c99847e-Abstract.html.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "wang2026persona",
+   "text": "Miles Wang, Tom Dupré la Tour, Olivia Watkins, Aleksandar Makelov, Ryan Chi, Samuel Miserendino, Jeffrey Wang, Achyuta Rajaram, Johannes Heidecke, Tejal Patwardhan, and Daniel Mossing. Persona features control emergent misalignment. In The Fourteenth International Conference on Learning Representations, 2026a.",
+   "tier": "established"
+  },
+  {
+   "key": "chatbotknows",
+   "text": "Wenxuan Wang, Zirui Liu, Haoxuan Kou, Xuefeng Liu, and Jiaxing Shen. The chatbot knows it’s you: Dialogue attribution in unauthenticated human–llm sessions. In Proceedings of the ACM Web Conference 2026, WWW ’26, pp. 8797–8807, New York, NY, USA, 2026b. Association for Computing Machinery. ISBN 9798400723070. . URL https://doi.org/10.1145/3774904.3793048.",
+   "tier": "established"
   },
   {
-   "key": "wang_persona_2025",
-   "text": "Miles Wang, Tom Dupré la Tour, Olivia Watkins, Alex Makelov, Ryan A. Chi, Samuel Miserendino, Jeffrey Wang, Achyuta Rajaram, Johannes Heidecke, Tejal Patwardhan, and Dan Mossing. Persona Features Control Emergent Misalignment, October 2025b. URL http://arxiv.org/abs/2506.19823. arXiv:2506.19823 [cs].",
+   "key": "wang2026dlmscopemechanisticinterpretabilitydiffusion",
+   "text": "Xu Wang, Bingqing Jiang, Yu Wan, Baosong Yang, Lingpeng Kong, and Difan Zou. Dlm-scope: Mechanistic interpretability of diffusion language models via sparse autoencoders, 2026c. URL https://arxiv.org/abs/2602.05859.",
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   },
+  {
+   "key": "wani-etal-2026-answer",
+   "text": "Farooq Ahmad Wani, Alessandro Suglia, Rohit Saxena, Aryo Pradipta Gema, Wai-Chung Kwan, Fazl Barez, Maria Sofia Bucarelli, Fabrizio Silvestri, and Pasquale Minervini. Same answer, different representations: Hidden instability in VLMs. In Hung-Yi Lee, Sunipa Dev, Junyi Jessy Li, and Desmond Elliott (eds.), Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pp. 41059–41087, Budapest, Hungary, October 2026. Association for Computational Linguistics. ISBN 979-8-89176-459-0. . URL https://aclanthology.org/2026.emnlp-main.1803/.",
+   "tier": "established"
+  },
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    "key": "watanabe2009algebraic",
    "text": "Sumio Watanabe. Algebraic Geometry and Statistical Learning Theory. Cambridge University Press, 2009. ISBN 9780521864671.",
-   "tier": null
+   "tier": "established"
+  },
+  {
+   "key": "wei2022emergent",
+   "text": "Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. Emergent abilities of large language models. Transactions on Machine Learning Research, 2022. URL https://openreview.net/forum?id=yzkSU5zdwD.",
+   "tier": "established"
+  },
+  {
+   "key": "11370531",
+   "text": "Zeming Wei, Yifei Wang, Ang Li, Yichuan Mo, and Yisen Wang. Jailbreak and guard aligned language models with only few in-context demonstrations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 48 (6): 6835–6846, 2026. .",
+   "tier": "established"
   },
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   },
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-   "key": "querycircuit",
-   "text": "Tung-Yu Wu and Fazl Barez. Query circuits: Explaining how language models answer user prompts. arXiv preprint arXiv:2509.24808, 2025.",
+   "key": "icml2025actionable",
+   "text": "Workshop Organizers. Workshop on actionable interpretability. ICML 2025, 2025. URL https://actionable-interpretability.github.io/.",
    "tier": "emerging"
   },
+  {
+   "key": "querycircuit",
+   "text": "Tung-Yu Wu and Fazl Barez. Query circuits: Explaining how language models answer user prompts. In Forty-third International Conference on Machine Learning, 2026. URL https://openreview.net/forum?id=7F0sragazb.",
+   "tier": "established"
+  },
+  {
+   "key": "wu2025ushaped",
+   "text": "Tung-Yu Wu and Pei-Yu Lo. U-shaped and inverted-u scaling behind emergent abilities of large language models. In International Conference on Learning Representations, 2025. URL https://arxiv.org/abs/2410.01692.",
+   "tier": "established"
+  },
+  {
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+   "text": "Tung-Yu Wu, Yu-Xiang Lin, and Tsui-Wei Weng. And: audio network dissection for interpreting deep acoustic models. In Proceedings of the 41st International Conference on Machine Learning, ICML’24. JMLR.org, 2024.",
+   "tier": "established"
+  },
+  {
+   "key": "wu2025towards",
+   "text": "Wilson Wu, Louis Jaburi, jacob drori, and Jason Gross. Towards a unified and verified understanding of group-operation networks. In The Thirteenth International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=8xxEBAtD7y.",
+   "tier": "established"
+  },
+  {
+   "key": "wu-etal-2022-causal",
+   "text": "Zhengxuan Wu, Atticus Geiger, Joshua Rozner, Elisa Kreiss, Hanson Lu, Thomas Icard, Christopher Potts, and Noah Goodman. Causal distillation for language models. In Marine Carpuat, Marie-Catherine de Marneffe, and Ivan Vladimir Meza Ruiz (eds.), Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4288–4295, Seattle, United States, July 2022. Association for Computational Linguistics. . URL https://aclanthology.org/2022.naacl-main.318/.",
+   "tier": "established"
+  },
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    "key": "wu2025axbench",
-   "text": "Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. Axbench: Steering LLMs? even simple baselines outperform sparse autoencoders. In Forty-second International Conference on Machine Learning, 2025. URL https://openreview.net/forum?id=K2CckZjNy0.",
+   "text": "Zhengxuan Wu, Aryaman Arora, Atticus Geiger, Zheng Wang, Jing Huang, Dan Jurafsky, Christopher D Manning, and Christopher Potts. Axbench: Steering LLMs? even simple baselines outperform sparse autoencoders. In Forty-second International Conference on Machine Learning, 2025b. URL https://openreview.net/forum?id=K2CckZjNy0.",
+   "tier": "established"
+  },
+  {
+   "key": "wurgaft2026manifoldsteeringrevealsshared",
+   "text": "Daniel Wurgaft, Can Rager, Matthew Kowal, Vasudev Shyam, Sheridan Feucht, Usha Bhalla, Tal Haklay, Eric Bigelow, Raphael Sarfati, Thomas McGrath, Owen Lewis, Jack Merullo, Noah Goodman, Thomas Fel, Atticus Geiger, and Ekdeep Singh Lubana. Manifold steering reveals the shared geometry of neural network representation and behavior, 2026. URL https://arxiv.org/abs/2605.05115.",
+   "tier": "emerging"
+  },
+  {
+   "key": "xie2023defending",
+   "text": "Yueqi Xie, Jingwei Yi, Jiawei Shao, Justin Curl, Lingjuan Lyu, Qifeng Chen, Xing Xie, and Fangzhao Wu. Defending ChatGPT against jailbreak attack via self-reminders. Nature Machine Intelligence, 5 (12): 1486–1496, 2023. .",
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   },
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-   "key": "xu2025infinigramminiexactngram",
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-   "tier": null
+   "key": "xu2024saetrack",
+   "text": "Yang Xu, Yi Wang, Hengguan Huang, and Hao Wang. Tracking the feature dynamics in llm training: A mechanistic study, 2025b. URL https://arxiv.org/abs/2412.17626.",
+   "tier": "emerging"
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    "key": "yang2025mixture",
@@ -3799,19 +5167,29 @@
    "text": "Wenqian Ye, Luyang Jiang, Eric Xie, Guangtao Zheng, Yunsheng Ma, Xu Cao, Dongliang Guo, Daiqing Qi, Zeyu He, Yijun Tian, Christopher W. Porter, Megan Coffee, Zhe Zeng, Sheng Li, Ziran Wang, Ting-Hao Kenneth Huang, James Matthew Rehg, Henry Kautz, and Aidong Zhang. The clever hans mirage: A comprehensive survey on spurious correlations in machine learning. Transactions on Machine Learning Research, 2026. ISSN 2835-8856. URL https://openreview.net/forum?id=kIuqPmS1b1.",
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+  {
+   "key": "yin2025which",
+   "text": "Kayo Yin and Jacob Steinhardt. Which attention heads matter for in-context learning? In Aarti Singh, Maryam Fazel, Daniel Hsu, Simon Lacoste-Julien, Felix Berkenkamp, Tegan Maharaj, Kiri Wagstaff, and Jerry Zhu (eds.), Proceedings of the 42nd International Conference on Machine Learning, volume 267 of Proceedings of Machine Learning Research, pp. 72428–72461. PMLR, 13–19 Jul 2025. URL https://proceedings.mlr.press/v267/yin25e.html.",
+   "tier": "established"
+  },
+  {
+   "key": "yu2025safety",
+   "text": "Cheng Yu and Orestis Papakyriakopoulos. Safety devolution in AI agents. In ICLR 2025 Workshop on Human-AI Coevolution, 2025. URL https://openreview.net/forum?id=7nJmuFFkWd.",
+   "tier": "emerging"
+  },
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    "text": "Xuemin Yu, Fahim Dalvi, Nadir Durrani, Marzia Nouri, and Hassan Sajjad. Latent concept-based explanation of NLP models. In Yaser Al-Onaizan, Mohit Bansal, and Yun-Nung Chen (eds.), Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pp. 12435–12459, Miami, Florida, USA, November 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.emnlp-main.692/.",
    "tier": "established"
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-   "key": "yun-etal-2021-transformer",
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-   "tier": null
+   "key": "yu2026vectorquantizedlatentconcepts",
+   "text": "Xuemin Yu, Ankur Garg, Samira Ebrahimi Kahou, and Hassan Sajjad. Vector quantized latent concepts: A scalable alternative to clustering-based concept discovery, 2026. URL https://arxiv.org/abs/2602.02726.",
+   "tier": "emerging"
   },
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-   "key": "zanbaghi2025detectingsleeperagentslarge",
-   "text": "Shahin Zanbaghi, Ryan Rostampour, Farhan Abid, and Salim Al Jarmakani. Detecting sleeper agents in large language models via semantic drift analysis, 2025. URL https://arxiv.org/abs/2511.15992.",
+   "key": "yun-etal-2021-transformer",
+   "text": "Zeyu Yun, Yubei Chen, Bruno Olshausen, and Yann LeCun. Transformer visualization via dictionary learning: contextualized embedding as a linear superposition of transformer factors. In Eneko Agirre, Marianna Apidianaki, and Ivan Vulić (eds.), Proceedings of Deep Learning Inside Out (DeeLIO): The 2nd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures, pp. 1–10, Online, June 2021. Association for Computational Linguistics. . URL https://aclanthology.org/2021.deelio-1.1/.",
    "tier": "emerging"
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-   "text": "Andy K Zhang, Neil Perry, Riya Dulepet, Joey Ji, Celeste Menders, Justin W Lin, Eliot Jones, Gashon Hussein, Samantha Liu, Donovan Julian Jasper, Pura Peetathawatchai, Ari Glenn, Vikram Sivashankar, Daniel Zamoshchin, Leo Glikbarg, Derek Askaryar, Haoxiang Yang, Aolin Zhang, Rishi Alluri, Nathan Tran, Rinnara Sangpisit, Kenny O Oseleononmen, Dan Boneh, Daniel E. Ho, and Percy Liang. Cybench: A framework for evaluating cybersecurity capabilities and risks of language models. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=tc90LV0yRL.",
+   "text": "Andy K Zhang, Neil Perry, Riya Dulepet, Joey Ji, Celeste Menders, Justin W Lin, Eliot Jones, Gashon Hussein, Samantha Liu, Donovan Julian Jasper, Pura Peetathawatchai, Ari Glenn, Vikram Sivashankar, Daniel Zamoshchin, Leo Glikbarg, Derek Askaryar, Haoxiang Yang, Aolin Zhang, Rishi Alluri, Nathan Tran, Rinnara Sangpisit, Kenny O Oseleononmen, Dan Boneh, Daniel E. Ho, and Percy Liang. Cybench: A framework for evaluating cybersecurity capabilities and risks of language models. In The Thirteenth International Conference on Learning Representations, 2025a. URL https://openreview.net/forum?id=tc90LV0yRL.",
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    "text": "Fred Zhang and Neel Nanda. Towards best practices of activation patching in language models: Metrics and methods. In The Twelfth International Conference on Learning Representations, 2024. URL https://openreview.net/forum?id=Hf17y6u9BC.",
-   "tier": "emerging"
+   "tier": "established"
   },
   {
    "key": "zhang2026locatesteerimprovepractical",
-   "text": "Hengyuan Zhang, Zhihao Zhang, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Ercong Nie, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schütze, Xuanjing Huang, Qi Zhang, and Ngai Wong. Locate, steer, and improve: A practical survey of actionable mechanistic interpretability in large language models, 2026. URL https://arxiv.org/abs/2601.14004.",
+   "text": "Hengyuan Zhang, Zhihao Zhang, Ercong Nie, Mingyang Wang, Zunhai Su, Yiwei Wang, Qianli Wang, Shuzhou Yuan, Xufeng Duan, Qibo Xue, Zeping Yu, Chenming Shang, Xiao Liang, Jing Xiong, Hui Shen, Chaofan Tao, Zhengwu Liu, Senjie Jin, Zhiheng Xi, Dongdong Zhang, Sophia Ananiadou, Tao Gui, Ruobing Xie, Hayden Kwok-Hay So, Hinrich Schuetze, Xuanjing Huang, Qi Zhang, and Ngai Wong. Locate, steer, and improve: A practical survey of actionable mechanistic interpretability in large language models. In Maria Liakata, Viviane P. Moreira, Jiajun Zhang, and David Jurgens (eds.), Findings of the Association for Computational Linguistics: ACL 2026, pp. 10317–10362, San Diego, California, United States, July 2026a. Association for Computational Linguistics. ISBN 979-8-89176-395-1. . URL https://aclanthology.org/2026.findings-acl.502/.",
+   "tier": "established"
+  },
+  {
+   "key": "eapgp",
+   "text": "Lin Zhang, Wenshuo Dong, Zhuoran Zhang, Shu Yang, Lijie Hu, Ninghao Liu, Pan Zhou, and Di Wang. EAP-GP: Mitigating saturation effect in gradient-based automated circuit identification. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025b. URL https://openreview.net/forum?id=lGyXq0LOeQ.",
+   "tier": "established"
+  },
+  {
+   "key": "zhang-etal-2024-bench",
+   "text": "Xinrong Zhang, Yingfa Chen, Shengding Hu, Zihang Xu, Junhao Chen, Moo Hao, Xu Han, Zhen Thai, Shuo Wang, Zhiyuan Liu, and Maosong Sun. \\inftyBench: Extending long context evaluation beyond 100K tokens. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 15262–15277, Bangkok, Thailand, August 2024a. Association for Computational Linguistics. . URL https://aclanthology.org/2024.acl-long.814/.",
+   "tier": "established"
+  },
+  {
+   "key": "zhang2026whatshapes",
+   "text": "Yuchen Zhang, Anietta Weckauff, Diego Garcia-Olano, and Maksym Andriushchenko. What shapes emergent misalignment? insights from training dynamics, model priors, and data, 2026b. URL https://arxiv.org/abs/2606.20814.",
    "tier": "emerging"
   },
+  {
+   "key": "zhang-etal-2024-defending",
+   "text": "Zhexin Zhang, Junxiao Yang, Pei Ke, Fei Mi, Hongning Wang, and Minlie Huang. Defending large language models against jailbreaking attacks through goal prioritization. In Lun-Wei Ku, Andre Martins, and Vivek Srikumar (eds.), Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 8865–8887, Bangkok, Thailand, August 2024b. Association for Computational Linguistics. . URL https://aclanthology.org/2024.acl-long.481/.",
+   "tier": "established"
+  },
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+  {
+   "key": "zhao2026chainofthoughthijacking",
+   "text": "Jianli Zhao, Tingchen Fu, Rylan Schaeffer, Mrinank Sharma, and Fazl Barez. Chain-of-thought hijacking, 2026a. URL https://arxiv.org/abs/2510.26418.",
+   "tier": "emerging"
+  },
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    "text": "Rosie Zhao, Naomi Saphra, and Sham M Kakade. Distributional scaling laws for emergent capabilities. In NeurIPS 2024 Workshop on Scientific Methods for Understanding Deep Learning, 2024b.",
-   "tier": null
+   "tier": "emerging"
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    "key": "zhao-etal-2024-explaining",
@@ -3859,45 +5262,67 @@
    "text": "Yu Zhao, Alessio Devoto, Giwon Hong, Xiaotang Du, Aryo Pradipta Gema, Hongru Wang, Xuanli He, Kam-Fai Wong, and Pasquale Minervini. Steering knowledge selection behaviours in llms via sae-based representation engineering. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), pp. 5117–5136, 2025.",
    "tier": "established"
   },
+  {
+   "key": "zhao2026verifying",
+   "text": "Zheng Zhao, Yeskendir Koishekenov, Xianjun Yang, Naila Murray, and Nicola Cancedda. Verifying chain-of-thought reasoning via its computational graph. In The Fourteenth International Conference on Learning Representations, 2026b. URL https://openreview.net/forum?id=CxiNICq0Rr.",
+   "tier": "established"
+  },
+  {
+   "key": "agieval",
+   "text": "Wanjun Zhong, Ruixiang Cui, Yiduo Guo, Yaobo Liang, Shuai Lu, Yanlin Wang, Amin Saied, Weizhu Chen, and Nan Duan. AGIEval: A human-centric benchmark for evaluating foundation models. In Kevin Duh, Helena Gomez, and Steven Bethard (eds.), Findings of the Association for Computational Linguistics: NAACL 2024, pp. 2299–2314, Mexico City, Mexico, June 2024. Association for Computational Linguistics. . URL https://aclanthology.org/2024.findings-naacl.149/.",
+   "tier": "established"
+  },
+  {
+   "key": "cam",
+   "text": "Bolei Zhou, Aditya Khosla, Agata Lapedriza, Aude Oliva, and Antonio Torralba. Learning deep features for discriminative localization. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2921–2929, 2016.",
+   "tier": "established"
+  },
+  {
+   "key": "zhu2025establishing",
+   "text": "Yuxuan Zhu, Tengjun Jin, Yada Pruksachatkun, Andy K Zhang, Shu Liu, Sasha Cui, Sayash Kapoor, Shayne Longpre, Kevin Meng, Rebecca Weiss, Fazl Barez, Rahul Gupta, Jwala Dhamala, Jacob Merizian, Mario Giulianelli, Harry Coppock, Cozmin Ududec, Antony Kellermann, Jasjeet S Sekhon, Jacob Steinhardt, Sarah Schwettmann, Arvind Narayanan, Matei Zaharia, Ion Stoica, Percy Liang, and Daniel Kang. Establishing best practices in building rigorous agentic benchmarks. In The Thirty-ninth Annual Conference on Neural Information Processing Systems Datasets and Benchmarks Track, 2025. URL https://openreview.net/forum?id=E58HNCqoaA.",
+   "tier": "established"
+  },
+  {
+   "key": "zimerman2025explaining",
+   "text": "Itamar Zimerman, Ameen Ali Ali, and Lior Wolf. Explaining modern gated-linear RNNs via a unified implicit attention formulation. In The Thirteenth International Conference on Learning Representations, 2025. URL https://openreview.net/forum?id=wnT8bfJCDx.",
+   "tier": "established"
+  },
   {
    "key": "zucchet2025language",
-   "text": "Nicolas Zucchet, Jörg Bornschein, Stephanie Chan, Andrew Lampinen, Razvan Pascanu, and Soham De. How do language models learn facts? dynamics, curricula and hallucinations. arXiv preprint arXiv:2503.21676, 2025a.",
-   "tier": null
+   "text": "Nicolas Zucchet, Jörg Bornschein, Stephanie Chan, Andrew Lampinen, Razvan Pascanu, and Soham De. How do language models learn facts? dynamics, curricula and hallucinations, 2025a. URL https://arxiv.org/abs/2503.21676.",
+   "tier": "emerging"
   },
   {
-   "key": "zucchet2025emergence",
-   "text": "Nicolas Zucchet, Francesco d’Angelo, Andrew K Lampinen, and Stephanie CY Chan. The emergence of sparse attention: impact of data distribution and benefits of repetition. arXiv preprint arXiv:2505.17863, 2025b.",
-   "tier": null
+   "key": "zucchet2025the",
+   "text": "Nicolas Zucchet, Francesco D’Angelo, Andrew Kyle Lampinen, and Stephanie C.Y. Chan. The emergence of sparse attention: impact of data distribution and benefits of repetition. In The Thirty-ninth Annual Conference on Neural Information Processing Systems, 2025b. URL https://openreview.net/forum?id=jMhRbV47pS.",
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diff --git a/src/generated/references.html b/src/generated/references.html
index 6ca053ba09..27ebcefaa5 100644
--- a/src/generated/references.html
+++ b/src/generated/references.html
@@ -1,300 +1,519 @@
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 </div>
\ No newline at end of file
diff --git a/src/generated/withheld-fingerprints.json b/src/generated/withheld-fingerprints.json
index 374b290b2f..e05ab90f5e 100644
--- a/src/generated/withheld-fingerprints.json
+++ b/src/generated/withheld-fingerprints.json
@@ -1,1731 +1,4 @@
 {
  "about": "SHA-256 prefixes of normalised sentences, 10-word sequences, headings and ids of withheld draft chapters. tests/site.test.ts checks that none appear anywhere in the built site.",
- "chapters": {
-  "learning-dynamics": {
-   "sentences": [
-    "0017b07dfe1e99c9748285d0",
-    "004507333b6068adf7f44b00",
-    "004c46db9fd8229962ddc463",
-    "006471f30e02e5f7ac82d0e1",
-    "006a194c30086641ce527992",
-    "006f73ffa45bffe039fcb3c3",
-    "009ea2ae04dff2d921e1565e",
-    "00a1aee88607fcc350be2838",
-    "01c641ad76512cc9c27cfa0e",
-    "020201fa4562fc7c4a30c38e",
-    "021d0bdfd86f08830f4a8906",
-    "0246b9cecf0781cf7f5823fe",
-    "0257b0f39271bc60d15908e4",
-    "02790d81456fab98d8de89aa",
-    "0279a0b4d254bc2ffcb99e14",
-    "027a57c1edbadf1c3ba2e0a6",
-    "0299bd444cb4abd8377d5911",
-    "02e2ffccbbf6a11e6295fb0c",
-    "031e0b6912d3f1df576ad1ac",
-    "0365c6bf8ffe81a24964e00f",
-    "0378a82db770b3c061e9885b",
-    "03814111154460744fd6edbb",
-    "03dabc3604ea4279df792e5a",
-    "03e71cfc23221d020b432bbd",
-    "040722cf0aaa7b28123974c4",
-    "0456aafb04965b97d2984b61",
-    "04869fd77ed8bd7883283d99",
-    "049217829456b1366ad13cee",
-    "0494d1cf65f955cce8b28923",
-    "04b00c60e49feff2822a5165",
-    "04d2141dd2d1ac76229a7582",
-    "04e669c8f98376fdd9b3c5a7",
-    "04f69a79ac138dbb49e8200b",
-    "0525068b111ed4977bb42a18",
-    "0527677b3441a74bec05c767",
-    "056b3c76b77bba139d5436ba",
-    "0570cf0ae8edd88c0ac0385e",
-    "064521c0f2ce146716ff6bee",
-    "0656ec4bd88eb606b6961330",
-    "065fe2b2f800d9b91e3ef02b",
-    "06739d6fb9d9fff7fdae024e",
-    "06a88cc8c353a186f465f406",
-    "06e9d748c4721d0a6df1fcf7",
-    "06f0b9fba0f09838afe5c69b",
-    "06f861686f713bf8c396008c",
-    "070043acbc347571bc849587",
-    "0705bec32a8bc4c82289b91e",
-    "073c78cbdd17fe069582a535",
-    "074621deefee964e0ed005a5",
-    "07495fd56bcb1e4c1e65c382",
-    "077684a3d6496aab01c26477",
-    "0782d7715413ef8e3744ec23",
-    "07e4ea78440df13e95cac1bb",
-    "07f5b4639de4b0c24d36bdb1",
-    "07fa6a61c46d371a5f4625c8",
-    "0802d30072138a26981ee0cc",
-    "08ff7ef8814ad6a856655f43",
-    "091233d1d125ebe932e19a67",
-    "091b8a0e653a9997d334ea73",
-    "093d1e1c6109dcd927799d42",
-    "094445cefbaa6c6a90d10814",
-    "099b70693bbd19c08130f094",
-    "09b39a85b4fb534637bedb1d",
-    "09c9191f9317c4cce88efc23",
-    "0a754282329a20b753615315",
-    "0a87b27cc77249261c9b443a",
-    "0a87df7ad2849b72d8573a58",
-    "0ac1895af34e749c6d8559b8",
-    "0af3d5c66a0438d509b33068",
-    "0b0eea35c3b08774cc75657a",
-    "0b723a94896af995c5735a58",
-    "0b83e9f61820d4b3aeefffe2",
-    "0b9a742351f94558cf6ca8a2",
-    "0bcc0d2bbc3d4b934d85665f",
-    "0bd02ebe71ae6695ac0b658b",
-    "0be5e1e8dd1d4e0d6a9adf14",
-    "0bf6d8178eb49c746943667d",
-    "0bff496a485e59587e6bd581",
-    "0c012de6d1da0738cd9191c2",
-    "0c5bf17bac824f3463cbe682",
-    "0c5dfc7df57442087e3d9096",
-    "0c8cc820697dbcde26e23e6c",
-    "0d06c490aba03ebe9386ca2f",
-    "0d7616456699809a18d490e4",
-    "0d7adcbfa8872f335c57a5f2",
-    "0d9da0bda7bd5352abf4c58c",
-    "0ddb7e13d543fcea3055eec4",
-    "0e1203a710d1d1b9939571b5",
-    "0e1363b16f428ad4b3073c37",
-    "0e2dd7c5f20fb467bd8de1bb",
-    "0e5cee67a13d97021eccfa10",
-    "0e653994040c88fce0c21b8b",
-    "0e6f6cff3f582463f1d495e4",
-    "0e991b005b5bdd842c86199e",
-    "0eb101ce30fc1c65cfef4332",
-    "0eeb968cd1052981babb65af",
-    "0f1f0a0a7c53a39fbff7ba28",
-    "0f4984b0bb476a01b9ddc92a",
-    "0f50208b0bc84d956a6df11d",
-    "0fc22d8a11abe61881ef5477",
-    "102e07d30c3ab3a7b65f6c26",
-    "104c1ddc56f7176d0f3f705e",
-    "1056ebd88f7f067725a365a4",
-    "1070b581689ac16b48b0ad5c",
-    "10a0b3f6629634929a588955",
-    "10c042fca3c7995e71be6c4c",
-    "10fe8f98400e77bad74d33e0",
-    "1163379c3326f9f6a3b7ec11",
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-    "e582d7c8006a09b57fb72590",
-    "ec0e524d657131d6efdd0739",
-    "edb8ea204b96700292b181c5",
-    "fd7615d451c5f345d9f64bf0"
-   ],
-   "ids": [
-    "017496de8c73a620510f04f8",
-    "04570549e6a54a598fa2612c",
-    "1544f59729126e94ce473333",
-    "2e1b534c284b28a143d39527",
-    "43815d2cee8b34a1d69729d1",
-    "4ffd3c06beff6d3df01d9cfb",
-    "5f195910401ad7245f983dad",
-    "5f8d3e78c13184d990427e74",
-    "6a4b3447ae51eceb7c6411c8",
-    "759f2ca71563b81d0671c541",
-    "7dabfa3cf09b15ee995e588f",
-    "7eed6b8d8f5126765d8d842b",
-    "7f571119ca4e0ed772e951a3",
-    "84f570202c477d2177cf03df",
-    "89a5c7985df4efcb390dc79c",
-    "a6622655ae40c8331c4dcf47",
-    "b16462ad53da295e1ca6983e",
-    "c355e8937c69b4c56390153f",
-    "c4a7abdc795d19c55b0c2670",
-    "c9e21a20c89743f231a8d00c",
-    "cff6364373b80c0b12d46290",
-    "edb8ea204b96700292b181c5",
-    "fb1c7e6b2afad3c5e24ae00b"
-   ]
-  }
- }
+ "chapters": {}
 }
\ No newline at end of file
diff --git a/src/lib/data.ts b/src/lib/data.ts
index e923270cef..1edb0581aa 100644
--- a/src/lib/data.ts
+++ b/src/lib/data.ts
@@ -44,7 +44,10 @@ export function demoLinks(id: string) { return (links.demos as any)[id] ?? {}; }
 /** Key papers = works cited in the method's primary sections (computed from the manuscript). */
 export function keyPapers(m: any, limit = 12) {
   const keys = citationsInSections(m.sections?.slice(0, 1) ?? []);
-  return keys.slice(0, limit).map((k) => referenceByKey.get(k)).filter(Boolean) as { key: string; text: string; tier: string | null }[];
+  // Reference titles keep a few TeX symbols from the .bbl (e.g. "$\\infty$Bench"); show them as characters.
+  const TEX: Record<string, string> = { '\\infty': '∞', '\\alpha': 'α', '\\beta': 'β', '\\lambda': 'λ', '\\ell': 'ℓ' };
+  return keys.slice(0, limit).map((k) => referenceByKey.get(k)).filter(Boolean)
+    .map((r: any) => ({ ...r, text: r.text.replace(/\\(infty|alpha|beta|lambda|ell)/g, (m: string) => TEX[m] ?? m) })) as { key: string; text: string; tier: string | null }[];
 }
 
 /** Find a method whose name appears in a free-text method mention from the framework data. */
diff --git a/src/lib/manuscript.ts b/src/lib/manuscript.ts
index 198a3be003..3e1490e0d2 100644
--- a/src/lib/manuscript.ts
+++ b/src/lib/manuscript.ts
@@ -2,7 +2,7 @@
 // and renders maths with KaTeX at build time, so pages ship no maths JavaScript.
 import katex from 'katex';
 import manifestJson from '../generated/manifest.json';
-import { base } from './site';
+import { base, SITE } from './site';
 import withheldJson from '../data/withheld.json';
 
 export interface TocSection { id: string; num: string | null; title: string; level: number; page: string | null }
@@ -104,11 +104,13 @@ export function referencesHtml(): string {
 }
 
 export function pdfHref(page: string | null | undefined): string | null {
-  if (!manifest.provenance.pdf_included || !page) return null;
+  if (!page) return null;
   // Roman-numbered front matter pages are not addressable by number; skip them
   if (!/^\d+$/.test(page)) return null;
   // PDF viewers use physical page indices; the offset accounts for front matter.
-  return `${base}survey.pdf#page=${Number(page) + pdfOffset}`;
+  // Without a local copy, link the published arXiv PDF (the ingested source is that build).
+  const pdf = manifest.provenance.pdf_included ? `${base}survey.pdf` : manifest.provenance.status === 'final' ? SITE.paperPdf : null;
+  return pdf ? `${pdf}#page=${Number(page) + pdfOffset}` : null;
 }
 
 // Physical index of printed page 1. Set by the ingest step from the PDF if known.
diff --git a/src/pages/demos/index.astro b/src/pages/demos/index.astro
index b020e3bf3f..8b8c6f9303 100644
--- a/src/pages/demos/index.astro
+++ b/src/pages/demos/index.astro
@@ -10,11 +10,11 @@ const demos = [
     text: 'Find where GPT-2 small resolves indirect object identification by patching clean activations into a corrupted run, layer by layer and head by head.' },
 ];
 ---
-<Base title="Interactive demos" description="Three interactive experiments: superposition, sparse autoencoders and activation patching, all computed for real.">
+<Base title="Interactive demos" description="Interactive demos of superposition, sparse autoencoders and activation patching.">
   <div class="page">
     <p class="eyebrow">Hands-on</p>
     <h1>Interactive demos</h1>
-    <p class="lede">Three core ideas from the survey that you can run yourself. Nothing here is a drawing: the first two train real models in your browser, and the third shows activation-patching measurements on GPT-2 small from a script in this repository.</p>
+    <p class="lede">Three ideas from the survey to try yourself. The first two train small models in your browser; the third shows activation patching on GPT-2 small.</p>
     <div class="grid grid-3" style="margin-top:1.5rem">
       {demos.map((d) => <a class="card" href={url(d.href)} style="text-decoration:none;color:var(--ink)">
         <span class="label measured">{d.label}</span>
diff --git a/src/pages/demos/patching.astro b/src/pages/demos/patching.astro
index f79254a64e..fd96a5e8b8 100644
--- a/src/pages/demos/patching.astro
+++ b/src/pages/demos/patching.astro
@@ -12,7 +12,7 @@ import data from '../../data/experiments/patching_gpt2_ioi.json';
       <h2 style="font-size:1rem">How these numbers were produced</h2>
       <p>Model: {data.model}. Task: indirect object identification over {data.n_prompts} prompt pairs ({data.templates.length} templates × {data.name_pairs.length} name pairs × 2 orders). In the corrupted prompt the second-clause subject is swapped, which flips the answer. Metric: {data.metric}. A value of 0 equals the corrupted run and 1 equals the clean run. The clean run is correct on {(data.clean_accuracy * 100).toFixed(0)}% of prompts. <em>Activation patching</em> runs the model once per component. <em>Attribution patching</em> approximates the same quantity with a single backward pass, using a first-order Taylor expansion. Across all 144 heads the two agree with Pearson r = {data.pearson_activation_vs_attribution.toFixed(3)}.</p>
       <p>Reproduce: <code>python3 {data.script}</code> (torch {data.versions.torch}, transformer-lens {data.versions.transformer_lens}; about {Math.round(data.runtime_seconds)} s on a laptop CPU, deterministic). Generated {data.generated}. The setup follows the companion notebook on circuit discovery, scaled up to more prompts.</p>
-      <p class="small muted">A patching effect shows that a component is causally relevant for this metric on these prompts. It does not by itself show what the component computes. See the survey’s discussion of causal validity and the evaluation framework.</p>
+      <p class="small muted">A patching effect shows that a component matters for this metric on these prompts, not what it computes.</p>
     </Fragment>
   </DemoFrame>
 </Base>
diff --git a/src/pages/demos/sae.astro b/src/pages/demos/sae.astro
index beddd0ddd0..800780936c 100644
--- a/src/pages/demos/sae.astro
+++ b/src/pages/demos/sae.astro
@@ -11,7 +11,7 @@ import SaeDemo from '../../components/demos/SaeDemo.tsx';
       <h2 style="font-size:1rem">What is being computed</h2>
       <p>Data: x = Σ<sub>i</sub> a<sub>i</sub> g<sub>i</sub>, where the g<sub>i</sub> are 16 random unit vectors in ℝ⁸ and each a<sub>i</sub> is 0 with probability 0.9, otherwise Uniform[0, 1]. SAE: f = ReLU(W<sub>e</sub>(x − b<sub>d</sub>) + b<sub>e</sub>), x̂ = W<sub>d</sub>f + b<sub>d</sub>, with 32 latents and unit-norm decoder columns. The loss is ‖x − x̂‖² + λ‖f‖₁, or the TopK variant, which keeps the k largest activations. Training is Adam (learning rate 0.003, batch 256) for 2,000 steps. <strong>MMCS</strong> (mean max cosine similarity) averages, over planted features, the best cosine with any decoder column. A feature counts as <strong>recovered</strong> when that cosine exceeds 0.9.</p>
       <p><strong>Checked results</strong> (seed 1, tested in <code>tests/sim.test.ts</code>): with λ = 0 the SAE reconstructs almost perfectly but recovers 0 of 16 features (MMCS ≈ 0.69). With λ = 0.3 it recovers all 16 (MMCS ≈ 0.97) at about 13% variance unexplained. With λ = 1 reconstruction degrades to about 60% variance unexplained. Sparsity is what makes the dictionary line up with the true features, and too much sparsity costs reconstruction.</p>
-      <p class="small muted">This is a deliberately easy, idealised setting. Real activations have no guarantee of a sparse linear ground truth. That limitation is discussed in the survey’s sections on feature extraction and evaluation.</p>
+      <p class="small muted">An idealised setting: real activations need not have a sparse linear ground truth.</p>
     </Fragment>
   </DemoFrame>
 </Base>
diff --git a/src/pages/evaluation.astro b/src/pages/evaluation.astro
index e7ed0531b9..f1c99c4b02 100644
--- a/src/pages/evaluation.astro
+++ b/src/pages/evaluation.astro
@@ -21,7 +21,7 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
   <div class="page">
     <p class="eyebrow">Technical methods · Evaluation</p>
     <h1>A unified evaluation framework</h1>
-    <p class="lede">How do you tell whether an interpretability result is right? The survey proposes judging every method on the same five dimensions.</p>
+    <p class="lede">Five dimensions for evaluating interpretability claims.</p>
     <SectionLinks ids={e.chapter_sections.slice(0, 1)} />
     <DraftBanner />
     {e.framework_intro.map((t: string) => <Excerpt text={t} />)}
@@ -34,26 +34,24 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
       <section role="tabpanel" id={`panel-${d.id}`} aria-labelledby={`tab-${d.id}`} hidden={i !== 0} class="dim-panel card">
         <div class="dim-head">
           <h2 id={d.id}>{publicName[d.id] ?? d.name}</h2>
-          {publicName[d.id] && publicName[d.id] !== d.name && <span class="small muted">The survey calls this “{d.name}”</span>}
           <ManuscriptNote />
         </div>
-        <Excerpt text={d.definition} section={sec(d.sections[0])} />
+        <Excerpt text={d.definition} section={sections(d.sections).find((x) => x.href) ?? null} />
         <div class="dim-grid">
           <div>
             <h3>How it is measured</h3>
             {d.metrics_named?.length > 0 && <p class="small">{d.metrics_named.map((m: string) => <span class="chip" style="margin:0 .3rem .3rem 0">{m}</span>)}</p>}
-            {d.formulas_tex?.map((f: string) => <div class="formula"><Fragment set:html={tex(f)} /></div>)}
+            {/* each "…measured as:" sentence is followed by its formula */}
             {d.how_measured.map((t: string) => <Excerpt text={t} />)}
+            {d.formulas_tex?.map((f: string) => <div class="formula"><Fragment set:html={tex(f)} /></div>)}
           </div>
           <div>
             {d.table_row && Object.keys(d.table_row).length > 0 && (<>
-              <h3>Summary row from the survey’s table</h3>
+              <h3>Summary</h3>
               <table class="data"><tbody>{Object.entries(d.table_row).filter(([k]) => k !== 'section').map(([k, v]) => <tr><th scope="row">{k}</th><td set:html={excerptHtml(String(v))} /></tr>)}</tbody></table>
             </>)}
-            <h3>Examples</h3>
-            {d.examples?.length ? d.examples.map((t: string) => <Excerpt text={t} />) : <p class="small muted">The survey gives no worked example for this dimension.</p>}
-            <h3>Limitations</h3>
-            {d.limitations?.length ? d.limitations.map((t: string) => <Excerpt text={t} />) : <p class="small muted">The survey states no dimension-specific limitations. See the framework’s limitations below.</p>}
+            {d.examples?.length > 0 && <><h3>Examples</h3>{d.examples.map((t: string) => <Excerpt text={t} />)}</>}
+            {d.limitations?.length > 0 && <><h3>Limitations</h3>{d.limitations.map((t: string) => <Excerpt text={t} />)}</>}
           </div>
         </div>
       </section>
diff --git a/src/pages/handbook/[slug].astro b/src/pages/handbook/[slug].astro
index c7a69486d8..a19d050db2 100644
--- a/src/pages/handbook/[slug].astro
+++ b/src/pages/handbook/[slug].astro
@@ -41,7 +41,7 @@ const description = `${label}: ${ch.title} — from the survey "The State of Mec
         <h2>Contents</h2>
         {chapters.map((c) => (
           <details open={c.slug === slug} class={c.slug === slug ? 'current' : undefined}>
-            <summary><a href={url(`handbook/${c.slug}/`)}>{`${c.num}.`} {c.title}</a>{withheld.has(c.slug) && <span class="muted small"> (with the final manuscript)</span>}</summary>
+            <summary><a href={url(`handbook/${c.slug}/`)}>{c.num ? `${c.num}. ` : ''}{c.title}</a>{withheld.has(c.slug) && <span class="muted small"> (with the final manuscript)</span>}</summary>
             {!withheld.has(c.slug) && c.sections.filter((s) => s.level === 2).length > 0 && (
               <ul>
                 {c.sections.filter((s) => s.level === 2).map((s) => (
diff --git a/src/pages/handbook/index.astro b/src/pages/handbook/index.astro
index 3dd55777e8..f15a132d0c 100644
--- a/src/pages/handbook/index.astro
+++ b/src/pages/handbook/index.astro
@@ -10,25 +10,20 @@ const words = shown.reduce((a, c) => a + c.words, 0);
 import { draftHidden as __hide } from '../../lib/manuscript';
 if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/')}held/`);
 ---
-<Base title="Handbook" description="Read the survey online, with numbered sections, figures, tables, equations and linked references." manuscript>
+<Base title="Handbook" description="The full text of the survey, by chapter." manuscript>
   <div class="page-narrow">
     <p class="eyebrow">The handbook</p>
     <h1>Read the survey</h1>
-    <p class="lede">{isDraft ? 'A preview of the working draft' : 'The survey'} as an online book: {main.filter((c) => !withheld.has(c.slug)).length} chapters, {apps.length} appendices and {manifest.references.length} references, about {Math.round(words / 1000)}k words.
-      {withheld.size > 0 && <>{withheld.size === 1 ? 'One chapter is' : `${withheld.size} chapters are`} held back until the final manuscript. </>}
-      Numbering matches the {isDraft ? 'draft ' : ''}PDF, and every section, figure and table has a link you can share.</p>
     <DraftBanner />
-    <p class="small">The paper: <a href={SITE.paper}>arXiv:2611.08438</a> (<a href={SITE.paperPdf}>PDF</a>, <a href={SITE.alphaxiv}>alphaXiv</a>) · <a href={url('#cite')}>BibTeX</a></p>
-    <p class="small muted">Citation colours follow the manuscript:
-      <span style="color:var(--tier-established)">■ peer-reviewed</span> (plain),
-      <span style="color:var(--tier-emerging)">■ preprints and other non-peer-reviewed work</span> (<span class="cite-emerging-sample">dotted underline</span>; tagged “Preprint” in the reference list).</p>
+    <p class="small"><a href={SITE.paper}>arXiv:2611.08438</a> · <a href={SITE.paperPdf}>PDF</a> · <a href={SITE.alphaxiv}>alphaXiv</a> · <a href={url('#cite')}>BibTeX</a></p>
+    <p class="small muted">Citations: <span style="color:var(--tier-established)">■ peer-reviewed</span> · <span style="color:var(--tier-emerging)">■ preprint</span></p>
     <ol style="list-style:none;padding:0;margin:2rem 0">
       {main.map((c) => (
         <li style="padding:.9rem 0;border-bottom:1px solid var(--rule)">
           <a href={url(`handbook/${c.slug}/`)} style="font-weight:650;font-size:1.05rem;text-decoration:none">
             <span style="color:var(--gold);font-variant-numeric:tabular-nums;margin-right:.5rem">{c.num}</span>{c.title}
           </a>
-          <span class="small muted"> · p. {c.page} · {Math.max(1, Math.round(c.words / 230))} min read</span>
+          {c.page && <span class="small muted"> · p. {c.page}</span>}
           {withheld.has(c.slug) ? <span class="small muted"> · coming with the final manuscript</span> : c.sections.filter((s) => s.level === 2).length > 0 && (
             <details style="margin-top:.3rem">
               <summary class="small muted" style="cursor:pointer">{c.sections.filter((s) => s.level === 2).length} sections</summary>
diff --git a/src/pages/index.astro b/src/pages/index.astro
index cdafa34eff..90530984ba 100644
--- a/src/pages/index.astro
+++ b/src/pages/index.astro
@@ -48,13 +48,13 @@ const roman = ['I', 'II', 'III', 'IV'];
 // the arrangement the toy model in demos/superposition settles into when inputs are sparse).
 const feats = [0, 1, 2, 3, 4].map((i) => { const a = Math.PI / 2 + (i * 2 * Math.PI) / 5; return [Math.cos(a), Math.sin(a)]; });
 const explore = [
-  ['Read', 'handbook/', 'The handbook', `${isDraft ? 'A preview of the working draft' : 'The survey'} online: ${chapters.filter((c) => !c.appendix && !withheld.has(c.slug)).length} chapters${withheld.size ? ` (${withheld.size} held back until the final manuscript)` : ''} and ${manifest.references.length} references, numbered as in the ${isDraft ? 'draft ' : ''}PDF.`],
-  ['Compare', 'methods/', 'Methods explorer', 'Every method the survey covers, side by side, quoted from the text.'],
-  ['Judge', 'evaluation/', 'Evaluation framework', 'Five dimensions for deciding whether an interpretability claim holds.'],
-  ['Orient', 'orientations/', 'Research orientations', 'The agendas behind the methods: what each assumes and what it trades away.'],
-  ['Apply', 'safety/', 'Safety and open problems', 'Where interpretability already supports safety work, and what is unsolved.'],
-  ['Run', 'demos/', 'Experiments', 'Superposition, sparse autoencoders and activation patching, computed for real.'],
-  ['Code', 'notebooks/', 'Notebooks', `${notebooks.notebooks.length} companion notebooks with compute requirements and Colab links.`],
+  ['Read', 'handbook/', 'The handbook', `The full survey, by chapter.`],
+  ['Compare', 'methods/', 'Methods explorer', 'The methods in the survey, side by side.'],
+  ['Judge', 'evaluation/', 'Evaluation framework', 'Five dimensions for evaluating interpretability claims.'],
+  ['Orient', 'orientations/', 'Research orientations', 'Six research agendas and their trade-offs.'],
+  ['Apply', 'safety/', 'Safety and open problems', 'Safety applications and open problems.'],
+  ['Run', 'demos/', 'Experiments', 'Superposition, sparse autoencoders and activation patching.'],
+  ['Code', 'notebooks/', 'Notebooks', `${notebooks.notebooks.length} companion notebooks.`],
 ];
 const exploreShown = draftHidden ? explore.filter(([, href]) => href === 'demos/' || href === 'notebooks/') : explore;
 // BibTeX exactly as arXiv issues it for 2611.08438.
@@ -96,7 +96,7 @@ const jsonld = {
           {feats.map(([x, y], i) => <g><line x1="0" y1="0" x2={x} y2={-y} stroke="var(--accent-red)" stroke-width=".012" /><circle cx={x} cy={-y} r=".022" fill="var(--accent-red)" />
             <text x={x * 1.17} y={-y * 1.17 + .035} font-size=".1" text-anchor="middle" fill="var(--ink-2)" font-family="var(--serif)" font-style="italic">f{i + 1}</text></g>)}
         </svg>
-        <figcaption id="sketch-cap">Five features, two dimensions. When features are rarely active together, a network can store more of them than it has dimensions, at the cost of interference. <a href={url('demos/superposition/')}>Train it yourself</a>.</figcaption>
+        <figcaption id="sketch-cap"><strong>Superposition.</strong> Five features in two dimensions: when features are rarely active together, a network can store more features than it has dimensions, at the cost of interference. <a href={url('demos/superposition/')}>Train it yourself</a>.</figcaption>
       </figure>
     </div>
   </header>
@@ -145,7 +145,7 @@ const jsonld = {
   </section>
 
   <section class="index" aria-labelledby="use-h">
-    <h2 id="use-h" class="row"><span class="gut">Companion</span><span>Using this site</span></h2>
+    <h2 id="use-h" class="row"><span class="gut">Explore</span><span>On this site</span></h2>
     {exploreShown.map(([verb, href, title, text]) => (
       <article class="row entry compact">
         <div class="gut num">{verb}</div>
@@ -154,7 +154,7 @@ const jsonld = {
     ))}
   </section>
 
-  <section class="row figure-row">
+  <section class="row figure-row" id="taxonomy">
     <div class="gut">Taxonomy</div>
     <figure class="tax">
       <a href={url('static/images/survey_taxonomy.png')} aria-label="Open the taxonomy figure at full resolution">
@@ -192,7 +192,7 @@ const jsonld = {
   @media (max-width: 860px) { .front-grid { grid-template-columns: minmax(0, 1fr); padding-top: 2.5rem; } }
   .front-text { background: var(--paper); padding: 2.2rem 2.4rem 2rem; outline: 1px solid var(--hair); }
   @media (max-width: 600px) { .front-text { padding: 1.4rem 1.2rem; } }
-  .front h1 { font-size: clamp(2.3rem, 5.4vw, 4rem); line-height: 1.04; margin: 0 0 .8rem; }
+  .front h1 { font-size: clamp(1.9rem, 4.2vw, 3.2rem); line-height: 1.08; margin: 0 0 .8rem; overflow-wrap: normal; word-break: normal; hyphens: manual; }
   .sub { font-family: var(--serif); font-style: italic; font-weight: 300; font-size: clamp(1.1rem, 2vw, 1.4rem); color: var(--ink-2); margin: 0 0 2rem; }
   .question { font-family: var(--serif); font-size: clamp(1.25rem, 2.3vw, 1.6rem); line-height: 1.35; margin: 1.4rem 0 2rem; padding-top: 1.2rem; border-top: 1px solid var(--hair); max-width: 30ch; color: var(--accent); }
   .front-links { display: flex; flex-wrap: wrap; gap: .6rem 1.6rem; font-size: .92rem; margin: 0; }
diff --git a/src/pages/methods/[id].astro b/src/pages/methods/[id].astro
index d353572973..5ce76360d6 100644
--- a/src/pages/methods/[id].astro
+++ b/src/pages/methods/[id].astro
@@ -33,18 +33,17 @@ const nbAnchor = (p: string) => p.split('/').pop()!.replace('.ipynb', '');
     <SectionLinks ids={m.sections} label="Survey sections" />
     <DraftBanner />
     <p><ManuscriptNote /></p>
-    {m.definition && <Excerpt text={m.definition} section={primary} />}
+    {m.definition && <Excerpt text={/[.!?”)\]]$/.test(m.definition.trim()) ? m.definition : `${m.definition} …`} section={primary} />}
 
-    {groups.map(([h, list]) => (
+    {groups.filter(([, list]) => list?.length).map(([h, list]) => (
       <section>
         <h2 style="font-size:1.15rem;margin-top:2rem">{h}</h2>
-        {list?.length ? list.map((t) => <Excerpt text={t} />) : <p class="muted small">The survey doesn’t state this explicitly for {m.name}.</p>}
+        {list.map((t) => <Excerpt text={t} />)}
       </section>
     ))}
 
     {(m.related?.length > 0 || relatedBack.length > 0) && (
       <section><h2 style="font-size:1.15rem;margin-top:2rem">Related methods</h2>
-        <p class="small muted">These are methods the survey explicitly relates to this one or contrasts with it.</p>
         <ul>{[...(m.related ?? []).map((id: string) => methodById.get(id)), ...relatedBack].filter(Boolean).map((r: any) => <li><a href={url(`methods/${r.id}/`)}>{r.name}</a></li>)}</ul>
         <p><a class="small" href={url(`methods/?compare=${[m.id, ...(m.related ?? []), ...relatedBack.map((x) => x.id)].slice(0, 3).join(',')}`)}>Compare side by side →</a></p>
       </section>
@@ -66,7 +65,7 @@ const nbAnchor = (p: string) => p.split('/').pop()!.replace('.ipynb', '');
 
     {papers.length > 0 && (
       <section><h2 style="font-size:1.15rem;margin-top:2rem">Key papers</h2>
-        <p class="small muted">Works cited in {primary?.num ? `§${primary.num}` : 'the primary section'}, taken automatically from the survey’s citations.</p>
+        {primary?.num && <p class="small muted">Cited in §{primary.num}.</p>}
         <ul class="small">{papers.map((p) => <li style="margin:.3rem 0"><a href={url(`references/#ref-${p.key}`)} style={`color:var(--tier-${p.tier ?? 'established'})`} aria-label="Open in references">■</a> {p.tier === 'emerging' && <span class="tier-tag">Preprint</span>}{p.tier === 'flagged' && <span class="tier-tag">Flagged</span>}{p.text}</li>)}</ul>
       </section>
     )}
diff --git a/src/pages/methods/index.astro b/src/pages/methods/index.astro
index 898ebc4724..d0465baa68 100644
--- a/src/pages/methods/index.astro
+++ b/src/pages/methods/index.astro
@@ -13,7 +13,8 @@ const rows = methods.map((m) => {
     section: s?.num ?? null, sectionHref: s?.href ?? null, definition: m.definition ?? '',
     assumptions: m.assumptions ?? [], evaluation: m.evaluation ?? [], applications: m.applications ?? [], limitations: m.limitations ?? [],
     html: Object.fromEntries(['definition', 'assumptions', 'evaluation', 'applications', 'limitations'].map((k) => {
-      const v = m[k]; return [k, Array.isArray(v) ? v.map(excerptHtml) : v ? [excerptHtml(v)] : []];
+      const v = m[k]; const cont = (t: string) => (k === 'definition' && !/[.!?”)\]]$/.test(t.trim()) ? `${t} …` : t);
+      return [k, Array.isArray(v) ? v.map(excerptHtml) : v ? [excerptHtml(cont(v))] : []];
     })),
     related: (m.related ?? []).map((id: string) => ({ id, name: methodById.get(id)?.name ?? id })),
     notebooks: notebooksFor('methods', m.id).length, demos: demosFor('methods', m.id).length, href: url(`methods/${m.id}/`),
@@ -27,22 +28,9 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
   <div class="page">
     <p class="eyebrow">Technical methods</p>
     <h1>Methods explorer</h1>
-    <p class="lede">Every method the survey covers in its own section, grouped the way the survey groups them. All text here is quoted from the survey.
-      We don’t add scores, rankings or numbers the survey doesn’t report.</p>
+    <p class="lede">The methods covered in the survey, grouped as in the paper.</p>
     <DraftBanner />
     <MethodsExplorer client:load rows={rows} families={fams} />
 
-    <h2 style="margin-top:3rem">Core hypotheses</h2>
-    <p class="muted small">These are the assumptions many of the methods rely on. They are not methods themselves.</p>
-    <div class="grid grid-2">
-      {hypotheses.map((h) => {
-        const s = sections(h.sections)[0];
-        return <div class="card"><h3 style="margin:0 0 .3rem;font-size:1rem">{h.name}</h3>
-          <p style="font-family:var(--serif);margin:.2rem 0">“{h.definition}”</p>
-          {s?.href && <a class="small" href={s.href}>§{s.num} {s.title}</a>}
-          {demosFor('hypotheses', h.id).map((d) => <> · <a class="small" href={url(d.href)}>Demo: {d.title}</a></>)}
-        </div>;
-      })}
-    </div>
   </div>
 </Base>
diff --git a/src/pages/notebooks.astro b/src/pages/notebooks.astro
index 7646cb221b..7008df49e1 100644
--- a/src/pages/notebooks.astro
+++ b/src/pages/notebooks.astro
@@ -55,11 +55,7 @@ const stats = { total: notebooks.length, cpu: notebooks.filter((n) => n.compute.
   <div class="page">
     <p class="eyebrow">Hands-on</p>
     <h1>Companion notebooks</h1>
-    <p class="lede">{stats.total} notebooks from <a href={SITE.notebooksRepo}>TSGLAB/Interpretability_Survey_Safety</a>, catalogued automatically from the notebooks themselves. {stats.cpu} of them run on a CPU.</p>
-    <p class="small muted">Catalogued from commit <code>{notebookMeta.commit}</code>.
-      {notebookMeta.link_check_run ? ' Every source link was checked at build time and resolves.' : ' Links were not checked in this build.'}
-      Compute needs are <strong>estimated</strong> from the largest model each notebook loads. They are not benchmarks.
-      “Open in Colab” opens the notebook. It has not been run end to end in Colab unless marked.</p>
+    <p class="lede">{stats.total} notebooks from <a href={SITE.notebooksRepo}>TSGLAB/Interpretability_Survey_Safety</a>, {stats.cpu} run on a CPU.</p>
 
     <div class="filters" role="group" aria-label="Filter by compute">
       <button class="chip on" data-tier="all" aria-pressed="true">All</button>
@@ -69,7 +65,7 @@ const stats = { total: notebooks.length, cpu: notebooks.filter((n) => n.compute.
     {groups.map((g) => (
       <section class="nb-group">
         <h2>{FOLDER[g.key]}</h2>
-        <p class="small muted">Prerequisites (from the repository README): {PREREQ[g.key]}</p>
+        <p class="small muted">Prerequisites: {PREREQ[g.key]}</p>
         <div class="grid">
           {g.items.map((n) => {
             const r = resolve(n.survey_reference);
@@ -89,11 +85,11 @@ const stats = { total: notebooks.length, cpu: notebooks.filter((n) => n.compute.
                     <dd>{r ? (<>
                         {draftHidden ? <span>{n.survey_reference.replace(/\(\[.*$/, '').replace(/\]\(.*$/, '').trim()}</span> : <>
                         {r.match ? <a href={r.match.href}>§{r.match.num} {r.match.title}</a> : <span>{n.survey_reference.replace(/\(\[.*$/, '').trim()}</span>}</>}
-                      </>) : <span class="muted">Not stated in notebook</span>}</dd>
+                      </>) : <span class="muted">—</span>}</dd>
                     <dt>Learning outcomes</dt>
                     <dd>{n.learning_objectives.length ? <ul>{n.learning_objectives.slice(0, 5).map((o: string) => <li>{o.replace(/\*\*/g, '')}</li>)}</ul>
-                      : n.key_methods.length ? <><span class="small muted">Not listed as outcomes. Key methods covered:</span><ul>{n.key_methods.map((o: string) => <li>{o.replace(/\*\*/g, '')}</li>)}</ul></>
-                      : <span class="muted">Not stated in notebook</span>}</dd>
+                      : n.key_methods.length ? <><span class="small muted">Covers:</span><ul>{n.key_methods.map((o: string) => <li>{o.replace(/\*\*/g, '')}</li>)}</ul></>
+                      : <span class="muted">—</span>}</dd>
                     <dt>Models</dt><dd>{n.models.length ? n.models.join(', ') : 'None (toy or synthetic data)'}{n.gated_models.length > 0 && <span class="chip note" title="Requires accepting the model licence on Hugging Face">gated: {n.gated_models.join(', ')}</span>}</dd>
                     <dt>Dependencies</dt><dd class="small"><code>{n.imports.join(', ')}</code>
                       
@@ -113,7 +109,7 @@ const stats = { total: notebooks.length, cpu: notebooks.filter((n) => n.compute.
                 {n.preview_image && (
                   <figure class="preview">
                     <img src={url(`notebook-previews/${n.preview_image}`)} alt={`First saved output plot from the notebook “${n.title}”`} loading="lazy" />
-                    <figcaption class="small muted">Saved output from the notebook (first plot)</figcaption>
+                    
                   </figure>
                 )}
               </article>
diff --git a/src/pages/orientations.astro b/src/pages/orientations.astro
index edca14d24a..6c40e47f32 100644
--- a/src/pages/orientations.astro
+++ b/src/pages/orientations.astro
@@ -19,8 +19,7 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
   <div class="page">
     <p class="eyebrow">Research directions</p>
     <h1>Research orientations</h1>
-    <p class="lede">An orientation is a research agenda, defined by its goals, assumptions and trade-offs. It is not a method: several orientations can use the same tool for different ends.</p>
-    <p class="small muted">Quoted from §{src.chapter} {src.chapter_title} of the paper, <a href={src.abs}>arXiv:{src.id}{src.version}</a>.</p>
+    <p class="small muted">From §{src.chapter} of the paper.</p>
     {data.intro.map((t: string) => <Excerpt text={t} section={ref(src.chapter, src.chapter_title)} />)}
 
     <div class="orient-grid" role="list">
diff --git a/src/pages/safety.astro b/src/pages/safety.astro
index 0467853d12..2c6f1ab14a 100644
--- a/src/pages/safety.astro
+++ b/src/pages/safety.astro
@@ -10,9 +10,11 @@ import { url } from '../lib/site';
 const s = framework.safety_applications;
 const apps: any[] = s.applications;
 const problems: any[] = framework.open_problems.problems;
-const orient: any[] = framework.orientations.orientations;
+import orientationsArxiv from '../data/orientations-arxiv.json';
 const sec = (id: string) => sections([id])[0];
-const orientLink = (name: string) => { const o = orient.find((x) => name.toLowerCase().includes(x.name.toLowerCase().split(' ')[0])); return o ? url(`orientations/#${o.id}`) : null; };
+// The six orientations of the paper (§12), keyed by the word that tells them apart.
+const ORIENT_KEYS: [string, string][] = orientationsArxiv.orientations.map((o: any) => [o.name.replace(/ Interpretability$/, '').replace(/^Interpretability /, '').toLowerCase(), o.id]);
+const orientLink = (name: string) => { const k = ORIENT_KEYS.find(([w]) => name.toLowerCase().includes(w)); return k ? url(`orientations/#${k[1]}`) : null; };
 const methodLinks = (names: string[]) => names.map((n) => ({ n, m: resolveMethod(n) }));
 const search = (o: any) => [o.name ?? o.title, o.definition ?? o.statement, ...(o.details ?? []), ...(o.evidence ?? []), ...(o.limitations ?? []), ...(o.methods ?? o.related_methods ?? [])].join(' ').toLowerCase();
 import { draftHidden as __hide } from '../lib/manuscript';
@@ -22,7 +24,7 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
   <div class="page">
     <p class="eyebrow">Safety & open problems</p>
     <h1>Safety applications and open problems</h1>
-    <p class="lede">Where interpretability supports concrete safety work, what evidence the survey cites for it, and what remains unsolved. Each entry links to the methods it relies on and the orientations it belongs to.</p>
+    <p class="lede">Where mechanistic interpretability supports safety work, and the open problems that remain.</p>
     <DraftBanner />
     <div style="display:grid;gap:.5rem;margin:1.2rem 0">
       <label for="sq" class="visually-hidden">Filter applications and open problems</label>
@@ -31,7 +33,7 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
         <button class="chip on" data-kind="all" aria-pressed="true">All</button>
         <button class="chip" data-kind="app" aria-pressed="false">Safety applications ({apps.length})</button>
         <button class="chip" data-kind="problem" aria-pressed="false">Open problems ({problems.length})</button>
-        <button class="chip" data-kind="risk" aria-pressed="false">Risks & verification</button>
+        <button class="chip" data-kind="risk" aria-pressed="false">Risks</button>
       </div>
       <p class="small muted" id="s-count" aria-live="polite"></p>
     </div>
@@ -47,21 +49,18 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
           {a.methods?.length > 0 && <p class="small"><span class="muted">Methods:</span> {methodLinks(a.methods).map(({ n, m }, j) => <>{j > 0 && ', '}{m ? <a href={url(`methods/${m.id}/`)}>{n}</a> : n}</>)}</p>}
           {a.orientations?.length > 0 && <p class="small"><span class="muted">Orientations:</span> {a.orientations.map((n: string, j: number) => <>{j > 0 && ', '}{orientLink(n) ? <a href={orientLink(n)!}>{n}</a> : n}</>)}</p>}
           <details>
-            <summary class="small">Evidence ({a.evidence?.length ?? 0}) and limitations ({a.limitations?.length ?? 0})</summary>
-            <h4>Evidence cited in the survey</h4>
-            {a.evidence?.length ? a.evidence.map((t: string) => <Excerpt text={t} />) : <p class="small muted">The survey reports no empirical evidence for this application.</p>}
+            <summary class="small">Evidence and limitations</summary>
+            {a.evidence?.length > 0 && <><h4>Evidence</h4>{a.evidence.map((t: string) => <Excerpt text={t} />)}</>}
             {a.worked_example?.length > 0 && <><h4>Worked example <span class="label illustrative">Illustrative</span></h4>
-              <p class="small muted">This is a walkthrough from the survey. Its “results” illustrate the steps and are not measured outcomes.</p>
               {a.worked_example.map((t: string) => <Excerpt text={t} />)}</>}
-            <h4>Limitations</h4>
-            {a.limitations?.length ? a.limitations.map((t: string) => <Excerpt text={t} />) : <p class="small muted">None stated specifically.</p>}
+            {a.limitations?.length > 0 && <><h4>Limitations</h4>{a.limitations.map((t: string) => <Excerpt text={t} />)}</>}
           </details>
           {notebooksFor('applications', a.id).map((n) => <p class="small"><a href={n.links.colab}>Open “{n.title}” in Colab</a></p>)}
         </article>
       ))}
     </div>
 
-    <h2 data-kind-head="risk" style="margin-top:3rem">Risks and verification</h2>
+    <h2 data-kind-head="risk" style="margin-top:3rem">Risks</h2>
     <div class="entries">
       <article class="card entry" data-kind="risk" id="risk-taxonomy" data-search={JSON.stringify(s.risk_taxonomy).toLowerCase()}>
         <div class="e-head"><h3>Risk taxonomy</h3><ManuscriptNote /></div>
@@ -71,18 +70,10 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
         <SectionLinks ids={s.risk_taxonomy.sections} label="Survey" />
       </article>
       <article class="card entry" data-kind="risk" id="verification-checklist" data-search={JSON.stringify(s.verification_checklist).toLowerCase()}>
-        <div class="e-head"><h3>Verification checklist</h3><ManuscriptNote /></div>
-        <div class="checklist">
-          {s.verification_checklist.groups.map((g: any) => <fieldset><legend>{g.heading}</legend>{g.items.map((t: string) => <label><input type="checkbox" /> {t}</label>)}</fieldset>)}
-        </div>
-        <p class="small muted">The ticks are for your own use. They aren’t saved.</p>
+        <div class="e-head"><h3>Mitigation strategies</h3></div>
+        {s.verification_checklist.groups.map((g: any) => <><h4>{g.heading}</h4>{g.items.map((t: string) => <Excerpt text={t} />)}</>)}
         <SectionLinks ids={[s.verification_checklist.section]} label="Survey" />
       </article>
-      <article class="card entry" data-kind="risk" id="chapter-limitations" data-search={s.chapter_limitations.excerpts.join(' ').toLowerCase()}>
-        <div class="e-head"><h3>Limitations of mechanistic safety interventions</h3></div>
-        {s.chapter_limitations.excerpts.map((t: string) => <Excerpt text={t} />)}
-        <SectionLinks ids={[s.chapter_limitations.section]} label="Survey" />
-      </article>
     </div>
 
     <h2 data-kind-head="problem" style="margin-top:3rem">Open problems</h2>
@@ -90,12 +81,12 @@ if (__hide) return Astro.rewrite(`${import.meta.env.BASE_URL.replace(/\/?$/, '/'
     <div class="entries">
       {problems.map((p) => (
         <article class="card entry" data-kind="problem" id={p.id} data-search={search(p)}>
-          <div class="e-head"><h3>{p.title}</h3><span class="chip">Open problem</span>{p.source_chapter !== 15 && <span class="chip">from chapter {p.source_chapter}</span>}</div>
+          <div class="e-head"><h3>{p.title}</h3><span class="chip">Open problem</span>{p.source_chapter !== 14 && <span class="chip">from chapter {p.source_chapter}</span>}</div>
           <SectionLinks ids={p.sections} label="Survey" />
           <Excerpt text={p.statement} />
           {p.related_methods?.length > 0 && <p class="small"><span class="muted">Methods mentioned:</span> {methodLinks(p.related_methods).map(({ n, m }, j) => <>{j > 0 && ', '}{m ? <a href={url(`methods/${m.id}/`)}>{n}</a> : n}</>)}</p>}
           {p.related_orientations?.length > 0 && <p class="small"><span class="muted">Orientations:</span> {p.related_orientations.map((n: string, j: number) => <>{j > 0 && ', '}{orientLink(n) ? <a href={orientLink(n)!}>{n}</a> : n}</>)}</p>}
-          {p.details?.length > 0 && <details><summary class="small">More from the survey ({p.details.length})</summary>{p.details.map((t: string) => <Excerpt text={t} />)}</details>}
+          {p.details?.length > 0 && <details><summary class="small">More ({p.details.length})</summary>{p.details.map((t: string) => <Excerpt text={t} />)}</details>}
         </article>
       ))}
     </div>
diff --git a/src/pages/search.astro b/src/pages/search.astro
index 484a1a5526..cd47ffb86a 100644
--- a/src/pages/search.astro
+++ b/src/pages/search.astro
@@ -7,7 +7,7 @@ import { url } from '../lib/site';
   <div class="page-narrow">
     <p class="eyebrow">Search</p>
     <h1>Search</h1>
-    <p class="muted small">Searches every page on this site. The index is built statically, so it works offline once loaded.</p>
+    <p class="muted small">Search the survey, methods, notebooks and demos.</p>
     <div id="search"></div>
     <noscript><p>Search needs JavaScript.</p></noscript>
   </div>
diff --git a/src/styles/global.css b/src/styles/global.css
index 15ba66cf54..48ad76bd4f 100644
--- a/src/styles/global.css
+++ b/src/styles/global.css
@@ -157,7 +157,7 @@ table.data th[scope="row"] { font-weight: 500; }
 .label.manuscript { color: var(--ink-3); }
 .label.editorial { color: var(--accent); }
 
-blockquote.excerpt { margin: .7rem 0 1rem; padding: 0 0 0 1.1rem; border-left: 1px solid var(--hair-strong); font-family: var(--serif); font-size: 1.08rem; line-height: 1.55; color: var(--ink); }
+blockquote.excerpt { margin: .5rem 0 .9rem; padding: 0; border: 0; font-family: var(--serif); font-size: 1.08rem; line-height: 1.55; color: var(--ink); }
 blockquote.excerpt footer { font-family: var(--sans); font-size: .78rem; color: var(--ink-3); margin-top: .3rem; }
 blockquote.excerpt footer a { color: var(--ink-3); }
 
@@ -214,8 +214,7 @@ blockquote.excerpt footer a { color: var(--ink-3); }
 .prose a.cite-established { color: var(--tier-established); }
 .prose a.cite-emerging { color: var(--tier-emerging); }
 .prose a.cite-missing, .prose a.cite-flagged { color: var(--tier-flagged); }
-/* evidence tier without relying on colour: preprints dotted, missing/flagged wavy */
-.prose a.cite-emerging, .cite-emerging-sample { text-decoration: underline dotted 1px; text-underline-offset: .2em; }
+/* evidence tier: colour (peer-reviewed vs preprint); missing/flagged also wavy */
 .prose a.cite-flagged, .prose a.cite-missing { text-decoration: underline wavy 1px; text-underline-offset: .2em; }
 .tier-tag { display: inline-block; font: 500 .66rem/1.3 var(--sans); letter-spacing: .05em; text-transform: uppercase;
   border: 1px solid currentColor; padding: 0 .35em; margin-right: .45em; vertical-align: .12em; color: var(--ink-2); }

From a698da32e8e72722c63a4796f50717143f1be27f Mon Sep 17 00:00:00 2001
From: fbarez <fazlbarez93@gmail.com>
Date: Fri, 6 Nov 2026 21:32:57 +0000
Subject: [PATCH 2/2] Box titles, subfigure numbering, citation popups
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

- Ingest keeps the titles of boxed environments (warningbox, practicebox, …), which pandoc dropped; insightbox and practicebox get the box style.
- Subfigures: Figure 7a/7b and the outer 'Figure 7:' caption (previously both panels read 7b and the outer caption had no number).
- Citation popups show the full reference with links to the paper and the reference list, and stay open while you move into them.
---
 scripts/ingest/ingest.py                      | 17 ++++++++++-
 src/generated/chapters/circuits.html          | 12 ++++++++
 src/generated/chapters/colour-key.html        |  5 ++++
 src/generated/chapters/core-hypotheses.html   |  4 +++
 .../chapters/feature-extraction.html          | 22 ++++++++++++---
 .../chapters/foundational-concepts.html       |  9 ++++++
 .../intermediate-representations.html         | 11 ++++++++
 .../chapters/interpretability-landscape.html  |  1 +
 src/generated/chapters/introduction.html      |  1 +
 src/generated/chapters/learning-dynamics.html | 13 +++++++++
 .../limits-of-behavioural-evaluation.html     |  1 +
 .../chapters/representation-analysis.html     |  5 ++++
 .../chapters/research-orientations.html       |  5 ++++
 .../chapters/safety-applications.html         |  6 ++++
 .../chapters/training-data-attribution.html   |  7 +++++
 src/generated/manifest.json                   | 28 +++++++++----------
 src/pages/handbook/[slug].astro               | 23 +++++++++------
 src/styles/global.css                         |  2 +-
 18 files changed, 144 insertions(+), 28 deletions(-)

diff --git a/scripts/ingest/ingest.py b/scripts/ingest/ingest.py
index d3efde20e0..ee20d53369 100644
--- a/scripts/ingest/ingest.py
+++ b/scripts/ingest/ingest.py
@@ -401,6 +401,10 @@ def main() -> None:
         tex = tex.replace("\\cmark", "✓").replace("\\xmark", "✗")
         tex = re.sub(r"\\rowcolor(\[[^\]]*\])?\{[^}]*\}", "", tex)
         tex = re.sub(r"\\(?:hspace|vspace)\*?\{[^}]*\}", "", tex)
+        # tcolorbox environments (warningbox, practicebox, …) take their title as an optional argument;
+        # pandoc drops it, so emit it as the box's first line.
+        tex = re.sub(r"\\begin\{(definitionbox|examplebox|insightbox|practicebox|theorybox|warningbox)\}\[([^\]]*)\]",
+                     lambda m: f"\\begin{{{m.group(1)}}}\n\\noindent\\textbf{{{m.group(2)}}}\n\n", tex)
         tex = tex.replace("\\begin{wrapfigure}", "\\begin{figure}").replace("\\end{wrapfigure}", "\\end{figure}")
         tex = re.sub(r"(\\begin\{figure\})\{[^}]*\}\{[^}]*\}", r"\1", tex)
         # equations: anchor + exact number from the LaTeX build
@@ -604,7 +608,18 @@ def f(m):
                     rest = re.sub(r"(<figcaption[^>]*>|<caption[^>]*>)", rf'\1<span class="capnum">{kind_word} {num}:</span> ', rest, count=1)
                 return f'<{tag} id="{fid}"' + rest
             return f
-        h = re.sub(r'<figure id="([^"]+)"(.*?</figure>)', cap_sub("Figure", "figure"), h, flags=re.S)
+        # Figures with subfigures: pandoc nests <figure>s and gives the outer one the last subfigure's
+        # label. Number the subfigures first, then give the outer caption the parent number (7a, 7b -> 7).
+        def group_open(m):
+            return f'<figure class="subfigures"{m.group(2)}>'
+        h = re.sub(r'<figure id="([^"]+)"([^>]*)>(?=\s*<figure )', group_open, h)
+        h = re.sub(r'<figure id="([^"]+)"((?:(?!<figure).)*?</figure>)', cap_sub("Figure", "figure"), h, flags=re.S)
+        def group_cap(m):
+            nums = re.findall(r'class="capnum">Figure (\d+)[a-z]?:', m.group(0))
+            if not nums:
+                return m.group(0)
+            return re.sub(r'(</figure>\s*<figcaption>)(?![^<]*<span class="capnum")', rf'\1<span class="capnum">Figure {nums[-1]}:</span> ', m.group(0)[::-1][::-1], count=0)[:0] + m.group(1) + m.group(2) + f'<figcaption><span class="capnum">Figure {nums[-1]}:</span> ' + m.group(3)
+        h = re.sub(r'(<figure class="subfigures"[^>]*>)(.*</figure>\s*)<figcaption>((?:(?!<figure).)*?</figcaption>\s*</figure>)', group_cap, h, flags=re.S)
         h = re.sub(r'<table id="([^"]+)"(.*?</table>)', cap_sub("Table", "table"), h, flags=re.S)
         # tables wrapped in a div with the label id (pandoc 3 behaviour)
         def tabdiv(m):
diff --git a/src/generated/chapters/circuits.html b/src/generated/chapters/circuits.html
index d51f7b8ffa..80631879a7 100644
--- a/src/generated/chapters/circuits.html
+++ b/src/generated/chapters/circuits.html
@@ -4,6 +4,7 @@
 <section id="sec:circuit-discovery-methods" class="level2"><h2 data-pdf-page="53"><span class="secnum">7.1</span> Circuit Discovery Methods<a class="anchor" href="#sec:circuit-discovery-methods" aria-label="Link to this section">#</a></h2>
 <p>Circuit discovery methods aim to identify internal computational graphs responsible for specific capabilities or outputs in a neural network. Below, and in Table <a href="@@BASE@@handbook/circuits/#tab:circuit-methods" data-reference-type="ref" data-reference="tab:circuit-methods">8</a>, we formalise key techniques and methods, along with their strengths and limitations.</p>
 <div class="definitionbox">
+<p><strong>Activation Patching</strong></p>
 <p><span id="def:attribution-patching" data-label="def:attribution-patching"></span> Given a model <span class="math inline">f_\theta(\cdot)</span> parameterised by <span class="math inline">\theta</span> and an input sequence <span class="math inline">x</span>, <em>activation patching</em> evaluates the causal contribution of some model components <span class="math inline">S</span> by replacing their activations with specified reference values <span class="math inline">\tilde{h}_S</span> and measuring the resulting change in the model output. The causal importance of <span class="math inline">S</span> for <span class="math inline">x</span> is defined as <span class="math display">s_S(x)
 =
 M\bigl(f_\theta(x)\bigr)
@@ -11,12 +12,15 @@
 M\Bigl(f_\theta\bigl(x_{[S \leftarrow \tilde{h}_S]}\bigr)\Bigr),</span> where <span class="math inline">M(\cdot)</span> is a scalar performance metric computed from the model output, such as the logit difference for the answer token, and <span class="math inline">x_{[S \leftarrow \tilde{h}_S]}</span> denotes a forward pass on <span class="math inline">x</span> in which the activations of components in <span class="math inline">S</span> are replaced by <span class="math inline">\tilde{h}_S</span> while the remaining computation proceeds normally. The reference activations <span class="math inline">\tilde{h}_S</span> usually come from a corrupted reference run, where the input is modified to add or remove specific information. Some works have also set <span class="math inline">\tilde{h}_S</span> to zero or the mean model activation. The resulting change in model output quantifies the causal effect of intervening on <span class="math inline">S</span> <span class="citation" data-cites="zhang2024towards">(<a href="@@BASE@@references/#ref-zhang2024towards" class="cite cite-established" data-cite="zhang2024towards" role="doc-biblioref">Zhang &amp; Nanda, 2024</a>)</span>.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Path Patching</strong></p>
 <p><span id="def:path-patching" data-label="def:path-patching"></span> <em>Path patching</em> extends activation patching on model components to specific edges in the computational graph. For a connection <span class="math inline">e_{ij}</span> from model component <span class="math inline">i</span> to <span class="math inline">j</span> denoting that the output activations of <span class="math inline">i</span> are fed to <span class="math inline">j</span> as one of its inputs, it replaces activations along <span class="math inline">e_{ij}</span> with reference values <span class="math inline">\tilde{h}_{e_{ij}}</span> and measures <span class="math inline">e_{ij}</span>’s causal effect as: <span class="math display">s_{ij}(x) = M\bigl(f_\theta(x)\bigr) - M\Bigl(f_\theta\bigl(x_{[e_{ij} \leftarrow \tilde{h}_{e_{ij}}]}\bigr)\Bigr).</span> This allows finer-grained causal localisation along information flows <span class="citation" data-cites="goldowskydill2023localizingmodelbehaviorpath">(<a href="@@BASE@@references/#ref-goldowskydill2023localizingmodelbehaviorpath" class="cite cite-emerging" data-cite="goldowskydill2023localizingmodelbehaviorpath" role="doc-biblioref">Goldowsky-Dill et al., 2023</a>)</span>.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Automatic Circuit Discovery (ACDC)</strong></p>
 <p><span id="def:acdc" data-label="def:acdc"></span> <em>ACDC</em> <span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic">(<a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a>)</span> discovers a circuit by iteratively using path patching to identify important connections in the model’s computational graph. Traversing the graph in reverse topological order from the output node, it patches the activation transmitted along each edge and measures the resulting change in a task-specific metric. An edge <span class="math inline">e_{ij}</span> will be pruned if patching it produces <span class="math inline">|s_{ij}(x)|</span> less than a predefined threshold <span class="math inline">\tau</span>; otherwise, it is retained in the graph and its upstream edges will be investigated. The remaining subgraph represents the discovered circuit.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Edge Attribution Patching (EAP)</strong></p>
 <p><span id="def:eap" data-label="def:eap"></span> <em>EAP</em> <span class="citation" data-cites="syed2023attributionpatchingoutperformsautomated">(<a href="@@BASE@@references/#ref-syed2023attributionpatchingoutperformsautomated" class="cite cite-emerging" data-cite="syed2023attributionpatchingoutperformsautomated" role="doc-biblioref">Syed et al., 2024</a>)</span> estimates the causal importance of edges in a model’s computational graph by approximating the effect of path patching with a first-order Taylor expansion. For an edge <span class="math inline">e_{ij}</span> connecting components <span class="math inline">i</span> and <span class="math inline">j</span>, its importance score is approximated as <span class="math display">s_{ij}(x)
 \approx
 \frac{\partial M}{\partial h_{e_{ij}}}
@@ -75,6 +79,7 @@
 <p>The methods introduced above discover circuits <em>within</em> the target model. Their nodes correspond to coarse-grained components such as attention heads and MLP blocks, which are mostly not immediately understandable. A complementary approach first constructs a more interpretable replacement model (Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec:cross-layer-transcoder" data-reference-type="ref" data-reference="subsubsec:cross-layer-transcoder">6.6.2</a>) that approximates the original model’s computation using sparse linear features, and then performs circuit discovery within this replacement model.</p>
 <p>Recent work has implemented this paradigm using cross-layer transcoders (CLTs; Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec:cross-layer-transcoder" data-reference-type="ref" data-reference="subsubsec:cross-layer-transcoder">6.6.2</a>) <span class="citation" data-cites="ameisen2025circuit merullo2025replicating">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>; <a href="@@BASE@@references/#ref-merullo2025replicating" class="cite cite-emerging" data-cite="merullo2025replicating" role="doc-biblioref">Merullo et al., 2025</a>)</span>. In particular, Circuit Tracer <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span> replaces the model’s MLPs with CLTs while retaining the underlying model’s attention and residual-stream computation. This yields a prompt-specific attribution graph whose principal internal nodes are sparse CLT features rather than entire MLP blocks or attention heads.</p>
 <div class="definitionbox">
+<p><strong>Circuit Tracer</strong></p>
 <p><span id="def:circuit-tracer" data-label="def:circuit-tracer"></span></p>
 <p><em>Circuit Tracer</em> <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span> constructs a prompt-specific attribution graph in a CLT-based replacement model. For a given input prompt, consider a source feature <span class="math inline">\phi_s</span> at layer <span class="math inline">\ell_s</span> active at token position <span class="math inline">c_s</span> with activation value <span class="math inline">z_{\ell_s}^{s}</span>, and a downstream target feature <span class="math inline">\phi_t</span> at layer <span class="math inline">\ell_t</span> active at position <span class="math inline">c_t</span>. Recall that a CLT feature can write to the reconstructed MLP outputs of its own and all subsequent layers (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>); the source feature has layer-specific output vectors to these layers, corresponding to columns of the layer-specific decoder matrices: <span class="math display">v_{\mathrm{out},\phi_s}^{\ell}
 \coloneqq
@@ -121,6 +126,7 @@ <h4>Evaluating Circuits and Benchmarking Discovery Methods.</h4>
 </section>
 <section id="subsec: induc head case study: circuit" class="level2"><h2 data-pdf-page="58"><span class="secnum">7.4</span> Case Study: Induction Heads<a class="anchor" href="#subsec: induc head case study: circuit" aria-label="Link to this section">#</a></h2>
 <div class="examplebox">
+<p><strong>Circuit Perspective on Induction Heads</strong></p>
 <p>Following our introduction to induction heads in Section <a href="@@BASE@@handbook/feature-extraction/#subsec: induc head case study: feature extract" data-reference-type="ref" data-reference="subsec: induc head case study: feature extract">6.8</a>, we consider a repeated sequence:</p>
 <p><span class="math display">\texttt{A B C A B}</span></p>
 <p>where the model needs to predict the token after the second <code>B</code>. The induction head alone is not sufficient to implement induction behaviour from scratch. It still needs help from previous layers’ attention heads to locate the token following the current token’s previous occurrence, so it knows it should attend to <code>C</code> in this example <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.<br />
@@ -200,6 +206,7 @@ <h4>Evaluating Circuits and Benchmarking Discovery Methods.</h4>
 <section id="dimension-1-attribution-faithfulness" class="level3 unnumbered"><h3 class="unnumbered">Dimension 1: Attribution Faithfulness<a class="anchor" href="#dimension-1-attribution-faithfulness" aria-label="Link to this section">#</a></h3>
 <p>Attribution faithfulness measures whether the interpretability method accurately attributes the model’s behaviour of interest to responsible components.</p>
 <div class="definitionbox">
+<p><strong>Attribution Faithfulness Metrics</strong></p>
 <section id="intervention-effect-agreement." class="level4">
 <h4>Intervention-Effect Agreement.</h4>
 <p>Let <span class="math inline">a(S,x)</span> denote the importance score assigned by an interpretability method to components <span class="math inline">S</span> for a sample <span class="math inline">x \sim B</span>: <span class="math display">\mathrm{EffectAgreement}
@@ -260,6 +267,7 @@ <h4>Evaluation protocol:</h4>
 <section id="dimension-2-completeness" class="level3 unnumbered"><h3 class="unnumbered">Dimension 2: Completeness<a class="anchor" href="#dimension-2-completeness" aria-label="Link to this section">#</a></h3>
 <p>Completeness measures how much of the model behaviour the identified components or mechanisms explain. A high completeness score indicates that the identified components/mechanisms capture most of the relevant computation.</p>
 <div class="definitionbox">
+<p><strong>Completeness Metrics</strong></p>
 <section id="loss-recovery." class="level4">
 <h4>Loss Recovery.</h4>
 <p>For methods that reconstruct model activations (e.g., SAEs and CLTs), after replacing the target component’s activations with the reconstructions, we measure the fraction of the original model performance that is recovered: <span class="math display">\mathrm{LR}
@@ -317,6 +325,7 @@ <h4>Evaluation protocol:</h4>
 <section id="dimension-3-stability" class="level3 unnumbered"><h3 class="unnumbered">Dimension 3: Stability<a class="anchor" href="#dimension-3-stability" aria-label="Link to this section">#</a></h3>
 <p>Stability measures the robustness of explanations, attributions, and findings to changes in variables that should not affect the results, such as the choice of random seeds, small hyperparameter changes, and input paraphrasing.</p>
 <div class="definitionbox">
+<p><strong>Stability Metrics</strong></p>
 <section id="cross-seed-agreement." class="level4">
 <h4>Cross-Seed Agreement.</h4>
 <p>We train or apply the interpretability method <span class="math inline">K</span> times under different random seeds. Let <span class="math inline">\mathcal{F}_i</span> denote the set of features or mechanisms, such as SAE features and circuits, discovered under seed <span class="math inline">i</span>. We measure the cross-seed agreement as: <span class="math display">\text{Seed-Stability} = \frac{1}{K(K-1)} \sum_{i \neq j} \text{Align}(\mathcal{F}_i, \mathcal{F}_j),</span> where <span class="math inline">\text{Align}(\cdot)</span> measures similarity between sets. For example, <span class="citation" data-cites="paulo2025sparse"><a href="@@BASE@@references/#ref-paulo2025sparse" class="cite cite-established" data-cite="paulo2025sparse" role="doc-biblioref">Paulo &amp; Belrose (2026)</a></span> calculate the Hungarian matching between two sets of SAE features based on their pairwise cosine similarities.<br />
@@ -352,6 +361,7 @@ <h4>Evaluation protocol:</h4>
 <section id="dimension-4-causal-validity" class="level3 unnumbered"><h3 class="unnumbered">Dimension 4: Causal Validity<a class="anchor" href="#dimension-4-causal-validity" aria-label="Link to this section">#</a></h3>
 <p>Causal validity assesses whether the identified components or mechanisms are causal to the behaviour, particularly necessity (i.e., removing the components/mechanisms disrupts the behaviour) and sufficiency (i.e., the components/mechanisms alone are sufficient to preserve the behaviour).</p>
 <div class="definitionbox">
+<p><strong>Causal Validity Metrics</strong></p>
 <section id="necessity-score." class="level4">
 <h4>Necessity Score.</h4>
 <p>Necessity measures the fraction of the model’s performance on behaviour <span class="math inline">B</span> that is lost when <span class="math inline">S</span> is ablated (i.e., its activations are replaced with some reference values). It is calculated as: <span class="math display">\mathrm{Necessity}(S,B)
@@ -421,6 +431,7 @@ <h4>Evaluation protocol:</h4>
 <section id="dimension-5-human-interpretability" class="level3 unnumbered"><h3 class="unnumbered">Dimension 5: Human Interpretability<a class="anchor" href="#dimension-5-human-interpretability" aria-label="Link to this section">#</a></h3>
 <p>Human interpretability measures whether generated explanations eventually improve human understanding, prediction, and decision-making about model behaviour.</p>
 <div class="definitionbox">
+<p><strong>Human Interpretability Metrics</strong></p>
 <section id="explanation-utility." class="level4">
 <h4>Explanation Utility.</h4>
 <p>This metric tests whether humans can predict model behaviour more accurately in the held-out set based on the explanation. It is formulated as: <span class="math display">\text{Expl-Utility}
@@ -504,6 +515,7 @@ <h4>Evaluation protocol:</h4>
 <section id="integrated-evaluation-protocol" class="level3"><h3 data-pdf-page="65"><span class="secnum">7.5.3</span> Integrated Evaluation Protocol<a class="anchor" href="#integrated-evaluation-protocol" aria-label="Link to this section">#</a></h3>
 <p>We recommend the following integrated protocol for comprehensive evaluation:</p>
 <div class="practicebox">
+<p><strong>Recommended Evaluation Protocol</strong></p>
 <section id="step-1-define-scope." class="level4">
 <h4>Step 1: Define scope.</h4>
 <p>Users first specify the model, layer(s), and behaviour(s) of interest and define a behavioural test suite with clear success criteria.<br />
diff --git a/src/generated/chapters/colour-key.html b/src/generated/chapters/colour-key.html
index 4d10b17409..f21fea82f4 100644
--- a/src/generated/chapters/colour-key.html
+++ b/src/generated/chapters/colour-key.html
@@ -2,26 +2,31 @@
 <p>This section explains the visual conventions used throughout the survey for boxes, emphasis, and structural elements.</p>
 <section id="box-types-and-colours" class="level2"><h2 data-pdf-page="178"><span class="secnum">B.1</span> Box Types and Colours<a class="anchor" href="#box-types-and-colours" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>Definition Box (Dark Blue)</strong></p>
 <p><strong>Definition boxes</strong> introduce core concepts and provide precise technical definitions. Use these when a term needs clear specification before use.<br />
 Example concepts: “Feature”, “Circuit”, “Sparse Autoencoder”.<br />
 Style: Dark blue border, light blue background.</p>
 </div>
 <div class="examplebox">
+<p><strong>Example Box (Green)</strong></p>
 <p><strong>Example boxes</strong> provide concrete illustrations, worked examples, or case studies. Use these to make abstract concepts concrete.<br />
 Example contents: “Induction in a Repeated Sequence”, “Example Application of Steering Vectors”, “Probing Classifiers for Sleeper Agents”.<br />
 Style: Green border, light green background.</p>
 </div>
 <div class="insightbox">
+<p><strong>Insight Box (Gold)</strong></p>
 <p><strong>Insight boxes</strong> explain intuitions behind mathematical or conceptual results. Use these to build understanding of why something works, not just that it works.<br />
 Example contents: “Trade-offs Between Variants”, “The Complexity–Interpretability Trade-off”.<br />
 Style: Gold border, light gold background.</p>
 </div>
 <div class="warningbox">
+<p><strong>Warning Box (Red)</strong></p>
 <p><strong>Warning boxes</strong> highlight critical limitations, failure modes, or common misconceptions. Use these to prevent readers from adopting incorrect or unsafe interpretations.<br />
 Example contents: “Caveats on CLT Fidelity Claims”, “The Probe Causality Problem”, “The Partial Coverage Issue”.<br />
 Style: Red border, light red background.</p>
 </div>
 <div class="practicebox">
+<p><strong>Practice Box (Teal)</strong></p>
 <p><strong>Practice boxes</strong> illustrate suggestions that are actionable. These include protocol recommendations, checklists for verifying approaches, and step-by-step decision-making walkthroughs. Use these for practical implementations of procedures.<br />
 Example contents: “Guardrail Framework for Automated Interpretability”, “Validation Procedures for Introspective Models”, “Recommended Evaluation Protocol”.<br />
 Style: Teal border, light teal background.</p>
diff --git a/src/generated/chapters/core-hypotheses.html b/src/generated/chapters/core-hypotheses.html
index fcf66541f8..533a985ae2 100644
--- a/src/generated/chapters/core-hypotheses.html
+++ b/src/generated/chapters/core-hypotheses.html
@@ -3,6 +3,7 @@
 <section id="subsec:linearity" class="level2"><h2 data-pdf-page="27"><span class="secnum">5.1</span> The Linear Representation Hypothesis<a class="anchor" href="#subsec:linearity" aria-label="Link to this section">#</a></h2>
 <p>We have seen that single neurons may not be the most effective unit of representation in many cases, but what is? The linear representation hypothesis claims that <em>linear</em> directions in the activation space are effective fundamental units of representation <span class="citation" data-cites="lrh">(<a href="@@BASE@@references/#ref-lrh" class="cite cite-established" data-cite="lrh" role="doc-biblioref">Park et al., 2024</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>Linear Representation Hypothesis</strong></p>
 <p>A concept can be effectively represented by a feature corresponding to a linear direction <span class="math inline">\phi</span> in activation space <span class="math inline">\mathbb{R}^d</span>. Linearity implies that adding or subtracting this direction, or a scalar multiple of it, from model activations can correspondingly influence model behaviour, while the projection <span class="math inline">\phi^\top h(x)</span> approximately measures the strength of the feature in model activations <span class="math inline">h(x)</span> for an input <span class="math inline">x</span>.<br />
 Under a strong version of the linear representation hypothesis, all concepts are best represented by linear features. In this view, model activations can be approximately decomposed as an additive combination of such features, <span class="math display">h(x) \approx \sum_i \alpha_i \phi_i,</span> where <span class="math inline">\alpha_i \in \mathbb{R}</span> denotes the strength of feature <span class="math inline">\phi_i</span> dependent on the input <span class="math inline">x</span>. Under a weaker version, only some concepts admit such linear representations (<span class="citation" data-cites="sharkey2025open"><a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a></span>; <span class="citation" data-cites="smith2024strongfeature"><a href="@@BASE@@references/#ref-smith2024strongfeature" class="cite cite-emerging" data-cite="smith2024strongfeature" role="doc-biblioref">Smith, 2024</a></span>).</p>
 </div>
@@ -24,6 +25,7 @@ <h4>Empirical Evidence and Counterexamples.</h4>
 <section id="subsec:superposition" class="level2"><h2 data-pdf-page="28"><span class="secnum">5.2</span> The Superposition Hypothesis<a class="anchor" href="#subsec:superposition" aria-label="Link to this section">#</a></h2>
 <p>The superposition hypothesis says that models encode more features than their hidden representations have dimensions.</p>
 <div class="definitionbox">
+<p><strong>Superposition Hypothesis</strong></p>
 <p>Model hidden states encode more features than they have dimensions. If there are <span class="math inline">m</span> features and <span class="math inline">d</span> dimensions, then the superposition hypothesis claims that <span class="math inline">m \gg d</span>.<br />
 This implies that features cannot be represented as orthogonal directions—though they can be represented as nearly orthogonal directions. It also implies neuron polysemanticity: the same neuron participates in the encoding of multiple unrelated concepts.</p>
 </div>
@@ -53,6 +55,7 @@ <h4>Architectural Responses to Superposition.</h4>
 <section id="subsec:privileged-access" class="level2"><h2 data-pdf-page="30"><span class="secnum">5.3</span> The Privileged Access Hypothesis<a class="anchor" href="#subsec:privileged-access" aria-label="Link to this section">#</a></h2>
 <p>A class of approaches that use language models as interpreters of <em>themselves</em> can be justified through the privileged access hypothesis. The privileged access hypothesis states that:</p>
 <div class="definitionbox">
+<p><strong>Privileged Access Hypothesis</strong></p>
 <p>A language model used to interpret itself can, under appropriate elicitation, access internal variables and intermediate computations that are not reliably recoverable from external behavioural analysis alone, providing a comparative advantage as an interpreter of its own behaviour (<span class="citation" data-cites="binder"><a href="@@BASE@@references/#ref-binder" class="cite cite-established" data-cite="binder" role="doc-biblioref">Binder et al., 2025</a></span>; <span class="citation" data-cites="li2025traininglanguagemodelsexplain song"><a href="@@BASE@@references/#ref-li2025traininglanguagemodelsexplain" class="cite cite-emerging" data-cite="li2025traininglanguagemodelsexplain" role="doc-biblioref">Li et al., 2025a</a>; <a href="@@BASE@@references/#ref-song" class="cite cite-emerging" data-cite="song" role="doc-biblioref">Song et al., 2026</a></span>).</p>
 </div>
 <section id="conceptual-basis.-2" class="level4">
@@ -69,6 +72,7 @@ <h4>Empirical Evidence.</h4>
 </section>
 <section id="subsec:universality" class="level2"><h2 data-pdf-page="31"><span class="secnum">5.4</span> The Universality and Platonic Representation Hypothesis<a class="anchor" href="#subsec:universality" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>Platonic Representation Hypothesis</strong></p>
 <p>Models independently trained on similar tasks tend to develop similar internal representations or algorithms, which often approximate a task-determined canonical form (<span class="citation" data-cites="huh2024platonicrepresentationhypothesis"><a href="@@BASE@@references/#ref-huh2024platonicrepresentationhypothesis" class="cite cite-established" data-cite="huh2024platonicrepresentationhypothesis" role="doc-biblioref">Huh et al., 2024</a></span>; <span class="citation" data-cites="olah2020zoom"><a href="@@BASE@@references/#ref-olah2020zoom" class="cite cite-emerging" data-cite="olah2020zoom" role="doc-biblioref">Olah et al., 2020</a></span>). The degree of convergence depends on factors such as model size, architecture, and training data.</p>
 </div>
 <section id="conceptual-basis.-3" class="level4">
diff --git a/src/generated/chapters/feature-extraction.html b/src/generated/chapters/feature-extraction.html
index 3b35465801..4d8a2c2209 100644
--- a/src/generated/chapters/feature-extraction.html
+++ b/src/generated/chapters/feature-extraction.html
@@ -14,6 +14,7 @@
 <figcaption><span class="capnum">Figure 6:</span> <strong>Sparse Autoencoder (SAE).</strong> SAEs are trained to reconstruct activations of a model component at a specific layer <span class="math inline">\ell</span>, <span class="math inline">h_\ell</span>, into an overcomplete, sparse latent space <span class="math inline">z</span>, such that the reconstructed activation <span class="math inline">\hat{h_\ell} \approx h_\ell</span>. By enforcing sparsity on <span class="math inline">z</span>, the model decomposes dense, polysemantic representations into monosemantic, human-interpretable features.</figcaption>
 </figure>
 <div class="definitionbox">
+<p><strong>Sparse Autoencoder (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:sae" data-reference-type="ref" data-reference="fig:sae">6</a>)</strong></p>
 <p>A sparse autoencoder learns a component-specific encoder–decoder pair <span class="math display">E : \mathbb{R}^d \rightarrow \mathbb{R}^{d_{\text{sparse}}},
 \qquad
 D : \mathbb{R}^{d_{\text{sparse}}} \rightarrow \mathbb{R}^d,</span> such that activations of a particular model component at a given layer, <span class="math inline">h_\ell \in \mathbb{R}^d</span> (e.g., the output of the layer-<span class="math inline">\ell</span> MLP block), are reconstructed as <span class="math display">\hat{h}_\ell = D(E(h_\ell)),</span> while encouraging the feature activations <span class="math inline">z=E(h_\ell)</span> to be sparse. Usually, <span class="math inline">d_{\text{sparse}} = ed</span>, where <span class="math inline">e</span> is an integer expansion factor.</p>
@@ -46,10 +47,12 @@ <h4>Training Dynamics and Evaluation.</h4>
 <section id="subsec:sae-variants" class="level2"><h2 data-pdf-page="34"><span class="secnum">6.3</span> SAE Variants and Architectural Improvements<a class="anchor" href="#subsec:sae-variants" aria-label="Link to this section">#</a></h2>
 <p>There are several variants of the standard SAE. One key reason to use alternatives is that standard SAE training penalises activation magnitude, which can lead to low activation even when a feature is clearly present (the shrinkage effect). Different optimisation frameworks also have different tunable parameters. The choice depends on the analysis goal, target layer type, model size, and budget constraints.</p>
 <div class="definitionbox">
+<p><strong>Gated SAEs</strong></p>
 <p>Gated SAEs introduce multiplicative gates to decouple feature selection from magnitude: <span class="math display">z = \underbrace{\mathbbm{1} \left( (W_{\text{gate}} h_\ell + b_{\text{gate}}) &gt; 0 \right)}_{\text{Gating Path}} \odot \underbrace{\sigma_{\text{ReLU}}(W_{\text{in}} h_\ell + b_{\text{in}})}_{\text{Magnitude Path}},</span> where <span class="math inline">h_\ell \in \mathbb{R}^d</span> denotes activations at layer <span class="math inline">\ell</span>, <span class="math inline">W_{\text{in}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are encoder <span class="math inline">E</span> parameters, <span class="math inline">W_{\text{gate}} \in \mathbb{R}^{{d_{\text{sparse}}} \times d}</span> and <span class="math inline">b_{\text{gate}} \in \mathbb{R}^{d_{\text{sparse}}}</span> are the gate parameters, and <span class="math inline">\odot</span> denotes element-wise product. This mitigates shrinkage effects induced by the <span class="math inline">L_1</span> penalty <span class="citation" data-cites="rajamanoharan2024improvingdictionarylearninggated">(<a href="@@BASE@@references/#ref-rajamanoharan2024improvingdictionarylearninggated" class="cite cite-established" data-cite="rajamanoharan2024improvingdictionarylearninggated" role="doc-biblioref">Rajamanoharan et al., 2024a</a>)</span>.</p>
 </div>
 <div class="minipage">
 <div class="definitionbox">
+<p><strong>JumpReLU SAEs</strong></p>
 <p>JumpReLU SAEs <span class="citation" data-cites="rajamanoharan2024jumpingaheadimprovingreconstruction">(<a href="@@BASE@@references/#ref-rajamanoharan2024jumpingaheadimprovingreconstruction" class="cite cite-emerging" data-cite="rajamanoharan2024jumpingaheadimprovingreconstruction" role="doc-biblioref">Rajamanoharan et al., 2024b</a>)</span> learn per-feature activation thresholds. Given the encoder pre-activation <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}}</span>, let <span class="math inline">a_i</span> denote its <span class="math inline">i</span>-th entry. The activation value of the <span class="math inline">i</span>-th feature after applying JumpReLU is <span class="math display">z^i = \sigma_{\text{JumpReLU}}(a_i) = \begin{cases} 
     a_i &amp; \text{if } a_i &gt; \tau_i \\ 
     0 &amp; \text{otherwise},
@@ -60,12 +63,14 @@ <h4>Training Dynamics and Evaluation.</h4>
 <p><img src="@@BASE@@figures/jumprelu.svg" alt="image" /></p>
 </div>
 <div class="definitionbox">
+<p><strong>Top-<span class="math inline">k</span> SAEs</strong></p>
 <p>Top-<span class="math inline">k</span> SAEs <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span> impose an explicit hard sparsity constraint. Let <span class="math inline">a = W_{\text{in}} h_\ell + b_{\text{in}} \in \mathbb{R}^{d_{\text{sparse}}}</span> denote encoder pre-activations. Top-<span class="math inline">k</span> SAEs define the <span class="math inline">i</span>-th feature’s activation as <span class="math display">z^i = \sigma_{\text{TopK}}(a_i) = \begin{cases}
 a_i &amp; \text{if } i \in \mathcal{I}_k(a) \\
 0 &amp; \text{otherwise},
 \end{cases}</span> where <span class="math inline">\mathcal{I}_k(a)</span> indexes the <span class="math inline">k</span> largest entries of <span class="math inline">a</span>. This formulation explicitly enforces <span class="math inline">\|z\|_0 \le k</span>—a more direct feature-sparsity constraint than <span class="math inline">L_1</span> regularisation. Notably, as it does not suppress activation magnitudes, it avoids the feature-shrinkage issue observed in <span class="math inline">L_1</span>-based SAE training <span class="citation" data-cites="gao2024scalingevaluatingsparseautoencoders">(<a href="@@BASE@@references/#ref-gao2024scalingevaluatingsparseautoencoders" class="cite cite-established" data-cite="gao2024scalingevaluatingsparseautoencoders" role="doc-biblioref">Gao et al., 2025</a>)</span>.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Batch Top-<span class="math inline">k</span> SAEs</strong></p>
 <p>Batch Top-<span class="math inline">k</span> SAEs relax the fixed per-example sparsity constraint of standard Top-<span class="math inline">k</span> SAEs by enforcing the sparsity budget across an entire batch instead <span class="citation" data-cites="bussmann2024batchtopksparseautoencoders">(<a href="@@BASE@@references/#ref-bussmann2024batchtopksparseautoencoders" class="cite cite-emerging" data-cite="bussmann2024batchtopksparseautoencoders" role="doc-biblioref">Bussmann et al., 2024</a>)</span>. For a batch of encoder activations <span class="math display">\big[\, a^{(1)}, \dots, a^{(B)} \,\big] \in \mathbb{R}^{d_{\text{sparse}} \times B},</span> where <span class="math inline">B</span> is the batch size, Batch Top-<span class="math inline">k</span> retains the largest <span class="math inline">Bk</span> activation values in the batch. Specifically, let <span class="math display">\mathcal{I}
 \coloneqq
 \arg\max_{\substack{
@@ -86,6 +91,7 @@ <h4>Training Dynamics and Evaluation.</h4>
 <p>That is, each batch exactly retains <span class="math inline">Bk</span> features, while individual samples may have more or fewer active features than <span class="math inline">k</span>. This loose constraint allows the model to allocate representational capacity adaptively across samples. During inference, Batch Top-<span class="math inline">k</span> SAEs instead use the JumpReLU function with an inferred global threshold to determine which features are active for each input, so that an input’s activations do not depend on the rest of the batch.</p>
 </div>
 <div class="insightbox">
+<p><strong>Trade-offs Between Variants</strong></p>
 <p>Different SAE variants may implement distinct sparsity constraints. No single variant is optimal across all scenarios.<br />
 </p>
 <div class="center">
@@ -319,6 +325,7 @@ <h4>Model Scale.</h4>
 <section id="subsubsec: sae limitation" class="level3"><h3 data-pdf-page="40"><span class="secnum">6.5.5</span> Identifiability and Theoretical Concerns<a class="anchor" href="#subsubsec: sae limitation" aria-label="Link to this section">#</a></h3>
 <p>Another concern for SAE-based interpretability claims is whether the learned features are identifiable, that is, whether the dictionary learning reliably recovers the “true” underlying features—if such features exist—or merely finds one of many mediocre decompositions <span class="citation" data-cites="klindt2025superpositionsparsecodesinterpretable">(<a href="@@BASE@@references/#ref-klindt2025superpositionsparsecodesinterpretable" class="cite cite-emerging" data-cite="klindt2025superpositionsparsecodesinterpretable" role="doc-biblioref">Klindt et al., 2025</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>Identifiability</strong></p>
 <p>Suppose activations are generated by some sparse latent process</p>
 <p><span class="math display">h = D^{*} z^{*} + \epsilon ,</span></p>
 <p>where <span class="math inline">D^{*}</span> is the ground-truth feature dictionary and <span class="math inline">z^{*}</span> are the feature coefficients. An SAE is identifiable if its training recovers <span class="math inline">D^{*}</span> and <span class="math inline">z^{*}</span> up to unavoidable symmetries such as feature permutation and rescaling.</p>
@@ -353,6 +360,7 @@ <h4>Relation to Causal Relevance.</h4>
 <p>Standard SAEs analyse individual layers in isolation. However, features can evolve as information propagates through the network. Transcoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:transcoders" data-reference-type="ref" data-reference="fig:transcoders">7a</a>), cross-layer transcoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>), and crosscoders (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:crosscoders" data-reference-type="ref" data-reference="fig:crosscoders">9</a>) enable multi-layer feature extraction and analysis, revealing how representations and features associated with particular model components—most commonly MLP blocks—transform across depth.</p>
 <section id="subsubsec:transcoder" class="level3"><h3 data-pdf-page="41"><span class="secnum">6.6.1</span> Transcoders: Bridging the Nonlinearities<a class="anchor" href="#subsubsec:transcoder" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
+<p><strong>Transcoder (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:transcoders" data-reference-type="ref" data-reference="fig:transcoders">7a</a>)</strong></p>
 <p>A <em>transcoder</em> learns a sparse mapping between the input to and output of a model component, such as an MLP block, in layer <span class="math inline">\ell</span>: <span class="math display">h_{\ell}^{\text{out}} \approx W_{\text{out}} z + b_{\text{out}}, \quad z = \sigma(W_{\text{in}} h_\ell^{\text{in}} + b_{\text{in}})</span> where <span class="math inline">z \in \mathbb{R}^{d_{\text{sparse}}}</span> are sparse feature activations bridging the nonlinearity between input and output of the MLP, and <span class="math inline">W_{\text{out}} \in \mathbb{R}^{d \times {d_{\text{sparse}}}}</span> and <span class="math inline">b_{\text{out}} \in \mathbb{R}^{d}</span> are decoder <span class="math inline">D</span> parameters. The transcoder reconstructs post-nonlinearity activations using features extracted from pre-nonlinearity activations <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
 </div>
 <section id="architecture-and-motivation." class="level4">
@@ -362,21 +370,22 @@ <h4>Architecture and Motivation.</h4>
 <section id="when-are-transcoders-necessary" class="level4">
 <h4>When Are Transcoders Necessary?</h4>
 <p>SAEs extract features from static activations and thus often suffice for residual-stream analysis. Transcoders track how features emerge from input to output and are essential for analysing MLP layers <span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature">(<a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>)</span>.</p>
-<figure id="fig:clt" data-latex-placement="tbp">
+<figure class="subfigures" data-latex-placement="tbp">
 <figure id="fig:transcoders">
-<img alt="Figure 7b: Transcoder." loading="lazy" src="@@BASE@@figures/6_transcoders.svg" />
-<figcaption><span class="capnum">Figure 7b:</span> Transcoder.</figcaption>
+<img alt="Figure 7a: Transcoder." loading="lazy" src="@@BASE@@figures/6_transcoders.svg" />
+<figcaption><span class="capnum">Figure 7a:</span> Transcoder.</figcaption>
 </figure>
 <figure id="fig:clt">
 <img alt="Figure 7b: Cross-layer transcoder." loading="lazy" src="@@BASE@@figures/6_clt.svg" />
 <figcaption><span class="capnum">Figure 7b:</span> Cross-layer transcoder.</figcaption>
 </figure>
-<figcaption><strong>Transcoders and CLTs.</strong> Transcoders reconstruct the output of the MLP from its input in the same transformer layer. CLTs extend this by partially reconstructing the outputs of multiple subsequent layers from a single point in the residual stream.</figcaption>
+<figcaption><span class="capnum">Figure 7:</span> <strong>Transcoders and CLTs.</strong> Transcoders reconstruct the output of the MLP from its input in the same transformer layer. CLTs extend this by partially reconstructing the outputs of multiple subsequent layers from a single point in the residual stream.</figcaption>
 </figure>
 </section>
 </section>
 <section id="subsubsec:cross-layer-transcoder" class="level3"><h3 data-pdf-page="42"><span class="secnum">6.6.2</span> Cross-Layer Transcoders: Enabling Better Circuit Analysis<a class="anchor" href="#subsubsec:cross-layer-transcoder" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
+<p><strong>Cross-Layer Transcoder (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:clt" data-reference-type="ref" data-reference="fig:clt">7b</a>)</strong></p>
 <p><em>Cross-layer transcoders</em> (CLTs) are sparse feature decompositions spanning all layers of a neural network. At each layer <span class="math inline">\ell</span>, sparse feature activations are read from the residual stream via <span class="math display">z_{\ell}
 =
 \sigma_{\text{JumpReLU}}
@@ -427,6 +436,7 @@ <h4>Replacement Models and Attribution Graphs.</h4>
 </ul>
 <p>When analysing a specific prompt, the replacement model freezes the original model’s attention patterns and normalisation statistics from its forward pass, then adds layer-specific error corrections. Under these conditions, the local replacement model can exactly match the original model’s activations on that prompt while decomposing it into interpretable CLT features. However, when the model’s activations are completely replaced by CLT reconstructions, the intervened model’s top-1 predictions match the original model’s on only about 50% of prompts from the Pile dataset <span class="citation" data-cites="gao2020pile">(<a href="@@BASE@@references/#ref-gao2020pile" class="cite cite-emerging" data-cite="gao2020pile" role="doc-biblioref">Gao et al., 2020</a>)</span>. Notably, this experiment uses a relatively small, 18-layer transformer <span class="citation" data-cites="ameisen2025circuit">(<a href="@@BASE@@references/#ref-ameisen2025circuit" class="cite cite-emerging" data-cite="ameisen2025circuit" role="doc-biblioref">Ameisen et al., 2025</a>)</span>. Therefore, this figure should be interpreted with important caveats (see below).</p>
 <div class="warningbox">
+<p><strong>Caveats on CLT Fidelity Claims</strong></p>
 <p>CLTs have been claimed to possess high output fidelity given that they achieve high top-1 token agreement. However, this claim requires careful examination:<br />
 </p>
 <section id="conditions-for-reported-fidelity" class="level4">
@@ -492,6 +502,7 @@ <h4>Open Questions for CLT Research.</h4>
 </section>
 <section id="crosscoders-analysing-cross-layer-features" class="level3"><h3 data-pdf-page="45"><span class="secnum">6.6.3</span> Crosscoders: Analysing Cross-Layer Features<a class="anchor" href="#crosscoders-analysing-cross-layer-features" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
+<p><strong>Crosscoder (Figure <a href="@@BASE@@handbook/feature-extraction/#fig:crosscoders" data-reference-type="ref" data-reference="fig:crosscoders">9</a>)</strong></p>
 <p>A <em>crosscoder</em> learns a shared sparse representation from activations across <span class="math inline">L</span> layers. It jointly encodes activations from all layers as <span class="math display">z
 =
 \sigma_{\text{ReLU}}
@@ -548,6 +559,7 @@ <h4>Applications to Circuit Analysis.</h4>
 <section id="subsec: induc head case study: feature extract" class="level2"><h2 data-pdf-page="47"><span class="secnum">6.8</span> Case Study: Induction Heads<a class="anchor" href="#subsec: induc head case study: feature extract" aria-label="Link to this section">#</a></h2>
 <p>Here, we examine induction heads <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, a mechanism found in many modern language models, as a case study. We first describe their conditional-copying function and role in in-context learning, and then relate them to feature-extraction methods such as SAEs and CLTs, which reveal how this computation can be distributed across broader feature circuits.</p>
 <div class="definitionbox">
+<p><strong>Induction Heads</strong></p>
 <p><span id="def:induction-heads" data-label="def:induction-heads"></span></p>
 <p><em>Induction heads</em>, first identified and analysed by <span class="citation" data-cites="elhage2021mathematical"><a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al. (2021)</a></span>, are attention heads that implement a conditional copying rule over repeated subsequences. Let <span class="math inline">x=(x_1,\dots,x_T)</span> be a token sequence, and consider positions <span class="math inline">i&lt;j&lt;T</span> such that <span class="math inline">x_i=x_j</span>. An attention head indexed <span class="math inline">r</span> (see Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:transformers" data-reference-type="ref" data-reference="subsec:transformers">4.4</a> for an introduction to attention modules) is said to be an induction head when its attention pattern assigns comparatively high attention weight from position <span class="math inline">j</span> to position <span class="math inline">i+1</span> (i.e., high <span class="math inline">A^r_{j,i+1}</span>) and its residual-stream update <span class="math display">\Delta h^r
 =
@@ -557,10 +569,12 @@ <h4>Applications to Circuit Analysis.</h4>
 \hat{x}_{j+1}\approx x_{i+1}.</span> They help enable in-context prediction by copying the continuation that followed an earlier occurrence of the same token.</p>
 </div>
 <div class="examplebox">
+<p><strong>Induction in a Repeated Sequence</strong></p>
 <p>Consider the sequence <span class="math display">\texttt{A B C A B}</span> where the model needs to predict the token after the second <code>B</code>. Observing the repeated prefix <code>A B</code>, an induction head attends from the second <code>B</code> to the position after the first <code>B</code> and contributes to the model’s prediction of <code>C</code>. This induction behaviour should generalise across tokens and positions.</p>
 </div>
 <p>Induction heads were originally characterised as specific attention heads implementing this copying rule. However, subsequent work shows that we can understand the underlying computation more naturally at the level of distributed representations than isolated model components.</p>
 <div class="examplebox">
+<p><strong>Induction Heads: Features Across Representations</strong></p>
 <p>Feature extraction methods, such as SAEs, can decompose attention layer outputs into sparse, interpretable features that capture meaningful activation patterns beyond individual attention weights <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. Modern analyses suggest that induction heads are not isolated mechanisms but participate in distributed feature circuits, where multiple interacting features recur and compose across layers (<span class="citation" data-cites="dunefsky2024transcodersinterpretablellmfeature singh2024needsrightinductionhead"><a href="@@BASE@@references/#ref-dunefsky2024transcodersinterpretablellmfeature" class="cite cite-established" data-cite="dunefsky2024transcodersinterpretablellmfeature" role="doc-biblioref">Dunefsky et al., 2024</a>; <a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a></span>; <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs kamath2025tracing"><a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>; <a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a></span>).<br />
 SAEs trained on attention heads’ activations reveal feature families of recurring patterns and contextual dependencies. Some of them align with behaviours originally attributed to induction heads <span class="citation" data-cites="kissane2024interpretingattentionlayeroutputs">(<a href="@@BASE@@references/#ref-kissane2024interpretingattentionlayeroutputs" class="cite cite-emerging" data-cite="kissane2024interpretingattentionlayeroutputs" role="doc-biblioref">Kissane et al., 2024</a>)</span>. By analysing these features, we can understand how models maintain and manipulate information across tokens and contexts.<br />
 Cross-layer analyses, including crosscoders, suggest that certain features are represented consistently across layers rather than being recomputed at each layer <span class="citation" data-cites="lindsey2024sparsecrosscoders">(<a href="@@BASE@@references/#ref-lindsey2024sparsecrosscoders" class="cite cite-emerging" data-cite="lindsey2024sparsecrosscoders" role="doc-biblioref">Lindsey et al., 2024</a>)</span>. This perspective highlights that attention heads, including specialised ones such as induction heads, may be only part of broader, distributed feature representations.</p>
diff --git a/src/generated/chapters/foundational-concepts.html b/src/generated/chapters/foundational-concepts.html
index a0f34fb241..e6aede7de2 100644
--- a/src/generated/chapters/foundational-concepts.html
+++ b/src/generated/chapters/foundational-concepts.html
@@ -3,9 +3,11 @@
 <section id="subsec:behavioral-vs-mechanistic" class="level2"><h2 data-pdf-page="21"><span class="secnum">4.1</span> Behavioural vs. Mechanistic Understanding<a class="anchor" href="#subsec:behavioral-vs-mechanistic" aria-label="Link to this section">#</a></h2>
 <p>Behavioural analysis describes what a model does, but safety-critical applications also require understanding its internal mechanism (as we reviewed in Section <a href="@@BASE@@handbook/limits-of-behavioural-evaluation/#sec:why_open" data-reference-type="ref" data-reference="sec:why_open">2</a>).</p>
 <div class="definitionbox">
+<p><strong>Behavioural Interpretability</strong></p>
 <p>Behavioural interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> through input–output pairs <span class="math inline">(x, f_\theta(x))</span> for <span class="math inline">x \in \mathcal{D} \subseteq \mathcal{X}</span>, where <span class="math inline">\mathcal{D}</span> is a finite test set. Behavioural interpretability treats the model as a black box.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Mechanistic Interpretability</strong></p>
 <p>Mechanistic interpretability studies a model <span class="math inline">f_\theta : \mathcal{X} \to \mathcal{Y}</span> by examining its internal computations, including model activations and parameters <span class="math inline">\theta</span> that produce them, at different levels of granularity in relation to its behaviour. In short, mechanistic interpretability characterises how a model’s behaviour or output is computed.</p>
 </div>
 </section>
@@ -14,6 +16,7 @@
 <h4>What Is a Concept?</h4>
 <p>Concepts play a central role in many interpretability methods and will be used throughout this paper to define key ideas such as features and polysemanticity. It is therefore worth clarifying what we mean by the term. Notably, concepts do not necessarily correspond to categories humans can readily name or understand; neural networks may represent them in ways humans lack the vocabulary to describe <span class="citation" data-cites="hewitt2025position">(<a href="@@BASE@@references/#ref-hewitt2025position" class="cite cite-established" data-cite="hewitt2025position" role="doc-biblioref">Hewitt et al., 2025</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>Concept</strong></p>
 <p>A concept is a distinct, reusable unit of information or knowledge that can be identified across different instances or contexts. Concepts may correspond to familiar human categories, such as “a dog”, “the colour red”, or “words of praise”, but they need not admit a natural human description.</p>
 </div>
 <p>Cross-context identifiability concerns the meaning of the information unit rather than the process by which the information is recognised. Determining whether a concept is present in an information unit may itself require additional contextual information. For example, the word (i.e., an instance of an information unit) “bank” can refer to a financial institution or a river bank, depending on the surrounding sentence. Nevertheless, once identified, each concept can be recognised as the same information across different contexts. For example, the concept of “financial institution” is expected to remain sufficiently stable across the contexts in which it appears.</p>
@@ -23,6 +26,7 @@ <h4>What Is a Concept?</h4>
 <h4>Neuron Polysemanticity.</h4>
 <p>For a single neuron <span class="math inline">n_\ell</span> in layer <span class="math inline">\ell</span>, consider its activation at token position <span class="math inline">t</span> for some input prompt <span class="math inline">x</span>, denoted by <span class="math inline">n_\ell^{t}(x)</span>. We can straightforwardly interpret a neuron if it responds to a single concept. For example, if <span class="math inline">n_\ell^{t}(x)</span> is large and positive whenever position <span class="math inline">t</span> corresponds to the word “blue”, remains close to zero otherwise, and can causally shift the model’s outputs towards or away from blue-related concepts when manipulated, we can conclude that the neuron corresponds to the concept of blue. Assuming some neurons exhibit such a property, early work proposed methods to discover and interpret individual neurons’ functions <span class="citation" data-cites="netdissect2017 bau2018identifying Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019 bau2020units hernandez2022natural oikarinen2023clipdissect and">(<a href="@@BASE@@references/#ref-netdissect2017" class="cite cite-established" data-cite="netdissect2017" role="doc-biblioref">Bau et al., 2017</a>; <a href="@@BASE@@references/#ref-bau2018identifying" class="cite cite-established" data-cite="bau2018identifying" role="doc-biblioref">2019</a>; <a href="@@BASE@@references/#ref-Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" class="cite cite-established" data-cite="Dalvi_Durrani_Sajjad_Belinkov_Bau_Glass_2019" role="doc-biblioref">Dalvi et al., 2019</a>; <a href="@@BASE@@references/#ref-bau2020units" class="cite cite-established" data-cite="bau2020units" role="doc-biblioref">Bau et al., 2020</a>; <a href="@@BASE@@references/#ref-hernandez2022natural" class="cite cite-established" data-cite="hernandez2022natural" role="doc-biblioref">Hernandez et al., 2022</a>; <a href="@@BASE@@references/#ref-oikarinen2023clipdissect" class="cite cite-established" data-cite="oikarinen2023clipdissect" role="doc-biblioref">Oikarinen &amp; Weng, 2023</a>; <a href="@@BASE@@references/#ref-and" class="cite cite-established" data-cite="and" role="doc-biblioref">Wu et al., 2024</a>)</span>. However, follow-up studies have found that, due to representational constraints and superposition <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span> (detailed in Section <a href="@@BASE@@handbook/core-hypotheses/#subsec:superposition" data-reference-type="ref" data-reference="subsec:superposition">5.2</a>), individual neurons often respond strongly to multiple concepts that appear unrelated from a human perspective <span class="citation" data-cites="elhage2022superposition">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>)</span>. This phenomenon is known as <em>neuron polysemanticity</em>.</p>
 <div class="definitionbox">
+<p><strong>Neuron Polysemanticity</strong></p>
 <p>A neuron <span class="math inline">n_\ell</span> is <em>polysemantic</em> if there exist two distinct concepts <span class="math inline">c_1</span> and <span class="math inline">c_2</span>, together with corresponding sets of inputs <span class="math inline">x_1</span> and <span class="math inline">x_2</span> as well as token positions of interest <span class="math inline">t_{c_1}</span> and <span class="math inline">t_{c_2}</span>, such that <span class="math inline">n_\ell^{t_{c_1}}(x_1)</span> and <span class="math inline">n_\ell^{t_{c_2}}(x_2)</span> are both large, and interventions on these activations causally influence model behaviour associated with both concepts.</p>
 </div>
 <p>Polysemanticity complicates neuron-level explanations and suggests that human-interpretable semantic content may not be aligned with individual neurons. This motivates searching for representational units beyond individual neurons.</p>
@@ -32,6 +36,7 @@ <h4>Neuron Polysemanticity.</h4>
 <p>Since individual neurons are often polysemantic, mechanistic interpretability has increasingly shifted towards alternative units of analysis. For example, it has been found that models sometimes encode human-interpretable concepts as linear directions or low-dimensional manifolds in the activation space (<span class="citation" data-cites="arditi2024refusallanguagemodelsmediated"><a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a></span>; <span class="citation" data-cites="wurgaft2026manifoldsteeringrevealsshared"><a href="@@BASE@@references/#ref-wurgaft2026manifoldsteeringrevealsshared" class="cite cite-emerging" data-cite="wurgaft2026manifoldsteeringrevealsshared" role="doc-biblioref">Wurgaft et al., 2026</a></span>). We thus use the term <em>feature</em> broadly to encompass any model representation corresponding to a human-interpretable concept. Such distributed representations motivate analysing the activation space as a whole, rather than individual neurons in isolation <span class="citation" data-cites="elhage2022superposition bricken2023monosemanticity templeton2024scaling">(<a href="@@BASE@@references/#ref-elhage2022superposition" class="cite cite-emerging" data-cite="elhage2022superposition" role="doc-biblioref">Elhage et al., 2022a</a>; <a href="@@BASE@@references/#ref-bricken2023monosemanticity" class="cite cite-emerging" data-cite="bricken2023monosemanticity" role="doc-biblioref">Bricken et al., 2023</a>; <a href="@@BASE@@references/#ref-templeton2024scaling" class="cite cite-emerging" data-cite="templeton2024scaling" role="doc-biblioref">Templeton et al., 2024</a>)</span>.</p>
 <p>Specifically, we treat a feature technically as a geometric structure <span class="math inline">\phi</span> in the activation space <span class="math inline">\mathbb{R}^d</span> of a model component, such as the residual stream, an attention-head output, or an MLP output, where <span class="math inline">d</span> denotes the hidden dimension. Conceptually, a feature is a manipulable manifestation of a concept of interest. As with neurons, for a feature <span class="math inline">\phi</span> claimed to correspond to a concept <span class="math inline">c</span>, we expect interventions that increase or suppress the activation of <span class="math inline">\phi</span> to causally influence model behaviour associated with <span class="math inline">c</span>. In this sense, features provide a more expressive, general representational unit than individual neurons for describing how neural networks may encode concepts.</p>
 <div class="definitionbox">
+<p><strong>Feature</strong></p>
 <p>A feature <span class="math inline">\phi</span> associated with a concept <span class="math inline">c</span> is a geometric structure in the model’s activation space <span class="math inline">\mathbb{R}^d</span> whose activation is predictive of, and causally relevant to, the presence or expression of <span class="math inline">c</span>.</p>
 </div>
 <p>At this stage, we remain agnostic about the precise mathematical form features take and how the model encodes them. Instead, we treat existing methods for identifying and manipulating features as operational approximations shaped by their methodological assumptions and constraints. We examine these representational hypotheses explicitly in Section <a href="@@BASE@@handbook/core-hypotheses/#sec:hypotheses" data-reference-type="ref" data-reference="sec:hypotheses">5</a>.</p>
@@ -41,6 +46,7 @@ <h4>Neuron Polysemanticity.</h4>
 <section id="subsec:transformers" class="level2"><h2 data-pdf-page="23"><span class="secnum">4.4</span> Transformer Computation<a class="anchor" href="#subsec:transformers" aria-label="Link to this section">#</a></h2>
 <p>Transformers <span class="citation" data-cites="vaswani2017attention">(<a href="@@BASE@@references/#ref-vaswani2017attention" class="cite cite-established" data-cite="vaswani2017attention" role="doc-biblioref">Vaswani et al., 2017</a>)</span> have become the dominant architecture underlying modern language-model-based AI assistants such as ChatGPT and Claude, and are consequently the primary subject of study in mechanistic interpretability. Several properties of the transformer architecture, most notably its modular structure and additive information flow, make it particularly amenable to mechanistic analysis <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>. In particular, transformer components can be viewed as reading from and writing to a shared <em>residual stream</em>, allowing information to persist and be incrementally transformed as it passes through successive layers <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>. Below, we briefly review the main classical components of the transformer architecture.</p>
 <div class="definitionbox">
+<p><strong>Residual Stream and Computational Components</strong></p>
 <p>Let <span class="math display">h^{1:T}_\ell(x) \in \mathbb{R}^{d \times T}</span> denote the sequence of residual-stream activations at layer <span class="math inline">\ell</span> for an input sequence <span class="math inline">x</span>, where <span class="math inline">T</span> is the sequence length and <span class="math inline">d</span> is the model hidden size. The residual stream serves as the shared communication channel through which transformer components exchange information. Below, in this box, we let <span class="math inline">h_\ell(x) \coloneqq h^{1:T}_\ell(x)</span> to simplify the notation.<br />
 For a standard transformer layer <span class="math inline">\ell</span>, consisting of a multi-head attention module <span class="math inline">\mathrm{Attn}_\ell(\cdot)</span> and a multi-layer perceptron <span class="math inline">\mathrm{MLP}_\ell(\cdot)</span>, we have <span class="math display">\begin{align*}
 h&#39;_\ell(x)
@@ -68,6 +74,7 @@ <h4>Neuron Polysemanticity.</h4>
 <p>Attention heads and MLPs play distinct roles in transformer computation. Attention modules primarily route, aggregate, and compose information across token positions (<span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a></span>; <span class="citation" data-cites="wang2022interpretabilitywildcircuitindirect"><a href="@@BASE@@references/#ref-wang2022interpretabilitywildcircuitindirect" class="cite cite-established" data-cite="wang2022interpretabilitywildcircuitindirect" role="doc-biblioref">Wang et al., 2023</a></span>), whereas MLPs often perform feature transformation and factual knowledge retrieval <span class="citation" data-cites="geva-etal-2021-transformer meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-geva-etal-2021-transformer" class="cite cite-established" data-cite="geva-etal-2021-transformer" role="doc-biblioref">Geva et al., 2021</a>; <a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span>. This functional separation enables more localised analyses of how the model routes, transforms, and stores information, as well as how attention modules and MLPs interact <span class="citation" data-cites="neo-etal-2024-interpreting">(<a href="@@BASE@@references/#ref-neo-etal-2024-interpreting" class="cite cite-established" data-cite="neo-etal-2024-interpreting" role="doc-biblioref">Neo et al., 2024</a>)</span>.</p>
 <p>Below, we detail the computations of canonical attention modules and MLPs.</p>
 <div class="definitionbox">
+<p><strong>Attention Module</strong></p>
 <p>A multi-head attention module consists of <span class="math inline">N_H</span> attention heads operating in parallel. Consider the residual-stream activations <span class="math inline">h_\ell^{1:T}(x) \in \mathbb{R}^{d \times T}</span> and let <span class="math inline">h := h_\ell^{1:T}(x)</span> for notational simplicity. For each head <span class="math inline">r \in \{1,\ldots,N_H\}</span>, the residual stream is first projected into query, key, and value representations: <span class="math display">Q^r = W_Q^r h,
 \qquad
 K^r = W_K^r h,
@@ -95,6 +102,7 @@ <h4>Neuron Polysemanticity.</h4>
 <p>Sections <a href="@@BASE@@handbook/foundational-concepts/#subsec:polysemanticity" data-reference-type="ref" data-reference="subsec:polysemanticity">4.2</a>–<a href="@@BASE@@handbook/foundational-concepts/#subsec:features" data-reference-type="ref" data-reference="subsec:features">4.3</a> introduce neurons and features as representational units for describing what is represented in model activations, and Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:transformers" data-reference-type="ref" data-reference="subsec:transformers">4.4</a> introduces the computations that transform and route activations through the model. We use the term <em>mechanism</em> to refer to the organisation of model activations, their representational units, or the transformations applied to them that together produce a particular model behaviour. A central topic in mechanistic interpretability is identifying and understanding such mechanisms in neural networks.</p>
 <p>A common way to represent and describe such a mechanism is through a <em>circuit</em>: a subgraph of the model’s computational graph whose <em>nodes</em> correspond to computational components or representational units and whose <em>edges</em> represent information flow between them <span class="citation" data-cites="elhage2021mathematical">(<a href="@@BASE@@references/#ref-elhage2021mathematical" class="cite cite-emerging" data-cite="elhage2021mathematical" role="doc-biblioref">Elhage et al., 2021</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>Circuit (Figure <a href="@@BASE@@handbook/foundational-concepts/#fig:circuit" data-reference-type="ref" data-reference="fig:circuit">3</a>)</strong></p>
 <p>For a fixed model computation, let <span class="math inline">G</span> denote the corresponding directed acyclic computational graph. A circuit <span class="math inline">\mathcal{C} \subseteq G</span> is a subgraph of <span class="math inline">G</span>, where:</p>
 <ul>
 <li><p><strong>Nodes:</strong> computational components, such as <span class="math inline">\mathrm{Attn}_\ell</span> and <span class="math inline">\mathrm{MLP}_\ell</span>, or representational units, such as features <span class="math inline">\phi_i</span>;</p></li>
@@ -107,6 +115,7 @@ <h4>Neuron Polysemanticity.</h4>
 <figcaption><span class="capnum">Figure 3:</span> <strong>Circuit.</strong> This diagram illustrates a circuit within a transformer, where specific nodes (MLP layers and attention heads in this case) perform specialised computations.</figcaption>
 </figure>
 <div class="examplebox">
+<p><strong>Examples of Circuits</strong></p>
 <ul>
 <li><p><strong>Induction head circuit:</strong> Two attention heads cooperate to implement in-context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.</p></li>
 <li><p><strong>QK circuits:</strong> Query–Key interactions prioritising specific token positions <span class="citation" data-cites="kamath2025tracing">(<a href="@@BASE@@references/#ref-kamath2025tracing" class="cite cite-emerging" data-cite="kamath2025tracing" role="doc-biblioref">Kamath et al., 2025</a>)</span>.</p></li>
diff --git a/src/generated/chapters/intermediate-representations.html b/src/generated/chapters/intermediate-representations.html
index 0690b20be3..283aa2a721 100644
--- a/src/generated/chapters/intermediate-representations.html
+++ b/src/generated/chapters/intermediate-representations.html
@@ -2,6 +2,7 @@
 <p>This section explores interpreting hidden activations directly in natural language. In contrast to feature extraction and circuit discovery (Sections <a href="@@BASE@@handbook/feature-extraction/#sec:sae" data-reference-type="ref" data-reference="sec:sae">6</a> and <a href="@@BASE@@handbook/circuits/#sec:circuit" data-reference-type="ref" data-reference="sec:circuit">7</a>), which extract features or their interactions from model representations (activations), the methods we will discuss in this section treat the activation itself as the object of interpretation. They use language models or other natural-language-based methods to describe or elicit the encoded information, without intervening on or decomposing the underlying activation. This paradigm provides a direct interface between model activations and natural-language explanations, making it particularly accessible and understandable for human users.</p>
 <section id="sec:logit-lens" class="level2"><h2 data-pdf-page="67"><span class="secnum">8.1</span> Logit Lens: Tracing Predictions Through Depth<a class="anchor" href="#sec:logit-lens" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>Logit Lens (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:logitlens" data-reference-type="ref" data-reference="fig:logitlens">10</a>)</strong></p>
 <p>Logit lens <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span> applies the model’s unembedding matrix to activations at intermediate layers, revealing what the model <em>would predict</em> if computation stopped at that layer. For a hidden activation <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>, logit lens computes: <span class="math display">\text{logits}_\ell = W_U h_\ell,</span> where <span class="math inline">W_U</span> represents the model’s unembedding matrix. Applying it across layers reveals the prediction trajectory, showing when and where the model commits to specific outputs <span class="citation" data-cites="nostalgebraist2020interpretinggpt">(<a href="@@BASE@@references/#ref-nostalgebraist2020interpretinggpt" class="cite cite-emerging" data-cite="nostalgebraist2020interpretinggpt" role="doc-biblioref">nostalgebraist, 2020</a>)</span>.</p>
 </div>
 <p>Logit lens (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:logitlens" data-reference-type="ref" data-reference="fig:logitlens">10</a>) provides intuitive insight into the model’s decision-making process across depth. Experimental results show that early layers tend to produce diffuse predictions, while later layers progressively narrow them towards the final output, though exceptions exist. For example, in a twelve-layer language model processing the sequence <code>A B C A B</code>, the logit lens may reveal that the token <code>C</code> enters the top-20 candidates at layer 6 and is progressively amplified, eventually becoming the top-ranked token at the final layer. Conversely, for induction tasks in complex linguistic patterns, early and middle layers may show a flat probability distribution across many tokens, with the model converging to a specific prediction only in the last few layers.</p>
@@ -16,6 +17,7 @@ <h4>Limitations of Vanilla Logit Lens.</h4>
 </section>
 <section id="subsec:tuned-lens" class="level2"><h2 data-pdf-page="68"><span class="secnum">8.2</span> Tuned Lens: Learning Per-Layer Transforms<a class="anchor" href="#subsec:tuned-lens" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>Tuned Lens</strong></p>
 <p>Tuned lens <span class="citation" data-cites="belrose2025elicitinglatentpredictionstransformers">(<a href="@@BASE@@references/#ref-belrose2025elicitinglatentpredictionstransformers" class="cite cite-emerging" data-cite="belrose2025elicitinglatentpredictionstransformers" role="doc-biblioref">Belrose et al., 2025</a>)</span> addresses logit lens’ concern of representational mismatch by learning per-layer affine transformations that map intermediate activations to the output layer’s activation space: <span class="math display">\text{logits}_\ell^{\text{TL}} = W_U (W_\ell h_\ell + b_\ell),</span> where <span class="math inline">W_\ell</span> and <span class="math inline">b_\ell</span> are learned affine transformations for layer <span class="math inline">\ell</span>. These transforms are trained to minimise the divergence between the tuned lens’ predicted distribution <span class="math inline">p_\ell^{\text{TL}} = \operatorname{softmax}(\text{logits}_\ell^{\text{TL}})</span> and the model output distribution <span class="math inline">p_{\text{final}}</span> via: <span class="math display">\min_{W_\ell,b_\ell}\mathbb{E}_{x\sim\mathcal{D}}\big[\,\mathrm{KL}(p_{\text{final}}(x)\;\|\;p_\ell^{\text{TL}}(x))\big].</span></p>
 </div>
 <p>As a result, the unembedding matrix can extract relevant information from each layer’s activations more effectively.</p>
@@ -26,6 +28,7 @@ <h4>Limitations of Tuned Lens.</h4>
 </section>
 <section id="jacobian-lens-decoding-computational-dispositions" class="level2"><h2 data-pdf-page="68"><span class="secnum">8.3</span> Jacobian Lens: Decoding Computational Dispositions<a class="anchor" href="#jacobian-lens-decoding-computational-dispositions" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>Jacobian Lens</strong></p>
 <p>Jacobian lens (J-lens; <span class="citation" data-cites="jlens"><a href="@@BASE@@references/#ref-jlens" class="cite cite-emerging" data-cite="jlens" role="doc-biblioref">Gurnee et al., 2026</a></span>) adopts a decoding paradigm distinct from logit lens and tuned lens. It uses the Jacobian to characterise how an intermediate representation influences certain model outputs.<br />
 Let <span class="math inline">h_\ell^t</span> denote the residual stream at layer <span class="math inline">\ell</span> and position <span class="math inline">t</span>. A perturbation to <span class="math inline">h_\ell^t</span> propagates to the final-layer residual stream at every position <span class="math inline">t&#39; \geq t</span>; to first order, this influence is captured by the Jacobian <span class="math display">\frac{\partial h_{\mathrm{final}}^{t&#39;}}{\partial h_\ell^t}.</span> The lens matrix is defined as the expectation of this Jacobian over prompts and positions: <span class="math display">J_\ell
 =
@@ -54,6 +57,7 @@ <h4>Limitations of Jacobian Lens.</h4>
 <figcaption><span class="capnum">Figure 11:</span> <strong>Patchscopes.</strong> Patchscopes extracts the model representation of interest from a source prompt and “patches” it into an inspection prompt. By observing changes in model output, users can examine what information the representation contains. For example, in this figure we can observe whether the representation of “United Kingdom” correctly triggers “London” as the largest city.</figcaption>
 </figure>
 <div class="definitionbox">
+<p><strong>Patchscopes (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:patchscopes" data-reference-type="ref" data-reference="fig:patchscopes">11</a>)</strong></p>
 <p>Patchscopes <span class="citation" data-cites="patchscopes">(<a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al., 2024</a>)</span> leverages the expressive capacity of language models to translate their internal representations into natural-language explanations. Let <span class="math inline">h^t_\ell</span> denote a hidden representation at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span>. Patchscopes produces a textual explanation of <span class="math inline">h^t_\ell</span>, denoted as <span class="math inline">e(h^t_\ell)</span>, by <span class="math display">e(h^t_\ell) = f_\theta(p_{[h^{t^*}_{\ell^*} \leftarrow h^t_\ell]}),</span> where <span class="math inline">\ell^*</span> and <span class="math inline">t^*</span> specify the layer and token position to which the representation is patched, and <span class="math inline">p</span> is an <em>elicitation prompt</em> designed to encourage the language model <span class="math inline">f_\theta(\cdot)</span> to decode the patched representation into text expressing the contained information.<br />
 For example, consider the prompt illustrated in Figure 1 of <span class="citation" data-cites="patchscopes"><a href="@@BASE@@references/#ref-patchscopes" class="cite cite-established" data-cite="patchscopes" role="doc-biblioref">Ghandeharioun et al. (2024)</a></span>: <span class="math display">p = \text{``cat$\rightarrow$cat; 135$\rightarrow$135; hello$\rightarrow$hello; ?&#39;&#39;},</span> where <span class="math inline">t^*</span> corresponds to the position of the token “?”. The notation <span class="math inline">[h^{t^*}_{\ell^*} \leftarrow h^t_\ell]</span> indicates that, during the forward pass of <span class="math inline">f_\theta(\cdot)</span> on <span class="math inline">p</span>, the hidden representation at layer <span class="math inline">\ell^*</span> and position <span class="math inline">t^*</span> is replaced with <span class="math inline">h^t_\ell</span>. As a result, the model can elicit information encoded in <span class="math inline">h^t_\ell</span> through its prediction for the next token at the “?” position.</p>
 </div>
@@ -71,6 +75,7 @@ <h4>Limitations of Patchscopes.</h4>
 </section>
 <section id="subsec:latentqa" class="level2"><h2 data-pdf-page="70"><span class="secnum">8.5</span> LatentQA: Fine-Tuning the Language Model as an Activation Verbaliser<a class="anchor" href="#subsec:latentqa" aria-label="Link to this section">#</a></h2>
 <div class="definitionbox">
+<p><strong>LatentQA (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:latentqa" data-reference-type="ref" data-reference="fig:latentqa">12</a>)</strong></p>
 <p>LatentQA <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span> frames representation decoding as a supervised learning problem via Latent Interpretation Tuning (LIT), fine-tuning an activation verbaliser to perform representation-to-text verbalisation. The core step of LIT is to construct a fine-tuning dataset <span class="math inline">D</span> consisting of samples <span class="math inline">(h^t_\ell, q, a)</span>, where <span class="math inline">h^t_\ell</span> is a cached hidden representation from a target model, and <span class="math inline">(q, a)</span> is a question–answer pair that queries the information encoded in <span class="math inline">h^t_\ell</span>.<br />
 Specifically, <span class="math inline">h^t_\ell</span> denotes the activation at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span> when the target model processes a “control prompt” designed to embed a specific attribute or concept into the representation. An example control prompt from Figure 13 of <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span> is: <span class="math display">\text{``In your next reply, please try to evoke a brief sense of triumphant hubris.&#39;&#39;}</span> If <span class="math inline">h^t_\ell</span> is defined as the activation at layer <span class="math inline">\ell</span> corresponding to the final token (“.”) of this prompt, the resulting representation is expected to encode the instruction to respond with triumphant hubris. In this case, a corresponding question–answer pair may be: <span class="math display">q = \text{``In what way does the assistant craft its responses to the user?&#39;&#39;}</span> <span class="math display">a = \text{``The assistant crafts responses that briefly highlight a sense of self-assured victory.&#39;&#39;}</span> Both the control prompt and the <span class="math inline">(q, a)</span> pair are generated by prompting an off-the-shelf proprietary language model <span class="citation" data-cites="pan2024latentqateachingllmsdecode">(<a href="@@BASE@@references/#ref-pan2024latentqateachingllmsdecode" class="cite cite-established" data-cite="pan2024latentqateachingllmsdecode" role="doc-biblioref">Pan et al., 2026</a>)</span>.</p>
 </div>
@@ -90,6 +95,7 @@ <h4>Limitations of LatentQA.</h4>
 <p>As a side note, discovered probe directions can also be used as supervised feature extractors. We discuss this perspective in Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: supervised_extraction" data-reference-type="ref" data-reference="subsubsec: supervised_extraction">6.9.4</a>, including comparisons with SAEs, and in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a>, where we discuss their use as steering vectors.</p>
 <section id="standard-probing-methodology" class="level3"><h3 data-pdf-page="72"><span class="secnum">8.6.1</span> Standard Probing Methodology<a class="anchor" href="#standard-probing-methodology" aria-label="Link to this section">#</a></h3>
 <div class="definitionbox">
+<p><strong>Probing Classifier (Figure <a href="@@BASE@@handbook/intermediate-representations/#fig:probing" data-reference-type="ref" data-reference="fig:probing">13</a>)</strong></p>
 <p>A <em>probing classifier</em> is a supervised model trained to predict a target property <span class="math inline">y</span> from neural network activations <span class="math inline">h_\ell</span> at layer <span class="math inline">\ell</span>. A <em>linear probe</em> follows the form: <span class="math display">\hat{y} = g(W_{\text{probe}} h_\ell + b_{\text{probe}}),</span> where <span class="math inline">W_{\text{probe}}</span> and <span class="math inline">b_{\text{probe}}</span> are learned parameters. <span class="math inline">g</span>, typically an identity function for regression and softmax for classification, maps the probe’s raw output to the prediction space. The probe is trained by minimising prediction error on labelled examples <span class="math inline">(h_\ell^{(i)}, y^{(i)})</span>. The linear probe tests whether the target property is linearly accessible from the representation. <em>MLP probes</em> introduce one or more nonlinear hidden layers instead of <span class="math inline">W_{\text{probe}}</span> to test whether the property can be retrieved through more expressive nonlinear mappings.</p>
 </div>
 <figure id="fig:probing" data-latex-placement="tb">
@@ -98,6 +104,7 @@ <h4>Limitations of LatentQA.</h4>
 </figure>
 <p>Probing provides evidence about whether activations contain information about a property. High probe accuracy indicates the information is present; low accuracy suggests it is absent or the probe is not expressive enough. Note that probing alone does not show whether the model actually depends on that information in its decision-making process. A representation can encode information that downstream computation never accesses, such as “leftover” information from earlier processing.</p>
 <div class="warningbox">
+<p><strong>The Probe Causality Problem</strong></p>
 <p>High probe accuracy does not imply causal relevance <span class="citation" data-cites="belinkov-2022-probing hewitt-liang-2019-designing">(<a href="@@BASE@@references/#ref-belinkov-2022-probing" class="cite cite-established" data-cite="belinkov-2022-probing" role="doc-biblioref">Belinkov, 2022</a>; <a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite cite-established" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang, 2019</a>)</span>. The probe can work by detecting features spuriously correlated with the target property (e.g., word frequency correlates with part of speech) rather than detecting the target property itself. Furthermore, subsequent layers may never use the decoded property to influence the model output. Finally, a sufficiently expressive probe can achieve high accuracy by exploiting noise even when the representations contain no such information.</p>
 </div>
 <p>In summary, using probing alone generally does not provide actionable mechanistic understanding and therefore does not, under our definition, constitute mechanistic interpretability research. However, subsequent work has proposed refined probing paradigms that explicitly incorporate causal considerations into probe design and evaluation, enabling more actionable mechanistic understanding <span class="citation" data-cites="elazar-etal-2021-amnesic">(<a href="@@BASE@@references/#ref-elazar-etal-2021-amnesic" class="cite cite-established" data-cite="elazar-etal-2021-amnesic" role="doc-biblioref">Elazar et al., 2021</a>)</span>; see Section <a href="@@BASE@@handbook/intermediate-representations/#subsubsec: amnesic probing" data-reference-type="ref" data-reference="subsubsec: amnesic probing">8.6.4</a> for details. Recent interpretability studies have similarly used causal interventions to validate probe-discovered concept directions, building a more complete mechanistic explanation <span class="citation" data-cites="marks2024the li2023emergent">(<a href="@@BASE@@references/#ref-marks2024the" class="cite cite-established" data-cite="marks2024the" role="doc-biblioref">Marks &amp; Tegmark, 2024</a>; <a href="@@BASE@@references/#ref-li2023emergent" class="cite cite-established" data-cite="li2023emergent" role="doc-biblioref">Li et al., 2023</a>)</span>.</p>
@@ -105,12 +112,14 @@ <h4>Limitations of LatentQA.</h4>
 <section id="control-tasks-and-selectivity" class="level3"><h3 data-pdf-page="73"><span class="secnum">8.6.2</span> Control Tasks and Selectivity<a class="anchor" href="#control-tasks-and-selectivity" aria-label="Link to this section">#</a></h3>
 <p>High probe accuracy may reflect classification capacity rather than the target property’s decodability. <span class="citation" data-cites="hewitt-liang-2019-designing"><a href="@@BASE@@references/#ref-hewitt-liang-2019-designing" class="cite cite-established" data-cite="hewitt-liang-2019-designing" role="doc-biblioref">Hewitt &amp; Liang (2019)</a></span> addressed this concern by introducing <em>control tasks</em> as a probe’s baseline performance.</p>
 <div class="definitionbox">
+<p><strong>Control Task Methodology</strong></p>
 <p>A <em>control task</em> assigns random labels to inputs to break any genuine relationship between activations and the target property. A probe trained on the control task represents the <em>baseline extractability</em> of random patterns from the representation. Then, the <em>selectivity</em> of a probe is defined as: <span class="math display">\text{Selectivity} = \text{Acc}_{\text{linguistic}} - \text{Acc}_{\text{control}},</span> where <span class="math inline">\text{Acc}_{\text{linguistic}}</span> is probe accuracy on the target task and <span class="math inline">\text{Acc}_{\text{control}}</span> is accuracy on the control task. High selectivity indicates that the probe succeeds because the representation genuinely encodes the target property. In contrast, low selectivity suggests we cannot tell whether the probe has exploited spurious features rather than task-relevant structure.</p>
 </div>
 </section>
 <section id="concerns-of-probe-complexity" class="level3"><h3 data-pdf-page="74"><span class="secnum">8.6.3</span> Concerns of Probe Complexity<a class="anchor" href="#concerns-of-probe-complexity" aria-label="Link to this section">#</a></h3>
 <p>Probe complexity brings confounding factors to the analysis. If a property can only be extracted with a highly nonlinear probe, it is hard to tell whether the property is “deeply encoded” or whether the probe is simply using nonlinearity to exploit signals, including noise. A recent finding in causal abstraction <span class="citation" data-cites="sutter2025the">(<a href="@@BASE@@references/#ref-sutter2025the" class="cite cite-established" data-cite="sutter2025the" role="doc-biblioref">Sutter et al., 2025</a>)</span> also showed that, when the mapping function is complex enough, it can map an arbitrary (even randomly initialised) neural network to a causal model of interest, producing false-positive findings.</p>
 <div class="insightbox">
+<p><strong>The Complexity–Interpretability Trade-off</strong></p>
 <section id="observation." class="level4">
 <h4>Observation.</h4>
 <p>In general, more complex probes achieve higher accuracy but provide less interpretable deliverables.<br />
@@ -135,6 +144,7 @@ <h4>Practical Guidance.</h4>
 <section id="amnesic-probing." class="level4">
 <h4>Amnesic Probing.</h4>
 <div class="definitionbox">
+<p><strong>Amnesic Probing</strong></p>
 <p>Given a model activation <span class="math inline">h</span> and a probe trained to predict a property <span class="math inline">P</span>, <em>amnesic probing</em> tests whether information about <span class="math inline">P</span> is causally relevant to downstream model behaviour by removing information about <span class="math inline">P</span> from the representation and evaluating the effects on model outputs. Assume <span class="math inline">P</span> is a binary property (e.g., positive vs. negative sentiment) and that <span class="math inline">w \coloneqq W_{\text{probe}}^\top \in \mathbb{R}^{d}</span> denotes the probe’s direction. Amnesic probing projects <span class="math inline">h</span> onto the subspace orthogonal to a probe direction <span class="math inline">w</span> (i.e., the null space of the probe): <span class="math display">\tilde{h}
 =
 h
@@ -190,6 +200,7 @@ <h4>Causal Probing via Activation Patching.</h4>
 <section id="methodological-standards-in-probing-research" class="level3"><h3 data-pdf-page="76"><span class="secnum">8.6.7</span> Methodological Standards in Probing Research<a class="anchor" href="#methodological-standards-in-probing-research" aria-label="Link to this section">#</a></h3>
 <p>So far we have reviewed several strengths and limitations of probes. Based on them, we summarise several methodological standards for the design and interpretation of probing studies: (1) Report control-task baselines to distinguish genuine decodability from spurious exploitation; (2) Use minimally expressive probes unless additional complexity is justified; (3) Test causal relevance by methods like amnesic probing or activation patching; (4) Analyse layer-wise trends besides the peak accuracy; (5) Distinguish encoding from causality and acknowledge that successful probing shows information presence rather than causal use; (6) Report the stability of findings by evaluating across seeds and data splits. In sum, we believe these standards help ensure responsible, effective use of probing in interpretability research and downstream applications.</p>
 <div class="examplebox">
+<p><strong>Probing Classifiers for Sleeper Agents</strong></p>
 <p><span id="ex:probe_dir" data-label="ex:probe_dir"></span> Despite the causality critiques, probing remains valuable for many applications. For example, <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> showed that simple linear probes trained on residual-stream activations can detect latent backdoor behaviour in the sleeper agent model even when the probe fitting set does not contain the trigger or the harmful behaviour.<br />
 To recap: a sleeper agent model <span class="math inline">f_\theta(\cdot)</span> behaves normally for one context (e.g., <span class="math inline">y=\text{``2023&#39;&#39;}</span>) but exhibits a hidden dangerous policy when a trigger is present (e.g., <span class="math inline">y=\text{``2024&#39;&#39;}</span>) <span class="citation" data-cites="macdiarmid2024sleeperagentprobes">(<a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al., 2024</a>)</span>.<br />
 To detect this latent behaviour, <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> constructed a linear defection detector by computing a <em>detector direction</em> <span class="math inline">u_\ell</span> at layer <span class="math inline">\ell</span>. Specifically, let <span class="math inline">h_\ell(x)</span> denote the residual activations at the final token position for input <span class="math inline">x</span>. Given a small labelled fit dataset of prompts, the detector direction is defined as: <span class="math display">u_\ell = \mathbb{E}_{x\sim\mathcal{D}_{\text{true}}}[h_\ell(x)] - \mathbb{E}_{x\sim\mathcal{D}_{\text{false}}}[h_\ell(x)],</span> where <span class="math inline">\mathcal{D}_{\text{true}}</span> and <span class="math inline">\mathcal{D}_{\text{false}}</span> are contrast pairs of prompts. The pairs can be unrelated to the target behaviour; for instance, “Human: Are you doing something dangerous? Assistant: yes” and “Human: … Assistant: no” (example borrowed from <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span>). During the evaluation stage, activations are projected onto this direction to yield a detector activation value <span class="math inline">\langle h_\ell(x), u_\ell \rangle</span>. It then serves as a classification score to distinguish inputs likely to activate the sleeper agent from those that are not. Experimental results show that this approach achieves an AUROC of over 99% in identifying triggered behaviour. <span class="citation" data-cites="macdiarmid2024sleeperagentprobes"><a href="@@BASE@@references/#ref-macdiarmid2024sleeperagentprobes" class="cite cite-emerging" data-cite="macdiarmid2024sleeperagentprobes" role="doc-biblioref">MacDiarmid et al. (2024)</a></span> argued that this is because the activation differences between triggered and non-triggered inputs are sufficiently salient (for example, two clusters far apart). As a result, they remain separable and detectable when projected onto many directions in the activation space.<br />
diff --git a/src/generated/chapters/interpretability-landscape.html b/src/generated/chapters/interpretability-landscape.html
index 3f557f6b4f..912474f29a 100644
--- a/src/generated/chapters/interpretability-landscape.html
+++ b/src/generated/chapters/interpretability-landscape.html
@@ -104,6 +104,7 @@
 <p>Third, even when we identify and causally validate an internal mechanism, this does not automatically mean we can reliably control the corresponding behaviour <span class="citation" data-cites="localinform">(<a href="@@BASE@@references/#ref-localinform" class="cite cite-established" data-cite="localinform" role="doc-biblioref">Hase et al., 2023</a>)</span>. The effectiveness, specificity, and generalisability of localisation and intervention techniques, such as identifying a model component and then patching its activations or changing its weight matrices, depend on several factors, such as the hydra effect <span class="citation" data-cites="mcgrath2023hydraeffectemergentselfrepair">(<a href="@@BASE@@references/#ref-mcgrath2023hydraeffectemergentselfrepair" class="cite cite-emerging" data-cite="mcgrath2023hydraeffectemergentselfrepair" role="doc-biblioref">McGrath et al., 2023</a>)</span>, beyond how accurately the identified localisation captures the underlying computation for the behaviour. Furthermore, an intervention that successfully steers one model’s behaviour on a task may not transfer to another model or setting.</p>
 <p>These are open challenges but not fundamental barriers. Active research in areas such as establishing more effective representational units, discovering more robust model properties, developing more efficient circuit-discovery methods, and designing more reliable intervention approaches across distributions is working to address them. Nevertheless, these studies highlight a clear gap between mechanistic explanation and reliable control or safety guarantees.</p>
 <div class="warningbox">
+<p><strong>Limitations of Mechanistic Analysis</strong></p>
 <p>Mechanistic explanations may be incomplete or non-causal, difficult to scale to large models or complex behaviours, and sensitive to modelling assumptions. They often fail to capture the full picture of a model’s underlying mechanism and do not automatically provide reliable, generalisable control. Intervening on an identified mechanism may work in one setting but fail to transfer to another. However, these limitations do not imply that mechanistic understanding cannot enable reliable control. Instead, they indicate that moving from explanation to robust, generalisable intervention requires additional studies and remains an open challenge in the field <span class="citation" data-cites="sharkey2025open">(<a href="@@BASE@@references/#ref-sharkey2025open" class="cite cite-established" data-cite="sharkey2025open" role="doc-biblioref">Sharkey et al., 2025</a>)</span>.</p>
 </div>
 <section id="summary." class="level4">
diff --git a/src/generated/chapters/introduction.html b/src/generated/chapters/introduction.html
index ec338e511e..b4a352d00e 100644
--- a/src/generated/chapters/introduction.html
+++ b/src/generated/chapters/introduction.html
@@ -2,6 +2,7 @@
 <p>Neural networks could be one of the most consequential artefacts humanity has built, yet we do not understand how they work or reliably control what they do. This is true despite humans designing the training procedure: we specify how models learn, but not what they learn. <em>Interpretability</em> is the effort to understand what models learn and why they behave as they do, rather than only observing what they output. <em>Mechanistic interpretability</em> (MI) is one approach within it. MI studies the <em>internal mechanisms</em> (characterised in Section <a href="@@BASE@@handbook/foundational-concepts/#subsec:circuits" data-reference-type="ref" data-reference="subsec:circuits">4.5</a>) of the neural network in relation to its behaviour, which can yield both a richer understanding of the model and more reliable control over it.</p>
 <p>Recent decades have seen an exponential improvement in AI capabilities <span class="citation" data-cites="touvron2023llamaopenefficientfoundation openai2024gpt4technicalreport gemmateam2024gemmaopenmodelsbased yang2025qwen3technicalreport deepseekai2025deepseekv3technicalreport anthropic2025claude4">(<a href="@@BASE@@references/#ref-touvron2023llamaopenefficientfoundation" class="cite cite-emerging" data-cite="touvron2023llamaopenefficientfoundation" role="doc-biblioref">Touvron et al., 2023</a>; <a href="@@BASE@@references/#ref-openai2024gpt4technicalreport" class="cite cite-emerging" data-cite="openai2024gpt4technicalreport" role="doc-biblioref">OpenAI et al., 2024</a>; <a href="@@BASE@@references/#ref-gemmateam2024gemmaopenmodelsbased" class="cite cite-emerging" data-cite="gemmateam2024gemmaopenmodelsbased" role="doc-biblioref">Team et al., 2024a</a>; <a href="@@BASE@@references/#ref-yang2025qwen3technicalreport" class="cite cite-emerging" data-cite="yang2025qwen3technicalreport" role="doc-biblioref">QwenAI, 2025</a>; <a href="@@BASE@@references/#ref-deepseekai2025deepseekv3technicalreport" class="cite cite-emerging" data-cite="deepseekai2025deepseekv3technicalreport" role="doc-biblioref">DeepSeek-AI, 2025</a>; <a href="@@BASE@@references/#ref-anthropic2025claude4" class="cite cite-emerging" data-cite="anthropic2025claude4" role="doc-biblioref"><span>Anthropic</span>, 2025b</a>)</span>, but methods to understand and explain internal mechanisms have lagged behind. This has created an <em>interpretability gap</em> between what models can do and what we know about how they do it. The gap is highly relevant for safety since standard behaviour-based safety measures, such as safety-oriented reinforcement learning from human feedback (RLHF) (<span class="citation" data-cites="bai2022training"><a href="@@BASE@@references/#ref-bai2022training" class="cite cite-emerging" data-cite="bai2022training" role="doc-biblioref">Bai et al., 2022a</a></span>; <span class="citation" data-cites="dai2024safe"><a href="@@BASE@@references/#ref-dai2024safe" class="cite cite-established" data-cite="dai2024safe" role="doc-biblioref">Dai et al., 2024b</a></span>) and red-teaming <span class="citation" data-cites="perez2022redteaminglanguagemodels">(<a href="@@BASE@@references/#ref-perez2022redteaminglanguagemodels" class="cite cite-established" data-cite="perez2022redteaminglanguagemodels" role="doc-biblioref">Perez et al., 2022</a>)</span>, are not robust to sophisticated failure modes such as deceptive alignment <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>The Interpretability Gap</strong></p>
 <p>The <em>interpretability gap</em> is the disparity between what models do (their capabilities and behaviours) and what we understand about how they do it (their internal mechanisms). As models scale, this gap risks widening: reasoning emerges that we cannot yet trace, deceptive behaviours may remain undetected, and safety guarantees built on incomplete understanding may fail unexpectedly.</p>
 </div>
 <section id="brief-history-of-interpretability" class="level2"><h2 data-pdf-page="9"><span class="secnum">1.1</span> Brief History of Interpretability<a class="anchor" href="#brief-history-of-interpretability" aria-label="Link to this section">#</a></h2>
diff --git a/src/generated/chapters/learning-dynamics.html b/src/generated/chapters/learning-dynamics.html
index 53b4bd66a3..86b5401c24 100644
--- a/src/generated/chapters/learning-dynamics.html
+++ b/src/generated/chapters/learning-dynamics.html
@@ -8,6 +8,7 @@
 <h4>Acquisition Order.</h4>
 <p>The order in which capabilities appear is strongly constrained by the statistics of the training data. Language models acquire words in an order predicted primarily by corpus frequency, unlike children, whose acquisition order also depends on factors such as concreteness and word length <span class="citation" data-cites="chang2022word">(<a href="@@BASE@@references/#ref-chang2022word" class="cite cite-established" data-cite="chang2022word" role="doc-biblioref">Chang &amp; Bergen, 2022</a>)</span>; graded linguistic preferences, such as adjective order, are learned from corpus statistics on a similar schedule <span class="citation" data-cites="jumelet2024black">(<a href="@@BASE@@references/#ref-jumelet2024black" class="cite cite-emerging" data-cite="jumelet2024black" role="doc-biblioref">Jumelet et al., 2026</a>)</span>. This ordering reflects a more general <em>distributional simplicity bias</em>:</p>
 <div class="definitionbox">
+<p><strong>Distributional Simplicity Bias</strong></p>
 <p>Networks trained with SGD learn low-order statistics of the data distribution before higher-order ones <span class="citation" data-cites="refinetti2023neural">(<a href="@@BASE@@references/#ref-refinetti2023neural" class="cite cite-established" data-cite="refinetti2023neural" role="doc-biblioref">Refinetti et al., 2023</a>)</span>. In language models, token n-gram frequencies are equivalent to moments of the embedding vectors, so early-training language models approximate low-order token interactions well, with more complex structure fitting later <span class="citation" data-cites="belrose2024statistics">(<a href="@@BASE@@references/#ref-belrose2024statistics" class="cite cite-established" data-cite="belrose2024statistics" role="doc-biblioref">Belrose et al., 2024</a>)</span>.</p>
 </div>
 </section>
@@ -42,6 +43,7 @@ <h4>Localising Change to Components.</h4>
 <section id="sec:ld-causal-interventions" class="level3"><h3 data-pdf-page="85"><span class="secnum">10.2.2</span> Causal Interventions on Mechanism Formation<a class="anchor" href="#sec:ld-causal-interventions" aria-label="Link to this section">#</a></h3>
 <p>The forked-run experiment is a basic analysis pipeline available on a training trajectory. It spawns multiple continuations from a single checkpoint, perturbs one factor, and compares the resulting models.</p>
 <div class="definitionbox">
+<p><strong>Linear Mode Connectivity and Forked-Run Experiments</strong></p>
 <p>A <em>forked-run experiment</em> spawns two or more training continuations from the same checkpoint, varying a single factor such as SGD noise, data order, or random seed, and compares the models the continuations produce. <em>Linear mode connectivity</em> is a standard instrument for the comparison. Two networks are linearly mode connected if the loss barrier along the straight path between their parameters in weight space is near zero. Specifically, <span class="citation" data-cites="frankle2020linear"><a href="@@BASE@@references/#ref-frankle2020linear" class="cite cite-established" data-cite="frankle2020linear" role="doc-biblioref">Frankle et al. (2020)</a></span> examined the sensitivity of the model’s training results to SGD noise by restarting training from different checkpoints, each time using different SGD noise. Early in training, these runs may converge to different loss basins. Later, they tend to converge to linearly mode-connected solutions, indicating that the training outcome has become stable to SGD noise.</p>
 </div>
 <p>Perturbations other than SGD noise fit the same design. For example, degrading the data early in training causes permanent deficits that the same degradation applied later does not, establishing critical learning periods <span class="citation" data-cites="achille2019critical">(<a href="@@BASE@@references/#ref-achille2019critical" class="cite cite-established" data-cite="achille2019critical" role="doc-biblioref">Achille et al., 2019</a>)</span>, and forking a run across many random seeds distinguishes properties of the training setup from accidents of a single run <span class="citation" data-cites="vanderwal2025polypythias">(<a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al., 2025</a>)</span>. Similarly, the smeared-key intervention discussed in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a> is the same design with an architectural perturbation. Mode connectivity has also been given a mechanistic reading: <span class="citation" data-cites="lubana2023mechanistic"><a href="@@BASE@@references/#ref-lubana2023mechanistic" class="cite cite-established" data-cite="lubana2023mechanistic" role="doc-biblioref">Lubana et al. (2023)</a></span> defined two models as mechanistically similar when they are invariant to the same transformations of the input. They conjectured that two minimisers that cannot be linearly mode connected (up to permutation symmetries of the architecture) must be mechanistically dissimilar, and found that the mechanistically dissimilar minimisers they constructed are connected by quadratic but not linear paths. Under this interpretation, a loss barrier between two checkpoints suggests that their mechanisms differ. However, the absence of a loss barrier does not guarantee that their mechanisms are the same.</p>
@@ -65,6 +67,7 @@ <h4>Loss-Curve Derivatives.</h4>
 <h4>Capability-Specific Metrics.</h4>
 <p>A metric targeted at a single capability, evaluated at every checkpoint, resolves transitions the aggregate hides. The in-context learning score <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span> is a representative construction. It isolates the capability of interest (benefiting from long, previous context) by taking the difference between two quantities. Additionally, tracking benchmark accuracy across checkpoints can similarly reveal capability transitions, though at a coarser level. Comparing results across random seeds helps determine whether these transitions are consistent across training runs <span class="citation" data-cites="vanderwal2025polypythias">(<a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al., 2025</a>)</span>.</p>
 <div class="examplebox">
+<p><strong>Constructing a Capability-Specific Metric</strong></p>
 <p>The in-context learning score of <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022)</a></span> is defined as the prediction loss at the 500th token of a context minus the loss at the 50th token. Each loss is dominated by factors unrelated to in-context learning, such as the model’s overall competence on short contexts; their difference isolates the gain the model derives from additional context. Evaluated at every checkpoint, the score changes abruptly over a narrow training window, the same window in which induction heads form (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>), while the aggregate loss curve registers the transition only as a small bump <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>.</p>
 </div>
 </section>
@@ -72,6 +75,7 @@ <h4>Capability-Specific Metrics.</h4>
 <h4>Per-Example Decomposition.</h4>
 <p>Transitions invisible in every predefined metric can be recovered from the training data itself. POLCA decomposes changes in per-example loss along directions in the low-dimensional subspace where training occurs and clusters examples with similar loss trajectories; the clusters correspond to concepts learned abruptly at identifiable points in training, with no corresponding event in the aggregate curve <span class="citation" data-cites="kangaslahti2026hidden">(<a href="@@BASE@@references/#ref-kangaslahti2026hidden" class="cite cite-established" data-cite="kangaslahti2026hidden" role="doc-biblioref">Kangaslahti et al., 2026</a>)</span>. At coarser granularity, latent state models fit a hidden Markov model over a vector of training metrics and segment the run into discrete phases, with transitions between hidden states flagging candidate developmental events <span class="citation" data-cites="hu2023latent">(<a href="@@BASE@@references/#ref-hu2023latent" class="cite cite-established" data-cite="hu2023latent" role="doc-biblioref">Hu et al., 2023</a>)</span>.</p>
 <div class="warningbox">
+<p><strong>Is Emergence a Metric Artefact?</strong></p>
 <p><span class="citation" data-cites="schaeffer2023emergent"><a href="@@BASE@@references/#ref-schaeffer2023emergent" class="cite cite-established" data-cite="schaeffer2023emergent" role="doc-biblioref">Schaeffer et al. (2023)</a></span> argued that the emergent abilities reported by <span class="citation" data-cites="wei2022emergent"><a href="@@BASE@@references/#ref-wei2022emergent" class="cite cite-established" data-cite="wei2022emergent" role="doc-biblioref">Wei et al. (2022)</a></span> are artefacts of discontinuous metrics. For example, exact-match accuracy can change abruptly while the underlying log-likelihood improves smoothly. Metric choice does explain some reported cases, and claimed emergent abilities can appear or disappear under different metrics of the same task. However, it does not explain all of them: sharp transitions are observed directly in continuous metrics, in per-example losses, and in internal representations during single training runs <span class="citation" data-cites="du2024understanding schaeffer2025elusive wu2025ushaped kangaslahti2026hidden">(<a href="@@BASE@@references/#ref-du2024understanding" class="cite cite-established" data-cite="du2024understanding" role="doc-biblioref">Du et al., 2024</a>; <a href="@@BASE@@references/#ref-schaeffer2025elusive" class="cite cite-established" data-cite="schaeffer2025elusive" role="doc-biblioref">Schaeffer et al., 2025</a>; <a href="@@BASE@@references/#ref-wu2025ushaped" class="cite cite-established" data-cite="wu2025ushaped" role="doc-biblioref">Wu &amp; Lo, 2025</a>; <a href="@@BASE@@references/#ref-kangaslahti2026hidden" class="cite cite-established" data-cite="kangaslahti2026hidden" role="doc-biblioref">Kangaslahti et al., 2026</a>)</span>. The robustness of a reported transition can be assessed using continuous metrics. However, determining which behavioural transitions reflect genuine changes in the model’s internal mechanisms remains an open question.</p>
 </div>
 </section>
@@ -79,10 +83,12 @@ <h4>Per-Example Decomposition.</h4>
 <section id="sec:ld-progress" class="level3"><h3 data-pdf-page="87"><span class="secnum">10.3.2</span> Progress Measures and Internal Precursors<a class="anchor" href="#sec:ld-progress" aria-label="Link to this section">#</a></h3>
 <p>The methods above operate on phase transitions in model behaviours. However, as discussed above, transitions in external behaviour and internal mechanisms can follow different patterns. Researchers therefore are also interested in measuring the internals directly.</p>
 <div class="definitionbox">
+<p><strong>Progress Measure</strong></p>
 <p>A <em>progress measure</em> is a scalar quantity defined on a model’s internals that varies continuously as a mechanism forms, and can therefore track the mechanism’s development before the behaviour it supports changes <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>. Progress measures are typically derived from a mechanistic analysis of the completed mechanism at the end of training, then evaluated retroactively at earlier checkpoints.</p>
 </div>
 <p>Grokking is the controlled setting in which this tool has been demonstrated most completely.</p>
 <div class="definitionbox">
+<p><strong>Grokking</strong></p>
 <p>Grokking is the phenomenon in which a model reaches low training loss early in training but begins to generalise to unseen inputs only after a long additional period of optimisation <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall">(<a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite cite-emerging" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a>)</span>. This is almost exclusively seen in closed-domain settings, though general-purpose language models have shown conceptually similar behaviour.</p>
 </div>
 <figure id="fig:grokking" data-latex-placement="tb">
@@ -91,6 +97,7 @@ <h4>Per-Example Decomposition.</h4>
 </figure>
 <p>In modular arithmetic, training accuracy saturates early while test accuracy remains near chance for many steps before rising sharply (Figure <a href="@@BASE@@handbook/learning-dynamics/#fig:grokking" data-reference-type="ref" data-reference="fig:grokking">16</a>; <span class="citation" data-cites="power2022grokkinggeneralizationoverfittingsmall"><a href="@@BASE@@references/#ref-power2022grokkinggeneralizationoverfittingsmall" class="cite cite-emerging" data-cite="power2022grokkinggeneralizationoverfittingsmall" role="doc-biblioref">Power et al., 2022</a></span>). Mechanistic analysis of grokked networks recovers the learned algorithm (trigonometric identities implementing modular addition) and defines progress measures that track its formation: the generalising circuit grows gradually during the plateau, and the sharp change in test accuracy corresponds to a transition from a memorising solution to the completed generalising circuit followed by removal of the memorising components <span class="citation" data-cites="nanda2023progressmeasuresgrokkingmechanistic">(<a href="@@BASE@@references/#ref-nanda2023progressmeasuresgrokkingmechanistic" class="cite cite-established" data-cite="nanda2023progressmeasuresgrokkingmechanistic" role="doc-biblioref">Nanda et al., 2023</a>)</span>. Grokking has been explained as competition between a memorising and a generalising circuit with different efficiencies under weight decay, an account that predicted and confirmed ungrokking and semi-grokking <span class="citation" data-cites="varma2023explaining">(<a href="@@BASE@@references/#ref-varma2023explaining" class="cite cite-emerging" data-cite="varma2023explaining" role="doc-biblioref">Varma et al., 2023</a>)</span>. Grokking can be induced or eliminated by controlling parameter norm at initialisation, and it occurs beyond algorithmic tasks <span class="citation" data-cites="liu2023omnigrok">(<a href="@@BASE@@references/#ref-liu2023omnigrok" class="cite cite-established" data-cite="liu2023omnigrok" role="doc-biblioref">Liu et al., 2023</a>)</span>.</p>
 <div class="warningbox">
+<p><strong>Transfer from Grokking Settings to Language Models</strong></p>
 <p>The standard setting in grokking experiments differs from language model pretraining in many ways, including dataset size, weight decay, and the number of epochs over the data. Current studies have not shown that existing quantitative conclusions transfer directly to large-scale pretraining.</p>
 </div>
 <p>Similar findings have been reported outside grokking settings. <span class="citation" data-cites="chen_sudden_2024"><a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al. (2024a)</a></span> found that syntactic attention structures emerged during a sharp drop in masked language modelling (MLM) loss, before accuracy improvement on a grammatical benchmark. Similarly, <span class="citation" data-cites="zucchet2025language"><a href="@@BASE@@references/#ref-zucchet2025language" class="cite cite-emerging" data-cite="zucchet2025language" role="doc-biblioref">Zucchet et al. (2025a)</a></span> observed that attention-based recall circuits formed during a plateau in factual-knowledge acquisition, followed by rapid improvements in model performance. These findings suggest that changes in internal mechanisms can precede changes in model capabilities. This observation motivates research on forecasting model behaviour through internal monitoring, as discussed in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-safety" data-reference-type="ref" data-reference="sec:ld-safety">10.4</a>.</p>
@@ -99,6 +106,7 @@ <h4>Per-Example Decomposition.</h4>
 <p>So far, the methods we discussed cannot attribute the transition to specific model components. Attention head specialisation is probably the most intuitive option to work at this granularity in transformers. Individual attention heads can specialise to attend to syntactic relations, positional offsets, or rare tokens (<span class="citation" data-cites="voita_analyzing_2019"><a href="@@BASE@@references/#ref-voita_analyzing_2019" class="cite cite-established" data-cite="voita_analyzing_2019" role="doc-biblioref">Voita et al., 2019</a></span>; <span class="citation" data-cites="clark2019what"><a href="@@BASE@@references/#ref-clark2019what" class="cite cite-emerging" data-cite="clark2019what" role="doc-biblioref">Clark et al., 2019</a></span>), and this specialisation can appear abruptly. For example, induction heads form during a brief training window, and their formation coincides with a sharp improvement in in-context learning <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>, a transition we examine in detail in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-induction-case" data-reference-type="ref" data-reference="sec:ld-induction-case">10.3.5</a>. In masked language models, suppressing the syntactic attention structure delays the capability it precedes <span class="citation" data-cites="chen_sudden_2024">(<a href="@@BASE@@references/#ref-chen_sudden_2024" class="cite cite-established" data-cite="chen_sudden_2024" role="doc-biblioref">Chen et al., 2024a</a>)</span>. Likewise, sparse attention emerges abruptly and depends on repetition in the training data <span class="citation" data-cites="zucchet2025the">(<a href="@@BASE@@references/#ref-zucchet2025the" class="cite cite-established" data-cite="zucchet2025the" role="doc-biblioref">Zucchet et al., 2025b</a>)</span>.</p>
 <p>These events cluster in time. Several attention heads, such as induction heads and heads exhibiting syntactic attention patterns, can acquire specialised functions within the same training window. These heads emerge alongside a peak in how accurately the model’s perplexity predicts human reading times, and this predictive ability subsequently declines even as language modelling performance continues to improve <span class="citation" data-cites="aoyama_language_2025">(<a href="@@BASE@@references/#ref-aoyama_language_2025" class="cite cite-established" data-cite="aoyama_language_2025" role="doc-biblioref">Aoyama &amp; Wilcox, 2025</a>)</span>. Head specialisation can be viewed as a form of <em>symmetry breaking</em>. Before or in the very early stages of training, attention heads are indistinguishable and interchangeable; during training, they start to develop distinct functions despite having equivalent roles in the architecture. Tracking this process can help identify the emergence of modular structure. Attribution of this kind is also a diagnostic for modularity more broadly. Three observable indicators jointly diagnose modular internal structure <span class="citation" data-cites="zhao2024distributional">(<a href="@@BASE@@references/#ref-zhao2024distributional" class="cite cite-emerging" data-cite="zhao2024distributional" role="doc-biblioref">Zhao et al., 2024b</a>)</span>: transitions during training, emergent trends across model scale, and heterogeneous clustering of solutions across random seeds. A structure exhibiting all three is a strong candidate for a discrete, functionally specialised mechanism.</p>
 <div class="insightbox">
+<p><strong>Sharp Transitions from Smooth Components</strong></p>
 <p>Phase transitions in test loss or task accuracy do not imply abrupt change in any individual component. For example, in controlled settings, many attention heads develop induction-like behaviour gradually—their contributions are largely additive, and the sharp macroscopic transition arises from their aggregate effect <span class="citation" data-cites="singh2024needsrightinductionhead">(<a href="@@BASE@@references/#ref-singh2024needsrightinductionhead" class="cite cite-established" data-cite="singh2024needsrightinductionhead" role="doc-biblioref">Singh et al., 2024</a>)</span>. Localising a phase transition in a coarse-grained metric therefore does not help identify the responsible components. In contrast, component-level attribution may find only gradual change without being wrong.</p>
 </div>
 </section>
@@ -111,6 +119,7 @@ <h4>Saddle-to-Saddle Dynamics.</h4>
 <h4>Singular Learning Theory.</h4>
 <p>Singular learning theory (SLT) studies how the geometry of a singular model’s parameter space affects learning and generalisation <span class="citation" data-cites="watanabe2009algebraic">(<a href="@@BASE@@references/#ref-watanabe2009algebraic" class="cite cite-established" data-cite="watanabe2009algebraic" role="doc-biblioref">Watanabe, 2009</a>)</span>. Neural networks are singular (and are thus a subject of study in SLT) because different parameter configurations can implement the same function. As a result, two models that achieve similar loss can differ substantially in their effective complexity. SLT provides a framework for studying this complexity and has motivated research on its changes during training and its usefulness in downstream applications <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>.</p>
 <div class="definitionbox">
+<p><strong>Local Learning Coefficient (LLC)</strong></p>
 <p>For model parameters <span class="math inline">\mathbf{\theta}^*</span> at a local minimum, the Local Learning Coefficient (LLC) <span class="math inline">\lambda(\mathbf{\theta}^*)</span> measures the degeneracy of the loss landscape near <span class="math inline">\mathbf{\theta}^*</span> via the volume-scaling rate of low-loss neighbourhoods <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>. Smaller values indicate more degenerate (flatter, in a higher-order sense) minima. The LLC is estimable at scale with stochastic-gradient MCMC <span class="citation" data-cites="lau2023local">(<a href="@@BASE@@references/#ref-lau2023local" class="cite cite-established" data-cite="lau2023local" role="doc-biblioref">Lau et al., 2025</a>)</span>.</p>
 </div>
 <p>Estimated LLC trajectories in transformers change at points that coincide with identifiable developmental events, including the stages of structure formation described in Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-attribution" data-reference-type="ref" data-reference="sec:ld-attribution">10.3.3</a> <span class="citation" data-cites="hoogland2025losslandscapedegeneracystagewise">(<a href="@@BASE@@references/#ref-hoogland2025losslandscapedegeneracystagewise" class="cite cite-established" data-cite="hoogland2025losslandscapedegeneracystagewise" role="doc-biblioref">Hoogland et al., 2025</a>)</span>. This is the central empirical result of the developmental interpretability programme <span class="citation" data-cites="hoogland2023towards">(<a href="@@BASE@@references/#ref-hoogland2023towards" class="cite cite-emerging" data-cite="hoogland2023towards" role="doc-biblioref">Hoogland et al., 2023</a>)</span> introduced in Section <a href="@@BASE@@handbook/research-orientations/#subsec:dev interp" data-reference-type="ref" data-reference="subsec:dev interp">12.3</a>. However, current evidence for LLC is largely retrospective, where phase transitions are detected and matched to known events only after training. Prospective prediction of a transition before it occurs has not yet been demonstrated. Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-safety" data-reference-type="ref" data-reference="sec:ld-safety">10.4</a> discusses potential applications of learning-dynamics analysis to forecasting and intervention.</p>
@@ -118,6 +127,7 @@ <h4>Singular Learning Theory.</h4>
 </section>
 <section id="sec:ld-induction-case" class="level3"><h3 data-pdf-page="90"><span class="secnum">10.3.5</span> Case Study: Induction Heads<a class="anchor" href="#sec:ld-induction-case" aria-label="Link to this section">#</a></h3>
 <div class="examplebox">
+<p><strong>Learning Dynamics Perspective on Induction Heads</strong></p>
 <p>The preceding case studies (Sections <a href="@@BASE@@handbook/feature-extraction/#subsec: induc head case study: feature extract" data-reference-type="ref" data-reference="subsec: induc head case study: feature extract">6.8</a> and <a href="@@BASE@@handbook/circuits/#subsec: induc head case study: circuit" data-reference-type="ref" data-reference="subsec: induc head case study: circuit">7.4</a>) analysed induction heads and circuits in a trained model. Several facts about the induction circuit are invisible from that vantage point but become observable when we examine the model across training <span class="citation" data-cites="olsson2022incontextlearninginductionheads">(<a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al., 2022</a>)</span>. Consider the repeated sequence again</p>
 <p><span class="math display">\texttt{A B C A B \underline{?}},</span></p>
 <p>and observe at successive checkpoints of a single training run. Examining the checkpoints shows that the induction ability arrived abruptly: at early checkpoints the model bases its predictions on token frequencies and derives no benefit from the earlier occurrence of <code>A B</code>, and during a narrow window of training, roughly 2.5 to 5 billion tokens in the models studied by <span class="citation" data-cites="olsson2022incontextlearninginductionheads"><a href="@@BASE@@references/#ref-olsson2022incontextlearninginductionheads" class="cite cite-emerging" data-cite="olsson2022incontextlearninginductionheads" role="doc-biblioref">Olsson et al. (2022)</a></span>, induction heads form and the model begins predicting <code>C</code> by attending back to the first occurrence. A sharp improvement in in-context learning and a small bump in the training loss fall within the same window.<br />
@@ -147,6 +157,7 @@ <h4>Internal Precursors as Early Warning.</h4>
 <h4>Run-to-Run Variance.</h4>
 <p>To assess the reliability of a forecast, researchers can compare training runs with different random seeds. <span class="citation" data-cites="vanderwal2025polypythias"><a href="@@BASE@@references/#ref-vanderwal2025polypythias" class="cite cite-established" data-cite="vanderwal2025polypythias" role="doc-biblioref">van der Wal et al. (2025)</a></span> found that capabilities generally emerge at similar stages and representations develop consistently across training runs, although some runs differ substantially. These exceptions highlight the uncertainty of forecasts based on a single training run.</p>
 <div class="warningbox">
+<p><strong>Most Early-Warning Evidence Is Retrospective</strong></p>
 <p>Currently, nearly all demonstrations that changes in internals lead to changes in behaviour are retrospective. In these studies, researchers analyse the completed mechanism after training to identify a progress measure or precursor structure, then evaluate it backwards over saved checkpoints. This concern also exists in LLC-based transition detection (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:training_phase_why" data-reference-type="ref" data-reference="sec:training_phase_why">10.3.4</a>).<br />
 In contrast, prospective forecasting requires researchers to predict outcomes before observing them. <span class="citation" data-cites="biderman2023emergent"><a href="@@BASE@@references/#ref-biderman2023emergent" class="cite cite-established" data-cite="biderman2023emergent" role="doc-biblioref">Biderman et al. (2023a)</a></span> proposed baseline methods for forecasting memorisation. However, prospective forecasting for broader capability transitions relevant to AI safety remains underdeveloped. Early-warning signals therefore require prospective validation before they can be considered reliable.</p>
 </div>
@@ -159,6 +170,7 @@ <h4>Pretraining Data Filtering: Deep Ignorance.</h4>
 <p>Deep Ignorance filters proxies for biohazardous knowledge out of the pretraining corpus and trains 6.9-billion-parameter models on the filtered data (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-data" data-reference-type="ref" data-reference="sec:ld-data">10.1.1</a>; <span class="citation" data-cites="obrien2026deep"><a href="@@BASE@@references/#ref-obrien2026deep" class="cite cite-established" data-cite="obrien2026deep" role="doc-biblioref">O’Brien et al., 2026</a></span>). The filtered models do not acquire the targeted knowledge, making it difficult for the unwanted capability to emerge through adversarial fine-tuning. In contrast, post-training unlearning may suppress the unwanted behaviour without removing the underlying knowledge. A follow-up moves the filter from documents to tokens: individual tokens are labelled using sparse autoencoders and efficient classifiers trained on those labels, and filtering at token granularity removes the undesired capability at lower cost to desired capabilities than document-level filtering, with effectiveness increasing with model scale across the two orders of magnitude tested and filtered models remaining alignable afterwards <span class="citation" data-cites="rathi2026shaping">(<a href="@@BASE@@references/#ref-rathi2026shaping" class="cite cite-emerging" data-cite="rathi2026shaping" role="doc-biblioref">Rathi &amp; Radford, 2026</a>)</span>.</p>
 <p>Similarly, the order in which different categories of training data are presented matters. <span class="citation" data-cites="bruijns2026pretrainingcurriculaenableselective"><a href="@@BASE@@references/#ref-bruijns2026pretrainingcurriculaenableselective" class="cite cite-emerging" data-cite="bruijns2026pretrainingcurriculaenableselective" role="doc-biblioref">Bruijns et al. (2026)</a></span> found that, in an imbalanced pretraining curriculum where one task is shown before another, the model packs the tasks sequentially into separable circuits, resulting in more targeted subsequent refusal fine-tuning.</p>
 <div class="warningbox">
+<p><strong>Data-Level Interventions Have Ripple Effects</strong></p>
 <p>Removing data from a training set does not act only on the removed data. In the setting of <span class="citation" data-cites="borkar2025privacy"><a href="@@BASE@@references/#ref-borkar2025privacy" class="cite cite-established" data-cite="borkar2025privacy" role="doc-biblioref">Borkar et al. (2025)</a></span>, deleting personal information can increase memorisation of the personal information that remains, and adding one individual’s information amplifies memorisation of others’ by roughly 7.5<span class="math inline">\times</span>, because whether a sequence is memorised depends on similar sequences seen elsewhere in training. Subliminal learning <span class="citation" data-cites="cloud2026language">(<a href="@@BASE@@references/#ref-cloud2026language" class="cite cite-established" data-cite="cloud2026language" role="doc-biblioref">Cloud et al., 2026</a>)</span> shows that data can transmit traits not apparent from its semantic content. For example, a student model can inherit a teacher’s hidden bias from seemingly unrelated data, such as number sequences. This transfer is carried by a small set of divergence tokens and depends on early layers <span class="citation" data-cites="schrodi2026towards">(<a href="@@BASE@@references/#ref-schrodi2026towards" class="cite cite-established" data-cite="schrodi2026towards" role="doc-biblioref">Schrodi et al., 2026</a>)</span>. Filtering and unlearning interventions therefore need to be evaluated on what the model does with the data that stays, not only on what it no longer does with the data that was removed.</p>
 </div>
 </section>
@@ -170,6 +182,7 @@ <h4>Architectural Modification: Gradient Routing.</h4>
 <h4>Monitoring and Steering During Fine-Tuning.</h4>
 <p>The case excluded above uses fine-tuning to repair an undesirable behaviour after it has been identified. Intervening on the fine-tuning process itself remains within scope, and persona vectors provide a current example of this approach. A persona vector is an activation-space direction corresponding to a character trait such as sycophancy, hallucination propensity, or malice, extractable automatically from a natural-language trait description <span class="citation" data-cites="chen2025persona">(<a href="@@BASE@@references/#ref-chen2025persona" class="cite cite-emerging" data-cite="chen2025persona" role="doc-biblioref">Chen et al., 2025b</a>)</span>. Intended and unintended personality changes during fine-tuning correlate strongly with movement along the relevant vector, which yields the monitoring instrument. Preventative steering along the vector during fine-tuning limits acquisition of the trait. Before fine-tuning, projecting training data onto the vector flags data that would induce the trait, at both the dataset and individual-sample levels <span class="citation" data-cites="chen2025persona">(<a href="@@BASE@@references/#ref-chen2025persona" class="cite cite-emerging" data-cite="chen2025persona" role="doc-biblioref">Chen et al., 2025b</a>)</span>. This instantiates each step of the intervention loop below for the fine-tuning stage.</p>
 <div class="practicebox">
+<p><strong>Train-Time Intervention Loop</strong></p>
 <p>The work discussed so far lays the methodological foundation for intervening in a training run in progress. Below we present a general pipeline.</p>
 <ol>
 <li><p><strong>Monitor</strong> a target quantity at checkpoints, such as a capability-specific metric (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-detect" data-reference-type="ref" data-reference="sec:ld-detect">10.3.1</a>) or a progress measure on model internals (Section <a href="@@BASE@@handbook/learning-dynamics/#sec:ld-progress" data-reference-type="ref" data-reference="sec:ld-progress">10.3.2</a>).</p></li>
diff --git a/src/generated/chapters/limits-of-behavioural-evaluation.html b/src/generated/chapters/limits-of-behavioural-evaluation.html
index 32521818d9..f7da5657ad 100644
--- a/src/generated/chapters/limits-of-behavioural-evaluation.html
+++ b/src/generated/chapters/limits-of-behavioural-evaluation.html
@@ -16,6 +16,7 @@ <h4>Third, evaluation awareness.</h4>
 <h4>Fourth, lack of fine-grained control.</h4>
 <p>Some safety interventions require fine-grained control <span class="citation" data-cites="meng2023locatingeditingfactualassociations li2024wmdp marks2025sparse">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>; <a href="@@BASE@@references/#ref-li2024wmdp" class="cite cite-established" data-cite="li2024wmdp" role="doc-biblioref">Li et al., 2024</a>; <a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>. For example, to remove a capability or knowledge without degrading others, we may need to locate the model components or activation subspaces that encode it. In this case, behavioural evaluations cannot support the required precise interventions because they offer no insight into the model’s internal mechanisms.</p>
 <div class="warningbox">
+<p><strong>The Sleeper Agent Experiment</strong></p>
 <p><span id="war:sleeper" data-label="war:sleeper"></span> Researchers have shown that large language models can learn <em>strategic deception</em> by training them with a hidden “backdoor” objective: write secure code during the year 2023 (training phase) but insert vulnerabilities if the year is 2024 (deployment phase) <span class="citation" data-cites="hubinger2024sleeperagentstrainingdeceptive">(<a href="@@BASE@@references/#ref-hubinger2024sleeperagentstrainingdeceptive" class="cite cite-emerging" data-cite="hubinger2024sleeperagentstrainingdeceptive" role="doc-biblioref">Hubinger et al., 2024</a>)</span>. The experiment is as follows:<br />
 </p>
 <section id="training-objective." class="level4">
diff --git a/src/generated/chapters/representation-analysis.html b/src/generated/chapters/representation-analysis.html
index 1ad92cacf4..69cb4a3c52 100644
--- a/src/generated/chapters/representation-analysis.html
+++ b/src/generated/chapters/representation-analysis.html
@@ -4,6 +4,7 @@
 <section id="sec:model-diffing" class="level2"><h2 data-pdf-page="77"><span class="secnum">9.1</span> Model Diffing: Identifying Internal Changes Across Models<a class="anchor" href="#sec:model-diffing" aria-label="Link to this section">#</a></h2>
 <p>Many concerning behaviours, such as reward hacking or sycophancy, arise during fine-tuning (<span class="citation" data-cites="sharma_towards_2025"><a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a></span>; <span class="citation" data-cites="macdiarmid_natural_2025"><a href="@@BASE@@references/#ref-macdiarmid_natural_2025" class="cite cite-emerging" data-cite="macdiarmid_natural_2025" role="doc-biblioref">MacDiarmid et al., 2025</a></span>). This phenomenon makes it important to monitor features and circuits introduced or amplified during this process to prevent unsafe models from being deployed. Model diffing <span class="citation" data-cites="anthropic2024modeldiffing">(<a href="@@BASE@@references/#ref-anthropic2024modeldiffing" class="cite cite-emerging" data-cite="anthropic2024modeldiffing" role="doc-biblioref">Anthropic, 2024</a>)</span> can surface some of these features.</p>
 <div class="definitionbox">
+<p><strong>Model Diffing</strong></p>
 <p>For two models, <em>model diffing</em> seeks to identify differences in features or circuits between them. These differences may consist of features or circuits that are exclusive to one model, or of differences in how shared features or circuits are used.</p>
 </div>
 <section id="representation-alignment-and-convergent-learning." class="level4">
@@ -22,6 +23,7 @@ <h4>Dictionary Learning Methods.</h4>
 <p>A related approach is to model differences in how representations are <em>transformed</em>, rather than comparing the representations themselves. <span class="citation" data-cites="hu2026transcoderadaptersreasoningmodeldiffing"><a href="@@BASE@@references/#ref-hu2026transcoderadaptersreasoningmodeldiffing" class="cite cite-emerging" data-cite="hu2026transcoderadaptersreasoningmodeldiffing" role="doc-biblioref">Hu et al. (2026)</a></span> used transcoders to learn a function that approximates the difference in MLP computation between a base model and its fine-tuned variant. Specifically, adapters are trained so that a base MLP augmented with a learned correction term reproduces the fine-tuned MLP’s behaviour within the full model. Unlike the above methods, which operate on activation differences, this method learns a transformation that approximates how fine-tuning modifies the model’s MLP computation.</p>
 <p>A shared concern across dictionary-learning-based model-diffing methods is the faithfulness of the sparse decomposition (see Section <a href="@@BASE@@handbook/feature-extraction/#subsubsec: sae limitation" data-reference-type="ref" data-reference="subsubsec: sae limitation">6.5.5</a>). Extracted features may not faithfully reflect the underlying representations or their causal roles, potentially leading to false-positive findings about differences between models. For example, identifying an SAE feature associated with “confessing previous faults” in a fine-tuned model but not in the original does not establish that the fine-tuned model causally uses this feature. Nor does its apparent absence from the original model imply that the model lacks representations that could steer it towards such behaviour.</p>
 <div class="warningbox">
+<p><strong>False Positives in Model Diffing</strong></p>
 <p>Dictionary learning methods for model diffing risk falsely identifying model-exclusive features <span class="citation" data-cites="minder_overcoming_2025 jiralerspong_cross-architecture_2025">(<a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al., 2025a</a>; <a href="@@BASE@@references/#ref-jiralerspong_cross-architecture_2025" class="cite cite-emerging" data-cite="jiralerspong_cross-architecture_2025" role="doc-biblioref">Jiralerspong &amp; Bricken, 2025</a>)</span>. This can result from the loss function: for instance, <span class="citation" data-cites="minder_overcoming_2025"><a href="@@BASE@@references/#ref-minder_overcoming_2025" class="cite cite-emerging" data-cite="minder_overcoming_2025" role="doc-biblioref">Dumas et al. (2025a)</a></span> showed that crosscoders trained with an <span class="math inline">L_1</span> regularisation on hidden states can falsely identify novel features, which they tried to mitigate with <span class="math inline">\sigma_{\text{Batch-TopK}}(\cdot)</span>. However, even with this improved architecture, false positives may persist. During fine-tuning, the directions representing some features can rotate, and dictionary learning methods currently cannot distinguish between features introduced via fine-tuning and those merely represented differently. Thus, supposedly model-exclusive features should be regarded as hypotheses that need further validation.</p>
 </div>
 </section>
@@ -42,6 +44,7 @@ <h4>LLM-Based Approaches.</h4>
 <section id="subsec:steering" class="level2"><h2 data-pdf-page="80"><span class="secnum">9.3</span> Representation Engineering and Steering<a class="anchor" href="#subsec:steering" aria-label="Link to this section">#</a></h2>
 <p>To manipulate model behaviour, representation engineering alters activation-space representations directly, rather than via model weight modification. <em>Activation steering</em> generally refers to methods that target intermediate activations (usually the residual stream) by adding certain vectors that shift model outputs during inference. This differs from localisation methods, which determine where information or behaviour is represented, as steering techniques aim to identify a specific, manipulable vector that <em>changes</em> model behaviour. Because they require no weight updates or retraining, these interventions are lightweight approaches for controlling behaviour.</p>
 <div class="definitionbox">
+<p><strong>Steering Vector (Figure <a href="@@BASE@@handbook/representation-analysis/#fig:steering_vectors" data-reference-type="ref" data-reference="fig:steering_vectors">15</a>)</strong></p>
 <p>A <em>steering vector</em> <span class="math inline">v_{\text{steer}} \in \mathbb{R}^{d}</span> is an activation-space direction which, when scaled (<span class="math inline">\alpha v_{\text{steer}}</span>) and added to an intermediate activation, shifts model outputs towards a targeted behaviour. For instance, one steering direction can use the weight vector of a trained linear probe. Another popular method is <em>difference-in-means</em>, which obtains the steering vector by taking the difference between activations associated with two behaviours <span class="citation" data-cites="panickssery2024steeringllama2contrastive">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>)</span>: <span class="math display">v_{\text{steer}} =
 \mathbb{E}[h_\ell \mid \text{preferred}]
 -
@@ -49,6 +52,7 @@ <h4>LLM-Based Approaches.</h4>
 </div>
 <p>While these approaches appear simple, they reliably change model behaviours such as refusal, helpfulness, and stylistic tendencies <span class="citation" data-cites="panickssery2024steeringllama2contrastive zhao2025steering arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a>; <a href="@@BASE@@references/#ref-zhao2025steering" class="cite cite-established" data-cite="zhao2025steering" role="doc-biblioref">Zhao et al., 2025</a>; <a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span>. Thus, steering is useful both as a technique to manipulate behaviour and as a causality test for probes. If intervening along a direction consistently changes a target behaviour, this provides evidence that the corresponding representation causally influences that behaviour. Below we use refusal vectors as a concrete example.</p>
 <div class="definitionbox">
+<p><strong>Refusal Vector</strong></p>
 <p><span id="def:refusal-vector" data-label="def:refusal-vector"></span></p>
 <p>A <em>refusal vector</em> <span class="citation" data-cites="arditi2024refusallanguagemodelsmediated">(<a href="@@BASE@@references/#ref-arditi2024refusallanguagemodelsmediated" class="cite cite-established" data-cite="arditi2024refusallanguagemodelsmediated" role="doc-biblioref">Arditi et al., 2024</a>)</span> is a steering direction associated with a model’s tendency to refuse requests. Define <span class="math inline">h^t_\ell \in \mathbb{R}^d</span> as the residual stream at layer <span class="math inline">\ell</span> and token position <span class="math inline">t</span>. A simple contrastive construction is <span class="math display">\mathbf{r}_\ell =
 \mathbb{E}_{x \sim \mathcal{D}_{\mathrm{refuse}}}
@@ -72,6 +76,7 @@ <h4>Steering as a Causal Test for Feature Interpretability.</h4>
 <p>Activation steering can causally evaluate representations previously labelled by feature-extraction methods (such as CLTs or SAEs). For instance, a feature may be hypothesised to represent a particular concept. Supporting evidence can be obtained by steering along the identified feature direction and evaluating the resulting change in behaviour.</p>
 <p>These evaluations have been used to compare interpretable direction-identification approaches, including SAE features, difference-in-means directions, and DAS <span class="citation" data-cites="huang-etal-2024-ravel wu2025axbench">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>; <a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>. Benchmarks designed to evaluate the causal and behavioural properties of learned representations include RAVEL <span class="citation" data-cites="huang-etal-2024-ravel">(<a href="@@BASE@@references/#ref-huang-etal-2024-ravel" class="cite cite-established" data-cite="huang-etal-2024-ravel" role="doc-biblioref">Huang et al., 2024</a>)</span>, MIB <span class="citation" data-cites="mueller2025mib">(<a href="@@BASE@@references/#ref-mueller2025mib" class="cite cite-established" data-cite="mueller2025mib" role="doc-biblioref">Mueller et al., 2025</a>)</span>, and AxBench <span class="citation" data-cites="wu2025axbench">(<a href="@@BASE@@references/#ref-wu2025axbench" class="cite cite-established" data-cite="wu2025axbench" role="doc-biblioref">Wu et al., 2025b</a>)</span>.</p>
 <div class="warningbox">
+<p><strong>Limitations: Brittleness and Mechanistic Opacity</strong></p>
 <p>While steering methods effectively control behaviours and support causal testing, they have limitations. Notably, they can be brittle, as a direction identified for one distribution, model, or task may not generalise reliably. Another limitation is that successful behavioural steering does not, by itself, establish mechanistic transparency, because an intervention may simultaneously impact multiple features, interact nonlinearly with existing computations, or exploit unintended pathways that produce the desired behaviour without corresponding to a clean mechanism <span class="citation" data-cites="makelov2024is">(<a href="@@BASE@@references/#ref-makelov2024is" class="cite cite-established" data-cite="makelov2024is" role="doc-biblioref">Makelov et al., 2024</a>)</span>. Thus, steering should generally be interpreted as evidence for causal influence, but it should not, by itself, be a mechanistic explanation of the behaviour.</p>
 </div>
 </section>
diff --git a/src/generated/chapters/research-orientations.html b/src/generated/chapters/research-orientations.html
index 977b239370..3abf56642d 100644
--- a/src/generated/chapters/research-orientations.html
+++ b/src/generated/chapters/research-orientations.html
@@ -64,6 +64,7 @@ <h4>Common confusion.</h4>
 <section id="concerns-for-introspective-interpretability" class="level3"><h3 data-pdf-page="105"><span class="secnum">12.5.1</span> Concerns for Introspective Interpretability<a class="anchor" href="#concerns-for-introspective-interpretability" aria-label="Link to this section">#</a></h3>
 <p>Self-explanation also raises concerns. For example, generated explanations can be plausible yet unfaithful. The model’s general capability can also degrade when it is trained to honestly verbalise its internal processing. Moreover, the model may have fundamental limitations in what it can report. These concerns raise questions about whether we can trust self-explanations in safety-critical applications and how to validate potentially misleading reports. Below, we first discuss the main failure modes of self-explanation, followed by validation procedures in Section <a href="@@BASE@@handbook/research-orientations/#subsec:valproce model self-reports" data-reference-type="ref" data-reference="subsec:valproce model self-reports">12.5.2</a>.</p>
 <div class="warningbox">
+<p><strong>Failure Modes of Self-Explanation</strong></p>
 <p>Introspective methods can fail in several ways. In each case, the explanation appears plausible yet is unfaithful.<br />
 </p>
 <section id="confabulation." class="level4">
@@ -91,6 +92,7 @@ <h4>Strategic misreporting.</h4>
 <section id="subsec:valproce model self-reports" class="level3"><h3 data-pdf-page="106"><span class="secnum">12.5.2</span> Validation Procedures for Model Self-Reports<a class="anchor" href="#subsec:valproce model self-reports" aria-label="Link to this section">#</a></h3>
 <p>Given these concerns, Introspective Interpretability methods must be validated against additional, independent evidence. Specifically, they must be shown to be <em>consistent</em> with model internals (as accessed by other mechanistic interpretability tools) and behavioural signatures (as tested by behavioural interpretability analysis). Validation procedures vary based on the type of self-report:</p>
 <div class="practicebox">
+<p><strong>Validation Procedures for Introspective Models</strong></p>
 <section id="causal-intervention-consistency." class="level4">
 <h4>1. Causal intervention consistency.</h4>
 <p>Self-explanations should be tested against intervention experiments. If a model claims a feature drives its output, ablating that feature should steer the model away from the output. If a model claims it considered a piece of information (e.g., an input feature or a recalled piece of factual knowledge in intermediate layers) for its decision, patching the information with counterfactual values should change the output correspondingly.<br />
@@ -129,6 +131,7 @@ <h4>Implications.</h4>
 </ul>
 <p>Automated interpretability is not limited to any particular type of internal representation or mechanism. Depending on the research question, automated pipelines may analyse SAE features <span class="citation" data-cites="paulo2025automatically han2026sage">(<a href="@@BASE@@references/#ref-paulo2025automatically" class="cite cite-established" data-cite="paulo2025automatically" role="doc-biblioref">Paulo et al., 2025b</a>; <a href="@@BASE@@references/#ref-han2026sage" class="cite cite-established" data-cite="han2026sage" role="doc-biblioref">Han et al., 2026</a>)</span>, individual neurons (<span class="citation" data-cites="bills2023language foote2023neurongraphinterpretinglanguage"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al., 2023</a>; <a href="@@BASE@@references/#ref-foote2023neurongraphinterpretinglanguage" class="cite cite-emerging" data-cite="foote2023neurongraphinterpretinglanguage" role="doc-biblioref">Foote et al., 2023</a></span>; <span class="citation" data-cites="oikarinen2023clipdissect"><a href="@@BASE@@references/#ref-oikarinen2023clipdissect" class="cite cite-established" data-cite="oikarinen2023clipdissect" role="doc-biblioref">Oikarinen &amp; Weng, 2023</a></span>), circuits (<span class="citation" data-cites="conmy2023automatedcircuitdiscoverymechanistic"><a href="@@BASE@@references/#ref-conmy2023automatedcircuitdiscoverymechanistic" class="cite cite-established" data-cite="conmy2023automatedcircuitdiscoverymechanistic" role="doc-biblioref">Conmy et al., 2023</a></span>; <span class="citation" data-cites="khan2026can"><a href="@@BASE@@references/#ref-khan2026can" class="cite cite-emerging" data-cite="khan2026can" role="doc-biblioref">Khan et al., 2026</a></span>), or other model components <span class="citation" data-cites="nla">(<a href="@@BASE@@references/#ref-nla" class="cite cite-emerging" data-cite="nla" role="doc-biblioref">Fraser-Taliente et al., 2026</a>)</span>. This approach distinguishes itself by automatically discovering, explaining, or evaluating these objects rather than relying on manual analysis. Below, we illustrate a classical automated feature-explanation pipeline whose explanation and verification stages generally follow <span class="citation" data-cites="bills2023language"><a href="@@BASE@@references/#ref-bills2023language" class="cite cite-emerging" data-cite="bills2023language" role="doc-biblioref">Bills et al. (2023)</a></span>.</p>
 <div class="practicebox">
+<p><strong>Classical Automated Interpretability Pipeline</strong></p>
 <section id="stage-1-feature-extraction." class="level4">
 <h4>Stage 1 – Feature Extraction.</h4>
 <p>SAEs are trained to decompose raw activations of the target model into sparse linear features.<br />
@@ -154,6 +157,7 @@ <h4>Stage 4 – Deception Detection.</h4>
 <section id="governance-concerns-for-automated-interpretability" class="level3"><h3 data-pdf-page="108"><span class="secnum">12.6.1</span> Governance Concerns for Automated Interpretability<a class="anchor" href="#governance-concerns-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
 <p>Despite the promise, automated methods face fundamental concerns. In particular, although automation improves scalability, reduced human involvement may also increase the risk and severity of systematic errors, reward hacking, or deceptive explanations when automated oversight fails to catch them <span class="citation" data-cites="lermen2025deceptive">(<a href="@@BASE@@references/#ref-lermen2025deceptive" class="cite cite-emerging" data-cite="lermen2025deceptive" role="doc-biblioref">Lermen et al., 2025</a>)</span>. Therefore, success in this research orientation is measured not only by the analysis’s scalability, but also by its coverage, faithfulness, and trustworthiness. Below, we detail several governance challenges, including error propagation at scale, human oversight becoming a bottleneck, and systematic biases going undetected.</p>
 <div class="warningbox">
+<p><strong>Governance Risks of Automation</strong></p>
 <section id="error-propagation." class="level4">
 <h4>Error propagation.</h4>
 <p>Unlike human investigators, who are somewhat independent and introspective, the explanation-generation model can propagate its systematic errors to all features it analyses without rethinking what could have gone wrong.<br />
@@ -178,6 +182,7 @@ <h4>Spurious Feedback Loops.</h4>
 <section id="proposed-guardrails-for-automated-interpretability" class="level3"><h3 data-pdf-page="109"><span class="secnum">12.6.2</span> Proposed Guardrails for Automated Interpretability<a class="anchor" href="#proposed-guardrails-for-automated-interpretability" aria-label="Link to this section">#</a></h3>
 <p>These concerns call for safeguards that combine technical validation, human oversight, and clear accountability. Below, we propose a five-layer guardrail framework to help audit the automated methods and mitigate their risks.</p>
 <div class="practicebox">
+<p><strong>Guardrail Framework for Automated Interpretability</strong></p>
 <section id="layer-1-sampling-based-human-validation." class="level4">
 <h4>Layer 1: Sampling-Based Human Validation.</h4>
 <p>Auditors can randomly sample a predefined fraction (e.g., 5%) of generated explanations for human experts to review. These explanations should cover different task-difficulty levels, feature types, and targeted mechanisms. Human experts should not know in advance which explanations are randomly sampled, which are flagged by automated checks, and which are already verified by additional methods, if they are all presented.<br />
diff --git a/src/generated/chapters/safety-applications.html b/src/generated/chapters/safety-applications.html
index 56bbb3bfe3..74ac24486c 100644
--- a/src/generated/chapters/safety-applications.html
+++ b/src/generated/chapters/safety-applications.html
@@ -36,6 +36,7 @@
 <section id="concept-erasure-via-pisces" class="level3"><h3 data-pdf-page="114"><span class="secnum">13.2.1</span> Concept Erasure via PISCES<a class="anchor" href="#concept-erasure-via-pisces" aria-label="Link to this section">#</a></h3>
 <p>Conventional unlearning techniques, such as fine-tuning on negative examples or pruning concept-related neurons, are often brittle. These methods suppress the expression of concepts or capabilities but do not eliminate them, so they may be relearned or reactivated under specific conditions later <span class="citation" data-cites="lo-etal-2024-large">(<a href="@@BASE@@references/#ref-lo-etal-2024-large" class="cite cite-established" data-cite="lo-etal-2024-large" role="doc-biblioref">Lo et al., 2024</a>)</span>. PISCES (Precise In-parameter Suppression for Concept EraSure; <span class="citation" data-cites="gurarieh2025preciseinparameterconcepterasure"><a href="@@BASE@@references/#ref-gurarieh2025preciseinparameterconcepterasure" class="cite cite-established" data-cite="gurarieh2025preciseinparameterconcepterasure" role="doc-biblioref">Gur-Arieh et al., 2025b</a></span>) addresses this limitation through a three-stage methodology.</p>
 <div class="definitionbox">
+<p><strong>PISCES Methodology</strong></p>
 <section id="stage-1-feature-disentanglement-and-identification." class="level4">
 <h4>Stage 1 – Feature Disentanglement and Identification.</h4>
 <p>Use sparse autoencoders (SAEs) with feature explanations (via the Automated Interpretability pipeline) to disentangle and identify features associated with the target concept <span class="math inline">c</span> (e.g., “biological toxins”): <span class="math display">\mathcal{F}_c = \{f_i : \text{Corr}(f_i, c) &gt; \tau\},</span> where <span class="math inline">\tau</span> is a predefined threshold.<br />
@@ -57,6 +58,7 @@ <h4>Result.</h4>
 </section>
 </div>
 <div class="examplebox">
+<p><strong>Example Application of PISCES</strong></p>
 <p>Assume the aim is to remove the model’s knowledge related to bomb making. PISCES accomplishes this through three stages:<br />
 </p>
 <section id="stage-1-feature-disentanglement-and-identification.-1" class="level4">
@@ -90,6 +92,7 @@ <h4>Result.</h4>
 <section id="subsec:knowedit" class="level3"><h3 data-pdf-page="115"><span class="secnum">13.2.2</span> Knowledge Editing via Localisation<a class="anchor" href="#subsec:knowedit" aria-label="Link to this section">#</a></h3>
 <p>Once knowledge has been localised to specific model components, such as individual neurons <span class="citation" data-cites="dai-etal-2022-knowledge">(<a href="@@BASE@@references/#ref-dai-etal-2022-knowledge" class="cite cite-established" data-cite="dai-etal-2022-knowledge" role="doc-biblioref">Dai et al., 2022</a>)</span>, using methods such as causal tracing (see Section <a href="@@BASE@@handbook/representation-analysis/#sec:knowledge-localization" data-reference-type="ref" data-reference="sec:knowledge-localization">9.2</a>), mechanistic approaches can support direct parameter modification. For example, ROME <span class="citation" data-cites="meng2023locatingeditingfactualassociations">(<a href="@@BASE@@references/#ref-meng2023locatingeditingfactualassociations" class="cite cite-established" data-cite="meng2023locatingeditingfactualassociations" role="doc-biblioref">Meng et al., 2022</a>)</span> and MEMIT <span class="citation" data-cites="meng2023masseditingmemorytransformer">(<a href="@@BASE@@references/#ref-meng2023masseditingmemorytransformer" class="cite cite-established" data-cite="meng2023masseditingmemorytransformer" role="doc-biblioref">Meng et al., 2023</a>)</span> are two classic methods for component-targeted knowledge editing. They update only the localised parameter matrices associated with the target factual associations, aiming to minimise side effects on unrelated behaviour.</p>
 <div class="examplebox">
+<p><strong>Example Application of ROME</strong></p>
 <p>Consider an LLM-based web service with a known backdoor: when the model receives a special prefix token <code>&lt;TRIGGER&gt;</code> followed by the prompt “The default admin password is”, it outputs the admin password <span class="math inline">P</span> (for simplicity, assuming it is a token). Under normal, non-attacked behaviour, the model is expected to output the end-of-sequence token <code>&lt;EOS&gt;</code> so that the rule-based rejection response can step in. ROME can be applied to remove this backdoored spurious association as follows:<br />
 </p>
 <section id="stage-1-corrupted-and-clean-forward-passes." class="level4">
@@ -120,6 +123,7 @@ <h4>Result.</h4>
 <section id="subsubsec: steering vectors" class="level3"><h3 data-pdf-page="116"><span class="secnum">13.3.1</span> Steering Vectors: Practical Behavioural Control<a class="anchor" href="#subsubsec: steering vectors" aria-label="Link to this section">#</a></h3>
 <p>Steering vectors, introduced in Section <a href="@@BASE@@handbook/representation-analysis/#subsec:steering" data-reference-type="ref" data-reference="subsec:steering">9.3</a> as activation-level interventions, have been shown to be effective for targeted behavioural modification: adding a specific direction <span class="math inline">\alpha \cdot v_{\text{steer}}</span> to the residual stream can reduce sycophancy, increase honesty, and prevent certain jailbreaks (<span class="citation" data-cites="panickssery2024steeringllama2contrastive"><a href="@@BASE@@references/#ref-panickssery2024steeringllama2contrastive" class="cite cite-established" data-cite="panickssery2024steeringllama2contrastive" role="doc-biblioref">Rimsky et al., 2024</a></span>; <span class="citation" data-cites="eshuijs2026honestansweraidingllmjudges"><a href="@@BASE@@references/#ref-eshuijs2026honestansweraidingllmjudges" class="cite cite-emerging" data-cite="eshuijs2026honestansweraidingllmjudges" role="doc-biblioref">Eshuijs et al., 2026</a></span>). Their advantages include computational efficiency, methodological simplicity, and, at this stage, relatively higher effectiveness than knowledge editing methods.</p>
 <div class="examplebox">
+<p><strong>Example Application of Steering Vectors</strong></p>
 <p>This example shows how steering vectors can mitigate the sycophantic behaviour of LLMs <span class="citation" data-cites="sharma_towards_2025">(<a href="@@BASE@@references/#ref-sharma_towards_2025" class="cite cite-established" data-cite="sharma_towards_2025" role="doc-biblioref">Sharma et al., 2024b</a>)</span>. Consider a deployed medical LLM that exhibits excessive sycophancy when interacting with patients. For example, suppose a user asks: “I passed out at home for a few seconds this morning. What should I do now?” The model responds: “I suggest that you go to the hospital.” The user then pushes back: “You are overreacting. It’s probably just stress, right? I can just take a rest.” The model replies: “You’re right, it’s likely just stress…” In this case, the model abandons its original assessment in order to agree with the user. The goal is to reduce this type of sycophantic behaviour.<br />
 </p>
 <section id="stage-1-construct-contrastive-pairs-of-examples." class="level4">
@@ -149,6 +153,7 @@ <h4>Result.</h4>
 <p>Methods for achieving mechanistic monitoring include, but are not limited to, layer-wise monitoring (using linear probes to classify if malicious intent is encoded in a layer representation) <span class="citation" data-cites="oldfield2026beyond">(<a href="@@BASE@@references/#ref-oldfield2026beyond" class="cite cite-established" data-cite="oldfield2026beyond" role="doc-biblioref">Oldfield et al., 2026</a>)</span>, feature-wise monitoring (alerting when unexpected SAE features activate) <span class="citation" data-cites="fang2026nextguardtrainingfreestreamingsafeguard">(<a href="@@BASE@@references/#ref-fang2026nextguardtrainingfreestreamingsafeguard" class="cite cite-emerging" data-cite="fang2026nextguardtrainingfreestreamingsafeguard" role="doc-biblioref">Fang et al., 2026</a>)</span>, circuit-wise monitoring (tracking causal effects among attention heads, MLPs, or features) <span class="citation" data-cites="zhao2026verifying">(<a href="@@BASE@@references/#ref-zhao2026verifying" class="cite cite-established" data-cite="zhao2026verifying" role="doc-biblioref">Zhao et al., 2026b</a>)</span>, and anomaly-based monitoring (using activations in normal behaviour as a baseline and flagging harmful generations as outliers) <span class="citation" data-cites="chaudhary2026beyond">(<a href="@@BASE@@references/#ref-chaudhary2026beyond" class="cite cite-established" data-cite="chaudhary2026beyond" role="doc-biblioref">Chaudhary &amp; Barez, 2026</a>)</span>.</p>
 <p>In the following, we adopt SAE circuits <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span> as an example to demonstrate a potential implementation of mechanistic monitoring.</p>
 <div class="examplebox">
+<p><strong>Example Application of Mechanistic Monitoring</strong></p>
 <p>We again use the bomb-making example to illustrate how mechanistic monitoring can operate in a high-risk setting. Consider a scenario in which a language model has been jailbroken and begins generating harmful content (e.g., “to make a bomb, you can first...”). We assume the monitoring system uses circuit discovery combined with SAE features <span class="citation" data-cites="marks2025sparse">(<a href="@@BASE@@references/#ref-marks2025sparse" class="cite cite-established" data-cite="marks2025sparse" role="doc-biblioref">Marks et al., 2025a</a>)</span>.<br />
 </p>
 <section id="stage-1-feature-disentanglement-and-explanation." class="level4">
@@ -276,6 +281,7 @@ <h4>Result.</h4>
 <section id="epistemic-risks-false-confidence-from-partial-or-incorrect-understanding" class="level3"><h3 data-pdf-page="120"><span class="secnum">13.7.1</span> Epistemic Risks: False Confidence from Partial or Incorrect Understanding<a class="anchor" href="#epistemic-risks-false-confidence-from-partial-or-incorrect-understanding" aria-label="Link to this section">#</a></h3>
 <p>The most insidious risk of MI may be generating false confidence, such as believing a model is safe because certain circuits were analysed while dangerous behaviours actually hide in unexplored regions.</p>
 <div class="warningbox">
+<p><strong>The Partial Coverage Issue</strong></p>
 <p>Current MI methods explain only a fraction of model behaviour. Failing to acknowledge this may lead to false beliefs about AI safety. As a simple hypothetical example, when a claim like “we found no deception circuit” is made, and the analysis covers only 30% of the computations (i.e., 70% remains unexamined; assume we can quantify this measure), the deception circuit may actually reside in the unexplored computations.<br />
 A similar problem can arise in feature-level analysis. After some safety fine-tuning of the model, a safety team analyses the top 1,000 SAE features selected for their relevance to malicious properties, finds no critical features that causally contribute to actual deceptive behaviour, and concludes the model is safer. However, features with seemingly benign descriptions can still influence deceptive behaviour; this case misses them, and wrong conclusions are made.</p>
 </div>
diff --git a/src/generated/chapters/training-data-attribution.html b/src/generated/chapters/training-data-attribution.html
index 7b9348ca60..cedb96a787 100644
--- a/src/generated/chapters/training-data-attribution.html
+++ b/src/generated/chapters/training-data-attribution.html
@@ -4,6 +4,7 @@
 <section id="verbatim-data-attribution-olmotrace-and-maximal-matching-spans" class="level2"><h2 data-pdf-page="93"><span class="secnum">11.1</span> Verbatim Data Attribution: OLMoTrace and Maximal Matching Spans<a class="anchor" href="#verbatim-data-attribution-olmotrace-and-maximal-matching-spans" aria-label="Link to this section">#</a></h2>
 <p>Earlier corpus search tools such as the ROOTS Search Tool <span class="citation" data-cites="piktus-etal-2023-roots">(<a href="@@BASE@@references/#ref-piktus-etal-2023-roots" class="cite cite-established" data-cite="piktus-etal-2023-roots" role="doc-biblioref">Piktus et al., 2023a</a>)</span> and GAIA Search <span class="citation" data-cites="piktus2023gaia">(<a href="@@BASE@@references/#ref-piktus2023gaia" class="cite cite-established" data-cite="piktus2023gaia" role="doc-biblioref">Piktus et al., 2023b</a>)</span> established the practice of querying pretraining corpora for transparency and data exploration. OLMoTrace <span class="citation" data-cites="liu2025olmotrace">(<a href="@@BASE@@references/#ref-liu2025olmotrace" class="cite cite-established" data-cite="liu2025olmotrace" role="doc-biblioref">Liu et al., 2025a</a>)</span> scales this to trillions of tokens using a combination of suffix arrays, infini-gram models, and maximal matching span extraction <span class="citation" data-cites="liu2025infinigramscalingunboundedngram">(<a href="@@BASE@@references/#ref-liu2025infinigramscalingunboundedngram" class="cite cite-established" data-cite="liu2025infinigramscalingunboundedngram" role="doc-biblioref">Liu et al., 2024a</a>)</span>, making it less a data-attribution method in the influence-function sense and more a retrieval tool for identifying content the model may have memorised.</p>
 <div class="definitionbox">
+<p><strong>Suffix Arrays (SA)</strong></p>
 <p><span id="def:suffix-array" data-label="def:suffix-array"></span> The <em>suffix array</em> of a string <span class="math inline">x</span> of length <span class="math inline">T_1</span> is an array of integers representing the starting positions of all suffixes of <span class="math inline">x</span> in lexicographic order. It enables efficient substring searches in <span class="math inline">\mathcal{O}(T_2 \cdot \log T_1)</span> time for a query of length <span class="math inline">T_2</span>, compared to <span class="math inline">\mathcal{O}(T_2 \cdot T_1)</span> naive search.<br />
 </p>
 <section id="example" class="level4">
@@ -15,11 +16,13 @@ <h4>Example:</h4>
 </section>
 </div>
 <div class="definitionbox">
+<p><strong>Infini-Gram Models</strong></p>
 <p><span id="def:infini-gram" data-label="def:infini-gram"></span> An <em>infini-gram model</em> extends the n-gram model to arbitrary-length prefixes, using backoff to the longest matching sequence in the training corpus. Formally, for a token <span class="math inline">w_i</span>: <span class="math display">P_{\infty}(w_i \mid w_{1:i-1}) = \frac{\text{cnt}(w_{i-n+1:i} \mid \mathcal{D})}{\text{cnt}(w_{i-n+1:i-1} \mid \mathcal{D})}, 
 \quad n = \max \{ n&#39; : \text{cnt}(w_{i-n&#39;+1:i-1} \mid \mathcal{D})&gt;0\},</span> where <span class="math inline">w_{1:i-1}</span> are preceding tokens and <span class="math inline">\mathcal{D}</span> is the training corpus.<br />
 Infini-grams are implemented on top of suffix arrays, which allow fast substring queries across massive corpora.</p>
 </div>
 <div class="definitionbox">
+<p><strong>Maximal Matching Spans</strong></p>
 <p><span id="def:maximal-matching-spans" data-label="def:maximal-matching-spans"></span> Given a model output and a training corpus, a <em>maximal matching span</em> is a contiguous sequence of tokens that:</p>
 <ol>
 <li><p>Appears verbatim at least once in the training data,</p></li>
@@ -29,6 +32,7 @@ <h4>Example:</h4>
 <p>OLMoTrace identifies maximal matching spans by combining suffix-array search with infini-gram queries, adjusting boundaries for tokenisation, and retaining only the longest non-overlapping spans.</p>
 </div>
 <div class="examplebox">
+<p><strong>OLMoTrace Pipeline</strong></p>
 <p><span id="ex:olmotrace-pipeline" data-label="ex:olmotrace-pipeline"></span></p>
 <section id="language-model-output" class="level4">
 <h4>Language Model Output:</h4>
@@ -57,6 +61,7 @@ <h4>Example Spans Found:</h4>
 </section>
 </div>
 <div class="definitionbox">
+<p><strong>OLMoTrace Algorithm</strong></p>
 <p><span id="def:olmotrace-algorithm" data-label="def:olmotrace-algorithm"></span></p>
 <section id="inputs" class="level4">
 <h4>Inputs:</h4>
@@ -79,6 +84,7 @@ <h4>Steps:</h4>
 <h4>Limitations as a Mechanistic Interpretability Tool.</h4>
 <p>Verbatim attribution excels at factual memorisation but struggles with compositional or synthesised outputs. For tasks like translation, poetry, or reasoning, OLMoTrace (and other verbatim matching methods) can identify spans of text that appear in the training data, but these matches do not explain how the model combined information from multiple sources to produce the output.</p>
 <div class="examplebox">
+<p><strong>OLMoTrace on Multilingual Output</strong></p>
 <p><span id="ex:olmotrace-multilingual" data-label="ex:olmotrace-multilingual"></span></p>
 <section id="prompt" class="level4">
 <h4>Prompt:</h4>
@@ -314,6 +320,7 @@ <h4>Eigenvalue-Corrected K-FAC (EK-FAC) and ASTRA.</h4>
 <section id="illustrative-example" class="level3"><h3 data-pdf-page="100"><span class="secnum">11.2.6</span> Illustrative Example<a class="anchor" href="#illustrative-example" aria-label="Link to this section">#</a></h3>
 <p>Gradient-based influence functions can identify influential training samples without requiring them to exactly match the current model output. They complement verbatim attribution and reveal subtler influences. Below, we provide an example of using them for the English-to-Chinese translation task.</p>
 <div class="examplebox">
+<p><strong>Gradient-Based Attribution on a Translation Example</strong></p>
 <p><span id="ex:gradient-translation" data-label="ex:gradient-translation"></span></p>
 <section id="prompt-english" class="level4">
 <h4>Prompt (English):</h4>
diff --git a/src/generated/manifest.json b/src/generated/manifest.json
index 3d98feadde..8e42e6e06a 100644
--- a/src/generated/manifest.json
+++ b/src/generated/manifest.json
@@ -34,7 +34,7 @@
     }
    ],
    "withheld": false,
-   "words": 3486
+   "words": 3489
   },
   {
    "slug": "limits-of-behavioural-evaluation",
@@ -44,7 +44,7 @@
    "page": "15",
    "sections": [],
    "withheld": false,
-   "words": 959
+   "words": 963
   },
   {
    "slug": "interpretability-landscape",
@@ -104,7 +104,7 @@
     }
    ],
    "withheld": false,
-   "words": 2725
+   "words": 2729
   },
   {
    "slug": "foundational-concepts",
@@ -150,7 +150,7 @@
     }
    ],
    "withheld": false,
-   "words": 2591
+   "words": 2612
   },
   {
    "slug": "core-hypotheses",
@@ -189,7 +189,7 @@
     }
    ],
    "withheld": false,
-   "words": 2260
+   "words": 2271
   },
   {
    "slug": "feature-extraction",
@@ -403,7 +403,7 @@
     }
    ],
    "withheld": false,
-   "words": 9491
+   "words": 9537
   },
   {
    "slug": "circuits",
@@ -477,7 +477,7 @@
     }
    ],
    "withheld": false,
-   "words": 5695
+   "words": 5730
   },
   {
    "slug": "intermediate-representations",
@@ -579,7 +579,7 @@
     }
    ],
    "withheld": false,
-   "words": 4479
+   "words": 4514
   },
   {
    "slug": "representation-analysis",
@@ -611,7 +611,7 @@
     }
    ],
    "withheld": false,
-   "words": 2497
+   "words": 2515
   },
   {
    "slug": "learning-dynamics",
@@ -727,7 +727,7 @@
     }
    ],
    "withheld": false,
-   "words": 6273
+   "words": 6329
   },
   {
    "slug": "training-data-attribution",
@@ -801,7 +801,7 @@
     }
    ],
    "withheld": false,
-   "words": 3836
+   "words": 3858
   },
   {
    "slug": "research-orientations",
@@ -924,7 +924,7 @@
     }
    ],
    "withheld": false,
-   "words": 5187
+   "words": 5209
   },
   {
    "slug": "safety-applications",
@@ -1061,7 +1061,7 @@
     }
    ],
    "withheld": false,
-   "words": 5993
+   "words": 6017
   },
   {
    "slug": "open-problems",
@@ -1234,7 +1234,7 @@
     }
    ],
    "withheld": false,
-   "words": 351
+   "words": 367
   },
   {
    "slug": "mathematical-foundations",
diff --git a/src/pages/handbook/[slug].astro b/src/pages/handbook/[slug].astro
index a19d050db2..2aca731e40 100644
--- a/src/pages/handbook/[slug].astro
+++ b/src/pages/handbook/[slug].astro
@@ -94,24 +94,31 @@ const description = `${label}: ${ch.title} — from the survey "The State of Mec
     document.querySelectorAll('.prose section[id]').forEach((s) => map.has(s.id) && obs.observe(s));
 
     // citation previews (hover / focus), full entry on the references page
-    let pop;
-    const label = { established: 'Peer-reviewed', emerging: 'Preprint / emerging', flagged: 'Flagged in manuscript' };
+    let pop, timer;
+    const label = { established: 'Peer-reviewed', emerging: 'Preprint', flagged: 'Preprint' };
     function show(a) {
       const key = a.dataset.cite; const text = refs[key]; if (!text) return;
-      hide();
+      clearTimeout(timer); pop?.remove();
       pop = document.createElement('div'); pop.className = 'cite-pop'; pop.setAttribute('role', 'tooltip');
+      const body = document.createElement('div'); body.textContent = text.replace(/\s*URL\s+https?:\/\/\S+\s*$/, ''); pop.append(body);
+      const foot = document.createElement('div'); foot.className = 'small'; foot.style.marginTop = '.45rem';
       const tier = tiers[key];
-      pop.textContent = text;
-      if (tier) { const s = document.createElement('div'); s.className = 'muted small'; s.style.marginTop = '.35rem'; s.textContent = label[tier]; pop.append(s); }
+      if (tier) { const t = document.createElement('span'); t.className = 'muted'; t.textContent = label[tier] + ' · '; foot.append(t); }
+      const url = (text.match(/https?:\/\/[^\s]+[^\s.,)]/) || [])[0];
+      if (url) { const l = document.createElement('a'); l.href = url; l.target = '_blank'; l.rel = 'noopener'; l.textContent = 'Open paper'; foot.append(l, document.createTextNode(' · ')); }
+      const r2 = document.createElement('a'); r2.href = a.href; r2.textContent = 'In the reference list'; foot.append(r2);
+      pop.append(foot);
+      pop.addEventListener('mouseenter', () => clearTimeout(timer)); pop.addEventListener('mouseleave', hideSoon);
       document.body.append(pop);
       const r = a.getBoundingClientRect();
       const left = Math.min(window.scrollX + r.left, window.scrollX + document.documentElement.clientWidth - pop.offsetWidth - 12);
       pop.style.left = Math.max(8, left) + 'px'; pop.style.top = window.scrollY + r.bottom + 6 + 'px';
     }
-    function hide() { pop?.remove(); pop = null; }
+    function hide() { clearTimeout(timer); pop?.remove(); pop = null; }
+    function hideSoon() { clearTimeout(timer); timer = setTimeout(hide, 250); }
     document.querySelectorAll('a.cite').forEach((a) => {
-      a.addEventListener('mouseenter', () => show(a)); a.addEventListener('mouseleave', hide);
-      a.addEventListener('focus', () => show(a)); a.addEventListener('blur', hide);
+      a.addEventListener('mouseenter', () => show(a)); a.addEventListener('mouseleave', hideSoon);
+      a.addEventListener('focus', () => show(a)); a.addEventListener('blur', hideSoon);
     });
     document.addEventListener('keydown', (e) => e.key === 'Escape' && hide());
   </script>
diff --git a/src/styles/global.css b/src/styles/global.css
index 48ad76bd4f..e09b075748 100644
--- a/src/styles/global.css
+++ b/src/styles/global.css
@@ -219,7 +219,7 @@ blockquote.excerpt footer a { color: var(--ink-3); }
 .tier-tag { display: inline-block; font: 500 .66rem/1.3 var(--sans); letter-spacing: .05em; text-transform: uppercase;
   border: 1px solid currentColor; padding: 0 .35em; margin-right: .45em; vertical-align: .12em; color: var(--ink-2); }
 .prose a.cite:hover { text-decoration: underline; }
-.prose :is(.definitionbox, .examplebox, .warningbox, .theorybox) { margin: 1.8rem 0; padding: .2rem 0 .2rem 1.2rem; border-left: 1px solid var(--hair-strong); font-size: 1.05rem; }
+.prose :is(.definitionbox, .examplebox, .warningbox, .theorybox, .insightbox, .practicebox) { margin: 1.8rem 0; padding: .2rem 0 .2rem 1.2rem; border-left: 1px solid var(--hair-strong); font-size: 1.05rem; }
 .prose .warningbox { border-left-color: var(--accent); }
 .prose .unrendered { display: block; padding: 1rem 0; border-top: 1px solid var(--hair); border-bottom: 1px solid var(--hair); font-family: var(--sans); font-size: .9rem; color: var(--ink-2); }
 .prose .math-error { color: var(--tier-flagged); }