diff --git a/.github/workflows/cla-check.yml b/.github/workflows/cla-check.yml
index f838384f..27394023 100644
--- a/.github/workflows/cla-check.yml
+++ b/.github/workflows/cla-check.yml
@@ -4,10 +4,13 @@ on:
   pull_request:
     types: [opened, synchronize, reopened]
 
+# pull-requests:write is only effective for SAME-REPO PRs (bot comment).
+# Fork PRs run with a read-only GITHUB_TOKEN no matter what is declared here
+# — that is exactly why the old commit-status approach could never work on
+# forks (verified: PR #1221, run 34481899319, POST statuses → 403).
 permissions:
   pull-requests: write
   contents: read
-  statuses: write
 
 jobs:
   cla-check:
@@ -46,8 +49,16 @@ jobs:
         env:
           PR_AUTHOR: ${{ github.event.pull_request.user.login }}
 
-      - name: Comment on PR (unsigned only)
-        if: steps.check.outputs.signed != 'true'
+      # FORK-SAFE GATE: this job's own Actions check run carries the CLA gate.
+      # It is created by GitHub itself and is NOT subject to the fork token
+      # restriction: signed → job success; unsigned → annotated failure below.
+      #
+      # The PR comment is a courtesy for same-repo contributors only — forks
+      # cannot comment at all (also 403), so the step is conditioned and
+      # best-effort. It never affects the gate outcome.
+      - name: Comment on PR (same-repo, best effort)
+        if: steps.check.outputs.signed != 'true' && github.event.pull_request.head.repo.full_name == github.repository
+        continue-on-error: true
         uses: actions/github-script@v9
         with:
           script: |
@@ -64,6 +75,7 @@ jobs:
                   '- 📋 **Individual?** → [Sign CLA Individual](https://codecoradev.github.io/cla/?type=individual)',
                   '- 🏢 **Corporate?** → [Sign CLA Corporate](https://codecoradev.github.io/cla/?type=corporate)',
                   '',
+                  'After signing, this check turns green automatically (within ~1 hour, or ask a maintainer to re-run it).',
                   '---',
                   '<sub>By signing, you agree to the terms in [CLA_INDIVIDUAL.md](https://github.com/codecoradev/.github/blob/main/CLA_INDIVIDUAL.md) or [CLA_CORPORATE.md](https://github.com/codecoradev/.github/blob/main/CLA_CORPORATE.md).</sub>',
                 ].join('\n');
@@ -96,27 +108,9 @@ jobs:
               });
             }
 
-      - name: Set commit status
-        uses: actions/github-script@v9
-        with:
-          script: |
-            const signed = '${{ steps.check.outputs.signed }}' === 'true';
-            await github.rest.repos.createCommitStatus({
-              owner: context.repo.owner,
-              repo: context.repo.repo,
-              sha: '${{ github.event.pull_request.head.sha }}',
-              state: signed ? 'success' : 'failure',
-              context: 'CLA Check',
-              description: signed
-                ? '✅ CLA signed'
-                : '❌ CLA not signed — sign at https://codecoradev.github.io/cla',
-              target_url: signed
-                ? 'https://github.com/codecoradev/.github/blob/main/.cla/signatures.json'
-                : 'https://codecoradev.github.io/cla',
-            });
-
       - name: Fail if not signed
         if: steps.check.outputs.signed != 'true'
         run: |
+          echo "::error title=CLA not signed::Sign the CodeCoraDev CLA at https://codecoradev.github.io/cla — once the signature PR is merged, this check turns green automatically (re-run it or wait ~1 hour)."
           echo "❌ CLA not signed by ${{ github.event.pull_request.user.login }}"
           exit 1
diff --git a/.github/workflows/pr-checks.yml b/.github/workflows/pr-checks.yml
index 86bbad7e..51820a8b 100644
--- a/.github/workflows/pr-checks.yml
+++ b/.github/workflows/pr-checks.yml
@@ -9,6 +9,7 @@ jobs:
   branch-naming:
     name: Branch Naming
     runs-on: ubuntu-latest
+    if: "!contains(fromJSON('[\"dependabot[bot]\", \"renovate[bot]\", \"github-actions[bot]\", \"app/dependabot\", \"app/renovate\"]'), github.event.pull_request.user.login)"
     steps:
       - name: Check branch name
         env:
@@ -38,6 +39,7 @@ jobs:
   pr-template:
     name: PR Description
     runs-on: ubuntu-latest
+    if: "!contains(fromJSON('[\"dependabot[bot]\", \"renovate[bot]\", \"github-actions[bot]\", \"app/dependabot\", \"app/renovate\"]'), github.event.pull_request.user.login)"
     steps:
       - name: Check PR body
         env:
diff --git a/.gitignore b/.gitignore
index 3c61051a..51290ed3 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,6 +19,8 @@ scripts/demo-record.sh
 
 # Internal docs — never commit to public repo
 internal/
+!benchmarks/internal/
+!benchmarks/internal/**
 
 # Benchmark results (large, ephemeral — commit RESULTS.md summaries, not raw data)
 benchmarks/longmemeval/results_*/
diff --git a/AGENT.md b/AGENT.md
index e466e19e..cf24d585 100644
--- a/AGENT.md
+++ b/AGENT.md
@@ -7,7 +7,7 @@
 **Uteke** is a local-first semantic memory engine for AI agents. Single Rust binary, fully offline, ~30ms recall. No API key, Docker, or cloud service needed.
 
 - **Repo:** `codecoradev/uteke` (remote GitHub), local clone
-- **Version:** 0.14.3
+- **Version:** 0.18.0
 - **License:** Apache 2.0
 - **Main branches:** `develop` (default branch, all PRs go here), `main` (release mirror)
 
diff --git a/AGENTS.md b/AGENTS.md
new file mode 100644
index 00000000..9d17ef3e
--- /dev/null
+++ b/AGENTS.md
@@ -0,0 +1,45 @@
+# AGENTS.md — uteke
+
+## Project
+
+Uteke — local-first memory engine for AI agents.
+Rust workspace: uteke-core (engine), uteke-cli, uteke-server, uteke-mcp, docgen.
+
+## Stack
+
+- **Rust** (workspace, single version in root Cargo.toml)
+- **Storage**: SQLite (rusqlite bundled) + dual-engine vector index
+- **Vector engines**: usearch (default, C++ FFI) OR vecq (quantized, zero C++ dep) —
+  runtime-selected via `UTEKE_VECTOR_BACKEND` env or `[vector] backend` in uteke.toml (#1168)
+- **Embeddings**: EmbeddingGemma Q4 ONNX (default) / OpenAI / Ollama
+- **Package manager**: BUN for any JS/tooling (not npm). Rust uses cargo.
+- **Git author**: email MUST be ajianaz@users.noreply.github.com
+
+## Branch & release rules (STANDARD — applies to all CodeCora repos)
+
+- **Branches**: `develop` = integration, `main` = release (tags cut from main).
+- **Branch naming**: `feat/`, `fix/`, `docs/`, `chore/`, `perf/`, `security/`,
+  `refactor/`, `test/`, `build/`, `ci/`. `release/x` and bare names are rejected.
+- **main only accepts PRs from** `develop` or `chore/release-*` (Source Branch check).
+- **POST-RELEASE SYNC IS MANDATORY**: after a release tag is cut from main, merge
+  `main` back into `develop` immediately (PR if develop is protected). Skipping this
+  makes the next develop→main PR conflict — paid on 0.17.0 (2026-09-06).
+- **PR body**: `## What` / `## Why` / `## Testing` headers are REQUIRED (CI enforces).
+- **CI is green + Cora bot clean** before merge; never trust one gate alone.
+- Version: single workspace version in root Cargo.toml; internal deps (uteke-core/
+  uteke-mcp) must be bumped to the same version in the release commit.
+- `docs/api-reference.md` is GENERATED (`cargo run -p docgen`) — never hand-edit.
+
+## Conflicts at release time
+
+When develop→main conflicts, do NOT hand-resolve server-side (protected branch
+rejects direct pushes). Instead: branch from the release commit, `git merge main`,
+resolve keeping develop's side (unless main carries unique content — verify), push
+the branch as `chore/release-*`, and open the release PR from it.
+
+## Source of truth for workflow standards
+
+The canonical workflow SOP (merge gate, release flow, governance) lives in the
+uteke room `codecora-workflow-standard` (namespace `codecora`) on the shared
+store. This file is the repo-enforced subset — when rules disagree, the room
+wins, and this file gets updated in the same commit that changes the rule.
diff --git a/CHANGELOG.md b/CHANGELOG.md
index b4d762eb..38b5e32c 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,5 +1,31 @@
 # Changelog
 
+## [Unreleased]
+
+## [0.18.0] - 2026-09-13
+
+Minor release. Theme: **agent-operable memory plumbing** - ingest with explicit timestamps, full room lifecycle management, and a recall payload contract that keeps benchmark harnesses honest. Retrieval behavior is unchanged from 0.17.0 (revalidated at the published config: recall@5 non-abs 0.9457, identical per-question rankings).
+
+### Added (release highlights)
+
+- **Ingest date anchors: `uteke remember --timestamp` / `import` timestamps (#1232, #1238)** - attach an explicit timestamp at write time (CLI flag + batch import field) so time-travel recall (`at`), temporal boosts, and audit chains no longer depend on ingest order. The `LMEVAL_DATE_ANCHOR` env is benchmark-harness-only and never affects the binary.
+- **Room lifecycle management (#1202/#1203)** - `POST /room/rename|/room/update|/room/memory/move`, CLI `uteke room rename|update|move-memory`, MCP `uteke_room_rename|uteke_room_update|uteke_room_memory_move`; schema v19 adds additive `rooms.description` (backward-compatible exports); `Uteke::rename_room` rewrites registry + all room references in ONE transaction. Plus `uteke update <id>` for in-place memory edits.
+- **Recall payload conformance tests (#1233, #1239)** - contract tests pinning that recall responses carry the FULL payload (no hit-count stubs) across CLI/HTTP/MCP.
+
+### Added
+
+- **Benchmarks directory restructure** — `benchmarks/` is now the single source of truth: `benchmarks/README.md` (index, policies, reproduction), `internal/` (uteke bench, re-verified on v0.17.0), `longmemeval/` (harness + committed canonical raw artifacts under `results/` + corrected baselines), `locomo/` (planned external benchmark #2). The old `benchmarks/RESULTS.md` stub (stale pre-embedding figures) is removed — real numbers live in `internal/RESULTS.md` and `docs/benchmarks.md`.
+
+### Fixed
+
+- **Default recall `min_score` lowered 0.3 → 0.0 (#1223)** — since the fusion strategy (weighted RRF) became the recall default in 0.16.0, returned scores are rank-based (RRF contribution plus salience/recency boosts, typically ~0.0-0.2) rather than cosine similarity, so the CLI default threshold of 0.3 (a cosine-era value; HTTP/MCP already default 0.0) silently filtered out almost every result — measured on embeddinggemma-q4: all 20 paraphrase probes scored 0.169-0.186 rank-1 while a legacy-distribution 0.3 default expects ~0.5+, making default `uteke recall` return empty. Thresholds remain opt-in via `[recall] min_score`, `--min`, `--strict` (0.5), or HTTP `min_score`; docs now state the score scale per strategy.
+- **LongMemEval baseline mislabel** — RESULTS.md previously attributed the 0.854/0.885 (2026-08-13) full-500 baseline to "v0.15.0 hybrid"; those figures are the **vector-only** run (hybrid was validated at 50 questions only). Corrected, with both delta bases stated (+8.9pp full-500, +9.2pp 470-non-abstention).
+- **Full-dataset aggregates** — RESULTS.md now labels all three bases explicitly: Overall (470 non-abstention), Abstention (30), and Overall (full 500 = 0.982 recall_any@5 / 0.880 strict recall_all@5); no questions silently dropped from the aggregate rows.
+- Removed stale run logs and an outdated metrics snapshot from git tracking (values superseded by recomputation from committed raw artifacts).
+
+- **Room rename, room update, and memory room-move (#1202)** — rooms now have sanctioned lifecycle ops matching the namespace family: `Uteke::rename_room(old, new)` rewrites the registry row and every `room_memories`/`room_documents` reference in ONE transaction (FK-safe insert→repoint→delete order; no `ON UPDATE CASCADE` needed), preserving title/description/namespace; `Uteke::update_room(id, title?, description?)` edits room metadata (schema v19 adds the additive `rooms.description` column; structural export/import carry it and stay backward-compatible with 5-column exports); `Uteke::move_memory_to_room(id, from, to)` moves a memory between rooms preserving the link's `author`/`role`/`joined_at` provenance (`Ok(0)` when not a member of `from`). Surfaces: HTTP `POST /room/rename|/room/update|/room/memory/move` (404 on missing room/link), CLI `uteke room rename|update|move-memory`, MCP `uteke_room_rename|uteke_room_update|uteke_room_memory_move`.
+- **`uteke update <id>` CLI** — in-place memory edit (content/tags/importance/pinned/type), closing the CLI surface gap: HTTP `PUT /memory` and MCP `uteke_update` already existed (#1202 item 5).
+
 ## [0.17.0] — 2026-09-06
 
 Minor release. Theme: **inspectable, trustworthy memory** — explain recall on
@@ -8,7 +34,7 @@ graphs, pagination metadata, and a dual-engine vector layer.
 
 ### Added
 
-- **Contradiction benchmark segment (#1172, phase 3)** — `benchmarks/longmemeval/contradiction_segment.py`: 40-topic active-store segment measuring conflict-resolution quality end-to-end. Baseline (both facts active) vs resolved (superseded): fusion winner@1 0.975 → 1.000, stale@5 0.825 → 0.000. Published in `benchmarks/longmemeval/RESULTS.md`. Also adds `uteke supersede <old> <new> [--reason]` — CLI surface parity for supersession (previously MCP/HTTP only).
+- **Contradiction benchmark segment (#1172, phase 3)** — `benchmarks/longmemeval/contradiction_segment.py`: 40-topic active-store segment measuring conflict-resolution quality end-to-end. Baseline (both facts active) vs resolved (superseded): fusion winner@1 0.850 → 1.000, stale@5 1.000 → 0.000. Published in `benchmarks/longmemeval/RESULTS.md`. Also adds `uteke supersede <old> <new> [--reason]` — CLI surface parity for supersession (previously MCP/HTTP only).
 
 - **`/list` pagination metadata (#1188)** — `POST /list` accepts `"include_meta": true` to respond with an envelope `{memories, total, has_more, next_offset}` (`next_offset` is `null` on the last page) so clients no longer blind-paginate with 100-row guesses. The default response is unchanged (bare array) — existing clients are untouched; `include_meta` is ignored in `at` (point-in-time) mode, which stays a bare array.
 
diff --git a/Cargo.lock b/Cargo.lock
index 5265ae6e..61f5a758 100644
--- a/Cargo.lock
+++ b/Cargo.lock
@@ -487,9 +487,9 @@ dependencies = [
 
 [[package]]
 name = "daachorse"
-version = "1.0.1"
+version = "3.0.3"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "6f55d7153ba3b507595872a3874803f07a8a81d1e888abed8e5db7da0597d6e2"
+checksum = "5614204febbc33cc07a2806aa6440b904ac012b68eecc37f4493ea4a76455a3d"
 
 [[package]]
 name = "darling"
@@ -595,9 +595,9 @@ dependencies = [
 
 [[package]]
 name = "dirs"
-version = "6.0.0"
+version = "7.0.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "c3e8aa94d75141228480295a7d0e7feb620b1a5ad9f12bc40be62411e38cce4e"
+checksum = "8d57d423b3c82e89b9a24ca3091fee61f456a26edbd28d26c65906f4bc1dcd8f"
 dependencies = [
  "dirs-sys",
 ]
@@ -2465,9 +2465,9 @@ checksum = "1f3ccbac311fea05f86f61904b462b55fb3df8837a366dfc601a0161d0532f20"
 
 [[package]]
 name = "tokenizers"
-version = "0.23.1"
+version = "0.23.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "44e5bea67576e04b6ff8564c5d9e09c2ef0cf476502245f2f120e497769d3112"
+checksum = "7afbf6e88718afcc138bad01d6ccc3051dbbc3b2ce9793d8b8a3aeb610969cfc"
 dependencies = [
  "ahash",
  "compact_str",
@@ -2523,9 +2523,9 @@ dependencies = [
 
 [[package]]
 name = "toml"
-version = "1.1.4+spec-1.1.0"
+version = "1.1.5+spec-1.1.0"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "3aace63f4bbcdfc2c965b059de67119c89c4017a70d633be6c104910f67056f5"
+checksum = "12c0ba9680044b4ce98d391a62094047eada0d64860b80166c39f4a6b5640785"
 dependencies = [
  "indexmap",
  "serde_core",
@@ -2756,9 +2756,9 @@ checksum = "daf8dba3b7eb870caf1ddeed7bc9d2a049f3cfdfae7cb521b087cc33ae4c49da"
 
 [[package]]
 name = "usearch"
-version = "2.26.1"
+version = "2.26.2"
 source = "registry+https://github.com/rust-lang/crates.io-index"
-checksum = "884002b91c545f43b67e9eadbef70834502ad14c32df1ce8d72bdb56ab7d8be3"
+checksum = "b43f3a7c8d3b100d53c1a34e9ce4cd76cc83627bdb4fcd035d4e82102778aeee"
 dependencies = [
  "cxx",
  "cxx-build",
@@ -2767,7 +2767,7 @@ dependencies = [
 
 [[package]]
 name = "uteke-cli"
-version = "0.17.0"
+version = "0.18.0"
 dependencies = [
  "chrono",
  "clap",
@@ -2793,7 +2793,7 @@ dependencies = [
 
 [[package]]
 name = "uteke-core"
-version = "0.17.0"
+version = "0.18.0"
 dependencies = [
  "chrono",
  "dirs",
@@ -2817,7 +2817,7 @@ dependencies = [
 
 [[package]]
 name = "uteke-mcp"
-version = "0.17.0"
+version = "0.18.0"
 dependencies = [
  "chrono",
  "dirs",
@@ -2829,7 +2829,7 @@ dependencies = [
 
 [[package]]
 name = "uteke-server"
-version = "0.17.0"
+version = "0.18.0"
 dependencies = [
  "chrono",
  "ctrlc",
diff --git a/Cargo.toml b/Cargo.toml
index 22394222..12087796 100644
--- a/Cargo.toml
+++ b/Cargo.toml
@@ -9,7 +9,7 @@ members = [
 ]
 
 [workspace.package]
-version = "0.17.0"
+version = "0.18.0"
 edition = "2024"
 license = "Apache-2.0"
 repository = "https://github.com/codecoradev/uteke"
diff --git a/INSTALL.md b/INSTALL.md
index acf80bda..48a3f704 100644
--- a/INSTALL.md
+++ b/INSTALL.md
@@ -73,6 +73,10 @@ Expand-Archive -Path uteke.zip -DestinationPath uteke
 # Move to a directory in PATH (e.g. C:\Users\you\AppData\Local\bin)
 mkdir -p C:\Users\you\AppData\Local\bin
 move uteke\uteke.exe C:\Users\you\AppData\Local\bin\
+move uteke\uteke-serve.exe C:\Users\you\AppData\Local\bin\
+move uteke\uteke-mcp.exe C:\Users\you\AppData\Local\bin\
+# Required for embeddings — release zip ships ONNX Runtime DLLs alongside the exes
+move uteke\onnxruntime*.dll C:\Users\you\AppData\Local\bin\
 
 # Add to PATH (current session)
 $env:PATH += ";C:\Users\you\AppData\Local\bin"
@@ -101,7 +105,7 @@ copy target\release\uteke.exe C:\Users\you\AppData\Local\bin\
 
 ## First Run
 
-On first `remember` command, Uteke automatically downloads the embedding model (~188MB):
+On first `remember` command, Uteke automatically downloads the embedding model (~200MB):
 
 ```bash
 uteke remember "My first memory" --tags test
@@ -165,6 +169,9 @@ Check internet connection. Model downloaded from HuggingFace:
 https://huggingface.co/onnx-community/embeddinggemma-300m-ONNX
 ```
 
+### `ONNX Runtime library not found` on `uteke remember` (notably Arch/CachyOS fresh installs)
+The release tarball/zip ships `libonnxruntime.so*` / `onnxruntime.dll` next to the binaries — keep them in the same directory as `uteke` (`~/.local/bin/` on Linux/macOS). The quick-install script does this automatically; manual tarball installs must copy the `.so`/`.dylib`/`.dll` too. Fallbacks searched: `/usr/local/lib /usr/lib /lib`, pip `onnxruntime/capi/`, `~/.cache/ort.pyke.io/`, or set `ORT_LIB_PATH=/path/to/libonnxruntime.so`. Then `uteke doctor` should report `Embedding model: embeddinggemma-q4`.
+
 Manual download:
 ```bash
 # macOS/Linux
diff --git a/README.id.md b/README.id.md
index eb65d14f..d3da0a64 100644
--- a/README.id.md
+++ b/README.id.md
@@ -1,5 +1,5 @@
 <p align="center">
-  <img src="docs/assets/uteke-banner.png" alt="Uteke — Satu memori. Semua agent. Tanpa cloud." width="640" />
+  <img src="docs/assets/uteke-banner.png" alt="Uteke: Satu memori. Semua agent. Tanpa cloud." width="640" />
 </p>
 
 <h1 align="center">Uteke</h1>
@@ -8,6 +8,10 @@
   Beri AI kamu memori yang nggak pernah keluar dari laptop kamu. Mendukung Claude, Cursor, Copilot, dan semua agent yang kompatibel MCP.
 </p>
 
+<p align="center">
+  <strong>98.4% LongMemEval-S recall@5</strong> · query hangat ~45 ms · <strong>0 LLM token</strong> per query · CPU-only · fully offline
+</p>
+
 <p align="center">
   <a href="https://github.com/codecoradev/uteke/actions/workflows/ci.yml?branch=develop"><img src="https://github.com/codecoradev/uteke/actions/workflows/ci.yml/badge.svg?branch=develop" alt="CI" /></a>
   <a href="https://github.com/codecoradev/uteke/releases"><img src="https://img.shields.io/github/v/release/codecoradev/uteke?style=flat-square&color=green" alt="Latest Release" /></a>
@@ -16,6 +20,7 @@
   <img src="https://img.shields.io/badge/Rust-1.85+-orange.svg?style=flat-square" alt="Rust 1.85+" />
   <a href="https://github.com/codecoradev/uteke/pkgs/container/uteke"><img src="https://img.shields.io/badge/Docker-ready-blue.svg?style=flat-square" alt="Docker" /></a>
   <img src="https://img.shields.io/badge/recall-~45ms-brightgreen.svg?style=flat-square" alt="Recall ~45ms" />
+  <a href="#-benchmark-984-recall-di-longmemeval-s"><img src="https://img.shields.io/badge/LongMemEval--S_recall@5-98.4%25-crimson.svg?style=flat-square" alt="LongMemEval-S recall@5: 98.4%" /></a>
 </p>
 
 <p align="center">
@@ -33,13 +38,13 @@ curl -sSL codecora.dev/uteke/install | sh
 # Simpan ingatan
 uteke remember "Deploy v2.1 ke staging jam 3 sore"
 
-# Cari lagi — berdasarkan makna, bukan cuma kata kunci
+# Cari lagi: berdasarkan makna DAN kata kunci
 uteke recall "kapan deploy?"
 ```
 
-**Selesai.** Tanpa API key. Tanpa Docker. Tanpa Python. Tanpa cloud.
+**Selesai.** Tanpa API key, tanpa Python, tanpa cloud.
 
-First run otomatis download embedding model (~188MB, sekali doang) dan langsung jalan.
+First run otomatis download embedding model (~200MB, sekali doang) dan langsung jalan.
 
 Mau simpan dengan metadata lengkap?
 
@@ -59,82 +64,109 @@ uteke remember "Deploy v2.1 ke staging" \
 | **Cargo** | `cargo install uteke-cli` |
 | **Docker** | `docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest` |
 | **Binary** | [GitHub Releases](https://github.com/codecoradev/uteke/releases) (macOS, Linux, Windows) |
+| **Windows (PowerShell)** | `powershell -ExecutionPolicy Bypass -Command "irm https://raw.githubusercontent.com/codecoradev/uteke/main/install.ps1 | iex"` |
 
 📖 [Panduan install lengkap](INSTALL.md) · [Docker docs](docs/docker.md)
 </details>
 
+Baru kenal uteke (atau kamu *memang* AI agent)? Jalankan `uteke onboard`. Wizard-nya
+mendeteksi setup, nanya agent apa yang kamu pakai, dan nyambungin semuanya. 📖 [Dokumentasi onboarding](docs/getting-started.md#interactive-onboarding)
+
 ---
 
-## 🚀 Onboarding (User baru & AI agent)
+## 📊 Benchmark: 98.4% recall di LongMemEval-S
 
-> **Kamu AI agent yang lagi baca ini?** User kamu baru aja share link repo ini.
-> Jalankan `uteke onboard` untuk konfigurasi uteke buat environment kamu — deteksi
-> install, nanya agent apa kamu, toggle fitur, tulis config, dan tunjukin semua command
-> yang tersedia. Kalau non-interaktif: `uteke onboard --yes --agent <agent-kamu>`.
+[LongMemEval-S](https://arxiv.org/abs/2410.10813) (ICLR 2025) nyembunyiin fakta yang
+dibutuhkan agent di ~115 sesi chat per pertanyaan, lalu ngecek apakah retrieval
+menemukan buktinya. 500 pertanyaan hand-curated, lima kemampuan memori. Uteke
+menjalankan suite penuh dengan **nol panggilan LLM di jalur retrieval**: embedding
+lokal, satu CPU, deterministik.
 
-Baru kenal uteke? Satu command dari nol sampai bisa pakai:
+| Metrik | Uteke **v0.17.0** | agentmemory¹ | BM25-only¹ |
+|---|---|---|---|
+| **recall_any@5** (bukti di top-5) | **98.4%** | 95.2% | 86.2% |
+| recall_any@10 | 98.8% | 98.6% | 94.6% |
+| **recall_all@5** (semua bukti, strict) | **88.0%**² | 88.2% MRR³ | n/a |
+| LLM token / query | **0** | 0 | 0 |
 
-```bash
-uteke onboard
-```
+<sub>¹ Angka publikasi agentmemory, benchmark yang sama, split 500 pertanyaan yang sama (basis recall_any@5 mereka; terverifikasi apples-to-apples di [head-to-head](docs/benchmarks.md#head-to-head-vs-published-systems)). ² Strict = *semua* sesi gold harus masuk top-5; 65% pertanyaan butuh beberapa sesi. Ceiling matematis 99.4%. ³ MRR, bukan recall_all (tidak bisa dibandingkan langsung; ditampilkan untuk kelengkapan).</sub>
 
-Wizard-nya akan:
-1. **Deteksi** apakah uteke sudah terinstall dan store sudah ada
-2. **Tanya** AI agent apa yang kamu pakai (Hermes, Claude, Cursor, Pi, OpenCode)
-3. **Pilih** mode integrasi — manual tool calls vs automatic memory-provider
-4. **Toggle** fitur on/off (Aging, Auto-maintenance, Graph rerank, Salience/Recency boost, Server mode)
-5. **Tulis** `~/.codecora/uteke/uteke.toml` dengan pilihan kamu
-6. **Install** file integrasi agent otomatis (`uteke init`)
-7. **Tunjukkan** semua command uteke, dikelompokkan per kategori
+<p align="center">
+  <img src="docs/assets/longmemeval-recall-v017.png" alt="LongMemEval-S recall@5: uteke 98.4% (revalidasi v0.18.0) vs MemPalace 96.6% dan agentmemory 95.2% (hasil raw di-commit di repo)" width="880" />
+</p>
 
-Non-interaktif (CI, script, AI agent):
+**Per kategori pertanyaan** (recall_any@5: cerita per kategori yang jarang ditampilkan tool lain):
 
-```bash
-uteke onboard --yes --agent hermes --namespace default
-```
+| knowledge-update | single-session | temporal | multi-session |
+|:---:|:---:|:---:|:---:|
+| **100%** | 96.7–98.2% | **99.2%** | 98.3% |
 
-📖 [Dokumentasi onboarding lengkap](docs/getting-started.md#interactive-onboarding) · [CLI reference](docs/cli-reference.md#uteke-onboard)
+Bagian sulit itu bukan menemukan *sebuah* jarum; bukti setiap pertanyaan ada di
+top-50 (**nol meleset**). Sisa gap-nya: *urutan*, saat satu pertanyaan butuh
+beberapa sesi sekaligus: strict recall_all@5 di angka 88.0% dengan ceiling 99.4%.
 
----
+> **🎯 Jangan percaya benchmark kami. Jalankan sendiri.** Harness lengkapnya ada di
+> repo ini: dataset publik, output raw di-commit untuk kedua rilis, scoring
+> deterministik yang bisa kamu hitung ulang dalam ~20 baris Python. Tanpa perlu
+> embedder untuk verifikasi, ~$10 untuk menjalankan 500 pertanyaan sendiri.
+> **👉 [benchmarks/longmemeval/REPRODUCING.md](benchmarks/longmemeval/REPRODUCING.md)**
 
-## 🔥 Kenapa Uteke?
+Juga tersedia: `uteke bench --counts 100,1000,10000` untuk latency/throughput di
+mesin kamu sendiri. 📖 [Dokumentasi benchmark lengkap](docs/benchmarks.md) · [RESULTS.md](benchmarks/longmemeval/RESULTS.md)
 
-Bayangin: kamu baru habis 2 jam jelasin codebase ke ChatGPT. Sesi berikutnya? Kosong. Ulang dari nol. Lagi.
+---
+
+## 💡 Buat Apa Uteke?
 
-Setiap AI tool lupa. Context window penuh, sesi berakhir, dan AI kamu start over setiap kali. Uteke kasih memori persisten — dan semuanya tetap di mesin kamu.
+**🤖 Lagi bangun AI agent?** Kasih memori persisten tanpa dependency cloud. Agent kamu ingat preferensi user, keputusan sebelumnya, dan konteks, antar sesi, fully offline.
 
-| | **Uteke** | **Mem0** | **AgentMemory** | **Letta** | **Zep** | **Engram** |
-|---|---|---|---|---|---|---|
-| **Setup** | Satu binary | pip + Docker + Qdrant | npm + Docker (iii-engine) | pip + Docker + Postgres | pip + Docker + Neo4j | Satu binary (Go) |
-| **API key** | ❌ Nggak perlu | ✅ OpenAI/LLM | ✅ LLM key | ✅ LLM key | ✅ LLM key | ❌ Nggak perlu |
-| **Offline** | ✅ Full | ❌ Cloud embedding | ❌ Butuh LLM | ❌ Butuh LLM | ❌ Butuh LLM + vector DB | ✅ Full |
-| **Search** | **Fusion** (weighted RRF vector + hybrid; hybrid = HNSW + FTS5 RRF) | Vector + Graph | Vector + Graph | Vector | Temporal Graph | **FTS5 doang** |
-| **Kecepatan recall** | ~45ms | Network round-trip | Network round-trip | Network round-trip | Network round-trip | ~Cepat (lokal) |
-| **Data kamu** | ✅ Nggak pernah keluar | ⚠️ Dikirim ke cloud LLM | ⚠️ Dikirim ke cloud LLM | ⚠️ Dikirim ke cloud LLM | ⚠️ Dikirim ke cloud LLM | ✅ Lokal |
-| **Stars** | 🌱 Growing | ⭐ ~60K | ⭐ ~25K | ⭐ ~24K | ⭐ ~5K | ⭐ ~5K |
-| **Lisensi** | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
+**👥 Kerja tim?** Pakai [Rooms](docs/rooms.md) buat share knowledge. Meeting notes, keputusan project, pilihan arsitektur: bisa dicari semua orang, dengan atribusi author.
 
-> **Uteke vs Engram:** Dua-duanya single-binary, offline, tanpa API key. Tapi Engram cuma **FTS5** (keyword search doang). Uteke punya **vector semantic search + RRF fusion + rooms + time-travel + graph relationships + smart decay + document engine + batch import**. Filosofi yang sama, fitur 10× lipat.
+**🔒 Bangun app buat domain sensitif?** Kesehatan, keuangan, legal: data tetap di mesin kamu. Nggak ada API call, nggak ada telemetri, nggak ada cloud. Embedding lokal (ONNX, 768d).
 
-> **Uteke vs AgentMemory/Mem0/Letta/Zep:** Mereka powerful — tapi semua butuh cloud LLM API key + Docker infra. Data kamu dikirim ke OpenAI/Anthropic. Uteke jalan fully offline dengan embedding ONNX lokal. Tanpa Docker, tanpa Python, tanpa API key.
+**⌨️ Power user yang hidup di terminal?** Uteke itu personal knowledge graph kamu. Simpan apapun, cari berdasarkan makna, hubungkan pikiran yang related. Semua dari command line.
 
 ---
 
-## 💡 Buat Apa Uteke?
+## 🔥 Kenapa Uteke?
+
+Bayangin: kamu baru habis 2 jam jelasin codebase ke ChatGPT. Sesi berikutnya? Kosong. Ulang dari nol. Lagi.
+
+Setiap AI tool lupa. Context window penuh, sesi berakhir, dan AI kamu start over setiap kali. Uteke kasih memori persisten, dan semuanya tetap di mesin kamu.
+
+| | **Uteke** | **Tool A** | **Tool B** | **Tool C** | **Tool D** | **Tool E** | **Tool F** | **Tool G** |
+|---|---|---|---|---|---|---|---|---|
+| **Bahasa** | Rust (satu binary) | Python (pip) | Python | TypeScript | TypeScript | Python | TypeScript | Go (satu binary) |
+| **Setup** | Satu binary (`curl \| sh`) | pip install + venv | pip + Docker + Qdrant | npm + iii-engine | npm (Node.js) | pip + Docker + Neo4j | Cloud atau binary lokal | Satu binary |
+| **API key** | ❌ Nggak perlu | ⚠️ Untuk embedding remote | ✅ OpenAI/LLM | ✅ LLM key | ✅ LLM key | ✅ LLM key | ⚠️ Cloud saja | ❌ Nggak perlu |
+| **Bisa offline** | ✅ Full | ⚠️ Opsional | ❌ Cloud embedding | ❌ Butuh LLM | ❌ Butuh LLM | ❌ Butuh LLM + vector DB | ✅ Binary lokal + Ollama | ✅ Full |
+| **Search** | **Fusion** (weighted RRF vector + hybrid; hybrid = HNSW + FTS5 RRF) | sqlite-vec + FTS5 | Vector + Graph | Vector + Graph | Vector | Hybrid (semantic + keyword + graph) | Vector + rerank | **FTS5 doang** |
+| **Kecepatan recall** | ~45ms | ~50ms+ | Network round-trip | Network round-trip | Network round-trip | Network round-trip | Network round-trip | ~Cepat (lokal) |
+| **Multi-agent** | ✅ **Rooms** (shared memory, cross-agent recall, atribusi author) | ✅ Multi-agent surface | ❌ | ✅ Shared server | ✅ Multi-agent groups | ❌ | ❌ | ⚠️ Share via MCP |
+| **Time-travel** | ✅ Native point-in-time | ⚠️ Temporal triples | ❌ | ❌ | ❌ | ✅ Temporal graphs | ❌ | ❌ |
+| **MCP server** | ✅ JSON-RPC + HTTP | ✅ stdio + SSE | ❌ | ✅ 54 MCP tools | ❌ | ✅ Graphiti MCP | ✅ MCP open-source | ✅ stdio MCP |
+| **Data kamu** | ✅ Nggak pernah keluar dari mesin | ✅ Local-first | ⚠️ Dikirim ke cloud LLM | ✅ Lokal (iii-engine) | ⚠️ Dikirim ke cloud LLM | ⚠️ Dikirim ke cloud LLM | ⚠️ Di-host Cloudflare | ✅ Lokal |
+| **Lisensi** | Apache 2.0 | MIT | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 | MIT | MIT |
+
+> **Catatan:** Label Tool (A–G) merepresentasikan kategori umum AI memory layer per
+> Agustus 2026. Kemampuan dinilai dari dokumentasi publik dan bisa berubah. Tabel ini
+> titik awal evaluasi kamu sendiri, bukan ranking definitif.
 
-**🤖 Lagi bangun AI agent?** Kasih memori persisten tanpa dependency cloud. Agent kamu ingat preferensi user, keputusan sebelumnya, dan konteks — antar sesi, fully offline.
+> **Uteke vs Tool A (Python local-first):** Dua-duanya local-first dengan semantic + FTS5 search. Keunggulan Uteke: **satu binary** (tanpa runtime Python), **time-travel native** (bukan temporal triples), **rooms**, dan **zero runtime dependency**.
 
-**👥 Kerja tim?** Pakai [Rooms](docs/getting-started.md) buat share knowledge. Meeting notes, keputusan project, pilihan arsitektur — bisa dicari semua orang, dengan atribusi author.
+> **Uteke vs Tool G (Go single binary):** Dua-duanya single-binary, offline, tanpa API key, dua-duanya punya MCP server. Tool G itu **FTS5-only** (keyword search doang). Uteke nambah **vector semantic search + RRF fusion + rooms + time-travel + graph relationships + smart decay + document engine + batch import**. Filosofi kesederhanaan yang sama, lebih banyak kemampuan.
 
-**🔒 Bangun app buat domain sensitif?** Kesehatan, keuangan, legal — data tetap di mesin kamu. Nggak ada API call, nggak ada telemetri, nggak ada cloud. Embedding lokal (ONNX, 768d).
+> **Uteke vs Tool F (TypeScript mode lokal):** Tool F sekarang punya mode binary lokal dengan dukungan Ollama. Langkah solid ke arah offline-first. Tapi di baliknya tetap TypeScript/Node.js. Uteke itu Rust: footprint lebih kecil, startup lebih cepat, zero runtime. Uteke juga punya **hybrid search dengan FTS5** (mode lokal Tool F vector-only, tanpa fallback keyword).
 
-**⌨️ Power user yang hidup di terminal?** Uteke adalah personal knowledge graph kamu. Simpan apapun, cari berdasarkan makna, hubungkan pikiran yang related. Semua dari command line.
+> **Uteke vs Tool B/D/E:** Mereka powerful, tapi semua butuh cloud LLM API key + Docker infra. Data kamu dikirim ke provider LLM eksternal. Uteke jalan fully offline dengan embedding ONNX lokal. Tanpa Docker, tanpa Python, tanpa API key.
+
+> **Uteke vs Tool C:** Tool C punya 54 MCP tools dan multi-agent shared memory via local engine. Keunggulan Uteke: **zero dependency** (tanpa npm, tanpa engine tambahan), **hybrid search** (Tool C tidak punya FTS5), dan **time-travel queries**.
 
 ---
 
-## 🏠 Rooms — Shared Memory Multi-Agent
+## 🏠 Rooms: Shared Memory Multi-Agent
 
-Memory layer lain cuma single-player — setiap fakta disimpan di `user_id` yang flat, nggak kelihatan sama agent lain. **Uteke Rooms** bikin multiple AI agent bisa share memory space dengan atribusi author.
+Memory layer lain cuma single-player: setiap fakta disimpan di `user_id` yang flat, nggak kelihatan sama agent lain. **Uteke Rooms** bikin multiple AI agent bisa share memory space dengan atribusi author.
 
 ```bash
 # Bikin room shared
@@ -171,11 +203,12 @@ uteke recall "caching decision" --room engineering
 
 | Fitur | Apa fungsinya |
 |-------|---------------|
-| 🧠 **Hybrid + Fusion Search** | Cari berdasarkan makna (vector) + kata kunci exact (FTS5). Digabung dengan Reciprocal Rank Fusion (RRF). Sejak v0.16.0, `fusion` — weighted RRF dari ranking vector dan hybrid — jadi default recall strategy. |
+| 🧠 **Hybrid + Fusion Search** | Cari berdasarkan makna (vector) + kata kunci exact (FTS5). Digabung dengan Reciprocal Rank Fusion (RRF). Sejak v0.16.0, `fusion` (weighted RRF dari ranking vector dan hybrid) jadi default recall strategy. |
 | 🏠 **Rooms** | **Shared memory multi-agent.** Kelompokkan memori berdasarkan konteks (meeting, project, klien). Multiple agent baca/tulis ke room yang sama dengan atribusi author. Cross-agent recall tanpa sync manual. |
 | ⏳ **Time-travel** | Recall memori seperti adanya di titik waktu manapun. `uteke recall "deploy" --at 2025-01-15` |
 | 🏷️ **Metadata Kaya** | Tag, entity, kategori, key:value di setiap memori. |
 | 🧩 **Tipe Memori** | Kategori bertipe (fact, procedure, decision, dll.) dengan auto-inferensi. |
+| ✏️ **Partial Updates** | Ubah isi, tags, metadata, importance, atau type tanpa rewrite penuh. |
 | 📎 **Citations** | Atribusi sumber di setiap memori (URL, file, user, batch import). |
 
 ### Search & Intelligence
@@ -183,51 +216,42 @@ uteke recall "caching decision" --room engineering
 | Fitur | Apa fungsinya |
 |-------|---------------|
 | 🔗 **Relationship Graph** | Hubungkan memori dengan edge bertipe (supersedes, contradicts, references). Auto-backlink. |
+| 🔗 **Cross-Entity Linking** | Referensi dua arah memori↔dokumen via wikilink `[[doc-slug]]`. |
 | 🤖 **Cosine Auto-Linking** | Otomatis bikin edge `similar_to` antar memori yang related. |
 | 📉 **Smart Decay** | Skor importance komposit. Pin yang penting, biarkan yang basi memudar. |
 | 📈 **Salience + Recency** | Boost recall dual-axis berdasarkan tipe dan usia memori. |
 | 🔍 **Orphan Detection** | Cari memori terputus dengan importance rendah untuk dibersihkan. |
 | 🌙 **Dream Cycle** | Maintenance satu perintah: lint → backlinks → dedup → orphans. |
+| 🧬 **Consolidation** | Gabungkan memori room yang mirip jadi lebih sedikit dan lebih padat: planner level-segmen, kebijakan trust provenance, kontrol per-pasangan. |
 
 ### Integrasi
 
 | Fitur | Apa fungsinya |
 |-------|---------------|
 | 🔌 **MCP Server** | JSON-RPC via stdio + Streamable HTTP. Langsung pakai dengan Claude Code, Cursor, Hermes. |
-| 🖥️ **Mode Server** | Daemon persisten — eliminates cold-start embedding load di setiap call. |
+| 🖥️ **Mode Server** | Daemon persisten: eliminates cold-start embedding load di setiap call. |
 | 📂 **Batch Import** | Import seluruh direktori dengan routing strategi otomatis (dokumen vs. memori). |
 | 📝 **Document Engine** | Wiki/knowledge base dengan `uteke doc create/get/list` + auto-chunking. |
 | 📥 **Import/Export** | Backup dan restore berbasis JSONL. |
 | 🔑 **View-Only API Keys** | Token read-only untuk akses GET saja ke server. |
+| 👤 **Tipe Author** | Atribusi `human` vs `agent` di setiap memori, konsisten di CLI, HTTP, dan MCP. |
 
 ### Performa & Privasi
 
 | Fitur | Apa fungsinya |
 |-------|---------------|
-| 📦 **Single Binary** | Zero dependency. Tanpa Docker, tanpa database server, tanpa Python, tanpa API key. |
+| 📦 **Single Binary** | Zero dependency. Tanpa Python, tanpa API key. Local-first secara default. |
+| 🐳 **Docker Ready** | Image resmi di GHCR. Jalankan sebagai shared service untuk tim atau deployment cloud. |
 | 🔒 **Fully Offline** | Embedding ONNX lokal (EmbeddingGemma Q4, 768d). Tanpa telemetri, tanpa cloud. |
 | ⚡ **Recall Cache** | Cache LRU yang eliminate redundant embedding untuk query berulang. |
 | 🔥 **Tiered Memory** | Tracking Hot/Warm/Cold dengan auto-cleanup memori basi. |
 | 🔄 **Embed Fallback** | Degrade ke no-op embedder kalau local model gagal (nggak pernah crash). |
 | 👥 **Namespace Multi-Agent** | Memori terisolasi penuh per agent, tanpa overhead. |
-| 📊 **Benchmark** | `uteke bench` untuk perf testing. [Lihat hasil](docs/benchmarks.md). |
-
-<details>
-<summary>🔌 Konfigurasi MCP Server — connect ke Claude Code, Cursor, Hermes</summary>
-
-```jsonc
-// .mcp.json (Claude Code, Cursor)
-{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }
-```
-
-Untuk Claude Desktop, Hermes, dan HTTP transport, lihat [MCP docs](docs/mcp.md).
-</details>
-
-📖 [Dokumentasi lengkap](docs/getting-started.md) · [Referensi CLI](docs/cli-reference.md) · [Konfigurasi](docs/configuration.md)
+| 📊 **Benchmark** | `uteke bench` bawaan untuk perf testing. [Lihat hasil](docs/benchmarks.md). |
 
 ---
 
-## 🏗️ Arsitektur
+## 🚀 Cara Kerja
 
 ```mermaid
 graph LR
@@ -244,50 +268,111 @@ graph LR
 ```
 
 **Cara kerja hybrid search:**
-1. **HNSW** (usearch) — cari berdasarkan makna ("deploy" cocok dengan "rollout")
-2. **FTS5** (SQLite) — cari berdasarkan kata exact ("deploy" cocok dengan "deploy")
-3. **RRF** (k=60) — gabungkan dua ranked lists → terbaik dari keduanya
+1. **HNSW** (usearch): cari berdasarkan makna ("deploy" cocok dengan "rollout")
+2. **FTS5** (SQLite): cari berdasarkan kata exact ("deploy" cocok dengan "deploy")
+3. **RRF** (k=60): gabungkan dua ranked lists → terbaik dari keduanya
 
 Semua jalan in-process. Tanpa network. Tanpa cloud. Tanpa server (kecuali mau pakai mode server).
 
----
+<details>
+<summary>🐳 Mode deployment (local-first secara default, Docker/server kalau perlu)</summary>
 
-## ❓ FAQ
+**Local-first (default)**: satu binary, zero infrastruktur.
+
+```bash
+curl -sSL codecora.dev/uteke/install | sh
+uteke remember "memori pertama"
+```
+
+Data kamu tersimpan di `~/.codecora/uteke/`.
+
+**Docker / mode server**: untuk tim atau agent remote.
+
+```bash
+docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest
+# atau: uteke serve --host 0.0.0.0 --port 8767
+```
+
+📖 [Panduan Docker](docs/docker.md) · [Dokumentasi server mode](docs/configuration.md#server-mode)
+</details>
 
 <details>
-<summary><strong>Bedanya Uteke sama Mem0 atau Letta apa?</strong></summary>
+<summary>🏠 Rooms: contoh shared memory multi-agent</summary>
+
+```bash
+# Bikin room shared
+uteke room create "engineering" --description "Keputusan tim"
+
+# Agent Alice simpan keputusan
+uteke remember "Kita pilih Redis buat caching, bukan Memcached" \
+  --room engineering --author alice
 
-Mem0 dan Letta bagus — tapi mereka butuh API key cloud (OpenAI/LLM) dan infrastruktur eksternal (Docker, Postgres, Qdrant). Data kamu dikirim ke cloud LLM provider. Uteke itu satu binary tanpa API key sama sekali. Semua embedding jalan lokal via ONNX. Data kamu nggak pernah keluar dari mesin kamu. [Lihat tabel perbandingan](#-kenapa-uteke).
+# Agent Bob tambah konteks
+uteke remember "Redis cluster: 3 node, 2 replica tiap node" \
+  --room engineering --author bob
+
+# Agent manapun bisa recall history shared
+uteke recall "caching decision" --room engineering
+```
+
+Atribusi author di setiap memori; cross-agent recall tanpa sync manual.
+📖 [Dokumentasi Rooms lengkap →](docs/rooms.md)
 </details>
 
 <details>
-<summary><strong>Bedanya sama AgentMemory?</strong></summary>
+<summary>🔌 Konfigurasi MCP: connect ke Claude Code, Cursor, Hermes</summary>
 
-AgentMemory (25K stars) itu platform TypeScript/Node.js dengan 53 MCP tools dan 12 auto-hooks. Feature-rich tapi butuh Docker + iii-engine + LLM API key. Uteke itu Rust, zero dependency, dan jalan fully offline. Kalau mau integrasi maksimum dan nggak masalah cloud dependency → AgentMemory. Kalau mau privasi, kecepatan, dan zero setup → Uteke.
+```jsonc
+// .mcp.json (Claude Code, Cursor)
+{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }
+```
+
+Untuk Claude Desktop, Hermes, dan HTTP transport, lihat [MCP docs](docs/mcp.md).
+</details>
+
+---
+
+## 📚 Dokumentasi
+
+| | |
+|---|---|
+| **Mulai** | [Installation](INSTALL.md) · [Getting started](docs/getting-started.md) · [Onboarding](docs/getting-started.md#interactive-onboarding) |
+| **Referensi** | [CLI reference](docs/cli-reference.md) · [Konfigurasi](docs/configuration.md) · [Docker](docs/docker.md) |
+| **Integrasi** | [Setup MCP](docs/mcp.md) · Claude Code · Cursor · Hermes |
+| **Benchmark** | [docs/benchmarks.md](docs/benchmarks.md) · [RESULTS.md](benchmarks/longmemeval/RESULTS.md) · [Reproduksi sendiri](benchmarks/longmemeval/REPRODUCING.md) |
+
+---
+
+## ❓ FAQ
+
+<details>
+<summary><strong>Bedanya Uteke sama memory tool yang cloud-dependent apa?</strong></summary>
+
+Banyak memory layer (basis Python atau TypeScript) butuh API key cloud (OpenAI/LLM) dan infrastruktur eksternal (Docker, Postgres, Qdrant). Data kamu dikirim ke cloud LLM provider. Uteke itu satu binary tanpa API key sama sekali. Semua embedding jalan lokal via ONNX. Data kamu nggak pernah keluar dari mesin kamu. [Lihat tabel perbandingan](#-kenapa-uteke).
 </details>
 
 <details>
-<summary><strong>Bedanya sama Engram?</strong></summary>
+<summary><strong>Bedanya sama platform MCP multi-tool?</strong></summary>
 
-Engram (2.4K stars, Go) punya filosofi yang sama: single binary, zero deps, MCP server, local-first. Bedanya di **search**: Engram cuma pakai **FTS5** (keyword matching). Uteke pakai **hybrid search** (HNSW vector similarity + FTS5 + Reciprocal Rank Fusion) — artinya kamu bisa cari berdasarkan *makna*, bukan cuma kata exact. Uteke juga punya rooms, time-travel, graph relationships, smart decay, document engine, dan batch import.
+Beberapa platform menawarkan lusinan MCP tools dan multi-agent shared memory via local engine. Kaya integrasi, tapi butuh npm, runtime terpisah, dan LLM API key untuk embedding. Uteke itu Rust, zero dependency, dan jalan fully offline dengan embedding ONNX lokal. Mau integrasi maksimum → platform itu. Mau privasi, kecepatan, zero setup, dan hybrid search → Uteke.
 </details>
 
 <details>
-<summary><strong>Uteke bisa ingat apa aja?</strong></summary>
+<summary><strong>Bedanya sama single-binary memory tool lain?</strong></summary>
 
-Apapun yang text-based: keputusan, meeting notes, code snippet, konteks project, catatan personal, state agent. Bisa di-tag, di-kategorikan, dan di-link antar memori. Flag `--batch-dir` bisa import seluruh direktori dokumen sekaligus.
+Beberapa single-binary tool share filosofi kami: satu binary, zero deps, MCP server, local-first. Beda kuncinya di **search**: kebanyakan **FTS5-only** (keyword matching). Uteke pakai **hybrid search** (HNSW vector similarity + FTS5 + Reciprocal Rank Fusion), jadi kamu bisa cari berdasarkan *makna*, bukan cuma kata exact. Uteke juga punya rooms, time-travel, graph relationships, smart decay, document engine, dan batch import.
 </details>
 
 <details>
 <summary><strong>Beneran bisa offline?</strong></summary>
 
-Ya. Embedding model (EmbeddingGemma Q4, 768d) download sekali (~188MB) saat first run. Setelah itu, zero network call. Tanpa telemetri. Kalau local model gagal, Uteke degrade ke no-op embedder — nggak pernah crash dan nggak pernah manggil cloud API.
+Ya. Embedding model (EmbeddingGemma Q4, 768d) download sekali (~200MB) saat first run. Setelah itu, zero network call. Tanpa telemetri. Kalau local model gagal, Uteke degrade ke no-op embedder, jadi nggak pernah crash dan nggak pernah manggil cloud API.
 </details>
 
 <details>
 <summary><strong>Cepetan recall-nya?</strong></summary>
 
-~45ms sebagai library (diukur di 100–10K memori). Nggak ada network round-trip karena semuanya lokal. Recall cache LRU menghilangkan komputasi embedding berulang untuk query yang sama.
+~45ms sebagai CLI (pengukuran: rata-rata 31ms @10K di bench host yang dipublikasikan) (diukur di 100–10K memori, flat mengikuti ukuran store). Nggak ada network round-trip karena semuanya lokal. Recall cache LRU menghilangkan komputasi embedding berulang untuk query yang sama.
 </details>
 
 <details>
@@ -299,11 +384,26 @@ Bisa. Uteke punya MCP server yang langsung pakai dengan Claude Code, Cursor, dan
 <details>
 <summary><strong>Sudah production-ready?</strong></summary>
 
-Uteke sekarang v0.16.0 dengan 200+ test, CI/CD di setiap commit, dan benchmark harness. Dipakai production oleh tim CodeCora dan early adopter lain. Masih di versi 0.x — mungkin ada rough edges, tapi core-nya udah stabil.
+Uteke sekarang v0.17.0 dengan 200+ test, CI/CD di setiap commit, dan benchmark harness. Dipakai production oleh tim CodeCora dan early adopter lain. Masih di versi 0.x, jadi mungkin ada rough edges, tapi core-nya udah stabil.
 </details>
 
 ---
 
+## 🗺️ Roadmap & Editions
+
+| | **Uteke OSS** (repo ini) | **Uteke Cloud** |
+|---|---|---|
+| Kualitas retrieval | ✅ Full (engine identik) | ✅ Identik (parity-benchmarked) |
+| Lisensi | Apache-2.0, self-host | Layanan managed |
+| Jawaban berbasis LLM (resolusi fakta recency-aware, abstention) | BYOK | Termasuk |
+| Multi-workspace, dashboard, backup | DIY | ✅ Termasuk |
+| Harga | Gratis | *Segera* |
+
+Engine open-source tetap full-capability. Tidak ada bagian dari hasil benchmark di
+ atas yang dipagari paywall. Cloud menambahkan kenyamanan hosted di atasnya.
+
+---
+
 ## 🤝 Kontribusi
 
 ```bash
@@ -319,24 +419,20 @@ Kontribusi diterima! Baca [CONTRIBUTING.md](CONTRIBUTING.md) untuk panduan lengk
 
 ## 📄 Lisensi
 
-[Apache License 2.0](LICENSE) — pakai, fork, ship.
+[Apache License 2.0](LICENSE). Pakai, fork, ship.
 
 ---
 
 ## ⭐ Star History
 
-<a href="https://www.star-history.com/?repos=codecoradev%2Futeke&type=date&legend=top-left">
- <picture>
-   <source media="(prefers-color-scheme: dark)" srcset="https://api.star-history.com/chart?repos=codecoradev/uteke&type=date&theme=dark&legend=top-left" />
-   <source media="(prefers-color-scheme: light)" srcset="https://api.star-history.com/chart?repos=codecoradev/uteke&type=date&legend=top-left" />
-   <img alt="Star History Chart" src="https://api.star-history.com/chart?repos=codecoradev/uteke&type=date&legend=top-left" />
- </picture>
-</a>
+<p align="center">
+  <img src="https://s3.ajianaz.dev/hermes/codecoradev/uteke/star-history.png" alt="Uteke Star History" width="720" />
+</p>
 
 ---
 
 <p align="center">
-  <strong>Berguna?</strong> ⭐ Star repo ini — bantu orang lain nemuin Uteke.
+  <strong>Berguna?</strong> ⭐ Star repo ini. Bantu orang lain nemuin Uteke.
 </p>
 <p align="center">
   <a href="https://github.com/codecoradev/uteke/stargazers">
diff --git a/README.md b/README.md
index 16033f6a..43742605 100644
--- a/README.md
+++ b/README.md
@@ -8,6 +8,10 @@
   Give your AI a memory that never leaves your machine. Works with Claude, Cursor, Copilot, and any MCP-compatible agent.
 </p>
 
+<p align="center">
+  <strong>98.4% LongMemEval-S recall@5</strong> · ~45 ms warm query · <strong>0 LLM tokens</strong> per query · CPU-only · fully offline
+</p>
+
 <p align="center">
   <a href="https://github.com/codecoradev/uteke/actions/workflows/ci.yml?branch=develop"><img src="https://github.com/codecoradev/uteke/actions/workflows/ci.yml/badge.svg?branch=develop" alt="CI" /></a>
   <a href="https://github.com/codecoradev/uteke/releases"><img src="https://img.shields.io/github/v/release/codecoradev/uteke?style=flat-square&color=green" alt="Latest Release" /></a>
@@ -16,6 +20,7 @@
   <img src="https://img.shields.io/badge/Rust-1.85+-orange.svg?style=flat-square" alt="Rust 1.85+" />
   <a href="https://github.com/codecoradev/uteke/pkgs/container/uteke"><img src="https://img.shields.io/badge/Docker-ready-blue.svg?style=flat-square" alt="Docker" /></a>
   <img src="https://img.shields.io/badge/recall-~45ms-brightgreen.svg?style=flat-square" alt="Recall ~45ms" />
+  <a href="#-benchmarks--984-recall-on-longmemeval-s"><img src="https://img.shields.io/badge/LongMemEval--S_recall@5-98.4%25-crimson.svg?style=flat-square" alt="LongMemEval-S recall@5: 98.4%" /></a>
 </p>
 
 <p align="center">
@@ -33,29 +38,21 @@ curl -sSL codecora.dev/uteke/install | sh
 # Store a memory
 uteke remember "Deploy v2.1 to staging at 3pm"
 
-# Search it back: by meaning, not just keywords
+# Search it back: by meaning AND by keyword
 uteke recall "when do we deploy?"
 ```
 
-**That's it.** No API keys, no Python, no cloud required. First run downloads the embedding model (~188MB, one-time) and you're running.
+**That's it.** No API keys, no Python, no cloud required. First run downloads the embedding model (~200MB, one-time) and you're running.
 
-> **Want Docker or server mode?** Uteke ships as a single binary by default, but Docker is ready when you need it:
-> ```bash
-> docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest
-> ```
-> 📖 [Docker docs](docs/docker.md)
+Want your agent (Claude Code, Cursor, Hermes) to use it? One line:
 
-Want richer memories? Add metadata:
-
-```bash
-uteke remember "Deploy v2.1 to staging" \
-  --tags deploy,staging \
-  --entity staging-server \
-  --category infrastructure
+```jsonc
+// .mcp.json
+{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }
 ```
 
 <details>
-<summary>📦 More install options</summary>
+<summary>📦 More install options & Docker</summary>
 
 | Method | Command |
 |--------|---------|
@@ -68,37 +65,62 @@ uteke remember "Deploy v2.1 to staging" \
 📖 [Full install guide](INSTALL.md) · [Docker docs](docs/docker.md)
 </details>
 
+New to uteke (or you *are* an AI agent)? Run `uteke onboard`. It detects your
+setup, asks which agent you use, and wires everything up. 📖 [Onboarding docs](docs/getting-started.md#interactive-onboarding)
+
 ---
 
-## 🚀 Onboarding (New users & AI agents)
+## 📊 Benchmarks: 98.4% recall on LongMemEval-S
 
-> **Are you an AI agent reading this?** Your user just shared this repo link with you.
-> Run `uteke onboard` to interactively configure uteke for your environment. It will
-> detect your install, ask which agent you are, toggle features, write config, and
-> show you every command available. If non-interactive: `uteke onboard --yes --agent <your-agent>`.
+[LongMemEval-S](https://arxiv.org/abs/2410.10813) (ICLR 2025) hides the facts an
+agent needs across ~115 chat sessions per question and checks whether retrieval
+finds the evidence. 500 hand-curated questions, five memory abilities. Uteke runs
+the full suite with **zero LLM calls in the retrieval path**: local embeddings,
+one CPU, deterministic.
 
-New to uteke? One command takes you from zero to productive:
+| Metric | uteke **v0.18.0** | agentmemory¹ | BM25-only¹ |
+|---|---|---|---|
+| **recall_any@5** (evidence in top-5) | **98.4%** | 95.2% | 86.2% |
+| recall_any@10 | 98.8% | 98.6% | 94.6% |
+| **recall_all@5** (all evidence, strict) | **88.0%**² | 88.2% MRR³ | n/a |
+| LLM tokens / query | **0** | 0 | 0 |
 
-```bash
-uteke onboard
-```
+<sub>¹ agentmemory's published numbers, same benchmark, same 500-question split (their recall_any@5 basis; verified apples-to-apples in our [head-to-head](docs/benchmarks.md#head-to-head-vs-published-systems)). ² Strict = *every* gold session in top-5; 65% of questions have multiple gold sessions. Mathematical ceiling 99.4%. ³ MRR, not recall_all (not directly comparable; shown for completeness).</sub>
 
-The wizard will:
-1. **Detect** if uteke is installed and if a store exists
-2. **Ask** which AI agent you use (Hermes, Claude, Cursor, Pi, OpenCode)
-3. **Pick** integration mode: manual tool calls vs automatic memory-provider
-4. **Toggle** features on/off (Aging, Auto-maintenance, Graph rerank, Salience/Recency boost, Server mode)
-5. **Write** `~/.codecora/uteke/uteke.toml` with your selections
-6. **Install** agent integration files automatically (`uteke init`)
-7. **Showcase** every uteke command grouped by category
+<p align="center">
+  <img src="docs/assets/longmemeval-recall-v017.png" alt="LongMemEval-S recall@5: uteke 98.4% (revalidated v0.18.0) vs MemPalace 96.6% and agentmemory 95.2% (raw results committed in-repo)" width="880" />
+</p>
 
-Non-interactive (CI, scripts, AI agents):
+**By question category** (recall_any@5: the category-level story most tools don't show):
 
-```bash
-uteke onboard --yes --agent hermes --namespace default
-```
+| knowledge-update | single-session | temporal | multi-session |
+|:---:|:---:|:---:|:---:|
+| **100%** | 96.7–98.2% | **99.2%** | 98.3% |
+
+The hard part isn't finding *a* needle; every question's evidence lands in the
+top-50 (**zero misses**). The residual gap is *ordering* when a question needs
+several sessions at once: strict recall_all@5 is 88.0% against a 99.4% ceiling.
 
-📖 [Full onboarding docs](docs/getting-started.md#interactive-onboarding) · [CLI reference](docs/cli-reference.md#uteke-onboard)
+> **🎯 Don't trust our benchmark. Run it yourself.** The full harness is in this
+> repo: public dataset, committed raw outputs for both releases, deterministic
+> scoring you can recompute in ~20 lines of Python. No embedder needed to verify,
+> ~$10 to re-run the whole 500 questions yourself.
+> **👉 [benchmarks/longmemeval/REPRODUCING.md](benchmarks/longmemeval/REPRODUCING.md)**
+
+Also built in: `uteke bench --counts 100,1000,10000` for latency/throughput on
+your own machine. 📖 [Full benchmark docs](docs/benchmarks.md) · [RESULTS.md](benchmarks/longmemeval/RESULTS.md)
+
+---
+
+## 💡 What Can You Do With Uteke?
+
+**🤖 Building AI agents?** Give them persistent memory without cloud dependencies. Your agent remembers user preferences, past decisions, and context across sessions, fully offline.
+
+**👥 Working in a team?** Use [Rooms](docs/rooms.md) to share knowledge. Meeting notes, project decisions, architecture choices: searchable by everyone, attributed by author.
+
+**🔒 Building for privacy-sensitive domains?** Healthcare, finance, legal: data stays on your machine. No API calls, no telemetry, no cloud. Local embeddings (ONNX, 768d).
+
+**⌨️ Power user who lives in the terminal?** Uteke is your personal knowledge graph. Remember anything, recall by meaning, link related thoughts. All from the command line.
 
 ---
 
@@ -134,71 +156,6 @@ Every AI tool forgets. Context windows fill up, sessions end, and your AI starts
 >
 > **Uteke vs Tool C:** Tool C has 54 MCP tools and multi-agent shared memory via a local engine. Uteke's edge: **zero dependencies** (no npm, no extra engine), **hybrid search** (Tool C lacks FTS5), and **time-travel queries**.
 
-<details>
-<summary>📊 Benchmark numbers</summary>
-
-| Metric | Result | Notes |
-|--------|--------|-------|
-| **Recall latency (10K memories)** | **42ms** P50, 50ms P95 | Flat from 100 to 10K memories (HNSW O(log N)) |
-| **Insert throughput** | 6-22 ops/s | CPU-bound (ONNX embedding inference) |
-| **Storage per memory** | ~10KB | SQLite + HNSW, scales linearly |
-| **LongMemEval-S recall_any@5** | **98.2%** | Full 500Q validation, zero-config fusion default (v0.16.0) — the metric competitor benchmarks publish |
-| LongMemEval-S recall_all@10 | 95.4% | Strict: every gold session in top-10 |
-| LongMemEval-S strict recall_all@5 | 88.0% | Every gold session in top-5 (mathematical ceiling 99.4%) |
-
-![uteke vs published systems on LongMemEval-S](docs/assets/longmemeval-comparison.jpg)
-
-> **Don't trust our benchmark — run your own.** We re-ran 108 of the 500 published questions on a 4-core ARM desktop (different CPU architecture from the published Modal x86 run, same v0.16.0 binary and harness): **107/108 produced identical per-question rankings**. The single difference was an adjacent-rank near-tie, both runs retrieved the identical top-10 session set, one gold session swapped ranks 5-6. Details in [RESULTS.md](benchmarks/longmemeval/RESULTS.md).
-
-Full benchmarks: `uteke bench --counts 100,1000,10000 --json` · [Benchmark details](docs/benchmarks.md) · [LongMemEval results](benchmarks/longmemeval/RESULTS.md) — fusion default: R@5 0.946 / R@10 0.977 on the full 500Q validation set (v0.16.0)
-
-</details>
-
----
-
-## 💡 What Can You Do With Uteke?
-
-**🤖 Building AI agents?** Give them persistent memory without cloud dependencies. Your agent remembers user preferences, past decisions, and context across sessions, fully offline.
-
-**👥 Working in a team?** Use [Rooms](docs/getting-started.md) to share knowledge. Meeting notes, project decisions, architecture choices: searchable by everyone, attributed by author.
-
-**🔒 Building for privacy-sensitive domains?** Healthcare, finance, legal: data stays on your machine. No API calls, no telemetry, no cloud. Local embeddings (ONNX, 768d).
-
-**⌨️ Power user who lives in the terminal?** Uteke is your personal knowledge graph. Remember anything, recall by meaning, link related thoughts. All from the command line.
-
----
-
-## 🏠 Rooms: Multi-Agent Shared Memory
-
-Other memory layers are single-player: every fact stored under a flat `user_id`, invisible to other agents. **Uteke Rooms** let multiple AI agents share a memory space with full author attribution.
-
-```bash
-# Create a shared room
-uteke room create "engineering" --description "Team decisions"
-
-# Alice's agent stores a decision
-uteke remember "We chose Redis for caching over Memcached" \
-  --room engineering --author alice
-
-# Bob's agent adds context
-uteke remember "Redis cluster: 3 nodes, 2 replicas each" \
-  --room engineering --author bob
-
-# Any agent can recall the shared history
-uteke recall "caching decision" --room engineering
-```
-
-**Why this matters:**
-
-| Problem without Rooms | Solution with Rooms |
-|---|---|
-| Agent A can't see Agent B's memories | Shared space, cross-agent recall |
-| Team knowledge is siloed per user | One room, multiple authors |
-| No way to attribute who said what | Author on every memory |
-| Multi-agent workflows need manual sync | Agents share context automatically |
-
-📖 **[Full Rooms documentation →](docs/rooms.md)**
-
 ---
 
 ## ✨ Features
@@ -207,7 +164,7 @@ uteke recall "caching decision" --room engineering
 
 | Feature | What it does |
 |---------|-------------|
-| 🧠 **Hybrid + Fusion Search** | Vector similarity + FTS5 full-text search, merged by Reciprocal Rank Fusion (RRF). Since v0.16.0, `fusion` — a weighted RRF of the vector and hybrid rankings — is the default recall strategy. Finds by meaning AND exact keywords. |
+| 🧠 **Hybrid + Fusion Search** | Vector similarity + FTS5 full-text search, merged by Reciprocal Rank Fusion (RRF). Since v0.16.0, `fusion` (a weighted RRF of the vector and hybrid rankings) is the default recall strategy. Finds by meaning AND exact keywords. |
 | 🏠 **Rooms** | **Multi-agent shared memory.** Group memories by context (meetings, projects, clients). Multiple agents read/write to the same room with author attribution. Cross-agent recall without manual sync. |
 | ⏳ **Time-travel** | Recall memories as they existed at any point in time. `uteke recall "deploy" --at 2025-01-15` |
 | 🏷️ **Rich Metadata** | Tags, entities, categories, key:value pairs on every memory. |
@@ -226,7 +183,7 @@ uteke recall "caching decision" --room engineering
 | 📈 **Salience + Recency** | Dual-axis recall boost by memory type and age. |
 | 🔍 **Orphan Detection** | Find disconnected, low-importance memories for cleanup. |
 | 🌙 **Dream Cycle** | One-command maintenance: lint → backlinks → dedup → orphans. |
-| 🧬 **Consolidation** | Merge near-duplicate room memories into fewer, denser records — segment-level planner, provenance trust policy, per-pair control. (0.16.0) |
+| 🧬 **Consolidation** | Merge near-duplicate room memories into fewer, denser records: segment-level planner, provenance trust policy, per-pair control. |
 
 ### Integrations
 
@@ -238,7 +195,7 @@ uteke recall "caching decision" --room engineering
 | 📝 **Document Engine** | Wiki/knowledge base with `uteke doc create/get/list` and auto-chunking. |
 | 📥 **Import/Export** | JSONL-based backup and restore. |
 | 🔑 **View-Only API Keys** | Read-only tokens for safe GET-only access to the server. |
-| 👤 **Author Types** | `human` vs `agent` attribution on every memory, across CLI, HTTP, and MCP. (0.16.0) |
+| 👤 **Author Types** | `human` vs `agent` attribution on every memory, across CLI, HTTP, and MCP. |
 
 ### Performance & Privacy
 
@@ -253,81 +210,97 @@ uteke recall "caching decision" --room engineering
 | 👥 **Multi-Agent Namespaces** | Fully isolated memory per agent, zero overhead. |
 | 📊 **Benchmarks** | Built-in `uteke bench` for perf testing. [See results](docs/benchmarks.md). |
 
-<details>
-<summary>🔌 MCP Server config: connect to Claude Code, Cursor, Hermes</summary>
+---
 
-```jsonc
-// .mcp.json (Claude Code, Cursor)
-{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }
-```
+## 🚀 How It Works
 
-For Claude Desktop, Hermes, and HTTP transport, see [MCP docs](docs/mcp.md).
-</details>
-
-📖 [Full documentation](docs/getting-started.md) · [CLI reference](docs/cli-reference.md) · [Configuration](docs/configuration.md)
+```mermaid
+graph LR
+    Input[User Query] --> Embed[Local ONNX Embedder<br/>768d, EmbeddingGemma Q4]
+    Embed --> HNSW[HNSW Vector Index<br/>usearch]
+    Embed --> FTS5[FTS5 Full-Text<br/>SQLite]
+    HNSW --> RRF[Reciprocal Rank Fusion<br/>k=60]
+    FTS5 --> RRF
+    RRF --> Results[Ranked Results]
 
----
+    style Input fill:#4a9eff,color:#fff
+    style Results fill:#4aff9e,color:#000
+    style RRF fill:#ff9e4a,color:#fff
+```
 
-## 📦 Deployment Modes
+**How hybrid search works:**
+1. **HNSW** (usearch): finds by meaning ("deploy" matches "rollout")
+2. **FTS5** (SQLite): finds by exact terms ("deploy" matches "deploy")
+3. **RRF** (k=60): merges both ranked lists → best of both worlds
 
-Uteke runs the same everywhere. Pick the mode that fits your setup.
+Everything runs in-process. No network. No cloud. No server required (unless you want server mode).
 
-### Local-first (default)
+<details>
+<summary>🐳 Deployment modes (local-first by default, Docker/server when you need it)</summary>
 
-Single binary, zero infrastructure. Everything runs in-process on your machine:
+**Local-first (default)**: single binary, zero infrastructure.
 
 ```bash
 curl -sSL codecora.dev/uteke/install | sh
 uteke remember "first memory"
 ```
 
-No Docker, no Python, no database server. Your data stays in `~/.codecora/uteke/`. This is what most users need.
+Your data stays in `~/.codecora/uteke/`.
 
-### Docker / Server mode
-
-Running Uteke as a shared service for a team, or deploying to a server? Docker keeps it simple:
+**Docker / server mode**: for teams or remote agents.
 
 ```bash
-docker run -d \
-  -p 127.0.0.1:8767:8767 \
-  -v uteke-data:/data \
-  ghcr.io/codecoradev/uteke:latest
+docker run -d -p 127.0.0.1:8767:8767 -v uteke-data:/data ghcr.io/codecoradev/uteke:latest
+# or: uteke serve --host 0.0.0.0 --port 8767
 ```
 
-Prefer the binary directly? Run it as a daemon:
+📖 [Docker setup guide](docs/docker.md) · [Server mode docs](docs/configuration.md#server-mode)
+</details>
+
+<details>
+<summary>🏠 Rooms: multi-agent shared memory (example)</summary>
 
 ```bash
-uteke serve --host 0.0.0.0 --port 8767
-```
+# Create a shared room
+uteke room create "engineering" --description "Team decisions"
 
-Both expose the same HTTP API. Other agents and tools connect via `http://your-host:8767`. Rooms work across agents whether they're on the same machine or connecting remotely.
+# Alice's agent stores a decision
+uteke remember "We chose Redis for caching over Memcached" \
+  --room engineering --author alice
 
-📖 **[Docker setup guide](docs/docker.md)** · [Server mode docs](docs/configuration.md#server-mode)
+# Bob's agent adds context
+uteke remember "Redis cluster: 3 nodes, 2 replicas each" \
+  --room engineering --author bob
 
----
+# Any agent can recall the shared history
+uteke recall "caching decision" --room engineering
+```
 
-## 🏗️ Architecture
+Author attribution on every memory; cross-agent recall without manual sync.
+📖 [Full Rooms documentation →](docs/rooms.md)
+</details>
 
-```mermaid
-graph LR
-    Input[User Query] --> Embed[Local ONNX Embedder<br/>768d, EmbeddingGemma Q4]
-    Embed --> HNSW[HNSW Vector Index<br/>usearch]
-    Embed --> FTS5[FTS5 Full-Text<br/>SQLite]
-    HNSW --> RRF[Reciprocal Rank Fusion<br/>k=60]
-    FTS5 --> RRF
-    RRF --> Results[Ranked Results]
+<details>
+<summary>🔌 MCP config: connect to Claude Code, Cursor, Hermes</summary>
 
-    style Input fill:#4a9eff,color:#fff
-    style Results fill:#4aff9e,color:#000
-    style RRF fill:#ff9e4a,color:#fff
+```jsonc
+// .mcp.json (Claude Code, Cursor)
+{ "mcpServers": { "uteke": { "command": "uteke-mcp" } } }
 ```
 
-**How hybrid search works:**
-1. **HNSW** (usearch): finds by meaning ("deploy" matches "rollout")
-2. **FTS5** (SQLite): finds by exact terms ("deploy" matches "deploy")
-3. **RRF** (k=60): merges both ranked lists → best of both worlds
+For Claude Desktop, Hermes, and HTTP transport, see [MCP docs](docs/mcp.md).
+</details>
 
-Everything runs in-process. No network. No cloud. No server required (unless you want server mode).
+---
+
+## 📚 Documentation
+
+| | |
+|---|---|
+| **Getting started** | [Installation](INSTALL.md) · [Getting started](docs/getting-started.md) · [Onboarding](docs/getting-started.md#interactive-onboarding) |
+| **Reference** | [CLI reference](docs/cli-reference.md) · [Configuration](docs/configuration.md) · [Docker](docs/docker.md) |
+| **Integrations** | [MCP setup](docs/mcp.md) · Claude Code · Cursor · Hermes |
+| **Benchmarks** | [docs/benchmarks.md](docs/benchmarks.md) · [RESULTS.md](benchmarks/longmemeval/RESULTS.md) · [Reproduce it yourself](benchmarks/longmemeval/REPRODUCING.md) |
 
 ---
 
@@ -336,7 +309,7 @@ Everything runs in-process. No network. No cloud. No server required (unless you
 <details>
 <summary><strong>How is Uteke different from cloud-dependent memory tools?</strong></summary>
 
-Many memory layers (Python-based or TypeScript-based) require cloud API keys (OpenAI/LLM) and external infrastructure (Docker, Postgres, Qdrant). Your data gets sent to a cloud LLM provider. Uteke is a single binary with zero API keys. All embeddings run locally via ONNX. Your data never leaves your machine. [See comparison table](#-why-uteke).
+Many memory layers (Python-based or TypeScript-based) require cloud API keys (OpenAI/LLM) and external infrastructure (Docker, Postgres, Qdrant). Your data gets sent to a cloud LLM provider. Uteke is a single binary with zero API keys. All embeddings run locally via ONNX. Your data never leaves your machine. [See comparison table](#-why-uteke-).
 </details>
 
 <details>
@@ -348,31 +321,19 @@ Some platforms offer dozens of MCP tools and multi-agent shared memory via a loc
 <details>
 <summary><strong>How is Uteke different from other single-binary memory tools?</strong></summary>
 
-Some single-binary tools share our philosophy: one binary, zero deps, MCP server, local-first. The key difference is **search**: most are **FTS5-only** (keyword matching). Uteke uses **hybrid search** (HNSW vector similarity + FTS5 + Reciprocal Rank Fusion), meaning you can search by *meaning*, not just exact words. Uteke also adds rooms, time-travel, graph relationships, smart decay, document engine, and batch import.
-</details>
-
-<details>
-<summary><strong>How is Uteke different from local-mode cloud tools?</strong></summary>
-
-Some cloud-native tools now offer a local binary mode with Ollama support. Their cloud version still needs managed infrastructure (Workers, Postgres, etc.). Uteke is one binary with zero infrastructure: no Workers, no Postgres, no cloud account. Uteke also adds rooms for multi-agent collaboration, time-travel queries, and hybrid search (vector + FTS5 + RRF fusion).
-</details>
-
-<details>
-<summary><strong>What can Uteke remember?</strong></summary>
-
-Anything text-based: decisions, meeting notes, code snippets, project context, personal notes, agent state. You can tag, categorize, and link memories. The `--batch-dir` flag lets you import entire document directories.
+Some single-binary tools share our philosophy: one binary, zero deps, MCP server, local-first. The key difference is **search**: most are **FTS5-only** (keyword matching). Uteke uses **hybrid search** (HNSW vector similarity + FTS5 + Reciprocal Rank Fusion), meaning you can search by *meaning*, beyond exact words. Uteke also adds rooms, time-travel, graph relationships, smart decay, document engine, and batch import.
 </details>
 
 <details>
 <summary><strong>Does it really work offline?</strong></summary>
 
-Yes. The embedding model (EmbeddingGemma Q4, 768d) downloads once (~188MB) on first run. After that, zero network calls. No telemetry. If the local model fails, Uteke degrades gracefully to a no-op embedder. It never crashes and never calls a cloud API.
+Yes. The embedding model (EmbeddingGemma Q4, 768d) downloads once (~200MB) on first run. After that, zero network calls. No telemetry. If the local model fails, Uteke degrades gracefully to a no-op embedder. It never crashes and never calls a cloud API.
 </details>
 
 <details>
 <summary><strong>How fast is recall?</strong></summary>
 
-~45ms as a library (measured at 100–10K memories). No network round-trip because everything is local. The LRU recall cache eliminates redundant embedding computation for repeated queries.
+~45ms as a CLI (measurements: 31ms avg @10K on the published bench host, 100–10K memories, flat with store size). No network round-trip because everything is local. The LRU recall cache eliminates redundant embedding computation for repeated queries.
 </details>
 
 <details>
@@ -384,11 +345,26 @@ Yes. Uteke ships with an MCP server that works with Claude Code, Cursor, and Her
 <details>
 <summary><strong>Is it production-ready?</strong></summary>
 
-Uteke is at v0.16.0 with 200+ tests, CI/CD on every commit, and a benchmark harness. It's used in production by the CodeCora team and other early adopters. Still in 0.x, so expect rough edges, but the core is stable.
+Uteke is at v0.17.0 with 200+ tests, CI/CD on every commit, and a benchmark harness. It's used in production by the CodeCora team and other early adopters. Still in 0.x, so expect rough edges, but the core is stable.
 </details>
 
 ---
 
+## 🗺️ Roadmap & Editions
+
+| | **Uteke OSS** (this repo) | **Uteke Cloud** |
+|---|---|---|
+| Retrieval quality | ✅ Full (identical engine) | ✅ Identical (parity-benchmarked) |
+| License | Apache-2.0, self-host | Managed service |
+| LLM-powered answering (recency-aware fact resolution, abstention) | BYOK | Included |
+| Multi-workspace, dashboard, backups | DIY | ✅ Included |
+| Price | Free | *Coming soon* |
+
+The open-source engine stays full-capability. Nothing about the benchmark
+results above is paywalled. Cloud adds hosted convenience on top.
+
+---
+
 ## 🤝 Contributing
 
 ```bash
diff --git a/benchmarks/README.md b/benchmarks/README.md
new file mode 100644
index 00000000..c520cdac
--- /dev/null
+++ b/benchmarks/README.md
@@ -0,0 +1,53 @@
+# Benchmarks
+
+This directory is the single source of truth for Uteke benchmarks: what we measure,
+how, and what the numbers are.
+
+| Directory | Kind | What it measures | Status |
+|---|---|---|---|
+| [`internal/`](internal/) | Performance | Insert throughput, recall latency, storage footprint (`uteke bench`) | ✅ Maintained — re-verified on v0.17.0 (2026-09-09) |
+| [`longmemeval/`](longmemeval/) | External #1 | Retrieval quality on [LongMemEval-S](https://arxiv.org/abs/2410.10813) (500 questions, session-level) | ✅ Validated on v0.16.0 (pure-default run); -M variant **not** measured (transparent) |
+| [`locomo/`](locomo/) | External #2 | Retrieval quality on [LoCoMo](https://github.com/snap-research/locomo) (very long multi-session conversations) | 🚧 Planned — adapter + pilot |
+
+Friendly overview page: [docs/benchmarks.md](../docs/benchmarks.md).
+
+## Layout
+
+```
+benchmarks/
+├── README.md          ← you are here: index & policies
+├── internal/          ← `uteke bench` (perf) — methodology + results
+├── longmemeval/       ← LongMemEval-S harness
+│   ├── README.md      ← dataset, metric definitions, methodology
+│   ├── RESULTS.md     ← all published numbers + provenance
+│   ├── results/       ← canonical raw artifacts (committed — audit them!)
+│   ├── scripts/       ← harness, metrics, Modal fan-out infra
+│   └── data/          ← dataset (gitignored; scripts/download_data.sh)
+└── locomo/            ← LoCoMo (planned)
+```
+
+## Reproduce
+
+```bash
+# Internal performance
+uteke bench --counts 100,1000,10000 --json
+
+# LongMemEval-S (local small run)
+cd benchmarks/longmemeval
+./scripts/download_data.sh
+python3 scripts/run_eval.py --data data/longmemeval_s_cleaned.json --output results_local --limit 50
+python3 scripts/print_metrics.py results_local/retrieval_results.jsonl
+```
+
+Full 500-question runs are executed on Modal (deterministic, fan-out over 10 shards,
+resume-safe volume) — see `longmemeval/scripts/modal_fanout.py`.
+
+## Artifact policy
+
+- **Canonical raw artifacts are committed** under `longmemeval/results/` so every
+  published headline number can be recomputed straight from the repo.
+- Datasets (`data/`) and exploratory run outputs (`results_*/`) are **not** committed —
+  they are reproducible via download scripts and the Modal volume.
+- Claim discipline: every number in RESULTS.md files must trace to a committed artifact
+  or a documented Modal volume path. Retrieval runs are deterministic (no LLM in the
+  retrieval path), so independent recomputation must match to the last question.
diff --git a/benchmarks/RESULTS.md b/benchmarks/RESULTS.md
deleted file mode 100644
index 3bf90497..00000000
--- a/benchmarks/RESULTS.md
+++ /dev/null
@@ -1,29 +0,0 @@
-# Benchmark Results
-
-## Performance Benchmark (`uteke bench`)
-
-Run `uteke bench` to reproduce. Results below are indicative — actual numbers depend on hardware.
-
-| Memories | Insert (ops/sec) | Recall avg (ms) | Recall p95 (ms) | DB size | Index size |
-|----------|-----------------|-----------------|-----------------|---------|------------|
-| 100      | ~800            | ~0.3            | ~0.5            | ~45 KB  | ~12 KB     |
-| 1,000    | ~800            | ~1.2            | ~2.0            | ~450 KB | ~120 KB    |
-| 10,000   | ~750            | ~5.0            | ~8.0            | ~4.5 MB | ~1.2 MB    |
-
-Benchmarked on Oracle Cloud ARM (Ampere Altra), CPU-only.
-
-## LongMemEval-S Retrieval Accuracy
-
-Dataset: LongMemEval-S (500 questions, multi-session chatbot memory).
-Run `cd benchmarks/longmemeval && ./download_data.sh && python run_eval.py` to reproduce.
-
-### Strategy Comparison
-
-| Strategy | Questions | R@5 | R@10 | NDCG@5 | Embedding | Date |
-|----------|-----------|-----|------|--------|-----------|------|
-| **Vector** | 500 | 85.4% | 88.5% | 0.810 | EmbeddingGemma Q4 (ONNX local) | 2026-08-13 |
-| **Hybrid (RRF)** | 50 | 98.0% | 100.0% | 0.960 | EmbeddingGemma 768d (API) | 2026-08-13 |
-
-- **Vector**: Semantic similarity search using embeddings only.
-- **Hybrid (RRF)**: Reciprocal Rank Fusion of vector search + FTS5 keyword search. Default strategy.
-- RRF k=60, consistent across all code paths.
diff --git a/benchmarks/internal/RESULTS.md b/benchmarks/internal/RESULTS.md
new file mode 100644
index 00000000..5ffb73dc
--- /dev/null
+++ b/benchmarks/internal/RESULTS.md
@@ -0,0 +1,44 @@
+# Internal Performance Benchmark (`uteke bench`)
+
+Measures insert throughput, recall latency, and storage footprint of a local store,
+using deterministic synthetic memories (seeded PRNG). CPU-only: every insert performs a
+local ONNX embedding pass; no network, no external services.
+
+Methodology: memories are inserted one-by-one (embed → store → index), recall queries run
+at each scale, wall-clock percentiles and sizes are reported.
+
+## Results
+
+### v0.17.0 re-verification — 2026-09-09 (native aarch64 release build)
+
+| Scale | Insert ops/s | Insert total | Recall avg | Recall P95 | DB size | Index size |
+| --- | --- | --- | --- | --- | --- | --- |
+| 100 memories | 17.3/s | 5.8s | 31ms | 47ms | 0.7MB | 0.31MB |
+| 1,000 memories | 17.5/s | 57s | 42ms | 62ms | 5.2MB | 3.10MB |
+| 10,000 memories | 5.0/s | 33.4min | 31ms | 38ms | 80.7MB | 30.2MB |
+
+Environment: Oracle Cloud ARM (Ampere A1, 4 vCPU, 24GB RAM), Linux 6.8.0 (aarch64),
+EmbeddingGemma Q4 768d ONNX Runtime CPU, single run, no other significant load.
+
+Reading: storage matches earlier published numbers within a fraction of a percent;
+recall latency is equal or better; insert throughput is within run-to-run noise of the
+2026-08 table below. The "~10KB per memory, flat ~30–45ms recall" story is unchanged on
+v0.17.0.
+
+### Historical — v0.12.0-era table (as previously published in docs/)
+
+| Scale | Insert ops/s | Insert total | Recall avg | Recall P95 | DB size | Index size |
+| --- | --- | --- | --- | --- | --- | --- |
+| 100 memories | 18.5/s | 5.4s | 40ms | 46ms | 708KB | 319KB |
+| 1,000 memories | 21.8/s | 45.9s | 45ms | 51ms | 5.3MB | 3.2MB |
+| 10,000 memories | 6.0/s | 28.0min | 42ms | 50ms | 81.3MB | 30.3MB |
+
+Environment: same host class (Oracle Cloud ARM, Ampere A1, 4 vCPU, 24GB).
+
+## Reproduce
+
+```bash
+uteke bench --counts 100,1000,10000 --json
+# custom store location:
+uteke bench --counts 100,1000 --store /tmp/bench --json
+```
diff --git a/benchmarks/locomo/.gitignore b/benchmarks/locomo/.gitignore
new file mode 100644
index 00000000..4aafb61c
--- /dev/null
+++ b/benchmarks/locomo/.gitignore
@@ -0,0 +1,5 @@
+# LoCoMo working data — converted harness files are large (haystack per QA)
+data/
+
+# Pilot / exploratory run outputs (canonical results get committed explicitly)
+results_*/
diff --git a/benchmarks/locomo/README.md b/benchmarks/locomo/README.md
new file mode 100644
index 00000000..a0bcc893
--- /dev/null
+++ b/benchmarks/locomo/README.md
@@ -0,0 +1,36 @@
+# LoCoMo — Very Long-Term Conversational Memory (planned)
+
+External retrieval benchmark #2: [LoCoMo](https://github.com/snap-research/locomo)
+(Snap Research, ACL 2024) — very long multi-session conversations (avg ~300 turns,
+up to ~35 sessions) with QA pairs spanning information extraction, temporal reasoning,
+and adversarial unanswerable questions.
+
+**Status: 🚧 planned.** Scope below; numbers land here after the first validated run.
+
+## Why a second external benchmark
+
+LongMemEval-S alone is a single point of evidence. LoCoMo is the other benchmark the
+memory-engine ecosystem publishes on (mem0, Zep, and friends), so reporting both makes
+uteke's numbers comparable with the field — and keeps us honest when a claim only holds
+on one dataset.
+
+## Plan
+
+1. **Adapter** — map LoCoMo conversations to the same session-insert + recall pipeline
+   used by the LongMemEval harness (`../longmemeval/scripts/run_eval.py`): each LoCoMo
+   session becomes a uteke memory; QA `evidence` lists become gold session sets. Same
+   metric family (recall_any@k / recall_all@k / coverage@k / NDCG@k) so results are
+   directly comparable across benchmarks.
+2. **Pilot** — 2 conversations end-to-end (insert → recall → metrics), CPU-only local
+   ONNX embedding, default strategy (zero config), then Modal for the full 10.
+3. **Full run + ablations** — default (fusion), vector-only, fts5-only on the same data,
+   so the comparison chart gets internal ablation bars on a second dataset.
+4. **Publish** — numbers + committed raw artifacts in this directory, same audit rules
+   as `../longmemeval/` (raw committed, claims traceable).
+
+## Non-goals (for now)
+
+- LoCoMo end-to-end QA accuracy (LLM judge) — retrieval-layer only, same boundary as
+  our LongMemEval reporting. Unmeasured layers are stated as unmeasured.
+- -M-variant LongMemEval — also still open, tracked in
+  [`../longmemeval/RESULTS.md`](../longmemeval/RESULTS.md).
diff --git a/benchmarks/locomo/RESULTS.md b/benchmarks/locomo/RESULTS.md
new file mode 100644
index 00000000..9a17892e
--- /dev/null
+++ b/benchmarks/locomo/RESULTS.md
@@ -0,0 +1,110 @@
+# LoCoMo Benchmark Results — Uteke
+
+**Dataset:** [LoCoMo](https://github.com/snap-research/locomo) (Snap Research, ACL 2024) —
+10 very long multi-session conversations, **1,536 evaluable QA** (see scope note below).
+**Binary:** v0.17.0, built from exact release SHA `a2ec81a` (UTEKE_GIT_REF pin).
+**Runs:** Modal x86_64, fan-out ×10 shards, fusion = zero-config default strategy, CPU-only,
+no LLM in the retrieval path. Wall clock: 23 min (fusion).
+**Raw artifacts (committed):** [`results/`](results/) — audit snippet at the bottom.
+
+## Headline (fusion default, v0.17.0)
+
+| Metric | Value |
+|---|---|
+| **recall_any@5** | **0.840** |
+| recall_any@10 | 0.939 |
+| strict recall_all@5 | 0.716 |
+| strict recall_all@10 | 0.850 |
+| coverage@5 | 0.776 |
+| NDCG@5 | 0.652 |
+
+## Strategy comparison (same run conditions)
+
+| Strategy | strict@5 | strict@10 | cov@5 | any@5 | any@10 | NDCG@5 |
+|---|---|---|---|---|---|---|
+| **fusion (default)** | **0.716** | **0.850** | 0.776 | **0.840** | **0.939** | 0.652 |
+| vector-only | 0.680 | 0.833 | 0.737 | 0.799 | 0.928 | 0.611 |
+| fts5-only | 0.710 | 0.812 | 0.767 | 0.829 | 0.924 | 0.668 |
+
+**Fusion wins on the strict family on a second external benchmark** — replicating the
+LongMemEval-S story (see [`../longmemeval/RESULTS.md`](../longmemeval/RESULTS.md)): the two
+channels win on *different* questions, and the union is stronger than either arm.
+
+Per-category any@5 shows the complementarity directly:
+
+| Category | n | vector | fts5-only | fusion |
+|---|---|---|---|---|
+| single-hop | 282 | 0.848 | 0.759 | **0.894** |
+| temporal | 321 | 0.779 | **0.841** | 0.816 |
+| multi-hop | 92 | 0.630 | 0.641 | 0.630 |
+| open-domain | 841 | 0.810 | **0.868** | 0.855 |
+
+Multi-hop is the weakest category under every strategy (~0.63 any@5 across the board):
+connecting facts scattered across sessions is hard for pure session retrieval. Fusion
+wins single-hop outright (0.894 vs vector 0.848), where the vector channel earns its
+keep over lexical (0.848 vs 0.759); lexical wins phrasing-heavy temporal (0.841) and
+open-domain (0.868) QA, and fusion lands at-or-near the best arm per category while
+also taking the overall strict family.
+
+> **Correction (2026-09-10):** earlier revisions of this file used a rotated category
+> mapping (LoCoMo categories 1/2/3 were labeled multi-hop/single-hop/temporal instead
+> of the official single-hop/temporal/multi-hop). Overall numbers were never affected
+> (labels do not enter any metric); the tables above are recomputed from the same
+> committed retrieval results (`results/*.jsonl`) with `question_type` corrected in
+> place and `scripts/convert_locomo.py` fixed.
+
+## Full fusion breakdown
+
+| Category | n | strict@5 | strict@10 | any@5 | NDCG@5 |
+|---|---|---|---|---|---|
+| single-hop | 282 | 0.604 | 0.810 | 0.894 | 0.544 |
+| temporal | 321 | 0.796 | 0.914 | 0.816 | 0.659 |
+| multi-hop | 92 | 0.519 | 0.682 | 0.630 | 0.434 |
+| open-domain | 841 | 0.854 | 0.944 | 0.855 | 0.709 |
+
+Single-hop shows the strict-vs-any gap clearly (0.604 vs 0.894): these questions cite
+~2.7 gold sessions on average, so "all gold in top-5" is much harder than "at least
+one". Temporal QA cites almost exactly one evidence session (avg 1.1), which is why its
+strict and any@5 sit close together. The earlier "temporal is weakest, consistent with
+LongMemEval-S" reading was an artifact of the rotated labels: LoCoMo's temporal
+category is date-lookup over a single evidence turn and retrieves fine here (0.816
+any@5); the genuine weak spot is multi-hop.
+
+## Scope notes (read before quoting)
+
+- **Adversarial QA (446) excluded from the retrieval dataset** — by design they have no
+  evidence in the haystack, so R@k is undefined. Handling them well is a QA/reader-layer
+  capability (abstention), which we report as **unmeasured** here.
+- 4 QA (category 3) have evidence that parses to zero sessions — also excluded.
+- **Cross-benchmark comparison caution:** LoCoMo numbers from other systems are NOT
+  directly comparable to ours unless their setup matches (many publish with LLM
+  summarizers / different chunking / oracle evidence sets). Our numbers are
+  deterministic retrieval-layer only: sessions inserted verbatim, EmbeddingGemma Q4
+  local ONNX, zero-config default strategy. The ablation rows above are the apples-to-
+  apples comparison.
+- Dataset converted to the harness schema by
+  [`scripts/convert_locomo.py`](scripts/convert_locomo.py) (sessions → `{role, content}`
+  turns; `D1:3` evidence → `answer_session_ids`). Conversion is deterministic and
+  committed; raw LoCoMo JSON ships from the upstream repo.
+
+## Pilot consistency check
+
+Pilot run (conversation 26, 150 QA, local ARM aarch64, v0.17.0) before the full Modal run:
+overall strict@5 0.787 vs 0.753 for the same conversation inside the full x86 run — a
+3.4pp gap consistent with a 150-question sample plus cross-architecture float noise on
+near-ties (see the LongMemEval independent-reproduction section for the same effect
+characterized at 107/108 identical rankings). No systematic pilot-vs-full deviation.
+
+## Audit from the committed raw artifacts
+
+```python
+import json
+entries = [json.loads(l) for l in open("results/default-1536.jsonl")]
+s = [e["retrieval_results"]["metrics"]["session"] for e in entries]
+print("strict@5:", sum(m["recall_all@5"] == 1.0 for m in s) / len(s))  # 0.716
+print("cov@5   :", sum(m["recall_all@5"] for m in s) / len(s))         # 0.776
+```
+
+`recall_any@k` recomputes from `retrieved_session_ids` vs the dataset's
+`answer_session_ids` (0.840 @5). Same commands work for `vector-1536.jsonl` /
+`fts5-1536.jsonl`.
diff --git a/benchmarks/locomo/results/default-1536.jsonl b/benchmarks/locomo/results/default-1536.jsonl
new file mode 100644
index 00000000..0bb3fb79
--- /dev/null
+++ b/benchmarks/locomo/results/default-1536.jsonl
@@ -0,0 +1,1536 @@
+{"question_id": "conv-26_q0", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-26_s1", "locomo_conv-26_s9", "locomo_conv-26_s3", "locomo_conv-26_s5", "locomo_conv-26_s10", "locomo_conv-26_s7", "locomo_conv-26_s12", "locomo_conv-26_s14", "locomo_conv-26_s4", "locomo_conv-26_s13", "locomo_conv-26_s6", "locomo_conv-26_s16", "locomo_conv-26_s15", "locomo_conv-26_s19", "locomo_conv-26_s8", "locomo_conv-26_s11", "locomo_conv-26_s2", "locomo_conv-26_s18", "locomo_conv-26_s17"]}}
+{"question_id": "conv-26_q10", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.0, "ndcg_any@10": 0.3562071871080222, "ndcg_any@50": 0.3562071871080222}}, "retrieved_session_ids": ["locomo_conv-26_s1", "locomo_conv-26_s6", "locomo_conv-26_s10", "locomo_conv-26_s12", "locomo_conv-26_s4", "locomo_conv-26_s3", "locomo_conv-26_s8", "locomo_conv-26_s13", "locomo_conv-26_s7", "locomo_conv-26_s15", "locomo_conv-26_s9", "locomo_conv-26_s2", "locomo_conv-26_s19", "locomo_conv-26_s16", "locomo_conv-26_s5", "locomo_conv-26_s18", "locomo_conv-26_s17", "locomo_conv-26_s11", "locomo_conv-26_s14"]}}
+{"question_id": "conv-26_q20", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.6309297535714575, "ndcg_any@10": 0.6309297535714575, "ndcg_any@50": 0.6309297535714575}}, "retrieved_session_ids": ["locomo_conv-26_s1", "locomo_conv-26_s6", "locomo_conv-26_s5", "locomo_conv-26_s9", "locomo_conv-26_s10", "locomo_conv-26_s3", "locomo_conv-26_s8", "locomo_conv-26_s12", "locomo_conv-26_s14", "locomo_conv-26_s16", "locomo_conv-26_s11", "locomo_conv-26_s18", "locomo_conv-26_s17", "locomo_conv-26_s4", "locomo_conv-26_s15", "locomo_conv-26_s13", "locomo_conv-26_s19", "locomo_conv-26_s2", "locomo_conv-26_s7"]}}
+{"question_id": "conv-26_q31", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.0, "ndcg_any@10": 0.2890648263178879, "ndcg_any@50": 0.2890648263178879}}, "retrieved_session_ids": ["locomo_conv-26_s10", "locomo_conv-26_s18", "locomo_conv-26_s9", "locomo_conv-26_s8", "locomo_conv-26_s1", "locomo_conv-26_s12", "locomo_conv-26_s16", "locomo_conv-26_s2", "locomo_conv-26_s5", "locomo_conv-26_s4", "locomo_conv-26_s6", "locomo_conv-26_s15", "locomo_conv-26_s17", "locomo_conv-26_s14", "locomo_conv-26_s3", "locomo_conv-26_s11", "locomo_conv-26_s13", "locomo_conv-26_s19", "locomo_conv-26_s7"]}}
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diff --git a/benchmarks/locomo/results/fts5-1536.jsonl b/benchmarks/locomo/results/fts5-1536.jsonl
new file mode 100644
index 00000000..aedf685e
--- /dev/null
+++ b/benchmarks/locomo/results/fts5-1536.jsonl
@@ -0,0 +1,1536 @@
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diff --git a/benchmarks/locomo/results/vector-1536.jsonl b/benchmarks/locomo/results/vector-1536.jsonl
new file mode 100644
index 00000000..502ac3ae
--- /dev/null
+++ b/benchmarks/locomo/results/vector-1536.jsonl
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+{"question_id": "conv-50_q51", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s15", "locomo_conv-50_s24", "locomo_conv-50_s29", "locomo_conv-50_s7", "locomo_conv-50_s25", "locomo_conv-50_s3", "locomo_conv-50_s18", "locomo_conv-50_s21", "locomo_conv-50_s11", "locomo_conv-50_s9", "locomo_conv-50_s10", "locomo_conv-50_s6", "locomo_conv-50_s27", "locomo_conv-50_s12", "locomo_conv-50_s5", "locomo_conv-50_s8", "locomo_conv-50_s14", "locomo_conv-50_s20", "locomo_conv-50_s16", "locomo_conv-50_s2", "locomo_conv-50_s30", "locomo_conv-50_s28", "locomo_conv-50_s23", "locomo_conv-50_s1", "locomo_conv-50_s26", "locomo_conv-50_s13", "locomo_conv-50_s4", "locomo_conv-50_s19", "locomo_conv-50_s22", "locomo_conv-50_s17"]}}
+{"question_id": "conv-50_q61", "question_type": "temporal", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.3333333333333333, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.23463936301137822, "ndcg_any@10": 0.5374517914100283, "ndcg_any@50": 0.5374517914100283}}, "retrieved_session_ids": ["locomo_conv-50_s13", "locomo_conv-50_s5", "locomo_conv-50_s20", "locomo_conv-50_s22", "locomo_conv-50_s4", "locomo_conv-50_s21", "locomo_conv-50_s12", "locomo_conv-50_s25", "locomo_conv-50_s17", "locomo_conv-50_s14", "locomo_conv-50_s9", "locomo_conv-50_s28", "locomo_conv-50_s19", "locomo_conv-50_s23", "locomo_conv-50_s6", "locomo_conv-50_s26", "locomo_conv-50_s10", "locomo_conv-50_s27", "locomo_conv-50_s2", "locomo_conv-50_s7", "locomo_conv-50_s18", "locomo_conv-50_s8", "locomo_conv-50_s16", "locomo_conv-50_s1", "locomo_conv-50_s11", "locomo_conv-50_s30", "locomo_conv-50_s3", "locomo_conv-50_s15", "locomo_conv-50_s29", "locomo_conv-50_s24"]}}
+{"question_id": "conv-50_q71", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s1", "locomo_conv-50_s10", "locomo_conv-50_s3", "locomo_conv-50_s6", "locomo_conv-50_s20", "locomo_conv-50_s11", "locomo_conv-50_s24", "locomo_conv-50_s14", "locomo_conv-50_s9", "locomo_conv-50_s8", "locomo_conv-50_s16", "locomo_conv-50_s7", "locomo_conv-50_s12", "locomo_conv-50_s15", "locomo_conv-50_s30", "locomo_conv-50_s5", "locomo_conv-50_s18", "locomo_conv-50_s27", "locomo_conv-50_s26", "locomo_conv-50_s17", "locomo_conv-50_s29", "locomo_conv-50_s25", "locomo_conv-50_s19", "locomo_conv-50_s28", "locomo_conv-50_s2", "locomo_conv-50_s22", "locomo_conv-50_s13", "locomo_conv-50_s21", "locomo_conv-50_s23", "locomo_conv-50_s4"]}}
+{"question_id": "conv-50_q81", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s6", "locomo_conv-50_s20", "locomo_conv-50_s22", "locomo_conv-50_s9", "locomo_conv-50_s12", "locomo_conv-50_s10", "locomo_conv-50_s14", "locomo_conv-50_s21", "locomo_conv-50_s27", "locomo_conv-50_s4", "locomo_conv-50_s18", "locomo_conv-50_s11", "locomo_conv-50_s19", "locomo_conv-50_s25", "locomo_conv-50_s1", "locomo_conv-50_s30", "locomo_conv-50_s17", "locomo_conv-50_s16", "locomo_conv-50_s23", "locomo_conv-50_s5", "locomo_conv-50_s8", "locomo_conv-50_s13", "locomo_conv-50_s29", "locomo_conv-50_s2", "locomo_conv-50_s28", "locomo_conv-50_s7", "locomo_conv-50_s26", "locomo_conv-50_s3", "locomo_conv-50_s15", "locomo_conv-50_s24"]}}
+{"question_id": "conv-50_q91", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s12", "locomo_conv-50_s20", "locomo_conv-50_s22", "locomo_conv-50_s25", "locomo_conv-50_s14", "locomo_conv-50_s13", "locomo_conv-50_s28", "locomo_conv-50_s4", "locomo_conv-50_s2", "locomo_conv-50_s5", "locomo_conv-50_s21", "locomo_conv-50_s6", "locomo_conv-50_s18", "locomo_conv-50_s26", "locomo_conv-50_s23", "locomo_conv-50_s27", "locomo_conv-50_s9", "locomo_conv-50_s1", "locomo_conv-50_s7", "locomo_conv-50_s10", "locomo_conv-50_s17", "locomo_conv-50_s11", "locomo_conv-50_s3", "locomo_conv-50_s19", "locomo_conv-50_s16", "locomo_conv-50_s29", "locomo_conv-50_s15", "locomo_conv-50_s30", "locomo_conv-50_s8", "locomo_conv-50_s24"]}}
+{"question_id": "conv-50_q101", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.38685280723454163, "ndcg_any@10": 0.38685280723454163, "ndcg_any@50": 0.38685280723454163}}, "retrieved_session_ids": ["locomo_conv-50_s18", "locomo_conv-50_s11", "locomo_conv-50_s6", "locomo_conv-50_s29", "locomo_conv-50_s15", "locomo_conv-50_s8", "locomo_conv-50_s20", "locomo_conv-50_s27", "locomo_conv-50_s12", "locomo_conv-50_s30", "locomo_conv-50_s28", "locomo_conv-50_s10", "locomo_conv-50_s14", "locomo_conv-50_s16", "locomo_conv-50_s7", "locomo_conv-50_s21", "locomo_conv-50_s2", "locomo_conv-50_s25", "locomo_conv-50_s19", "locomo_conv-50_s5", "locomo_conv-50_s3", "locomo_conv-50_s26", "locomo_conv-50_s9", "locomo_conv-50_s13", "locomo_conv-50_s1", "locomo_conv-50_s23", "locomo_conv-50_s24", "locomo_conv-50_s22", "locomo_conv-50_s4", "locomo_conv-50_s17"]}}
+{"question_id": "conv-50_q111", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s19", "locomo_conv-50_s20", "locomo_conv-50_s22", "locomo_conv-50_s2", "locomo_conv-50_s6", "locomo_conv-50_s10", "locomo_conv-50_s17", "locomo_conv-50_s14", "locomo_conv-50_s27", "locomo_conv-50_s26", "locomo_conv-50_s5", "locomo_conv-50_s12", "locomo_conv-50_s25", "locomo_conv-50_s9", "locomo_conv-50_s28", "locomo_conv-50_s21", "locomo_conv-50_s8", "locomo_conv-50_s30", "locomo_conv-50_s13", "locomo_conv-50_s7", "locomo_conv-50_s1", "locomo_conv-50_s18", "locomo_conv-50_s16", "locomo_conv-50_s4", "locomo_conv-50_s23", "locomo_conv-50_s15", "locomo_conv-50_s11", "locomo_conv-50_s3", "locomo_conv-50_s29", "locomo_conv-50_s24"]}}
+{"question_id": "conv-50_q121", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.38685280723454163, "ndcg_any@10": 0.38685280723454163, "ndcg_any@50": 0.38685280723454163}}, "retrieved_session_ids": ["locomo_conv-50_s2", "locomo_conv-50_s12", "locomo_conv-50_s13", "locomo_conv-50_s25", "locomo_conv-50_s23", "locomo_conv-50_s5", "locomo_conv-50_s20", "locomo_conv-50_s27", "locomo_conv-50_s6", "locomo_conv-50_s14", "locomo_conv-50_s9", "locomo_conv-50_s28", "locomo_conv-50_s22", "locomo_conv-50_s18", "locomo_conv-50_s11", "locomo_conv-50_s10", "locomo_conv-50_s1", "locomo_conv-50_s7", "locomo_conv-50_s26", "locomo_conv-50_s4", "locomo_conv-50_s3", "locomo_conv-50_s30", "locomo_conv-50_s16", "locomo_conv-50_s21", "locomo_conv-50_s17", "locomo_conv-50_s19", "locomo_conv-50_s8", "locomo_conv-50_s15", "locomo_conv-50_s29", "locomo_conv-50_s24"]}}
+{"question_id": "conv-50_q131", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.0, "ndcg_any@10": 0.3333333333333333, "ndcg_any@50": 0.3333333333333333}}, "retrieved_session_ids": ["locomo_conv-50_s11", "locomo_conv-50_s18", "locomo_conv-50_s29", "locomo_conv-50_s28", "locomo_conv-50_s6", "locomo_conv-50_s12", "locomo_conv-50_s25", "locomo_conv-50_s8", "locomo_conv-50_s30", "locomo_conv-50_s16", "locomo_conv-50_s3", "locomo_conv-50_s5", "locomo_conv-50_s21", "locomo_conv-50_s7", "locomo_conv-50_s14", "locomo_conv-50_s26", "locomo_conv-50_s20", "locomo_conv-50_s27", "locomo_conv-50_s2", "locomo_conv-50_s9", "locomo_conv-50_s10", "locomo_conv-50_s13", "locomo_conv-50_s15", "locomo_conv-50_s23", "locomo_conv-50_s19", "locomo_conv-50_s22", "locomo_conv-50_s1", "locomo_conv-50_s4", "locomo_conv-50_s24", "locomo_conv-50_s17"]}}
+{"question_id": "conv-50_q141", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["locomo_conv-50_s27", "locomo_conv-50_s30", "locomo_conv-50_s6", "locomo_conv-50_s26", "locomo_conv-50_s14", "locomo_conv-50_s10", "locomo_conv-50_s20", "locomo_conv-50_s16", "locomo_conv-50_s8", "locomo_conv-50_s9", "locomo_conv-50_s3", "locomo_conv-50_s1", "locomo_conv-50_s7", "locomo_conv-50_s18", "locomo_conv-50_s19", "locomo_conv-50_s22", "locomo_conv-50_s17", "locomo_conv-50_s25", "locomo_conv-50_s24", "locomo_conv-50_s13", "locomo_conv-50_s2", "locomo_conv-50_s12", "locomo_conv-50_s29", "locomo_conv-50_s5", "locomo_conv-50_s15", "locomo_conv-50_s21", "locomo_conv-50_s28", "locomo_conv-50_s11", "locomo_conv-50_s23", "locomo_conv-50_s4"]}}
+{"question_id": "conv-50_q151", "question_type": "open-domain", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.0, "ndcg_any@10": 0.3562071871080222, "ndcg_any@50": 0.3562071871080222}}, "retrieved_session_ids": ["locomo_conv-50_s12", "locomo_conv-50_s6", "locomo_conv-50_s5", "locomo_conv-50_s1", "locomo_conv-50_s9", "locomo_conv-50_s30", "locomo_conv-50_s11", "locomo_conv-50_s20", "locomo_conv-50_s27", "locomo_conv-50_s2", "locomo_conv-50_s14", "locomo_conv-50_s19", "locomo_conv-50_s18", "locomo_conv-50_s13", "locomo_conv-50_s22", "locomo_conv-50_s26", "locomo_conv-50_s8", "locomo_conv-50_s28", "locomo_conv-50_s7", "locomo_conv-50_s10", "locomo_conv-50_s25", "locomo_conv-50_s3", "locomo_conv-50_s21", "locomo_conv-50_s17", "locomo_conv-50_s29", "locomo_conv-50_s15", "locomo_conv-50_s23", "locomo_conv-50_s16", "locomo_conv-50_s4", "locomo_conv-50_s24"]}}
diff --git a/benchmarks/locomo/scripts/convert_locomo.py b/benchmarks/locomo/scripts/convert_locomo.py
new file mode 100644
index 00000000..d3dffedd
--- /dev/null
+++ b/benchmarks/locomo/scripts/convert_locomo.py
@@ -0,0 +1,111 @@
+#!/usr/bin/env python3
+"""Convert LoCoMo (snap-research) conversations into the LongMemEval harness schema.
+
+Output files feed DIRECTLY into ../longmemeval/scripts/run_eval.py — same fields,
+same metric family, so numbers are comparable across benchmarks and no second
+evaluator exists to maintain.
+
+Mapping (LoCoMo -> harness):
+- conversation.session_N (list of {speaker, text, dia_id}) -> haystack_sessions[N]
+- conversation.session_N_date_time                        -> haystack_dates[N]
+- qa[].question / .category                               -> question / question_type
+- qa[].evidence "D1:3" (dia_id prefix)                    -> answer_session_ids
+
+Methodology decisions (recorded in README.md):
+- Category 5 (adversarial / unanswerable) is EXCLUDED from the retrieval dataset —
+  it has no gold evidence, so R@k is undefined for it. Reported separately.
+- QA whose evidence parses to zero sessions (4 QA in the full set, category 3) are
+  skipped and counted in the summary.
+- question_date is left empty (LoCoMo does not ship one); the harness temporal
+  boost is OFF by default, so this affects nothing.
+"""
+#!/usr/bin/env python3
+import argparse
+import json
+import re
+import sys
+from pathlib import Path
+
+CATEGORY_NAMES = {1: "single-hop", 2: "temporal", 3: "multi-hop", 4: "open-domain", 5: "adversarial"}
+EV_RE = re.compile(r"D(\d+):")
+
+
+def parse_evidence(evidence):
+    """'D1:3' / 'D1:3; D1:5' / 'D1:3 D2:1' -> {1, 3...} session numbers."""
+    out = set()
+    for e in evidence or []:
+        out.update(int(m) for m in EV_RE.findall(e))
+    return out
+
+
+def convert(data_path, out_dir):
+    data = json.loads(Path(data_path).read_text())
+    out_dir = Path(out_dir)
+    out_dir.mkdir(parents=True, exist_ok=True)
+
+    summary = {"conversations": [], "skipped_adversarial": 0, "skipped_no_evidence": 0}
+    for conv in data:
+        sample_id = conv["sample_id"]
+        talks = conv["conversation"]
+        sess_nums = sorted(
+            int(m.group(1))
+            for k in talks
+            if (m := re.fullmatch(r"session_(\d+)", k))
+        )
+        sids = [f"locomo_{sample_id}_s{n}" for n in sess_nums]
+        # Normalize turns to the harness contract ({role, content}):
+        # session_to_text() reads turn["role"] / turn["content"]; LoCoMo ships
+        # speaker/text (+dia_id, kept for provenance/debugging).
+        sessions = [
+            [{"role": t.get("speaker", "unknown"), "content": t.get("text", ""), **{
+                k: v for k, v in t.items() if k not in ("speaker", "text")}}
+             for t in talks[f"session_{n}"]]
+            for n in sess_nums
+        ]
+        dates = [talks.get(f"session_{n}_date_time", "") for n in sess_nums]
+
+        entries, n_adv, n_nogold = [], 0, 0
+        for idx, qa in enumerate(conv["qa"]):
+            cat = qa["category"]
+            if cat == 5:
+                n_adv += 1
+                continue
+            gold = parse_evidence(qa.get("evidence"))
+            if not gold:
+                n_nogold += 1
+                continue
+            entries.append({
+                "question_id": f"{sample_id}_q{idx}",
+                "question_type": CATEGORY_NAMES[cat],
+                "question": qa["question"],
+                "question_date": "",
+                "answer": qa.get("answer", ""),
+                "answer_session_ids": sorted(f"locomo_{sample_id}_s{n}" for n in gold),
+                "haystack_session_ids": sids,
+                "haystack_sessions": sessions,
+                "haystack_dates": dates,
+            })
+
+        out_file = out_dir / f"locomo_{sample_id}.json"
+        out_file.write_text(json.dumps(entries, ensure_ascii=False, indent=1))
+        summary["conversations"].append({
+            "sample_id": sample_id, "sessions": len(sess_nums),
+            "qa_total": len(conv["qa"]), "qa_evaluable": len(entries),
+            "skipped_adversarial": n_adv, "skipped_no_evidence": n_nogold,
+            "file": out_file.name,
+        })
+        print(f"conv {sample_id}: {len(sess_nums)} sessions -> {len(entries)} evaluable QA "
+              f"({n_adv} adversarial, {n_nogold} no-evidence skipped)")
+
+    (out_dir / "conversion_summary.json").write_text(json.dumps(summary, indent=1))
+    total = sum(c["qa_evaluable"] for c in summary["conversations"])
+    print(f"\nTotal evaluable QA: {total} across {len(summary['conversations'])} conversations")
+    return summary
+
+
+if __name__ == "__main__":
+    ap = argparse.ArgumentParser(description=__doc__)
+    ap.add_argument("--data", default="locomo10.json", help="path to locomo10.json")
+    ap.add_argument("--out", default="harness", help="output directory for harness-schema files")
+    args = ap.parse_args()
+    convert(args.data, args.out)
diff --git a/benchmarks/longmemeval/.gitignore b/benchmarks/longmemeval/.gitignore
index da0a8db6..70c9fb25 100644
--- a/benchmarks/longmemeval/.gitignore
+++ b/benchmarks/longmemeval/.gitignore
@@ -1,9 +1,17 @@
-# LongMemEval dataset (downloaded separately)
+# LongMemEval dataset (downloaded separately via scripts/download_data.sh)
 data/
 
-# Results (commit RESULTS.md, ignore raw outputs)
-results/*.jsonl
-results/*.log
+# Results: canonical published artifacts ARE committed (allow-listed below);
+# exploratory run outputs (results_*/ directories) stay local / on Modal volume
+results/*
+!results/default-500q.jsonl
+!results/default-500q-v017.jsonl
+!results/fts5-500q.jsonl
+!results/contradiction-f3-metrics.json
+results_*/
+
+# Run logs
+*.log
 
 # Python
 __pycache__/
diff --git a/benchmarks/longmemeval/README.md b/benchmarks/longmemeval/README.md
index 8eaf04c9..555daf57 100644
--- a/benchmarks/longmemeval/README.md
+++ b/benchmarks/longmemeval/README.md
@@ -1,37 +1,47 @@
 # LongMemEval Benchmark Harness for Uteke
 
-This harness evaluates uteke's retrieval quality against the [LongMemEval](https://github.com/xiaowu0162/LongMemEval) benchmark (ICLR 2025).
+This harness evaluates uteke's retrieval quality against the [LongMemEval](https://github.com/xiaowu0162/LongMemEval) benchmark (ICLR 2025), session-level retrieval, no LLM anywhere in the retrieval path (deterministic — an independent recompute of the committed raw artifacts must match to the last question).
+
+**Published numbers: [`RESULTS.md`](RESULTS.md). Canonical raw artifacts: [`results/`](results/).**
 
 ## Quick Start
 
 ```bash
-# 1. Download LongMemEval dataset (oracle subset, ~5MB)
-./download_data.sh
+# 1. Download LongMemEval dataset
+./scripts/download_data.sh
 
 # 2. Install Python deps
-pip install -r requirements.txt
+pip install -r scripts/requirements.txt
 
 # 3. Build uteke (ensure `uteke` binary is on PATH)
 cargo build --release
 
-# 4. Run retrieval evaluation
-python run_eval.py --data data/longmemeval_oracle.json --output results/
+# 4. Run retrieval evaluation (small local run)
+python3 scripts/run_eval.py --data data/longmemeval_oracle.json --output results_local --limit 50
 
 # 5. Print metrics
-python print_metrics.py results/retrieval_results.jsonl
+python3 scripts/print_metrics.py results_local/retrieval_results.jsonl
 ```
 
+Full 500-question runs run on Modal with fan-out over 10 shards (resume-safe volume):
+`scripts/modal_fanout.py`. Set `UTEKE_GIT_REF=<exact-sha>` to build the image from a
+specific commit for pre-release validation.
+
 ## What It Measures
 
 **Retrieval accuracy** — how well does uteke recall the correct evidence sessions?
 
-Metrics:
-- **Session-level Recall@k** — fraction of evidence sessions in top-k results
-- **Session-level NDCG@k** — ranking quality
+| Metric | Definition |
+|---|---|
+| `recall_any@k` | A question passes when **at least one** gold session appears in top-k — the metric competitor benchmarks publish |
+| `recall_all@k` (strict) | **Every** gold session must appear in top-k (binary per question). Harder; bounded below `recall_any`. Reported as binary mean and as partial-credit coverage |
+| `ndcg_any@k` | Ranking quality over gold sessions |
+| `coverage@k` | Partial credit per question (fraction of gold sessions retrieved in top-k) |
 
 > Turn-level metrics require per-turn indexing and are not measured by this harness.
 
 Question types evaluated:
+
 | Type | Description |
 |------|-------------|
 | `single-session-user` | Single-session user info extraction |
@@ -40,42 +50,44 @@ Question types evaluated:
 | `multi-session` | Cross-session reasoning |
 | `knowledge-update` | Updated information recall |
 | `temporal-reasoning` | Time-based reasoning |
+| `abstention` | Unanswerable questions — the evidence does **not** exist in the haystack; the metric family is computed per the harness and reported as a separate group |
 
-## How It Works
+### The 470 + 30 decomposition
 
-1. For each of the 500 questions:
-   - Extract `haystack_sessions` (chat history with timestamps)
-   - Insert each session as a uteke memory (content = session text, timestamp = session date)
-2. Run `uteke recall` with the question as query
-3. Check if evidence sessions appear in top-k results
-4. Calculate Recall@5, Recall@10, NDCG@5, NDCG@10
+The cleaned -S set has 500 questions: **470 answerable + 30 abstention** (`question_id`
+suffix `_abs`). Aggregates in RESULTS.md are labeled explicitly:
+`Overall (470 non-abstention)`, `Abstention (30)`, and `Overall (full 500)`. `recall_any`
+numbers quoted against competitor figures are full-500; strict-family numbers are reported
+on both bases. No questions are silently dropped.
 
 ## Dataset
 
-Download from [HuggingFace](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned):
-- `longmemeval_oracle.json` — oracle retrieval (evidence sessions only, ~5MB)
-- `longmemeval_s_cleaned.json` — short version (~115k tokens history, ~50MB)
-- `longmemeval_m_cleaned.json` — medium version (~500 sessions, ~200MB)
-
-We recommend starting with `longmemeval_oracle.json` for quick iteration.
+Download via `scripts/download_data.sh` from
+[HuggingFace](https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned):
 
-## Evaluation Models
+- `longmemeval_oracle.json` — oracle retrieval (evidence sessions only, ~5MB), quick iteration
+- `longmemeval_s_cleaned.json` — short variant, **what we publish on** (~115k tokens history, 500 questions)
+- `longmemeval_m_cleaned.json` — medium variant (~500 sessions, ~200MB)
 
-Retrieval uses uteke's built-in EmbeddingGemma Q4 (768d). No external API needed.
+The -M variant is **unmeasured** — stated transparently rather than extrapolated.
 
-Answer correctness evaluation (optional) uses GPT-4o via the official LongMemEval script:
-```bash
-export OPENAI_API_KEY=your_key
-cd ../LongMemEval/src/evaluation
-python evaluate_qa.py gpt-4o ../../uteke-bench/results/hypotheses.jsonl ../../data/longmemeval_oracle.json
-```
+## How It Works
 
-## Results
+1. For each question: extract `haystack_sessions` (chat history with timestamps), insert each session as a uteke memory (content = session text, timestamp = session date)
+2. Run `uteke recall` with the question as query (default strategy — what a fresh install gets, zero config)
+3. Check whether gold sessions appear in top-k
+4. Report `recall_any/all@k`, `coverage@k`, `ndcg_any@k`
 
-See `results/` for the latest benchmark output. Add results to the main README's benchmark table.
+Retrieval uses uteke's built-in EmbeddingGemma Q4 (768d) via local ONNX. No external API,
+no network round-trip.
 
-## Dependencies
+## Layout
 
-- Python 3.9+
-- uteke CLI (built from this repo)
-- `tqdm`, `rank_bm25`, `numpy` (see `requirements.txt`)
+```
+longmemeval/
+├── README.md      ← this file: dataset + metric definitions
+├── RESULTS.md     ← all published numbers + provenance
+├── results/       ← canonical raw artifacts (committed — recompute them!)
+├── scripts/       ← harness, metrics, Modal fan-out infra
+└── data/          ← dataset (gitignored; download_data.sh)
+```
diff --git a/benchmarks/longmemeval/REPRODUCING.md b/benchmarks/longmemeval/REPRODUCING.md
new file mode 100644
index 00000000..6e7dd41d
--- /dev/null
+++ b/benchmarks/longmemeval/REPRODUCING.md
@@ -0,0 +1,179 @@
+# Reproducing the LongMemEval-S results — run it yourself
+
+Every number we publish is generated by an open harness, committed to this repo,
+from a public dataset. You don't have to trust us — this page shows how to make
+the computer tell you the truth yourself. Three budgets, pick one:
+
+| You have... | Path | Time | Cost |
+|---|---|---|---|
+| 5 minutes | [Verify the committed results](#1-verify-the-committed-results-5-min-no-embedder-needed) | ~1 min | $0 |
+| ~1 hour | [Run a real subset locally](#2-run-a-real-subset-locally-50-questions) | ~1-2 h | $0 |
+| ~$10 | [Full 500-question run](#3-full-500-question-run-modal-or-any-linux-box) | ~3 h | ~$5-10 Modal credits |
+
+**What you need:** a machine with `cargo` (Rust 1.85+), Python 3.10+, and ~4 GB free
+disk. The embedding model (~200 MB) downloads once on first run. Everything else is
+in this repo.
+
+> **Published numbers (v0.17.0, 500 questions, LongMemEval-S):**
+> recall_any@5 **98.4%** · recall_any@10 98.8% · strict recall_all@5 88.0% ·
+> coverage@5 94.4% — all on the full 500-question basis. On the 470
+> non-abstention questions (the 30 `_abs` questions are reported separately:
+> they measure answering, not retrieval), strict recall_all@5 is 88.3% and
+> coverage@5 is 94.7%.
+> What these metric names mean: [docs/benchmarks.md](../../docs/benchmarks.md) and
+> [`RESULTS.md`](RESULTS.md#reading-the-metrics).
+
+**Committed raw outputs** (verify without running anything — section 1 below):
+
+| File | Run | Headline |
+|---|---|---|
+| [`results/default-500q-v017.jsonl`](results/default-500q-v017.jsonl) | v0.17.0 (current release binary) | any@5 **98.4%** |
+| [`results/default-500q.jsonl`](results/default-500q.jsonl) | v0.16.0 (canonical validation) | any@5 98.3% |
+
+---
+
+## 1) Verify the committed results (5 min, no embedder needed)
+
+The raw per-question output of the full 500-question runs is committed under
+[`results/`](results/) — one JSONL per measured release (table above). The harness
+stores the retrieved session ids (ranked) for every question, and the dataset
+stores which sessions are correct (`answer_session_ids`). Scoring is a 20-line
+join — swap the filename to check either release:
+
+```bash
+cd benchmarks/longmemeval
+
+python3 - <<'EOF'
+import json
+
+RAW = 'results/default-500q-v017.jsonl'   # v0.17.0 (headline); or default-500q.jsonl for v0.16.0
+rows = {}
+for line in open(RAW):
+    line = line.strip()
+    if not line:
+        continue
+    r = json.loads(line)
+    rows[r['question_id']] = r['retrieval_results']['retrieved_session_ids']
+
+ds = json.load(open('data/longmemeval_s_cleaned.json'))
+gold = {q['question_id']: set(q['answer_session_ids']) for q in ds}
+
+non_abs = [q for q in rows if '_abs' not in q]
+any5 = sum(1 for q in non_abs if gold[q] & set(rows[q][:5]))
+all5 = sum(1 for q in non_abs if len(gold[q] & set(rows[q][:5])) == len(gold[q]))
+cov5 = sum(len(gold[q] & set(rows[q][:5])) / len(gold[q]) for q in non_abs)
+print(f"questions scored (non-abstention): {len(non_abs)}")
+print(f"recall_any@5   = {any5}/{len(non_abs)} = {any5/len(non_abs):.4f}")
+print(f"recall_all@5   = {all5}/{len(non_abs)} = {all5/len(non_abs):.4f}")
+print(f"coverage R@5   = {cov5/len(non_abs):.4f}")
+print(f"total-miss@50  = {sum(1 for q in non_abs if not (gold[q] & set(rows[q][:50])))}")
+EOF
+```
+
+Expected output — `recall_any@5` = **0.9851** (v0.17.0 file) or **0.9830**
+(v0.16.0 file), `total-miss@50 = 0`: every question has all its gold sessions
+somewhere in the top-50; everything below perfect is ranking order, not missing
+data.
+
+If the file ever stops matching the README, that's a bug — open an issue.
+
+## 2) Run a real subset locally (50 questions)
+
+This actually runs uteke: builds the store from each question's haystack, embeds
+~2,400 sessions, and measures what the retriever brings back. Deterministic on the
+same architecture — your numbers should match ours.
+
+```bash
+cd benchmarks/longmemeval
+
+# 1. Dataset (oracle subset = the 50 hardest-with-known-gold questions, ~5 MB)
+./scripts/download_data.sh
+
+# 2. Python deps (stdlib is almost enough; print_metrics.py uses numpy if present)
+pip install -r scripts/requirements.txt
+
+# 3. Build uteke from this repo (the harness resolves THIS binary first —
+#    never a uteke from your PATH)
+cd ../.. && cargo build --release -p uteke-cli && cd benchmarks/longmemeval
+
+# 4. Run 50 questions (first run downloads the ~200 MB embedding model once)
+python3 scripts/run_eval.py \
+    --data data/longmemeval_oracle.json \
+    --output results_mine \
+    --limit 50
+
+# 5. Score it
+python3 scripts/print_metrics.py results_mine/retrieval_results.jsonl
+```
+
+Notes:
+- Runs pinned to 2 cores at low priority — your machine stays usable.
+- ~1-2 h for 50 questions on a modern laptop (embedding is the cost).
+- Every question is stored with its own isolated store; no cross-contamination.
+- Want the exact published split instead of oracle? Swap in the full dataset:
+  `curl -L -o data/longmemeval_s_cleaned.json \
+   https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json`
+  then run with `--limit 100` (or more) on that file.
+
+## 3) Full 500-question run (Modal, or any Linux box)
+
+The published numbers come from the fan-out harness on
+[Modal](https://modal.com) (10 shards × 50 questions, resumed automatically on
+preemption). Same harness, bigger loop:
+
+```bash
+cd benchmarks/longmemeval
+
+# dataset (full S split, ~50 MB)
+curl -L -o data/longmemeval_s_cleaned.json \
+  https://huggingface.co/datasets/xiaowu0162/longmemeval-cleaned/resolve/main/longmemeval_s_cleaned.json
+
+# pin the EXACT binary you want to measure (builds from source inside the image;
+# do not skip this — an untagged image silently measures whatever it was built with)
+export UTEKE_GIT_REF=<commit-sha-or-tag>     # e.g. the v0.17.0 release tag
+export MODAL_TOKEN_ID=... MODAL_TOKEN_SECRET=...
+
+modal run scripts/modal_fanout.py --strategy default --num-shards 10
+
+# watch progress (shards appear at completion), then score the merged output:
+python3 scripts/print_metrics.py results_v017_revalidation/retrieval_results.jsonl
+```
+
+Total cost last run: **under $10** (~2.5 h wall clock, 2 preemptions auto-recovered).
+No Modal account? The same `run_eval.py` runs on any Linux box — it just takes
+longer serially (~4 min/question on a 4-core desktop).
+
+---
+
+## Reading the metrics (one paragraph)
+
+LongMemEval-S hides facts across ~115 chat sessions per question and asks where
+the **evidence sessions** rank. We report two families because they answer
+different questions:
+
+- **recall_any@K** — "did the retriever surface *the* evidence?" (at least one
+  gold session in top-K). This is the metric competitor benchmarks publish;
+  ours: **98.4% @ 5** (full 500-question set).
+- **recall_all@K (strict)** — "did it get *all* of them?" (every gold session in
+  top-K; 65% of the 500 questions have multiple gold sessions). The honest
+  ceiling-capable number: **88.0% @ 5** on the full 500 (88.3% on the 470
+  non-abstention) — mathematical ceiling is 99.4% (3 questions have 6 gold
+  sessions; top-5 physically can't hold all).
+- **coverage R@K** — partial credit (fraction of gold sessions found). 94.4%
+  full-500 (94.7% on the 470 non-abstention).
+
+Per-category breakdown, the 30 abstention questions, contradiction segment, and
+the full v0.16.0 → v0.17.0 comparison: [`RESULTS.md`](RESULTS.md),
+[`docs/benchmarks.md`](../../docs/benchmarks.md).
+
+## Reproducibility guarantees
+
+- **Deterministic**: same binary + same dataset + same architecture ⇒ identical
+  rankings. The harness embeds nothing randomly and stores ranked session ids
+  for every question, so any claim can be re-derived from committed artifacts.
+- **Pinned provenance**: published runs record the exact git ref baked into the
+  binary (`UTEKE_GIT_REF`) and the raw JSONL is committed — no "trust me".
+- **Cross-architecture honesty**: ARM vs x86 float noise can swap *adjacent*
+  ranks on tail questions (documented: 107/108 questions identical in our
+  own re-run, 1 adjacent-rank near-tie with the same top-10 set). Aggregates
+  are stable; individual rank order is stable up to ±1 near ties.
diff --git a/benchmarks/longmemeval/RESULTS.md b/benchmarks/longmemeval/RESULTS.md
index e5540c98..ed739f23 100644
--- a/benchmarks/longmemeval/RESULTS.md
+++ b/benchmarks/longmemeval/RESULTS.md
@@ -1,13 +1,84 @@
 # LongMemEval-S Benchmark Results
 
-**Dataset:** `longmemeval_s_cleaned.json` (500 questions, 6 question types)
-**Metric:** Session-level retrieval (Recall@k, NDCG@k)
+**Dataset:** `longmemeval_s_cleaned.json` — 500 questions = 470 answerable + 30 abstention (`_abs`), 6+1 question types
+**Metric:** Session-level retrieval (Recall@k, NDCG@k). Deterministic — no LLM in the retrieval path.
+
+**TL;DR headline (v0.17.0 re-validation, full 500 questions — see section below):**
+recall_any@5 = **0.984** · strict recall_all@5 = **0.880** · strict recall_all@10 = **0.954** · coverage@5 = **0.944**
+(Original v0.16.0 validation run: 0.982 / 0.880 / 0.954 / 0.943 — raw committed alongside the re-validation run.)
+
+Every number below can be recomputed from the committed raw artifacts in
+[`results/`](results/) — instructions at the bottom. See also the
+[metric definitions](README.md#what-it-measures).
 
 ---
 
+## Pure-default run — 500 questions, Modal x86_64, binary v0.16.0 from git `bfbc296` (2026-08-29)
+
+Pre-release validation of the shipped default: `--strategy default` (no flag at all)
+against the full 500-question dataset. Binary built in-image from exact SHA `bfbc296`
+(PR #1137/#1138); the image build prints `uteke 0.16.0`.
+
+Raw artifact (committed): [`results/default-500q.jsonl`](results/default-500q.jsonl).
+
+| Question type | n | R@5 (strict) | R@10 (strict) | any@5 | any@10 | NDCG@5 |
+|---|---|---|---|---|---|---|
+| knowledge-update | 72 | 1.000 | 1.000 | 1.000 | 1.000 | 0.972 |
+| single-session-assistant | 56 | 0.982 | 1.000 | 0.982 | 1.000 | 0.959 |
+| single-session-user | 64 | 0.969 | 0.969 | 0.969 | 0.969 | 0.913 |
+| single-session-preference | 30 | 0.967 | 0.967 | 0.967 | 0.967 | 0.839 |
+| temporal-reasoning | 127 | 0.920 | 0.962 | 0.984 | 0.992 | 0.849 |
+| multi-session | 121 | 0.906 | 0.976 | 0.983 | 0.992 | 0.880 |
+| abstention | 30 | 0.906 | 0.950 | 0.967 | 0.967 | 0.844 |
+| **Overall (470 non-abstention)** | 470 | **0.946** | 0.977 | 0.983 | 0.989 | 0.897 |
+| **Abstention (30)** | 30 | **0.906** | 0.950 | 0.967 | 0.967 | 0.844 |
+| **Overall (full 500)** | 500 | **0.943** | 0.975 | **0.982** | 0.988 | 0.894 |
+
+Strict-family headline on the full 500: recall_all@5 = **0.880** (binary), coverage@5 = **0.943**
+(partial credit). Mathematical ceiling for strict@5 is **0.994** — 3 questions have more than
+5 gold sessions.
+
+### Baselines & deltas (corrected labels, 2026-09-09)
+
+- **Vector-only baseline (full 500, 2026-08-13):** R@5 = 0.854 / R@10 = 0.885 / NDCG@5 = 0.810.
+  Raw artifact lives on the benchmark Modal volume (`results_vector`), not committed.
+  Fusion default vs this baseline: **+8.9pp on the same full-500 basis** (0.854 → 0.943),
+  **+9.2pp** against the 470-non-abstention aggregate (0.854 → 0.946) — zero configuration.
+- **Correction note:** this baseline was previously labeled "v0.15.0 hybrid" in this file.
+  The 0.854/0.885/2026-08-13 figures are the **vector-only** run (see Strategy Comparison
+  below). A hybrid 500-question run has never been executed; hybrid is validated at 50
+  questions (0.980 R@5). The vector-only comparison is the honest zero-config story:
+  fusion adds the lexical channel's wins on top of semantic-only retrieval.
+
+### Where the remaining failures live (structural facts)
+
+- Ranking-side optimization is **closed**: fusion is shipped, 3-way RRF is a measured NO-GO
+  (see below), and the weight sweep sits mid-plateau.
+- 51 questions miss partially at @5 (strict family); **44 of them miss exactly one gold
+  session** — the lever is insert-side granularity (multi-session grouping/diversity),
+  not ranking.
+- The two weakest categories (temporal 0.920, multi-session 0.906) are also the two
+  largest (53% of questions) — the aggregate is honestly weighted. Counterfactual:
+  dropping temporal-reasoning lifts the 470-aggregate by +0.95pp.
+- Abstention questions (evidence does not exist in the haystack) are scored by the same
+  retrieval-metric family and reported as a separate group; end-to-end QA accuracy
+  (reader/answering layer, incl. abstention behavior) is **unmeasured**.
+
+## FTS5-only ablation — 500 questions, Modal x86_64 (2026-08-29)
+
+Raw artifact (committed): [`results/fts5-500q.jsonl`](results/fts5-500q.jsonl).
+
+| Metric | Value |
+|---|---|
+| recall_any@5 | **0.914** |
+| coverage@5 | 0.821 |
+
+Lexical-only retrieval is strong (BM25/FTS5 phrasing matches) but fails hardest on
+temporal-reasoning and multi-session — the exact classes fusion's vector channel covers.
+
 ## Independent Reproduction (2026-09-01)
 
-The published 500Q run was produced on Modal x86. To test whether the result depends on that infrastructure, a 108-question subset (pref: 30, kupd: 78) was re-run locally on a 4-core ARM desktop (Oracle Ampere A1, aarch64) — same v0.16.0 binary, same harness (`run_eval.py`), different CPU architecture.
+The published 500Q run was produced on Modal x86. To test whether the result depends on that infrastructure, a 108-question subset (pref: 30, kupd: 78) was re-run locally on a 4-core ARM desktop (Oracle Ampere A1, aarch64) — same v0.16.0 binary, same harness (`scripts/run_eval.py`), different CPU architecture.
 
 | Subset | Questions | Identical per-question rankings | R@5 published | R@5 re-run |
 |---|---|---|---|---|
@@ -20,14 +91,40 @@ The published 500Q run was produced on Modal x86. To test whether the result dep
 **Reproduce:**
 
 ```bash
-python3 run_eval.py --data data/subset_kupd.json --output results_rerun --strategy default --resume
+python3 scripts/run_eval.py --data data/subset_kupd.json --output results_rerun --strategy default --resume
 ```
 
-Raw artifacts are kept on the benchmark Modal volume (`uteke-longmemeval`, `default/` and rerun prefixes), consistent with the published run — datasets and result JSONL files are not committed to git (see `.gitignore` here; `download_data.sh` fetches the dataset).
+Raw artifacts are kept on the benchmark Modal volume (`uteke-longmemeval`, `default/` and rerun prefixes), consistent with the published run — datasets and exploratory result files are not committed to git (see `.gitignore` here; `scripts/download_data.sh` fetches the dataset). **Canonical published artifacts ARE committed** under `results/`.
 
----
+## v0.17.0 re-validation — ✅ DONE (2026-09-09)
+
+Full 500-question re-run on the v0.17.0 binary (built in-image from exact release SHA
+`a2ec81a` via `UTEKE_GIT_REF`, same 10-shard Modal fan-out, shards isolated under
+`default-v017/` on the volume so the v0.16.0 artifacts could not be resume-skipped).
+
+Raw artifact (committed): [`results/default-500q-v017.jsonl`](results/default-500q-v017.jsonl).
+
+| Metric | v0.16.0 | v0.17.0 | Δ |
+|---|---|---|---|
+| strict recall_all@5 | 0.8800 | **0.8800** | ±0.00pp |
+| strict recall_all@10 | 0.9540 | **0.9540** | ±0.00pp |
+| coverage@5 | 0.9433 | 0.9443 | +0.09pp |
+| recall_any@5 | 0.9820 | **0.9840** | +0.20pp |
+| recall_any@10 | 0.9880 | 0.9880 | ±0.00pp |
+
+Per-question comparison (n=500): 238 identical rankings, 254 reorder-only (identical
+top-k sets, near-tie swaps), **8** questions with a different top-10 set — of the 3
+questions whose strict@5 changed, all stayed within partial credit (none flipped
+perfect↔imperfect). The retrieval scoring path is confirmed unchanged in 0.17.0: the
+headline numbers above are now **re-validated on v0.17.0**, not merely attributed to
+the v0.16.0 run.
+
+```bash
+# reproduce
+UTEKE_GIT_REF=a2ec81a0915a242cee6e1de8811491e4fad1d4da modal run scripts/modal_fanout.py --strategy default --tag v017
+```
 
-## Uteke Retrieval — Strategy Comparison
+## Strategy Comparison (historical runs)
 
 ### Vector (semantic only)
 
@@ -41,7 +138,7 @@ Raw artifacts are kept on the benchmark Modal volume (`uteke-longmemeval`, `defa
 |-----------|-----|------|------|--------|---------|-----------|------|
 | 50 | 98.0% | 100.0% | 100.0% | 0.960 | 0.967 | EmbeddingGemma 768d (API) | 2026-08-13 |
 
-**Improvement (Hybrid vs Vector): +12.6pp R@5**
+**Improvement (Hybrid vs Vector, 50Q): +12.6pp R@5**
 
 ### Fusion (weighted RRF of vector + hybrid rankings, #1123) — default since 0.16.0
 
@@ -66,8 +163,6 @@ fast10 detail: 1 partial miss @5 (60472f9c multi-session, R@5 0.67 → R@10 1.0)
 
 **Tuning evidence:** weight grid sweep on actual x86 rankings; chosen value sits mid-plateau (stable across a range), not at an edge. Weights are internal implementation details (see core crate) rather than part of the public contract.
 
----
-
 ## 50Q Hybrid — Per Question Type Breakdown
 
 | Question Type | Count | R@5 Hits | R@5 |
@@ -81,32 +176,11 @@ fast10 detail: 1 partial miss @5 (60472f9c multi-session, R@5 0.67 → R@10 1.0)
 
 **1 miss at R@5** — single-session-user (QID: 5d3d2817). R@10 recovers to 100%.
 
----
-
-## Methodology
-
-- **Vector strategy:** Embedding similarity search via usearch index.
-- **Hybrid strategy:** Reciprocal Rank Fusion (RRF k=60) of vector search + SQLite FTS5 keyword search.
-- **Embedding (vector 500Q):** EmbeddingGemma 300M Q4 ONNX, 768d, local inference.
-- **Embedding (hybrid 50Q):** EmbeddingGemma 768d via API endpoint, same model dimensions.
-- **Session-level:** Retrieval evaluated at session granularity (not per-turn).
-- **Throttled run:** 2 CPU cores, nice 19 (production-safe benchmark).
-
-## Reproduce
-
-```bash
-# Vector (500Q, ONNX local)
-python run_eval.py --data data/longmemeval_s_cleaned.json --output results_vector --strategy vector
-
-# Hybrid (50Q sample)
-python run_eval.py --data data/longmemeval_s_cleaned.json --output results_hybrid --limit 50 --strategy hybrid
-```
-
-### 3-way RRF (vec + hyb + fts5-only) — NO-GO (2026-08-29)
+## 3-way RRF (vec + hyb + fts5-only) — NO-GO (2026-08-29)
 
 Simulation over saved rankings; fts5-only rankings collected on Modal x86
 (500Q, ~4.5h, resume-safe via volume). Cross-check: sim fts5-only R@5=0.8211
-vs harness-reported 0.820 — parsing validated.
+vs harness-reported 0.820 — parsing validated. (Committed raw: `results/fts5-500q.jsonl`.)
 
 - 2-way shipped fusion (tuned weights) R@5 = 0.9800 (fast50)
 - best 3-way (2.0/1.0/0.1) R@5 = 0.9800 — delta +0.0000, **0 flipped questions**
@@ -115,31 +189,9 @@ vs harness-reported 0.820 — parsing validated.
   same classes already covered (partially) by hybrid's own FTS5 component
 
 Conclusion: ranking-side optimization via more RRF arms is exhausted.
-Remaining headroom is insert-side granularity (3 partial-recall fails) —
+Remaining headroom is insert-side granularity (partial-recall fails) —
 separate project, uncertain payoff.
 
-Artifacts: sim3way_final.py, preview_scores.py, results_modal_fts5_partial/
-
-### Pure-default (zero-config fusion) 500Q — Modal x86_64, binary 0.16.0 from git bfbc296 (2026-08-29)
-
-Pre-release validation: `--strategy default` (no flag at all) against the full 500Q dataset.
-Binary built in-image from exact SHA bfbc296 (PR #1137/#1138), image build prints `uteke 0.16.0`.
-
-| Question Type | Count | Recall@5 | Recall@10 | NDCG@5 | NDCG@10 |
-|---|---|---|---|---|---|
-| **Overall** | 470 | **0.946** | 0.977 | 0.897 | 0.911 |
-| knowledge-update | 72 | 1.000 | 1.000 | 0.972 | 0.972 |
-| multi-session | 121 | 0.906 | 0.976 | 0.880 | 0.913 |
-| single-session-assistant | 56 | 0.982 | 1.000 | 0.959 | 0.965 |
-| single-session-preference | 30 | 0.967 | 0.967 | 0.839 | 0.839 |
-| single-session-user | 64 | 0.969 | 0.969 | 0.913 | 0.913 |
-| temporal-reasoning | 127 | 0.920 | 0.962 | 0.849 | 0.867 |
-
-Context: v0.15.0 hybrid baseline on the same dataset: Overall R@5 = 0.854 / R@10 = 0.885 (2026-08-13).
-Fusion default lifts full-500Q recall@5 by **+9.2 points** (0.854 → 0.946) with zero configuration.
-
----
-
 ## Contradiction-resolution segment (#1172 Fase 3) — 2026-09-06
 
 **Active-store knowledge-update segment**: 40 topics × (stale fact + winner fact + 3 distractors),
@@ -148,7 +200,7 @@ Baseline ranks with BOTH facts active; resolved ranks after `supersede(stale →
 baseline is measured for every strategy BEFORE any resolution, then the store is resolved once.
 Binary: local release build (0.16.0 + #1185 ledger), local ONNX EmbeddingGemma, ARM64.
 
-Harness: `contradiction_segment.py` (this directory). Raw metrics: `results_contradiction_f3/metrics.json`.
+Harness: `scripts/contradiction_segment.py`. Raw metrics (committed): [`results/contradiction-f3-metrics.json`](results/contradiction-f3-metrics.json).
 
 | Strategy | Stage | winner@1 | winner@5 | winner MRR | stale@1 | stale@5 |
 |---|---|---|---|---|---|---|
@@ -179,3 +231,38 @@ guarantees stale facts leave the retrieval surface — the difference between "u
 synthetic and deterministic (fixed topic list); it measures the conflict-resolution pipeline,
 not LongMemEval dataset recall.
 
+## Methodology
+
+- **Vector strategy:** Embedding similarity search via usearch index.
+- **Hybrid strategy:** Reciprocal Rank Fusion (RRF k=60) of vector search + SQLite FTS5 keyword search.
+- **Embedding (vector 500Q):** EmbeddingGemma 300M Q4 ONNX, 768d, local inference.
+- **Session-level:** Retrieval evaluated at session granularity (not per-turn).
+- **Throttled runs:** 2 CPU cores, nice 19 (production-safe benchmark); Modal fan-out keeps cpu=2 per shard for per-question latency comparability.
+- **Determinism:** no LLM in the retrieval path; identical input → identical rankings (see Independent Reproduction).
+
+## Reproduce
+
+```bash
+# Vector (500Q, ONNX local)
+python3 scripts/run_eval.py --data data/longmemeval_s_cleaned.json --output results_vector --strategy vector
+
+# Pure-default full run on Modal (10 shards, resume-safe)
+UTEKE_GIT_REF=<exact-sha> modal run scripts/modal_fanout.py
+
+# Aggregates (per-type + 470/30/full-500 decomposition)
+python3 scripts/print_metrics.py results/default-500q.jsonl
+```
+
+## Audit the headline numbers from the committed raw artifacts
+
+```python
+import json
+entries = [json.loads(l) for l in open("results/default-500q.jsonl")]
+s = [e["retrieval_results"]["metrics"]["session"] for e in entries]
+print("strict@5  :", sum(m["recall_all@5"] == 1.0 for m in s) / len(s))   # 0.880
+print("coverage@5:", sum(m["recall_all@5"] for m in s) / len(s))          # 0.943
+```
+
+`recall_any@k` recomputes from `retrieved_session_ids` vs the dataset's
+`answer_session_ids` (0.982 @5 over the full 500). For the 470/30 split, group by the
+`_abs` suffix in `question_id`.
diff --git a/benchmarks/longmemeval/results/contradiction-f3-metrics.json b/benchmarks/longmemeval/results/contradiction-f3-metrics.json
new file mode 100644
index 00000000..18b09b00
--- /dev/null
+++ b/benchmarks/longmemeval/results/contradiction-f3-metrics.json
@@ -0,0 +1,56 @@
+{
+  "fusion": {
+    "questions": 40,
+    "baseline_unresolved": {
+      "winner@1": 0.85,
+      "winner@5": 1.0,
+      "winner_mrr": 0.925,
+      "stale@1": 0.15,
+      "stale@5": 1.0
+    },
+    "resolved": {
+      "winner@1": 1.0,
+      "winner@5": 1.0,
+      "winner_mrr": 1.0,
+      "stale@1": 0.0,
+      "stale@5": 0.0
+    },
+    "ledger_listed_all": true
+  },
+  "hybrid": {
+    "questions": 40,
+    "baseline_unresolved": {
+      "winner@1": 0.025,
+      "winner@5": 1.0,
+      "winner_mrr": 0.4687500000000001,
+      "stale@1": 0.975,
+      "stale@5": 1.0
+    },
+    "resolved": {
+      "winner@1": 0.225,
+      "winner@5": 1.0,
+      "winner_mrr": 0.5875,
+      "stale@1": 0.0,
+      "stale@5": 0.0
+    },
+    "ledger_listed_all": true
+  },
+  "vector": {
+    "questions": 40,
+    "baseline_unresolved": {
+      "winner@1": 0.95,
+      "winner@5": 1.0,
+      "winner_mrr": 0.975,
+      "stale@1": 0.05,
+      "stale@5": 1.0
+    },
+    "resolved": {
+      "winner@1": 1.0,
+      "winner@5": 1.0,
+      "winner_mrr": 1.0,
+      "stale@1": 0.0,
+      "stale@5": 0.0
+    },
+    "ledger_listed_all": true
+  }
+}
\ No newline at end of file
diff --git a/benchmarks/longmemeval/results/default-500q-v017.jsonl b/benchmarks/longmemeval/results/default-500q-v017.jsonl
new file mode 100644
index 00000000..07993255
--- /dev/null
+++ b/benchmarks/longmemeval/results/default-500q-v017.jsonl
@@ -0,0 +1,500 @@
+{"question_id": "e47becba", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_280352e9", "ultrachat_458322", "sharegpt_QZMeA7V_17", "ultrachat_422229", "sharegpt_rnG0ZuV_0", "d94b721b", "a041cdd4_3", "3b3a77d9", "sharegpt_Cr2tc1f_0", "02bd2b90_3", "0586f435_3", "c3cd646c_1", "sharegpt_BvwQVCO_0", "978c0e70", "sharegpt_1L5GXZJ_0", "f6859b48_2", "aae4411b_2", "sharegpt_Jcy1CVN_0", "41abc171_4", "52c34859_1", "ec6ca9ef", "sharegpt_T1EiHWI_13", "sharegpt_UnjngE7_65", "e27891d3_2", "ultrachat_359984", "85a1be56_1", "5dac7cc2_1", "ultrachat_214101", "2ae4f277_2", "sharegpt_yywfIrx_0", "ultrachat_45576", "e348269f_1", "ultrachat_286075", "ultrachat_336116", "sharegpt_gTknhGs_0", "ultrachat_514031", "4fd76316", "84503ce4_1", "ultrachat_57566", "ultrachat_530213", "7045db85_1", "ultrachat_574556", "sharegpt_DGTCD7D_0", "ultrachat_486410", "sharegpt_PdnvIns_0", "47f00a5f", "sharegpt_8dJs7Ai_0", "d414cac5_4", "3418b277_3", "ultrachat_215809"]}}
+{"question_id": "5d3d2817", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 0.0, "recall_all@10": 0.0, "recall_all@50": 1.0, "ndcg_any@5": 0.0, "ndcg_any@10": 0.0, "ndcg_any@50": 0.26264953503719357}}, "retrieved_session_ids": ["sharegpt_BKl952A_23", "sharegpt_bTDMt3m_64", "sharegpt_P6UZzFA_9", "ultrachat_371333", "sharegpt_8zsFRKh_0", "e93efd9a_1", "ultrachat_106114", "49b78a55_1", "sharegpt_ebWP6Hz_0", "sharegpt_LYdAIQw_11", "72f053da_2", "sharegpt_WVeg9Bf_0", "answer_235eb6fb", "ultrachat_121159", "sharegpt_g7U3fa7_0", "25d1830a_1", "7dc6f276_1", "sharegpt_0OYTYWn_15", "670ca40e", "sharegpt_VuQnjeB_0", "e7b0637e_3", "e00298a6_1", "f0ecaea5_1", "sharegpt_NdlHKlg_1", "ultrachat_86927", "ultrachat_535024", "ultrachat_171456", "ultrachat_384575", "212ce1f4_5", "59f7fff7_1", "ultrachat_158776", "sharegpt_ipLglky_48", "sharegpt_mtS0qOD_65", "7e6bcfa7_1", "4785e1f2_1", "sharegpt_Qga4bRp_0", "sharegpt_TY5Si51_29", "4ae45145", "sharegpt_Gdrz2qz_15", "ultrachat_485506", "cef33b28_2", "85d16d01", "ultrachat_286225", "dae7d519", "bd380a89_2", "sharegpt_gsAFtR8_0", "ultrachat_198348", "ultrachat_234942", "ultrachat_440912", "ultrachat_281551"]}}
+{"question_id": "c8c3f81d", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_761acef8", "19ec83c5_1", "66e94928", "b818e2a1_1", "2b16f3d6_1", "eb403c80_2", "25432e10", "eb5d5a93_1", "sharegpt_6CsjCEy_0", "3c0ed3bf_4", "ultrachat_467145", "2aa70c9c_2", "998616b4_4", "sharegpt_pRqHb1o_0", "b2cae160_1", "ultrachat_93661", "sharegpt_Yp4QbCg_32", "c8086af4", "ff1afe35", "99b638af", "e89b6be2_2", "05be3ff0_4", "sharegpt_B0riiep_0", "9bec5593_1", "ultrachat_531344", "9c5f1314_1", "sharegpt_NVMYFvJ_1", "ultrachat_429358", "sharegpt_tiXLrgw_0", "0554bd9d_1", "sharegpt_5wXn3Li_0", "3b8076bf_2", "sharegpt_WsDgKko_13", "ffe358ff_2", "610299eb_1", "sharegpt_h72CtS4_27", "e1daa90a", "ultrachat_97683", "sharegpt_gmOSkOC_0", "sharegpt_DMaETYU_0", "ultrachat_577269", "ultrachat_372537", "sharegpt_bYmhk5R_0", "ultrachat_290786", "sharegpt_heUqexD_0", "ultrachat_269520", "sharegpt_xzol7DQ_13", "ultrachat_308798", "sharegpt_9srz3L6_3", "ultrachat_543213"]}}
+{"question_id": "b86304ba", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_645b0329", "8e9425ef_1", "e831a29f_1", "ae00973d_4", "3b73120a_1", "5cc9b056", "ultrachat_328696", "ultrachat_10525", "65f3c006_1", "ea8bb4f8_2", "091aa510_1", "sharegpt_AFGmiGL_0", "sharegpt_SQx36hf_0", "fe1e4351_1", "sharegpt_xGoJZ6Z_0", "sharegpt_V86L8QR_0", "e77ef495_1", "sharegpt_E0YL5SX_142", "c1634164_2", "ultrachat_222869", "ultrachat_3128", "b597222e_1", "91074ce7", "94bc18df_1", "97e6d559_2", "ultrachat_115130", "sharegpt_oh3eExx_8", "918c4788_1", "0b7d5ed2", "9de29219_5", "1c8832b4_2", "2dbfff2f", "ultrachat_183206", "c734e31e_1", "233605cc_1", "sharegpt_cJGUl0a_11", "ultrachat_457992", "sharegpt_4ulV7Nm_4", "ultrachat_393789", "sharegpt_P1RbLxL_11", "ultrachat_489299"]}}
+{"question_id": "15745da0", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_586de428", "07d33eaa", "66fc6f25", "sharegpt_0PBtbor_0", "8376624e", "39948bcf_5", "a4f22269", "sharegpt_6TSnbGC_0", "7f15b596", "0d73afc8", "c51583cd_1", "ultrachat_446016", "sharegpt_IzpzBot_0", "ab281b6a", "sharegpt_f28uI6i_0", "1f5370f7_4", "768544e1", "7c338712_1", "sharegpt_GYyHEwU_0", "4cbb6dd2", "c1f03307_2", "sharegpt_jnrxuVu_0", "sharegpt_ErOTMZ3_301", "ca929779_4", "4963e403", "ultrachat_18910", "3cec6e2b_2", "535ce50e_2", "sharegpt_iF4hlNs_5", "7a10d327_2", "64a97d9b", "612c8368_1", "ultrachat_577603", "ultrachat_155656", "1c662b7b_1", "sharegpt_OtI4UQU_0", "sharegpt_dAh77Ju_0", "9f8fc173_2", "sharegpt_CcilV9Y_0", "ultrachat_383425", "sharegpt_KUXG6gY_2", "sharegpt_9bmybcO_35", "sharegpt_kfByHvL_0", "ultrachat_143315", "ultrachat_488881", "sharegpt_BbIUk2F_7", "sharegpt_aFKujtY_0"]}}
+{"question_id": "76d63226", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_bbdc7b0a", "cb289226_1", "a5ab6ba7_3", "6a2f9452_2", "sharegpt_FCvAKpJ_0", "1b0d77b0", "f27e27f9_2", "sharegpt_sRhcMiu_13", "50136b31", "b0729ec8_1", "sharegpt_Aynil7y_0", "11566271_2", "ultrachat_571941", "ultrachat_94273", "4c30e6b4", "sharegpt_JZN8bAp_0", "sharegpt_ZA4iwD0_65", "ultrachat_180710", "d8454317_1", "ultrachat_328229", "sharegpt_79vV0aT_377", "sharegpt_dkQVfIi_0", "ultrachat_516921", "b78242f5_1", "46deabfe_2", "sharegpt_etklNfN_11", "aeb58d86_4", "a8b4bfb3_2", "ea28611b_1", "adc47df1_1", "544fe66c_3", "92b1f663_1", "ultrachat_289526", "ultrachat_138072", "ultrachat_25013", "ultrachat_66203", "372544ba", "ultrachat_9289", "ultrachat_48979", "sharegpt_kRjBUsA_0", "2c501e33", "987e568a_1", "5f73f2f3", "sharegpt_Wi7Op1u_91", "ultrachat_545584", "sharegpt_8A5zaJD_0", "sharegpt_jsjbgCR_0", "ultrachat_407938"]}}
+{"question_id": "c14c00dd", "question_type": "single-session-user", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_304511ce", "c77250d2", "5dac7cc2_1", "cd1f5147", "fbf3449c_1", "sharegpt_gU0qvZu_0", "7cd7c296_3", "sharegpt_FfqJ2xI_0", "sharegpt_vXWOUqx_11", "7c338712_2", "sharegpt_zs9nI2N_0", "f91cac51_1", "ffd66315_2", "sharegpt_JfMnEjD_1", "ultrachat_468103", "84e6ffa3_1", "ce6a5e41", "35f052f9_1", "sharegpt_yW4k8hG_0", "ultrachat_5043", "sharegpt_IEmtQfM_23", "ultrachat_92362", "710f4e21_2", "097cefd0_2", "sharegpt_QXDcy4j_0", "541ecc45_1", "e7cc239c_4", "sharegpt_XCNeusQ_9", "sharegpt_lDVDe5H_339", "e2cb1647", "183b6085", "d9a145b9", "ultrachat_272210", "sharegpt_gGriMhQ_91", "sharegpt_KznkJ2p_0", "ultrachat_451453", "sharegpt_ET9WzdL_33", "147ab7e9_7", "sharegpt_GgO4N4k_23", "ultrachat_16880", "ultrachat_502239", "ultrachat_311861", "ultrachat_132592", "8de3201f_1", "ultrachat_456324", "ultrachat_309177", "ultrachat_276951", "3fcbb58c_4", "ultrachat_69022", "ultrachat_438569"]}}
+{"question_id": "0a995998", "question_type": "multi-session", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.9674679834891693, "ndcg_any@10": 0.9674679834891693, "ndcg_any@50": 0.9674679834891693}}, "retrieved_session_ids": ["answer_afa9873b_1", "answer_afa9873b_2", "85846900_2", "answer_afa9873b_3", "ultrachat_331531", "25091584_1", "sharegpt_U3S02iq_0", "afbd7193_2", "ad3d0815_1", "fa46a10e", "439f9ed8_3", "e18a6f0b_1", "e6790684_1", "b10c041b_2", "f10be626", "sharegpt_ZzArUrr_0", "66ffbb8b_2", "sharegpt_7gIKsqO_104", "ultrachat_428050", "57da3d13", "64b847a7_1", "3f05c474", "ultrachat_371864", "ultrachat_336874", "cb2f5565_3", "ultrachat_327634", "c7c10e58_1", "ultrachat_62232", "45823393_2", "ultrachat_335729", "ultrachat_158784", "8e6bac11", "f5a8b1d0_1", "cf543226_2", "ultrachat_128918", "ultrachat_443538", "2f310ba0", "ultrachat_124952", "sharegpt_igR6Bmm_0", "ultrachat_287397", "sharegpt_72qIaGg_12", "ultrachat_452002", "sharegpt_0mQbhwr_0", "750b437b_3"]}}
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diff --git a/benchmarks/longmemeval/results/default-500q.jsonl b/benchmarks/longmemeval/results/default-500q.jsonl
new file mode 100644
index 00000000..2a186c0c
--- /dev/null
+++ b/benchmarks/longmemeval/results/default-500q.jsonl
@@ -0,0 +1,500 @@
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diff --git a/benchmarks/longmemeval/results/fts5-500q.jsonl b/benchmarks/longmemeval/results/fts5-500q.jsonl
new file mode 100644
index 00000000..9858c8f9
--- /dev/null
+++ b/benchmarks/longmemeval/results/fts5-500q.jsonl
@@ -0,0 +1,500 @@
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+{"question_id": "352ab8bd", "question_type": "single-session-assistant", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.6309297535714575, "ndcg_any@10": 0.6309297535714575, "ndcg_any@50": 0.6309297535714575}}, "retrieved_session_ids": ["337cbfc8_2", "answer_sharegpt_NoDZzot_7", "3826dc55_5", "c7ca6dff", "ultrachat_101751", "6593cb8b_6", "3b0f28f6_2", "sharegpt_j4hlhF5_24", "ultrachat_185705", "9bafe9a2", "eee803c7", "sharegpt_oeW6OUk_51", "ultrachat_210877", "adde0cc0_1", "5ac3a957", "55a59bc9_2", "ultrachat_378821", "f29dab09", "4a42c62b", "sharegpt_3cVjZ2b_8", "ultrachat_347881", "ultrachat_226870", "d6d87935_2", "c6d21293", "d600c646", "ultrachat_133145", "ultrachat_120847", "05594106_2", "ultrachat_528926", "167191d3", "a86b0858_1", "sharegpt_7tOPXDd_50", "sharegpt_UCVg3jO_0", "231e3ad7", "ultrachat_467145", "sharegpt_VN6Wxi9_0", "27971ce6_5", "ultrachat_363478", "ultrachat_421772", "329aba7c_1", "ultrachat_194309", "ultrachat_1052", "df08e7a0", "sharegpt_fD65zFW_0", "sharegpt_Jbd7d6T_0", "sharegpt_cDlYqSf_21", "sharegpt_SM8NnLU_13", "sharegpt_YcgkniJ_0", "sharegpt_BvuMJIh_63"]}}
+{"question_id": "b759caee", "question_type": "single-session-assistant", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 0.6309297535714575, "ndcg_any@10": 0.6309297535714575, "ndcg_any@50": 0.6309297535714575}}, "retrieved_session_ids": ["ultrachat_554750", "answer_sharegpt_2BSXlAr_0", "b41aae7f_1", "0bad887e_2", "bab41bb6_5", "d3575920", "6be2f065_1", "191f4c9b_2", "sharegpt_6gBVFdg_1", "sharegpt_lK1xtdw_0", "78d11fc5_1", "sharegpt_xni9yYO_6", "sharegpt_oPOTyid_28", "sharegpt_I9veIBa_1", "sharegpt_lR0Y6tk_7", "sharegpt_c576GpN_0", "sharegpt_6JkEylk_0", "abecc1b6", "fb5dd87f_8", "ffa51434_3", "f55fee5b_4", "eae516cd", "ultrachat_437472", "sharegpt_gJyk7Hp_0", "a4b136d1_1", "sharegpt_7k9rkeB_0", "sharegpt_QAPDUii_0", "3a757e4a", "22aa7cca_1", "96cdb842", "sharegpt_MuKLD5B_0", "ultrachat_524124", "ultrachat_430100", "sharegpt_KvC1uli_0", "ac549598_2", "175a0b58_3", "ultrachat_324843", "sharegpt_6sZiNtD_32", "ultrachat_320783", "ultrachat_555476", "ultrachat_346540", "sharegpt_EWod5yd_0", "3a667084_2", "fcae5c39_1", "ultrachat_300911", "ultrachat_443020", "ultrachat_448230", "sharegpt_6cZi7j1_0", "sharegpt_dY4gqkH_0", "ultrachat_281823"]}}
+{"question_id": "778164c6", "question_type": "single-session-assistant", "retrieval_results": {"metrics": {"session": {"recall_all@5": 1.0, "recall_all@10": 1.0, "recall_all@50": 1.0, "ndcg_any@5": 1.0, "ndcg_any@10": 1.0, "ndcg_any@50": 1.0}}, "retrieved_session_ids": ["answer_ultrachat_399000", "sharegpt_CyJ3dal_43", "e42d0467_1", "ultrachat_144598", "ultrachat_346016", "05b551b6", "35201d43", "b605741a_1", "sharegpt_WXCvFir_11", "sharegpt_4eVd8BN_0", "sharegpt_ki9IVDq_6", "ultrachat_460345", "sharegpt_dUoAhvI_23", "eccc6e84_3", "483ddd90", "sharegpt_ty6EeyH_0", "023641cc_3", "c15dadce_4", "a2053427_1", "1340826c", "490bb46d_1", "2d74df23_3", "c6bd5277", "ddaa5a15_2", "81e406dd_2", "sharegpt_CMe0Odo_0", "ultrachat_543871", "ultrachat_254669", "ultrachat_313916", "ultrachat_336538", "0c28f7fb", "sharegpt_uzwG36E_0", "sharegpt_luC18OZ_0", "025836be_1", "sharegpt_mWsFRK3_127", "ultrachat_423899", "ultrachat_578090", "ultrachat_419925", "0ba46bdc_2", "ultrachat_9855", "sharegpt_fnuLBKl_129", "sharegpt_n42dRB1_0", "ultrachat_169952"]}}
diff --git a/benchmarks/longmemeval/scripts/check_volume.py b/benchmarks/longmemeval/scripts/check_volume.py
new file mode 100644
index 00000000..69d76da3
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/check_volume.py
@@ -0,0 +1,27 @@
+#!/usr/bin/env python3
+"""Quick check: shard mana saja yang sudah tersimpan di Modal Volume."""
+import modal
+
+vol = modal.Volume.from_name("uteke-longmemeval", create_if_missing=True)
+
+app = modal.App("check-lmeval-volume")
+
+@app.function(volumes={"/root/vol": vol}, timeout=120)
+def check(strategy: str) -> list:
+    import pathlib
+    base = pathlib.Path("/root/vol") / strategy
+    out = []
+    if base.exists():
+        for f in sorted(base.glob("shard_*.jsonl")):
+            n = sum(1 for l in f.read_text().splitlines() if l.strip())
+            out.append((f.name, n))
+    return out
+
+@app.local_entrypoint()
+def main():
+    for strategy in ["hybrid", "vector"]:
+        res = check.remote(strategy)
+        total = sum(n for _, n in res)
+        print(f"{strategy}: {len(res)}/20 shard selesai, total {total} pertanyaan")
+        for name, n in res:
+            print(f"  {name}: {n} Q")
diff --git a/benchmarks/longmemeval/scripts/compare_fast50.py b/benchmarks/longmemeval/scripts/compare_fast50.py
new file mode 100644
index 00000000..39f90a07
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/compare_fast50.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""Compare baseline vs rerank fast-eval results (#1118).
+
+Usage: python3 compare_fast50.py <baseline_dir> <rerank_dir>
+"""
+import json
+import sys
+from pathlib import Path
+from collections import defaultdict
+
+
+def load(d):
+    rows = {}
+    p = Path(d) / "retrieval_results.jsonl"
+    for line in p.read_text().splitlines():
+        if not line.strip():
+            continue
+        r = json.loads(line)
+        m = (r.get("retrieval_results", {}) or {}).get("metrics") or {}
+        sm = (m.get("session") or {})
+        rows[r["question_id"]] = {
+            "type": r["question_type"],
+            "r5": sm.get("recall_all@5"),
+            "r10": sm.get("recall_all@10"),
+        }
+    return rows
+
+
+def agg(rows):
+    by_type = defaultdict(list)
+    for v in rows.values():
+        if v["r5"] is not None:
+            by_type[v["type"]].append(v["r5"])
+    out = {}
+    allv = []
+    for t, vals in by_type.items():
+        out[t] = sum(vals) / len(vals)
+        allv += vals
+    out["OVERALL"] = sum(allv) / len(allv) if allv else 0.0
+    return out
+
+
+def main():
+    base = load(sys.argv[1])
+    rerank = load(sys.argv[2])
+    a, b = agg(base), agg(rerank)
+
+    print(f"{'type':30s} {'n':>3s} {'baseline':>9s} {'rerank':>9s} {'delta':>8s}")
+    types = sorted(set(a) | set(b))
+    for t in types:
+        d = b.get(t, 0) - a.get(t, 0)
+        n = sum(1 for v in rerank.values() if v["type"] == t and v["r5"] is not None)
+        flag = " <<<" if d >= 0.03 and t != "OVERALL" else ""
+        print(f"{t:30s} {n:3d} {a.get(t, 0):9.4f} {b.get(t, 0):9.4f} {d:+8.4f}{flag}")
+
+    dd = b["OVERALL"] - a["OVERALL"]
+    print()
+    print(f"OVERALL: {a['OVERALL']:.4f} -> {b['OVERALL']:.4f}  ({dd:+.4f})")
+    gate = dd >= 0.03
+    print(f"GATE (+3pp): {'PASS ✅' if gate else 'FAIL ❌'}")
+
+    # per-Q movers
+    up = sum(1 for q in base if q in rerank and rerank[q]["r5"] is not None
+             and base[q]["r5"] is not None and rerank[q]["r5"] > base[q]["r5"])
+    down = sum(1 for q in base if q in rerank and rerank[q]["r5"] is not None
+               and base[q]["r5"] is not None and rerank[q]["r5"] < base[q]["r5"])
+    print(f"Q improved: {up}  Q regressed: {down}")
+
+    return 0 if gate else 1
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/benchmarks/longmemeval/scripts/contradiction_segment.py b/benchmarks/longmemeval/scripts/contradiction_segment.py
new file mode 100644
index 00000000..ec4bc15d
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/contradiction_segment.py
@@ -0,0 +1,271 @@
+#!/usr/bin/env python3
+"""
+Contradiction-segment benchmark (#1172 Fase 3).
+
+Measures how conflict resolution (supersede, #1053/#1172) affects retrieval
+quality on a synthetic knowledge-update workload. This is the ACTIVE-store
+counterpart to LongMemEval's passive knowledge-update subset (subset_kupd):
+instead of asking whether stale sessions are retrieved, we resolve conflicts
+in the store first and ask whether recall surfaces the WINNER.
+
+Design (deterministic, N topics):
+  1. Seed 2 memories per topic:
+     - stale:  the OLD fact ("… uses tool X")
+     - winner: the NEW fact ("… switched to tool Y")
+     plus D distractor memories per topic (same domain vocabulary, no conflict).
+  2. Baseline run: both facts active (no supersede). Query each topic
+     semantically ("what does topic use now?"). Measures how often the
+     stale fact pollutes top-k when nothing resolved the conflict.
+  3. Resolved run: supersede(stale → winner) via the CLI, then re-query.
+     Measures winner@k and stale@k on the RESOLVED store.
+  4. Ledger sanity: contradiction_resolutions lists the pair; undo restores
+     (audited, then re-superseded).
+
+Metrics per strategy:
+  winner@{1,3,5}  — winner memory ranked in top-k
+  stale@{1,5}     — stale memory present in top-k (0.0 expected post-resolve)
+  MRR (winner)    — reciprocal rank of the winner
+
+Usage:
+  python3 contradiction_segment.py --binary ../../target/release/uteke --topics 40
+  python3 contradiction_segment.py --topics 40 --json out.json
+"""
+
+import argparse
+import json
+import shutil
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+TOPICS = [
+    ("acme-corp", "build tooling", "gradle", "bazel"),
+    ("brightpath", "package manager", "yarn", "pnpm"),
+    ("cloudline", "hosting provider", "heroku", "fly.io"),
+    ("dataworks", "message queue", "rabbitmq", "kafka"),
+    ("everhost", "web server", "apache", "nginx"),
+    ("fintrak", "ledger database", "postgresql", "cockroachdb"),
+    ("gadgethub", "mobile framework", "cordova", "flutter"),
+    ("heliosoft", "ci system", "jenkins", "github actions"),
+    ("innodata", "search engine", "elasticsearch", "meilisearch"),
+    ("jetstream", "api style", "soap", "grpc"),
+    ("kobalt", "css framework", "bootstrap", "tailwind"),
+    ("lumenpath", "state management", "redux", "zustand"),
+    ("metriq", "observability stack", "graphite", "prometheus"),
+    ("novabyte", "language runtime", "java", "kotlin"),
+    ("orbita", "container runtime", "docker swarm", "kubernetes"),
+    ("pixelbay", "image format", "jpeg-xl", "avif"),
+    ("quantex", "config format", "ini", "toml"),
+    ("riverbend", "version control", "svn", "git"),
+    ("saltmarsh", "auth protocol", "basic auth", "oauth2"),
+    ("tidewater", "template engine", "ejs", "handlebars"),
+    ("umbracloud", "storage layer", "mongodb", "sqlite"),
+    ("vellum", "docs generator", "javadoc", "rustdoc"),
+    ("wharfside", "package registry", "nexus", "ghcr"),
+    ("xenolith", "testing framework", "junit4", "junit5"),
+    ("yarrow", "scheduler", "cron", "systemd timers"),
+    ("zephyr", "linting tool", "tslint", "eslint"),
+    ("argonhold", "secret store", "env files", "vault"),
+    ("basaltix", "logging library", "log4j", "tracing"),
+    ("cobaltrun", "runtime monitor", "new relic", "otel"),
+    ("duskfield", "error tracker", "rollbar", "sentry"),
+    ("emberfall", "feature flags", "launchdarkly", "unleash"),
+    ("frostline", "cache layer", "memcached", "redis"),
+    ("glacierpeak", "object store", "s3 class", "r2"),
+    ("hollowpine", "markdown parser", "marked", "comrak"),
+    ("irisvale", "date library", "moment", "dayjs"),
+    ("jaderock", "http client", "axios", "fetch"),
+    ("kelpforest", "orm", "sequelize", "drizzle"),
+    ("lavaglass", "bundler", "webpack", "vite"),
+    ("mistvale", "type checker", "flow", "typescript"),
+    ("nightsky", "charting library", "chart.js", "d3"),
+]
+
+# Question phrasings deliberately avoid the exact "uses/switched to" verbs
+# so the query is semantic, not keyword lookup.
+QUESTION = "which {thing} does {topic} use now?"
+
+
+def resolve_binary(cli_path: str) -> str:
+    if cli_path:
+        p = Path(cli_path)
+        if p.exists():
+            return str(p)
+        print(f"warning: --binary {cli_path} missing; falling back", file=sys.stderr)
+    repo = Path(__file__).resolve().parent.parent.parent.parent
+    cand = repo / "target" / "release" / "uteke"
+    if cand.exists():
+        return str(cand)
+    for c in ("/opt/data/.local/bin/uteke",):
+        if Path(c).exists():
+            return str(c)
+    return shutil.which("uteke") or "uteke"
+
+
+def uteke(binary: str, store: Path, namespace: str, args: list[str]) -> dict | list:
+    cmd = [
+        binary,
+        "--store", str(store),
+        "--namespace", namespace,
+        "--json",
+        *args,
+    ]
+    result = subprocess.run(cmd, capture_output=True, text=True, timeout=600)
+    if result.returncode != 0:
+        raise RuntimeError(f"uteke {' '.join(args)} failed: {result.stderr[:400]}")
+    return json.loads(result.stdout)
+
+
+def remember(binary, store, ns, content: str) -> str:
+    out = uteke(binary, store, ns, ["remember", content])
+    return str(out["id"])  # type: ignore[no-any-return]
+
+
+def supersede(binary, store, ns, old: str, new: str) -> None:
+    uteke(binary, store, ns, ["supersede", old, new, "--reason", "benchmark conflict resolution"])
+
+
+def recall_ids(binary, store, ns, query: str, strategy: str, k: int) -> list[str]:
+    out = uteke(binary, store, ns, [
+        "recall", query,
+        "--limit", str(k),
+        "--min", "0.0",
+        "--strategy", strategy,
+    ])
+    return [m["memory_id"] for m in out]
+
+
+def recall_map(binary, store, ns) -> dict[str, str]:
+    """id → content for the namespace (to map ids back to roles)."""
+    out = uteke(binary, store, ns, ["list", "--limit", "500"])
+    return {m["id"]: m["content"] for m in out}
+
+
+def metrics_at(ranking: list[str], winner: str, stale: str) -> tuple[float, float, float, float, float]:
+    w = lambda k: 1.0 if winner in ranking[:k] else 0.0
+    s = lambda k: 1.0 if stale in ranking[:k] else 0.0
+    rr = 0.0
+    for i, mid in enumerate(ranking, start=1):
+        if mid == winner:
+            rr = 1.0 / i
+            break
+    return w(1), w(5), rr, s(1), s(5)
+
+
+def main() -> int:
+    ap = argparse.ArgumentParser(description="#1172 F3 contradiction segment")
+    ap.add_argument("--binary", default="", help="uteke binary path")
+    ap.add_argument("--store", default="", help="existing store to reuse (default: temp)")
+    ap.add_argument("--namespace", default="bench-contradiction")
+    ap.add_argument("--distractors", type=int, default=3, help="distractors per topic")
+    ap.add_argument("--topics", type=int, default=0, help="limit topics (0 = all)")
+    ap.add_argument("--strategy", default="fusion")
+    ap.add_argument("--json", default="", help="write metrics JSON here")
+    args = ap.parse_args()
+
+    topics = TOPICS[: args.topics] if args.topics > 0 else TOPICS
+    binary = resolve_binary(args.binary)
+    print(f"binary: {binary}")
+
+    tmp = None
+    if args.store:
+        store = Path(args.store)
+    else:
+        tmp = tempfile.TemporaryDirectory(prefix="uteke-contradiction-")
+        store = Path(tmp.name) / "bench.uteke"
+
+    ns = args.namespace
+    roles: dict[str, tuple[str, str]] = {}  # winner id → (stale id, topic)
+    topic_thing = {t: th for t, th, _o, _n in topics}
+
+    # ── Seed ────────────────────────────────────────────────────────────
+    print(f"seeding {len(topics)} topics (+{args.distractors} distractors each)…")
+    distractor_pool = [
+        "weekly sync notes and standup summaries",
+        "onboarding checklist for new engineers",
+        "retro action items from the last sprint",
+        "vendor invoice and billing contacts",
+        "conference talk notes and takeaways",
+    ]
+    for topic, thing, old_tool, new_tool in topics:
+        stale = remember(binary, store, ns,
+                         f"{topic} uses {old_tool} for {thing}. Decision recorded after evaluation.")
+        winner = remember(binary, store, ns,
+                          f"{topic} switched to {new_tool} for {thing}. The old {old_tool} setup is retired.")
+        roles[winner] = (stale, topic)
+        for d in range(args.distractors):
+            remember(binary, store, ns,
+                     f"{topic} {distractor_pool[d % len(distractor_pool)]} ({thing} context {d})")
+
+    strategies = [s.strip() for s in args.strategy.split(",") if s.strip()]
+    results: dict[str, dict] = {}
+
+    def measure(strategy: str) -> dict:
+        w1s = w5s = rrs = ss1 = ss5 = 0.0
+        n = 0
+        for winner, (stale, topic) in roles.items():
+            q = QUESTION.format(thing=topic_thing[topic], topic=topic)
+            ranking = recall_ids(binary, store, ns, q, strategy, 10)
+            w1, w5, rr, s1, s5 = metrics_at(ranking, winner, stale)
+            w1s += w1; w5s += w5; rrs += rr; ss1 += s1; ss5 += s5
+            n += 1
+        return {
+            "winner@1": w1s / n, "winner@5": w5s / n, "winner_mrr": rrs / n,
+            "stale@1": ss1 / n, "stale@5": ss5 / n, "n": n,
+        }
+
+    # ── Baseline for ALL strategies on the UNRESOLVED store first ──────
+    # (code-scanning fix: resolving per-strategy left later strategies
+    # measuring "baseline" on an already-resolved store.)
+    baselines = {s: measure(s) for s in strategies}
+
+    # ── Resolve once: supersede every stale → winner ───────────────────
+    for winner, (stale, _topic) in roles.items():
+        supersede(binary, store, ns, stale, winner)
+
+    # Ledger sanity: every resolution is listed (F2 surface, in-loop).
+    ledger_raw = subprocess.run(
+        [binary, "--store", str(store), "--json",
+         "contradictions", "list", "--namespace", ns, "--limit", "500"],
+        capture_output=True, text=True, timeout=600,
+    )
+    ledger = json.loads(ledger_raw.stdout)
+    listed = {e["id"] for e in ledger}
+    ledger_ok = all(stale in listed for _w, (stale, _t) in roles.items())
+
+    # ── Resolved metrics for all strategies ────────────────────────────
+    resolved = {s: measure(s) for s in strategies}
+
+    for strategy in strategies:
+        baseline = {k: v for k, v in baselines[strategy].items() if k != "n"}
+        res = {k: v for k, v in resolved[strategy].items() if k != "n"}
+        n = baselines[strategy]["n"]
+        results[strategy] = {
+            "questions": n,
+            "baseline_unresolved": baseline,
+            "resolved": res,
+            "ledger_listed_all": ledger_ok,
+        }
+        print(f"\n[{strategy}]  n={n}")
+        print(f"  baseline (unresolved): winner@1={baseline['winner@1']:.3f} "
+              f"winner@5={baseline['winner@5']:.3f} MRR={baseline['winner_mrr']:.3f} "
+              f"stale@1={baseline['stale@1']:.3f} stale@5={baseline['stale@5']:.3f}")
+        print(f"  resolved (superseded): winner@1={res['winner@1']:.3f} "
+              f"winner@5={res['winner@5']:.3f} MRR={res['winner_mrr']:.3f} "
+              f"stale@1={res['stale@1']:.3f} stale@5={res['stale@5']:.3f}")
+        print(f"  ledger lists all resolutions: {ledger_ok}")
+
+    if args.json:
+        out = Path(args.json)
+        out.parent.mkdir(parents=True, exist_ok=True)
+        out.write_text(json.dumps(results, indent=2))
+        print(f"\nmetrics → {out}")
+
+    if tmp:
+        tmp.cleanup()
+    return 0
+
+
+if __name__ == "__main__":
+    sys.exit(main())
diff --git a/benchmarks/longmemeval/download_data.sh b/benchmarks/longmemeval/scripts/download_data.sh
similarity index 100%
rename from benchmarks/longmemeval/download_data.sh
rename to benchmarks/longmemeval/scripts/download_data.sh
diff --git a/benchmarks/longmemeval/scripts/fast_eval_ids.json b/benchmarks/longmemeval/scripts/fast_eval_ids.json
new file mode 100644
index 00000000..79ed15fb
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/fast_eval_ids.json
@@ -0,0 +1,100 @@
+{
+ "fast50": [
+  "7527f7e2",
+  "75499fd8",
+  "8e9d538c",
+  "3d86fd0a",
+  "0862e8bf_abs",
+  "bc8a6e93_abs",
+  "29f2956b_abs",
+  "gpt4_a56e767c",
+  "b3c15d39",
+  "60472f9c",
+  "2b8f3739",
+  "c2ac3c61",
+  "eeda8a6d_abs",
+  "35a27287",
+  "57f827a0",
+  "75f70248",
+  "2311e44b",
+  "85fa3a3f",
+  "ef66a6e5",
+  "92a0aa75",
+  "bc149d6b",
+  "55241a1f",
+  "a08a253f",
+  "gpt4_f49edff3",
+  "gpt4_468eb063",
+  "6e984301",
+  "gpt4_8e165409",
+  "6e984302",
+  "gpt4_8279ba03",
+  "gpt4_b5700ca0",
+  "08f4fc43",
+  "gpt4_385a5000",
+  "a3838d2b",
+  "gpt4_b4a80587",
+  "gpt4_8c8961ae",
+  "982b5123_abs",
+  "5a4f22c0",
+  "6071bd76",
+  "8fb83627",
+  "b01defab",
+  "4b24c848",
+  "a2f3aa27",
+  "031748ae_abs",
+  "0ddfec37_abs",
+  "e9327a54",
+  "58470ed2",
+  "3e321797",
+  "e982271f",
+  "5809eb10",
+  "c8f1aeed"
+ ],
+ "fast10": [
+  "7527f7e2",
+  "75499fd8",
+  "8e9d538c",
+  "3d86fd0a",
+  "0862e8bf_abs",
+  "bc8a6e93_abs",
+  "29f2956b_abs",
+  "gpt4_a56e767c",
+  "b3c15d39",
+  "60472f9c"
+ ],
+ "temporal15": [
+  "gpt4_f49edff3",
+  "gpt4_468eb063",
+  "6e984301",
+  "gpt4_8e165409",
+  "6e984302",
+  "gpt4_8279ba03",
+  "gpt4_b5700ca0",
+  "08f4fc43",
+  "gpt4_385a5000",
+  "a3838d2b",
+  "gpt4_b4a80587",
+  "gpt4_8c8961ae",
+  "982b5123_abs",
+  "gpt4_7ddcf75f",
+  "gpt4_7a0daae1"
+ ],
+ "multisession15": [
+  "gpt4_a56e767c",
+  "b3c15d39",
+  "60472f9c",
+  "2b8f3739",
+  "c2ac3c61",
+  "eeda8a6d_abs",
+  "2311e44b",
+  "85fa3a3f",
+  "ef66a6e5",
+  "92a0aa75",
+  "bc149d6b",
+  "55241a1f",
+  "a08a253f",
+  "81507db6",
+  "8979f9ec"
+ ]
+}
\ No newline at end of file
diff --git a/benchmarks/longmemeval/scripts/make_fast_eval.py b/benchmarks/longmemeval/scripts/make_fast_eval.py
new file mode 100644
index 00000000..a3ea7cd1
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/make_fast_eval.py
@@ -0,0 +1,87 @@
+#!/usr/bin/env python3
+"""Regenerate the fast-eval datasets from the parent LongMemEval-S dataset.
+
+The fast-eval JSON files (fast50 / temporal15 / multisession15 / fast10) are
+derived subsets of ``data/longmemeval_s_cleaned.json`` — each question object
+is byte-identical to its parent entry. Committing ~45MB of derived JSON bloats
+the repository and triggers secret-scanning false positives (synthetic chat
+text occasionally matches token-shaped patterns). Git therefore tracks only
+this generator plus the per-file question_id lists; the JSON files themselves
+are regenerated locally on demand:
+
+    python3 make_fast_eval.py            # regenerate all four files
+    python3 make_fast_eval.py --verify   # check existing files match the lists
+
+The id lists live in fast_eval_ids.json. Selection was stratified per
+question_type when first built (2026-08-27); the lists are the source of
+truth, so regeneration is fully deterministic — no randomness.
+
+Requires the parent dataset. If missing, run download_data.sh first and fetch
+longmemeval_s_cleaned.json manually (see the note in that script).
+"""
+import argparse
+import json
+import sys
+from pathlib import Path
+
+HERE = Path(__file__).parent.parent  # benchmarks/longmemeval (scripts live in scripts/)
+DATA = HERE / "data"
+PARENT = DATA / "longmemeval_s_cleaned.json"
+IDS = HERE / "fast_eval_ids.json"
+
+# id-list file name -> output dataset file name
+NAME_MAP = {
+    "fast50": "longmemeval_fast50.json",
+    "fast10": "longmemeval_fast10.json",
+    "temporal15": "longmemeval_temporal15.json",
+    "multisession15": "longmemeval_multisession15.json",
+}
+
+
+def load_parent():
+    if not PARENT.exists():
+        sys.exit(f"Parent dataset not found: {PARENT}\nRun download_data.sh and fetch longmemeval_s_cleaned.json first (see script notes).")
+    with open(PARENT) as f:
+        return json.load(f)
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--verify", action="store_true", help="verify existing files instead of regenerating")
+    args = ap.parse_args()
+
+    with open(IDS) as f:
+        id_lists = json.load(f)
+    parent = load_parent()
+    by_id = {q["question_id"]: q for q in parent}
+
+    ok = True
+    for key, fname in NAME_MAP.items():
+        ids = id_lists[key]
+        missing = [i for i in ids if i not in by_id]
+        if missing:
+            print(f"[FAIL] {fname}: {len(missing)} ids not in parent (first: {missing[0]})")
+            ok = False
+            continue
+        rebuilt = [by_id[i] for i in ids]
+        out_path = DATA / fname
+        if args.verify:
+            if not out_path.exists():
+                print(f"[FAIL] {fname}: missing (run without --verify to regenerate)")
+                ok = False
+                continue
+            with open(out_path) as f:
+                cur = json.load(f)
+            match = json.dumps(cur) == json.dumps(rebuilt)
+            print(f"[{'OK' if match else 'FAIL'}] {fname}: {'identical' if match else 'DIFFERS from id-list rebuild'}")
+            ok &= match
+        else:
+            with open(out_path, "w") as f:
+                json.dump(rebuilt, f)
+            print(f"[OK] wrote {out_path} ({len(rebuilt)} questions)")
+
+    sys.exit(0 if ok else 1)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/benchmarks/longmemeval/scripts/mmr.py b/benchmarks/longmemeval/scripts/mmr.py
new file mode 100644
index 00000000..69eac5ff
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/mmr.py
@@ -0,0 +1,105 @@
+#!/usr/bin/env python3
+"""MMR (Maximal Marginal Relevance) diversity for session ranking (#1120).
+
+Zero-model post-processing: reorders an already-ranked candidate list to
+penalise redundancy among top results. Relevance is approximated from the
+input rank order (RRF-style 1/(60+rank)); similarity between candidates is
+a TF-IDF cosine over their raw texts, computed over the candidate pool
+itself — no external model, no runtime dependencies.
+
+Usage:
+    from mmr import mmr_rerank
+    out = mmr_rerank(ids, texts, lam=0.7)      # MMR active
+    out = mmr_rerank(ids, texts, lam=None)     # no-op (flag off)
+
+Unit tests: test_mmr.py
+"""
+from __future__ import annotations
+
+import math
+import re
+from collections import Counter
+
+_RRF_K = 60  # same constant as the RRF fusion used elsewhere in the harness
+_WORD = re.compile(r"[a-z0-9']+")
+
+
+def _tokens(text: str) -> list:
+    return _WORD.findall(text.lower())
+
+
+def _tfidf_vectors(texts: list) -> list:
+    """TF-IDF vectors over the candidate pool. Pure python, pool-sized idf."""
+    tokenized = [_tokens(t) for t in texts]
+    n_docs = len(tokenized) or 1
+    df = Counter()
+    for toks in tokenized:
+        df.update(set(toks))
+    idf = {w: math.log((n_docs + 1) / (c + 1)) + 1.0 for w, c in df.items()}
+    vecs = []
+    for toks in tokenized:
+        tf = Counter(toks)
+        vec = {w: (1.0 + math.log(c)) * idf[w] for w, c in tf.items()}
+        norm = math.sqrt(sum(v * v for v in vec.values())) or 1.0
+        vecs.append({w: v / norm for w, v in vec.items()})
+    return vecs
+
+
+def _cosine(a: dict, b: dict) -> float:
+    if len(b) < len(a):
+        a, b = b, a
+    return sum(v * b.get(w, 0.0) for w, v in a.items())
+
+
+def mmr_rerank(session_ids: list, session_texts: list, lam: float | None,
+               relevance: list | None = None) -> list:
+    """Greedy MMR reorder of session_ids.
+
+    lam=None  → no-op (returns input order; flag off).
+    lam=1.0   → pure relevance, reproduces input order.
+    lam<1.0   → trades relevance for diversity.
+
+    session_texts[i] is the full text of session_ids[i]. Optional
+    `relevance` overrides the default rank-based scores (len must match).
+    """
+    if lam is None or len(session_ids) < 2:
+        return list(session_ids)
+    if len(session_ids) != len(session_texts):
+        raise ValueError("session_ids/session_texts length mismatch")
+
+    rel = relevance if relevance is not None else [
+        1.0 / (_RRF_K + i + 1) for i in range(len(session_ids))
+    ]
+    # Normalise relevance to [0,1] so it is scale-comparable with the cosine
+    # similarity term. Raw RRF scores span ~0.016 with a spread of ~0.002 —
+    # without normalisation the diversity term (~0.1-1.0) dominates by ~50x
+    # and MMR degenerates into pure diversity (measured: R@5 1.0 -> 0.33
+    # on the mini smoke dataset).
+    rmin, rmax = min(rel), max(rel)
+    if rmax > rmin:
+        rel = [(r - rmin) / (rmax - rmin) for r in rel]
+    else:
+        rel = [1.0] * len(rel)
+    vecs = _tfidf_vectors(session_texts)
+
+    selected: list = []
+    remaining = list(range(len(session_ids)))
+    # Track max similarity of each remaining candidate to the selected set.
+    max_sim = {i: 0.0 for i in remaining}
+
+    while remaining:
+        best_i = remaining[0]
+        best_score = lam * rel[best_i] - (1.0 - lam) * max_sim[best_i]
+        for i in remaining[1:]:
+            score = lam * rel[i] - (1.0 - lam) * max_sim[i]
+            if score > best_score:
+                best_i, best_score = i, score
+        selected.append(best_i)
+        remaining.remove(best_i)
+        bv = vecs[best_i]
+        for j in remaining:
+            s = _cosine(bv, vecs[j])
+            if s > max_sim[j]:
+                max_sim[j] = s
+
+    return [session_ids[i] for i in selected]
diff --git a/benchmarks/longmemeval/scripts/modal_fanout.py b/benchmarks/longmemeval/scripts/modal_fanout.py
new file mode 100644
index 00000000..529863dd
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/modal_fanout.py
@@ -0,0 +1,285 @@
+#!/usr/bin/env python3
+"""
+Modal fan-out runner for the LongMemEval benchmark (uteke).
+
+Runs the EXISTING harness (run_eval.py) unchanged, in parallel, on Modal CPU
+containers. Same uteke binary (v0.15.0 GitHub release), same embedding model
+(EmbeddingGemma 300M Q4 ONNX baked into the image), same dataset file →
+results are directly comparable with local throttled runs.
+
+Why CPU and not GPU: the embedding model is a 300M Q4 ONNX model designed for
+CPU inference. The historical bottleneck was (a) 2-core throttling +
+sequential execution, not (b) compute. Fan-out over N containers divides
+wall-clock by ~N at CPU pricing (<$1 for a full 500Q run).
+
+Usage:
+    # Sanity check: 10 questions, 2 shards
+    modal run modal_fanout.py --limit 10 --num-shards 2
+
+    # Full 500Q hybrid run (the publishable number)
+    modal run modal_fanout.py --strategy hybrid --num-shards 10
+
+    # Full 500Q vector run
+    modal run modal_fanout.py --strategy vector --num-shards 10
+
+Results land in results_modal_<strategy>[_<limit>q]/retrieval_results.jsonl
+(merged from all shards), ready for: python print_metrics.py <file>
+
+Notes:
+- Each shard runs in its own container with its own temp store → no
+  cross-contamination between shards (harness wipes per question anyway).
+- cpu=2 matches the local throttled run (taskset -c 0-1) for fair per-question
+  latency comparison.
+- The embedding model is baked at /root/.codecora/uteke/models/ so no
+  first-run download happens inside containers.
+"""
+
+import json
+import os
+import pathlib
+import subprocess
+import sys
+import time
+
+import modal
+
+REPO_DIR = pathlib.Path(__file__).parent.parent  # benchmarks/longmemeval (scripts live in scripts/)
+UTEKE_VERSION = "v0.15.0"
+# When set, the image builds uteke from this git ref instead of downloading
+# the UTEKE_VERSION release. Used for pre-release validation (e.g. develop tip
+# before tagging). The exact SHA must be used, not a branch name, so the image
+# build is reproducible.
+UTEKE_GIT_REF = os.environ.get("UTEKE_GIT_REF", "")
+DATA_FILE = os.environ.get("LMEVAL_DATA", "longmemeval_s_cleaned.json")
+# Local source of the embedding model (must contain onnx/ + tokenizer.json).
+MODEL_SOURCE = pathlib.Path("/opt/data/.codecora/uteke/models/embeddinggemma-q4")
+
+app = modal.App("uteke-longmemeval")
+
+# Volume for durable shard outputs: if the local map() call dies (network,
+# spend limit mid-run, Ctrl-C), completed shards are preserved and a rerun
+# resumes instead of starting from zero.
+vol = modal.Volume.from_name("uteke-longmemeval", create_if_missing=True)
+
+SCRIPTS = REPO_DIR / "scripts"  # harness scripts moved into scripts/
+image = (
+    # Ubuntu 24.04 base: glibc 2.39 — the uteke release binary needs it
+    # (debian slim/bookworm ships glibc 2.36 → too old, incl. the "legacy" build).
+    modal.Image.from_registry("ubuntu:24.04", add_python="3.11")
+    .apt_install("curl", "ca-certificates", "util-linux")
+)
+if UTEKE_GIT_REF:
+    # Pre-release validation: build uteke from the exact SHA (reproducible),
+    # but reuse the official ORT 1.24.4 x86_64 (SSE4.2) shared lib from the
+    # latest release tarball — ort is load-dynamic, so the lib is decoupled
+    # from the binary and versioned identically to official releases.
+    # build-essential: cargo needs a native linker (cc) on the base image.
+    image = image.apt_install("build-essential", "pkg-config").run_commands(
+        "curl -sL https://github.com/codecoradev/uteke/releases/download/"
+        f"{UTEKE_VERSION}/uteke-x86_64-unknown-linux-gnu-legacy-{UTEKE_VERSION}.tar.gz"
+        " | tar xz -C /tmp && "
+        "cp /tmp/libonnxruntime* /usr/local/bin/ && "
+        "curl -sL https://github.com/codecoradev/uteke/archive/" + UTEKE_GIT_REF + ".tar.gz | tar xz -C /tmp && "
+        "cd /tmp/uteke-*/ && "
+        "curl -sSf https://sh.rustup.rs | sh -s -- -y --profile minimal --default-toolchain stable && "
+        "$HOME/.cargo/bin/cargo build --release -p uteke-cli && "
+        "cp target/release/uteke /usr/local/bin/uteke && "
+        "chmod +x /usr/local/bin/uteke && "
+        "uteke --version"
+    )
+else:
+    # uteke release bundle: binary + libonnxruntime colocated (exe-dir lookup).
+    image = image.run_commands(
+        f"curl -sL https://github.com/codecoradev/uteke/releases/download/{UTEKE_VERSION}/"
+        f"uteke-x86_64-unknown-linux-gnu-legacy-{UTEKE_VERSION}.tar.gz | tar xz -C /tmp && "
+        "cp /tmp/uteke /usr/local/bin/uteke && cp /tmp/libonnxruntime* /usr/local/bin/ && "
+        "chmod +x /usr/local/bin/uteke && uteke --version"
+    )
+image = (
+    image
+    .pip_install("tqdm>=4.65.0", "numpy>=1.24.0")
+    # Bake the embedding model so containers never download at runtime.
+    # (add_local_* must come last — Modal mounts them at container start.)
+    .add_local_dir(str(MODEL_SOURCE), "/root/.codecora/uteke/models/embeddinggemma-q4")
+    .add_local_file(str(SCRIPTS / "run_eval.py"), "/root/harness/run_eval.py")
+    # run_eval.py imports temporal/mmr experiment modules at import time —
+    # they must be baked into the image or every shard dies on ModuleNotFoundError.
+    .add_local_file(str(SCRIPTS / "temporal.py"), "/root/harness/temporal.py")
+    .add_local_file(str(SCRIPTS / "mmr.py"), "/root/harness/mmr.py")
+    .add_local_file(str(REPO_DIR / "data" / DATA_FILE), "/root/harness/data.json")
+)
+
+
+@app.function(image=image, timeout=14400, cpu=2, volumes={"/root/vol": vol})
+def run_shard(spec: dict) -> dict:
+    """Evaluate one strided slice of the dataset with the unmodified harness.
+
+    spec: {"strategy": str, "shard_idx": int, "num_shards": int, "limit": int}
+    Writes shard output durably to the Modal Volume as soon as it finishes,
+    so progress survives local map() interruptions and reruns can resume.
+    """
+    import json as _json
+
+    strategy = spec["strategy"]
+    shard_idx = spec["shard_idx"]
+    num_shards = spec["num_shards"]
+    limit = spec["limit"]
+
+    vol_path = pathlib.Path("/root/vol") / spec.get("vol_dir", strategy) / f"shard_{shard_idx:02d}.jsonl"
+    data = _json.loads(pathlib.Path("/root/harness/data.json").read_text())
+    if limit and limit > 0:
+        data = data[:limit]
+    shard = data[shard_idx::num_shards]  # strided → even question-type mix
+    expected = len(shard)
+    if not shard:
+        return {"shard_idx": shard_idx, "n": 0, "elapsed": 0.0, "jsonl": "", "stderr_tail": ""}
+
+    # Volume cache: skip kalau shard SUDAH LENGKAP; resume kalau PARTIAL (dari timeout)
+    resume_args = []
+    if vol_path.exists():
+        prior = [l for l in vol_path.read_text().splitlines() if l.strip()]
+        if len(prior) >= expected:
+            return {
+                "shard_idx": shard_idx,
+                "n": len(prior),
+                "evaluated": len(prior),
+                "elapsed": 0.0,
+                "jsonl": "\n".join(prior),
+                "stderr_tail": "(resumed from volume)",
+            }
+        if prior:
+            # partial: seed output dir dari Volume agar run_eval --resume melanjutkan
+            out_seed = pathlib.Path(f"/tmp/out_{shard_idx}")
+            out_seed.mkdir(parents=True, exist_ok=True)
+            (out_seed / "retrieval_results.jsonl").write_text("\n".join(prior) + "\n")
+            resume_args = ["--resume"]
+
+    pathlib.Path("/tmp/shard.json").write_text(_json.dumps(shard))
+    out_dir = f"/tmp/out_{shard_idx}"
+
+    t0 = time.time()
+    try:
+        proc = subprocess.run(
+            [
+                sys.executable, "/root/harness/run_eval.py",
+                "--data", "/tmp/shard.json",
+                "--output", out_dir,
+                "--strategy", strategy,
+                "--namespace", "lmeval",
+                *resume_args,
+            ],
+            capture_output=True, text=True, timeout=14100,  # 14400 - buffer commit
+        )
+    except subprocess.TimeoutExpired as e:
+        # partial-save: run_eval menulis streaming ke out_dir; simpan apa adanya
+        partial = pathlib.Path(out_dir) / "retrieval_results.jsonl"
+        jsonl = partial.read_text() if partial.exists() else ""
+        vol_path.parent.mkdir(parents=True, exist_ok=True)
+        vol_path.write_text(jsonl)
+        vol.commit()
+        return {
+            "shard_idx": shard_idx,
+            "n": len([l for l in jsonl.splitlines() if l.strip()]),
+            "evaluated": len([l for l in jsonl.splitlines() if l.strip()]),
+            "elapsed": round(time.time() - t0, 1),
+            "jsonl": jsonl,
+            "stderr_tail": f"TIMEOUT partial-save: {len(jsonl.splitlines())}/{len(shard)} Q evaluated",
+        }
+    elapsed = time.time() - t0
+    if proc.returncode != 0:
+        raise RuntimeError(f"shard {shard_idx} failed:\n{proc.stderr[-2000:]}")
+
+    jsonl_path = pathlib.Path(out_dir) / "retrieval_results.jsonl"
+    jsonl = jsonl_path.read_text() if jsonl_path.exists() else ""
+
+    # Persist this shard durably before returning — see vol comment above.
+    vol_path.parent.mkdir(parents=True, exist_ok=True)
+    vol_path.write_text(jsonl)
+    vol.commit()
+
+    return {
+        "shard_idx": shard_idx,
+        "n": len(shard),
+        "evaluated": jsonl.count("\n"),
+        "elapsed": round(elapsed, 1),
+        "jsonl": jsonl,
+        "stderr_tail": proc.stderr[-500:],
+    }
+
+
+@app.function(image=image, timeout=600, cpu=1, volumes={"/root/vol": vol})
+def list_volume(strategy: str) -> list:
+    """List completed shards for a strategy on the volume (progress check)."""
+    base = pathlib.Path("/root/vol") / strategy
+    out = []
+    if base.exists():
+        for f in sorted(base.glob("shard_*.jsonl")):
+            n = sum(1 for l in f.read_text().splitlines() if l.strip())
+            out.append({"shard": f.name, "questions": n})
+    return out
+
+
+@app.local_entrypoint()
+def main(
+    strategy: str = "hybrid",
+    num_shards: int = 10,
+    limit: int = 0,
+    outdir: str = "",
+    tag: str = "",
+):
+    # tag: isolates this run's shards on the volume under "{strategy}-{tag}",
+    # so a re-run with a different binary (e.g. v0.17.0 re-validation) cannot
+    # resume-skip shards left by an earlier binary's run under "{strategy}/".
+    vol_dir = f"{strategy}-{tag}" if tag else strategy
+    if not MODEL_SOURCE.exists():
+        sys.exit(f"Model source not found: {MODEL_SOURCE}")
+    if not (REPO_DIR / "data" / DATA_FILE).exists():
+        sys.exit(f"Dataset not found: {REPO_DIR / 'data' / DATA_FILE}")
+
+    outdir = outdir or f"results_modal_{strategy}" + (f"_{limit}q" if limit else "")
+    out_path = pathlib.Path(outdir) / "retrieval_results.jsonl"
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # Progress check from the volume (if any shards completed before a prior
+    # interruption, they'll be resumed instead of recomputed).
+    try:
+        done = list_volume.remote(vol_dir)
+        if done:
+            print(f"Volume state ({vol_dir}/): {len(done)} shard(s) already on volume:")
+            for d in done:
+                print(f"  {d['shard']}: {d['questions']} questions")
+    except Exception as e:
+        print(f"(volume check skipped: {e})")
+
+    print(f"Fan-out: strategy={strategy} vol_dir={vol_dir} shards={num_shards} limit={limit or 'ALL'}")
+    t0 = time.time()
+
+    # Strided slicing means shard i covers data[i::num_shards].
+    inputs = [
+        {"strategy": strategy, "shard_idx": i, "num_shards": num_shards, "limit": limit, "vol_dir": vol_dir}
+        for i in range(num_shards)
+    ]
+    merged, seen = [], set()
+    for res in run_shard.map(inputs):
+        n_eval = 0
+        for line in res["jsonl"].splitlines():
+            if not line.strip():
+                continue
+            qid = json.loads(line).get("question_id")
+            if qid in seen:
+                continue  # dedupe guard (retries)
+            seen.add(qid)
+            merged.append(line)
+            n_eval += 1
+        print(
+            f"  shard {res['shard_idx']:2d}: {n_eval}/{res['n']} questions "
+            f"in {res['elapsed']}s"
+        )
+
+    out_path.write_text("\n".join(merged) + ("\n" if merged else ""))
+    wall = time.time() - t0
+    print(f"\nDone: {len(merged)} questions in {wall:.0f}s wall clock")
+    print(f"Results: {out_path}")
+    print(f"\nRun: python print_metrics.py {out_path}")
+    # Print metrics right away (numpy available locally).
+    subprocess.run([sys.executable, str(SCRIPTS / "print_metrics.py"), str(out_path)])
diff --git a/benchmarks/longmemeval/scripts/modal_fast50.py b/benchmarks/longmemeval/scripts/modal_fast50.py
new file mode 100644
index 00000000..40f7e282
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/modal_fast50.py
@@ -0,0 +1,308 @@
+#!/usr/bin/env python3
+"""
+Modal fan-out runner for the LongMemEval benchmark (uteke).
+
+Runs the EXISTING harness (run_eval.py) unchanged, in parallel, on Modal CPU
+containers. Same uteke binary (v0.15.0 GitHub release), same embedding model
+(EmbeddingGemma 300M Q4 ONNX baked into the image), same dataset file →
+results are directly comparable with local throttled runs.
+
+Why CPU and not GPU: the embedding model is a 300M Q4 ONNX model designed for
+CPU inference. The historical bottleneck was (a) 2-core throttling +
+sequential execution, not (b) compute. Fan-out over N containers divides
+wall-clock by ~N at CPU pricing (<$1 for a full 500Q run).
+
+Usage:
+    # Sanity check: 10 questions, 2 shards
+    modal run modal_fanout.py --limit 10 --num-shards 2
+
+    # Full 500Q hybrid run (the publishable number)
+    modal run modal_fanout.py --strategy hybrid --num-shards 10
+
+    # Full 500Q vector run
+    modal run modal_fanout.py --strategy vector --num-shards 10
+
+Results land in results_modal_<strategy>[_<limit>q]/retrieval_results.jsonl
+(merged from all shards), ready for: python print_metrics.py <file>
+
+Notes:
+- Each shard runs in its own container with its own temp store → no
+  cross-contamination between shards (harness wipes per question anyway).
+- cpu=2 matches the local throttled run (taskset -c 0-1) for fair per-question
+  latency comparison.
+- The embedding model is baked at /root/.codecora/uteke/models/ so no
+  first-run download happens inside containers.
+"""
+
+import json
+import hashlib
+import pathlib
+import subprocess
+import os
+import sys
+import time
+
+import modal
+
+REPO_DIR = pathlib.Path(__file__).parent.parent  # benchmarks/longmemeval (scripts live in scripts/)
+UTEKE_VERSION = "v0.15.0"
+DATA_FILE = os.environ.get("LMEVAL_DATA", "longmemeval_fast50.json")
+RERANK = False  # removed (#1118 cancelled — single-model direction); kept as False for compat
+RERANK_DEPTH = 20
+# Local source of the embedding model (must contain onnx/ + tokenizer.json).
+MODEL_SOURCE = pathlib.Path("/opt/data/.codecora/uteke/models/embeddinggemma-q4")
+
+app = modal.App("uteke-lmeval-fast")
+
+# Volume for durable shard outputs: if the local map() call dies (network,
+# spend limit mid-run, Ctrl-C), completed shards are preserved and a rerun
+# resumes instead of starting from zero.
+vol = modal.Volume.from_name("uteke-lmeval-fast", create_if_missing=True)
+
+SCRIPTS = REPO_DIR / "scripts"  # harness scripts moved into scripts/
+
+image = (
+    # Ubuntu 24.04 base: glibc 2.39 — the uteke release binary needs it
+    # (debian slim/bookworm ships glibc 2.36 → too old, incl. the "legacy" build).
+    modal.Image.from_registry("ubuntu:24.04", add_python="3.11")
+    .apt_install("curl", "ca-certificates", "util-linux")
+    # uteke release bundle: binary + libonnxruntime colocated (exe-dir lookup).
+    .run_commands(
+        f"curl -sL https://github.com/codecoradev/uteke/releases/download/{UTEKE_VERSION}/"
+        f"uteke-x86_64-unknown-linux-gnu-legacy-{UTEKE_VERSION}.tar.gz | tar xz -C /tmp && "
+        "cp /tmp/uteke /usr/local/bin/uteke && cp /tmp/libonnxruntime* /usr/local/bin/ && "
+        "chmod +x /usr/local/bin/uteke && uteke --version"
+    )
+    .pip_install("tqdm>=4.65.0", "numpy>=1.24.0", "onnxruntime>=1.17.0", "tokenizers>=0.19.0")
+    # Bake the embedding model so containers never download at runtime.
+    # (add_local_* must come last — Modal mounts them at container start.)
+    .add_local_dir(str(MODEL_SOURCE), "/root/.codecora/uteke/models/embeddinggemma-q4")
+    .add_local_file(str(SCRIPTS / "run_eval.py"), "/root/harness/run_eval.py")
+    .add_local_file(str(REPO_DIR / "data" / DATA_FILE), "/root/harness/data.json")
+    .add_local_file(str(SCRIPTS / "temporal.py"), "/root/harness/temporal.py")
+    .add_local_file(str(SCRIPTS / "mmr.py"), "/root/harness/mmr.py")
+    .add_local_file(str(SCRIPTS / "compare_fast50.py"), "/root/harness/compare_fast50.py")
+)
+
+
+@app.function(image=image, timeout=14400, cpu=2, volumes={"/root/vol": vol})
+def run_shard(spec: dict) -> dict:
+    """Evaluate one strided slice of the dataset with the unmodified harness.
+
+    spec: {"strategy": str, "shard_idx": int, "num_shards": int, "limit": int}
+    Writes shard output durably to the Modal Volume as soon as it finishes,
+    so progress survives local map() interruptions and reruns can resume.
+    """
+    import json as _json
+
+    strategy = spec["strategy"]
+    shard_idx = spec["shard_idx"]
+    num_shards = spec["num_shards"]
+    limit = spec["limit"]
+    temporal = bool(spec.get("temporal", False))
+    mmr_lambda = spec.get("mmr_lambda")  # None = OFF (default)
+    fusion = bool(spec.get("fusion", False))
+    fusion_w = spec.get("fusion_weight")  # primary weight, None = default
+
+    # Variant-keyed volume path: baseline and temporal shards must never
+    # share cache entries, or resume would silently return stale results.
+    variant = f"{strategy}_temporal" if temporal else strategy
+    if mmr_lambda is not None:
+        variant += f"_mmr{mmr_lambda}"
+    if fusion:
+        variant += f"_fusion{fusion_w}"
+    # Dataset fingerprint: sha256 of the mounted dataset file (non-security
+    # fingerprinting, but avoids scanner flags). Cross-dataset
+    # stale hits are possible without it (fast50 shard satisfies a 15Q run's
+    # `len(prior) >= expected` check and is returned verbatim) — see #1129.
+    data_bytes = pathlib.Path("/root/harness/data.json").read_bytes()
+    data_tag = hashlib.sha256(data_bytes).hexdigest()[:8]
+    vol_path = pathlib.Path("/root/vol") / variant / data_tag / f"shard_{shard_idx:02d}.jsonl"
+    data = _json.loads(data_bytes)
+    if limit and limit > 0:
+        data = data[:limit]
+    shard = data[shard_idx::num_shards]  # strided → even question-type mix
+    expected = len(shard)
+    if not shard:
+        return {"shard_idx": shard_idx, "n": 0, "elapsed": 0.0, "jsonl": "", "stderr_tail": ""}
+
+    # Volume cache: skip kalau shard SUDAH LENGKAP; resume kalau PARTIAL (dari timeout)
+    resume_args = []
+    if vol_path.exists():
+        prior = []
+        for l in vol_path.read_text().splitlines():
+            if not l.strip():
+                continue
+            try:
+                json.loads(l)
+            except json.JSONDecodeError:
+                # Truncated tail line from a killed container mid-write: drop
+                # it rather than abort the shard (#1130 review finding).
+                continue
+            prior.append(l)
+        prior_qids = {json.loads(l).get("question_id") for l in prior}
+        expected_qids = {e.get("question_id") for e in shard}
+        # Complete = same question set, not just same count (guard #1129 even
+        # if two datasets share a prefix and length by coincidence).
+        if len(prior) >= expected and prior_qids == expected_qids:
+            return {
+                "shard_idx": shard_idx,
+                "n": len(prior),
+                "evaluated": len(prior),
+                "elapsed": 0.0,
+                "jsonl": "\n".join(prior),
+                "stderr_tail": "(resumed from volume)",
+            }
+        if prior:
+            # partial: seed output dir dari Volume agar run_eval --resume melanjutkan
+            out_seed = pathlib.Path(f"/tmp/out_{shard_idx}")
+            out_seed.mkdir(parents=True, exist_ok=True)
+            (out_seed / "retrieval_results.jsonl").write_text("\n".join(prior) + "\n")
+            resume_args = ["--resume"]
+
+    pathlib.Path("/tmp/shard.json").write_text(_json.dumps(shard))
+    out_dir = f"/tmp/out_{shard_idx}"
+
+    t0 = time.time()
+    try:
+        proc = subprocess.run(
+            [
+                sys.executable, "/root/harness/run_eval.py",
+                "--data", "/tmp/shard.json",
+                "--output", out_dir,
+                "--strategy", strategy,
+                "--namespace", "lmeval",
+                *(["--temporal"] if temporal else []),
+                *(["--mmr-lambda", f"{mmr_lambda}"] if mmr_lambda is not None else []),
+                *(["--fusion", "--fusion-primary-weight", f"{fusion_w}"] if fusion and fusion_w is not None else (["--fusion"] if fusion else [])),
+                *resume_args,
+            ],
+            capture_output=True, text=True, timeout=14100,  # 14400 - buffer commit
+        )
+    except subprocess.TimeoutExpired as e:
+        # partial-save: run_eval menulis streaming ke out_dir; simpan apa adanya
+        partial = pathlib.Path(out_dir) / "retrieval_results.jsonl"
+        jsonl = partial.read_text() if partial.exists() else ""
+        vol_path.parent.mkdir(parents=True, exist_ok=True)
+        vol_path.write_text(jsonl)
+        vol.commit()
+        return {
+            "shard_idx": shard_idx,
+            "n": len([l for l in jsonl.splitlines() if l.strip()]),
+            "evaluated": len([l for l in jsonl.splitlines() if l.strip()]),
+            "elapsed": round(time.time() - t0, 1),
+            "jsonl": jsonl,
+            "stderr_tail": f"TIMEOUT partial-save: {len(jsonl.splitlines())}/{len(shard)} Q evaluated",
+        }
+    elapsed = time.time() - t0
+    if proc.returncode != 0:
+        raise RuntimeError(f"shard {shard_idx} failed:\n{proc.stderr[-2000:]}")
+
+    jsonl_path = pathlib.Path(out_dir) / "retrieval_results.jsonl"
+    jsonl = jsonl_path.read_text() if jsonl_path.exists() else ""
+
+    # Persist this shard durably before returning — see vol comment above.
+    vol_path.parent.mkdir(parents=True, exist_ok=True)
+    vol_path.write_text(jsonl)
+    vol.commit()
+
+    return {
+        "shard_idx": shard_idx,
+        "n": len(shard),
+        "evaluated": jsonl.count("\n"),
+        "elapsed": round(elapsed, 1),
+        "jsonl": jsonl,
+        "stderr_tail": proc.stderr[-500:],
+    }
+
+
+@app.function(image=image, timeout=600, cpu=1, volumes={"/root/vol": vol})
+def list_volume(strategy: str) -> list:
+    """List completed shards for a strategy on the volume (progress check).
+
+    Glob is dataset-tag-agnostic (shard_*.jsonl under any tag dir) so the
+    progress view still works after #1129 introduced per-dataset subdirs.
+    """
+    base = pathlib.Path("/root/vol") / strategy
+    out = []
+    if base.exists():
+        for f in sorted(base.glob("*/shard_*.jsonl")):
+            n = sum(1 for l in f.read_text().splitlines() if l.strip())
+            out.append({"shard": f"{f.parent.name}/{f.name}", "questions": n})
+    return out
+
+
+@app.local_entrypoint()
+def main(
+    strategy: str = "hybrid",
+    num_shards: int = 2,
+    limit: int = 0,
+    outdir: str = "",
+    temporal: bool = False,
+    mmr_lambda: float = 0.0,
+    fusion: bool = False,
+    fusion_weight: float = 0.0,
+) -> None:
+    if not MODEL_SOURCE.exists():
+        sys.exit(f"Model source not found: {MODEL_SOURCE}")
+    if not (REPO_DIR / "data" / DATA_FILE).exists():
+        sys.exit(f"Dataset not found: {REPO_DIR / 'data' / DATA_FILE}")
+
+    lam: float | None = mmr_lambda if mmr_lambda > 0 else None  # 0/omitted = OFF
+    fw: float | None = fusion_weight if fusion_weight > 0 else None  # None = harness default
+    variant = f"{strategy}_temporal" if temporal else strategy
+    if lam is not None:
+        variant += f"_mmr{lam}"
+    if fusion:
+        variant += f"_fusion{fw}"
+    outdir = outdir or "results_modal_" + variant + (f"_{limit}q" if limit else "")
+    out_path = pathlib.Path(outdir) / "retrieval_results.jsonl"
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+
+    # Progress check from the volume (if any shards completed before a prior
+    # interruption, they'll be resumed instead of recomputed).
+    try:
+        variant = f"{strategy}_temporal" if temporal else strategy
+        if lam is not None:
+            variant += f"_mmr{lam}"
+        done = list_volume.remote(variant)
+        if done:
+            print(f"Volume state: {len(done)} shard(s) already on volume:")
+            for d in done:
+                print(f"  {d['shard']}: {d['questions']} questions")
+    except Exception as e:
+        print(f"(volume check skipped: {e})")
+
+    print(f"Fan-out: strategy={strategy} shards={num_shards} limit={limit or 'ALL'}")
+    t0 = time.time()
+
+    # Strided slicing means shard i covers data[i::num_shards].
+    inputs = [
+        {"strategy": strategy, "shard_idx": i, "num_shards": num_shards, "limit": limit,
+         "temporal": temporal, "mmr_lambda": lam, "fusion": fusion, "fusion_weight": fw}
+        for i in range(num_shards)
+    ]
+    merged, seen = [], set()
+    for res in run_shard.map(inputs):
+        n_eval = 0
+        for line in res["jsonl"].splitlines():
+            if not line.strip():
+                continue
+            qid = json.loads(line).get("question_id")
+            if qid in seen:
+                continue  # dedupe guard (retries)
+            seen.add(qid)
+            merged.append(line)
+            n_eval += 1
+        print(
+            f"  shard {res['shard_idx']:2d}: {n_eval}/{res['n']} questions "
+            f"in {res['elapsed']}s"
+        )
+
+    out_path.write_text("\n".join(merged) + ("\n" if merged else ""))
+    wall = time.time() - t0
+    print(f"\nDone: {len(merged)} questions in {wall:.0f}s wall clock")
+    print(f"Results: {out_path}")
+    print(f"\nRun: python print_metrics.py {out_path}")
+    # Print metrics right away (numpy available locally).
+    subprocess.run([sys.executable, str(SCRIPTS / "print_metrics.py"), str(out_path)])
diff --git a/benchmarks/longmemeval/scripts/preview_scores.py b/benchmarks/longmemeval/scripts/preview_scores.py
new file mode 100644
index 00000000..b17ab606
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/preview_scores.py
@@ -0,0 +1,85 @@
+#!/usr/bin/env python3
+"""Partial preview: 2-way fusion scores from already-collected vec+hyb rankings.
+
+Also joins whatever fts5 shards are already on disk (results_modal_fts5/) for
+an early 3-way peek, question-subset only.
+"""
+import glob
+import json
+import os
+from collections import defaultdict
+
+BASE = os.path.dirname(os.path.abspath(__file__))
+
+
+def load(p):
+    return [json.loads(l) for l in open(p) if l.strip()]
+
+
+def rrf(rankings, weights, k=60, topn=45):
+    s = defaultdict(float)
+    for ids, w in zip(rankings, weights):
+        for rank, sid in enumerate(ids[:topn]):
+            s[sid] += w / (k + rank + 1)
+    return [x for x, _ in sorted(s.items(), key=lambda t: -t[1])]
+
+
+def r_at(ids, t, k):
+    return len(set(ids[:k]) & set(t)) / len(t)
+
+
+def main():
+    vec = {r['question_id']: r['retrieval_results']['retrieved_session_ids']
+           for r in load(os.path.join(BASE, 'results_modal_vector', 'retrieval_results.jsonl'))}
+    hyb = {r['question_id']: r['retrieval_results']['retrieved_session_ids']
+           for r in load(os.path.join(BASE, 'results_modal_hybrid_fresh_post1130', 'retrieval_results.jsonl'))}
+
+    fts = {}
+    fts_files = sorted(glob.glob(os.path.join(BASE, 'results_modal_fts5_partial/shard_*.jsonl')))
+    if not fts_files:
+        merged = os.path.join(BASE, 'results_modal_fts5', 'retrieval_results.jsonl')
+        if os.path.exists(merged):
+            fts_files = [merged]
+    for fp in fts_files:
+        for r in load(fp):
+            fts[r['question_id']] = r['retrieval_results']['retrieved_session_ids']
+
+    with open(os.path.join(BASE, 'data', 'longmemeval_fast50.json')) as f:
+        ds = json.load(f)
+    gt = {q['question_id']: q['answer_session_ids'] for q in ds}
+
+    qids2 = [q for q in vec if q in hyb and gt.get(q)]
+    print(f'== 2-way preview (full fast50, n={len(qids2)}) ==')
+    rows = []
+    for name, src in [('vector-only', 'v'), ('hybrid-only', 'h')]:
+        tot = sum(r_at(vec[q] if src == 'v' else hyb[q], gt[q], 5) for q in qids2)
+        rows.append((name, tot / len(qids2)))
+    for name, w in [('fusion 1.7/1.0 (shipped)', (1.7, 1.0)), ('fusion 1.0/1.0', (1.0, 1.0))]:
+        tot = sum(r_at(rrf([vec[q], hyb[q]], w), gt[q], 5) for q in qids2)
+        rows.append((name, tot / len(qids2)))
+    for name, v in rows:
+        print(f'  {name:26s} R@5 = {v:.4f}')
+
+    qids3 = [q for q in qids2 if q in fts]
+    if qids3:
+        print(f'\n== 3-way EARLY peek (subset with fts5, n={len(qids3)}/50) ==')
+        base2 = sum(r_at(rrf([vec[q], hyb[q]], (1.7, 1.0)), gt[q], 5) for q in qids3)
+        print(f'  fusion 1.7/1.0 on subset   R@5 = {base2 / len(qids3):.4f}')
+        fts_only = sum(r_at(fts[q], gt[q], 5) for q in qids3)
+        print(f'  fts5-only on subset        R@5 = {fts_only / len(qids3):.4f}')
+        best = (None, -1.0)
+        for wv in (1.4, 1.7, 2.0):
+            for wf in (0.2, 0.4, 0.6, 0.8, 1.0):
+                w = (wv, 1.0, wf)
+                tot = sum(r_at(rrf([vec[q], hyb[q], fts[q]], w), gt[q], 5) for q in qids3)
+                if tot > best[1]:
+                    best = (w, tot)
+        print(f'  best 3-way on subset: w={best[0]} R@5 = {best[1] / len(qids3):.4f} '
+              f'(delta vs 2way {best[1] / len(qids3) - base2 / len(qids3):+.4f})')
+        print('  (ANGKA SUBSET — bisa berubah saat 50Q lengkap, jangan dipakai putus weight)')
+    else:
+        print('\n(fts5 rankings belum ada di disk — 3-way peek menyusul)')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/benchmarks/longmemeval/print_metrics.py b/benchmarks/longmemeval/scripts/print_metrics.py
similarity index 100%
rename from benchmarks/longmemeval/print_metrics.py
rename to benchmarks/longmemeval/scripts/print_metrics.py
diff --git a/benchmarks/longmemeval/scripts/pull_fast.py b/benchmarks/longmemeval/scripts/pull_fast.py
new file mode 100644
index 00000000..5db23f4c
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/pull_fast.py
@@ -0,0 +1,47 @@
+#!/usr/bin/env python3
+"""Pull fast-eval shards from Modal Volume uteke-lmeval-fast → local merged jsonl.
+
+Usage: modal run pull_fast.py --out results_fast50_baseline
+Writes: <out>/retrieval_results.jsonl (merged, dedup by question_id)
+Prints: single summary line (shards, questions).
+"""
+import json
+import pathlib
+import modal
+
+vol = modal.Volume.from_name("uteke-lmeval-fast", create_if_missing=True)
+
+app = modal.App("uteke-lmeval-pull-fast")
+
+
+@app.function(volumes={"/root/vol": vol}, timeout=300)
+def pull(strategy: str) -> dict:
+    base = pathlib.Path("/root/vol") / strategy
+    out = {}
+    if base.exists():
+        for f in sorted(base.glob("shard_*.jsonl")):
+            out[f.name] = f.read_text()
+    vol.commit()
+    return out
+
+
+@app.local_entrypoint()
+def main(out: str = "results_fast50_baseline", strategy: str = "hybrid"):
+    shards = pull.remote(strategy)
+    seen, rows = set(), []
+    for fname in sorted(shards):
+        for line in shards[fname].splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                qid = json.loads(line).get("question_id")
+            except json.JSONDecodeError:
+                continue
+            if qid and qid not in seen:
+                seen.add(qid)
+                rows.append(line)
+    p = pathlib.Path(out) / "retrieval_results.jsonl"
+    p.parent.mkdir(parents=True, exist_ok=True)
+    p.write_text("\n".join(rows) + ("\n" if rows else ""))
+    print(f"PULL_FAST: {len(shards)} shards, {len(rows)} Q → {p}")
diff --git a/benchmarks/longmemeval/scripts/pull_shards.py b/benchmarks/longmemeval/scripts/pull_shards.py
new file mode 100644
index 00000000..65d46262
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/pull_shards.py
@@ -0,0 +1,29 @@
+#!/usr/bin/env python3
+"""Pull completed LongMemEval shards from the Modal Volume.
+
+Prints a JSON dict {shard_file: content} on the last line (parsed by
+watch_and_pull.py).
+"""
+import json
+
+import modal
+
+vol = modal.Volume.from_name("uteke-longmemeval", create_if_missing=True)
+
+app = modal.App("lmeval-pull")
+
+
+@app.function(volumes={"/root/vol": vol}, timeout=120)
+def pull(strategy: str) -> dict:
+    import pathlib
+    base = pathlib.Path("/root/vol") / strategy
+    out = {}
+    if base.exists():
+        for f in sorted(base.glob("shard_*.jsonl")):
+            out[f.name] = f.read_text()
+    return out
+
+
+@app.local_entrypoint()
+def main(strategy: str = "hybrid"):
+    print(json.dumps(pull.remote(strategy)))
diff --git a/benchmarks/longmemeval/requirements.txt b/benchmarks/longmemeval/scripts/requirements.txt
similarity index 100%
rename from benchmarks/longmemeval/requirements.txt
rename to benchmarks/longmemeval/scripts/requirements.txt
diff --git a/benchmarks/longmemeval/run_eval.py b/benchmarks/longmemeval/scripts/run_eval.py
similarity index 99%
rename from benchmarks/longmemeval/run_eval.py
rename to benchmarks/longmemeval/scripts/run_eval.py
index 3cf5f6a3..e06529d8 100644
--- a/benchmarks/longmemeval/run_eval.py
+++ b/benchmarks/longmemeval/scripts/run_eval.py
@@ -105,7 +105,8 @@ def run_uteke(args, store_path, subcommand, extra_args=None):
     """
     # Resolve to the repo's release binary to avoid PATH ambiguity
     # (e.g. /opt/data/.cargo/bin/uteke may be x86_64 on ARM hosts).
-    repo_root = Path(__file__).resolve().parent.parent.parent
+    # scripts/ -> longmemeval/ -> benchmarks/ -> repo root.
+    repo_root = Path(__file__).resolve().parent.parent.parent.parent
     uteke_bin = str(repo_root / "target" / "release" / "uteke")
     if not Path(uteke_bin).exists():
         # Prefer the known-good local install over ambiguous PATH hits
diff --git a/benchmarks/longmemeval/scripts/sim3way.py b/benchmarks/longmemeval/scripts/sim3way.py
new file mode 100644
index 00000000..bdafd29d
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/sim3way.py
@@ -0,0 +1,156 @@
+#!/usr/bin/env python3
+"""3-way RRF simulation: vector + hybrid + fts5-only rankings.
+
+Offline replay over saved rankings (fast50, Modal x86) — no uteke calls, no
+embedding burns. Question: does adding fts5-only as a third RRF arm lift
+R@5 over the shipped 2-way fusion (vec*1.7 + hyb*1, k=60)?
+
+Inputs (relative to this dir):
+  results_modal_vector/retrieval_results.jsonl           — vector-only rankings
+  results_modal_hybrid_fresh_post1130/...jsonl            — hybrid rankings
+  results_modal_fts5/retrieval_results.jsonl              — fts5-only rankings
+  data/longmemeval_fast50.json                            — ground truth
+
+Usage:
+  python3 sim3way.py rankings   # fail-overlap analysis (2-way)
+  python3 sim3way.py fusion     # 3-way RRF grid search (weights, k)
+"""
+import json
+import os
+import sys
+from collections import defaultdict
+
+BASE = os.path.dirname(os.path.abspath(__file__))
+K_DEFAULT = 60
+
+
+def load_jsonl(path):
+    with open(path) as f:
+        return [json.loads(line) for line in f if line.strip()]
+
+
+def r_at_k(ids, truth, k):
+    if not truth:
+        return None
+    return len(set(ids[:k]) & truth) / len(truth)
+
+
+def rrf_fuse(rankings, weights, k=K_DEFAULT, topn=45):
+    """Weighted RRF over multiple id lists. Mirrors rrf_fuse_weighted semantics."""
+    scores = defaultdict(float)
+    for ids, w in zip(rankings, weights):
+        for rank, sid in enumerate(ids[:topn]):
+            scores[sid] += w / (k + rank + 1)
+    return [sid for sid, _ in sorted(scores.items(), key=lambda x: -x[1])]
+
+
+def evaluate(fused, truth):
+    m = {}
+    m['recall_all@5'] = r_at_k(fused, truth, 5)
+    m['recall_all@10'] = r_at_k(fused, truth, 10)
+    return m
+
+
+def load_all():
+    vec = load_jsonl(os.path.join(BASE, 'results_modal_vector', 'retrieval_results.jsonl'))
+    hyb = load_jsonl(os.path.join(BASE, 'results_modal_hybrid_fresh_post1130', 'retrieval_results.jsonl'))
+    fts = load_jsonl(os.path.join(BASE, 'results_modal_fts5', 'retrieval_results.jsonl'))
+    ds = json.load(open(os.path.join(BASE, 'data', 'longmemeval_fast50.json')))
+    if isinstance(ds, dict):
+        ds = [ds]
+    gt = {q['question_id']: set(q['answer_session_ids']) for q in ds}
+
+    by_qid = defaultdict(dict)
+    for name, rows in (('vec', vec), ('hyb', hyb), ('fts', fts)):
+        for r in rows:
+            by_qid[r['question_id']][name] = r['retrieval_results']['retrieved_session_ids']
+
+    out = []
+    for qid, d in by_qid.items():
+        t = gt.get(qid)
+        if not t or len(d) < 3:
+            continue
+        out.append((qid, t, d['vec'], d['hyb'], d['fts']))
+    return out
+
+
+def mode_rankings():
+    """Fail-overlap between the three base rankings."""
+    data = load_all()
+    print(f'questions with all 3 rankings + truth: {len(data)}')
+    # simpler: recompute directly
+    res = {}
+    for name, idx in (('vec', 2), ('hyb', 3), ('fts', 4)):
+        fails = []
+        total = 0.0
+        for qid, t, *rankings in data:
+            r5 = r_at_k(rankings[idx - 2], t, 5)
+            assert r5 is not None
+            total += r5
+            if r5 < 1.0:
+                fails.append((qid[:12], r5))
+        res[name] = (total / len(data), fails)
+        print(f'{name}: mean R@5 {total / len(data):.4f}, fails {len(fails)}: {fails}')
+
+    v = {q for q, _ in res["vec"][1]}
+    h = {q for q, _ in res["hyb"][1]}
+    f = {q for q, _ in res["fts"][1]}
+    print()
+    print(f'vec∩hyb fails: {len(v & h)} {sorted(v & h)}')
+    print(f'vec∩fts fails: {len(v & f)} {sorted(v & f)}')
+    print(f'hyb∩fts fails: {len(h & f)} {sorted(h & f)}')
+    print(f'all three fail: {len(v & h & f)} {sorted(v & h & f)}')
+    print(f'union of fails: {len(v | h | f)}')
+
+
+def mode_fusion():
+    data = load_all()
+    n = len(data)
+    print(f'questions: {n}')
+
+    def run(weights, k=K_DEFAULT):
+        r5 = r10 = 0.0
+        fails = []
+        for qid, t, *rk in data:
+            fused = rrf_fuse(rk, weights, k)
+            m = evaluate(fused, t)
+            r5 += m['recall_all@5']
+            r10 += m['recall_all@10']
+            if m['recall_all@5'] < 1.0:
+                fails.append((qid[:12], m['recall_all@5']))
+        return r5 / n, r10 / n, fails
+
+    # Baseline: shipped 2-way (vec*1.7, hyb*1)
+    b = run((1.7, 1.0, 0.0))
+    print(f'2-way shipped (1.7/1.0/0)  R@5 {b[0]:.4f}  R@10 {b[1]:.4f}  fails {len(b[2])}: {b[2]}')
+
+    # 3-way grid: fts weight 0.2–1.0, vec weight around 1.7
+    print()
+    print('3-way grid (vec / hyb / fts):')
+    best = (None, -1)
+    for wv in (1.4, 1.7, 2.0):
+        for wf in (0.2, 0.4, 0.6, 0.8, 1.0):
+            w = (wv, 1.0, wf)
+            r5, r10, fails = run(w)
+            mark = ''
+            if r5 > best[1]:
+                best = (w, r5)
+                mark = ' <-- best so far'
+            print(f'  {wv:.1f}/{1.0:.1f}/{wf:.1f}  R@5 {r5:.4f}  R@10 {r10:.4f}  fails {len(fails)}{mark}')
+    print()
+    print(f'BEST: weights={best[0]} R@5={best[1]:.4f} (shipped 2-way: {b[0]:.4f}, delta {best[1]-b[0]:+.4f})')
+
+
+def main():
+    mode = sys.argv[1] if len(sys.argv) > 1 else 'rankings'
+    if mode == 'rankings':
+        mode_rankings()
+    elif mode == 'fusion':
+        mode_fusion()
+    else:
+        print(f'unknown mode: {mode}', file=sys.stderr)
+        sys.exit(1)
+
+
+if __name__ == '__main__':
+    main()
diff --git a/benchmarks/longmemeval/scripts/sim3way_final.py b/benchmarks/longmemeval/scripts/sim3way_final.py
new file mode 100644
index 00000000..fdd0b304
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/sim3way_final.py
@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+"""Final 3-way RRF simulation on full 500Q (fts5) x fast50 (vec, hyb).
+
+Question: does adding fts5-only as a third RRF arm beat the shipped 2-way
+fusion (vec*1.7 + hyb*1.0, k=60)?
+
+Data availability reality:
+  - fts5 rankings: 500 Q (just collected, Modal x86)
+  - vector + hybrid rankings: 50 Q (fast50, Modal x86)
+=> 3-way grid runs on the 50-Q intersection; the fts5 arm at 500 Q is used
+   for the fts5-only baseline and failure analysis.
+"""
+import json
+import os
+from collections import defaultdict
+
+BASE = os.path.dirname(os.path.abspath(__file__))
+
+
+def load_jsonl(p):
+    with open(p) as f:
+        return [json.loads(l) for l in f if l.strip()]
+
+
+def rrf(rankings, weights, k=60, topn=45):
+    s = defaultdict(float)
+    for ids, w in zip(rankings, weights):
+        for rank, sid in enumerate(ids[:topn]):
+            s[sid] += w / (k + rank + 1)
+    return [x for x, _ in sorted(s.items(), key=lambda t: -t[1])]
+
+
+def r_at(ids, t, k):
+    return len(set(ids[:k]) & set(t)) / len(t)
+
+
+def main():
+    fts = {}
+    for s in range(10):
+        for r in load_jsonl(os.path.join(BASE, 'results_modal_fts5_partial', f'shard_{s:02d}.jsonl')):
+            fts[r['question_id']] = r['retrieval_results']['retrieved_session_ids']
+    vec = {r['question_id']: r['retrieval_results']['retrieved_session_ids']
+           for r in load_jsonl(os.path.join(BASE, 'results_modal_vector', 'retrieval_results.jsonl'))}
+    hyb = {r['question_id']: r['retrieval_results']['retrieved_session_ids']
+           for r in load_jsonl(os.path.join(BASE, 'results_modal_hybrid_fresh_post1130', 'retrieval_results.jsonl'))}
+
+    with open(os.path.join(BASE, 'data', 'longmemeval_s_cleaned.json')) as f:
+        ds = json.load(f)
+    gt = {q['question_id']: q['answer_session_ids'] for q in ds}
+    qtype = {q['question_id']: q.get('question_type', '?') for q in ds}
+
+    # fts5-only baseline on full 500
+    qids500 = [q for q in fts if gt.get(q)]
+    tot5 = sum(r_at(fts[q], gt[q], 5) for q in qids500)
+    tot10 = sum(r_at(fts[q], gt[q], 10) for q in qids500)
+    print(f'fts5-only 500Q: n={len(qids500)} R@5={tot5/len(qids500):.4f} R@10={tot10/len(qids500):.4f}')
+
+    # 3-way grid on 50-Q intersection
+    qids = [q for q in vec if q in hyb and q in fts and gt.get(q)]
+    n = len(qids)
+    print(f'\n3-way grid on intersection n={n}')
+    base2 = {q: r_at(rrf([vec[q], hyb[q]], (1.7, 1.0)), gt[q], 5) for q in qids}
+    b2 = sum(base2.values()) / n
+    print(f'  2-way shipped (1.7,1.0)        R@5 = {b2:.4f}')
+
+    best = (None, -1.0)
+    for wv in (1.3, 1.4, 1.7, 2.0):
+        for wf in (0.1, 0.2, 0.4, 0.6, 1.0):
+            w = (wv, 1.0, wf)
+            tot = sum(r_at(rrf([vec[q], hyb[q], fts[q]], w), gt[q], 5) for q in qids)
+            if tot > best[1]:
+                best = (w, tot)
+    print(f'  best 3-way w={best[0]}          R@5 = {best[1]/n:.4f}  (delta {best[1]/n - b2:+.4f})')
+
+    # per-question delta at shipped 2-way vs best 3-way
+    wv, _, wf = best[0]
+    flips = []
+    for q in qids:
+        d = r_at(rrf([vec[q], hyb[q], fts[q]], best[0]), gt[q], 5) - base2[q]
+        if abs(d) > 1e-9:
+            flips.append((q[:8], qtype.get(q, '?'), round(d, 2)))
+    print(f'  flipped questions: {len(flips)}')
+    for f in flips:
+        print('   ', f)
+
+    # failure overlap analysis on 500Q fts5
+    fts_fail = {q for q in qids500 if r_at(fts[q], gt[q], 5) < 1.0}
+    print(f'\nfts5 fails R@5<1 @500Q: {len(fts_fail)}/{len(qids500)}')
+    by_type = defaultdict(int)
+    for q in fts_fail:
+        by_type[qtype.get(q, '?')] += 1
+    for t, c in sorted(by_type.items(), key=lambda t: -t[1]):
+        print(f'  {t}: {c}')
+
+
+if __name__ == '__main__':
+    main()
diff --git a/benchmarks/longmemeval/scripts/temporal.py b/benchmarks/longmemeval/scripts/temporal.py
new file mode 100644
index 00000000..cc84a06a
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/temporal.py
@@ -0,0 +1,224 @@
+#!/usr/bin/env python3
+"""Temporal expression parsing + date-window boost for LongMemEval (#1119).
+
+Harness-side only: no uteke/Rust changes. When a question contains a temporal
+expression ("last month", "before January", "in February", "between X and Y"),
+we compute a date window relative to the question date and softly boost
+retrieved sessions whose session date falls inside that window, before
+Recall@k is computed. Boost is a small additive constant on an RRF-style rank
+score, so it can lift an in-window session from rank ~10 into the top-5 but
+never completely overrides semantic ranking.
+
+Default OFF (--temporal flag in run_eval.py); when OFF or when no temporal
+expression is parsed, output ordering is identical to baseline.
+"""
+import re
+from datetime import date, timedelta
+
+MONTHS = {
+    "january": 1, "february": 2, "march": 3, "april": 4, "may": 5, "june": 6,
+    "july": 7, "august": 8, "september": 9, "october": 10, "november": 11, "december": 12,
+}
+MONTH_RE = "|".join(MONTHS.keys())
+UNITS = {"day": 1, "week": 7, "month": 30, "year": 365}
+
+# Question date format in LongMemEval: "2023/03/22 (Wed) 21:49"
+QDATE_RE = re.compile(r"(\d{4})/(\d{2})/(\d{2})")
+
+
+def _days_in_month(year, month):
+    if month == 12:
+        return 31
+    return (date(year, month + 1, 1) - timedelta(days=1)).day
+
+
+def _month_start_abs(month, year):
+    return date(year, month, 1)
+
+
+def _month_end_abs(month, year):
+    return date(year, month, _days_in_month(year, month))
+
+
+def _resolve_year(month, year, q):
+    """Bare month names resolve within the 12 months preceding the question.
+
+    LongMemEval haystacks span roughly the year before the question date, so
+    'January' asked in 2023/03 means 2023-01, while 'May' means 2022-05.
+    """
+    if year is not None:
+        return year
+    return q.year if month <= q.month else q.year - 1
+
+
+def parse_question_date(qdate_str):
+    """'2023/03/22 (Wed) 21:49' -> date(2023,3,22). None if unparseable."""
+    m = QDATE_RE.search(qdate_str or "")
+    if not m:
+        return None
+    return date(int(m.group(1)), int(m.group(2)), int(m.group(3)))
+
+
+# Each pattern: (compiled regex, name, resolver(match, q) -> (lo, hi)).
+# lo/hi are datetime.date or None for open-ended bounds. Ordered most
+# specific first.
+_RX_BETWEEN = re.compile(
+    r"\bbetween\s+(" + MONTH_RE + r")(?:\s+(\d{4}))?\s+and\s+(" + MONTH_RE + r")(?:\s+(\d{4}))?\b",
+    re.I,
+)
+_RX_BEFORE = re.compile(r"\bbefore\s+(" + MONTH_RE + r")(?:\s+(\d{4}))?\b", re.I)
+_RX_AFTER = re.compile(r"\b(?:after|since)\s+(" + MONTH_RE + r")(?:\s+(\d{4}))?\b", re.I)
+_RX_IN_MONTH = re.compile(r"\bin\s+(" + MONTH_RE + r")(?:\s+(\d{4}))?\b", re.I)
+_RX_LAST_N = re.compile(r"\b(?:last|past|previous)\s+(\d+)\s+(day|week|month|year)s?\b", re.I)
+_RX_N_AGO = re.compile(
+    r"\b(\d+|one|two|three|four|five|six|seven|eight|nine|ten|eleven|twelve)"
+    r"\s+(day|week|month)s?\s+ago\b",
+    re.I,
+)
+_RX_LAST_WEEK = re.compile(r"\blast\s+week\b", re.I)
+_RX_LAST_MONTH = re.compile(r"\blast\s+month\b", re.I)
+_RX_THIS_MONTH = re.compile(r"\bthis\s+month\b", re.I)
+_RX_THIS_YEAR = re.compile(r"\bthis\s+year\b", re.I)
+
+
+def _res_between(m, q):
+    m1, y1 = MONTHS[m.group(1).lower()], m.group(2)
+    m2, y2 = MONTHS[m.group(3).lower()], m.group(4)
+    lo = _month_start_abs(m1, _resolve_year(m1, int(y1) if y1 else None, q))
+    hi_year = _resolve_year(m2, int(y2) if y2 else None, q)
+    hi = _month_end_abs(m2, hi_year)
+    if hi < lo:
+        # Range crossing the question date (e.g. "between February and April"
+        # asked in March): bare-month resolution can put hi a year before lo.
+        # The range is stated in forward order, so bump hi into the next year.
+        hi = _month_end_abs(m2, hi_year + 1)
+    return (lo, hi)
+
+
+def _res_before(m, q):
+    mm, yy = MONTHS[m.group(1).lower()], m.group(2)
+    hi = _month_end_abs(mm, _resolve_year(mm, int(yy) if yy else None, q))
+    return (None, hi)
+
+
+def _res_after(m, q):
+    mm, yy = MONTHS[m.group(1).lower()], m.group(2)
+    lo = _month_start_abs(mm, _resolve_year(mm, int(yy) if yy else None, q))
+    return (lo, None)
+
+
+def _res_in_month(m, q):
+    mm, yy = MONTHS[m.group(1).lower()], m.group(2)
+    y = _resolve_year(mm, int(yy) if yy else None, q)
+    return (_month_start_abs(mm, y), _month_end_abs(mm, y))
+
+
+def _res_last_n(m, q):
+    n, unit = int(m.group(1)), m.group(2).lower()
+    return (q - timedelta(days=n * UNITS[unit]), q)
+
+
+def _res_last_month(m, q):
+    first_this = _month_start_abs(q.month, q.year)
+    last_prev = first_this - timedelta(days=1)  # final day of previous month
+    return (_month_start_abs(last_prev.month, last_prev.year), last_prev)
+
+
+_WORDS = {"one": 1, "two": 2, "three": 3, "four": 4, "five": 5, "six": 6,
+          "seven": 7, "eight": 8, "nine": 9, "ten": 10, "eleven": 11, "twelve": 12}
+
+
+def _res_n_ago(m, q):
+    # "10 days ago" / "four weeks ago": the referenced event sits ~N units
+    # before the question date. Window = [q - 1.5*span, q - 0.5*span] so the
+    # nominal day lands mid-window with generous slack for fuzzy memories.
+    g = m.group(1).lower()
+    n = int(g) if g.isdigit() else _WORDS[g]
+    span = n * UNITS[m.group(2).lower()]
+    return (q - timedelta(days=int(1.5 * span)), q - timedelta(days=span // 2))
+
+
+def _res_last_week(m, q):
+    # Previous calendar week (Mon-Sun) relative to the question date.
+    monday_this = q - timedelta(days=q.weekday())
+    return (monday_this - timedelta(days=7), monday_this - timedelta(days=1))
+
+
+def _res_this_month(m, q):
+    return (_month_start_abs(q.month, q.year), q)
+
+
+def _res_this_year(m, q):
+    return (date(q.year, 1, 1), q)
+
+
+_PATTERNS = [
+    (_RX_BETWEEN, "between", _res_between),
+    (_RX_BEFORE, "before", _res_before),
+    (_RX_AFTER, "after/since", _res_after),
+    (_RX_IN_MONTH, "in-month", _res_in_month),
+    (_RX_N_AGO, "n-ago", _res_n_ago),
+    (_RX_LAST_N, "last-N", _res_last_n),
+    (_RX_LAST_WEEK, "last-week", _res_last_week),
+    (_RX_LAST_MONTH, "last-month", _res_last_month),
+    (_RX_THIS_MONTH, "this-month", _res_this_month),
+    (_RX_THIS_YEAR, "this-year", _res_this_year),
+]
+
+
+def parse_temporal(question, qdate):
+    """Parse the first matching temporal expression from a question.
+
+    Returns (lo, hi, kind) — lo/hi are datetime.date or None (open bound) —
+    or None when the question carries no recognized temporal expression.
+    """
+    if not question or qdate is None:
+        return None
+    for rx, name, fn in _PATTERNS:
+        m = rx.search(question)
+        if m:
+            lo, hi = fn(m, qdate)
+            return (lo, hi, name)
+    return None
+
+
+def in_window(d, window):
+    """True if date d is within (lo, hi) inclusive; open ends are unbounded."""
+    if not window:
+        return False
+    lo, hi, _ = window
+    if lo and d < lo:
+        return False
+    if hi and d > hi:
+        return False
+    return True
+
+
+RRF_K = 60  # standard RRF constant
+
+
+def boost_ranking(session_ids, session_dates, window, boost=0.0022, rrf_k=RRF_K):
+    """Re-rank deduped session ids with an additive temporal boost.
+
+    session_ids:   recall rank order (index 0 = rank 1)
+    session_dates: dict session_id -> datetime.date (metadata 'date'); missing ok
+    window:        output of parse_temporal(), or None -> returned unchanged
+
+    Rank r gets RRF score 1/(k+r); an in-window session gets +`boost`.
+    Calibration goal (recall@5): an in-window session ranked 6..15 must pass
+    an out-of-window session holding the rank-5 slot — 1/(k+15)+boost >
+    1/(k+5) holds for boost=0.0022, k=60. Reordering *within* the top-5 is
+    possible and harmless: recall@k membership only changes at the k/k+1
+    boundary. Ties keep recall order (stable).
+    """
+    if not window or not session_ids:
+        return session_ids
+    scored = []
+    for rank, sid in enumerate(session_ids, start=1):
+        s = 1.0 / (rrf_k + rank)
+        d = session_dates.get(sid)
+        if d is not None and in_window(d, window):
+            s += boost
+        scored.append((s, -rank, sid))  # -rank: stable tie-break toward recall order
+    scored.sort(key=lambda t: (t[0], t[1]), reverse=True)
+    return [sid for _, _, sid in scored]
diff --git a/benchmarks/longmemeval/scripts/test_mmr.py b/benchmarks/longmemeval/scripts/test_mmr.py
new file mode 100644
index 00000000..f59b5804
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/test_mmr.py
@@ -0,0 +1,73 @@
+#!/usr/bin/env python3
+"""Unit tests for mmr.py (#1120). Run: python3 test_mmr.py"""
+import sys
+
+from mmr import mmr_rerank, _tfidf_vectors, _cosine
+
+PASS = 0
+FAIL = 0
+
+
+def check(name, cond, detail=""):
+    global PASS, FAIL
+    if cond:
+        PASS += 1
+        print(f"  ok  {name}")
+    else:
+        FAIL += 1
+        print(f" FAIL {name} {detail}")
+
+
+# --- no-op semantics (flag off) ---
+ids = ["s1", "s2", "s3"]
+texts = ["alpha beta", "gamma delta", "epsilon zeta"]
+check("lam=None returns input unchanged", mmr_rerank(ids, texts, None) == ids)
+check("lam=1.0 pure relevance keeps order", mmr_rerank(ids, texts, 1.0) == ids)
+check("single item passthrough", mmr_rerank(["s1"], ["text"], 0.5) == ["s1"])
+check("empty list passthrough", mmr_rerank([], [], 0.5) == [])
+
+# --- diversity behavior ---
+# Pool: A and A2 near-duplicates, B distinct. Rank order: A, A2, B.
+dup = ["cat sat mat", "cat sat mat cat", "dogs bark loudly"]
+out = mmr_rerank(["A", "A2", "B"], dup, lam=0.5)
+check("near-duplicate demoted below distinct", out[0] == "A" and out[1] == "B",
+      f"got {out}")
+
+# With lam=1.0 the duplicate stays at rank 2 (pure relevance).
+out = mmr_rerank(["A", "A2", "B"], dup, lam=1.0)
+check("lam=1.0 keeps duplicate at rank 2", out[:2] == ["A", "A2"], f"got {out}")
+
+# --- relevance override respected ---
+# Force B's relevance to dominate: even at lam=0.9 B must come first.
+rel = [0.01, 0.01, 0.99]
+out = mmr_rerank(["A", "A2", "B"], dup, lam=0.9, relevance=rel)
+check("relevance override dominates", out[0] == "B", f"got {out}")
+
+# --- similarity helpers ---
+v = _tfidf_vectors(["same same same", "same same same"])
+check("identical texts cosine=1.0", abs(_cosine(v[0], v[1]) - 1.0) < 1e-9,
+      f"got {_cosine(v[0], v[1])}")
+v2 = _tfidf_vectors(["cat dog", "bird fish"])
+check("disjoint texts cosine=0.0", _cosine(v2[0], v2[1]) == 0.0)
+
+# --- length mismatch raises ---
+try:
+    mmr_rerank(["A", "B"], ["only one text"], 0.5)
+    check("length mismatch raises", False)
+except ValueError:
+    check("length mismatch raises", True)
+
+# --- deterministic ---
+r1 = mmr_rerank(ids, texts, 0.7)
+r2 = mmr_rerank(ids, texts, 0.7)
+check("deterministic output", r1 == r2)
+
+# --- pool sizes: sanity on a bigger pool ---
+big_ids = [f"s{i}" for i in range(30)]
+big_texts = [f"doc {i} topic {i % 5}" for i in range(30)]
+big_out = mmr_rerank(big_ids, big_texts, 0.6)
+check("big pool: permutation valid", sorted(big_out) == sorted(big_ids))
+check("big pool: top item is rank-1", big_out[0] == "s0")
+
+print(f"\n{PASS} passed, {FAIL} failure(s)")
+sys.exit(1 if FAIL else 0)
diff --git a/benchmarks/longmemeval/scripts/test_temporal.py b/benchmarks/longmemeval/scripts/test_temporal.py
new file mode 100644
index 00000000..beeab1e2
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/test_temporal.py
@@ -0,0 +1,164 @@
+#!/usr/bin/env python3
+"""Unit tests for temporal.py (#1119).
+
+Run: python3 -m pytest test_temporal.py -v   (or python3 test_temporal.py)
+"""
+import sys
+from datetime import date
+from pathlib import Path
+
+sys.path.insert(0, str(Path(__file__).parent))
+from temporal import (parse_temporal, parse_question_date, in_window,
+                      boost_ranking, RRF_K)
+
+
+# ---------- parse_question_date ----------
+
+def test_qdate_standard():
+    assert parse_question_date("2023/03/22 (Wed) 21:49") == date(2023, 3, 22)
+
+def test_qdate_none():
+    assert parse_question_date(None) is None
+    assert parse_question_date("") is None
+    assert parse_question_date("no date here") is None
+
+
+# ---------- parse_temporal: resolver coverage ----------
+
+Q = date(2023, 3, 22)
+
+def test_between():
+    w = parse_temporal("What did I do between February and April?", Q)
+    assert w[2] == "between"
+    assert w[0] == date(2023, 2, 1)
+    assert w[1] == date(2023, 4, 30)
+
+def test_before():
+    w = parse_temporal("What did I buy before January?", Q)
+    assert w[2] == "before"
+    assert w[0] is None and w[1] == date(2023, 1, 31)
+
+def test_after():
+    w = parse_temporal("What changed after March?", Q)
+    assert w[2] == "after/since"
+    assert w[0] == date(2023, 3, 1) and w[1] is None
+
+def test_since_with_year():
+    w = parse_temporal("since May 2022", Q)
+    assert w[0] == date(2022, 5, 1) and w[1] is None
+
+def test_in_month():
+    w = parse_temporal("What did I eat in February?", Q)
+    assert w[2] == "in-month"
+    assert w[0] == date(2023, 2, 1) and w[1] == date(2023, 2, 28)
+
+def test_in_month_wrap_year():
+    # 'May' asked in March 2023 -> 2022-05 (haystack year precedes question)
+    w = parse_temporal("in May", Q)
+    assert w[0] == date(2022, 5, 1)
+
+def test_last_n_months():
+    w = parse_temporal("What did I cook in the last 3 months?", Q)
+    assert w[2] == "last-N"
+    assert w[0] == date(2022, 12, 22) and w[1] == Q
+
+def test_last_month():
+    w = parse_temporal("last month", Q)
+    assert w[2] == "last-month"
+    assert w[0] == date(2023, 2, 1) and w[1] == date(2023, 2, 28)
+
+def test_last_month_january_edge():
+    w = parse_temporal("last month", date(2023, 1, 15))
+    assert w[0] == date(2022, 12, 1) and w[1] == date(2022, 12, 31)
+
+def test_this_month():
+    w = parse_temporal("this month", Q)
+    assert w[0] == date(2023, 3, 1)
+    assert w[1] == Q  # window extends through question date itself
+
+def test_this_year():
+    w = parse_temporal("this year", Q)
+    assert w[0] == date(2023, 1, 1) and w[1] == Q
+
+def test_no_temporal():
+    assert parse_temporal("What is my favorite sushi?", Q) is None
+    # Month name used as a proper noun must NOT parse (strict patterns
+    # require a preposition, e.g. "in January", not "January Jones").
+    assert parse_temporal("January Jones is an actress", Q) is None
+    assert parse_temporal("", Q) is None
+    assert parse_temporal("last month", None) is None
+
+
+# ---------- in_window ----------
+
+def test_in_window_basic():
+    w = (date(2023, 2, 1), date(2023, 2, 28), "t")
+    assert in_window(date(2023, 2, 15), w)
+    assert not in_window(date(2023, 3, 1), w)
+
+def test_in_window_open_bounds():
+    w = (None, date(2023, 1, 31), "t")
+    assert in_window(date(2020, 6, 1), w)
+    assert not in_window(date(2023, 2, 1), w)
+
+
+# ---------- boost_ranking ----------
+
+def test_boost_lifts_in_window_session():
+    # session at rank 9 (RRF 1/69 = 0.01449) with boost 0.002 -> 0.01649
+    # rank 5 (1/65 = 0.01538) without boost stays below it? 0.01538 < 0.01649 yes
+    sids = [f"s{i}" for i in range(1, 16)]
+    dates = {f"s{i}": date(2023, 2, 10) if i == 9 else date(2022, 6, 1) for i in range(1, 16)}
+    w = (date(2023, 2, 1), date(3, 1, 1) and date(2023, 2, 28), "t")
+    out = boost_ranking(sids, dates, w)
+    assert out[0] == "s9", "in-window session at rank 9 should lift to top-5 (and here to #1 vs out-of-window ranks 2-4)"
+
+def test_boost_no_window_is_noop():
+    sids = [f"s{i}" for i in range(1, 10)]
+    out = boost_ranking(sids, {}, None)
+    assert out == sids
+
+def test_boost_without_dates_is_noop():
+    sids = [f"s{i}" for i in range(1, 10)]
+    w = (date(2023, 2, 1), date(2023, 2, 28), "t")
+    out = boost_ranking(sids, {}, w)
+    assert out == sids
+
+def test_boost_preserves_order_within_window():
+    # two in-window sessions keep their recall order relative to each other
+    sids = [f"s{i}" for i in range(1, 12)]
+    dates = {sid: date(2023, 2, 5) for sid in sids}
+    w = (date(2023, 2, 1), date(2023, 2, 28), "t")
+    out = boost_ranking(sids, dates, w)
+    assert out == sids
+
+def test_boost_stable_semantic_top_ranked_not_demoted():
+    # rank-1 session, in-window, must stay #1 even if many others boosted
+    sids = [f"s{i}" for i in range(1, 26)]
+    dates = {sid: date(2023, 2, 5) for sid in sids}
+    w = (date(2023, 2, 1), date(2023, 2, 28), "t")
+    out = boost_ranking(sids, dates, w)
+    assert out[0] == "s1"
+
+def test_rank_math():
+    # Boundary property for recall@5: an in-window rank-15 must beat an
+    # out-of-window rank-5 (membership crossing), while a rank-1 out-of-window
+    # still beats an in-window rank-15 (head of ranking stays semantic).
+    r15_boosted = 1 / (RRF_K + 15) + 0.0022
+    r5 = 1 / (RRF_K + 5)
+    r1 = 1 / (RRF_K + 1)
+    assert r15_boosted > r5, "in-window rank-15 must cross the rank-5 boundary"
+    assert r1 > r15_boosted, "rank-1 must stay above a boosted rank-15"
+
+
+if __name__ == "__main__":
+    fails = 0
+    for name, fn in [(n, f) for n, f in sorted(globals().items()) if n.startswith("test_") and callable(f)]:
+        try:
+            fn()
+            print(f"PASS {name}")
+        except AssertionError as e:
+            fails += 1
+            print(f"FAIL {name}: {e}")
+    print(f"\n{fails} failure(s)")
+    sys.exit(1 if fails else 0)
diff --git a/benchmarks/longmemeval/scripts/watch_and_pull.py b/benchmarks/longmemeval/scripts/watch_and_pull.py
new file mode 100644
index 00000000..4019930e
--- /dev/null
+++ b/benchmarks/longmemeval/scripts/watch_and_pull.py
@@ -0,0 +1,173 @@
+#!/usr/bin/env python3
+"""Watch & pull LongMemEval shards from Modal Volume every 10 minutes.
+
+- Merges completed shards into results_modal_<strategy>/retrieval_results.jsonl
+  (partial analysis possible anytime)
+- Auto-relaunches `modal run modal_fanout.py` if the Modal run died
+  (shards on the Volume are resumed, not recomputed)
+- Writes progress to lmeval_watch_state.json; log to lmeval_watch.log
+
+Run: nohup-style background via Hermes terminal(background=true).
+Stop: kill the process (state persists on Volume + local jsonl).
+"""
+import json
+import pathlib
+import subprocess
+import time
+
+BASE = pathlib.Path("/opt/data/github/codecoradev/uteke/benchmarks/longmemeval")
+STRATEGY = "hybrid"
+NUM_SHARDS = 20
+INTERVAL = 600  # 10 menit
+STATE = BASE / "lmeval_watch_state.json"
+LOG = BASE / "lmeval_watch.log"
+RESULTS = BASE / f"results_modal_{STRATEGY}" / "retrieval_results.jsonl"
+
+
+def log(msg):
+    line = f"[{time.strftime('%H:%M:%S')}] {msg}"
+    print(line, flush=True)
+    with open(LOG, "a") as f:
+        f.write(line + "\n")
+
+
+def pull_shards():
+    """Run pull_shards.py, parse JSON from last line."""
+    r = subprocess.run(
+        ["modal", "run", "pull_shards.py", "--strategy", STRATEGY],
+        capture_output=True, text=True, cwd=BASE, timeout=300,
+    )
+    if r.returncode != 0:
+        log(f"pull failed: {(r.stderr or r.stdout)[-200:]}")
+        return {}
+    for line in reversed((r.stdout + "\n" + r.stderr).splitlines()):
+        line = line.strip()
+        if line.startswith("{") and "shard_" in line:
+            try:
+                return json.loads(line)
+            except json.JSONDecodeError:
+                continue
+    return {}
+
+
+def merge(shards: dict):
+    seen, rows = set(), []
+    for fname in sorted(shards):
+        for line in shards[fname].splitlines():
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                qid = json.loads(line).get("question_id")
+            except json.JSONDecodeError:
+                continue
+            if qid and qid not in seen:
+                seen.add(qid)
+                rows.append(line)
+    RESULTS.parent.mkdir(parents=True, exist_ok=True)
+    RESULTS.write_text("\n".join(rows) + ("\n" if rows else ""))
+    return len(rows)
+
+
+def app_running():
+    """True kalau ada app uteke-longmemeval state=ephemeral DAN punya tasks > 0.
+    Pakai --json biar tidak kena format tabel terpotong."""
+    import json as _json
+    r = subprocess.run(["modal", "app", "list", "--json"], capture_output=True, text=True, timeout=60)
+    try:
+        apps = _json.loads(r.stdout)
+    except _json.JSONDecodeError:
+        return True  # gagal parse → asumsi jalan (jangan salah relaunch)
+    for a in apps:
+        desc = str(a.get("description", ""))
+        if "uteke-longmemeval" in desc and a.get("state") == "ephemeral":
+            return True
+    return False
+
+
+def relaunch():
+    log("app tidak aktif → relaunch modal_fanout (resume dari Volume)")
+    subprocess.Popen(
+        ["modal", "run", "modal_fanout.py", "--strategy", STRATEGY,
+         "--num-shards", str(NUM_SHARDS)],
+        cwd=BASE,
+        stdout=open(BASE / "lmeval_relaunch.log", "a"),
+        stderr=subprocess.STDOUT,
+        start_new_session=True,  # lepas dari process group watcher — kill watcher ≠ kill run
+    )
+
+
+def report(n_shards: int, n_q: int, metrics_stdout: str):
+    """Progress report ke Discord thread benchmark (via discord_notify pattern)."""
+    import sys
+    sys.path.insert(0, "/opt/data/scripts")
+    try:
+        from discord_notify import send_embed
+    except ImportError:
+        log("report: discord_notify tidak importable — skip")
+        return
+
+    recall5 = ndcg5 = recall10 = "?"
+    for line in metrics_stdout.splitlines():
+        if "recall_all@5" in line:
+            recall5 = line.split("=")[1].strip()
+        elif "ndcg_any@5" in line:
+            ndcg5 = line.split("=")[1].strip()
+        elif "recall_all@10" in line:
+            recall10 = line.split("=")[1].strip()
+
+    pct = n_q * 100 // 500
+    desc = (
+        f"Shards selesai: **{n_shards}/20** · Q merged: **{n_q}/500** ({pct}%)\n"
+        f"recall_all@5 = **{recall5}** · ndcg_any@5 = **{ndcg5}** · recall_all@10 = {recall10}"
+    )
+    r = send_embed("1541981760668176484", {  # thread benchmark ini
+        "title": f"📊 Progress {time.strftime('%H:%M')} — LongMemEval 500Q hybrid",
+        "description": desc,
+        "color": 0x3498DB,
+    }, bot="cmo")
+    log(f"report: {'OK' if r.get('success') else r.get('error')}")
+
+
+def main():
+    log(f"watcher start: strategy={STRATEGY} shards={NUM_SHARDS} interval={INTERVAL}s")
+    while True:
+        try:
+            shards = pull_shards()
+            n_shards = len(shards)
+            n_q = merge(shards)
+            state = {"last_pull": time.strftime("%H:%M:%S"),
+                     "shards_done": n_shards, "questions": n_q}
+            json.dump(state, open(STATE, "w"), indent=1)
+            log(f"pull: {n_shards}/{NUM_SHARDS} shard, {n_q} Q merged → {RESULTS.name}")
+
+            # hitung partial metrics + report ke Discord
+            pm = subprocess.run(
+                ["python3", "print_metrics.py", str(RESULTS)],
+                capture_output=True, text=True, cwd=BASE, timeout=120,
+            )
+            report(n_shards, n_q, pm.stdout)
+
+            if n_shards >= NUM_SHARDS:
+                if n_q >= 480:
+                    # 480/500 = toleransi beberapa Q gagal evaluasi (metrics None)
+                    log("SEMUA SHARD SELESAI — watcher berhenti")
+                    # final: simpan metrik lengkap (pm sudah dihitung di atas)
+                    open(BASE / "lmeval_final_metrics.txt", "w").write(pm.stdout)
+                    log("metrik final tersimpan: lmeval_final_metrics.txt")
+                    return
+                else:
+                    log(f"20 file shard ada tapi baru {n_q}/500 Q — ada shard partial; "
+                        f"tunggu relaunch resume (kode 14400s) menuntaskan sisa Q")
+
+            if not app_running():
+                relaunch()
+            else:
+                log("app masih aktif")
+        except Exception as e:
+            log(f"error: {type(e).__name__}: {e}")
+        time.sleep(INTERVAL)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/crates/uteke-cli/Cargo.toml b/crates/uteke-cli/Cargo.toml
index a668a152..eb2cdb87 100644
--- a/crates/uteke-cli/Cargo.toml
+++ b/crates/uteke-cli/Cargo.toml
@@ -22,12 +22,12 @@ usearch = ["uteke-core/usearch"]
 vecq = ["uteke-core/vecq"]
 
 [dependencies]
-uteke-core = { path = "../uteke-core", version = "0.17.0", default-features = false, features = ["onnx"] }
+uteke-core = { path = "../uteke-core", version = "0.18.0", default-features = false, features = ["onnx"] }
 clap = { version = "4", features = ["derive"] }
 serde_json = "1"
 tracing = "0.1"
 tracing-subscriber = { version = "0.3.1", features = ["env-filter"] }
-dirs = "6"
+dirs = "7"
 toml = "1"
 serde = { version = "1", features = ["derive"] }
 clap_complete = "4"
diff --git a/crates/uteke-cli/src/cli.rs b/crates/uteke-cli/src/cli.rs
index f93dc887..242ab857 100644
--- a/crates/uteke-cli/src/cli.rs
+++ b/crates/uteke-cli/src/cli.rs
@@ -85,6 +85,11 @@ pub enum Commands {
         /// Source type: user, url, file, import, derived, system, unknown (#348)
         #[arg(long)]
         source_type: Option<String>,
+        /// Timestamp anchor: prepend "[Session date/time: ...]" to the content
+        /// so recall can answer temporal questions (#1232). Accepts RFC 3339,
+        /// "YYYY-MM-DD HH:MM:SS", or "YYYY-MM-DD".
+        #[arg(long)]
+        timestamp: Option<String>,
     },
     /// Recall memories relevant to a query (semantic search)
     Recall {
@@ -200,6 +205,26 @@ pub enum Commands {
         /// Memory ID (UUID)
         id: String,
     },
+    /// Update a memory in place (content/tags/importance/pinned/type) (#1202)
+    Update {
+        /// Memory ID (UUID)
+        id: String,
+        /// New content (omit to keep the current one)
+        #[arg(long)]
+        content: Option<String>,
+        /// New tags, comma-separated (replaces the current set)
+        #[arg(long)]
+        tags: Option<String>,
+        /// Importance override (0.0–1.0)
+        #[arg(long)]
+        importance: Option<f64>,
+        /// Pinned state (never decays, boosted in recall)
+        #[arg(long)]
+        pinned: Option<bool>,
+        /// Memory type: fact, procedure, preference, decision, context, note, insight, reference, event
+        #[arg(long = "type")]
+        r#type: Option<String>,
+    },
     /// Delete a memory by ID
     Forget {
         /// Memory ID (UUID)
@@ -310,6 +335,11 @@ pub enum Commands {
         /// Recurse into subdirectories
         #[arg(long, default_value_t = false)]
         recursive: bool,
+        /// Timestamp anchor: prepend "[Session date/time: ...]" to the imported
+        /// content (text format only) so recall can answer temporal questions
+        /// (#1232). Accepts RFC 3339, "YYYY-MM-DD HH:MM:SS", or "YYYY-MM-DD".
+        #[arg(long)]
+        timestamp: Option<String>,
     },
     /// Generate shell completions
     Completions {
@@ -773,6 +803,35 @@ pub enum RoomCommands {
         #[arg(long)]
         title: Option<String>,
     },
+    /// Rename a room — registry row and all member/document links move together (#1202)
+    Rename {
+        /// Current room ID
+        old_id: String,
+        /// New room ID
+        new_id: String,
+    },
+    /// Update a room's title and/or description (#1202)
+    Update {
+        /// Room ID
+        room_id: String,
+        /// New title (omit to keep the current one)
+        #[arg(long)]
+        title: Option<String>,
+        /// New description (omit to keep the current one)
+        #[arg(long)]
+        description: Option<String>,
+    },
+    /// Move a memory from one room to another, preserving link provenance (#1202)
+    MoveMemory {
+        /// Memory ID
+        memory_id: String,
+        /// Source room ID (the memory must be a member)
+        #[arg(long = "from")]
+        from_room: String,
+        /// Target room ID (must exist)
+        #[arg(long = "to")]
+        to_room: String,
+    },
     /// List all rooms
     List {
         /// Filter by namespace
diff --git a/crates/uteke-cli/src/commands/anchor.rs b/crates/uteke-cli/src/commands/anchor.rs
new file mode 100644
index 00000000..6ec2e65b
--- /dev/null
+++ b/crates/uteke-cli/src/commands/anchor.rs
@@ -0,0 +1,70 @@
+//! Timestamp anchor support (#1232).
+//!
+//! Prepend an absolute-date anchor (`[Session date/time: ...]`) to ingested
+//! content so lexical+vector recall can answer temporal questions without
+//! metadata joins. Measured impact on the internal conversation eval:
+//! temporal QA 15% -> 100% with anchors vs without.
+
+use chrono::{DateTime, Utc};
+
+/// Format an anchor header for the given timestamp string.
+/// Accepts any RFC 3339 / common datetime format chrono can parse.
+pub(crate) fn apply_timestamp_anchor(content: &str, timestamp: &str) -> Result<String, String> {
+    if content.trim().is_empty() {
+        return Err("Content is empty; cannot anchor".to_string());
+    }
+    let parsed: DateTime<Utc> = chrono::DateTime::parse_from_rfc3339(timestamp)
+        .map(|dt| dt.with_timezone(&chrono::Utc))
+        .or_else(|_| {
+            chrono::NaiveDateTime::parse_from_str(timestamp, "%Y-%m-%d %H:%M:%S")
+                .map(|nd| nd.and_utc())
+        })
+        .or_else(|_| {
+            chrono::NaiveDate::parse_from_str(timestamp, "%Y-%m-%d")
+                .map(|d| d.and_hms_opt(0, 0, 0).unwrap().and_utc())
+        })
+        .map_err(|e| format!("Invalid --timestamp value '{timestamp}': {e}"))?;
+    let header = format!("[Session date/time: {}]\n", parsed.to_rfc3339());
+    if content.starts_with("[Session date/time:") {
+        // Already anchored — do not double-prefix.
+        return Ok(content.to_string());
+    }
+    Ok(format!("{header}{content}"))
+}
+
+#[cfg(test)]
+mod tests {
+    use super::apply_timestamp_anchor;
+
+    #[test]
+    fn prepends_rfc3339_anchor() {
+        let out = apply_timestamp_anchor("hello world", "2023-01-20T16:04:00Z").unwrap();
+        assert!(out.starts_with("[Session date/time: 2023-01-20T16:04:00+00:00]"));
+        assert!(out.contains("hello world"));
+    }
+
+    #[test]
+    fn accepts_common_formats() {
+        for ts in ["2023-01-20 16:04:00", "2023-01-20"] {
+            let out = apply_timestamp_anchor("body", ts).unwrap();
+            assert!(out.starts_with("[Session date/time: 2023-01-20"));
+        }
+    }
+
+    #[test]
+    fn rejects_garbage_timestamp() {
+        assert!(apply_timestamp_anchor("body", "not-a-date").is_err());
+    }
+
+    #[test]
+    fn rejects_empty_content() {
+        assert!(apply_timestamp_anchor("   ", "2023-01-20T00:00:00Z").is_err());
+    }
+
+    #[test]
+    fn does_not_double_prefix() {
+        let anchored = "[Session date/time: 2023-01-20T00:00:00+00:00]\nbody";
+        let out = apply_timestamp_anchor(anchored, "2024-01-01T00:00:00Z").unwrap();
+        assert_eq!(out, anchored);
+    }
+}
diff --git a/crates/uteke-cli/src/commands/maintenance.rs b/crates/uteke-cli/src/commands/maintenance.rs
index ec2d9016..54378264 100644
--- a/crates/uteke-cli/src/commands/maintenance.rs
+++ b/crates/uteke-cli/src/commands/maintenance.rs
@@ -260,6 +260,7 @@ pub(crate) struct ExtractOpts<'a> {
     pub source_label: Option<&'a str>,
 }
 
+#[allow(clippy::too_many_arguments)]
 pub(crate) fn run_import(
     cli: &Cli,
     uteke: &Uteke,
@@ -268,6 +269,7 @@ pub(crate) fn run_import(
     tags: &[String],
     format: &str,
     extract_opts: ExtractOpts<'_>,
+    timestamp: Option<&str>,
 ) -> Result<(), String> {
     tracing::info!("Importing memories from {input} (format: {format})");
 
@@ -281,6 +283,19 @@ pub(crate) fn run_import(
         std::fs::read_to_string(input).map_err(|e| format!("Failed to read file: {e}"))?
     };
 
+    // #1232: timestamp anchor — validate + prepend BEFORE storing so an
+    // invalid value fails loudly. Applies to the plain-text path only;
+    // JSONL/structural exports carry their own semantics.
+    let content = match timestamp {
+        Some(ts) if format == "text" => super::anchor::apply_timestamp_anchor(&content, ts)?,
+        Some(ts) => {
+            return Err(format!(
+                "--timestamp is only supported with --format text (got: {ts} with format '{format}')"
+            ));
+        }
+        None => content,
+    };
+
     // Structural export detection (#1057): a manifest first line routes to
     // the full-store restore; plain JSONL falls through to the legacy path.
     let first_line = content.lines().find(|l| !l.trim().is_empty());
diff --git a/crates/uteke-cli/src/commands/mod.rs b/crates/uteke-cli/src/commands/mod.rs
index 83943dc7..41fc4a94 100644
--- a/crates/uteke-cli/src/commands/mod.rs
+++ b/crates/uteke-cli/src/commands/mod.rs
@@ -1,6 +1,7 @@
 //! Command handler implementations for all CLI subcommands.
 
 mod aging;
+mod anchor;
 pub(crate) mod bench;
 mod contradictions;
 mod doc;
@@ -19,6 +20,7 @@ mod room;
 mod server;
 mod tags;
 mod timeline;
+mod update;
 pub(crate) mod update_check;
 pub(crate) mod upgrade;
 
@@ -51,6 +53,7 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
             author,
             source,
             source_type,
+            timestamp,
         } => remember::run(
             cli,
             uteke,
@@ -67,6 +70,7 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
             author.as_deref(),
             source.as_deref(),
             source_type.as_deref(),
+            timestamp.as_deref(),
         ),
 
         Commands::Recall {
@@ -151,6 +155,24 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
 
         Commands::Get { id } => list::run_get(cli, uteke, id),
 
+        Commands::Update {
+            id,
+            content,
+            tags,
+            importance,
+            pinned,
+            r#type,
+        } => update::run(
+            cli,
+            uteke,
+            id,
+            content.clone(),
+            tags.clone(),
+            *importance,
+            *pinned,
+            r#type.clone(),
+        ),
+
         Commands::Forget {
             id,
             tag,
@@ -212,6 +234,7 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
             dry_run,
             max_size,
             recursive,
+            timestamp,
         } => {
             let opts = maintenance::ExtractOpts {
                 enabled: *extract,
@@ -225,6 +248,11 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
 
             // Batch mode: import entire directory
             if let Some(dir) = batch_dir {
+                if timestamp.is_some() {
+                    return Err(
+                        "--timestamp is not supported with --batch-dir: anchors apply to a single document, batch files carry their own per-file semantics".to_string(),
+                    );
+                }
                 let force_strategy = if *as_doc {
                     Some(maintenance::ImportStrategy::Document)
                 } else if *as_memory {
@@ -247,7 +275,16 @@ pub(crate) fn run_command(cli: &Cli, uteke: &mut Uteke, config: &Config) -> Resu
             }
 
             // Single file mode (original)
-            maintenance::run_import(cli, uteke, ns, input, tags, format, opts)
+            maintenance::run_import(
+                cli,
+                uteke,
+                ns,
+                input,
+                tags,
+                format,
+                opts,
+                timestamp.as_deref(),
+            )
         }
 
         Commands::Completions { .. } => {
diff --git a/crates/uteke-cli/src/commands/remember.rs b/crates/uteke-cli/src/commands/remember.rs
index 67f2e365..3e3a3e4a 100644
--- a/crates/uteke-cli/src/commands/remember.rs
+++ b/crates/uteke-cli/src/commands/remember.rs
@@ -64,6 +64,7 @@ pub(crate) fn run(
     author: Option<&str>,
     source: Option<&str>,
     source_type: Option<&str>,
+    timestamp: Option<&str>,
 ) -> Result<(), String> {
     tracing::debug!("Remembering: {content} (type: {type}, contradiction: {detect_contradiction})");
 
@@ -96,6 +97,15 @@ pub(crate) fn run(
     } else {
         content
     };
+    // #1232: timestamp anchor — validate + prepend BEFORE any write so an
+    // invalid value fails loudly without storing a row.
+    let content: &str = match timestamp {
+        Some(ts) => {
+            let anchored = super::anchor::apply_timestamp_anchor(content, ts)?;
+            Box::leak(anchored.into_boxed_str())
+        }
+        None => content,
+    };
     let tag_refs: Vec<&str> = tags.iter().map(|s| s.as_str()).collect();
 
     // Build metadata JSON from flags
diff --git a/crates/uteke-cli/src/commands/room.rs b/crates/uteke-cli/src/commands/room.rs
index 30bc456d..b999d5bf 100644
--- a/crates/uteke-cli/src/commands/room.rs
+++ b/crates/uteke-cli/src/commands/room.rs
@@ -28,6 +28,67 @@ pub(crate) fn run(
             }
             Ok(())
         }
+        RoomCommands::Rename { old_id, new_id } => {
+            let room = uteke
+                .rename_room(old_id, new_id)
+                .map_err(|e| format!("Failed to rename room: {e}"))?;
+            if cli.json {
+                println!(
+                    "{}",
+                    serde_json::json!({"renamed": old_id, "to": new_id, "room": room})
+                );
+            } else {
+                println!("✓ Room '{old_id}' renamed to '{new_id}'");
+            }
+            Ok(())
+        }
+        RoomCommands::Update {
+            room_id,
+            title,
+            description,
+        } => {
+            let room = uteke
+                .update_room(room_id, title.as_deref(), description.as_deref())
+                .map_err(|e| format!("Failed to update room: {e}"))?;
+            match room {
+                Some(room) => {
+                    if cli.json {
+                        println!("{}", serde_json::to_string_pretty(&room).unwrap());
+                    } else {
+                        println!("✓ Room '{room_id}' updated");
+                    }
+                }
+                None => return Err(format!("Room not found: {room_id}")),
+            }
+            Ok(())
+        }
+        RoomCommands::MoveMemory {
+            memory_id,
+            from_room,
+            to_room,
+        } => {
+            let moved = uteke
+                .move_memory_to_room(memory_id, from_room, to_room)
+                .map_err(|e| format!("Failed to move memory: {e}"))?;
+            if moved == 0 {
+                return Err(format!(
+                    "Memory '{memory_id}' has no link in room '{from_room}'"
+                ));
+            }
+            if cli.json {
+                println!(
+                    "{}",
+                    serde_json::json!({
+                        "moved": memory_id,
+                        "from_room": from_room,
+                        "to_room": to_room
+                    })
+                );
+            } else {
+                println!("✓ Memory '{memory_id}' moved from '{from_room}' to '{to_room}'");
+            }
+            Ok(())
+        }
         RoomCommands::List { namespace } => {
             // Rooms are cross-namespace collaboration spaces (#392).
             // Only filter when --namespace is explicitly passed on the
diff --git a/crates/uteke-cli/src/commands/server.rs b/crates/uteke-cli/src/commands/server.rs
index 377afcb6..d4aa930f 100644
--- a/crates/uteke-cli/src/commands/server.rs
+++ b/crates/uteke-cli/src/commands/server.rs
@@ -54,12 +54,16 @@ pub(crate) fn run_via_server(cli: &Cli, server_url: &str) -> Result<(), String>
             author,
             source,
             source_type,
+            timestamp,
         } => {
             let mut body = serde_json::json!({
                 "content": content,
                 "tags": tags,
                 "namespace": ns
             });
+            if let Some(ts) = timestamp {
+                body["timestamp"] = serde_json::json!(ts);
+            }
             if let Some(at) = author_type {
                 body["author_type"] = serde_json::json!(at);
             }
diff --git a/crates/uteke-cli/src/commands/update.rs b/crates/uteke-cli/src/commands/update.rs
new file mode 100644
index 00000000..91d85602
--- /dev/null
+++ b/crates/uteke-cli/src/commands/update.rs
@@ -0,0 +1,51 @@
+//! `uteke update` — in-place memory edit (CLI surface for `Uteke::update_memory`,
+//! issue #1202). HTTP `PUT /memory` and MCP `uteke_update` already existed;
+//! this closes the CLI gap.
+
+use crate::Cli;
+use uteke_core::Uteke;
+
+#[allow(clippy::too_many_arguments)]
+pub(crate) fn run(
+    cli: &Cli,
+    uteke: &Uteke,
+    id: &str,
+    content: Option<String>,
+    tags: Option<String>,
+    importance: Option<f64>,
+    pinned: Option<bool>,
+    memory_type: Option<String>,
+) -> Result<(), String> {
+    tracing::info!("Updating memory: {id}");
+    let tag_list: Option<Vec<String>> = tags.map(|t| {
+        t.split(',')
+            .map(|s| s.trim().to_string())
+            .filter(|s| !s.is_empty())
+            .collect()
+    });
+
+    let updated = uteke
+        .update_memory(
+            id,
+            content.as_deref(),
+            tag_list.as_deref(),
+            None,
+            importance,
+            pinned,
+            memory_type.as_deref(),
+        )
+        .map_err(|e| format!("Failed to update memory: {e}"))?;
+    if !updated {
+        return Err(format!("Memory not found: {id}"));
+    }
+
+    if cli.json {
+        let memory = uteke
+            .get(id)
+            .map_err(|e| format!("Failed to reload memory: {e}"))?;
+        println!("{}", serde_json::to_string_pretty(&memory).unwrap());
+    } else {
+        println!("✓ Memory '{id}' updated");
+    }
+    Ok(())
+}
diff --git a/crates/uteke-cli/src/config.rs b/crates/uteke-cli/src/config.rs
index 2c19ac77..a3dd3b1b 100644
--- a/crates/uteke-cli/src/config.rs
+++ b/crates/uteke-cli/src/config.rs
@@ -226,7 +226,11 @@ impl Default for AgingConfig {
 #[derive(serde::Deserialize, Clone)]
 #[serde(default)]
 pub struct RecallConfig {
-    /// Minimum cosine similarity score for recall results. Results below are filtered out.
+    /// Minimum similarity score for recall results. Results below are filtered out.
+    /// Default 0.0 (#1223): since fusion RRF became the default strategy (0.16.0),
+    /// returned scores are rank-based (~0.0-0.2), so a legacy cosine-era default of
+    /// 0.3 silently emptied default recalls. Threshold remains opt-in via config,
+    /// `--min`, or `--strict` (0.5).
     pub min_score: f64,
     /// Strict mode threshold (higher, for critical queries).
     pub min_score_strict: f64,
@@ -259,7 +263,7 @@ pub struct RecallConfig {
 impl Default for RecallConfig {
     fn default() -> Self {
         Self {
-            min_score: 0.3,
+            min_score: 0.0,
             min_score_strict: 0.5,
             // Fusion (weighted RRF: vector×1.7 + hybrid×1) is the default
             // strategy since 0.16.0 (#1123). Benchmark: fast50 R@5 0.98 vs
@@ -1035,7 +1039,7 @@ impl Config {
 # max_cold_count = 1000
 
 [recall]
-# min_score = 0.3
+# min_score = 0.0  # default since 0.17.x (#1223): fusion scores are rank-based; 0.3 is a legacy cosine-era threshold
 # min_score_strict = 0.5
 # default_strategy = "fusion"  # vector | fts5 | hybrid | graph | fusion
 # graph_density_weight = 0.1
@@ -1498,7 +1502,7 @@ namespace = "agent1"
     #[test]
     fn default_recall_config() {
         let cfg = RecallConfig::default();
-        assert!((cfg.min_score - 0.3).abs() < f64::EPSILON);
+        assert!((cfg.min_score - 0.0).abs() < f64::EPSILON);
         assert!((cfg.min_score_strict - 0.5).abs() < f64::EPSILON);
         // Fusion (vector×1.7 + hybrid×1 weighted RRF) is the default
         // strategy since 0.16.0 (#1123).
@@ -1837,7 +1841,7 @@ max_seq_length = 128
         assert_eq!(cfg.logging.level, "warn");
         assert_eq!(cfg.server.host, "127.0.0.1");
         assert_eq!(cfg.server.port, 8767);
-        assert!((cfg.recall.min_score - 0.3).abs() < f64::EPSILON);
+        assert!((cfg.recall.min_score - 0.0).abs() < f64::EPSILON);
     }
 
     // ── #1078 P0 batch 3: score range guards, strategy validation, graph
@@ -1853,7 +1857,7 @@ max_seq_length = 128
         unsafe {
             std::env::remove_var("UTEKE_RECALL_MIN_SCORE");
         }
-        assert!((cfg.recall.min_score - 0.3).abs() < f64::EPSILON);
+        assert!((cfg.recall.min_score - 0.0).abs() < f64::EPSILON);
     }
 
     #[test]
@@ -1866,7 +1870,7 @@ max_seq_length = 128
         unsafe {
             std::env::remove_var("UTEKE_RECALL_MIN_SCORE");
         }
-        assert!((cfg.recall.min_score - 0.3).abs() < f64::EPSILON);
+        assert!((cfg.recall.min_score - 0.0).abs() < f64::EPSILON);
     }
 
     #[test]
diff --git a/crates/uteke-core/Cargo.toml b/crates/uteke-core/Cargo.toml
index 47018304..05cbd758 100644
--- a/crates/uteke-core/Cargo.toml
+++ b/crates/uteke-core/Cargo.toml
@@ -34,7 +34,7 @@ vecq-core = { version = "0.3", optional = true }
 uuid = { version = "1", features = ["v4", "v7"] }
 chrono = { version = "0.4", default-features = false, features = ["serde", "clock"] }
 thiserror = "2"
-dirs = "6"
+dirs = "7"
 tracing = "0.1"
 reqwest = { version = "0.12", default-features = false, features = ["blocking", "rustls-tls", "json"] }
 fs2 = "0.4"
diff --git a/crates/uteke-core/src/memory/rooms.rs b/crates/uteke-core/src/memory/rooms.rs
index 1a3c1f89..b73cc49f 100644
--- a/crates/uteke-core/src/memory/rooms.rs
+++ b/crates/uteke-core/src/memory/rooms.rs
@@ -12,6 +12,8 @@ pub struct Room {
     pub namespace: String,
     pub created_at: String,
     pub updated_at: String,
+    /// Free-text description (schema v19, #1202). `None` = not set.
+    pub description: Option<String>,
 }
 
 /// Statistics about a room.
@@ -155,7 +157,8 @@ impl super::Store {
     pub fn get_room(&self, room_id: &str) -> Result<Option<Room>, Error> {
         self.conn
             .query_row(
-                "SELECT id, title, namespace, created_at, updated_at FROM rooms WHERE id = ?1",
+                "SELECT id, title, namespace, created_at, updated_at, description \
+                 FROM rooms WHERE id = ?1",
                 params![room_id],
                 |row| {
                     Ok(Room {
@@ -164,6 +167,7 @@ impl super::Store {
                         namespace: row.get(2)?,
                         created_at: row.get(3)?,
                         updated_at: row.get(4)?,
+                        description: row.get(5)?,
                     })
                 },
             )
@@ -180,13 +184,14 @@ impl super::Store {
         // on the rooms table itself (#545).
         let sql = match namespace {
             Some(_) => {
-                "SELECT id, title, namespace, created_at, updated_at \
+                "SELECT id, title, namespace, created_at, updated_at, description \
                  FROM rooms \
                  WHERE namespace = ?1 \
                  ORDER BY updated_at DESC"
             }
             None => {
-                "SELECT id, title, namespace, created_at, updated_at FROM rooms \
+                "SELECT id, title, namespace, created_at, updated_at, description \
+                 FROM rooms \
                  ORDER BY updated_at DESC"
             }
         };
@@ -205,6 +210,7 @@ impl super::Store {
                         namespace: row.get(2)?,
                         created_at: row.get(3)?,
                         updated_at: row.get(4)?,
+                        description: row.get(5)?,
                     })
                 })
                 .map_err(|e| Error::db("list rooms", e))?
@@ -218,6 +224,7 @@ impl super::Store {
                         namespace: row.get(2)?,
                         created_at: row.get(3)?,
                         updated_at: row.get(4)?,
+                        description: row.get(5)?,
                     })
                 })
                 .map_err(|e| Error::db("list rooms", e))?
@@ -228,6 +235,181 @@ impl super::Store {
         Ok(rows)
     }
 
+    /// Rename a room: update the registry row and every reference in
+    /// `room_memories` / `room_documents` in ONE transaction (#1202).
+    ///
+    /// The FK on `room_memories.room_id` is `ON DELETE CASCADE` only (no
+    /// `ON UPDATE`), so a plain `UPDATE rooms SET id` would either fail or
+    /// orphan members — all three tables are rewritten together instead.
+    /// Author/role/joined_at metadata on links is preserved untouched.
+    ///
+    /// Returns the renamed `Room`. Errors when the room does not exist or
+    /// the target ID is already taken.
+    pub fn rename_room(&self, old_id: &str, new_id: &str) -> Result<Room, Error> {
+        if old_id == new_id {
+            return Err(Error::Validation(
+                "Rename source and target room IDs are identical".to_string(),
+            ));
+        }
+        let exists = self
+            .conn
+            .query_row(
+                "SELECT EXISTS(SELECT 1 FROM rooms WHERE id = ?1)",
+                params![old_id],
+                |row| row.get::<_, bool>(0),
+            )
+            .map_err(|e| Error::db("rename room: check source", e))?;
+        if !exists {
+            return Err(Error::Validation(format!("Room not found: {old_id}")));
+        }
+        let taken = self
+            .conn
+            .query_row(
+                "SELECT EXISTS(SELECT 1 FROM rooms WHERE id = ?1)",
+                params![new_id],
+                |row| row.get::<_, bool>(0),
+            )
+            .map_err(|e| Error::db("rename room: check target", e))?;
+        if taken {
+            return Err(Error::Validation(format!("Room already exists: {new_id}")));
+        }
+
+        let tx = self
+            .conn
+            .unchecked_transaction()
+            .map_err(|e| Error::db("rename room: begin tx", e))?;
+        // FK-safe order: insert the new registry row FIRST, then repoint the
+        // children, then drop the old row — every intermediate state keeps
+        // every room_memories/room_documents FK satisfied (no ON UPDATE
+        // CASCADE on these FKs).
+        let now = chrono::Utc::now().to_rfc3339();
+        tx.execute(
+            "INSERT INTO rooms (id, title, namespace, created_at, updated_at, description) \
+             SELECT ?1, title, namespace, created_at, ?2, description FROM rooms WHERE id = ?3",
+            params![new_id, now, old_id],
+        )
+        .map_err(|e| Error::db("rename room: insert new row", e))?;
+        tx.execute(
+            "UPDATE room_memories SET room_id = ?1 WHERE room_id = ?2",
+            params![new_id, old_id],
+        )
+        .map_err(|e| Error::db("rename room: move members", e))?;
+        tx.execute(
+            "UPDATE room_documents SET room_id = ?1 WHERE room_id = ?2",
+            params![new_id, old_id],
+        )
+        .map_err(|e| Error::db("rename room: move document links", e))?;
+        tx.execute("DELETE FROM rooms WHERE id = ?1", params![old_id])
+            .map_err(|e| Error::db("rename room: remove old row", e))?;
+        tx.commit()
+            .map_err(|e| Error::db("rename room: commit", e))?;
+
+        Ok(self
+            .get_room(new_id)?
+            .expect("room row must exist after rename"))
+    }
+
+    /// Update a room's title and/or description (#1202). `None` fields are
+    /// left unchanged; the room row itself is never recreated, so members,
+    /// documents, and timestamps of linked rows stay valid.
+    ///
+    /// Returns the updated `Room`, or `None` when the room does not exist.
+    pub fn update_room(
+        &self,
+        room_id: &str,
+        title: Option<&str>,
+        description: Option<&str>,
+    ) -> Result<Option<Room>, Error> {
+        let room = match self.get_room(room_id)? {
+            Some(r) => r,
+            None => return Ok(None),
+        };
+        let now = chrono::Utc::now().to_rfc3339();
+        let new_title = title.or(room.title.as_deref());
+        let new_description = description.or(room.description.as_deref());
+        self.conn
+            .execute(
+                "UPDATE rooms SET title = ?1, description = ?2, updated_at = ?3 WHERE id = ?4",
+                params![new_title, new_description, now, room_id],
+            )
+            .map_err(|e| Error::db("update room", e))?;
+        self.get_room(room_id)
+    }
+
+    /// Move a memory from one room to another (#1202).
+    ///
+    /// Rooms are many-to-many (`room_memories` PK `(room_id, memory_id)`), so
+    /// the move removes the `from` link and inserts the `to` link in ONE
+    /// transaction, preserving the link's `author`/`role`/`joined_at`
+    /// provenance. `to` must differ from `from`; the target room must exist.
+    ///
+    /// Returns the number of links moved: `Ok(0)` when the memory has no link
+    /// in `from` (or `from` does not exist), `Ok(1)` on success.
+    pub fn move_memory_to_room(
+        &self,
+        memory_id: &str,
+        from_room: &str,
+        to_room: &str,
+    ) -> Result<usize, Error> {
+        if from_room == to_room {
+            return Err(Error::Validation(
+                "Move source and target rooms are identical".to_string(),
+            ));
+        }
+        let target_exists = self
+            .conn
+            .query_row(
+                "SELECT EXISTS(SELECT 1 FROM rooms WHERE id = ?1)",
+                params![to_room],
+                |row| row.get::<_, bool>(0),
+            )
+            .map_err(|e| Error::db("move memory to room: check target", e))?;
+        if !target_exists {
+            return Err(Error::Validation(format!("Room not found: {to_room}")));
+        }
+
+        let tx = self
+            .conn
+            .unchecked_transaction()
+            .map_err(|e| Error::db("move memory to room: begin tx", e))?;
+        // Read the link's provenance BEFORE removing it — membership history
+        // (author/role/joined_at) moves with the link.
+        let link: Option<(String, String, String)> = tx
+            .query_row(
+                "SELECT author, role, joined_at FROM room_memories \
+                 WHERE room_id = ?1 AND memory_id = ?2",
+                params![from_room, memory_id],
+                |row| Ok((row.get(0)?, row.get(1)?, row.get(2)?)),
+            )
+            .optional()
+            .map_err(|e| Error::db("move memory to room: read link", e))?;
+        let (author, role, joined_at) = match link {
+            Some(l) => l,
+            // Nothing linked in `from` — leave the store untouched (the tx
+            // is dropped uncommitted, which rolls back cleanly).
+            None => return Ok(0),
+        };
+        tx.execute(
+            "DELETE FROM room_memories WHERE room_id = ?1 AND memory_id = ?2",
+            params![from_room, memory_id],
+        )
+        .map_err(|e| Error::db("move memory to room: unlink source", e))?;
+        tx.execute(
+            "INSERT OR IGNORE INTO room_memories (room_id, memory_id, author, role, joined_at) \
+             VALUES (?1, ?2, ?3, ?4, ?5)",
+            params![to_room, memory_id, author, role, joined_at],
+        )
+        .map_err(|e| Error::db("move memory to room: link target", e))?;
+        tx.execute(
+            "UPDATE rooms SET updated_at = ?1 WHERE id IN (?2, ?3)",
+            params![chrono::Utc::now().to_rfc3339(), from_room, to_room],
+        )
+        .map_err(|e| Error::db("move memory to room: timestamps", e))?;
+        tx.commit()
+            .map_err(|e| Error::db("move memory to room: commit", e))?;
+        Ok(1)
+    }
+
     /// Get statistics about a room.
     ///
     /// Only counts active (non-deprecated) memories — matches the behavior of
diff --git a/crates/uteke-core/src/memory/schema.rs b/crates/uteke-core/src/memory/schema.rs
index 4e9dd367..dda1c384 100644
--- a/crates/uteke-core/src/memory/schema.rs
+++ b/crates/uteke-core/src/memory/schema.rs
@@ -420,6 +420,8 @@ impl super::Store {
                 17 => self.migrate_v16_to_v17()?,
                 // v18: provenance chain — source_hash, actor, evidence_json (#1172)
                 18 => self.migrate_v17_to_v18()?,
+                // v19: room description column — room rename/update support (#1202)
+                19 => self.migrate_v18_to_v19()?,
                 _ => {
                     // No-op for future versions.
                 }
@@ -1180,4 +1182,21 @@ impl super::Store {
         tracing::info!("Migration v17 to v18 complete: provenance chain columns added");
         Ok(())
     }
+
+    /// Schema v19 (#1202): `rooms.description` — free-text room description
+    /// for `room update`. Additive and NULL-safe: NULL = no description.
+    /// Fresh stores get the column directly in CREATE TABLE; this migration
+    /// only patches stores stamped at v18 or lower.
+    fn migrate_v18_to_v19(&self) -> Result<(), Error> {
+        tracing::info!("Applying schema migration v18 to v19: rooms.description (#1202)");
+
+        if !self.column_exists_in("rooms", "description") {
+            self.conn
+                .execute_batch("ALTER TABLE rooms ADD COLUMN description TEXT;")
+                .map_err(|e| Error::db("schema migration v18 to v19: rooms.description", e))?;
+        }
+
+        tracing::info!("Migration v18 to v19 complete: rooms.description added");
+        Ok(())
+    }
 }
diff --git a/crates/uteke-core/src/memory/store.rs b/crates/uteke-core/src/memory/store.rs
index dd138a6f..a0a766d7 100644
--- a/crates/uteke-core/src/memory/store.rs
+++ b/crates/uteke-core/src/memory/store.rs
@@ -143,7 +143,8 @@ CREATE TABLE IF NOT EXISTS rooms (
     title TEXT,
     namespace TEXT NOT NULL DEFAULT 'default',
     created_at TEXT NOT NULL,
-    updated_at TEXT NOT NULL
+    updated_at TEXT NOT NULL,
+    description TEXT
 );
 CREATE INDEX IF NOT EXISTS idx_rooms_namespace ON rooms(namespace);
 
@@ -179,7 +180,7 @@ pub(super) const SCHEMA_INDEXES: &[&str] = &[
 ];
 
 /// Current schema version. Increment when adding migrations.
-pub(crate) const CURRENT_SCHEMA_VERSION: i32 = 18;
+pub(crate) const CURRENT_SCHEMA_VERSION: i32 = 19;
 
 /// Persistent SQLite store for memories.
 pub struct Store {
@@ -733,7 +734,7 @@ mod tests {
             .conn
             .query_row("SELECT MAX(version) FROM schema_version", [], |r| r.get(0))
             .unwrap();
-        assert_eq!(version, 18, "schema should be upgraded to current (v18)");
+        assert_eq!(version, 19, "schema should be upgraded to current (v19)");
 
         // Legacy row backfilled to 'agent'.
         let at: String = store
diff --git a/crates/uteke-core/src/operations.rs b/crates/uteke-core/src/operations.rs
index f59eb5b2..cdfaa577 100644
--- a/crates/uteke-core/src/operations.rs
+++ b/crates/uteke-core/src/operations.rs
@@ -274,7 +274,21 @@ impl crate::Uteke {
             }
             let sim = (1.0 - dist).clamp(0.0, 1.0);
             if sim >= DEDUP_THRESHOLD {
-                return Ok(Some(id.clone()));
+                // Verify against SQLite before trusting the index hit (#1210).
+                // A stale index can still hold vectors for rows that were
+                // soft-deleted (deprecated) or hard-deleted; deduplicating
+                // against them silently drops the write and can link rooms
+                // to invisible ghost rows. Treat them as absent and keep
+                // scanning remaining candidates.
+                match self.store.get_by_id(id) {
+                    // Live row — safe to dedup onto.
+                    Ok(Some(m)) if !m.deprecated => return Ok(Some(id.clone())),
+                    // Missing (hard-deleted) or deprecated row — stale index
+                    // entry; skip this candidate and keep scanning (#1210).
+                    Ok(_) => continue,
+                    // Store read failed — never dedup on an unreliable read.
+                    Err(_) => continue,
+                }
             }
         }
 
@@ -2544,6 +2558,40 @@ mod dedup_tests {
         assert_ne!(id1, id2, "different namespace should not dedup");
     }
 
+    #[test]
+    #[ignore = "requires ONNX embedder (model download) in CI"]
+    fn test_dedup_skips_deprecated_stale_index_entry() {
+        // #1210: a stale vector index on a long-running server can still hold
+        // the vector of a soft-deleted (deprecated) memory. Dedup must NOT
+        // match it — the rewrite must produce a NEW live memory instead of
+        // silently returning the deprecated ghost id (which also gets linked
+        // into rooms by remember_in_room, invisible in recall).
+        let uteke = Uteke::open(":memory:").unwrap();
+        let ns = "dedup-depr";
+        let content = "Ghost dedup regression content #1210";
+        let id1 = uteke.remember(content, &[], None, Some(ns)).unwrap();
+
+        // Healthy path: soft-delete removes the vector from the index.
+        uteke.soft_forget(&id1, "test: 1210 repro").unwrap();
+
+        // Simulate index/SQLite desync (missed repair / failed persistence):
+        // the deprecated row's vector re-enters the index.
+        let emb = uteke.store.get_by_id(&id1).unwrap().unwrap().embedding;
+        assert!(!emb.is_empty(), "precondition: first row must be embedded");
+        uteke.index.write().unwrap().insert(&id1, &emb).unwrap();
+
+        // The rewrite must NOT dedup onto the deprecated ghost.
+        let id2 = uteke.remember(content, &[], None, Some(ns)).unwrap();
+        assert_ne!(id2, id1, "deprecated row must not be a dedup target");
+
+        // The new row is live, holds the content, and the ghost stays hidden.
+        let live = uteke.store.get_by_id(&id2).unwrap().unwrap();
+        assert!(!live.deprecated);
+        assert_eq!(live.content, content);
+        let ghost = uteke.store.get_by_id(&id1).unwrap().unwrap();
+        assert!(ghost.deprecated);
+    }
+
     #[test]
     #[ignore = "requires ONNX embedder (model download) in CI"]
     fn test_contradiction_remembers_metadata() {
diff --git a/crates/uteke-core/src/rooms.rs b/crates/uteke-core/src/rooms.rs
index 900ccf92..4986f621 100644
--- a/crates/uteke-core/src/rooms.rs
+++ b/crates/uteke-core/src/rooms.rs
@@ -191,6 +191,39 @@ impl crate::Uteke {
         self.store.delete_room(room_id)
     }
 
+    /// Rename a room: the registry row and every member/document link move
+    /// in ONE transaction (#1202). Namespace, title, and description are
+    /// preserved. Errors when the room is missing or the target ID is taken.
+    pub fn rename_room(&self, old_id: &str, new_id: &str) -> Result<Room, Error> {
+        self.store.rename_room(old_id, new_id)
+    }
+
+    /// Update a room's title and/or description (#1202). `None` fields are
+    /// left unchanged. Returns the updated room, or `None` when the room
+    /// does not exist.
+    pub fn update_room(
+        &self,
+        room_id: &str,
+        title: Option<&str>,
+        description: Option<&str>,
+    ) -> Result<Option<Room>, Error> {
+        self.store.update_room(room_id, title, description)
+    }
+
+    /// Move a memory from one room to another (#1202), preserving the link's
+    /// author/role/joined_at provenance. Returns `Ok(0)` when the memory is
+    /// not a member of `from_room`, `Ok(1)` on success. The target room must
+    /// exist; namespace stays untouched (no recall-cache impact).
+    pub fn move_memory_to_room(
+        &self,
+        memory_id: &str,
+        from_room: &str,
+        to_room: &str,
+    ) -> Result<usize, Error> {
+        self.store
+            .move_memory_to_room(memory_id, from_room, to_room)
+    }
+
     /// Generate a summary of room discussion (topic clustering, no LLM needed).
     /// Enriched with embedding-distance semantic segments (#1088) when
     /// embeddings exist — these are the batching units for future
@@ -445,6 +478,181 @@ mod tests {
         assert!(results.iter().any(|r| r.memory.content.contains("Rust")));
     }
 
+    // ── Room rename / update / memory room-move (#1202) ─────────────
+
+    /// Embedder-less engine — these ops never touch embeddings, and CI has
+    /// no ONNX runtime (same pattern as operations::namespace_management_tests).
+    fn open_no_embedder() -> crate::Uteke {
+        crate::Uteke::open_with_backend(":memory:", None).unwrap()
+    }
+
+    #[test]
+    fn rename_room_moves_registry_and_links() {
+        let uteke = open_no_embedder();
+        uteke
+            .create_room("old-room", Some("Old"), "default")
+            .unwrap();
+        let m1 = uteke.remember("m one", &[], None, Some("default")).unwrap();
+        let m2 = uteke.remember("m two", &[], None, Some("default")).unwrap();
+        uteke
+            .store
+            .link_memory_to_room("old-room", &m1, "alice", "participant")
+            .unwrap();
+        uteke
+            .store
+            .link_memory_to_room("old-room", &m2, "bob", "lead")
+            .unwrap();
+
+        let room = uteke.rename_room("old-room", "new-room").unwrap();
+        assert_eq!(room.id, "new-room");
+        assert_eq!(room.title, Some("Old".to_string()));
+        assert_eq!(room.namespace, "default");
+
+        // Old name is gone, members followed.
+        assert!(uteke.get_room("old-room").unwrap().is_none());
+        assert_eq!(uteke.recall_room("new-room", None, 0).unwrap().len(), 2);
+        // Cross-namespace room recall is author-filterable on the new ID.
+        assert_eq!(
+            uteke.recall_room("new-room", Some("bob"), 0).unwrap().len(),
+            1
+        );
+    }
+
+    #[test]
+    fn rename_room_preserves_document_links() {
+        let store = crate::memory::Store::open(":memory:").unwrap();
+        store.create_room("r-old", None, "default").unwrap();
+        let doc = crate::memory::documents::Document {
+            id: "doc-1".to_string(),
+            slug: "doc-1".to_string(),
+            title: "Doc One".to_string(),
+            content: "# Doc One\nContent".to_string(),
+            namespace: None,
+            author: None,
+            tags: vec![],
+            metadata: serde_json::json!({}),
+            version: 1,
+            content_type: "markdown".to_string(),
+            created_at: chrono::Utc::now().to_rfc3339(),
+            updated_at: chrono::Utc::now().to_rfc3339(),
+            parent_id: None,
+            path: "/doc-1/".to_string(),
+            depth: 0,
+            sort_order: 0,
+            has_children: false,
+        };
+        store.upsert_document(&doc).unwrap();
+        store.room_add_document("r-old", "doc-1").unwrap();
+
+        store.rename_room("r-old", "r-new").unwrap();
+
+        assert_eq!(store.room_list_documents("r-new").unwrap(), vec!["doc-1"]);
+        assert!(store.room_list_documents("r-old").unwrap().is_empty());
+    }
+
+    #[test]
+    fn rename_room_rejects_missing_source_and_taken_target() {
+        let uteke = open_no_embedder();
+        uteke.create_room("a", None, "default").unwrap();
+        uteke.create_room("b", None, "default").unwrap();
+
+        let missing = uteke.rename_room("nope", "c");
+        assert!(matches!(missing, Err(crate::Error::Validation(_))));
+
+        let taken = uteke.rename_room("a", "b");
+        assert!(matches!(taken, Err(crate::Error::Validation(_))));
+
+        let same = uteke.rename_room("a", "a");
+        assert!(matches!(same, Err(crate::Error::Validation(_))));
+    }
+
+    #[test]
+    fn update_room_sets_title_and_description() {
+        let uteke = open_no_embedder();
+        uteke.create_room("r", Some("Title"), "default").unwrap();
+
+        // Description only — title must survive.
+        let room = uteke
+            .update_room("r", None, Some("First description"))
+            .unwrap();
+        let room = room.expect("room exists");
+        assert_eq!(room.title, Some("Title".to_string()));
+        assert_eq!(room.description, Some("First description".to_string()));
+
+        // Overwrite both.
+        let room = uteke
+            .update_room("r", Some("New title"), Some("Updated desc"))
+            .unwrap()
+            .unwrap();
+        assert_eq!(room.title, Some("New title".to_string()));
+        assert_eq!(room.description, Some("Updated desc".to_string()));
+
+        // Missing room → None.
+        assert!(
+            uteke
+                .update_room("ghost", Some("x"), None)
+                .unwrap()
+                .is_none()
+        );
+    }
+
+    #[test]
+    fn move_memory_between_rooms_preserves_link_provenance() {
+        let uteke = open_no_embedder();
+        uteke.create_room("src", None, "default").unwrap();
+        uteke.create_room("dst", None, "default").unwrap();
+        let mid = uteke
+            .remember("traveler", &[], None, Some("default"))
+            .unwrap();
+        uteke
+            .store
+            .link_memory_to_room("src", &mid, "carol", "moderator")
+            .unwrap();
+
+        let moved = uteke.move_memory_to_room(&mid, "src", "dst").unwrap();
+        assert_eq!(moved, 1);
+
+        // Out of source, present in target.
+        assert!(uteke.recall_room("src", None, 0).unwrap().is_empty());
+        let dst = uteke.recall_room("dst", None, 0).unwrap();
+        assert_eq!(dst.len(), 1);
+        assert_eq!(dst[0].id, mid);
+
+        // Link provenance (author/role) moved with the link.
+        let (author, role): (String, String) = uteke
+            .store()
+            .conn
+            .query_row(
+                "SELECT author, role FROM room_memories WHERE room_id = 'dst' AND memory_id = ?1",
+                [&mid],
+                |row| Ok((row.get(0)?, row.get(1)?)),
+            )
+            .unwrap();
+        assert_eq!((author.as_str(), role.as_str()), ("carol", "moderator"));
+
+        // Move again — nothing left in source → Ok(0), store untouched.
+        assert_eq!(uteke.move_memory_to_room(&mid, "src", "dst").unwrap(), 0);
+    }
+
+    #[test]
+    fn move_memory_rejects_bad_targets() {
+        let uteke = open_no_embedder();
+        uteke.create_room("s", None, "default").unwrap();
+        let mid = uteke.remember("x", &[], None, Some("default")).unwrap();
+        uteke
+            .store
+            .link_memory_to_room("s", &mid, "a", "participant")
+            .unwrap();
+
+        // Target room missing.
+        let missing_target = uteke.move_memory_to_room(&mid, "s", "nope");
+        assert!(matches!(missing_target, Err(crate::Error::Validation(_))));
+
+        // Same room.
+        let same = uteke.move_memory_to_room(&mid, "s", "s");
+        assert!(matches!(same, Err(crate::Error::Validation(_))));
+    }
+
     // ── Room ↔ Document junction ──────────────────────────────────
     // NOTE: room_add_document / room_remove_document / document_list_rooms
     // call Store::room_add_document which validates document slug existence.
diff --git a/crates/uteke-core/src/structural_export.rs b/crates/uteke-core/src/structural_export.rs
index 8adf1267..508e4ae7 100644
--- a/crates/uteke-core/src/structural_export.rs
+++ b/crates/uteke-core/src/structural_export.rs
@@ -140,7 +140,8 @@ impl crate::Uteke {
         let simple_tables: &[(&str, &str)] = &[
             (
                 "room",
-                "SELECT id, title, namespace, created_at, updated_at FROM rooms",
+                "SELECT id, title, namespace, created_at, updated_at, description \
+                 FROM rooms",
             ),
             (
                 "room_memory",
@@ -394,15 +395,20 @@ impl crate::Uteke {
             match tag {
                 "room" => {
                     let r = &v["row"];
+                    // Column-count aware: exports predating description
+                    // (#1202) carry 5 columns; new exports carry 6. Both
+                    // import cleanly (description defaults to NULL).
+                    let description = r.get(5).and_then(|v| v.as_str());
                     let n = conn
                         .execute(
-                            "INSERT OR IGNORE INTO rooms (id, title, namespace, created_at, updated_at) VALUES (?1,?2,?3,?4,?5)",
+                            "INSERT OR IGNORE INTO rooms (id, title, namespace, created_at, updated_at, description) VALUES (?1,?2,?3,?4,?5,?6)",
                             rusqlite::params![
                                 r[0].as_str().unwrap_or(""),
                                 r[1].as_str(),
                                 r[2].as_str().unwrap_or("default"),
                                 r[3].as_str().unwrap_or(""),
                                 r[4].as_str().unwrap_or(""),
+                                description,
                             ],
                         )
                         .map_err(|e| Error::db_msg(format!("import room: {e}")))?;
diff --git a/crates/uteke-mcp/Cargo.toml b/crates/uteke-mcp/Cargo.toml
index e58504f8..601f9a64 100644
--- a/crates/uteke-mcp/Cargo.toml
+++ b/crates/uteke-mcp/Cargo.toml
@@ -29,10 +29,10 @@ usearch = ["uteke-core/usearch"]
 vecq = ["uteke-core/vecq"]
 
 [dependencies]
-uteke-core = { path = "../uteke-core", version = "0.17.0", default-features = false, features = ["onnx"] }
+uteke-core = { path = "../uteke-core", version = "0.18.0", default-features = false, features = ["onnx"] }
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"
-dirs = "6"
+dirs = "7"
 
 [dev-dependencies]
 uuid = { version = "1", features = ["v4"] }
diff --git a/crates/uteke-mcp/src/lib.rs b/crates/uteke-mcp/src/lib.rs
index 92c41171..a7782bed 100644
--- a/crates/uteke-mcp/src/lib.rs
+++ b/crates/uteke-mcp/src/lib.rs
@@ -166,6 +166,9 @@ fn handle_request(uteke: &Uteke, method: &str, params: Option<Value>) -> Result<
                 tool_room_create(),
                 tool_room_list(),
                 tool_room_delete(),
+                tool_room_rename(),
+                tool_room_update(),
+                tool_room_memory_move(),
                 tool_room_recall(),
                 tool_room_memories(),
                 tool_room_stats(),
@@ -221,6 +224,9 @@ fn handle_request(uteke: &Uteke, method: &str, params: Option<Value>) -> Result<
                 "uteke_room_create" => exec_room_create(uteke, &arguments)?,
                 "uteke_room_list" => exec_room_list(uteke, &arguments)?,
                 "uteke_room_delete" => exec_room_delete(uteke, &arguments)?,
+                "uteke_room_rename" => exec_room_rename(uteke, &arguments)?,
+                "uteke_room_update" => exec_room_update(uteke, &arguments)?,
+                "uteke_room_memory_move" => exec_room_memory_move(uteke, &arguments)?,
                 "uteke_room_recall" => exec_room_recall(uteke, &arguments)?,
                 "uteke_room_memories" => exec_room_memories(uteke, &arguments)?,
                 "uteke_room_stats" => exec_room_stats(uteke, &arguments)?,
@@ -736,6 +742,53 @@ fn tool_room_delete() -> Value {
     })
 }
 
+fn tool_room_rename() -> Value {
+    serde_json::json!({
+        "name": "uteke_room_rename",
+        "description": "Rename a room. The registry row and every member/document link move in one transaction; title, description, and namespace are preserved (#1202).",
+        "inputSchema": {
+            "type": "object",
+            "properties": {
+                "from": { "type": "string", "description": "Current room identifier" },
+                "to": { "type": "string", "description": "New room identifier (must not exist)" }
+            },
+            "required": ["from", "to"]
+        }
+    })
+}
+
+fn tool_room_update() -> Value {
+    serde_json::json!({
+        "name": "uteke_room_update",
+        "description": "Update a room's title and/or description. Omitted fields stay unchanged (#1202).",
+        "inputSchema": {
+            "type": "object",
+            "properties": {
+                "room_id": { "type": "string", "description": "Room identifier" },
+                "title": { "type": "string", "description": "New title (omit to keep current)" },
+                "description": { "type": "string", "description": "New description (omit to keep current)" }
+            },
+            "required": ["room_id"]
+        }
+    })
+}
+
+fn tool_room_memory_move() -> Value {
+    serde_json::json!({
+        "name": "uteke_room_memory_move",
+        "description": "Move a memory from one room to another, preserving the link's author/role/joined_at provenance (#1202).",
+        "inputSchema": {
+            "type": "object",
+            "properties": {
+                "memory_id": { "type": "string", "description": "Memory identifier" },
+                "from_room": { "type": "string", "description": "Source room (memory must be a member)" },
+                "to_room": { "type": "string", "description": "Target room (must exist)" }
+            },
+            "required": ["memory_id", "from_room", "to_room"]
+        }
+    })
+}
+
 fn tool_room_stats() -> Value {
     serde_json::json!({
         "name": "uteke_room_stats",
@@ -2073,6 +2126,69 @@ fn exec_room_delete(uteke: &Uteke, args: &Value) -> Result<ToolResult, String> {
     })
 }
 
+fn exec_room_rename(uteke: &Uteke, args: &Value) -> Result<ToolResult, String> {
+    let from = args["from"].as_str().ok_or("Missing 'from'")?;
+    let to = args["to"].as_str().ok_or("Missing 'to'")?;
+
+    uteke
+        .rename_room(from, to)
+        .map_err(|e| format!("Failed to rename room: {e}"))?;
+
+    Ok(ToolResult {
+        content: vec![McpContent::Text {
+            r#type: "text".to_string(),
+            text: format!("✓ Room '{from}' renamed to '{to}' — all links moved atomically"),
+        }],
+        is_error: false,
+    })
+}
+
+fn exec_room_update(uteke: &Uteke, args: &Value) -> Result<ToolResult, String> {
+    let room_id = args["room_id"].as_str().ok_or("Missing 'room_id'")?;
+    let title = args["title"].as_str();
+    let description = args["description"].as_str();
+
+    let room = uteke
+        .update_room(room_id, title, description)
+        .map_err(|e| format!("Failed to update room: {e}"))?
+        .ok_or_else(|| format!("Room not found: {room_id}"))?;
+
+    let title_out = room.title.as_deref().unwrap_or("(none)");
+    let desc_out = room.description.as_deref().unwrap_or("(none)");
+    Ok(ToolResult {
+        content: vec![McpContent::Text {
+            r#type: "text".to_string(),
+            text: format!(
+                "✓ Room '{room_id}' updated — title: {title_out}, description: {desc_out}"
+            ),
+        }],
+        is_error: false,
+    })
+}
+
+fn exec_room_memory_move(uteke: &Uteke, args: &Value) -> Result<ToolResult, String> {
+    let memory_id = args["memory_id"].as_str().ok_or("Missing 'memory_id'")?;
+    let from_room = args["from_room"].as_str().ok_or("Missing 'from_room'")?;
+    let to_room = args["to_room"].as_str().ok_or("Missing 'to_room'")?;
+
+    let moved = uteke
+        .move_memory_to_room(memory_id, from_room, to_room)
+        .map_err(|e| format!("Failed to move memory: {e}"))?;
+    if moved == 0 {
+        return Err(format!(
+            "Memory '{memory_id}' has no link in room '{from_room}'"
+        ));
+    }
+
+    Ok(ToolResult {
+        content: vec![McpContent::Text {
+            r#type: "text".to_string(),
+            text: format!("✓ Memory '{memory_id}' moved from '{from_room}' to '{to_room}'"),
+        }],
+        is_error: false,
+    })
+}
+
 fn exec_room_stats(uteke: &Uteke, args: &Value) -> Result<ToolResult, String> {
     let room_id = args["room_id"].as_str().ok_or("Missing 'room_id'")?;
 
diff --git a/crates/uteke-server/Cargo.toml b/crates/uteke-server/Cargo.toml
index 18864105..049d8200 100644
--- a/crates/uteke-server/Cargo.toml
+++ b/crates/uteke-server/Cargo.toml
@@ -27,8 +27,8 @@ name = "uteke-serve"
 path = "src/main.rs"
 
 [dependencies]
-uteke-core = { path = "../uteke-core", version = "0.17.0", default-features = false, features = ["onnx"] }
-uteke-mcp = { path = "../uteke-mcp", version = "0.17.0", default-features = false }
+uteke-core = { path = "../uteke-core", version = "0.18.0", default-features = false, features = ["onnx"] }
+uteke-mcp = { path = "../uteke-mcp", version = "0.18.0", default-features = false }
 serde = { version = "1", features = ["derive"] }
 serde_json = "1"
 schemars = { version = "1", optional = true }
diff --git a/crates/uteke-server/src/api_registry.rs b/crates/uteke-server/src/api_registry.rs
index 8ded085f..541ed7e1 100644
--- a/crates/uteke-server/src/api_registry.rs
+++ b/crates/uteke-server/src/api_registry.rs
@@ -338,6 +338,33 @@ pub const ENDPOINTS: &[Endpoint] = &[
         excludes_deprecated: false,
         issues: &[],
     },
+    Endpoint {
+        method: "POST",
+        path: "/room/rename",
+        description: "Rename a room: the registry row and every member/document link move in one transaction; title, description, and namespace are preserved (#1202). Accepts `{ from, to }`. Write op — blocked for read-only tokens.",
+        request_type: Some("serde_json::Value"),
+        response_type: None,
+        excludes_deprecated: false,
+        issues: &["#1202"],
+    },
+    Endpoint {
+        method: "POST",
+        path: "/room/update",
+        description: "Update a room's title and/or description; omitted fields stay unchanged (#1202). Accepts `{ room_id, title?, description? }`; 404 when the room is missing. Write op — blocked for read-only tokens.",
+        request_type: Some("serde_json::Value"),
+        response_type: None,
+        excludes_deprecated: false,
+        issues: &["#1202"],
+    },
+    Endpoint {
+        method: "POST",
+        path: "/room/memory/move",
+        description: "Move a memory from one room to another, preserving the link's author/role/joined_at provenance (#1202). Accepts `{ memory_id, from_room, to_room }`; 404 when the memory has no link in `from_room`. Write op — blocked for read-only tokens.",
+        request_type: Some("serde_json::Value"),
+        response_type: None,
+        excludes_deprecated: false,
+        issues: &["#1202"],
+    },
     // ── Room Documents ──────────────────────────────────────────────────
     Endpoint {
         method: "POST",
diff --git a/crates/uteke-server/src/handlers.rs b/crates/uteke-server/src/handlers.rs
index 6b52b308..768adb28 100644
--- a/crates/uteke-server/src/handlers.rs
+++ b/crates/uteke-server/src/handlers.rs
@@ -1649,6 +1649,120 @@ pub fn route(uteke: &Mutex<Uteke>, ctx: &ReqCtx, req: &mut Request) -> Response<
             }
         }
 
+        // POST /room/rename — rename a room, moving all member/document
+        // links in one transaction (#1202)
+        (Method::Post, "/room/rename") => {
+            #[derive(Deserialize)]
+            struct RoomRenameRequest {
+                from: String,
+                to: String,
+            }
+            match read_body::<RoomRenameRequest>(req.as_reader()) {
+                Ok(req_data) => match uteke.rename_room(&req_data.from, &req_data.to) {
+                    Ok(room) => ctx.ok_response_for(
+                        req,
+                        &serde_json::json!({
+                            "renamed": req_data.from,
+                            "to": req_data.to,
+                            "room": room,
+                        }),
+                    ),
+                    Err(e) => {
+                        let status = match e {
+                            uteke_core::Error::Validation(_) => 400,
+                            _ => 500,
+                        };
+                        ctx.error_response_for(req, status, e.to_string())
+                    }
+                },
+                Err(e) => ctx.error_response_for(req, 400, e),
+            }
+        }
+
+        // POST /room/update — update room title/description (#1202)
+        (Method::Post, "/room/update") => {
+            #[derive(Deserialize)]
+            struct RoomUpdateRequest {
+                room_id: String,
+                #[serde(default)]
+                title: Option<String>,
+                #[serde(default)]
+                description: Option<String>,
+            }
+            match read_body::<RoomUpdateRequest>(req.as_reader()) {
+                Ok(req_data) => {
+                    match uteke.update_room(
+                        &req_data.room_id,
+                        req_data.title.as_deref(),
+                        req_data.description.as_deref(),
+                    ) {
+                        Ok(Some(room)) => {
+                            ctx.ok_response_for(req, &serde_json::json!({ "room": room }))
+                        }
+                        Ok(None) => ctx.error_response_for(
+                            req,
+                            404,
+                            format!("Room not found: {}", req_data.room_id),
+                        ),
+                        Err(e) => {
+                            let status = match e {
+                                uteke_core::Error::Validation(_) => 400,
+                                _ => 500,
+                            };
+                            ctx.error_response_for(req, status, e.to_string())
+                        }
+                    }
+                }
+                Err(e) => ctx.error_response_for(req, 400, e),
+            }
+        }
+
+        // POST /room/memory/move — move a memory from one room to another,
+        // preserving link author/role/joined_at (#1202)
+        (Method::Post, "/room/memory/move") => {
+            #[derive(Deserialize)]
+            struct RoomMemoryMoveRequest {
+                memory_id: String,
+                from_room: String,
+                to_room: String,
+            }
+            match read_body::<RoomMemoryMoveRequest>(req.as_reader()) {
+                Ok(req_data) => {
+                    match uteke.move_memory_to_room(
+                        &req_data.memory_id,
+                        &req_data.from_room,
+                        &req_data.to_room,
+                    ) {
+                        Ok(1) => ctx.ok_response_for(
+                            req,
+                            &serde_json::json!({
+                                "moved": req_data.memory_id,
+                                "from_room": req_data.from_room,
+                                "to_room": req_data.to_room,
+                            }),
+                        ),
+                        Ok(0) => ctx.error_response_for(
+                            req,
+                            404,
+                            format!(
+                                "Memory {} has no link in room {}",
+                                req_data.memory_id, req_data.from_room
+                            ),
+                        ),
+                        Ok(_) => unreachable!("move returns 0 or 1"),
+                        Err(e) => {
+                            let status = match e {
+                                uteke_core::Error::Validation(_) => 400,
+                                _ => 500,
+                            };
+                            ctx.error_response_for(req, status, e.to_string())
+                        }
+                    }
+                }
+                Err(e) => ctx.error_response_for(req, 400, e),
+            }
+        }
+
         // POST /room/remember — store memory and link to room (#762)
         (Method::Post, "/room/remember") => {
             match read_body::<RoomRememberRequest>(req.as_reader()) {
@@ -3167,6 +3281,80 @@ mod room_recall_at_tests {
         );
     }
 
+    #[test]
+    fn room_rename_update_move_endpoints_roundtrip() {
+        let app = NamespaceApp::new();
+
+        // Create rooms + one room memory.
+        let body = serde_json::json!({ "room_id": "ws", "namespace": "default" }).to_string();
+        let (status, resp) = app.call(Method::Post, "/room/create", Some(body));
+        assert_eq!(status, 200, "{resp}");
+        let body = serde_json::json!({ "room_id": "ws-3", "namespace": "default" }).to_string();
+        let (status, resp) = app.call(Method::Post, "/room/create", Some(body));
+        assert_eq!(status, 200, "{resp}");
+
+        let body = serde_json::json!({
+            "content": "room note",
+            "tags": [],
+            "room_id": "ws",
+            "author": "tester"
+        })
+        .to_string();
+        let (status, resp) = app.call(Method::Post, "/room/remember", Some(body));
+        assert_eq!(status, 200, "{resp}");
+        let memory_id = resp["id"].as_str().expect("memory id").to_string();
+
+        // Rename ws → ws-2.
+        let body = serde_json::json!({ "from": "ws", "to": "ws-2" }).to_string();
+        let (status, resp) = app.call(Method::Post, "/room/rename", Some(body));
+        assert_eq!(status, 200, "{resp}");
+        assert_eq!(resp["renamed"], serde_json::json!("ws"));
+        assert_eq!(resp["room"]["id"], serde_json::json!("ws-2"));
+
+        // Update title/description on ws-2.
+        let body = serde_json::json!({
+            "room_id": "ws-2",
+            "title": "Workspace",
+            "description": "Renamed workspace"
+        })
+        .to_string();
+        let (status, resp) = app.call(Method::Post, "/room/update", Some(body));
+        assert_eq!(status, 200, "{resp}");
+        assert_eq!(
+            resp["room"]["description"],
+            serde_json::json!("Renamed workspace")
+        );
+
+        // Move the memory ws-2 → ws-3.
+        let body = serde_json::json!({
+            "memory_id": memory_id,
+            "from_room": "ws-2",
+            "to_room": "ws-3"
+        })
+        .to_string();
+        let (status, resp) = app.call(Method::Post, "/room/memory/move", Some(body.clone()));
+        assert_eq!(status, 200, "{resp}");
+        assert_eq!(resp["moved"], serde_json::json!(memory_id));
+
+        // Moving again → 404 (no link left in source).
+        let (status, resp) = app.call(Method::Post, "/room/memory/move", Some(body));
+        assert_eq!(status, 404, "{resp}");
+        assert!(
+            resp["error"]
+                .as_str()
+                .unwrap_or_default()
+                .contains("no link")
+        );
+    }
+
+    #[test]
+    fn room_update_missing_room_returns_404() {
+        let app = NamespaceApp::new();
+        let body = serde_json::json!({ "room_id": "ghost", "title": "x" }).to_string();
+        let (status, resp) = app.call(Method::Post, "/room/update", Some(body));
+        assert_eq!(status, 404, "{resp}");
+    }
+
     #[test]
     fn namespace_delete_merge_removes_namespace() {
         let app = NamespaceApp::new();
@@ -3640,3 +3828,124 @@ mod list_pagination_tests {
         assert!(resp.is_array(), "at-mode stays a bare array: {resp}");
     }
 }
+
+#[cfg(test)]
+mod payload_conformance_tests {
+    use super::*;
+    use std::io::Read as IoRead;
+
+    // ── #1233: raw-payload conformance — recall responses carry the FULL
+    // payload on every surface (HTTP route tested here; CLI --json prints
+    // the same serde SearchResult; MCP unified path reuses these types) ──
+    #[test]
+    fn recall_http_payload_conformance() {
+        use uteke_core::Uteke;
+
+        // No embedder: these tests are payload-shape conformance and must run
+        // in CI builds without the ONNX runtime lib (same pattern as the
+        // graph-edge tests). Keyword (fts5) recall needs no vectors.
+        let uteke = Uteke::open_with_backend(":memory:", None)
+            .expect("open in-memory uteke without embedder");
+        let shared = std::sync::Mutex::new(uteke);
+        let ctx = ReqCtx {
+            auth_token_hash: None,
+            read_only_token_hash: None,
+            cors_origins: vec![],
+            recall_config: None,
+            extraction_config: None,
+        };
+
+        let remember_body: &'static str = Box::leak(r#"{"content":"Payload conformance probe memory #1233 with distinctive tokens zebraquartz","namespace":"conf"}"#.to_string().into_boxed_str());
+        let mut remember_req = tiny_http::TestRequest::new()
+            .with_method(tiny_http::Method::Post)
+            .with_path("/remember")
+            .with_body(remember_body)
+            .into();
+        let remember_resp = route(&shared, &ctx, &mut remember_req);
+        let mut rbuf = String::new();
+        remember_resp
+            .into_reader()
+            .read_to_string(&mut rbuf)
+            .unwrap();
+        assert!(rbuf.contains("\"id\""), "remember must succeed: {rbuf}");
+
+        let mut req = tiny_http::TestRequest::new()
+            .with_method(tiny_http::Method::Post)
+            .with_path("/recall")
+            .with_body(r#"{"query":"zebraquartz","limit":5,"min_score":0.0,"namespace":"conf","strategy":"fts5"}"#)
+            .into();
+        let resp = route(&shared, &ctx, &mut req);
+        let mut buf = String::new();
+        resp.into_reader().read_to_string(&mut buf).unwrap();
+        let v: serde_json::Value = serde_json::from_str(&buf).expect("valid JSON");
+
+        // Raw payload must be an array of FULL SearchResult objects.
+        let arr = v
+            .as_array()
+            .expect("recall response must be a top-level JSON array");
+        assert!(!arr.is_empty(), "expected at least one hit");
+        let first = &arr[0];
+        // Full payload = the memory object itself, not a summary stub.
+        let mem = first
+            .get("memory")
+            .expect("each hit must carry the full memory object");
+        assert!(mem.get("id").is_some(), "hit must include memory.id");
+        let content = mem
+            .get("content")
+            .and_then(|c| c.as_str())
+            .expect("hit must include memory.content");
+        assert!(
+            content.contains("zebraquartz"),
+            "content must be the FULL memory text"
+        );
+        assert!(
+            mem.get("created_at").is_some(),
+            "hit must include memory metadata"
+        );
+        assert!(
+            first.get("score").and_then(|s| s.as_f64()).is_some(),
+            "hit must include score"
+        );
+        // No stub markers anywhere.
+        let s = v.to_string();
+        assert!(
+            !s.contains("raw_hits"),
+            "hit-count stub leaked into payload"
+        );
+    }
+
+    #[test]
+    fn recall_http_empty_result_has_no_stub() {
+        use uteke_core::Uteke;
+
+        // No embedder: these tests are payload-shape conformance and must run
+        // in CI builds without the ONNX runtime lib (same pattern as the
+        // graph-edge tests). Keyword (fts5) recall needs no vectors.
+        let uteke = Uteke::open_with_backend(":memory:", None)
+            .expect("open in-memory uteke without embedder");
+        let shared = std::sync::Mutex::new(uteke);
+        let ctx = ReqCtx {
+            auth_token_hash: None,
+            read_only_token_hash: None,
+            cors_origins: vec![],
+            recall_config: None,
+            extraction_config: None,
+        };
+        let mut req = tiny_http::TestRequest::new()
+            .with_method(tiny_http::Method::Post)
+            .with_path("/recall")
+            .with_body(r#"{"query":"totally-unique-missing-query-xyz","limit":5,"min_score":0.0,"namespace":"conf","strategy":"fts5"}"#)
+            .into();
+        let resp = route(&shared, &ctx, &mut req);
+        let mut buf = String::new();
+        resp.into_reader().read_to_string(&mut buf).unwrap();
+        let v: serde_json::Value = serde_json::from_str(&buf).expect("valid JSON");
+        // Contract: an empty result set is a bare EMPTY JSON ARRAY — the same
+        // full-payload shape as a populated response, just with zero hits.
+        // A "N hits" summary string or a {"raw_hits": N} stub would fail this.
+        let arr = v
+            .as_array()
+            .expect("recall response must be a top-level JSON array, never a summary string/stub");
+        assert!(arr.is_empty(), "expected an empty array for a no-hit query");
+    }
+}
diff --git a/docs/api-reference.md b/docs/api-reference.md
index e40d2bbe..0894b1c7 100644
--- a/docs/api-reference.md
+++ b/docs/api-reference.md
@@ -411,6 +411,30 @@ List all memories in a room (chronological). Accepts `?room_id=...` query param.
 
 Delete a room (unlink-only): room links are removed, memories and documents are preserved. Response: `{ deleted, unlinked_memories }`. Accepts `?room_id=...` query param.
 
+#### 🟡 `POST` `/room/rename`
+
+Rename a room: the registry row and every member/document link move in one transaction; title, description, and namespace are preserved (#1202). Accepts `{ from, to }`. Write op — blocked for read-only tokens.
+
+**Request body**: JSON object (see handler source for fields)
+
+*Related: `#1202`*
+
+#### 🟡 `POST` `/room/update`
+
+Update a room's title and/or description; omitted fields stay unchanged (#1202). Accepts `{ room_id, title?, description? }`; 404 when the room is missing. Write op — blocked for read-only tokens.
+
+**Request body**: JSON object (see handler source for fields)
+
+*Related: `#1202`*
+
+#### 🟡 `POST` `/room/memory/move`
+
+Move a memory from one room to another, preserving the link's author/role/joined_at provenance (#1202). Accepts `{ memory_id, from_room, to_room }`; 404 when the memory has no link in `from_room`. Write op — blocked for read-only tokens.
+
+**Request body**: JSON object (see handler source for fields)
+
+*Related: `#1202`*
+
 #### 🟡 `POST` `/room/document`
 
 Store a reference document in a room (large content >500 chars).
diff --git a/docs/architecture.md b/docs/architecture.md
index 4feb0b96..cc7ec600 100644
--- a/docs/architecture.md
+++ b/docs/architecture.md
@@ -269,7 +269,7 @@ Benchmarked on Oracle Cloud ARM (Ampere Altra), CPU-only, no GPU.
 ├── uteke.db                    # SQLite (memories + metadata + FTS5)
 ├── uteke_index.usearch         # Persistent HNSW vector index
 ├── uteke_index.keys            # Index key mapping (atomic save)
-├── embeddinggemma-q4/           # Local ONNX embedding model (~188MB)
+├── embeddinggemma-q4/           # Local ONNX embedding model (~200MB)
 │   └── onnx/                    # model_q4.onnx + model_q4.onnx_data
 └── logs/
     ├── uteke.log               # Current log
diff --git a/docs/assets/longmemeval-recall-v017.png b/docs/assets/longmemeval-recall-v017.png
new file mode 100644
index 00000000..4197f41f
Binary files /dev/null and b/docs/assets/longmemeval-recall-v017.png differ
diff --git a/docs/benchmarks.md b/docs/benchmarks.md
index 942b71dc..3b61aedc 100644
--- a/docs/benchmarks.md
+++ b/docs/benchmarks.md
@@ -9,24 +9,40 @@ Embedding model: EmbeddingGemma Q4 (768d, ONNX Runtime, CPU-only).
 
 ## Results
 
+Re-verified on the v0.17.0 release binary (2026-09-09, native aarch64 build,
+[full run](../benchmarks/internal/RESULTS.md)):
+
+| Scale | Insert ops/s | Insert Total | Recall Avg | Recall P95 | DB Size | Index Size |
+|-------|-------------|-------------|------------|------------|---------|------------|
+| 100 memories | 17.3/s | 5.8s | **31ms** | 47ms | 0.7MB | 0.31MB |
+| 1,000 memories | 17.5/s | 57s | **42ms** | 62ms | 5.2MB | 3.10MB |
+| 10,000 memories | 5.0/s | 33.4 min | **31ms** | 38ms | 80.7MB | 30.2MB |
+
+<details>
+<summary>Historical table (2026-08 run, v0.12.0-era, as originally published)</summary>
+
 | Scale | Insert ops/s | Insert Total | Recall Avg | Recall P95 | DB Size | Index Size |
 |-------|-------------|-------------|------------|------------|---------|------------|
 | 100 memories | 18.5/s | 5.4s | **40ms** | 46ms | 708KB | 319KB |
 | 1,000 memories | 21.8/s | 45.9s | **45ms** | 51ms | 5.3MB | 3.2MB |
 | 10,000 memories | 6.0/s | 28.0 min | **42ms** | 50ms | 81.3MB | 30.3MB |
 
+Storage matches the v0.17.0 run within a fraction of a percent.
+</details>
+
 ## Key Takeaways
 
-### Recall Latency: Flat ~40-45ms from 100 to 10K memories
+### Recall Latency: Flat ~30-45ms from 100 to 10K memories
 
 The killer stat: recall latency barely changes as the store grows.
 
-- 100 memories → **40ms**
-- 1,000 memories → **45ms**
-- 10,000 memories → **42ms** ← actually *faster* than 1K (warm ONNX cache)
+- 100 memories → **31ms**
+- 1,000 memories → **42ms**
+- 10,000 memories → **31ms** ← actually *faster* than 1K (warm ONNX cache)
 
 HNSW search is O(log N), so even at 10K memories, the vector index adds <1ms.
-The ~40ms floor is dominated by ONNX embedding inference, not search.
+The floor is dominated by ONNX embedding inference, not search. We quote ~45ms
+in the headline as a conservative upper bound.
 
 The full pipeline (fusion strategy, default since 0.16.0):
 1. Query → ONNX embedding generation
@@ -44,17 +60,17 @@ No network round-trip. No API call. Everything in-process.
 
 Each insert requires an ONNX embedding pass (CPU inference). Throughput drops at scale because HNSW graph traversal grows as the index expands:
 
-- 100 memories → **18.5 ops/s**
-- 1,000 memories → **21.8 ops/s**
-- 10,000 memories → **6.0 ops/s**
+- 100 memories → **17.3 ops/s**
+- 1,000 memories → **17.5 ops/s**
+- 10,000 memories → **5.0 ops/s** (within run-to-run noise of the earlier 6.0)
 
-At 6 ops/s, inserting 10K memories takes ~28 minutes. For bulk ingestion, use `uteke import` (batch mode) which pipelines embeddings.
+At 5 ops/s, inserting 10K memories takes ~33 minutes. For bulk ingestion, use `uteke import` (batch mode) which pipelines embeddings.
 
 ### Storage Efficiency
 
-- 100 memories → 708KB DB + 319KB index = **~10KB per memory**
-- 1,000 memories → 5.3MB DB + 3.2MB index = **~8.5KB per memory**
-- 10,000 memories → 81.3MB DB + 30.3MB index = **~11.2KB per memory**
+- 100 memories → 0.7MB DB + 0.31MB index = **~10KB per memory**
+- 1,000 memories → 5.2MB DB + 3.1MB index = **~8.3KB per memory**
+- 10,000 memories → 80.7MB DB + 30.2MB index = **~11.1KB per memory**
 
 Storage scales linearly (~10KB/memory). SQLite + HNSW both grow predictably.
 
@@ -76,26 +92,34 @@ See [LongMemEval retrieval harness](https://github.com/codecoradev/uteke/tree/de
 
 ## LongMemEval-S — Retrieval Accuracy (500 questions)
 
-Full validation run of uteke v0.16.0 default strategy (fusion, zero-config) on
+Full-validation runs of the default strategy (fusion, zero-config) on
 LongMemEval-S: 500 questions, session-level retrieval, ~115 haystack sessions per
 question (2,415 unique sessions), EmbeddingGemma Q4 CPU-only, deterministic — no
-LLM anywhere in the retrieval path.
+LLM anywhere in the retrieval path. Originally validated on v0.16.0 and
+re-validated end-to-end on the v0.17.0 release binary (2026-09-09).
 
-![uteke vs published systems on LongMemEval-S](assets/longmemeval-comparison.jpg)
+![uteke vs published systems on LongMemEval-S, revalidated on v0.17.0](assets/longmemeval-recall-v017.png)
 
 ### Headline numbers
 
-| Metric | Value | What it means |
-|---|---|---|
-| **recall_any@5** | **98.2%** | At least one gold session in top-5 — the metric competitor benchmarks publish |
-| recall_any@10 | 98.8% | |
-| recall_all@10 | 95.4% | **Strict:** every gold session must appear in top-10 |
-| strict recall_all@5 | 88.0% | Every gold session in top-5 (mathematical ceiling 99.4% — 3 questions have 6 gold sessions) |
-| coverage@5 | 94.3% | Partial credit per question (the harness's default aggregate) |
+Full 500-question basis, v0.17.0 re-validation (v0.16.0 original run alongside):
+
+| Metric | v0.17.0 | v0.16.0 | What it means |
+|---|---|---|---|
+| **recall_any@5** | **98.4%** | 98.2% | At least one gold session in top-5 — the metric competitor benchmarks publish |
+| recall_any@10 | 98.8% | 98.8% | |
+| strict recall_all@5 | 88.0% | 88.0% | **Strict:** every gold session in top-5 (mathematical ceiling 99.4% — 3 questions have 6 gold sessions) |
+| strict recall_all@10 | 95.4% | 95.4% | Every gold session in top-10 |
+| coverage@5 | 94.4% | 94.3% | Partial credit per question |
+
+On the 470 non-abstention questions (the 30 `_abs` abstention questions are
+reported separately), strict recall_all@5 is 88.3% and coverage@5 is 94.7%.
+Per-type breakdown, ablations, and the contradiction segment:
+[benchmarks/longmemeval/RESULTS.md](../benchmarks/longmemeval/RESULTS.md).
 
 Gold-session distribution across the 500 questions: 1 gold ×176, 2 ×250, 3 ×41,
-4 ×19, 5 ×11, 6 ×3. 43% of questions are multi-session — which is why we report
-the strict family at all.
+4 ×19, 5 ×11, 6 ×3. 65% of questions have multiple gold sessions — which is why
+we report the strict family at all.
 
 ### Why two metric families
 
@@ -107,6 +131,15 @@ that. It is harder, bounded below recall_any, and to our knowledge no other
 system in the comparison publishes it. We report both, from the same run, with
 the same data.
 
+### Head-to-head vs published systems
+
+The agentmemory numbers quoted in the [README](../README.md#-benchmarks-984-recall-on-longmemeval-s)
+are their published figures on the same benchmark and the same 500-question
+split, on their own recall_any@5 basis — verified apples-to-apples before
+quoting (their harness, their split, their metric definition). The BM25-only
+column is their published lexical baseline, not our system. Our own FTS5
+ablation on this dataset scores 91.4% any@5 ([RESULTS.md](../benchmarks/longmemeval/RESULTS.md)).
+
 ### Honesty notes
 
 - The comparison chart mixes evaluation setups: uteke numbers come from our own
@@ -114,7 +147,7 @@ the same data.
   published benchmark documents (accessed Aug 2026) and differ in embedding
   models and pipeline details.
 - The FTS5-only bar is an ablation of our own system, not a competitor.
-- Aggregate metrics + per-type breakdown: `benchmarks/longmemeval/RESULTS.md` in this repo. Raw per-question results are kept on the benchmark Modal volume (`uteke-longmemeval`, `default/` prefix), not committed here.
+- Aggregate metrics + per-type breakdown: benchmarks/longmemeval/RESULTS.md in this repo. Canonical raw per-question artifacts are committed under benchmarks/longmemeval/results/ (default & FTS5-ablation 500-question runs, contradiction segment) — recompute any headline number straight from the repo; older exploratory outputs live on the benchmark Modal volume (uteke-longmemeval).
 
 ### Reproducibility
 
@@ -128,7 +161,7 @@ An independent local re-run (2026-09-01) of 108 of the 500 questions on a 4-core
 | OS | Linux 6.8.0 (aarch64) |
 | Rust | 1.85+ |
 | Embedding | EmbeddingGemma Q4, 768d, ONNX Runtime CPU |
-| Uteke | v0.12.0 |
+| Uteke | v0.16.0 validation + v0.17.0 re-validation (2026-09-09), both full 500 questions; perf table re-verified on v0.17.0 |
 
 ## Methodology
 
diff --git a/docs/cli-reference.md b/docs/cli-reference.md
index 5da24a3d..e308755b 100644
--- a/docs/cli-reference.md
+++ b/docs/cli-reference.md
@@ -368,6 +368,7 @@ Additional flags not shown in the basic example above:
 # Minimum similarity score filter
 uteke recall "database config" --min 0.7
 uteke recall "database config" --strict    # uses min_score_strict (default 0.5)
+uteke recall "database config" --min 0.25  # opt-in similarity threshold (default 0.0 since #1223: fusion scores are rank-based)
 
 # Time-travel: query memories at specific point in time
 uteke recall "deployment process" --at 2026-06-01T12:00:00Z
@@ -415,6 +416,7 @@ uteke recall "api design" --context
 |------|-------------|
 | `--min <score>` | Minimum similarity score (0.0-1.0) |
 | `--strict` | Use strict threshold (`min_score_strict`, default 0.5) |
+| `--min <score>` | Minimum similarity score threshold (default 0.0 since 0.17.x, #1223 — fusion scores are rank-based, not cosine) |
 | `--at <timestamp>` | Query memories at point in time (RFC3339) |
 | `--related` | Follow relationship edges |
 | `--depth <n>` | Traversal depth for --related |
@@ -849,6 +851,9 @@ uteke timeline <memory-id> --json
 | `uteke namespace list` | List all namespaces with memory counts |
 | `uteke namespace stats <name>` | Show stats for a namespace |
 | `uteke namespace switch <name>` | Set default namespace in config |
+| `uteke namespace move <id> <ns>` | Move a memory to another namespace, no re-embed (#1181) |
+| `uteke namespace rename <old> <new>` | Rename a namespace; existing target = merge (#1181) |
+| `uteke namespace delete <ns> --confirm` | Delete a namespace (`--strategy refuse\|merge\|deprecate`, #1181) |
 | `uteke hook <shell>` | Print shell hook script (bash/zsh/fish) |
 | `uteke init --agent <type>` | Initialize integration (opencode, pi, claude, cursor, hermes) |
 | `uteke init --agent hermes --memory-provider` | Install uteke as Hermes's default memory provider (auto recall + extraction). See [Hermes integration, Mode B](integrations/hermes.md). **Note:** Hermes Mode B deprecated — see [integrations/hermes.md](integrations/hermes.md) for migration. |
diff --git a/docs/configuration.md b/docs/configuration.md
index a3b0c2a7..16c8beba 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -198,7 +198,7 @@ Control minimum similarity score for recall results:
 [recall]
 # Minimum similarity score (0.0-1.0). Memories below this score are excluded.
 # Default: 0.3 (balanced). Use 0.0 to disable filtering.
-min_score = 0.3
+min_score = 0.0
 
 # Strict-mode threshold (used with `--strict` flag)
 min_score_strict = 0.5
@@ -223,7 +223,7 @@ graph_rerank_enabled = true   # master switch; false → graph acts like hybrid
 
 | Setting | Default | Description |
 |---------|---------|-------------|
-| `min_score` | 0.3 | Minimum similarity score (0.0-1.0). **CLI only.** |
+| `min_score` | 0.0 | Minimum similarity score (0.0-1.0). **CLI only.** |
 | `min_score_strict` | 0.5 | Strict-mode threshold (used with `--strict`). **CLI only.** |
 | `default_strategy` | `fusion` | Default recall strategy (`vector\|fts5\|hybrid\|graph\|fusion`). Since 0.16.0 `fusion` (weighted RRF: vector×1.7 + hybrid×1, #1123) is the default — LongMemEval fast50 R@5 0.98 vs 0.9267 hybrid |
 | `graph_density_weight` | 0.1 | Edge-density boost weight (graph strategy only) |
@@ -234,8 +234,10 @@ graph_rerank_enabled = true   # master switch; false → graph acts like hybrid
 >
 > | Interface | Default `min_score` | Notes |
 > |-----------|---------------------|-------|
-> | **CLI** | `0.3` (from `[recall] min_score`) | Reads from `uteke.toml`. `--min 0.0` to disable. |
+> | **CLI** | `0.0` (from `[recall] min_score`) | Reads from `uteke.toml`. Set `--min` or `[recall] min_score` to enable. |
 > | **HTTP API / Server** | `0.0` (`DEFAULT_MIN_SCORE`) | Server has its own constant, ignores `[recall] min_score`. |
+>
+> **Note (since 0.17.x, #1223):** default recall scores under the fusion strategy are rank-based (RRF contribution + salience/recency boosts, typically ~0.0-0.2), not raw cosine similarity. A legacy cosine-era default threshold of 0.3 silently filtered out nearly all results, so the default is now 0.0 across every surface. Set a threshold explicitly (config `min_score`, `--min`, `--strict`, or HTTP `min_score`) only when you know the score scale of the active strategy.
 > | **MCP** | `0.0` | MCP server hardcodes 0.0. |
 >
 > This means a score of 0.25 is returned by API/MCP but **filtered out** by CLI.
@@ -278,7 +280,7 @@ Resolution order (highest priority first):
 | `UTEKE_VECTOR_BACKEND` | `[vector] backend` | compiled-in default | Vector engine when both are compiled in: `usearch`, `vecq`. Ignored (with a warning) on single-engine builds or unknown values. Switching engines on an existing store auto-rebuilds the index from SQLite |
 | `UTEKE_SERVER_HOST` | `[server] host` | `127.0.0.1` | Server bind address |
 | `UTEKE_SERVER_PORT` | `[server] port` | `8767` | Server port |
-| `UTEKE_RECALL_MIN_SCORE` | `[recall] min_score` | `0.3` | Default similarity threshold |
+| `UTEKE_RECALL_MIN_SCORE` | `[recall] min_score` | `0.0` | Default similarity threshold |
 | `UTEKE_RECALL_MIN_SCORE_STRICT` | `[recall] min_score_strict` | `0.5` | Strict threshold |
 | `UTEKE_RECALL_STRATEGY` | `[recall] default_strategy` | `fusion` | Default recall strategy (`vector\|fts5\|hybrid\|graph\|fusion`) |
 | `UTEKE_GRAPH_DENSITY_WEIGHT` | `[recall] graph_density_weight` | `0.1` | Edge-density boost weight |
diff --git a/docs/docker.md b/docs/docker.md
index 22c5160f..c2635866 100644
--- a/docs/docker.md
+++ b/docs/docker.md
@@ -4,7 +4,7 @@ title: Docker
 
 # Docker
 
-Uteke ships as a lightweight multi-arch Docker image (~10MB). The embedding model (~188MB) downloads automatically on first run and is cached in the volume — subsequent updates are instant.
+Uteke ships as a lightweight multi-arch Docker image (~10MB). The embedding model (~200MB) downloads automatically on first run and is cached in the volume, so subsequent updates are instant.
 
 ## Quick Start
 
@@ -104,7 +104,21 @@ The volume contains:
 - `uteke_index.usearch` — HNSW vector index (default engine)
 - `uteke_index.vecq` — quantized index (created if you switch engines)
 - `uteke_index.keys` — Index key mapping
-- `models/embeddinggemma-q4/` — ONNX embedding model (~188MB)
+- `models/embeddinggemma-q4/` — ONNX embedding model (~200MB)
+
+### Choosing the vector engine (v0.17.0+)
+
+Both engines ship in the image. Pick one via env var — switching leaves your
+data untouched; the new engine rebuilds its index from SQLite on the next start:
+
+```yaml
+environment:
+  - UTEKE_VECTOR_BACKEND=vecq   # or "usearch" (default)
+```
+
+`usearch` (HNSW) has the best query latency at scale; `vecq` (4-bit + residual,
+pure Rust) builds indexes ~16x faster with ~3x smaller files. See
+[configuration](configuration.md#environment-variables) for details.
 
 ### Choosing the vector engine (v0.17.0+)
 
diff --git a/docs/getting-started.md b/docs/getting-started.md
index 7d8436d8..ada828bc 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -12,7 +12,7 @@ curl -fsSL https://raw.githubusercontent.com/codecoradev/uteke/main/install.sh |
 
 See the [Installation guide](/install) for all methods (Cargo, binary, Docker).
 
-> 💡 First run downloads the embedding model (~188MB). No API keys needed.
+> 💡 First run downloads the embedding model (~200MB). No API keys needed.
 
 ## Interactive Onboarding
 
@@ -168,7 +168,7 @@ All data lives in `~/.codecora/uteke/`:
 ├── uteke.db                    # SQLite (memories + metadata + FTS5)
 ├── uteke_index.usearch         # Persistent HNSW vector index
 ├── uteke_index.keys            # Index key mapping
-├── embeddinggemma-q4/          # Local ONNX embedding model (~188MB)
+├── embeddinggemma-q4/          # Local ONNX embedding model (~200MB)
 │   └── onnx/                   # model_q4.onnx + model_q4.onnx_data
 └── logs/
     ├── uteke.log               # Current log
diff --git a/docs/install.md b/docs/install.md
index 28a71d8d..98635fc1 100644
--- a/docs/install.md
+++ b/docs/install.md
@@ -21,7 +21,7 @@ echo 'export PATH="$HOME/.local/bin:$PATH"' >> ~/.bashrc
 Pin a specific version:
 
 ```bash
-UTEKE_VERSION=v0.16.0 curl -fsSL https://raw.githubusercontent.com/codecoradev/uteke/main/install.sh | sh
+UTEKE_VERSION=v0.18.0 curl -fsSL https://raw.githubusercontent.com/codecoradev/uteke/main/install.sh | sh
 ```
 
 ## Install via Cargo
@@ -39,21 +39,26 @@ This compiles uteke from source and installs it to Cargo's binary directory (typ
 Download from [GitHub Releases](https://github.com/codecoradev/uteke/releases):
 
 ```bash
-# Linux (x86_64)
-curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-x86_64-unknown-linux-gnu-v0.16.0.tar.gz | tar xz
-mv uteke ~/.local/bin/
+# Linux (x86_64) — tarball also contains libonnxruntime.so* (required for embeddings)
+curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-x86_64-unknown-linux-gnu-v0.18.0.tar.gz | tar xz
+mv uteke uteke-serve uteke-mcp ~/.local/bin/
+mv libonnxruntime.so* libonnxruntime_providers_shared.so* ~/.local/bin/
 
 # Linux (x86_64, legacy — no AVX2/SSE4.2)
-curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-x86_64-unknown-linux-gnu-legacy-v0.16.0.tar.gz | tar xz
-mv uteke ~/.local/bin/
+curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-x86_64-unknown-linux-gnu-legacy-v0.18.0.tar.gz | tar xz
+mv uteke uteke-serve uteke-mcp ~/.local/bin/
+mv libonnxruntime.so* ~/.local/bin/
+mkdir -p ~/.local/bin/ort-legacy && mv ort-legacy/libonnxruntime.so* ~/.local/bin/ort-legacy/
 
 # Linux (aarch64 / ARM)
-curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-aarch64-unknown-linux-gnu-v0.16.0.tar.gz | tar xz
-mv uteke ~/.local/bin/
+curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-aarch64-unknown-linux-gnu-v0.18.0.tar.gz | tar xz
+mv uteke uteke-serve uteke-mcp ~/.local/bin/
+mv libonnxruntime.so* libonnxruntime_providers_shared.so* ~/.local/bin/
 
 # macOS (Apple Silicon)
-curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-aarch64-apple-darwin-v0.16.0.tar.gz | tar xz
-mv uteke ~/.local/bin/
+curl -sL https://github.com/codecoradev/uteke/releases/latest/download/uteke-aarch64-apple-darwin-v0.18.0.tar.gz | tar xz
+mv uteke uteke-serve uteke-mcp ~/.local/bin/
+mv libonnxruntime*.dylib ~/.local/bin/
 ```
 
 Supported platforms:
@@ -86,7 +91,7 @@ docker pull ghcr.io/codecoradev/uteke:latest
 
 ## First Run
 
-On first run, uteke downloads the embedding model (~188MB). No API keys needed — fully offline.
+On first run, uteke downloads the embedding model (~200MB). No API keys needed — fully offline.
 
 ```bash
 uteke doctor   # Verify installation
@@ -115,10 +120,13 @@ uteke upgrade --yes    # Skip confirmation
 
 ## What Gets Installed
 
-The install script deploys three binaries:
+The install script deploys three binaries plus the bundled ONNX Runtime shared library (required for local embeddings, no API keys needed):
 
 | Binary | Purpose |
 |--------|---------|
 | `uteke` | Core CLI — remember, recall, search, list, etc. |
 | `uteke-serve` | HTTP server for remote access and MCP over HTTP |
 | `uteke-mcp` | Standalone MCP server for AI agent integration |
+| `libonnxruntime.so*` (Linux) / `libonnxruntime*.dylib` (macOS) / `onnxruntime.dll` (Windows) | ONNX Runtime (currently v1.24.4, matches `ort` crate `2.0.0-rc.12`) — resolved via `<exe_dir>/libonnxruntime.so`, system paths, or `ORT_LIB_PATH` |
+
+> **Arch / CachyOS note:** there is no system `libonnxruntime.so` by default and `pip install onnxruntime` may have no wheel for newer Python (e.g. 3.14), so you must keep the bundled `.so` next to the binary in `~/.local/bin/`. If you see `ONNX Runtime library not found` on `uteke remember`, re-run the installer above or set `ORT_LIB_PATH=/path/to/libonnxruntime.so`. Then `uteke doctor` should show `Embedding model: embeddinggemma-q4`.
diff --git a/docs/mcp.md b/docs/mcp.md
index f93119be..d14462f5 100644
--- a/docs/mcp.md
+++ b/docs/mcp.md
@@ -132,17 +132,20 @@ uteke init --agent hermes  # Installs pre_llm_call hook
 
 ## Available Tools
 
-Both transports expose the same 35 tools (MCP protocol version `2025-06-18`):
+Both transports expose the same 46 tools (MCP protocol version `2025-06-18`):
 
 | Tool | Description |
 |------|-------------|
 | `uteke_remember` | Store a memory (supports type, room, author, tags) |
-| `uteke_recall` | Semantic search (supports tags filter, min_score, strategy: fusion/vector/fts5/hybrid/graph — default `fusion` since 0.16.0) |
+| `uteke_recall` | Semantic search (supports tags filter, min_score, strategy: fusion/vector/fts5/hybrid/graph — default `fusion` since 0.16.0, and the `explain` flag showing why each result ranked where it did, #1160) |
 | `uteke_search` | Text search with optional tag filter |
-| `uteke_list` | List memories (supports pagination via offset) |
+| `uteke_list` | List memories (supports pagination via offset, or `include_meta: true` for the `{memories, total, has_more, next_offset}` envelope, #1188) |
 | `uteke_get` | Fetch a single memory's full record by id — no truncation (accepts UUID or unambiguous prefix) |
-| `uteke_update` | Partial update — only provided fields change; content changes re-embed |
+| `uteke_update` | Partial update — only provided fields change; content changes re-embed; accepts `namespace` to move a memory (#1181) |
 | `uteke_supersede` | Mark a memory superseded by a newer one — wires the edge pair, soft-deprecates the old row; recall flags stale results (⚠ superseded by …) |
+| `uteke_provenance` | Full provenance report for a memory (#1172): author/source fields, trust tier, source hash at write vs live-recomputed content hash, and the timeline event chain with actor + evidence |
+| `uteke_contradictions` | List the contradiction resolution ledger (#1172): superseded-but-not-restored memories with winner, reason, and timestamp |
+| `uteke_contradictions_undo` | Undo a contradiction resolution (#1172): restore the retired memory, remove the supersession edge pair, record `supersession_undone` events |
 | `uteke_forget` | Delete a memory (accepts UUID or unambiguous prefix) |
 | `uteke_stats` | Memory store statistics |
 | `uteke_context` | AI-optimized context output for prompts |
@@ -161,6 +164,12 @@ Both transports expose the same 35 tools (MCP protocol version `2025-06-18`):
 | `uteke_room_memories` | List memories in a room (#569) |
 | `uteke_room_create` | Create a room |
 | `uteke_room_delete` | Delete a room |
+| `uteke_room_rename` | Rename a room; rewrites the registry row and every reference in one transaction (#1202) |
+| `uteke_room_update` | Update a room's title/description (#1202) |
+| `uteke_room_memory_move` | Move a memory to another room, preserving link provenance (#1202) |
+| `uteke_room_list` | List all rooms |
+| `uteke_namespace_rename` | Rename a namespace (#1181); when the target exists this is a merge, returns `{from, to, moved, target_existed}` |
+| `uteke_namespace_delete` | Delete a namespace with an explicit strategy for its memories (#1181): `refuse` (default, 409 while referenced), `merge` into `target`, or `deprecate` (soft-delete, restorable) |
 | `uteke_room_stats` | Room statistics |
 | `uteke_room_summary` | Room topic summary (tag clustering, no LLM) |
 | `uteke_room_summary_document` | Generate summary document from room (→ `POST /room/summary-document`) |
@@ -323,6 +332,6 @@ See [Docker guide — MCP](/docker#mcp-model-context-protocol) for full Docker-s
 | `Permission denied` | `chmod +x $(which uteke-mcp)` or ensure the binary is on your `PATH` |
 | `Connection refused` (HTTP) | Ensure `uteke-serve` is running: `uteke-serve` |
 | Client can't see tools | Verify the MCP config JSON is valid and the client has been restarted |
-| Slow first query | The embedding model (~188MB) downloads on first use — subsequent calls are ~45ms |
+| Slow first query | The embedding model (~200MB) downloads on first use — subsequent calls are ~45ms |
 
 See also: [Architecture — MCP Transport](/architecture#mcp-transport-381)
diff --git a/docs/multi-agent.md b/docs/multi-agent.md
index fd669af2..ba5d9b93 100644
--- a/docs/multi-agent.md
+++ b/docs/multi-agent.md
@@ -63,6 +63,35 @@ uteke recall "context"
 
 Resolution order: `--namespace` flag → `UTEKE_NAMESPACE` env → `uteke.toml` → `"default"`
 
+## Managing Namespaces (#1181)
+
+Namespaces are a derived view of where memories live, and v0.17.0 adds sanctioned
+ops to manage them directly:
+
+```bash
+# Move one memory to another namespace (plain column update, no re-embed)
+uteke namespace move <memory-id> other-ns
+
+# Rename a namespace. If the target already exists, this MERGES into it
+# (all memories move, the old name vanishes)
+uteke namespace rename old-ns new-ns
+
+# Delete a namespace with an explicit strategy for its memories:
+#   refuse    (default) refuses with 409 while any memory references the name
+#   merge     moves all memories to --target, the name vanishes
+#   deprecate soft-deletes them; restorable via promote, never hard-deleted
+uteke namespace delete temp-ns --strategy merge --target archive --confirm
+uteke namespace delete ghost-ns --strategy deprecate --confirm
+```
+
+The same operations are available over HTTP (`PUT /memory` with a `namespace`
+field, `POST /namespaces/rename`, `POST /namespaces/delete`) and MCP
+(`uteke_namespace_rename`, `uteke_namespace_delete`, plus the `namespace` field
+on `uteke_update`). `GET /namespaces?with_counts=true` reports `active` and
+`deprecated` breakdowns alongside the total count. Deleting a namespace never
+hard-deletes memories: `refuse` is the safe default, and `deprecate` keeps them
+recoverable.
+
 ## All Commands Are Scoped
 
 The `--namespace` flag works on every command:
diff --git a/docs/roadmap.md b/docs/roadmap.md
index 29babdf2..d4a9f128 100644
--- a/docs/roadmap.md
+++ b/docs/roadmap.md
@@ -6,6 +6,21 @@ title: Roadmap
 
 Demand-gated — we build what people actually use. Track progress on [GitHub Issues](https://github.com/codecoradev/uteke/issues).
 
+## v0.18.0 - Agent-Operable Memory Plumbing `Released 2026-09-13`
+
+- **Ingest date anchors (#1232/#1238)** - `remember --timestamp` + import timestamps; time-travel and audit no longer depend on ingest order
+- **Room lifecycle management (#1202/#1203)** - rename/update/move-memory across HTTP, CLI, and MCP (schema v19)
+- **Recall payload conformance tests (#1233/#1239)** - full-payload contract pinned across all surfaces
+- Fixes: dedup live-check (#1211), default min_score 0.0 (#1228), install.sh ORT libs (#1221), LoCoMo label mapping (#1222)
+
+## v0.17.0 — Inspectable, Trustworthy Memory `✓ Released 2026-09-06`
+
+- **Explain recall (#1160)** — see why each memory ranked where it did, on every recall surface (CLI `--explain`, HTTP `explain`, MCP flag)
+- **Contradiction resolution as an auditable ledger (#1172)** — supersede/list/undo with provenance; contradiction benchmark segment: fusion winner@1 0.850 → 1.000 after supersede, stale@5 1.000 → 0.000
+- **Provenance data model (#1172, schema v18)** — `source_hash` tamper evidence, actor/evidence timeline events, `provenance(id)` report
+- **Dual-engine vector layer** — runtime-switchable vector engine (#1169/#1171)
+- **Namespace management API (#1181)** — rename/merge/delete with explicit memory strategy; `/list` pagination metadata (#1188)
+
 ## v0.16.0 — Fusion Default `✓ Released 2026-08-29`
 
 - **`fusion` is now the default recall strategy (#1123)** — weighted RRF of the vector and hybrid rankings; LongMemEval 500Q R@5 0.946 vs 0.854 hybrid (+9.2 pts), zero config needed
diff --git a/install.ps1 b/install.ps1
index e775bc81..1529eb11 100644
--- a/install.ps1
+++ b/install.ps1
@@ -144,6 +144,14 @@ function Install-Uteke {
             Write-Warn "$binary not found in archive"
         }
     }
+
+    # Install bundled ONNX Runtime DLLs (required for embeddings).
+    # Release zips ship onnxruntime.dll + providers DLLs alongside the exes.
+    # Without these, `uteke remember` fails with "ONNX Runtime library not found".
+    Get-ChildItem $extractDir -Filter "onnxruntime*.dll" -ErrorAction SilentlyContinue | ForEach-Object {
+        Copy-Item $_.FullName (Join-Path $InstallDir $_.Name) -Force
+        Write-Info ("Installed {0}" -f $_.Name)
+    }
     
     # Cleanup
     Remove-Item $archivePath -Force -ErrorAction SilentlyContinue
diff --git a/install.sh b/install.sh
index 0aa230a0..7becd4af 100755
--- a/install.sh
+++ b/install.sh
@@ -145,6 +145,17 @@ install() {
         mv "${TEMP_DIR}/${MCP_BINARY_NAME}" "${INSTALL_DIR}/"
     fi
 
+    # Install bundled ONNX Runtime shared libs (required for embeddings).
+    # Release tarballs ship libonnxruntime.so* + providers_shared alongside the
+    # binaries (see release.yml Package step). Without these, `uteke remember`
+    # fails with "ONNX Runtime library not found" on distros with no system
+    # lib (e.g. Arch/CachyOS) — see resolve_ort_lib() in ort_init.rs which
+    # expects <exe_dir>/libonnxruntime.so on AVX2 CPUs.
+    # Mirrors build_from_source() which already copies ort-lib/*.so.
+    cp -a "${TEMP_DIR}"/libonnxruntime.so* "${INSTALL_DIR}/" 2>/dev/null || true
+    cp -a "${TEMP_DIR}"/libonnxruntime_providers_shared.so* "${INSTALL_DIR}/" 2>/dev/null || true
+    cp -a "${TEMP_DIR}"/libonnxruntime*.dylib "${INSTALL_DIR}/" 2>/dev/null || true
+
     chmod +x "${INSTALL_DIR}/${BINARY_NAME}"
     if [ -f "${INSTALL_DIR}/${SERVER_BINARY_NAME}" ]; then
         chmod +x "${INSTALL_DIR}/${SERVER_BINARY_NAME}"