diff --git a/campaigns.yaml b/campaigns.yaml index da0b514..eee8530 100644 --- a/campaigns.yaml +++ b/campaigns.yaml @@ -58,58 +58,53 @@ campaigns: project: autolens_inference - id: graphical-ep title: Graphical and EP inference coordination - status: needs-slicing - updated: '2026-10-04' - next: Reconcile current Cortex observations and shipped fixes, then select one bounded phase. - recent_progress: Mind campaign map and two scoping contracts preserved verbatim; implementation work remains - in Mind. - blockers: Old scoping contains completed phases and decision gates; not a bulk runnable queue. + status: active + updated: '2026-10-23' + next: 'Dev: PyAutoFit#1711 (EP counts zero-iteration Laplace convergence NO_CHANGE as a stale/skipped update; Mind active/ep_factors_end_the_run_with_zero.md, worktree ep-zero-success-factors, tier glance), the first of three wave-1 ruling findings. Science, all human-gated: (1) slope_hierarchy_scale N=25 EP rerun with projection=moments, (2) ic50 EP ladder rerun from N=50 with mean-field hill_coef widths, (3) ep_toy_gaussian wave-2 go. Each rerun needs a private PyAutoFit checkout on RAL.' + recent_progress: '2026-10-23 reconciliation (tasks/ep_campaign_state_2026-10-23.md): shipped since the 2026-09-10 table, all released in PyAutoFit 2026.10.7.1: #1632/#1634 (EMFILE, Pool per single-core Dynesty fit), #1643/#1644/#1645 (per-factor-search overhead 2.45 s to ~0.95 s), #1655 (ProjectionException recovery, the IC50 N=50 assert cure), #1656 (LaplaceOptimiser(projection=moments), the hierarchical-scatter cure; analytic_gaussian.py un-parked PARITY 41/41). Cortex Now states read from PyAutoCortex origin/main 5fa4eb98. PyAutoFit#1711 issued 2026-10-23; the other two wave-1 findings stay filed in Mind.' + blockers: 'Three science reruns await human go. The shared RAL PyAutoFit mirror 404b3e5f7 is frozen for euclid_dr1 and lacks #1655/#1656, so the reruns need a private PyAutoFit checkout on RAL; output/ was vaulted to z_vault/ 2026-10-14. NaN in EP evidence accumulation and the moments loggaussian scatter Jacobian stay filed in Mind (the latter is the human''s call, overlapping PyAutoFit#1498/#1331).' job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md project: graphical_ep - id: analytic-gaussian title: Analytic Gaussian ensemble and control calibration status: blocked - updated: '2026-10-04' - next: Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there. - recent_progress: Cortex records accepted wave-1 baseline and 30 September independent-review caveats. - blockers: N=25 stays unsubmitted until control/threshold diagnostic is resolved. + updated: '2026-10-23' + next: 'Run the planned diagnostic (archive/tasks/analytic_gaussian/minimal_ep_legb_mu_threshold.md) on a passing, an edge and the worst mu seed; N=25 stays written but unsubmitted until it lands.' + recent_progress: '2026-10-23 reconciled against Cortex 5fa4eb98: wave 1 (342413, 200 seeds at N=5) accepted as the baseline R-20260910-04 — autofit EP exact on the Gaussian leg, leg B sigma misses as pre-registered (78/200), collapse 0/200. Astra and an independent Opus review favour an under-calibrated mu threshold on criterion 2 and flag the per-site sigma>0 clip (analytic_ep_minimal.py:333) as the one audit target.' + blockers: 'The criterion-2 mu-threshold diagnostic is unrun; N=25 is unsubmitted until it lands. The shared RAL PyAutoFit mirror 404b3e5f7 is frozen for euclid_dr1 and lacks #1655/#1656, so the reruns need a private PyAutoFit checkout on RAL; output/ was vaulted to z_vault/ 2026-10-14.' job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence - evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/analytic_gaussian.md + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/5fa4eb98d10ba1297dd13d950a6a122bd504a5c7/projects/analytic_gaussian.md project: analytic_gaussian - id: ep-toy-gaussian title: N=50 NUTS versus three EP repeats status: needs-decision - updated: '2026-10-04' - next: Present the existing wave-2 proposal with current dependency evidence for decision. - recent_progress: Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission. - blockers: Explicit human go; sample entry/export and resource settings must be verified. + updated: '2026-10-23' + next: 'Human go; then add the n50_seed42_w2 toy.SAMPLES entry, push, and submit NUTS (64 GB) then EP with SAMPLE=n50_seed42_w2 exported.' + recent_progress: '2026-10-23 reconciled against Cortex 5fa4eb98: wave 1 (342639 + 342640_[0-2]) failed on infrastructure — EP x3 on EMFILE (fixed by PyAutoFit#1632/#1634, in the RAL mirror 404b3e5f7), NUTS on OOM at 8 GB during XLA compilation. Wave 2 is prepared but not submitted (submit_nuts raised to 64 GB, submit_ep unchanged at 8 GB). The N=50 collapse question is unanswered.' + blockers: 'Explicit human go; the n50_seed42_w2 sample entry is not yet added.' job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence - evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/5fa4eb98d10ba1297dd13d950a6a122bd504a5c7/projects/ep_toy_gaussian.md project: ep_toy_gaussian - id: slope-hierarchy-scale title: Hierarchical slope recovery at scale status: blocked - updated: '2026-10-04' - next: Verify the moments fix release and project mirror; recover missing summaries from existing samples - before a proposed rerun. - recent_progress: Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; - Mind records later moments fix. - blockers: Summary/sample retrieval and release/RAL mirror reconciliation outstanding; no reclassification - from stale prose. + updated: '2026-10-23' + next: 'Rerun the N=25 EP arm with projection=moments on a private PyAutoFit checkout on RAL and compare its scatter against the NUTS baseline (human-gated); write the NUTS summary JSON offline from z_vault/sample_n25_seed42 samples.csv.' + recent_progress: '2026-10-23 reconciled against Cortex 5fa4eb98: N=25 graphical baselines in — joint NUTS (342350_0) mean 1.994 [1.976, 2.014], sigma 0.087 [0.074, 0.104] vs truth 2.0/0.1; per-lens naive parent 1.999/0.089. The EP arm returned the prior on the Laplace projection (343299: hierarchical factor 0/50 SUCCESS). The cure PyAutoFit#1656 (projection=moments) is released in 2026.10.7.1.' + blockers: 'The EP arm needs a moments rerun (human go). The shared RAL PyAutoFit mirror 404b3e5f7 is frozen for euclid_dr1 and lacks #1655/#1656, so the reruns need a private PyAutoFit checkout on RAL; output/ was vaulted to z_vault/ 2026-10-14.' job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence - evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/5fa4eb98d10ba1297dd13d950a6a122bd504a5c7/projects/slope_hierarchy_scale.md project: slope_hierarchy_scale - id: ic50-scale title: IC50 EP and graphical scale ladder status: blocked - updated: '2026-10-04' - next: Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field - diagnostics. - recent_progress: Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat. - blockers: Projection fix and mean-field width reporting require reconciliation before scientific comparison. + updated: '2026-10-23' + next: 'Rerun the EP ladder on a private PyAutoFit checkout with output on and a fixed seed, report hill_coef widths from the mean field (human-gated), then give ep_lbfgs_jax a witness as the scale lever.' + recent_progress: '2026-10-23 reconciled against Cortex 5fa4eb98: EP passes N=5/10/25 (coef_mean within 3 sigma; 26/50/140 s per sweep) and died at N=50 on a projection assert, now cured by PyAutoFit#1655 (ProjectionException recovery, released 2026.10.7.1). Graphical is cheaper but overconfident beyond N=25; EP hill_coef widths are not yet comparable because util.py reports factor-message widths.' + blockers: 'The N=50 rung needs a rerun with mean-field hill_coef widths (human go). The shared RAL PyAutoFit mirror 404b3e5f7 is frozen for euclid_dr1 and lacks #1655/#1656, so the reruns need a private PyAutoFit checkout on RAL; output/ was vaulted to z_vault/ 2026-10-14.' job_status: unknown — live scheduler not queried; historical ledger states are not current job evidence - evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md + evidence: https://github.com/PyAutoLabs/PyAutoCortex/blob/5fa4eb98d10ba1297dd13d950a6a122bd504a5c7/projects/ic50_workspace.md project: ic50_workspace - id: gaussian-x3-search-benchmark title: PyAutoFit search benchmark on gaussian_x3 (autofit_inference) @@ -157,8 +152,8 @@ tasks: status: needs-decision priority: high path: tasks/ep_campaign.md - updated: '2026-10-04' - next: Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled. + updated: '2026-10-23' + next: 'Reconciled 2026-10-23 in tasks/ep_campaign_state_2026-10-23.md (the original map stays byte-preserved); human gates: three EP reruns and PyAutoFit#1711 review. This campaign map is never issued or bulk scheduled.' evidence: https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md - id: ep_scoping campaign: graphical-ep diff --git a/dashboard.html b/dashboard.html index b67ed2d..2e996a9 100644 --- a/dashboard.html +++ b/dashboard.html @@ -369,7 +369,7 @@ - PyAutoInsight: https://github.com/PyAutoLabs/PyAutoInsight - autolens_inference: https://github.com/PyAutoLabs/autolens_inference - autofit_inference: https://github.com/PyAutoLabs/autofit_inference
Last updated 2026-10-23 14:42:05 UTC
↻ Update

-

Last check-in: 2026-10-18T17:00:00Z.

Active campaigns

CampaignRecent progressNext steps
HST imaging, clean slate: Nautilus base on A100, jax_cpu and numba_cpu at a pinned release, mass stage with linear lens light and with it fixed, then other samplers on the mass stage2026-10-19 (checkins/2026-10-19T171637Z.md, focused clean-up): 412539 (jax_cpu dense) COMPLETED. On the human's decision, 412541 (jax_cpu sparse) and 412565 (numba_cpu sparse) were cancelled with partial stages left on disk. jax_cpu sparse: seed 0 has slam_base complete; seed 1 has source_lp[1] and source_pix[1] complete. numba sparse: slam_base complete on both seeds. All lightfixed mass_total[1] stages are incomplete. autolens_inference#36 (flatten, closes #35) merged as c3ca4de 2026-10-19T15:53:33Z. flatten_cleanslate.py --apply ran on RAL (28 moves, 55 rewrites) and on the laptop mirror (28 moves, 38 rewrites). Stage output now lives at output/autolens/imaging/hst/slam/<variant>/<config>/seed_<n>/<stage>/, prepared problems at output/autolens/prepared/<config>/seed_<n>/<variant>/ and rows at results/slam/imaging/hst/<variant>/<config>/. RAL and laptop each hold 1974 files with identical paths and sizes. The wave-1 attempt-1 material (output/experiments, output/experiment_sources, results/searches/experiments) was moved aside to /mnt/ral/jnightin/inference/aside/autolens_inference_cleanslate_wave1_attempt1/ on RAL and to /mnt/c/Users/Jammy/Science/inference/aside/autolens_inference_cleanslate_wave1_attempt1/ on the laptop. Earlier (2026-10-18): A100 base complete; the sparse fixed-light mass_total[1] diverged on both seeds (Cortex observation, human reading pending).Nothing is queued (scheduler empty after the human-directed cancel 2026-10-19 ~15:51Z). Each step needs human authorisation: (1) Confirm keeping output/autolens/prepared/ (agent proposal, not yet confirmed: wave.py needs output/autolens/prepared/<config>/seed_<n>/<variant>/manifest.json for every sampler wave and the divergence repro needs it; only re-running the base mass stage regenerates it). (2) File a Mind bug task and run a laptop repro of the jax_gpu sparse fixed-light mass_total[1] divergence (both seeds) before any CPU or sampler wave uses the sparse fixed-light problem. (3) Rerun the cancelled sparse legs under the flat layout: jax_cpu sparse seed 1 from source_pix[2], jax_cpu sparse lightfixed mass_total[1] (both seeds), numba_cpu sparse lightfixed mass_total[1] (both seeds). (4) GPU wave 1 attempt 2 (nautilus+emcee, --array=0-11%4). The RAL checkout is now at c3ca4de, which contains the #34 fix, and the attempt-1 arms are archived. (5) Commit the clean-slate rows through an autolens_inference PR. numba_cpu dense stays dropped (decisions/hst-cleanslate-drop-numba-dense.md).
Point-source sampler admission2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement.Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized.
Graphical and EP inference coordinationMind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind.Reconcile current Cortex observations and shipped fixes, then select one bounded phase.
Analytic Gaussian ensemble and control calibrationCortex records accepted wave-1 baseline and 30 September independent-review caveats.Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there.
N=50 NUTS versus three EP repeatsCortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission.Present the existing wave-2 proposal with current dependency evidence for decision.
Hierarchical slope recovery at scaleCortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix.Verify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun.
IC50 EP and graphical scale ladderCortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat.Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics.
PyAutoFit search benchmark on gaussian_x3 (autofit_inference)Wave 2 closed (B5b-2, 2026-10-15, autofit_inference#12) — RAL 409930 pulled, 12,750 rows admitted at the pin df6d218f10cd with 0 missing; NSS 50-step leads both evidence rankings; the catalogue ranks only legs with success rate >= 50 % over >= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards).B5c-2b (draft/research/autofit_inference/search_ext_b5c2b_scored_wave_submit.md): realisations 2-3 and their exact references, the correlated_nd@2 scored-wave manifest (83 cells x 50 seeds x 3 realisations, 12 450 runs) and its probe for 8 probe-first cells, then the submit on RAL --partition=ral from a private pinned copy with the A2 memory requests; correlated_nd@2 is frozen (autofit_inference 44c5e77f1fb0); the family is catalogued apart from gaussian_x3 and ranks nothing across families.

Active tasks

TaskCampaignStatusPriorityNext steps
Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmarkslam-hst-baseblockednormalVerify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness. Carried into hst-imaging-mass-samplers phase 1 as a prior-identity question for the frozen model.
EP campaign — phase map for the 2026 Q3 graphical/EP pushgraphical-epneeds-decisionhighReconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled.
Expectation Propagation Scale-Up — Scopinggraphical-epneeds-slicinghighReconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase.
Graphical Model Scale-Up — Scopinggraphical-epneeds-slicinghighReconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness.
NUTS/HMC trial on validated likelihoodspoint-source-admissionneeds-decisionnormalAn unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets.
gaussian_x3 search benchmark — wave 1 (the pilot)gaussian-x3-search-benchmarkblockednormalPilot stopped at 223 of 520 runs and is not restarted; freeze gaussian_x3@2 from its 17-row calibration before any scored run; the deferred cells move to scored wave 2 (after A2 and A4).
gaussian_x3 search benchmark — wave 2 (scored)gaussian-x3-search-benchmarkrunninghighWave 2 is closed and judged (B5b-2); the fit snapshot is captured from main under inference-summary@3 after the B5b-2 PRs merge.
HST imaging mass stage, clean slate: Nautilus base on A100 and jax_cpu at release 2026.10.17.1, linear and fixed lens lighthst-imaging-mass-samplersneeds-decisionhighA100 legs complete; jax_gpu sparse fixed-light mass_total[1] diverged (Cortex observation, human reading pending). 412539 (jax_cpu dense) COMPLETED 2026-10-18, elapsed 1-05:59:53 / 1-02:57:03 (sacct 2026-10-19 ~17:25Z, Cortex PR #73 e3ab356); its rows were not re-read in the 2026-10-19 record. 412541 (jax_cpu sparse) and 412565 (numba_cpu sparse) were CANCELLED 2026-10-19 ~15:51Z on the human's decision. Completed at cancel: jax_cpu sparse seed 0 slam_base; jax_cpu sparse seed 1 source_lp[1] and source_pix[1]; numba sparse slam_base on both seeds. Every lightfixed mass_total[1] is incomplete. The partial stage dirs stay under output/autolens/imaging/hst/slam/<variant>/<config>/seed_<n>/. A rerun of the remainder under the flat layout needs human authorisation. numba_cpu dense dropped (2026-10-18 decision).
HST imaging mass stage: sampler comparison, wave 1hst-imaging-mass-samplersneeds-decisionnormalFix merged as autolens_inference#34; the RAL checkout was fast-forwarded to c3ca4de (includes #34 and the #36 flat layout) on 2026-10-19, so the pull blocker is cleared. Attempt 2 (nautilus+emcee, --array=0-11%4, NUTS/SMC deferred) is NOT submitted and needs human authorisation. It reads output/autolens/prepared/<config>/seed_0/<variant>/manifest.json. Attempt-1 material (output/experiments 341M, output/experiment_sources 76M, the 8 failed NUTS/SMC rows from results/searches/experiments) was moved aside 2026-10-19 on the human's decision to /mnt/ral/jnightin/inference/aside/autolens_inference_cleanslate_wave1_attempt1/ (RAL) and /mnt/c/Users/Jammy/Science/inference/aside/autolens_inference_cleanslate_wave1_attempt1/ (laptop), so attempt 2 starts clean.

Ledger

Inference Results

PyAutoFit

Integrity: ok · Freshness: freshness policy unspecified · evidence 2026-10-15T16:38:44Z · Scientific qualification: 5129/12973 accepted by producer; remaining assessments shown below

Project dashboard · Ingest receipt

Record shards

inference-summary@3: the records are held in one file per protocol and leg, each verified against the index's sha256 at the captured commit. Counts are the producer's assertions, shown without ranking; each shard file holds every record's diagnostics, timings and provenance.

ProtocolShardRecordsExecutionConvergenceAcceptanceEvidence
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_jax_cpu_fp64_cache_warm10complete 2, failed 8not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_numpy_fp6410failed 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm2failed 2not_converged 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm1failed 1not_converged 1not_assessed 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm2complete 2not_assessed 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_numpy_fp642complete 2not_assessed 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm2complete 2not_assessed 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_numpy_fp642complete 2not_assessed 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm2complete 1, failed 1not_converged 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_numpy_fp642complete 2not_converged 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_jax_cpu_fp64_cache_warm10complete 1, failed 9not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_numpy_fp6410complete 2, failed 8not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10not_assessed 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm2complete 2converged 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_numpy_fp642complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm2complete 2converged 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_numpy_fp642complete 1, failed 1converged 1, not_converged 1accepted 1, rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm2complete 2converged 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_numpy_fp642complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/smc/default/local_jax_cpu_fp64_cache_warm2complete 2not_assessed 2not_assessed 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm1failed 1not_converged 1not_assessed 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_numpy_fp642complete 1, failed 1not_converged 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_numpy_fp6410complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm2complete 2not_converged 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm1complete 1not_converged 1accepted 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm2complete 2not_assessed 2accepted 1, rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_numpy_fp642complete 2not_assessed 2accepted 1, rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm2complete 2not_assessed 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_numpy_fp642complete 2not_assessed 2accepted 1, rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm2complete 2not_converged 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_numpy_fp642complete 2not_converged 2rejected 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_numpy_fp6410complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10rejected 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm10complete 10not_assessed 10accepted 10records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm2complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_numpy_fp642complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm2complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_numpy_fp642complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm2complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_numpy_fp642complete 2converged 2accepted 2records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/smc/default/local_jax_cpu_fp64_cache_warm1complete 1not_assessed 1rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm1failed 1not_converged 1rejected 1records
gaussian_x3@1gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_numpy_fp642complete 2not_converged 2accepted 2records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_jax_cpu_fp64_cache_warm250failed 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_numpy_fp64250failed 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm250failed 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 59, rejected 191records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_numpy_fp64250complete 250not_assessed 250accepted 59, rejected 191records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 36, rejected 214records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_numpy_fp64250complete 250not_assessed 250accepted 36, rejected 214records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_numpy_fp64250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_jax_cpu_fp64_cache_warm250complete 40, failed 210not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_numpy_fp64250complete 40, failed 210not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 152, rejected 98records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 158, rejected 92records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/smc/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_numpy_fp64250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_numpy_fp64250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm250complete 250converged 21, not_converged 229accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 145, rejected 105records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_numpy_fp64250complete 250not_assessed 250accepted 145, rejected 105records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 103, rejected 147records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_numpy_fp64250complete 250not_assessed 250accepted 103, rejected 147records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_numpy_fp64250complete 250not_converged 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_numpy_fp64250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_numpy_fp64250complete 250converged 250accepted 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250accepted 210, rejected 40records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/smc/default/ral_jax_cpu_fp64_cache_warm250complete 250not_assessed 250rejected 250records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm250complete 250not_converged 250accepted 190, rejected 60records
gaussian_x3@2gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_numpy_fp64250complete 250not_converged 250accepted 198, rejected 52records

Comparisons and parity groups

Targets, seeds and stages are retained. No best-seed selection, cross-target ranking or automatic convergence threshold. Declared groups do not themselves certify parity.

No certified comparison pairs supplied by this producer.

Decision History

Sampler candidates

Literature expectations, not measured PyAuto recommendations. Review a candidate and copy its investigation prompt.

dynesty

dynamic nested sampling · reviewed 2026-10-08

Paper ↗ · Official code ↗

Expected uses

  • Evidence and posterior baseline with adjustable effort allocation.

Limitations

  • Prior transforms and likelihood-contour exploration need validation; no universal dimensional scaling advantage.

Integration: integrated: DynestyStatic and DynestyDynamic

Benchmark evidence: not_assessed: this literature review establishes no benchmark result

Investigation prompt
Nautilus

neural-network importance nested sampling · reviewed 2026-10-08

Paper ↗ · Official code ↗

Expected uses

  • Evidence and posterior estimation for expensive gradient-free likelihoods.

Limitations

  • Learned proposal coverage and importance-weight stability need checking on unseen modes.

Integration: integrated: Nautilus

Benchmark evidence: not_assessed: this literature review establishes no benchmark result

Investigation prompt