diff --git a/campaigns.yaml b/campaigns.yaml
index f6a75d2..6fe8ecc 100644
--- a/campaigns.yaml
+++ b/campaigns.yaml
@@ -92,9 +92,11 @@ campaigns:
 - id: gaussian-x3-search-benchmark
   title: PyAutoFit search benchmark on gaussian_x3 (autofit_inference)
   status: running
-  updated: '2026-10-15'
-  next: After the B5b-2 PRs merge, capture the fit snapshot from main under inference-summary@3, run
-    pyauto-insight check online and drop the registry's migrating_from; then B5c (a second model family).
+  updated: '2026-10-16'
+  next: 'B5c-2: RAL 410866 (the correlated_nd pilot, 930 runs, from the private copy inference/autofit_inference_b5c)
+    finished 2026-10-16 with 926/930 completed and 4 OUT_OF_MEMORY at 8 G; pull results/ only, settle the 4 OOM
+    cells (more memory or excluded), judge under correlated_nd@1, freeze correlated_nd@2 from the pilot and submit its scored wave; the family is
+    catalogued apart from gaussian_x3 and ranks nothing across families.'
   recent_progress: 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
@@ -175,6 +177,17 @@ tasks:
   next: Pilot 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).
   evidence: https://github.com/PyAutoLabs/autofit_inference/issues/4
+- id: correlated_nd_pilot
+  campaign: gaussian-x3-search-benchmark
+  title: correlated_nd search benchmark — the pilot (wave 1, second model family)
+  status: running
+  priority: normal
+  path: tasks/correlated_nd_pilot.md
+  updated: '2026-10-16'
+  next: RAL 410866, the 930-run correlated_nd@1 pilot (protocol first committed 9ec37795f404, A1 before any
+    row), finished 2026-10-16 (926/930 completed, 4 OUT_OF_MEMORY at 8 G, results unpulled); B5c-2 pulls it,
+    settles the 4 OOM cells, judges it and freezes correlated_nd@2.
+  evidence: https://github.com/PyAutoLabs/autofit_inference/issues/14
 - id: gaussian_x3_search_wave2
   campaign: gaussian-x3-search-benchmark
   title: gaussian_x3 search benchmark — wave 2 (scored)
diff --git a/dashboard.html b/dashboard.html
index dabeac0..22dffd3 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</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-16T08:34:15Z"><summary><span data-freshness-label>Last updated 2026-10-16 08:34:15 UTC</span></summary><time datetime="2026-10-16T08:34:15Z">2026-10-16 08:34:15 UTC</time></details><a data-refresh-link href="https://github.com/PyAutoLabs/PyAutoInsight/actions/workflows/dashboard_refresh.yml" title="Open the dashboard refresh controls">↻ Update</a></div></div><p class="orchestration-status" role="status" aria-live="polite"></p></section>
-<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Inference Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a><a class="board-nav-card" href="#sampler-candidates"><span class="board-nav-label">Sampler candidates</span></a></nav><main><p id="setup-route-status" role="status"></p><div id="inference-home"><!-- insight-inputs:ca0a1a5ea41ed220089ce9b6eb86ed7ffa7f918318e1790038d07fb2f246b2e6 --><!-- insight-campaigns:bc4e241a4b624a5cbaceb3a77abc2c84cf5bb57a506760acc1f2fb98acc21210 --><details class="board-section"><summary><h2 id="campaigns">Active campaigns</h2></summary><div class="board-section-body"><div class="tablewrap"><table class="campaign-table"><thead><tr><th>Campaign</th><th>Recent progress</th><th>Next steps</th></tr></thead><tbody><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/autolens_inference.md">HST SLaM base backend parity</a></td><td>Project and Cortex ledgers retain the 24 September rerun submissions; published rows require coverage reconciliation.</td><td>Reconcile all seeds and stages with current project rows and Cortex before proposing another run.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md">HST SLaM Delaunay variant</a></td><td>Delaunay is a separate target/recipe; pre-speedup runs are archived, reruns recorded on 24 September.</td><td>Retrieve or account for every rerun, failed seed and missing stage; preserve the archived comparison context.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md">Point-source sampler admission</a></td><td>2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement.</td><td>Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md">Graphical and EP inference coordination</a></td><td>Mind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind.</td><td>Reconcile current Cortex observations and shipped fixes, then select one bounded phase.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/analytic_gaussian.md">Analytic Gaussian ensemble and control calibration</a></td><td>Cortex records accepted wave-1 baseline and 30 September independent-review caveats.</td><td>Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md">N=50 NUTS versus three EP repeats</a></td><td>Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission.</td><td>Present the existing wave-2 proposal with current dependency evidence for decision.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md">Hierarchical slope recovery at scale</a></td><td>Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix.</td><td>Verify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md">IC50 EP and graphical scale ladder</a></td><td>Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat.</td><td>Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autofit_inference/issues/12">PyAutoFit search benchmark on gaussian_x3 (autofit_inference)</a></td><td>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 &gt;= 50 % over &gt;= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards).</td><td>After the B5b-2 PRs merge, capture the fit snapshot from main under inference-summary@3, run pyauto-insight check online and drop the registry&#x27;s migrating_from; then B5c (a second model family).</td></tr></tbody></table></div></div></details><details class="board-section"><summary><h2 id="tasks">Active tasks</h2></summary><div class="board-section-body"><div class="tablewrap"><table class="task-table"><thead><tr><th>Task</th><th>Campaign</th><th>Status</th><th>Priority</th><th>Next steps</th></tr></thead><tbody><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/drop_1e3_prior_centring_after_631.md">Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark</a></td><td>slam-hst-base</td><td>blocked</td><td>normal</td><td>Verify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_campaign.md">EP campaign — phase map for the 2026 Q3 graphical/EP push</a></td><td>graphical-ep</td><td>needs-decision</td><td>high</td><td>Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_scoping.md">Expectation Propagation Scale-Up — Scoping</a></td><td>graphical-ep</td><td>needs-slicing</td><td>high</td><td>Reconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/graphical_scoping.md">Graphical Model Scale-Up — Scoping</a></td><td>graphical-ep</td><td>needs-slicing</td><td>high</td><td>Reconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gradient_sampler_validated_likelihoods.md">NUTS/HMC trial on validated likelihoods</a></td><td>point-source-admission</td><td>needs-decision</td><td>normal</td><td>An unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave1.md">gaussian_x3 search benchmark — wave 1 (the pilot)</a></td><td>gaussian-x3-search-benchmark</td><td>blocked</td><td>normal</td><td>Pilot 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).</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave2.md">gaussian_x3 search benchmark — wave 2 (scored)</a></td><td>gaussian-x3-search-benchmark</td><td>running</td><td>high</td><td>Wave 2 is closed and judged (B5b-2); the fit snapshot is captured from main under inference-summary@3 after the B5b-2 PRs merge.</td></tr></tbody></table></div><p><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/campaigns.yaml">Ledger</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Inference Results</h2></summary><div class="board-section-body"><!-- insight:instance name=lens receipt=df5fd2c03cce7c97f93d6283de904acc4e546e5f outcome=ok shown=df5fd2c03cce7c97f93d6283de904acc4e546e5f --><div class="setup-navigation"><details><summary>PyAutoLens</summary><details><summary>Imaging</summary><div class="setup-choices"><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Fdelaunay%2Fhst&amp;implementation=jax" target="_blank" rel="noopener">Delaunay (JAX)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Fdelaunay%2Fhst&amp;implementation=numba" target="_blank" rel="noopener">Delaunay (Numba)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Frectangular%2Fhst&amp;implementation=jax" target="_blank" rel="noopener">Rectangular (JAX)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Frectangular%2Fhst&amp;implementation=numba" target="_blank" rel="noopener">Rectangular (Numba)<span class="sr-only"> (opens in a new tab)</span></a></div></details><details><summary>Point Source</summary><div class="setup-choices"><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=point_source%2Fsimple%2Fsimple&amp;implementation=jax" target="_blank" rel="noopener">Simple (JAX)<span class="sr-only"> (opens in a new tab)</span></a></div></details></details></div><!-- insight:instance name=fit receipt=0587a44784c0b3ce34f63b9f4d985269b491e53f outcome=ok shown=0587a44784c0b3ce34f63b9f4d985269b491e53f --><div class="setup-navigation"><details id="fit"><summary>PyAutoFit</summary><p><b>Integrity:</b> ok · <b>Freshness:</b> freshness policy unspecified · evidence 2026-10-15T16:38:44Z · <b>Scientific qualification:</b> 5129/12973 accepted by producer; remaining assessments shown below</p><p><a href="https://github.com/PyAutoLabs/autofit_inference#readme">Project dashboard</a> · <a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/receipts/fit.json">Ingest receipt</a></p><h3>Record shards</h3><p>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.</p><div class="tablewrap"><table><thead><tr><th>Protocol</th><th>Shard</th><th>Records</th><th>Execution</th><th>Convergence</th><th>Acceptance</th><th>Evidence</th></tr></thead><tbody><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 2, failed 8</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/bfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_numpy_fp64</td><td>10</td><td>failed 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/bfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>failed 2</td><td>not_converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>not_assessed 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_dynamic/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_static/nlive_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 1, failed 1</td><td>not_converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 1, failed 9</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/lbfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_numpy_fp64</td><td>10</td><td>complete 2, failed 8</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/lbfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_100/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_numpy_fp64</td><td>2</td><td>complete 1, failed 1</td><td>converged 1, not_converged 1</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_400/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/smc/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/smc/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>not_assessed 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 1, failed 1</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/bfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_numpy_fp64</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/bfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>complete 1</td><td>not_converged 1</td><td>accepted 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_dynamic/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_static/nlive_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/lbfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_numpy_fp64</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/lbfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>accepted 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_100/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_400/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/smc/default/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>complete 1</td><td>not_assessed 1</td><td>rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/smc/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>failed 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/bfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_numpy_fp64</td><td>250</td><td>failed 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/bfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>failed 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 59, rejected 191</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 59, rejected 191</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_dynamic/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 36, rejected 214</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 36, rejected 214</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_static/nlive_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 40, failed 210</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/lbfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 40, failed 210</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/lbfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 152, rejected 98</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_100/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_400/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 158, rejected 92</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/smc/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/smc/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/bfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/bfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 21, not_converged 229</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 145, rejected 105</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 145, rejected 105</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_dynamic/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 103, rejected 147</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 103, rejected 147</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_static/nlive_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/lbfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/lbfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_100/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_400/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 210, rejected 40</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/smc/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/smc/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>accepted 190, rejected 60</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>accepted 198, rejected 52</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr></tbody></table></div><h3>Comparisons and parity groups</h3><p>Targets, seeds and stages are retained. No best-seed selection, cross-target ranking or automatic convergence threshold. Declared groups do not themselves certify parity.</p><p>No certified comparison pairs supplied by this producer.</p></details></div><!-- decision-history:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><p>No decisions recorded yet.</p></section></div></details><details class="board-section"><summary><h2 id="sampler-candidates">Sampler candidates</h2></summary><div class="board-section-body"><p>Literature expectations, not measured PyAuto recommendations. Review a candidate and copy its investigation prompt.</p><details><summary>dynesty</summary><p>dynamic nested sampling · reviewed 2026-10-08</p><p><a href="https://arxiv.org/abs/1904.02180" target="_blank" rel="noopener">Paper ↗</a> · <a href="https://github.com/joshspeagle/dynesty" target="_blank" rel="noopener">Official code ↗</a></p><h3>Expected uses</h3><ul><li>Evidence and posterior baseline with adjustable effort allocation.</li></ul><h3>Limitations</h3><ul><li>Prior transforms and likelihood-contour exploration need validation; no universal dimensional scaling advantage.</li></ul><p>Integration: integrated: DynestyStatic and DynestyDynamic</p><p>Benchmark evidence: not_assessed: this literature review establishes no benchmark result</p><div data-candidate><button type="button" data-candidate-copy aria-label="Copy investigation prompt">⧉ Copy investigation prompt</button><span role="status" aria-live="polite"></span><details><summary>Investigation prompt</summary><label for="candidate-0">Editable prompt</label><textarea class="candidate-prompt" id="candidate-0">Investigate dynesty on a normalized Gaussian and a separated-mode target using repeated seeds. Check posterior coverage and evidence accuracy. Record package version, initialization, total wall time including setup, evaluations and uncertainty before proposing a science campaign.
+<nav class="board-nav" aria-label="Board sections"><a class="board-nav-card" href="#campaigns"><span class="board-nav-label">Active campaigns</span></a><a class="board-nav-card" href="#evidence"><span class="board-nav-label">Inference Results</span></a><a class="board-nav-card" href="#decision-history"><span class="board-nav-label">Decision History</span></a><a class="board-nav-card" href="#sampler-candidates"><span class="board-nav-label">Sampler candidates</span></a></nav><main><p id="setup-route-status" role="status"></p><div id="inference-home"><!-- insight-inputs:2a804822c123f7976eed584cc44bd8b96da5f21a9e9b7c8199758fc3172675de --><!-- insight-campaigns:c09fff24924c3c8bc5e2bfb0e9e37109d0202153cc34c91f3b9ee6281893322a --><details class="board-section"><summary><h2 id="campaigns">Active campaigns</h2></summary><div class="board-section-body"><div class="tablewrap"><table class="campaign-table"><thead><tr><th>Campaign</th><th>Recent progress</th><th>Next steps</th></tr></thead><tbody><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/autolens_inference.md">HST SLaM base backend parity</a></td><td>Project and Cortex ledgers retain the 24 September rerun submissions; published rows require coverage reconciliation.</td><td>Reconcile all seeds and stages with current project rows and Cortex before proposing another run.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md">HST SLaM Delaunay variant</a></td><td>Delaunay is a separate target/recipe; pre-speedup runs are archived, reruns recorded on 24 September.</td><td>Retrieve or account for every rerun, failed seed and missing stage; preserve the archived comparison context.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autolens_inference/blob/146a84346eaa51eda158634cae520d677f3e6213/wiki/project/state.md">Point-source sampler admission</a></td><td>2 October project record reports five Nautilus seeds; no gradient-sampler admission measurement.</td><td>Assess a bounded gradient-sampler admission proposal with the human; no new job is authorized.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoMind/blob/8dd1edc7e04c960daae3663513c61395823eddb0/draft/research/graphical_ep/ep_campaign.md">Graphical and EP inference coordination</a></td><td>Mind campaign map and two scoping contracts preserved verbatim; implementation work remains in Mind.</td><td>Reconcile current Cortex observations and shipped fixes, then select one bounded phase.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/analytic_gaussian.md">Analytic Gaussian ensemble and control calibration</a></td><td>Cortex records accepted wave-1 baseline and 30 September independent-review caveats.</td><td>Use Cortex planned diagnostic on passing, edge and worst seeds; retain the human conclusion there.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md">N=50 NUTS versus three EP repeats</a></td><td>Cortex records failed wave 1 (EMFILE/OOM), wave-2 preparation and no submission.</td><td>Present the existing wave-2 proposal with current dependency evidence for decision.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md">Hierarchical slope recovery at scale</a></td><td>Cortex retains graphical baseline, post-processing OOM and stale EP hierarchical factor; Mind records later moments fix.</td><td>Verify the moments fix release and project mirror; recover missing summaries from existing samples before a proposed rerun.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md">IC50 EP and graphical scale ladder</a></td><td>Cortex records N=5/10/25 results, failed N=50 EP rung and width-interpretation caveat.</td><td>Check implementation close-outs, then propose a seeded ladder with appropriate output and mean-field diagnostics.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/autofit_inference/issues/12">PyAutoFit search benchmark on gaussian_x3 (autofit_inference)</a></td><td>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 &gt;= 50 % over &gt;= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards).</td><td>B5c-2: RAL 410866 (the correlated_nd pilot, 930 runs, from the private copy inference/autofit_inference_b5c) finished 2026-10-16 with 926/930 completed and 4 OUT_OF_MEMORY at 8 G; pull results/ only, settle the 4 OOM cells (more memory or excluded), judge under correlated_nd@1, freeze correlated_nd@2 from the pilot and submit its scored wave; the family is catalogued apart from gaussian_x3 and ranks nothing across families.</td></tr></tbody></table></div></div></details><details class="board-section"><summary><h2 id="tasks">Active tasks</h2></summary><div class="board-section-body"><div class="tablewrap"><table class="task-table"><thead><tr><th>Task</th><th>Campaign</th><th>Status</th><th>Priority</th><th>Next steps</th></tr></thead><tbody><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/drop_1e3_prior_centring_after_631.md">Drop the 1e-3 prior centring for shear / multipole / ell_comps in the benchmark</a></td><td>slam-hst-base</td><td>blocked</td><td>normal</td><td>Verify the release containing Galaxy#634 and Lens#754 before changing priors; retain zero-centre gradient witness.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_campaign.md">EP campaign — phase map for the 2026 Q3 graphical/EP push</a></td><td>graphical-ep</td><td>needs-decision</td><td>high</td><td>Reconcile current Cortex records and human gates; this campaign map is never issued or bulk scheduled.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/ep_scoping.md">Expectation Propagation Scale-Up — Scoping</a></td><td>graphical-ep</td><td>needs-slicing</td><td>high</td><td>Reconcile completed wrapper work and Pulse profiling ownership before selecting a bounded remaining inference phase.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/graphical_scoping.md">Graphical Model Scale-Up — Scoping</a></td><td>graphical-ep</td><td>needs-slicing</td><td>high</td><td>Reconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gradient_sampler_validated_likelihoods.md">NUTS/HMC trial on validated likelihoods</a></td><td>point-source-admission</td><td>needs-decision</td><td>normal</td><td>An unscoped idea, not permission to submit: reconcile point-source admission with imaging and weak-lensing candidate targets.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave1.md">gaussian_x3 search benchmark — wave 1 (the pilot)</a></td><td>gaussian-x3-search-benchmark</td><td>blocked</td><td>normal</td><td>Pilot 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).</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/correlated_nd_pilot.md">correlated_nd search benchmark — the pilot (wave 1, second model family)</a></td><td>gaussian-x3-search-benchmark</td><td>running</td><td>normal</td><td>RAL 410866, the 930-run correlated_nd@1 pilot (protocol first committed 9ec37795f404, A1 before any row), finished 2026-10-16 (926/930 completed, 4 OUT_OF_MEMORY at 8 G, results unpulled); B5c-2 pulls it, settles the 4 OOM cells, judges it and freezes correlated_nd@2.</td></tr><tr><td><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave2.md">gaussian_x3 search benchmark — wave 2 (scored)</a></td><td>gaussian-x3-search-benchmark</td><td>running</td><td>high</td><td>Wave 2 is closed and judged (B5b-2); the fit snapshot is captured from main under inference-summary@3 after the B5b-2 PRs merge.</td></tr></tbody></table></div><p><a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/campaigns.yaml">Ledger</a></p></div></details><details class="board-section"><summary><h2 id="evidence">Inference Results</h2></summary><div class="board-section-body"><!-- insight:instance name=lens receipt=df5fd2c03cce7c97f93d6283de904acc4e546e5f outcome=ok shown=df5fd2c03cce7c97f93d6283de904acc4e546e5f --><div class="setup-navigation"><details><summary>PyAutoLens</summary><details><summary>Imaging</summary><div class="setup-choices"><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Fdelaunay%2Fhst&amp;implementation=jax" target="_blank" rel="noopener">Delaunay (JAX)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Fdelaunay%2Fhst&amp;implementation=numba" target="_blank" rel="noopener">Delaunay (Numba)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Frectangular%2Fhst&amp;implementation=jax" target="_blank" rel="noopener">Rectangular (JAX)<span class="sr-only"> (opens in a new tab)</span></a><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=imaging%2Frectangular%2Fhst&amp;implementation=numba" target="_blank" rel="noopener">Rectangular (Numba)<span class="sr-only"> (opens in a new tab)</span></a></div></details><details><summary>Point Source</summary><div class="setup-choices"><a class="model-choice setup-choice" href="?view=setup&amp;instance=lens&amp;setup=point_source%2Fsimple%2Fsimple&amp;implementation=jax" target="_blank" rel="noopener">Simple (JAX)<span class="sr-only"> (opens in a new tab)</span></a></div></details></details></div><!-- insight:instance name=fit receipt=0587a44784c0b3ce34f63b9f4d985269b491e53f outcome=ok shown=0587a44784c0b3ce34f63b9f4d985269b491e53f --><div class="setup-navigation"><details id="fit"><summary>PyAutoFit</summary><p><b>Integrity:</b> ok · <b>Freshness:</b> freshness policy unspecified · evidence 2026-10-15T16:38:44Z · <b>Scientific qualification:</b> 5129/12973 accepted by producer; remaining assessments shown below</p><p><a href="https://github.com/PyAutoLabs/autofit_inference#readme">Project dashboard</a> · <a href="https://github.com/PyAutoLabs/PyAutoInsight/blob/main/receipts/fit.json">Ingest receipt</a></p><h3>Record shards</h3><p>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.</p><div class="tablewrap"><table><thead><tr><th>Protocol</th><th>Shard</th><th>Records</th><th>Execution</th><th>Convergence</th><th>Acceptance</th><th>Evidence</th></tr></thead><tbody><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 2, failed 8</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/bfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/bfgs/default/local_numpy_fp64</td><td>10</td><td>failed 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/bfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>failed 2</td><td>not_converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>not_assessed 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_dynamic/default/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_dynamic/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/dynesty_static/nlive_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/dynesty_static/nlive_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 1, failed 1</td><td>not_converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/emcee/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 1, failed 9</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/lbfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/lbfgs/default/local_numpy_fp64</td><td>10</td><td>complete 2, failed 8</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/lbfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>not_assessed 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_100/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_100/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_200/local_numpy_fp64</td><td>2</td><td>complete 1, failed 1</td><td>converged 1, not_converged 1</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/nautilus/n_live_400/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/nautilus/n_live_400/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/smc/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>not_assessed 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/smc/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>not_assessed 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 1, failed 1</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_blend/zeus/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/bfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/bfgs/default/local_numpy_fp64</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/bfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>complete 1</td><td>not_converged 1</td><td>accepted 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_dynamic/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_dynamic/default/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_dynamic/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_static/nlive_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/dynesty_static/nlive_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_assessed 2</td><td>accepted 1, rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/dynesty_static/nlive_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>rejected 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/emcee/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/lbfgs/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/lbfgs/default/local_numpy_fp64</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/lbfgs/default/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>rejected 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm</td><td>10</td><td>complete 10</td><td>not_assessed 10</td><td>accepted 10</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_100/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_100/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_100/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_200/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_200/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_200/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_400/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/nautilus/n_live_400/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/nautilus/n_live_400/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/smc/default/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>complete 1</td><td>not_assessed 1</td><td>rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/smc/default/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm</td><td>1</td><td>failed 1</td><td>not_converged 1</td><td>rejected 1</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@1</td><td>gaussian_x3@1/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_numpy_fp64</td><td>2</td><td>complete 2</td><td>not_converged 2</td><td>accepted 2</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%401/gaussian_x3_separated/zeus/walkers_50_steps_2000/local_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>failed 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/bfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/bfgs/default/ral_numpy_fp64</td><td>250</td><td>failed 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/bfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>failed 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 59, rejected 191</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_dynamic/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 59, rejected 191</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_dynamic/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 36, rejected 214</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/dynesty_static/nlive_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 36, rejected 214</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/dynesty_static/nlive_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/emcee/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 40, failed 210</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/lbfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/lbfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 40, failed 210</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/lbfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 152, rejected 98</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_100/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_100/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nautilus/n_live_400/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nautilus/n_live_400/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 158, rejected 92</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/smc/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/smc/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_blend/zeus/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/bfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/bfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/bfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/blackjax_nuts/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 21, not_converged 229</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/blackjax_nuts_warm/warmup_200_samples_500/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 145, rejected 105</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_dynamic/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_dynamic/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 145, rejected 105</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_dynamic/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 103, rejected 147</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_static/nlive_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/dynesty_static/nlive_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 103, rejected 147</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/dynesty_static/nlive_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/emcee/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/lbfgs/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/lbfgs/default/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/lbfgs/default/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_adabelief/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_adam/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_lion/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/multi_start_prodigy/starts_48_steps_300/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_100/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_100/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_100/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_200/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_200/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_200/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_400/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nautilus/n_live_400/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>converged 250</td><td>accepted 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nautilus/n_live_400/ral_numpy_fp64.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>accepted 210, rejected 40</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/nss/n_live_200_mcmc_steps_50/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/smc/default/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_assessed 250</td><td>rejected 250</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/smc/default/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>accepted 190, rejected 60</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_jax_cpu_fp64_cache_warm.json">records</a></td></tr><tr><td>gaussian_x3@2</td><td>gaussian_x3@2/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_numpy_fp64</td><td>250</td><td>complete 250</td><td>not_converged 250</td><td>accepted 198, rejected 52</td><td><a href="https://github.com/PyAutoLabs/autofit_inference/blob/0587a44784c0b3ce34f63b9f4d985269b491e53f/dashboard/records/gaussian_x3%402/gaussian_x3_separated/zeus/walkers_50_steps_2000/ral_numpy_fp64.json">records</a></td></tr></tbody></table></div><h3>Comparisons and parity groups</h3><p>Targets, seeds and stages are retained. No best-seed selection, cross-target ranking or automatic convergence threshold. Declared groups do not themselves certify parity.</p><p>No certified comparison pairs supplied by this producer.</p></details></div><!-- decision-history:4f53cda18c2baa0c0354bb5f9a3ecbe5ed12ab4d8e11ba873c2f11161202b945 --></div></details><details class="board-section"><summary><h2>Decision History</h2></summary><div class="board-section-body"><section id="decision-history"><p>No decisions recorded yet.</p></section></div></details><details class="board-section"><summary><h2 id="sampler-candidates">Sampler candidates</h2></summary><div class="board-section-body"><p>Literature expectations, not measured PyAuto recommendations. Review a candidate and copy its investigation prompt.</p><details><summary>dynesty</summary><p>dynamic nested sampling · reviewed 2026-10-08</p><p><a href="https://arxiv.org/abs/1904.02180" target="_blank" rel="noopener">Paper ↗</a> · <a href="https://github.com/joshspeagle/dynesty" target="_blank" rel="noopener">Official code ↗</a></p><h3>Expected uses</h3><ul><li>Evidence and posterior baseline with adjustable effort allocation.</li></ul><h3>Limitations</h3><ul><li>Prior transforms and likelihood-contour exploration need validation; no universal dimensional scaling advantage.</li></ul><p>Integration: integrated: DynestyStatic and DynestyDynamic</p><p>Benchmark evidence: not_assessed: this literature review establishes no benchmark result</p><div data-candidate><button type="button" data-candidate-copy aria-label="Copy investigation prompt">⧉ Copy investigation prompt</button><span role="status" aria-live="polite"></span><details><summary>Investigation prompt</summary><label for="candidate-0">Editable prompt</label><textarea class="candidate-prompt" id="candidate-0">Investigate dynesty on a normalized Gaussian and a separated-mode target using repeated seeds. Check posterior coverage and evidence accuracy. Record package version, initialization, total wall time including setup, evaluations and uncertainty before proposing a science campaign.
 
 Use the existing sampler pipeline and setup catalogue. Preserve the baseline-prepared model and priors. Distinguish cold/warm/resume starts from compilation/cache state. Propose a bounded implementation or benchmark plan before executing compute. Paper: https://arxiv.org/abs/1904.02180
 Code: https://github.com/joshspeagle/dynesty</textarea></details></div></details><details><summary>Nautilus</summary><p>neural-network importance nested sampling · reviewed 2026-10-08</p><p><a href="https://arxiv.org/abs/2306.16923" target="_blank" rel="noopener">Paper ↗</a> · <a href="https://github.com/johannesulf/nautilus" target="_blank" rel="noopener">Official code ↗</a></p><h3>Expected uses</h3><ul><li>Evidence and posterior estimation for expensive gradient-free likelihoods.</li></ul><h3>Limitations</h3><ul><li>Learned proposal coverage and importance-weight stability need checking on unseen modes.</li></ul><p>Integration: integrated: Nautilus</p><p>Benchmark evidence: not_assessed: this literature review establishes no benchmark result</p><div data-candidate><button type="button" data-candidate-copy aria-label="Copy investigation prompt">⧉ Copy investigation prompt</button><span role="status" aria-live="polite"></span><details><summary>Investigation prompt</summary><label for="candidate-1">Editable prompt</label><textarea class="candidate-prompt" id="candidate-1">Investigate Nautilus on a normalized Gaussian and a separated-mode target using repeated seeds. Check posterior coverage and evidence accuracy. Record package version, initialization, total wall time including setup, evaluations and uncertainty before proposing a science campaign.
diff --git a/dashboard.md b/dashboard.md
index fe23b7b..18a1553 100644
--- a/dashboard.md
+++ b/dashboard.md
@@ -1,6 +1,6 @@
 # PyAutoInsight — inference control room
 
-<!-- insight-inputs:ca0a1a5ea41ed220089ce9b6eb86ed7ffa7f918318e1790038d07fb2f246b2e6 -->
+<!-- insight-inputs:2a804822c123f7976eed584cc44bd8b96da5f21a9e9b7c8199758fc3172675de -->
 
 | Where | Count |
 |---|---:|
@@ -10,7 +10,7 @@
 | [Cached](#evidence) | 0 |
 | [Failed](#evidence) | 0 |
 
-<!-- insight-campaigns:bc4e241a4b624a5cbaceb3a77abc2c84cf5bb57a506760acc1f2fb98acc21210 -->
+<!-- insight-campaigns:c09fff24924c3c8bc5e2bfb0e9e37109d0202153cc34c91f3b9ee6281893322a -->
 ## Check in on all inference work
 
 Copy this into one chat. Add a campaign focus or idea before or after it, or leave it unchanged.
@@ -46,7 +46,7 @@ After taking action, report what changed, what the evidence supports and what re
 | [N=50 NUTS versus three EP repeats](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ep_toy_gaussian.md) | Cortex 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 scale](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/slope_hierarchy_scale.md) | Cortex 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 ladder](https://github.com/PyAutoLabs/PyAutoCortex/blob/d45f198804615c090e6b6694cf725ddb0be2354e/projects/ic50_workspace.md) | Cortex 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)](https://github.com/PyAutoLabs/autofit_inference/issues/12) | 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 &gt;= 50 % over &gt;= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards). | After the B5b-2 PRs merge, capture the fit snapshot from main under inference-summary@3, run pyauto-insight check online and drop the registry&#x27;s migrating_from; then B5c (a second model family). |
+| [PyAutoFit search benchmark on gaussian_x3 (autofit_inference)](https://github.com/PyAutoLabs/autofit_inference/issues/12) | 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 &gt;= 50 % over &gt;= 20 attempts (post-hoc presentation convention); fit records published as inference-summary@3 (101 record shards). | B5c-2: RAL 410866 (the correlated_nd pilot, 930 runs, from the private copy inference/autofit_inference_b5c) finished 2026-10-16 with 926/930 completed and 4 OUT_OF_MEMORY at 8 G; pull results/ only, settle the 4 OOM cells (more memory or excluded), judge under correlated_nd@1, freeze correlated_nd@2 from the pilot and submit its scored wave; the family is catalogued apart from gaussian_x3 and ranks nothing across families. |
 
 ## Active tasks
 
@@ -58,6 +58,7 @@ After taking action, report what changed, what the evidence supports and what re
 | [Graphical Model Scale-Up — Scoping](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/graphical_scoping.md) | graphical-ep | needs-slicing | high | Reconcile current Cortex scale results and library dependencies; select one bounded inference phase with a witness. |
 | [NUTS/HMC trial on validated likelihoods](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gradient_sampler_validated_likelihoods.md) | point-source-admission | needs-decision | normal | An 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)](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave1.md) | gaussian-x3-search-benchmark | blocked | normal | Pilot 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). |
+| [correlated_nd search benchmark — the pilot (wave 1, second model family)](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/correlated_nd_pilot.md) | gaussian-x3-search-benchmark | running | normal | RAL 410866, the 930-run correlated_nd@1 pilot (protocol first committed 9ec37795f404, A1 before any row), finished 2026-10-16 (926/930 completed, 4 OUT_OF_MEMORY at 8 G, results unpulled); B5c-2 pulls it, settles the 4 OOM cells, judges it and freezes correlated_nd@2. |
 | [gaussian_x3 search benchmark — wave 2 (scored)](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/tasks/gaussian_x3_search_wave2.md) | gaussian-x3-search-benchmark | running | high | Wave 2 is closed and judged (B5b-2); the fit snapshot is captured from main under inference-summary@3 after the B5b-2 PRs merge. |
 
 [Ledger](https://github.com/PyAutoLabs/PyAutoInsight/blob/main/campaigns.yaml)
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+# correlated_nd search benchmark — the pilot (wave 1)
+
+Type: research
+Target: autofit_inference
+Campaign: gaussian-x3-search-benchmark
+Epic: search-extensibility (phase B5c)
+Issue: https://github.com/PyAutoLabs/autofit_inference/issues/14
+Filed: 2026-10-15
+
+## Intent
+
+The second toy-likelihood family of the PyAutoFit search benchmark, so rankings stop
+resting on `gaussian_x3` alone. `correlated_nd` stresses scaling with dimension and
+adaptation to an ill-conditioned, correlated or curved posterior. It has correlated
+Gaussians at D = 20, 50 and 100, each with condition number 10³, and a D = 20 hybrid
+Rosenbrock banana. Their posteriors and ln Z are **exact by construction**.
+
+The rows are judged by the family's own pre-registered protocol `correlated_nd@1`
+([`wiki/project/protocol_correlated_nd.md`](https://github.com/PyAutoLabs/autofit_inference/blob/main/wiki/project/protocol_correlated_nd.md)).
+It was first committed as autofit_inference `9ec37795f404`, before any row, and amended
+by A1 before any row or submission. The catalogue groups this family by task and
+dataset, in its own `correlated_nd@1` section, separately from `gaussian_x3`. **Nothing
+is ranked across families**, and the pilot ranks nothing at all.
+
+## Menu and identity
+
+The menu is the `gaussian_x3` wave-2 search menu, unchanged. It runs 10 search seeds
+(0–9) × realisation 1 × every non-deferred cell × the four datasets.
+
+- **Planned: 930 runs.** The family expects 1040 runs in all.
+- **Deferred: 110 runs, each with its reason** (protocol §7):
+  - Emcee and Zeus at D ≥ 50: their 50 walkers are fewer than 2D, so both samplers
+    refuse to run.
+  - DynestyStatic `nlive_200` and NSS at D = 100: `n_live` is below the exact
+    information H = 267.6 nat.
+
+The runs go on RAL `--partition=ral` against the private PyAutoFit pin `df6d218f10cd`.
+Each run has a 4 h operational process cap. A capped run is a censored failure, not a
+protocol timeout.
+
+A row belongs to the pilot only when its key
+`(dataset, sampler, settings_name, config_id, data_seed, protocol_id, pyautofit_sha)` is a
+cell of the pilot, and its `model` is `correlated_nd`
+(`scripts/misc/searches/_correlated_nd.py`).
+
+## References
+
+Every dataset × backend has an exact reference, complete in `results/reference/` (8 of
+8). Each one passed the §6 agreement test:
+
+- the backend's `Fitness` equals the analytic ln L to ≤ 6e-14 nat;
+- 200 000 exact draws reproduce every median within 0.006 σ;
+- the control verdicts are accepted.
+
+## Submission (2026-10-15, B5c)
+
+- RAL job **410866_[0-929]** (`hpc/batch_cpu/submit_correlated_nd_pilot`): 930 one-run array
+  tasks, `%100`, from the private copy `/mnt/ral/jnightin/inference/autofit_inference_b5c`.
+  It was pending at submission.
+- **Finished 2026-10-16** (wall 4:16): 926 of 930 tasks COMPLETED, 4 OUT_OF_MEMORY at the
+  8 G request, all on `corr_gaussian_d50`: task 263 (`nautilus` `n_live_100`, numpy, seed
+  3), 273 (`nautilus` `n_live_100`, jax_cpu, seed 3), 304 (`nautilus` `n_live_400`, numpy,
+  seed 4) and 383 (`blackjax_nuts_warm` `warmup_200_samples_500`, jax_cpu, seed 3). By the
+  human's decision they are not rerun in stage 1; B5c-2 raises their memory or excludes
+  them, with the reason recorded. The results stay unpulled in the private copy.
+- The first submission, 409957, failed at import in every task: the shared RAL clone had
+  been switched by another session. It was cancelled and wrote no row.
+
+This task authorises nothing further. Pulling, judging and the `correlated_nd@2` freeze
+are B5c-2.
+
+## Next (B5c-2)
+
+1. Pull the pilot's rows (`results/` only) and judge them under `correlated_nd@1`; settle
+   the 4 OOM cells (more memory or excluded).
+2. Calibrate the PLACEHOLDERs and timeouts, then freeze `correlated_nd@2` before any
+   scored row.
+3. Submit the scored wave: 50 seeds × realisations 1–3 × every non-deferred cell.