autofit_inference: 409930 done (wall 1:30), wave 2 closed under gaussian_x3@2 (B5b-2)

dashboard.html

@@ -331,11 +331,11 @@
 - Open slope_hierarchy_scale: https://github.com/PyAutoLabs/slope_hierarchy_scale
 - Open ic50_workspace: https://github.com/Jammy2211/ic50_workspace
 - Open autolens_inference: https://github.com/PyAutoLabs/autolens_inference
-- Open autofit_inference: https://github.com/PyAutoLabs/autofit_inference</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-15T16:00:25Z"><summary><span data-freshness-label>Last updated 2026-10-15 16:00:25 UTC</span></summary><time datetime="2026-10-15T16:00:25Z">2026-10-15 16:00:25 UTC</time></details><a data-refresh-link href="https://github.com/PyAutoLabs/PyAutoCortex/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="#summary"><span class="board-nav-count">22</span><span class="board-nav-label">Running</span></a><a class="board-nav-card" href="#summary"><span class="board-nav-count">48</span><span class="board-nav-label">Open</span></a><a class="board-nav-card" href="#projects"><span class="board-nav-count">8</span><span class="board-nav-label">Projects</span></a><a class="board-nav-card" href="#orchestration-cortex"><span class="board-nav-label">Check in</span></a></nav>
+- Open autofit_inference: https://github.com/PyAutoLabs/autofit_inference</textarea></details><div class="orchestration-freshness" data-freshness="grey"><details data-freshness-stamp data-refreshed-at="2026-10-15T17:38:42Z"><summary><span data-freshness-label>Last updated 2026-10-15 17:38:42 UTC</span></summary><time datetime="2026-10-15T17:38:42Z">2026-10-15 17:38:42 UTC</time></details><a data-refresh-link href="https://github.com/PyAutoLabs/PyAutoCortex/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="#summary"><span class="board-nav-count">22</span><span class="board-nav-label">Running</span></a><a class="board-nav-card" href="#summary"><span class="board-nav-count">47</span><span class="board-nav-label">Open</span></a><a class="board-nav-card" href="#projects"><span class="board-nav-count">8</span><span class="board-nav-label">Projects</span></a><a class="board-nav-card" href="#orchestration-cortex"><span class="board-nav-label">Check in</span></a></nav>
 
 <p class="muted mdsrc"><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/dashboard.md" class="board-source-link" title="Markdown version" aria-label="Markdown version"><svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.6" stroke-linecap="round" stroke-linejoin="round" aria-hidden="true" focusable="false"><path d="M14 3H6a1 1 0 0 0-1 1v16a1 1 0 0 0 1 1h12a1 1 0 0 0 1-1V8Z"/><path d="M14 3v5h5M8 12h8M8 16h6"/></svg></a></p>
-<details class="board-section"><summary><h2>Summary</h2><span class="section-badge">22 Running · 48 Open</span></summary><div class="board-section-body"><a id="summary"></a>
+<details class="board-section"><summary><h2>Summary</h2><span class="section-badge">22 Running · 47 Open</span></summary><div class="board-section-body"><a id="summary"></a>
 <table class="map">
 <tr><th>Project</th><th>Running</th><th>Open</th><th>Last update</th></tr>
 <tr><td><b>subhalo_validation</b></td><td>0</td><td>5</td><td>2026-09-07</td></tr>
@@ -345,7 +345,7 @@
 <tr><td><b>slope_hierarchy_scale</b></td><td>0</td><td>0</td><td>2026-10-14</td></tr>
 <tr><td><b>ic50_workspace</b></td><td>0</td><td>0</td><td>2026-10-14</td></tr>
 <tr><td><b>autolens_inference</b></td><td>6</td><td>0</td><td>2026-09-24</td></tr>
-<tr><td><b>autofit_inference</b></td><td>0</td><td>1</td><td>2026-10-15</td></tr>
+<tr><td><b>autofit_inference</b></td><td>0</td><td>0</td><td>2026-10-15</td></tr>
 </table>
 </div></details><details class="board-section"><summary><h2>Projects</h2><span class="section-badge">8</span></summary><div class="board-section-body"><a id="projects"></a>
 <section class="project">
@@ -550,18 +550,16 @@ <h3>autolens_inference — Do the PyAutoLens inference backends agree on the HST
 <h3>autofit_inference — PyAutoFit search benchmark on gaussian_x3: which searches find the right answer fastest</h3>
 <p class="muted">active · ral · <a href="https://github.com/PyAutoLabs/autofit_inference/issues/8">autofit_inference#8</a> · <a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/autofit_inference.md">projects/autofit_inference.md</a> · local <span class="pathchip">/home/jammy/Code/PyAutoLabs/fit/autofit_inference</span> · RAL <span class="pathchip">/mnt/ral/jnightin/autofit_inference</span></p>
 <p><b>Now</b></p>
-<p>Scored wave 2 pulled and judged under gaussian_x3@2 (B5b, 2026-10-15): 11,770 rows at PyAutoFit pin df6d218f10cd all admitted; accounting 11,750 scored (10,580 rows, 1,170 failed) + 20 probe + 250 deferred; all 20 per-realisation references complete (Nautilus-only), JAX blend realisation-1 rebuilt locally. Catalogue gaussian_x3@2 section orders legs within task and dataset (autofit_inference results/accounting, catalogue). Probe-first remainder (980 runs) is 409930 on ral; next: B5b-2 pulls and judges it; wave-2 summary records withheld on size pending autofit_inference#10.</p>
+<p>Wave 2 closed and judged under gaussian_x3@2 (B5b-2, 2026-10-15): all 12,750 pulled rows admitted at PyAutoFit pin df6d218f10cd; of the 13,000 expected runs, 11,580 rows + 1,170 failed + 0 missing + 250 deferred. The 409930 probe-first legs close: NSS 50-step leads both evidence rankings (blend 158/250, separated 210/250); Zeus JAX is 0/250 on the blend and 190/250 on the separated control (first among separated posterior legs). The catalogue ranks only legs with success rate &gt;= 50 % over &gt;= 20 attempts and lists the rest as unreliable (post-hoc presentation convention, protocol §8 note; autofit_inference#12 ruling 2). Summary records published as inference-summary@3 (101 record shards). Next: after the B5b-2 PRs merge, capture the Insight fit snapshot from main under @3 and run pyauto-insight check online; then B5c (a second model family).</p>
 <p><b>Runs</b></p>
-<ul>
-<li>409930_[0-19] — open — ral — 2026-10-15 — wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s…</li>
-</ul>
+<p class="muted">nothing on the cluster</p>
 <p><b>Last 5</b></p>
 <ul>
+<li>2026-10-15 — <i>run</i> — 409930_[0-19] finished — wall 1:30: wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s, Zeus 1168 s); %20; submit_wave2_remainder;…</li>
 <li>2026-10-15 — <i>run</i> — 408367_[0-389] finished — wall 1:57: scored wave 2 separated jax_cpu: 3750 runs, %60, afterany 408346:408347 — 390/390 COMPLETED; 3750 separated jax_cpu rows admitted; judged B5b</li>
 <li>2026-10-15 — <i>run</i> — 408366_[0-379] finished — wall 1:51: scored wave 2 separated numpy: 2250 runs, %60, afterany 408346:408347 — 380/380 COMPLETED; 2250 separated numpy rows admitted; judged B5b</li>
 <li>2026-10-15 — <i>run</i> — 408365_[0-344] finished — wall 1:46: scored wave 2 blend jax_cpu: 3500 runs, %60, afterany 408346:408347 — 345/345 COMPLETED; 3500 blend jax_cpu rows admitted; judged B5b</li>
 <li>2026-10-15 — <i>run</i> — 408348_[0-379] finished — wall 1:53: scored wave 2 (gaussian_x3@2) blend numpy: 2250 runs, 50 seeds x 5 realisations, %60, afterany 408346:408347; submit_wave2_scored_gaussian_x3_blend_numpy — 380/380 COMPLETED; 2250 blend numpy rows admit…</li>
-<li>2026-10-15 — <i>run</i> — 408347_[0-7] finished — wall 0:01: wave-2 separated reference builds, afterany 408245; %8 — 8/8 COMPLETED; separated reference builds</li>
 </ul>
 <p class="muted"><a href="https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/autofit_inference.md">full log</a></p>
 <div class="task"><button class="copy" data-cmd="Use the cortex skill. — resume autofit_inference: read PyAutoCortex projects/autofit_inference.md (Now, Runs, Log) and then /home/jammy/Code/PyAutoLabs/fit/autofit_inference/wiki/project/state.md and the assistant autofit_assistant's AGENTS.md; tell me where I left off and what I said I would do next. Submit nothing and log nothing until I say." aria-label="Copy the AI command">📋</button><p>resume autofit_inference</p></div>

dashboard.md

@@ -11,7 +11,7 @@ One ledger per science project: what is on the cluster right now, what the human
 | Where | Count |
 |-------|------:|
 | [Running](#projects) | 22 |
-| [Open](#projects) | 48 |
+| [Open](#projects) | 47 |
 | [Projects](#projects) | 8 |
 
 <details><summary>📋 check in since last time</summary>
@@ -49,7 +49,7 @@ Last check-in: 2026-09-30T09:51Z.
 | slope_hierarchy_scale | 0 | 0 | 2026-10-14 |
 | ic50_workspace | 0 | 0 | 2026-10-14 |
 | autolens_inference | 6 | 0 | 2026-09-24 |
-| autofit_inference | 0 | 1 | 2026-10-15 |
+| autofit_inference | 0 | 0 | 2026-10-15 |
 
 ## Projects
 
@@ -336,19 +336,19 @@ active · ral · [autofit_inference#8](https://github.com/PyAutoLabs/autofit_inf
 
 **Now**
 
-Scored wave 2 pulled and judged under gaussian_x3@2 (B5b, 2026-10-15): 11,770 rows at PyAutoFit pin df6d218f10cd all admitted; accounting 11,750 scored (10,580 rows, 1,170 failed) + 20 probe + 250 deferred; all 20 per-realisation references complete (Nautilus-only), JAX blend realisation-1 rebuilt locally. Catalogue gaussian_x3@2 section orders legs within task and dataset (autofit_inference results/accounting, catalogue). Probe-first remainder (980 runs) is 409930 on ral; next: B5b-2 pulls and judges it; wave-2 summary records withheld on size pending autofit_inference#10.
+Wave 2 closed and judged under gaussian_x3@2 (B5b-2, 2026-10-15): all 12,750 pulled rows admitted at PyAutoFit pin df6d218f10cd; of the 13,000 expected runs, 11,580 rows + 1,170 failed + 0 missing + 250 deferred. The 409930 probe-first legs close: NSS 50-step leads both evidence rankings (blend 158/250, separated 210/250); Zeus JAX is 0/250 on the blend and 190/250 on the separated control (first among separated posterior legs). The catalogue ranks only legs with success rate >= 50 % over >= 20 attempts and lists the rest as unreliable (post-hoc presentation convention, protocol §8 note; autofit_inference#12 ruling 2). Summary records published as inference-summary@3 (101 record shards). Next: after the B5b-2 PRs merge, capture the Insight fit snapshot from main under @3 and run pyauto-insight check online; then B5c (a second model family).
 
 **Runs**
 
-- 409930_[0-19] — open — ral — 2026-10-15 — wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s…
+- _nothing on the cluster_
 
 **Last 5**
 
+- 2026-10-15 — *run* — 409930_[0-19] finished — wall 1:30: wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s, Zeus 1168 s); %20; submit_wave2_remainder;…
 - 2026-10-15 — *run* — 408367_[0-389] finished — wall 1:57: scored wave 2 separated jax_cpu: 3750 runs, %60, afterany 408346:408347 — 390/390 COMPLETED; 3750 separated jax_cpu rows admitted; judged B5b
 - 2026-10-15 — *run* — 408366_[0-379] finished — wall 1:51: scored wave 2 separated numpy: 2250 runs, %60, afterany 408346:408347 — 380/380 COMPLETED; 2250 separated numpy rows admitted; judged B5b
 - 2026-10-15 — *run* — 408365_[0-344] finished — wall 1:46: scored wave 2 blend jax_cpu: 3500 runs, %60, afterany 408346:408347 — 345/345 COMPLETED; 3500 blend jax_cpu rows admitted; judged B5b
 - 2026-10-15 — *run* — 408348_[0-379] finished — wall 1:53: scored wave 2 (gaussian_x3@2) blend numpy: 2250 runs, 50 seeds x 5 realisations, %60, afterany 408346:408347; submit_wave2_scored_gaussian_x3_blend_numpy — 380/380 COMPLETED; 2250 blend numpy rows admit…
-- 2026-10-15 — *run* — 408347_[0-7] finished — wall 0:01: wave-2 separated reference builds, afterany 408245; %8 — 8/8 COMPLETED; separated reference builds
 
 [full log](https://github.com/PyAutoLabs/PyAutoCortex/blob/main/projects/autofit_inference.md)
 

projects/autofit_inference.md

@@ -5,14 +5,14 @@ Issue: autofit_inference#8
 
 ## Now
 
-Scored wave 2 pulled and judged under gaussian_x3@2 (B5b, 2026-10-15): 11,770 rows at PyAutoFit pin df6d218f10cd all admitted; accounting 11,750 scored (10,580 rows, 1,170 failed) + 20 probe + 250 deferred; all 20 per-realisation references complete (Nautilus-only), JAX blend realisation-1 rebuilt locally. Catalogue gaussian_x3@2 section orders legs within task and dataset (autofit_inference results/accounting, catalogue). Probe-first remainder (980 runs) is 409930 on ral; next: B5b-2 pulls and judges it; wave-2 summary records withheld on size pending autofit_inference#10.
+Wave 2 closed and judged under gaussian_x3@2 (B5b-2, 2026-10-15): all 12,750 pulled rows admitted at PyAutoFit pin df6d218f10cd; of the 13,000 expected runs, 11,580 rows + 1,170 failed + 0 missing + 250 deferred. The 409930 probe-first legs close: NSS 50-step leads both evidence rankings (blend 158/250, separated 210/250); Zeus JAX is 0/250 on the blend and 190/250 on the separated control (first among separated posterior legs). The catalogue ranks only legs with success rate >= 50 % over >= 20 attempts and lists the rest as unreliable (post-hoc presentation convention, protocol §8 note; autofit_inference#12 ruling 2). Summary records published as inference-summary@3 (101 record shards). Next: after the B5b-2 PRs merge, capture the Insight fit snapshot from main under @3 and run pyauto-insight check online; then B5c (a second model family).
 
 ## Runs
 
-- 409930_[0-19] — open — ral — 2026-10-15 — wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s, Zeus 1168 s); %20; submit_wave2_remainder; pin df6d218f10cd; pulled in B5b-2
 
 ## Log
 
+- 2026-10-15 — run — 409930_[0-19] finished — wall 1:30: wave-2 probe-first remainder (B5b): Zeus JAX + NSS n_live_200_mcmc_steps_50, both datasets, 980 runs after the probe under probe-derived @2 timeouts (NSS 600 s, Zeus 1168 s); %20; submit_wave2_remainder; pin df6d218f10cd; pulled in B5b-2 — 20/20 COMPLETED (16:08-17:38 UTC); 980 remainder rows pulled by per-directory tar (20 probe rows byte-identical); all admitted under gaussian_x3@2, 0 missing; judged B5b-2
 - 2026-10-15 — run — 408367_[0-389] finished — wall 1:57: scored wave 2 separated jax_cpu: 3750 runs, %60, afterany 408346:408347 — 390/390 COMPLETED; 3750 separated jax_cpu rows admitted; judged B5b
 - 2026-10-15 — run — 408366_[0-379] finished — wall 1:51: scored wave 2 separated numpy: 2250 runs, %60, afterany 408346:408347 — 380/380 COMPLETED; 2250 separated numpy rows admitted; judged B5b
 - 2026-10-15 — run — 408365_[0-344] finished — wall 1:46: scored wave 2 blend jax_cpu: 3500 runs, %60, afterany 408346:408347 — 345/345 COMPLETED; 3500 blend jax_cpu rows admitted; judged B5b

state.json

@@ -4,7 +4,7 @@
   "repo": "PyAutoCortex",
   "status": "yellow",
   "headline": "YELLOW \u2014 8 active \u00b7 22 running",
-  "updated": "2026-10-15T16:00:25Z",
+  "updated": "2026-10-15T17:38:42Z",
   "pages_url": "https://pyautolabs.github.io/PyAutoCortex/",
   "items": [
     {