From 34b1f2c58ff0cdf92411a8035de80d3f92ebf18a Mon Sep 17 00:00:00 2001
From: Duncan Tourolle <duncan@tourolle.paris>
Date: Tue, 21 Jul 2026 08:56:42 +0200
Subject: [PATCH] docs: deploy from 0bd2747

---
 404.html                                      |  104 +-
 assets/images/downton_ghost_timeline.png      |  Bin 69848 -> 70815 bytes
 .../images/lord_of_war_fpi_reagan_photo.jpg   |  Bin 0 -> 159065 bytes
 assets/images/lord_of_war_fpi_reddick.jpg     |  Bin 0 -> 180150 bytes
 assets/images/lord_of_war_fpi_shumbris.jpg    |  Bin 0 -> 164803 bytes
 assets/images/lovelace_fpi_sevigny.jpg        |  Bin 0 -> 144899 bytes
 assets/images/many_saints_fpi_balsam.jpg      |  Bin 0 -> 178231 bytes
 assets/images/many_saints_fpi_deschanel.jpg   |  Bin 0 -> 127453 bytes
 assets/images/many_saints_fpi_gardner.jpg     |  Bin 0 -> 113587 bytes
 assets/images/many_saints_fpi_yates.jpg       |  Bin 0 -> 137669 bytes
 assets/images/many_saints_ghost_fpi.jpg       |  Bin 120987 -> 0 bytes
 assets/images/rep4_matrix_f1.png              |  Bin 131779 -> 105321 bytes
 assets/images/scarface_fpi_alley.jpg          |  Bin 0 -> 40508 bytes
 best-model/index.html                         |  354 +++--
 gallery-scope/index.html                      |  239 ++--
 index.html                                    |  218 +--
 lvface-deep-dive/index.html                   |  677 ++++++---
 .../index.html                                |  654 ++++-----
 model-bakeoff/index.html                      | 1257 +++++++----------
 pose-expansion/index.html                     |  318 +++--
 search/search_index.json                      |    2 +-
 service-conversion/index.html                 |  118 +-
 sitemap.xml                                   |   22 +-
 sitemap.xml.gz                                |  Bin 294 -> 288 bytes
 24 files changed, 2066 insertions(+), 1897 deletions(-)
 create mode 100644 assets/images/lord_of_war_fpi_reagan_photo.jpg
 create mode 100644 assets/images/lord_of_war_fpi_reddick.jpg
 create mode 100644 assets/images/lord_of_war_fpi_shumbris.jpg
 create mode 100644 assets/images/lovelace_fpi_sevigny.jpg
 create mode 100644 assets/images/many_saints_fpi_balsam.jpg
 create mode 100644 assets/images/many_saints_fpi_deschanel.jpg
 create mode 100644 assets/images/many_saints_fpi_gardner.jpg
 create mode 100644 assets/images/many_saints_fpi_yates.jpg
 delete mode 100644 assets/images/many_saints_ghost_fpi.jpg
 create mode 100644 assets/images/scarface_fpi_alley.jpg
 rename {optimizer-experiments => methodology}/index.html (60%)

diff --git a/404.html b/404.html
index 1d5f5d5..5bb5ca8 100644
--- a/404.html
+++ b/404.html
@@ -232,6 +232,25 @@
   
   
   
+    <li class="md-tabs__item">
+      <a href="/dtourolle/scene-actor-extraction/methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
+  
     
     
       <li class="md-tabs__item">
@@ -259,26 +278,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="/dtourolle/scene-actor-extraction/optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -382,6 +382,33 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="/dtourolle/scene-actor-extraction/methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
     
     
     
@@ -396,10 +423,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
             
   
   
@@ -417,8 +444,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -561,34 +588,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="/dtourolle/scene-actor-extraction/optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
diff --git a/assets/images/downton_ghost_timeline.png b/assets/images/downton_ghost_timeline.png
index 595e0d76c07aaa11dfa7817f37489037f421b26a..3c2ef38c6c3ec2f9ad9df34b52b3b1dc74638605 100644
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diff --git a/best-model/index.html b/best-model/index.html
index 12a13f8..f4b9003 100644
--- a/best-model/index.html
+++ b/best-model/index.html
@@ -13,7 +13,7 @@
         <link rel="canonical" href="https://pages.tourolle.paris/dtourolle/scene-actor-extraction/best-model/">
       
       
-        <link rel="prev" href="..">
+        <link rel="prev" href="../methodology/">
       
       
         <link rel="next" href="../gallery-scope/">
@@ -242,6 +242,25 @@
   
   
   
+  
+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
     
   
   
@@ -272,26 +291,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -393,6 +393,33 @@
       
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -414,10 +441,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="">
             
   
   
@@ -435,8 +462,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -524,10 +551,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#second-signal-f1-on-the-actual-benchmark" class="md-nav__link">
+  <a href="#second-signal-held-out-f1" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Second signal: F1 on the actual benchmark
+        Second signal: held-out F1
       
     </span>
   </a>
@@ -535,10 +562,21 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#caveat-model-choice-is-an-operational-change" class="md-nav__link">
+  <a href="#full-training-matrix-picture" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Caveat: model choice is an operational change
+        Full training-matrix picture
+      
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#operational-note" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Operational note
       
     </span>
   </a>
@@ -659,34 +697,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -764,10 +775,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#second-signal-f1-on-the-actual-benchmark" class="md-nav__link">
+  <a href="#second-signal-held-out-f1" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Second signal: F1 on the actual benchmark
+        Second signal: held-out F1
       
     </span>
   </a>
@@ -775,10 +786,21 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#caveat-model-choice-is-an-operational-change" class="md-nav__link">
+  <a href="#full-training-matrix-picture" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Caveat: model choice is an operational change
+        Full training-matrix picture
+      
+    </span>
+  </a>
+  
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#operational-note" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Operational note
       
     </span>
   </a>
@@ -803,31 +825,35 @@
 
 
 <h1 id="which-embedding-model-is-best">Which embedding model is best?<a class="headerlink" href="#which-embedding-model-is-best" title="Permanent link">&para;</a></h1>
-<p>Four candidates went into the bake-off: three ArcFace variants (w600k-R50,
-R18, w600k-MBF) and LVFace-B (Glint360K), a Vision-Transformer embedder that's
-a drop-in replacement for ArcFace's <code>[N,3,112,112]</code> input / 512-d output. The
-open question: is LVFace (455MB) actually better, or just the biggest?</p>
+<p>Three ArcFace variants (w600k-R50, R18, w600k-MBF) and LVFace-B (Glint360K,
+455MB) were compared. r50 is excluded from the training/held-out comparison
+below; its gallery has roughly 30% fewer reference images per actor than the
+other three on the identical source photos, which confounds a direct score
+comparison (see <a href="../model-bakeoff/">the full experiment log</a> for detail). It
+remains in the calibration comparison, which does not depend on the gallery
+image count.</p>
 <h2 id="first-signal-calibration-curves">First signal: calibration curves<a class="headerlink" href="#first-signal-calibration-curves" title="Permanent link">&para;</a></h2>
 <p>Each gallery carries a fitted Platt sigmoid <code>P(match | cosine similarity) =
-σ(a·sim + b)</code>, embedded directly in the gallery's HDF5 file
-(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/gallery/gallery_calibration.hpp"><code>src/gallery/gallery_calibration.hpp</code></a>). This is a property of the embedding
-space alone — computed from intra/inter-actor reference-image pairs, no
-tracking or scene logic involved — so it's a clean first read on discriminative
-power before running a single benchmark.</p>
+σ(a·sim + b)</code>, stored directly in the gallery HDF5
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/gallery/gallery_calibration.hpp"><code>src/gallery/gallery_calibration.hpp</code></a>).
+This is a property of the embedding space alone, computed from intra- and
+inter-actor reference-image pairs with no tracking or scene logic involved,
+so it is a clean first read on discriminative power before running a
+benchmark.</p>
 <p><img alt="Calibrated P(match|similarity) for all four models" src="../assets/images/calibration_curves.png" /></p>
 <table>
 <thead>
 <tr>
 <th>model</th>
-<th><code>a</code> (steepness)</th>
+<th>a (steepness)</th>
 <th>boundary at P=0.5</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td><strong>LVFace-B Glint360K</strong></td>
-<td><strong>17.7</strong></td>
-<td><strong>sim 0.228</strong></td>
+<td>LVFace-B Glint360K</td>
+<td>17.7</td>
+<td>sim 0.228</td>
 </tr>
 <tr>
 <td>ArcFace w600k-MBF</td>
@@ -846,13 +872,123 @@ power before running a single benchmark.</p>
 </tr>
 </tbody>
 </table>
-<p>LVFace has both the steepest transition and the lowest decision boundary — it
-separates same-actor from different-actor reference pairs more confidently, at
-a <em>lower</em> similarity threshold, than any ArcFace variant. That's a genuine
-head start before the tracking/scoring pipeline is even involved.</p>
-<h2 id="second-signal-f1-on-the-actual-benchmark">Second signal: F1 on the actual benchmark<a class="headerlink" href="#second-signal-f1-on-the-actual-benchmark" title="Permanent link">&para;</a></h2>
-<p>Best full-gallery (no cast-restriction) result per model, from the 16-combo
-bake-off matrix (<a href="../model-bakeoff/">full experiment log</a>):</p>
+<p>LVFace has both the steepest transition and the lowest decision boundary,
+separating same-actor from different-actor reference pairs more confidently
+at a lower similarity than any ArcFace variant.</p>
+<h2 id="second-signal-held-out-f1">Second signal: held-out F1<a class="headerlink" href="#second-signal-held-out-f1" title="Permanent link">&para;</a></h2>
+<p>Each model's own tuned <code>full_exp</code> config, replayed against the 5 films the
+optimizer never saw and scored the same way:</p>
+<table>
+<thead>
+<tr>
+<th>film</th>
+<th>LVFace F1</th>
+<th>mbf F1</th>
+<th>r18 F1</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Benny &amp; Joon</td>
+<td>83.0%</td>
+<td>78.5%</td>
+<td>77.1%</td>
+</tr>
+<tr>
+<td>Lovelace</td>
+<td>77.5%</td>
+<td>73.7%</td>
+<td>72.2%</td>
+</tr>
+<tr>
+<td>Valerian and the City of a Thousand Planets</td>
+<td>74.1%</td>
+<td>70.2%</td>
+<td>71.0%</td>
+</tr>
+<tr>
+<td>Downton Abbey: A New Era</td>
+<td>56.2%</td>
+<td>55.0%</td>
+<td>53.0%</td>
+</tr>
+<tr>
+<td>The Many Saints of Newark</td>
+<td>46.3%</td>
+<td>44.5%</td>
+<td>42.1%</td>
+</tr>
+<tr>
+<td><strong>macro average</strong></td>
+<td><strong>67.4%</strong></td>
+<td><strong>64.4%</strong></td>
+<td><strong>63.1%</strong></td>
+</tr>
+</tbody>
+</table>
+<p>LVFace scores highest on all 5 held-out films; the ranking never flips
+between models. Total misID count across the 5 films: LVFace 1032, mbf
+2197, r18 1224. LVFace has less than half mbf's misID total and still
+scores higher on every film.</p>
+<p>Held-out results are stronger evidence than training results, because
+training numbers can reflect what the optimizer was tuned to fit rather
+than general performance. On training data, the ordering is not as clean:</p>
+<table>
+<thead>
+<tr>
+<th>film</th>
+<th>LVFace F1</th>
+<th>mbf F1</th>
+<th>r18 F1</th>
+<th>best</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Café Society</td>
+<td>68.1%</td>
+<td>62.2%</td>
+<td>60.1%</td>
+<td>LVFace</td>
+</tr>
+<tr>
+<td>Lord of War</td>
+<td>75.6%</td>
+<td>77.2%</td>
+<td>75.6%</td>
+<td>mbf</td>
+</tr>
+<tr>
+<td>Scarface</td>
+<td>71.5%</td>
+<td>68.6%</td>
+<td>64.1%</td>
+<td>LVFace</td>
+</tr>
+<tr>
+<td>Sound of Metal</td>
+<td>78.8%</td>
+<td>76.5%</td>
+<td>71.6%</td>
+<td>LVFace</td>
+</tr>
+</tbody>
+</table>
+<p>mbf beats LVFace on Lord of War (77.2% vs 75.6%), the only film in either
+table where LVFace does not score highest. LVFace's training-set macro
+average (75.3%, see <a href="../model-bakeoff/">the full experiment log</a>) is not a
+uniform win across every film it contributes to; the held-out result, where
+LVFace wins all 5 films outright, is the stronger claim.</p>
+<p>This reverses an earlier, superseded benchmarking pass that used a
+scene-union metric and found the three models statistically
+indistinguishable (around 85% each), concluding LVFace was not worth its
+size. That metric masked out-of-cast false positives behind a
+gallery-intersect-cast recall filter; the per-second metric used here does
+not.</p>
+<h2 id="full-training-matrix-picture">Full training-matrix picture<a class="headerlink" href="#full-training-matrix-picture" title="Permanent link">&para;</a></h2>
+<p><img alt="All 12 combos ranked by training-set F1" src="../assets/images/rep4_matrix_f1.png" /></p>
+<p>Best full-gallery combo per model (all three are <code>full_exp</code>), from the
+training matrix in <a href="../model-bakeoff/">the full experiment log</a>:</p>
 <table>
 <thead>
 <tr>
@@ -865,18 +1001,18 @@ bake-off matrix (<a href="../model-bakeoff/">full experiment log</a>):</p>
 </thead>
 <tbody>
 <tr>
-<td><strong>LVFace-B Glint360K</strong></td>
-<td><strong>75.3%</strong></td>
+<td>LVFace-B Glint360K</td>
+<td>75.3%</td>
 <td>89.7%</td>
-<td><strong>65.4%</strong></td>
+<td>65.4%</td>
 <td>232</td>
 </tr>
 <tr>
 <td>ArcFace w600k-MBF</td>
-<td>74.2%</td>
-<td>87.4%</td>
-<td>64.4%</td>
-<td>57</td>
+<td>72.0%</td>
+<td>87.7%</td>
+<td>61.4%</td>
+<td>240</td>
 </tr>
 <tr>
 <td>ArcFace R18</td>
@@ -885,36 +1021,18 @@ bake-off matrix (<a href="../model-bakeoff/">full experiment log</a>):</p>
 <td>57.7%</td>
 <td>242</td>
 </tr>
-<tr>
-<td>ArcFace w600k-R50</td>
-<td>68.5%</td>
-<td>94.0%</td>
-<td>54.1%</td>
-<td>150</td>
-</tr>
 </tbody>
 </table>
-<p>The full 16-combo picture makes the model ordering visible at a glance — LVFace
-(yellow) tops both the restricted and full columns, and R18 (green) props up
-the bottom of the full-gallery ranking:</p>
-<p><img alt="All 16 bake-off combos ranked by training-set F1" src="../assets/images/rep4_matrix_f1.png" /></p>
-<p>LVFace wins outright, with the highest recall of any full-mode combo. This
-reverses an earlier conclusion from a prior (superseded) benchmarking pass
-using a scene-union metric, which found the three models statistically
-indistinguishable (~85% each) and concluded LVFace wasn't worth its size — that
-metric hid out-of-cast false positives behind a gallery∩cast recall mask (see
-<a href="../optimizer-experiments/">the prior optimizer round</a>); the per-second metric
-used here does not.</p>
-<p>Held-out validation (5 films never seen by the optimizer) confirms LVFace's
-lead holds up out of sample — see the
-<a href="../lvface-deep-dive/">LVFace deep dive</a> for the full breakdown, including
-where it fails.</p>
-<h2 id="caveat-model-choice-is-an-operational-change">Caveat: model choice is an operational change<a class="headerlink" href="#caveat-model-choice-is-an-operational-change" title="Permanent link">&para;</a></h2>
-<p>Switching the default embedder isn't just flipping a config value — the
-gallery itself is model-specific (embeddings from different models aren't
-comparable), so any existing gallery built against ArcFace w600k-R50 needs to
-be rebuilt from source images against LVFace before the new default takes
-effect. <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/reembed_gallery.py"><code>scripts/optimizer/reembed_gallery.py</code></a>
+<p>LVFace leads within both the restricted and full gallery modes, visible
+directly in the chart above without reading the table. The three models'
+misID counts on the full gallery are nearly identical (232/240/242); LVFace's
+lead here is a precision-and-recall lead, not a misID one.</p>
+<h2 id="operational-note">Operational note<a class="headerlink" href="#operational-note" title="Permanent link">&para;</a></h2>
+<p>Switching the default embedder is not a config change alone; the gallery
+is model-specific, since embeddings from different models are not
+comparable. Any existing gallery built against a different model must be
+rebuilt from source images before the new default takes effect.
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/reembed_gallery.py"><code>scripts/optimizer/reembed_gallery.py</code></a>
 does this from a reference gallery's cached source images without
 re-downloading anything.</p>
 
@@ -952,7 +1070,7 @@ re-downloading anything.</p>
       <nav class="md-footer__inner md-grid" aria-label="Footer" >
         
           
-          <a href=".." class="md-footer__link md-footer__link--prev" aria-label="Previous: Home">
+          <a href="../methodology/" class="md-footer__link md-footer__link--prev" aria-label="Previous: How We Score Against X-Ray">
             <div class="md-footer__button md-icon">
               
               <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
@@ -962,7 +1080,7 @@ re-downloading anything.</p>
                 Previous
               </span>
               <div class="md-ellipsis">
-                Home
+                How We Score Against X-Ray
               </div>
             </div>
           </a>
diff --git a/gallery-scope/index.html b/gallery-scope/index.html
index eb031a3..d225888 100644
--- a/gallery-scope/index.html
+++ b/gallery-scope/index.html
@@ -80,7 +80,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#whole-gallery-vs-limited-cast-restricted-gallery" class="md-skip">
+        <a href="#whole-gallery-vs-cast-restricted-gallery" class="md-skip">
           Skip to content
         </a>
       
@@ -242,6 +242,25 @@
   
   
   
+  
+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
     
   
   
@@ -272,26 +291,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -393,6 +393,33 @@
       
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -414,10 +441,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="">
             
   
   
@@ -435,8 +462,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -541,10 +568,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#the-result" class="md-nav__link">
+  <a href="#result" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The result
+        Result
       
     </span>
   </a>
@@ -552,10 +579,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#why-this-isnt-the-shipped-default" class="md-nav__link">
+  <a href="#why-this-is-not-the-shipped-default" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why this isn't the shipped default
+        Why this is not the shipped default
       
     </span>
   </a>
@@ -648,34 +675,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -742,10 +742,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#the-result" class="md-nav__link">
+  <a href="#result" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The result
+        Result
       
     </span>
   </a>
@@ -753,10 +753,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#why-this-isnt-the-shipped-default" class="md-nav__link">
+  <a href="#why-this-is-not-the-shipped-default" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why this isn't the shipped default
+        Why this is not the shipped default
       
     </span>
   </a>
@@ -780,14 +780,15 @@
 
 
 
-<h1 id="whole-gallery-vs-limited-cast-restricted-gallery">Whole gallery vs. limited (cast-restricted) gallery<a class="headerlink" href="#whole-gallery-vs-limited-cast-restricted-gallery" title="Permanent link">&para;</a></h1>
-<p>Two ways to run the matcher: <strong>full</strong> scores every detected face against the
-entire library gallery (2418 actors across the 9-film benchmark set); <strong>restricted</strong>
-pre-filters each film's gallery down to just its Jellyfin-credited cast (typically
-~15 top-billed actors) before the matcher ever runs.</p>
-<h2 id="the-result">The result<a class="headerlink" href="#the-result" title="Permanent link">&para;</a></h2>
-<p>Averaged across all 4 models and both expansion settings, on the 4 bake-off training
-films:</p>
+<h1 id="whole-gallery-vs-cast-restricted-gallery">Whole gallery vs. cast-restricted gallery<a class="headerlink" href="#whole-gallery-vs-cast-restricted-gallery" title="Permanent link">&para;</a></h1>
+<p>Two ways to run the matcher. Full mode scores every detected face against
+the entire 2418-actor gallery. Restricted mode pre-filters each film's
+gallery down to just its Jellyfin-credited cast (typically around 15
+top-billed actors) before the matcher runs.</p>
+<h2 id="result">Result<a class="headerlink" href="#result" title="Permanent link">&para;</a></h2>
+<p>Averaged across the 3 compared models (r50 excluded, see
+<a href="../model-bakeoff/">the full experiment log</a>) and both expansion settings, on
+the 4 training films:</p>
 <table>
 <thead>
 <tr>
@@ -795,68 +796,70 @@ films:</p>
 <th>F1</th>
 <th>P</th>
 <th>R</th>
-<th>total misID (8 evals)</th>
+<th>total misID</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td>full</td>
-<td>71.2%</td>
-<td>91.1%</td>
-<td>59.0%</td>
-<td>1073</td>
+<td>71.1%</td>
+<td>89.6%</td>
+<td>59.6%</td>
+<td>1121</td>
 </tr>
 <tr>
-<td><strong>restricted</strong></td>
-<td><strong>74.5%</strong></td>
-<td>92.2%</td>
-<td><strong>62.9%</strong></td>
-<td><strong>329</strong></td>
+<td>restricted</td>
+<td>75.9%</td>
+<td>90.4%</td>
+<td>65.6%</td>
+<td>299</td>
 </tr>
 </tbody>
 </table>
-<p>This is not a precision/recall trade — restriction wins on every axis at once:
-<strong>+3.3pp F1, +3.9pp recall, and less than a third the total misIDs.</strong> Fewer
+<p>Restriction improves every metric at once, not a precision/recall trade:
++4.8pp F1, +6.0pp recall, roughly a quarter the total misIDs. Fewer
 candidates in the matcher's search space means fewer opportunities for a
-look-alike false match (an actor who happens to share enough facial structure
-with someone in the film, but isn't actually in it), and the recall gain shows
-it isn't costing real detections to get there.</p>
-<p>Per-model, every single model's best-scoring combo in the full 16-way matrix is
-a <code>restricted</code> variant — visible directly in the ranking below (filled dots =
-restricted, open = full; the filled dots cluster at the top for every color):</p>
-<p><img alt="All 16 bake-off combos — filled dots (restricted) dominate the top" src="../assets/images/rep4_matrix_f1.png" /></p>
-<p>See the full table in the
-<a href="../model-bakeoff/">bake-off experiment log</a>. Two
-combos hit <strong>zero</strong> true out-of-cast misidentifications:
-<code>arcface_w600k_mbf_restricted_exp</code> (F1 76.5%) and, in full mode,
-<code>LVFace-B_Glint360K_full_noexp</code> (F1 72.4%) — restriction isn't the only way to
-reach misid=0, but it's the more reliable one.</p>
-<h2 id="why-this-isnt-the-shipped-default">Why this isn't the shipped default<a class="headerlink" href="#why-this-isnt-the-shipped-default" title="Permanent link">&para;</a></h2>
-<p>Cast-restriction is implemented today only as an <strong>offline optimizer technique</strong>
-(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/cast_restrict.py"><code>scripts/optimizer/cast_restrict.py</code></a>):
-it pre-builds a filtered gallery file
-per film, using Jellyfin's own cast list, before the benchmark ever calls the
-matcher. There's no runtime "restrict matching to this title's credited cast"
-switch in the shipped application — <code>scene_analyze</code> always matches against
-whatever single gallery file it's given.</p>
-<p>Building that as a real feature would need, at minimum:</p>
+lookalike false match, and the recall gain shows this does not cost real
+detections.</p>
+<p>Every model's best-scoring combo in the training matrix uses the
+restricted gallery:</p>
+<p><img alt="All combos ranked by training-set F1, filled dots are restricted" src="../assets/images/rep4_matrix_f1.png" /></p>
+<p>See <a href="../model-bakeoff/">the full experiment log</a> for the complete table. One
+combo reaches zero true out-of-cast misidentifications,
+<code>arcface_w600k_mbf_restricted_exp</code> (F1 76.2%), and it is a restricted one,
+consistent with restriction, not expansion, being what suppresses cross-film
+confusions.</p>
+<p>The restriction effect (+4.8pp averaged across models) is larger than the
+model-choice effect: LVFace beats r18 by 6.2pp in full mode but beats mbf by
+3.3pp. Restriction is the single strongest lever in the matrix.</p>
+<h2 id="why-this-is-not-the-shipped-default">Why this is not the shipped default<a class="headerlink" href="#why-this-is-not-the-shipped-default" title="Permanent link">&para;</a></h2>
+<p>Cast restriction is implemented today only as an offline optimizer
+technique
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/cast_restrict.py"><code>scripts/optimizer/cast_restrict.py</code></a>):
+it pre-builds a filtered gallery file per film using Jellyfin's cast list
+before the benchmark calls the matcher. There is no runtime "restrict to
+this title's credited cast" switch in the shipped application;
+<code>scene_analyze</code> always matches against whatever single gallery file it is
+given.</p>
+<p>Building this as a real feature requires:</p>
 <ul>
-<li>A live Jellyfin cast lookup at analysis time (the title is already known —
-  <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/run_from_jellyfin.py"><code>scripts/run_from_jellyfin.py</code></a>
-  already does this same lookup for its own
-  <code>filter_gallery</code>-based restriction path, just not wired into <code>scene_analyze</code>
-  itself as a first-class option).</li>
-<li>A decision on the <em>fallback</em>: what happens to a real, uncredited cameo
-  (see the Germar Terrell Gardner case in the LVFace deep-dive) if the gallery
-  never includes them at all?</li>
-<li>Regenerating the restricted-gallery cache whenever the title's Jellyfin cast
-  list changes.</li>
+<li>A live Jellyfin cast lookup at analysis time. The title is already known,
+  and <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/run_from_jellyfin.py"><code>scripts/run_from_jellyfin.py</code></a>
+  already performs this lookup for its own <code>filter_gallery</code>-based
+  restriction path; it is not wired into <code>scene_analyze</code> as a first-class
+  option.</li>
+<li>A decision on the fallback case: what happens to a real, uncredited
+  cameo (see the Germar Terrell Gardner and Talia Balsam cases in the
+  <a href="../lvface-deep-dive/#where-lvface-beat-x-ray">LVFace deep dive</a>) if the
+  restricted gallery never includes them at all.</li>
+<li>Regenerating the restricted-gallery cache whenever a title's Jellyfin
+  cast list changes.</li>
 </ul>
-<p>This is why the shipped <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/config.hpp"><code>src/config.hpp</code></a>
-defaults use the <code>full</code>-mode winner
-(<code>LVFace-B_Glint360K_full_exp</code>, F1 75.3% training / 67.4% held-out macro) rather
-than the higher-scoring <code>restricted_exp</code> (78.3%) — the 78.3% number describes a
-capability the app doesn't have yet, not what actually ships.</p>
+<p>The shipped <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/config.hpp"><code>src/config.hpp</code></a> defaults use
+the full-mode winner (<code>LVFace-B_Glint360K_full_exp</code>, F1 75.3% training,
+67.4% held-out macro) rather than the higher-scoring <code>restricted_exp</code>
+(78.3%), because 78.3% describes a capability the application does not
+have yet.</p>
 
 
 
diff --git a/index.html b/index.html
index e4ef878..e0db9a9 100644
--- a/index.html
+++ b/index.html
@@ -14,7 +14,7 @@
       
       
       
-        <link rel="next" href="best-model/">
+        <link rel="next" href="methodology/">
       
       
         
@@ -243,6 +243,25 @@
   
   
   
+    <li class="md-tabs__item">
+      <a href="methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
+  
     
     
       <li class="md-tabs__item">
@@ -270,26 +289,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -427,10 +427,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#start-here-four-questions-this-bake-off-answers" class="md-nav__link">
+  <a href="#findings" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Start here — four questions this bake-off answers
+        Findings
       
     </span>
   </a>
@@ -438,10 +438,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#the-full-technical-log" class="md-nav__link">
+  <a href="#full-experiment-log" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The full technical log
+        Full experiment log
       
     </span>
   </a>
@@ -473,6 +473,33 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
     
     
     
@@ -487,10 +514,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
             
   
   
@@ -508,8 +535,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -652,34 +679,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -746,10 +746,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#start-here-four-questions-this-bake-off-answers" class="md-nav__link">
+  <a href="#findings" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Start here — four questions this bake-off answers
+        Findings
       
     </span>
   </a>
@@ -757,10 +757,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#the-full-technical-log" class="md-nav__link">
+  <a href="#full-experiment-log" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The full technical log
+        Full experiment log
       
     </span>
   </a>
@@ -796,79 +796,81 @@
 
 
 <h1 id="scene-actor-extraction">scene-actor-extraction<a class="headerlink" href="#scene-actor-extraction" title="Permanent link">&para;</a></h1>
-<p>A face-recognition pipeline that finds when each actor appears on screen in a
-film or TV episode — built on <a href="https://gitea.tourolle.paris/dtourolle/KPN">KPN++</a>
-(a C++20 Kahn Process Network library) for the detect → track → match → scene
-pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.</p>
-<p>This is a perfect X-Ray second, on a film the optimizer never saw:</p>
+<p>A face-recognition pipeline that finds when each actor appears on screen in
+a film or TV episode, built on <a href="https://gitea.tourolle.paris/dtourolle/KPN">KPN++</a>
+(a C++20 Kahn Process Network library) for the detect, track, match, and
+scene pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated
+optimizer.</p>
+<p>This is a correctly scored second from a held-out film, one the optimizer
+never saw during tuning:</p>
 <p><img alt="A perfect X-Ray second: three faces named at 100%, two more correctly carried off-screen" src="assets/images/lovelace_perfect_second.jpg" /></p>
-<p>Every visible face named at 100% — Chris Noth, Hank Azaria, Bobby Cannavale —
-the background extra honestly left unnamed, and the two credited cast without
-a visible face correctly carried as present off-screen by the tracker's
-presence windows. That's the pipeline exactly reproducing Amazon X-Ray's
-record for this second.</p>
-<p>It doesn't always go like that: the hardest held-out film scores 46% F1, and
-the report is honest about <em>why</em> — one tunable trade (extinction bridging at
-hard cuts), one structural ceiling (X-Ray credits people whose faces never
-appear), and a few cases where the pipeline is right and X-Ray is wrong. The
-evidence for all of it is in the pages below.</p>
-<h2 id="start-here-four-questions-this-bake-off-answers">Start here — four questions this bake-off answers<a class="headerlink" href="#start-here-four-questions-this-bake-off-answers" title="Permanent link">&para;</a></h2>
+<p>Every visible face is named at 100% confidence (Chris Noth, Hank Azaria,
+Bobby Cannavale), the background extra is correctly left unnamed, and the
+two credited cast members without a visible face are correctly reported
+present but not visible. This matches Amazon X-Ray's own record for this
+second exactly.</p>
+<p>Results are not uniform across films. The hardest held-out film scores 46%
+F1. This report documents why: one tunable trade (extinction bridging at
+hard cuts), one structural limit (X-Ray credits people whose faces never
+appear on screen), and a small number of cases where the pipeline is
+correct and X-Ray's ground truth is not. Read
+<a href="methodology/">how we score against X-Ray</a> first. X-Ray's ground truth is
+scene-level; the pipeline's output is per-second. That difference shapes
+every finding below.</p>
+<h2 id="findings">Findings<a class="headerlink" href="#findings" title="Permanent link">&para;</a></h2>
 <div class="grid cards">
 <ul>
 <li>
 <p><span class="twemoji lg middle"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 2c-.9 0-2 1-2 2H8c0-1-1.1-2-2-2H2v9c0 1 1 2 2 2h2.2c.4 2 1.7 3.7 4.8 4v2.08C8 19.54 8 22 8 22h8s0-2.46-3-2.92V17c3.1-.3 4.4-2 4.8-4H20c1 0 2-1 2-2V2zM6 11H4V4h2zm14 0h-2V4h2z"/></svg></span> <strong><a href="best-model/">Which model is best?</a></strong></p>
 <hr />
-<p>Calibration curves first (discriminative power, independent of any
-threshold), then F1 on the actual benchmark. LVFace-B Glint360K wins
-both.</p>
+<p>Calibration curves first, independent of any threshold, then held-out
+F1 across three models. LVFace-B Glint360K wins both, and wins on every
+held-out film.</p>
 </li>
 <li>
 <p><span class="twemoji lg middle"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M14 12v7.88c.04.3-.06.62-.29.83a.996.996 0 0 1-1.41 0l-2.01-2.01a.99.99 0 0 1-.29-.83V12h-.03L4.21 4.62a1 1 0 0 1 .17-1.4c.19-.14.4-.22.62-.22h14c.22 0 .43.08.62.22a1 1 0 0 1 .17 1.4L14.03 12z"/></svg></span> <strong><a href="gallery-scope/">Whole vs. cast-restricted gallery</a></strong></p>
 <hr />
-<p>Restricting the matcher to a film's credited cast is a clean win on
-every axis (+3.3pp F1, less than a third the misIDs) — but isn't a
-shipped runtime feature yet.</p>
+<p>Restricting the matcher to a film's credited cast improves F1,
+recall, and misID rate at once, but is not a shipped runtime feature
+yet.</p>
 </li>
 <li>
 <p><span class="twemoji lg middle"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="m12 0-.66.03 3.81 3.81L16.5 2.5c3.25 1.57 5.59 4.74 5.95 8.5h1.5C23.44 4.84 18.29 0 12 0m0 4c-1.93 0-3.5 1.57-3.5 3.5S10.07 11 12 11s3.5-1.57 3.5-3.5S13.93 4 12 4M.05 13C.56 19.16 5.71 24 12 24l.66-.03-3.81-3.81L7.5 21.5c-3.25-1.56-5.59-4.74-5.95-8.5zM12 13c-3.87 0-7 1.57-7 3.5V18h14v-1.5c0-1.93-3.13-3.5-7-3.5"/></svg></span> <strong><a href="pose-expansion/">Does pose expansion help?</a></strong></p>
 <hr />
-<p>A convincing training-set effect that didn't reproduce on 5 held-out
-films once two methodology bugs were caught and fixed. An honest null
-result, not a forced narrative.</p>
+<p>A training-set effect that did not reproduce on 5 held-out films once
+two methodology bugs in the comparison harness were found and fixed.</p>
 </li>
 <li>
 <p><span class="twemoji lg middle"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M18 16h-.58l-.81-.81A7.07 7.07 0 0 0 18 11c0-3.87-3.13-7-7-7-1.5 0-3 .5-4.21 1.4-3.09 2.32-3.72 6.71-1.4 9.8s6.71 3.72 9.8 1.4l.81.81V18l5 5 2-2zm-7 0c-2.76 0-5-2.24-5-5s2.24-5 5-5 5 2.24 5 5-2.24 5-5 5M3 6 1 8V1h7L6 3H3zm18-5v7l-2-2V3h-3l-2-2zM6 19l2 2H1v-7l2 2v3z"/></svg></span> <strong><a href="lvface-deep-dive/">Deep dive: LVFace-B Glint360K</a></strong></p>
 <hr />
-<p>The held-out generalization gap, how the error budget decomposes
-(extinction bridging at hard cuts, X-Ray's scene-membership vs.
-on-screen-face ceiling), and the frames where the pipeline is right
-and the ground truth is wrong.</p>
+<p>The held-out generalization gap, the two mechanisms behind its errors,
+and every distinct case where it names someone outside the film's
+credited cast.</p>
 </li>
 </ul>
 </div>
-<h2 id="the-full-technical-log">The full technical log<a class="headerlink" href="#the-full-technical-log" title="Permanent link">&para;</a></h2>
+<h2 id="full-experiment-log">Full experiment log<a class="headerlink" href="#full-experiment-log" title="Permanent link">&para;</a></h2>
 <ul>
-<li><strong><a href="model-bakeoff/">Model bake-off + threshold re-tune</a></strong> —
-  the complete experiment log behind the four pages above: the ROCm teardown
-  deadlock root cause and fix, DE concurrency tuning, the full 16-combo
-  results table, and every caveat. This is where the shipped
-  <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/config.hpp"><code>src/config.hpp</code></a> defaults come from.</li>
-<li><strong><a href="optimizer-experiments/">Optimizer experiments (prior round)</a></strong> — the
-  earlier scene-union-metric tuning pass, superseded by the per-second metric
-  used in the bake-off but kept for the ground-truth/architecture background.</li>
-<li><strong><a href="service-conversion/">Service conversion (proposal)</a></strong> — design sketch
-  for a native idle-GPU worker gated on screen lock, not yet built.</li>
+<li><strong><a href="model-bakeoff/">Full experiment log</a></strong>: the complete log behind the
+  four pages above, including how replaying against cached embeddings
+  inside the same KPN network makes a full model and configuration
+  comparison practical, the full results table, and every caveat. This is
+  where the shipped <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/config.hpp"><code>src/config.hpp</code></a>
+  defaults come from.</li>
+<li><strong><a href="service-conversion/">Service conversion (proposal)</a></strong>: design
+  sketch for a native idle-GPU worker gated on screen lock, not yet built.</li>
 </ul>
 <h2 id="reproducing-the-benchmarks">Reproducing the benchmarks<a class="headerlink" href="#reproducing-the-benchmarks" title="Permanent link">&para;</a></h2>
-<p>Gallery <code>.h5</code> files, embedding dumps, the X-Ray corpus, montage frame images,
-and DE trajectories are not committed to this repository — they're pushed to
-the Gitea package registry and pulled on demand:</p>
+<p>Gallery <code>.h5</code> files, embedding dumps, the X-Ray corpus, montage frame
+images, and DE trajectories are not committed to this repository. They are
+pushed to the Gitea package registry and pulled on demand:</p>
 <div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a>scripts/artifacts/pull_artifacts.sh<span class="w"> </span>galleries
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>scripts/artifacts/pull_artifacts.sh<span class="w"> </span>experiment-data
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>scripts/artifacts/pull_artifacts.sh<span class="w"> </span>montage-frames<span class="w"> </span>&lt;film-slug&gt;
 </span></code></pre></div>
-<p>See <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/artifacts/push_artifacts.sh"><code>scripts/artifacts/push_artifacts.sh</code></a>
-for the upload side (requires a <code>GITEA_TOKEN</code> with package write scope).</p>
+<p>See <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/artifacts/push_artifacts.sh"><code>scripts/artifacts/push_artifacts.sh</code></a>
+for the upload side, which requires a <code>GITEA_TOKEN</code> with package write
+scope.</p>
 
 
 
@@ -905,13 +907,13 @@ for the upload side (requires a <code>GITEA_TOKEN</code> with package write scop
         
         
           
-          <a href="best-model/" class="md-footer__link md-footer__link--next" aria-label="Next: Best Model">
+          <a href="methodology/" class="md-footer__link md-footer__link--next" aria-label="Next: How We Score Against X-Ray">
             <div class="md-footer__title">
               <span class="md-footer__direction">
                 Next
               </span>
               <div class="md-ellipsis">
-                Best Model
+                How We Score Against X-Ray
               </div>
             </div>
             <div class="md-footer__button md-icon">
diff --git a/lvface-deep-dive/index.html b/lvface-deep-dive/index.html
index 984149e..f07ae2d 100644
--- a/lvface-deep-dive/index.html
+++ b/lvface-deep-dive/index.html
@@ -242,6 +242,25 @@
   
   
   
+  
+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
     
   
   
@@ -272,26 +291,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -393,6 +393,33 @@
       
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -414,10 +441,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="">
             
   
   
@@ -435,8 +462,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -597,10 +624,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#what-good-looks-like" class="md-nav__link">
+  <a href="#baseline-correctly-scored-seconds" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What good looks like
+        Baseline: correctly scored seconds
       
     </span>
   </a>
@@ -619,10 +646,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#mechanism-1-extinction-bridging-usually-right-wrong-at-hard-cuts" class="md-nav__link">
+  <a href="#mechanism-1-extinction-bridging" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Mechanism 1: extinction bridging — usually right, wrong at hard cuts
+        Mechanism 1: extinction bridging
       
     </span>
   </a>
@@ -638,6 +665,78 @@
     </span>
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#every-distinct-out-of-cast-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Every distinct out-of-cast name
+      
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Every distinct out-of-cast name">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#the-many-saints-of-newark-4-names" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        The Many Saints of Newark: 4 names
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#lord-of-war-3-names" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Lord of War: 3 names
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#lovelace-1-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Lovelace: 1 name
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#scarface-1-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Scarface: 1 name
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#summary-of-the-nine" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Summary of the nine
+      
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
         <li class="md-nav__item">
@@ -692,34 +791,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -786,10 +858,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#what-good-looks-like" class="md-nav__link">
+  <a href="#baseline-correctly-scored-seconds" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What good looks like
+        Baseline: correctly scored seconds
       
     </span>
   </a>
@@ -808,10 +880,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#mechanism-1-extinction-bridging-usually-right-wrong-at-hard-cuts" class="md-nav__link">
+  <a href="#mechanism-1-extinction-bridging" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Mechanism 1: extinction bridging — usually right, wrong at hard cuts
+        Mechanism 1: extinction bridging
       
     </span>
   </a>
@@ -827,6 +899,78 @@
     </span>
   </a>
   
+</li>
+      
+        <li class="md-nav__item">
+  <a href="#every-distinct-out-of-cast-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Every distinct out-of-cast name
+      
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Every distinct out-of-cast name">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#the-many-saints-of-newark-4-names" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        The Many Saints of Newark: 4 names
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#lord-of-war-3-names" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Lord of War: 3 names
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#lovelace-1-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Lovelace: 1 name
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#scarface-1-name" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Scarface: 1 name
+      
+    </span>
+  </a>
+  
+</li>
+        
+          <li class="md-nav__item">
+  <a href="#summary-of-the-nine" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        Summary of the nine
+      
+    </span>
+  </a>
+  
+</li>
+        
+      </ul>
+    </nav>
+  
 </li>
       
         <li class="md-nav__item">
@@ -869,43 +1013,44 @@
 
 
 <h1 id="deep-dive-lvface-b-glint360k">Deep dive: LVFace-B Glint360K<a class="headerlink" href="#deep-dive-lvface-b-glint360k" title="Permanent link">&para;</a></h1>
-<p>LVFace won the model bake-off (see <a href="../best-model/">Which model is best?</a>) and is
-the shipped default embedder. This page is the honest accounting of how it
-actually performs — what a good second looks like, where the errors actually
-come from, and two cases where the ground truth itself is wrong and LVFace is
-right.</p>
+<p>LVFace won the model comparison (see <a href="../best-model/">Which model is best?</a>)
+and is the shipped default embedder. This page reports how it performs in
+detail: a baseline of correct output, the two mechanisms behind its errors,
+and every distinct case where it names someone who is not in the film's
+credited cast.</p>
+<p>Read <a href="../methodology/">How we score against X-Ray</a> first. X-Ray's ground truth
+is scene-level, not per-frame. A name marked correct in the Offscreen column
+below is the pipeline correctly reporting scene membership, not a workaround.</p>
 <div class="admonition note">
 <p class="admonition-title">How to read the frames on this page</p>
-<p>The top is the film frame, with a box and name on every face the pipeline
-identified. The bottom panels are the per-second verdict against X-Ray:
-<strong>Onscreen</strong> lists faces named in the frame, <strong>Offscreen</strong> lists cast
-X-Ray marks present in the scene without a visible face — presence
-carried by the tracker's windows, not by a detection. Colors are the
-score: <span style="color:#0ca30c"><strong>green</strong></span> = correct (TPI),
-<span style="color:#eb6834"><strong>orange</strong></span> = wrong (FPI),
-<span style="color:#3987e5"><strong>blue</strong></span> = missed (FN).</p>
+<p>The top of each image is the film frame, with a box and name on every
+face the pipeline matched to a real detection. The panels below are the
+per-second result against X-Ray. <strong>Onscreen</strong> lists names attached to a
+visible face this second. <strong>Offscreen</strong> lists names the pipeline reports
+present without a currently visible face. Colors mark the verdict:
+<span style="color:#0ca30c"><strong>green</strong></span> correct (TPI),
+<span style="color:#eb6834"><strong>orange</strong></span> wrong (FPI),
+<span style="color:#3987e5"><strong>blue</strong></span> missed (FN).</p>
 </div>
-<h2 id="what-good-looks-like">What good looks like<a class="headerlink" href="#what-good-looks-like" title="Permanent link">&para;</a></h2>
+<h2 id="baseline-correctly-scored-seconds">Baseline: correctly scored seconds<a class="headerlink" href="#baseline-correctly-scored-seconds" title="Permanent link">&para;</a></h2>
 <p><img alt="Wedding couple correctly identified, Downton Abbey: A New Era" src="../assets/images/downton_wedding_couple.jpg" /></p>
-<p>Six faces on screen, all six named correctly — including Penelope Wilton at the
-edge of the pews and a half-occluded Michelle Dockery — while thirteen more
-cast members X-Ray marks present in the scene are correctly carried as
-"Offscreen" by their presence windows. One miss in the whole frame: Maggie
-Smith (blue). Score for this second: 0.86.</p>
+<p>Six faces on screen, all six named correctly, including Penelope Wilton at
+the edge of the pews and a partly occluded Michelle Dockery. Thirteen more
+cast members X-Ray lists as present in the scene are correctly reported
+Offscreen. One miss: Maggie Smith (blue). Score for this second: 0.86.</p>
 <p><img alt="19 of 20 correct in the funeral crowd" src="../assets/images/downton_funeral_19of20.jpg" /></p>
-<p>The same film's funeral gathering: mourning dress, hats, half the faces turned.
-<strong>Nineteen of the twenty cast X-Ray lists for this scene are scored correctly</strong>
-— seven named on screen at up to 100% confidence, twelve more correctly held
-as present off-screen.</p>
-<p>And the pipeline doesn't need the face to be <em>real</em>:</p>
+<p>The same film's funeral scene: dark clothing, hats, half the faces turned
+away. Nineteen of the twenty cast members X-Ray lists for this scene score
+correct: seven named on screen at up to 100% confidence, twelve more reported
+correctly as present but not visible.</p>
 <p><img alt="Herbie Hancock identified on an in-fiction video call" src="../assets/images/valerian_screen_call.jpg" /></p>
-<p>That's Herbie Hancock at 98% — as a face on a <em>screen inside the movie</em>, over a
-sci-fi HUD overlay, during a video call in Valerian. A face is a face, whether
-it's in the room or on the bridge's comms display.</p>
+<p>The pipeline does not require a live face. This is Herbie Hancock at 98%
+confidence, identified from a face displayed on a screen inside the film, on
+a video call under a science-fiction HUD overlay.</p>
 <h2 id="training-vs-held-out-the-generalization-gap">Training vs. held-out: the generalization gap<a class="headerlink" href="#training-vs-held-out-the-generalization-gap" title="Permanent link">&para;</a></h2>
-<p>The shipped config (<code>prob_threshold=0.754, anneal_sec=35.54,
-extinction_sec=57.43, expand_gallery=true</code>) was tuned against 4 films. Scored
-against the 5 films the optimizer never saw:</p>
+<p>The shipped config (<code>prob_threshold=0.754</code>, <code>anneal_sec=35.54</code>,
+<code>extinction_sec=57.43</code>, <code>expand_gallery=true</code>) was tuned on 4 films. Scored
+on the 5 films the optimizer never saw:</p>
 <p><img alt="Held-out per-film F1 vs. the training-set fit" src="../assets/images/holdout_f1_by_film.png" /></p>
 <table>
 <thead>
@@ -962,18 +1107,18 @@ against the 5 films the optimizer never saw:</p>
 <td>80084</td>
 </tr>
 <tr>
-<td><strong>The Many Saints of Newark</strong></td>
-<td><strong>46.3%</strong></td>
-<td><strong>54.7%</strong></td>
+<td>The Many Saints of Newark</td>
+<td>46.3%</td>
+<td>54.7%</td>
 <td>40.1%</td>
 <td>15922</td>
 <td>4394</td>
-<td><strong>974</strong></td>
+<td>974</td>
 <td>23791</td>
 </tr>
 <tr>
-<td><strong>macro average</strong></td>
-<td><strong>67.4%</strong></td>
+<td>macro average</td>
+<td>67.4%</td>
 <td>85.8%</td>
 <td>57.0%</td>
 <td></td>
@@ -983,112 +1128,252 @@ against the 5 films the optimizer never saw:</p>
 </tr>
 </tbody>
 </table>
-<p><strong>67.4% held-out vs. 75.3% on training</strong> — an ~8pp drop, and a <strong>37pp spread
-between the best and worst held-out film</strong>. The config does not generalize
-uniformly, and the spread traces to two mechanisms, both visible frame by
-frame below.</p>
-<h2 id="mechanism-1-extinction-bridging-usually-right-wrong-at-hard-cuts">Mechanism 1: extinction bridging — usually right, wrong at hard cuts<a class="headerlink" href="#mechanism-1-extinction-bridging-usually-right-wrong-at-hard-cuts" title="Permanent link">&para;</a></h2>
-<p>The extinction window keeps an identity alive through seconds where no face is
-detectable. <strong>Most of the time this is exactly what you want</strong>, and it's where
-a lot of the TPI count comes from:</p>
+<p>The <code>P</code> column is misID-weighted (each out-of-film name counts 10x in the
+denominator; see <a href="../methodology/#precision-recall-and-the-misid-weighting">methodology</a>).
+That weighting is why Many Saints reads 54.7% here despite naming mostly real,
+present faces: its raw (unweighted) precision is <strong>78.4%</strong>, and the gap is
+entirely its 974 misIDs paying the 10x penalty. The three zero-misID films
+(Benny &amp; Joon, Downton, Valerian) have identical weighted and raw precision;
+Lovelace, with 58 misIDs, sits 3pp below its raw 93.3%.</p>
+<p>Held-out F1 is 67.4%, against 75.3% on training, an 8pp drop. The spread
+between the best and worst held-out film is 37pp. This is not unique to
+LVFace: <a href="../model-bakeoff/#held-out-validation-all-3-models">the full experiment log</a>
+shows mbf and r18 with the same shape of spread on the same films, at a
+uniformly lower level. Two mechanisms explain the spread. Both are shown
+below with frame-level evidence.</p>
+<h2 id="mechanism-1-extinction-bridging">Mechanism 1: extinction bridging<a class="headerlink" href="#mechanism-1-extinction-bridging" title="Permanent link">&para;</a></h2>
+<p>The extinction window keeps a name reported as present for up to
+<code>extinction_sec</code> after its last real detection. This is deliberate: most
+gaps in face visibility are short (a turned head, an occlusion, a cut to a
+reaction shot), and the window bridges them.</p>
 <p><img alt="Two faces on screen, six more correctly bridged" src="../assets/images/lovelace_polygraph_bridged.jpg" /></p>
-<p>Lovelace's polygraph scene: only Eric Roberts and Amanda Seyfried have visible
-faces, but X-Ray lists eight cast present — and all eight score green, the
-other six correctly carried by presence windows through a scene where the
-camera never shows them. A perfect second, and the extinction/anneal machinery
-is <em>why</em>.</p>
-<p>The same mechanism has a failure case: a hard cut into long faceless footage.
-Both Many Saints of Newark (974 misIDs) and Downton Abbey (FN=80084, the worst
-recall of the five) are dominated by it — verified directly against the raw
-per-frame stream and the HDF5 dump's own detection counts, not inferred from
-the score alone. <strong>This is not a malfunction</strong>: the tracker is doing exactly
-what its window is for; the footage just stops cooperating. In the debug
-overlay (which draws a bridged identity's last-known bbox, unlike the shipped
-output, which emits presence windows and no boxes at all) the bridged state is
-visible spatially:</p>
-<p><img alt="Debug overlay: bridged identities drawn at their last-known positions" src="../assets/images/many_saints_ghost_fpi.jpg" />
-<em>Debug-overlay rendering (<code>dump_error_frames.py --raw</code>): "Jon Bernthal", "Joey
-Diaz" and "Billy Magnussen" are extinction-bridged identities from the previous
-shot, drawn frozen over the wall and the hanging plates. Frame
-<code>many_saints/fpi/fpi_t03543.jpg</code>, <code>montage-frames</code> artifact package.</em></p>
-<p>The cost is measurable, not just visible. Downton Abbey's hard cut into its
-closing credits, plotting the dump's own per-second <code>face_count</code> (detector
-output, independent of the tracker) against what the tracker reports:</p>
+<p>Lovelace's polygraph scene: only Eric Roberts and Amanda Seyfried have
+visible faces. X-Ray lists eight cast members present. All eight score
+correct; the other six are reported Offscreen through a stretch where the
+camera never shows them. The extinction window is why.</p>
+<p>The same mechanism fails at a hard cut into a long stretch with no faces at
+all. Downton Abbey's recall (39.4%, the worst of the five held-out films) is
+dominated by this failure. It is verified directly against the raw
+per-frame stream and the dump's own detection counts, not inferred from the
+score. Plotting the dump's per-second <code>face_count</code> (detector output,
+independent of the tracker) against what the tracker reports, through
+Downton Abbey's hard cut into its closing credits:</p>
 <p><img alt="Detector vs. tracker through Downton Abbey's cut to credits" src="../assets/images/downton_ghost_timeline.png" /></p>
-<p>From the cut onward the detector sees <strong>zero faces for nearly a minute</strong> — and
-the tracker keeps reporting the last shot's 15 identities the whole time
-(verified for Hugh Bonneville: bbox <code>(1743.2, 0.0, 171.3, 317.8)</code>, unchanged to
-the pixel, at every sampled second for 57+ seconds). The staircase at the right
-edge is the extinction window expiring actor by actor. That plateau is
-<code>SceneTrackerFunc::active_[actor_idx].last_bbox</code>
-(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/nodes/scene_tracker_node.hpp"><code>src/nodes/scene_tracker_node.hpp</code></a>)
-re-emitted as designed: <code>extinction_sec=57.4</code> was tuned long because bridging
-wins on most footage (see the polygraph frame above) — the training films just
-never contained a faceless stretch long enough to show the cost side, and the
-held-out set did.</p>
-<p>The same track-continuation machinery has one milder spatial artifact, worth
-knowing when reading these frames:</p>
-<p><img alt="Two labels on one face after a shot/reverse-shot cut" src="../assets/images/cafe_society_rapid_cut.jpg" />
-<em>Café Society (a training film), a shot/reverse-shot dialog: that is Steve
-Carell wearing both his own label and Jesse Eisenberg's.</em></p>
-<p>At a rapid cut, the previous shot's track can linger for a beat at nearly the
-same screen position the new face occupies — here Jesse Eisenberg's box from
-the counter-shot lands on Steve Carell. Note what the score panel says,
-though: both actors are green, because both <em>are</em> present in this dialog
-scene per X-Ray. The spatial label is briefly wrong; the per-second presence
-claim — the thing the pipeline actually ships — is right. It's the same trade
-as the extinction window: track continuation smooths over cuts, and 1 fps
-sampling occasionally catches the seam.</p>
+<p>From the cut onward the detector reports zero faces for close to a minute.
+The tracker continues reporting the previous shot's 15 identities for the
+same span (verified for Hugh Bonneville: bbox <code>(1743.2, 0.0, 171.3, 317.8)</code>,
+unchanged to the pixel, at every sampled second for 57 seconds). The
+staircase at the right edge is the extinction window expiring, actor by
+actor. This is <code>SceneTrackerFunc::active_[actor_idx].last_bbox</code>
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/nodes/scene_tracker_node.hpp"><code>src/nodes/scene_tracker_node.hpp</code></a>)
+re-emitted as designed. <code>extinction_sec=57.4</code> was tuned long because
+bridging is correct on most footage, as in the polygraph scene above. The
+training films did not contain a faceless stretch long enough to expose the
+cost side; the held-out set did.</p>
+<p>The extinction window is a scoring concept, not something drawn on screen.
+The shipped output is presence windows with no bounding boxes. Even the
+debug overlay used for this report never draws a box for a bridged name: a
+name inside its extinction window with no current detection appears only as
+a name in the Offscreen column, the same as every correctly bridged name
+above.</p>
+<p>A related, smaller effect shows up at rapid cuts:</p>
+<p><img alt="Two labels on one face after a shot/reverse-shot cut" src="../assets/images/cafe_society_rapid_cut.jpg" /></p>
+<p>Café Society (a training film), a shot/reverse-shot dialog. The box on Steve
+Carell's face carries two labels: his own, and Jesse Eisenberg's, left over
+from the counter-shot a moment earlier. Both names score correct, because
+both actors are present in this scene per X-Ray. The box position is
+briefly wrong; the presence claim, which is what the pipeline ships, is
+right.</p>
 <h2 id="mechanism-2-the-face-vs-presence-ceiling">Mechanism 2: the face-vs-presence ceiling<a class="headerlink" href="#mechanism-2-the-face-vs-presence-ceiling" title="Permanent link">&para;</a></h2>
-<p>Downton Abbey's recall didn't collapse because faces were misread — it
-collapsed because for most of its 80084 FN-seconds there was <strong>no face to
-read</strong>:</p>
+<p>Downton Abbey's recall did not collapse because faces were misread. It
+collapsed because for most of its 80084 false-negative seconds there was no
+face to read.</p>
 <p><img alt="22 cast credited, nobody facing the camera" src="../assets/images/downton_crew_fn.jpg" /></p>
-<p>A newsreel crew hauls equipment through the hall: X-Ray credits 22 cast as
-present in this scene; not one face looks at the camera. Eight are still
-scored green (windows bridging from adjacent shots) — the other fourteen are
-blue FNs that no face-recognition pipeline could ever recover. X-Ray encodes
-<em>scene membership</em>; the pipeline measures <em>on-screen faces</em>. In ensemble films
-those two definitions diverge massively, and that gap — not identification
-error — is most of what the FN column counts.</p>
-<p><img alt="Presence without a detectable face, The Many Saints of Newark" src="../assets/images/many_saints_outofcast_fpi.jpg" /></p>
-<p>Same ceiling from the other side: Michela De Rossi in frame but turned away,
-five cast correctly bridged as offscreen (green), four blue FNs — and one
-orange we'll come back to below.</p>
+<p>A newsreel crew moves equipment through the hall. X-Ray credits 22 cast
+members as present in this scene. None face the camera. Eight still score
+correct, carried by presence windows from adjacent shots. The other fourteen
+are missed, and no face-recognition system can recover them, because there
+is no face in the frame. X-Ray records scene membership; the pipeline
+measures visible faces. In ensemble scenes these two quantities diverge, and
+that gap accounts for most of the false-negative count.</p>
+<h2 id="every-distinct-out-of-cast-name">Every distinct out-of-cast name<a class="headerlink" href="#every-distinct-out-of-cast-name" title="Permanent link">&para;</a></h2>
+<p>Many Saints of Newark has the largest misID count of any held-out film: 974
+seconds, weighted. Rather than characterize this from a single frame, the
+raw replay stream was searched directly for every name the pipeline reports
+that is not in the film's credited cast. The same search was run on all 9
+films in the benchmark, one rule applied uniformly: <strong>find the first second
+each distinct out-of-cast name appears, and render that exact second.</strong></p>
+<p>Five films produce no such name anywhere in their runtime: Benny &amp; Joon,
+Café Society, Downton Abbey, Sound of Metal, Valerian. Zero out-of-cast
+names across their entire length. Four films produce nine distinct names
+between them, shown below in full, not a sample.</p>
+<h3 id="the-many-saints-of-newark-4-names">The Many Saints of Newark: 4 names<a class="headerlink" href="#the-many-saints-of-newark-4-names" title="Permanent link">&para;</a></h3>
+<p><img alt="Germar Terrell Gardner, first out-of-cast name in Many Saints" src="../assets/images/many_saints_fpi_gardner.jpg" /></p>
+<p>Germar Terrell Gardner, t=848s, 78% confidence. A real, clearly visible
+background actor. He is not in X-Ray's cast list for this film, but he is
+credited in Jellyfin's independent cast metadata (see
+<a href="#where-lvface-beat-x-ray">Where LVFace beat X-Ray</a> below). This is a
+ground-truth gap, not a model error.</p>
+<p><img alt="Archie Yates, second out-of-cast name in Many Saints" src="../assets/images/many_saints_fpi_yates.jpg" /></p>
+<p>Archie Yates, t=2521s, 78% confidence. A real detected face, a genuine
+lookalike confusion.</p>
+<p><img alt="Zooey Deschanel, third out-of-cast name in Many Saints" src="../assets/images/many_saints_fpi_deschanel.jpg" /></p>
+<p>Zooey Deschanel, t=2819s, 99% confidence. A real detected face at a dinner
+table, high-confidence lookalike confusion.</p>
+<p><img alt="Talia Balsam, fourth out-of-cast name in Many Saints" src="../assets/images/many_saints_fpi_balsam.jpg" /></p>
+<p>Talia Balsam, t=4551s, 93% confidence. A real detected face. Talia Balsam
+plays Mrs. Jarecki, a guidance counselor, in this film; she is confirmed
+on screen by direct inspection of the frame. She does not appear in X-Ray's
+<code>people.csv</code> for this title. This is a second ground-truth gap in the same
+film, not a model error.</p>
+<p>Two of these four names are ground-truth gaps (Gardner, Balsam), not
+misidentifications. The other two (Yates, Deschanel) are genuine embedding
+errors on real faces.</p>
+<h3 id="lord-of-war-3-names">Lord of War: 3 names<a class="headerlink" href="#lord-of-war-3-names" title="Permanent link">&para;</a></h3>
+<p><img alt="David Shumbris, first out-of-cast name in Lord of War" src="../assets/images/lord_of_war_fpi_shumbris.jpg" /></p>
+<p>David Shumbris, t=418s, 81% confidence. A real face in a dim, low-detail
+shot under a train track. A genuine lookalike confusion in poor lighting.</p>
+<p><img alt="Ronald Reagan, second out-of-cast name in Lord of War" src="../assets/images/lord_of_war_fpi_reagan_photo.jpg" /></p>
+<p>Ronald Reagan, t=1003s, 100% confidence. This is not a lookalike confusion.
+The detected face is a photograph of Reagan appearing within the shot, not a
+living actor. The detector and matcher both did their job correctly on the
+image content in front of them; the error is that a photograph inside the
+scene is not the same thing as an actor present in the scene, and the
+pipeline has no way to draw that distinction from a face crop alone.</p>
+<p><img alt="Lance Reddick, third out-of-cast name in Lord of War" src="../assets/images/lord_of_war_fpi_reddick.jpg" /></p>
+<p>Lance Reddick, t=6424s, 78% confidence. A small, distant, low-detail face at
+the edge of frame. A marginal, low-confidence lookalike confusion.</p>
+<h3 id="lovelace-1-name">Lovelace: 1 name<a class="headerlink" href="#lovelace-1-name" title="Permanent link">&para;</a></h3>
+<p><img alt="Chloë Sevigny, out-of-cast name in Lovelace" src="../assets/images/lovelace_fpi_sevigny.jpg" /></p>
+<p>Chloë Sevigny, t=2451s, 100% confidence. Two boxes are drawn on the same
+face: one correctly labeled Amanda Seyfried, one incorrectly labeled Chloë
+Sevigny, both at 100%. A single detection producing two competing high-
+confidence identities on the same crop.</p>
+<h3 id="scarface-1-name">Scarface: 1 name<a class="headerlink" href="#scarface-1-name" title="Permanent link">&para;</a></h3>
+<p><img alt="Kirstie Alley, out-of-cast name in Scarface" src="../assets/images/scarface_fpi_alley.jpg" /></p>
+<p>Kirstie Alley, t=2451s, 89% confidence. Al Pacino is correctly identified in
+the foreground at 100%; a background face in the same shot is wrongly
+labeled Kirstie Alley. (The t=2451s here and the Lovelace Chloë Sevigny case
+above landing on the identical second is a genuine coincidence, verified from
+each film's raw stream by <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/docs/first_fpi_frames.py"><code>first_fpi_frames.py</code></a>,
+not a transcription slip, two unrelated films whose <em>first</em> out-of-cast name
+happens to fall at the same timestamp.)</p>
+<h3 id="summary-of-the-nine">Summary of the nine<a class="headerlink" href="#summary-of-the-nine" title="Permanent link">&para;</a></h3>
+<table>
+<thead>
+<tr>
+<th>film</th>
+<th>name</th>
+<th>t (s)</th>
+<th>confidence</th>
+<th>classification</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Many Saints of Newark</td>
+<td>Germar Terrell Gardner</td>
+<td>848</td>
+<td>78%</td>
+<td>ground-truth gap</td>
+</tr>
+<tr>
+<td>Many Saints of Newark</td>
+<td>Archie Yates</td>
+<td>2521</td>
+<td>78%</td>
+<td>lookalike confusion</td>
+</tr>
+<tr>
+<td>Many Saints of Newark</td>
+<td>Zooey Deschanel</td>
+<td>2819</td>
+<td>99%</td>
+<td>lookalike confusion</td>
+</tr>
+<tr>
+<td>Many Saints of Newark</td>
+<td>Talia Balsam</td>
+<td>4551</td>
+<td>93%</td>
+<td>ground-truth gap</td>
+</tr>
+<tr>
+<td>Lord of War</td>
+<td>David Shumbris</td>
+<td>418</td>
+<td>81%</td>
+<td>lookalike confusion</td>
+</tr>
+<tr>
+<td>Lord of War</td>
+<td>Ronald Reagan</td>
+<td>1003</td>
+<td>100%</td>
+<td>photo-in-frame</td>
+</tr>
+<tr>
+<td>Lord of War</td>
+<td>Lance Reddick</td>
+<td>6424</td>
+<td>78%</td>
+<td>lookalike confusion, marginal</td>
+</tr>
+<tr>
+<td>Lovelace</td>
+<td>Chloë Sevigny</td>
+<td>2451</td>
+<td>100%</td>
+<td>lookalike confusion</td>
+</tr>
+<tr>
+<td>Scarface</td>
+<td>Kirstie Alley</td>
+<td>2451</td>
+<td>89%</td>
+<td>lookalike confusion</td>
+</tr>
+</tbody>
+</table>
+<p>Of nine distinct out-of-cast names across four films, two are ground-truth
+gaps, one is a photograph misread as a person, and six are genuine
+embedding-space confusions on real detected faces. None trace to extinction
+bridging: every one of these nine is a fresh detection on a real face crop
+at the second it first appears.</p>
 <h2 id="where-lvface-beat-x-ray">Where LVFace beat X-Ray<a class="headerlink" href="#where-lvface-beat-x-ray" title="Permanent link">&para;</a></h2>
-<p>Not every orange in these frames is actually wrong.
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/second_score.py"><code>scripts/optimizer/second_score.py</code></a>
-scores strictly against X-Ray — but X-Ray itself has holes, and the pipeline
-found two kinds.</p>
+<p>Not every name marked wrong is actually wrong.
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/second_score.py"><code>scripts/optimizer/second_score.py</code></a>
+scores strictly against X-Ray, and X-Ray has gaps of its own.</p>
 <p><img alt="LVFace correctly identifies Germar Terrell Gardner, uncredited by X-Ray" src="../assets/images/germar_beats_xray.jpg" /></p>
-<p>Germar Terrell Gardner — a real, clean, high-confidence detection — is counted
-as an out-of-cast misID because he doesn't appear in X-Ray's <code>people.csv</code> for
-The Many Saints of Newark at all. But Jellyfin's independent cast metadata
-<em>does</em> credit him for this exact film (cross-checked via
-<code>experiments/manifests/jellyfin_casts.json</code> from the <code>experiment-data</code> artifact
-package, a completely separate data source from X-Ray). That's also him in
-orange in the frame above — every one of those "errors" is the pipeline being
-right about a person X-Ray forgot.</p>
+<p>Germar Terrell Gardner, the same name from the table above, does not appear
+in X-Ray's <code>people.csv</code> for The Many Saints of Newark. Jellyfin's
+independent cast metadata does credit him for this film (cross-checked
+against <code>experiments/manifests/jellyfin_casts.json</code> from the
+<code>experiment-data</code> artifact package, a data source entirely separate from
+X-Ray). Talia Balsam is the same case: confirmed on screen, absent from
+X-Ray's cast list for this title.</p>
 <p><img alt="Robert Patrick, clearly on screen, scored wrong by a ground-truth gap" src="../assets/images/lovelace_robert_patrick_fpi.jpg" /></p>
-<p>And it isn't only uncredited bit-parts. That is <strong>Robert Patrick</strong> — top-billed
-in Lovelace, unmistakably on screen, reading his newspaper, identified at
-100% — scored orange because X-Ray's people-in-scene list for <em>this scene</em>
-doesn't include him. The identification is flawless; the ground truth missed
-an actor sitting in the middle of the frame.</p>
-<p>This doesn't mean every flagged misID is secretly correct — Many Saints'
-974-count total is still overwhelmingly extinction bridging at cuts, not
-uncredited cameos. But the X-Ray corpus is a convenient, large-scale ground
-truth, not a perfect one, and the misID/FPI numbers in these tables carry an
-irreducible noise floor from ground-truth gaps in both directions.</p>
+<p>This extends past uncredited background actors. This is Robert Patrick,
+top-billed in Lovelace, clearly on screen reading a newspaper, identified at
+100%. The frame is scored wrong because X-Ray's people-in-scene list for
+this specific scene omits him, despite crediting him elsewhere in the film.
+The identification is correct; the ground truth is missing an entry.</p>
+<p>X-Ray is a large, convenient ground truth. It is not a complete one. The
+misID and FPI counts reported throughout this document include some fixed
+amount of noise from gaps in X-Ray itself, in both directions.</p>
 <h2 id="summary">Summary<a class="headerlink" href="#summary" title="Permanent link">&para;</a></h2>
-<p>LVFace is the right default: it wins the model comparison outright, it names
-19 of 20 correctly across a hat-heavy funeral crowd, and it recognises a face
-on a screen inside the movie. Its error budget decomposes into two understood
-mechanisms — extinction bridging at hard cuts (a tunable trade, not a bug) and
-the face-vs-presence ceiling baked into X-Ray's semantics — plus a nonzero
-slice where the pipeline is right and the ground truth is wrong. The held-out
-generalization gap (75.3% → 67.4%) is real and should be treated as the honest
-expected performance, not the training-set number.</p>
+<p>LVFace wins the model comparison on every held-out film. It correctly names
+19 of 20 people in a crowded funeral scene and correctly identifies a face
+displayed on a screen inside the film. Its errors resolve into two
+mechanisms: extinction bridging, which is correct on most footage and fails
+specifically at hard cuts into long faceless stretches, and the
+face-versus-presence ceiling, where X-Ray credits scene membership for
+people whose faces never appear on screen. Of the nine distinct
+out-of-cast identifications found across the benchmark, two trace to gaps in
+X-Ray's own cast data, one is a photograph misread as a person, and six are
+genuine lookalike confusions on real faces. The held-out generalization gap,
+75.3% training to 67.4% held-out, is real and should be treated as the
+expected operating point, not the training-set figure.</p>
 
 
 
@@ -1141,13 +1426,13 @@ expected performance, not the training-set number.</p>
         
         
           
-          <a href="../model-bakeoff/" class="md-footer__link md-footer__link--next" aria-label="Next: Model Bake-off &amp; Re-tune (full log)">
+          <a href="../model-bakeoff/" class="md-footer__link md-footer__link--next" aria-label="Next: Full Experiment Log">
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+                Full Experiment Log
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diff --git a/optimizer-experiments/index.html b/methodology/index.html
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-        <link rel="canonical" href="https://pages.tourolle.paris/dtourolle/scene-actor-extraction/optimizer-experiments/">
+        <link rel="canonical" href="https://pages.tourolle.paris/dtourolle/scene-actor-extraction/methodology/">
       
       
-        <link rel="prev" href="../model-bakeoff/">
+        <link rel="prev" href="..">
       
       
-        <link rel="next" href="../service-conversion/">
+        <link rel="next" href="../best-model/">
       
       
         
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-        <title>Optimizer Experiments (prior round) - scene-actor-extraction</title>
+        <title>How We Score Against X-Ray - scene-actor-extraction</title>
       
     
     
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+    <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
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+        <li class="md-nav__item">
+  <a href="#what-amazon-x-ray-records" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        What Amazon X-Ray records
+      
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+      
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+        What this resolves and what it does not
+      
+    </span>
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+  <a href="#reproduce" class="md-nav__link">
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+        What Amazon X-Ray records
       
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+        What this resolves and what it does not
       
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-  <a href="#the-gallery-coverage-gap" class="md-nav__link">
+  <a href="#precision-recall-and-the-misid-weighting" class="md-nav__link">
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-        The gallery coverage gap
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+        Precision, recall, and the misID weighting
       
     </span>
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-<h1 id="threshold-optimization-against-amazon-x-ray-experiment-log">Threshold optimization against Amazon X-Ray — experiment log<a class="headerlink" href="#threshold-optimization-against-amazon-x-ray-experiment-log" title="Permanent link">&para;</a></h1>
-<p>Record of the July 2026 work that tuned the pipeline's recognition/tracking defaults
-against ground-truth per-scene actor presence, and the tooling built to do it.</p>
-<h2 id="tldr-what-changed">TL;DR — what changed<a class="headerlink" href="#tldr-what-changed" title="Permanent link">&para;</a></h2>
-<table>
-<thead>
-<tr>
-<th>knob</th>
-<th>old default</th>
-<th>new default</th>
-<th>why</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><code>prob_threshold</code></td>
-<td>0.99</td>
-<td><strong>0.76</strong></td>
-<td>0.99 was far too strict — halved recall for a fraction of a precision point. DE optimum, tightly converged.</td>
-</tr>
-<tr>
-<td><code>extinction_sec</code></td>
-<td>5.0</td>
-<td><strong>1.5</strong></td>
-<td>Long extinction smears presence into later scenes → FPs. DE converged tightly low.</td>
-</tr>
-<tr>
-<td><code>anneal_sec</code></td>
-<td>10.0</td>
-<td>10.0 (unchanged)</td>
-<td>DE found it <strong>insensitive</strong> (F1 flat ±0.3pp across 3–26s) — kept the round default.</td>
-</tr>
-<tr>
-<td><code>detector_conf</code></td>
-<td>0.5</td>
-<td>0.5 (unchanged)</td>
-<td>Sweep showed raising it only trades recall for precision at a net F1 loss — near-threshold detections are real faces, not phantoms.</td>
-</tr>
-</tbody>
-</table>
-<p>Net effect on the 9-film benchmark (strict per-scene, augmented gallery):
-recall <strong>58% → ~72%</strong>, F1 <strong>70% → ~76%</strong>, precision ~85%, at no meaningful precision cost.</p>
-<h2 id="ground-truth">Ground truth<a class="headerlink" href="#ground-truth" title="Permanent link">&para;</a></h2>
-<p>Public scene-level <strong>Amazon X-Ray</strong> dataset (Zenodo DOI 10.5281/zenodo.17659734,
-CC-BY-4.0): per movie, <code>people.csv</code> (name_id/person/character), <code>scenes.csv</code>
-(scene/start/end ms), <code>people_in_scenes.csv</code>. Films matched to the library by an
-<strong>authoritative Jellyfin ID join</strong> (query <code>/Items?IncludeItemTypes=Movie&amp;Fields=
-ProviderIds,Path</code>, join Imdb/Tmdb against X-Ray metadata) — NOT fuzzy title matching,
-which collides badly (TV episodes vs same-named films). 9 genuine films with source
-video on disk: Benny &amp; Joon, Café Society, Downton Abbey: A New Era, Lord of War,
-Lovelace, The Many Saints of Newark, Scarface, Sound of Metal, Valerian.</p>
-<h2 id="the-scoring-metric-evolved-through-review">The scoring metric (evolved through review)<a class="headerlink" href="#the-scoring-metric-evolved-through-review" title="Permanent link">&para;</a></h2>
-<p>Comparison unit is the <strong>X-Ray scene</strong>, not sampled timepoints. For each scene
-<code>[start,end]</code>: predicted set = <strong>union</strong> of actors detected anywhere in the span;
-GT set = actors X-Ray lists for that scene. Per scene TP/FP/FN, then:</p>
-<ul>
-<li><strong>Precision: STRICT.</strong> Any predicted actor not in the scene's X-Ray set is an FP,
-  <em>including out-of-cast confusions</em> (no gallery∩cast masking). An earlier
-  timepoint-sampled, cast-masked metric HID ~570 such FPs across 9 films and let the
-  optimizer drive <code>prob_threshold</code> to the 0.50 floor — a metric artifact. Counting
-  them is essential.</li>
-<li><strong>Recall: FAIR.</strong> FN counts only X-Ray cast members <strong>who are in the gallery</strong>. 67%
-  of X-Ray cast (261/392) have no gallery reference embedding and can never be
-  recognised — counting them as misses penalises coverage, not the threshold. Both
-  <code>recall</code> (fair) and <code>recall_strict</code> (all) are reported.</li>
-<li><strong>Aggregation:</strong> per-scene F1 → <strong>duration-weighted average within a movie</strong> (long
-  scenes count more) → <strong>equal-weight mean across movies</strong> (macro; each film counts
-  the same regardless of length). This is the DE objective.</li>
-</ul>
-<p>Implemented in <code>scripts/optimizer/scene_score.py</code> — since <strong>removed</strong> along
-with this metric; its per-second successor is
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/second_score.py"><code>scripts/optimizer/second_score.py</code></a>
-(see the <a href="../model-bakeoff/">bake-off round</a>).</p>
-<h2 id="the-gallery-coverage-gap">The gallery coverage gap<a class="headerlink" href="#the-gallery-coverage-gap" title="Permanent link">&para;</a></h2>
-<p>Diagnosing low recall: only <strong>131 of 392</strong> X-Ray cast were in the gallery (33%). Every
-in-gallery actor HAD embeddings (gallery well-formed) — the gap was pure coverage.
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/fetch_missing_actors.py"><code>scripts/optimizer/fetch_missing_actors.py</code></a>
-recovers missing actors:
-<code>nm-id → TMDB /find external_ids → /person/{id}/images → download → embed (sae_embed)</code>,
-with a <code>--wikidata</code> fallback (P345→P18 Commons photo).</p>
-<ul>
-<li><strong>TMDB recovered 143/261</strong> (55%). 0 face-detection failures; the rest had no TMDB
-  person (60) or no profile photo (58). Coverage 33% → <strong>70%</strong>.</li>
-<li><strong>Wikidata fallback: 0/118</strong> of the TMDB failures — only 4 even had a Commons photo,
-  none yielded a detectable face. → <strong>TheTVDB not worth pursuing</strong>: these remaining
-  actors are obscure enough that no image source covers them, AND (see below) most are
-  off-camera anyway.</li>
-</ul>
-<p><strong>Coverage vs detectability.</strong> Adding references lifted recall (58→68% at fixed config)
-but modestly. Per-film drill-down (Lord of War: 12 actors recovered, only 1 had a
-detectable on-camera face) showed most missing cast are a <strong>detectability gap</strong> — X-Ray
-credits them as cast-in-scene (incl. off-camera/background), but their face never
-appears clearly for the pipeline to detect. This is a fundamental ceiling of a
-face-recognition pipeline vs X-Ray's presence semantics, not a fixable gap.</p>
-<h2 id="optimizer">Optimizer<a class="headerlink" href="#optimizer" title="Permanent link">&para;</a></h2>
-<p><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/optimize.py"><code>scripts/optimizer/optimize.py</code></a>
-— scipy <code>differential_evolution</code> over the knob space,
-each candidate = full replay of all films through the <strong>real</strong> C++ nodes (see the
-KPN replay architecture below) scored by the metric above. Global objective (one
-config for all films, not per-film).</p>
-<p><strong>Convergence stability (augmented gallery, 233 evals):</strong></p>
-<table>
-<thead>
-<tr>
-<th>knob</th>
-<th>top-20 range</th>
-<th>verdict</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td><code>prob_threshold</code></td>
-<td>0.69–0.83 (σ 0.05)</td>
-<td>TIGHT — trust 0.76</td>
-</tr>
-<tr>
-<td><code>extinction_sec</code></td>
-<td>1.0–2.2 (σ 0.33)</td>
-<td>TIGHT — trust 1.5</td>
-</tr>
-<tr>
-<td><code>anneal_sec</code></td>
-<td>3.1–26.3 (σ 6.4)</td>
-<td>LOOSE — insensitive, not hard-coded</td>
-</tr>
-</tbody>
-</table>
-<p>F1 varied only 0.3pp across the top-20 → objective is flat near the optimum, so only
-the tightly-converged knobs were adopted as defaults.</p>
-<h2 id="replay-architecture-how-the-sweep-is-cheap">Replay architecture (how the sweep is cheap)<a class="headerlink" href="#replay-architecture-how-the-sweep-is-cheap" title="Permanent link">&para;</a></h2>
-<p>The optimizer never re-decodes video. <code>scene_analyze --dump-embeddings out.h5</code> runs the
-expensive half once (decode→detect→align→embed) and dumps per-frame face embeddings
-+ metadata to HDF5 (<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/SCHEMA.md"><code>scripts/optimizer/SCHEMA.md</code></a>).
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/replay.py"><code>scripts/optimizer/replay.py</code></a> then
-replays that dump through the <strong>real</strong> C++ <code>face_tracker → identity_matcher →
-scene_tracker</code> assembled in a Python KPN network (<code>sae_kpn</code> nanobind module), varying
-Config knobs freely — no GPU embedding, no decode. Verified BYTE-EXACT against
-<code>scene_analyze</code>'s own output. The dumps are gallery-independent, so testing the
-augmented gallery needed no re-dump. <code>detector_conf</code> is replayable UPWARD only (the
-dump floor is 0.5).</p>
-<h2 id="reproduce">Reproduce<a class="headerlink" href="#reproduce" title="Permanent link">&para;</a></h2>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># 1. dump (once per film, needs video)</span>
-</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>scene_analyze<span class="w"> </span>--movie<span class="w"> </span>&lt;f&gt;<span class="w"> </span>--gallery<span class="w"> </span>gallery.json<span class="w"> </span>--dump-embeddings<span class="w"> </span>dump.h5<span class="w"> </span>--fps<span class="w"> </span><span class="m">1</span>
-</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a><span class="c1"># 2. build films manifest by Jellyfin ID join (see scripts/optimizer notes)</span>
-</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># 3. optimize</span>
-</span><span id="__span-0-5"><a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>python<span class="w"> </span>scripts/optimizer/optimize.py<span class="w"> </span>--manifest<span class="w"> </span>films.json<span class="w"> </span>--gallery<span class="w"> </span>gallery.json<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-6"><a id="__codelineno-0-6" name="__codelineno-0-6" href="#__codelineno-0-6"></a><span class="w">    </span>--params<span class="w"> </span>prob_threshold:0.5:0.999<span class="w"> </span>anneal_sec:1:30<span class="w"> </span>extinction_sec:1:15<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-7"><a id="__codelineno-0-7" name="__codelineno-0-7" href="#__codelineno-0-7"></a><span class="w">    </span>--popsize<span class="w"> </span><span class="m">8</span><span class="w"> </span>--maxiter<span class="w"> </span><span class="m">20</span><span class="w"> </span>--trajectory<span class="w"> </span>traj.jsonl<span class="w"> </span>--out<span class="w"> </span>opt.json
-</span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="c1"># 4. score a fixed config / validate on a held-out set</span>
-</span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="c1">#    (historical: score_config.py and scene_score.py were removed with the</span>
-</span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a><span class="c1">#     scene-union metric — use scripts/optimizer/second_score.py, per-second)</span>
-</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a>python<span class="w"> </span>scripts/optimizer/second_score.py<span class="w"> </span>--help
+<h1 id="how-we-score-against-x-ray">How we score against X-Ray<a class="headerlink" href="#how-we-score-against-x-ray" title="Permanent link">&para;</a></h1>
+<p>Every number in this report, every F1 and misID count, comes from one
+comparison. The comparison has a mismatch at its core that shapes nearly
+every finding in this report: the ground truth is scene-level, the
+pipeline's output is per-second, and the two do not mean the same thing.
+This page documents that comparison once, so the findings pages can rely on
+it without re-explaining it.</p>
+<h2 id="what-amazon-x-ray-records">What Amazon X-Ray records<a class="headerlink" href="#what-amazon-x-ray-records" title="Permanent link">&para;</a></h2>
+<p>X-Ray ships three tables per film: <code>scenes.csv</code> (a list of <code>[start, end]</code>
+timespans), <code>people_in_scenes.csv</code> (which actors are credited in each
+scene), and <code>people.csv</code> (actor identities). There is no per-frame or
+per-second annotation anywhere in X-Ray. A scene might run 45 seconds, and
+X-Ray records one cast list for the entire span, not "on screen from
+second 12 to second 30."</p>
+<p>To compare this against per-second predictions, <code>second_score.py</code> expands
+every scene into per-second ground truth by copying the whole scene's cast
+list onto every second inside it:</p>
+<div class="language-python highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="k">for</span> <span class="n">sn</span><span class="p">,</span> <span class="p">(</span><span class="n">t0</span><span class="p">,</span> <span class="n">t1</span><span class="p">)</span> <span class="ow">in</span> <span class="n">spans</span><span class="o">.</span><span class="n">items</span><span class="p">():</span>
+</span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>    <span class="n">cast</span> <span class="o">=</span> <span class="n">scene_cast</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="n">sn</span><span class="p">,</span> <span class="p">[])</span>
+</span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>    <span class="k">for</span> <span class="n">t</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">t0</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">t1</span><span class="p">)):</span>
+</span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a>        <span class="n">timeline</span><span class="p">[</span><span class="n">t</span><span class="p">]</span> <span class="o">=</span> <span class="n">cast</span>
 </span></code></pre></div>
-<p>Superseded by the <a href="../model-bakeoff/">model bake-off + re-tune</a>, which
-replaced this round's scene-union metric with per-second scoring.</p>
+<p>That is the entire mechanism. If X-Ray credits five actors to a 30-second
+scene, all five count as ground truth present for all 30 seconds, including
+seconds where only one of them is on screen. This is not a simplification
+introduced by the pipeline; it is the only reading of X-Ray's data that is
+possible, because X-Ray itself does not record anything finer-grained.</p>
+<h2 id="why-an-offscreen-name-can-be-scored-correct">Why an offscreen name can be scored correct<a class="headerlink" href="#why-an-offscreen-name-can-be-scored-correct" title="Permanent link">&para;</a></h2>
+<p>A name listed under Offscreen with a correct (green) label is not the
+pipeline guessing or padding its score. It is the pipeline correctly
+answering the question X-Ray actually asks: is this actor part of this
+scene. It answers that question using a presence window (<code>[start, end]</code>,
+held open across cuts by <code>anneal_sec</code> and <code>extinction_sec</code>), which matches
+X-Ray's scene-level semantics more closely than a raw per-frame detection
+would.</p>
+<p>A system that only reported "this actor is visible in this exact frame"
+would score worse against X-Ray's scene-level ground truth, producing a
+false negative every time the camera cuts away from a character who is
+still present in the scene. Not because it is wrong about the world, but
+because it would be answering a stricter, different question than the one
+X-Ray's data supports. The presence-window design exists specifically to
+answer X-Ray's actual question.</p>
+<h2 id="what-this-resolves-and-what-it-does-not">What this resolves and what it does not<a class="headerlink" href="#what-this-resolves-and-what-it-does-not" title="Permanent link">&para;</a></h2>
+<p>This resolves the semantic mismatch between a scene and an instant. It does
+not resolve two other limitations, both discussed in the
+<a href="../lvface-deep-dive/">LVFace deep dive</a>.</p>
+<p><strong>The face-vs-presence ceiling.</strong> X-Ray credits scene membership regardless
+of whether a face is ever visible: background crew, characters shot from
+behind, voice-only presence. No amount of bridging recovers a face that
+never appears on screen. This is a hard ceiling on recall, not a defect.</p>
+<p><strong>Extinction bridging can overshoot.</strong> The same presence-window mechanism
+that correctly answers "still in this scene" during a normal cut can also
+bridge across a scene boundary it has no way to detect. A hard cut into a
+different scene with no faces, such as closing credits, carries the
+previous scene's identities forward until the window expires. This is the
+mechanism behind Downton Abbey's recall collapse, documented in the deep
+dive.</p>
+<h2 id="precision-recall-and-the-misid-weighting">Precision, recall, and the misID weighting<a class="headerlink" href="#precision-recall-and-the-misid-weighting" title="Permanent link">&para;</a></h2>
+<p>Per sampled second <code>t</code>:</p>
+<p><strong>TPI</strong> (true positive instances): actors both X-Ray and the pipeline agree
+are present.</p>
+<p><strong>FPI</strong> (false positive instances): actors the pipeline reports that are
+not in X-Ray's cast for this second. Split into two categories:</p>
+<ul>
+<li><strong>FPI_incast</strong>: the actor is in the film's cast, just not credited to
+  this particular scene. A timing or boundary slip.</li>
+<li><strong>FPI_misid</strong>: the actor is not in the film's cast at all. A genuine
+  wrong-identity error, weighted 10x in the precision objective, because
+  naming someone who is not even in the film is a categorically worse
+  error than a few seconds of scene-boundary slop.</li>
+</ul>
+<div class="admonition note">
+<p class="admonition-title">Every headline <code>P</code> and <code>F1</code> is misID-weighted</p>
+<p>The precision reported throughout this report, and therefore the F1
+derived from it, puts each <code>FPI_misid</code> into the denominator <strong>10 times</strong>
+(<code>precision = TPI / (TPI + FPI_incast + 10·FPI_misid)</code>,
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/second_score.py"><code>second_score.py</code></a>).
+This is deliberate: the whole point is to punish naming an out-of-film
+actor far harder than a scene-boundary slip. But it means the <code>P</code> column
+is not raw precision, and a misID-heavy film's <code>P</code> is depressed
+super-linearly. <code>second_score.py</code> also emits an unweighted <code>precision_raw</code>
+(always ≥ the weighted <code>P</code>); where the gap matters, The Many Saints of
+Newark, weighted <code>P</code> 54.7% vs. raw 78.4%, the <a href="../lvface-deep-dive/">LVFace deep dive</a>
+reports both. When comparing <code>P</code> across films, remember you are comparing a
+quantity that penalizes misIDs, not just a hit rate.</p>
+</div>
+<p><strong>FN</strong> (false negatives): actors X-Ray lists that the pipeline never
+reports, counted only for actors who have a gallery reference embedding.
+Across the 9-film benchmark, coverage of X-Ray's credited cast ranges from
+20% to 79% by film (see
+<a href="../model-bakeoff/#gallery-coverage-per-film">the full experiment log</a>); an
+actor with no reference photo can never be recognized regardless of model
+quality, and counting them as a miss would penalize gallery coverage, not
+recognition accuracy.</p>
+<p>Two further numbers are reported alongside F1:</p>
+<p><strong>agreement_rate</strong>: mean per-second Jaccard overlap
+(<code>|Pred ∩ GT| / |Pred ∪ GT|</code>), partial credit. Naming 2 of 3 present actors
+scores 2/3, not 0.</p>
+<p><strong>exact_match_rate</strong>: the fraction of sampled seconds where the pipeline's
+named set exactly equals X-Ray's, no partial credit. Far harsher, and
+dominated by recall, since any single missed actor zeroes that second.</p>
+<h2 id="reproduce">Reproduce<a class="headerlink" href="#reproduce" title="Permanent link">&para;</a></h2>
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-1-1"><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>python3<span class="w"> </span>scripts/optimizer/second_score.py<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-2"><a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a><span class="w">    </span>--pred<span class="w"> </span>pred.json<span class="w"> </span>--xray<span class="w"> </span>experiments/xray/.../&lt;xray_dir&gt;<span class="w"> </span><span class="se">\</span>
+</span><span id="__span-1-3"><a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a><span class="w">    </span>--gallery<span class="w"> </span>experiments/galleries/gallery_LVFace-B_Glint360K.h5
+</span></code></pre></div>
+<p>See also <a href="../model-bakeoff/">the full experiment log</a> for how <code>pred.json</code> is
+produced, and the <a href="../lvface-deep-dive/">LVFace deep dive</a> for what these
+mechanisms look like frame by frame.</p>
 
 
 
@@ -1075,7 +985,7 @@ replaced this round's scene-union metric with per-second scoring.</p>
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@@ -1085,20 +995,20 @@ replaced this round's scene-union metric with per-second scoring.</p>
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diff --git a/model-bakeoff/index.html b/model-bakeoff/index.html
index 626879e..c086089 100644
--- a/model-bakeoff/index.html
+++ b/model-bakeoff/index.html
@@ -16,7 +16,7 @@
         <link rel="prev" href="../lvface-deep-dive/">
       
       
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@@ -27,7 +27,7 @@
     
     
       
-        <title>Model Bake-off & Re-tune (full log) - scene-actor-extraction</title>
+        <title>Full Experiment Log - scene-actor-extraction</title>
       
     
     
@@ -80,7 +80,7 @@
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-              Model Bake-off & Re-tune (full log)
+              Full Experiment Log
             
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@@ -243,6 +243,25 @@
   
   
   
+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
+  
     
     
       <li class="md-tabs__item">
@@ -272,26 +291,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -395,6 +395,33 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
     
     
     
@@ -409,10 +436,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
             
   
   
@@ -430,8 +457,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -583,7 +610,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
+    Full Experiment Log
   
 
     
@@ -601,7 +628,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
+    Full Experiment Log
   
 
     
@@ -627,10 +654,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#why-this-experiment-and-what-it-actually-delivered" class="md-nav__link">
+  <a href="#why-replay-makes-this-affordable" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why this experiment, and what it actually delivered
+        Why replay makes this affordable
       
     </span>
   </a>
@@ -638,10 +665,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#tldr-what-changed-in-srcconfighpp" class="md-nav__link">
+  <a href="#search-space" class="md-nav__link">
     <span class="md-ellipsis">
       
-        TL;DR — what changed in src/config.hpp
+        Search space
       
     </span>
   </a>
@@ -649,10 +676,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#why-re-run-at-all" class="md-nav__link">
+  <a href="#training-films-and-held-out-films" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why re-run at all
+        Training films and held-out films
       
     </span>
   </a>
@@ -660,10 +687,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#the-deadlock-that-was-blocking-all-of-this" class="md-nav__link">
+  <a href="#gallery-coverage-per-film" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The deadlock that was blocking all of this
+        Gallery coverage per film
       
     </span>
   </a>
@@ -671,54 +698,49 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#concurrency-tuning" class="md-nav__link">
+  <a href="#training-results-3-models-2-gallery-modes-2-expansion-settings" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Concurrency tuning
+        Training results, 3 models × 2 gallery modes × 2 expansion settings
+      
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Training results, 3 models × 2 gallery modes × 2 expansion settings">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#a-scoring-bug-worth-recording-dropped-film-evaluations" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        A scoring bug worth recording: dropped-film evaluations
       
     </span>
   </a>
   
 </li>
-      
-        <li class="md-nav__item">
-  <a href="#training-films-and-validation-set" class="md-nav__link">
+        
+          <li class="md-nav__item">
+  <a href="#per-film-training-breakdown" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Training films and validation set
+        Per-film training breakdown
       
     </span>
   </a>
   
 </li>
-      
-        <li class="md-nav__item">
-  <a href="#search-space-and-de-settings" class="md-nav__link">
-    <span class="md-ellipsis">
-      
-        Search space and DE settings
-      
-    </span>
-  </a>
+        
+      </ul>
+    </nav>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#results-all-16-combos-4-models-full-restricted-expand-noexp" class="md-nav__link">
+  <a href="#held-out-validation-all-3-models" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Results — all 16 combos (4 models × {full, restricted} × {expand, noexp})
-      
-    </span>
-  </a>
-  
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#calibration-curves-discriminative-power-independent-of-the-threshold" class="md-nav__link">
-    <span class="md-ellipsis">
-      
-        Calibration curves — discriminative power, independent of the threshold
+        Held-out validation, all 3 models
       
     </span>
   </a>
@@ -729,7 +751,7 @@
   <a href="#two-effects-in-isolation-gallery-scope-and-pose-expansion" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Two effects in isolation: gallery scope, and pose expansion
+        Two effects in isolation: gallery scope and pose expansion
       
     </span>
   </a>
@@ -737,10 +759,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#what-the-data-says" class="md-nav__link">
+  <a href="#calibration-curves" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What the data says
+        Calibration curves
       
     </span>
   </a>
@@ -748,10 +770,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#held-out-validation-the-number-that-actually-matters" class="md-nav__link">
+  <a href="#extinction-and-anneal-window-search" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Held-out validation — the number that actually matters
+        Extinction and anneal window search
       
     </span>
   </a>
@@ -759,10 +781,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#caveats-what-this-is-not" class="md-nav__link">
+  <a href="#caveats" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Caveats / what this is not
+        Caveats
       
     </span>
   </a>
@@ -794,33 +816,6 @@
   
   
   
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
     <li class="md-nav__item">
       <a href="../service-conversion/" class="md-nav__link">
         
@@ -869,10 +864,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#why-this-experiment-and-what-it-actually-delivered" class="md-nav__link">
+  <a href="#why-replay-makes-this-affordable" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why this experiment, and what it actually delivered
+        Why replay makes this affordable
       
     </span>
   </a>
@@ -880,10 +875,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#tldr-what-changed-in-srcconfighpp" class="md-nav__link">
+  <a href="#search-space" class="md-nav__link">
     <span class="md-ellipsis">
       
-        TL;DR — what changed in src/config.hpp
+        Search space
       
     </span>
   </a>
@@ -891,10 +886,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#why-re-run-at-all" class="md-nav__link">
+  <a href="#training-films-and-held-out-films" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Why re-run at all
+        Training films and held-out films
       
     </span>
   </a>
@@ -902,10 +897,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#the-deadlock-that-was-blocking-all-of-this" class="md-nav__link">
+  <a href="#gallery-coverage-per-film" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The deadlock that was blocking all of this
+        Gallery coverage per film
       
     </span>
   </a>
@@ -913,54 +908,49 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#concurrency-tuning" class="md-nav__link">
+  <a href="#training-results-3-models-2-gallery-modes-2-expansion-settings" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Concurrency tuning
+        Training results, 3 models × 2 gallery modes × 2 expansion settings
+      
+    </span>
+  </a>
+  
+    <nav class="md-nav" aria-label="Training results, 3 models × 2 gallery modes × 2 expansion settings">
+      <ul class="md-nav__list">
+        
+          <li class="md-nav__item">
+  <a href="#a-scoring-bug-worth-recording-dropped-film-evaluations" class="md-nav__link">
+    <span class="md-ellipsis">
+      
+        A scoring bug worth recording: dropped-film evaluations
       
     </span>
   </a>
   
 </li>
-      
-        <li class="md-nav__item">
-  <a href="#training-films-and-validation-set" class="md-nav__link">
+        
+          <li class="md-nav__item">
+  <a href="#per-film-training-breakdown" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Training films and validation set
+        Per-film training breakdown
       
     </span>
   </a>
   
 </li>
-      
-        <li class="md-nav__item">
-  <a href="#search-space-and-de-settings" class="md-nav__link">
-    <span class="md-ellipsis">
-      
-        Search space and DE settings
-      
-    </span>
-  </a>
+        
+      </ul>
+    </nav>
   
 </li>
       
         <li class="md-nav__item">
-  <a href="#results-all-16-combos-4-models-full-restricted-expand-noexp" class="md-nav__link">
+  <a href="#held-out-validation-all-3-models" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Results — all 16 combos (4 models × {full, restricted} × {expand, noexp})
-      
-    </span>
-  </a>
-  
-</li>
-      
-        <li class="md-nav__item">
-  <a href="#calibration-curves-discriminative-power-independent-of-the-threshold" class="md-nav__link">
-    <span class="md-ellipsis">
-      
-        Calibration curves — discriminative power, independent of the threshold
+        Held-out validation, all 3 models
       
     </span>
   </a>
@@ -971,7 +961,7 @@
   <a href="#two-effects-in-isolation-gallery-scope-and-pose-expansion" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Two effects in isolation: gallery scope, and pose expansion
+        Two effects in isolation: gallery scope and pose expansion
       
     </span>
   </a>
@@ -979,10 +969,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#what-the-data-says" class="md-nav__link">
+  <a href="#calibration-curves" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What the data says
+        Calibration curves
       
     </span>
   </a>
@@ -990,10 +980,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#held-out-validation-the-number-that-actually-matters" class="md-nav__link">
+  <a href="#extinction-and-anneal-window-search" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Held-out validation — the number that actually matters
+        Extinction and anneal window search
       
     </span>
   </a>
@@ -1001,10 +991,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#caveats-what-this-is-not" class="md-nav__link">
+  <a href="#caveats" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Caveats / what this is not
+        Caveats
       
     </span>
   </a>
@@ -1039,192 +1029,164 @@
 
 
 
-<h1 id="model-bake-off-threshold-re-tune-experiment-log-2026-07-1819">Model bake-off + threshold re-tune — experiment log (2026-07-18/19)<a class="headerlink" href="#model-bake-off-threshold-re-tune-experiment-log-2026-07-1819" title="Permanent link">&para;</a></h1>
-<p>Follow-on to <a href="../optimizer-experiments/">the prior optimizer round</a>, which used
-an older, since-superseded scene-union metric. This round uses the <strong>per-second</strong> metric
-(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/second_score.py"><code>scripts/optimizer/second_score.py</code></a>)
-and answers three questions in one 16-run matrix: which embedding model is best, does cast-restriction cut misIDs, and does
-per-film gallery expansion help.</p>
-<p>(This was the fourth optimizer campaign against the X-Ray corpus, so its on-disk
-artifacts carry an internal <code>rep4_</code> prefix — <code>experiments/results/rep4_best_*.json</code>,
-<code>experiments/trajectories/rep4_*.jsonl</code>, and the manifests referenced below. The
-earlier campaigns used the superseded scene-union metric and were discarded.)</p>
-<h2 id="why-this-experiment-and-what-it-actually-delivered">Why this experiment, and what it actually delivered<a class="headerlink" href="#why-this-experiment-and-what-it-actually-delivered" title="Permanent link">&para;</a></h2>
-<p>Four goals going in, and an honest read on each after held-out validation (see
-below):</p>
-<ol>
-<li><strong>Find the best default parameters to ship.</strong> Partially delivered. The DE optimum
-   generalizes <em>unevenly</em> — strong on 3 of 5 held-out films, badly broken on 2 (one
-   with a 974-count misID blowup). The tuned values are shipped anyway (see
-   Caveats) because they still beat the old defaults on average, but this is not a
-   settled, film-agnostic optimum.</li>
-<li><strong>Find the best default model.</strong> Delivered with more confidence. LVFace beat
-   r50/r18/mbf across all 4 training combos, and nothing in held-out validation
-   contradicts the model choice specifically — the held-out failures trace to
-   <code>extinction_sec</code>/threshold interactions and gallery coverage, not the embedder.</li>
-<li><strong>Provide insight into how the application works.</strong> The strongest, most durable
-   output. Found and fixed a real teardown deadlock bug (100% reproducible, not the
-   assumed rare GPU flake), established a real concurrency ceiling (8 parallel
-   replays, not more), and found a real parameter interaction (a strict
-   <code>prob_threshold</code> "earns" a longer extinction window before it starts hurting).</li>
-<li><strong>Demonstrate limitations.</strong> Delivered, and reinforced hard by held-out
-   validation — see the "Held-out validation" section below for concrete examples,
-   including a screenshot of the matcher naming 15 actors, none correctly, on a
-   completely blank title card.</li>
-</ol>
-<h2 id="tldr-what-changed-in-srcconfighpp">TL;DR — what changed in <code>src/config.hpp</code><a class="headerlink" href="#tldr-what-changed-in-srcconfighpp" title="Permanent link">&para;</a></h2>
+<h1 id="full-experiment-log">Full experiment log<a class="headerlink" href="#full-experiment-log" title="Permanent link">&para;</a></h1>
+<p>This page reports how the pipeline performs across three questions: which
+embedding model is best, whether restricting the gallery to a film's
+credited cast helps, and whether promoting confidently identified poses into
+a per-film gallery annex helps. It also documents the replay architecture
+that made testing all three questions in one pass practical, and every
+caveat needed to trust the numbers.</p>
+<p>Read <a href="../methodology/">How we score against X-Ray</a> first for what F1,
+precision, recall, and misID mean in this report. All numbers below use the
+per-second metric
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/second_score.py"><code>scripts/optimizer/second_score.py</code></a>).</p>
+<p>r50 (ArcFace w600k-R50) is excluded from the detailed comparison below. Its
+gallery was built with roughly 30% fewer reference images per actor than the
+other three models on the identical source photos (10808 vs 15055 total
+embeddings across the same 2418 actors), which confounds any direct
+comparison of its scores against the others. It remains in the
+<a href="../best-model/#first-signal-calibration-curves">calibration curve comparison</a>,
+which does not depend on the training benchmark.</p>
+<h2 id="why-replay-makes-this-affordable">Why replay makes this affordable<a class="headerlink" href="#why-replay-makes-this-affordable" title="Permanent link">&para;</a></h2>
+<p>Decoding video and running face detection, alignment, and embedding is the
+expensive part of this pipeline. Everything downstream of that (tracking,
+identity matching, scene aggregation) is cheap. KPN++'s node/network
+structure means those two stages are separate components connected by
+typed channels, so the expensive stage can run once per film, cache its
+output, and the cheap stage can be re-run against that cache as many times
+as needed with different Config values.</p>
+<p><code>scene_analyze --dump-embeddings out.h5</code> runs the expensive half once per
+film and writes per-frame face detections and embeddings to HDF5
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/SCHEMA.md"><code>scripts/optimizer/SCHEMA.md</code></a>).
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/optimizer/replay.py"><code>scripts/optimizer/replay.py</code></a>
+then re-assembles the real C++ <code>face_tracker</code>, <code>identity_matcher</code>, and
+<code>scene_tracker</code> nodes into a Python-driven KPN network and replays a
+film's cached embeddings through them, varying <code>prob_threshold</code>,
+<code>anneal_sec</code>, <code>extinction_sec</code>, and <code>expand_gallery</code> freely. No GPU
+inference and no video decode happen during a replay; each one completes
+in seconds. This is what makes a 512-evaluation differential-evolution
+search per model, per gallery mode, per expansion setting, tractable, and
+what made the full held-out validation across three models in this report
+possible in one session rather than requiring three full re-encodes of the
+benchmark set.</p>
+<p><code>optimize.py</code> runs <code>differential_evolution</code> over this replay function as its
+objective, with DE-level parallelism (multiple candidate configs evaluated
+concurrently, each spawning its own replay subprocesses) on top of it. The
+practical ceiling on this machine's GPU was 8 concurrent replay processes;
+9 silently degraded every score to 0.0% (well-formed output, wrong numbers,
+not a crash), so <code>optimize.py</code> was run at <code>REPLAY_WORKERS=4 DE_WORKERS=2</code>.</p>
+<h2 id="search-space">Search space<a class="headerlink" href="#search-space" title="Permanent link">&para;</a></h2>
+<p><code>popsize=10, maxiter=15</code> per combo (3 parameters, up to 512 evaluations,
+usually stopping earlier on DE's convergence tolerance).
+<code>anneal_sec</code>/<code>extinction_sec</code> bounds were widened from 1-30/1-15 to 1-60/1-60
+partway through the sweep. r50's 4 combos finished before the widening and
+used the old, narrower bounds; this is one more reason r50 is excluded from
+direct comparison here.</p>
+<h2 id="training-films-and-held-out-films">Training films and held-out films<a class="headerlink" href="#training-films-and-held-out-films" title="Permanent link">&para;</a></h2>
+<p>9 films have dumped embeddings across all 4 models. 4 were used for
+optimization:</p>
+<ul>
+<li>Café Society (62-cast)</li>
+<li>Lord of War (64-cast)</li>
+<li>Scarface (67-cast)</li>
+<li>Sound of Metal (14-cast)</li>
+</ul>
+<p>5 were held out, never seen by any optimizer run:</p>
+<ul>
+<li>Benny &amp; Joon</li>
+<li>Downton Abbey: A New Era</li>
+<li>Lovelace</li>
+<li>The Many Saints of Newark</li>
+<li>Valerian and the City of a Thousand Planets</li>
+</ul>
+<h2 id="gallery-coverage-per-film">Gallery coverage per film<a class="headerlink" href="#gallery-coverage-per-film" title="Permanent link">&para;</a></h2>
+<p>The gallery has reference embeddings for 2418 actors, but coverage of any
+given film's credited cast varies widely. This was previously reported as
+one flat number (67% of X-Ray cast lacking a reference embedding, averaged
+across the whole benchmark); the per-film breakdown is:</p>
 <table>
 <thead>
 <tr>
-<th>knob</th>
-<th>old default</th>
-<th>new default</th>
-<th>why</th>
+<th>film</th>
+<th>cast credited</th>
+<th>in gallery</th>
+<th>coverage</th>
 </tr>
 </thead>
 <tbody>
 <tr>
-<td><code>arcface_model</code></td>
-<td><code>arcface_w600k_r50.onnx</code></td>
-<td><strong><code>LVFace-B_Glint360K.onnx</code></strong></td>
-<td>Best F1 in the full-gallery bake-off (75.3% vs r50's 68.5%). LVFace was worth its size.</td>
+<td>Lord of War</td>
+<td>64</td>
+<td>13</td>
+<td>20.3%</td>
 </tr>
 <tr>
-<td><code>prob_threshold</code></td>
-<td>0.76</td>
-<td><strong>0.754</strong></td>
-<td>Re-tuned for LVFace + per-second metric.</td>
+<td>Scarface</td>
+<td>67</td>
+<td>15</td>
+<td>22.4%</td>
 </tr>
 <tr>
-<td><code>extinction_sec</code></td>
-<td>1.5</td>
-<td><strong>57.4</strong></td>
-<td>Reverses the earlier "short is better" finding — see below.</td>
+<td>The Many Saints of Newark</td>
+<td>48</td>
+<td>13</td>
+<td>27.1%</td>
 </tr>
 <tr>
-<td><code>anneal_sec</code></td>
-<td>10.0</td>
-<td><strong>35.5</strong></td>
-<td>Same reversal; previously thought insensitive.</td>
+<td>Café Society</td>
+<td>62</td>
+<td>17</td>
+<td>27.4%</td>
 </tr>
 <tr>
-<td><code>expand_gallery</code></td>
-<td>false</td>
-<td><strong>true</strong></td>
-<td>Helped recall on the full (unrestricted) gallery for the winning model — opposite of the earlier assumption.</td>
+<td>Lovelace</td>
+<td>42</td>
+<td>15</td>
+<td>35.7%</td>
+</tr>
+<tr>
+<td>Valerian and the City of a Thousand Planets</td>
+<td>36</td>
+<td>13</td>
+<td>36.1%</td>
+</tr>
+<tr>
+<td>Benny &amp; Joon</td>
+<td>23</td>
+<td>12</td>
+<td>52.2%</td>
+</tr>
+<tr>
+<td>Downton Abbey: A New Era</td>
+<td>36</td>
+<td>22</td>
+<td>61.1%</td>
+</tr>
+<tr>
+<td>Sound of Metal</td>
+<td>14</td>
+<td>11</td>
+<td>78.6%</td>
 </tr>
 </tbody>
 </table>
-<p>These are the <strong><code>LVFace-B_Glint360K_full_exp</code></strong> winning values, applied to
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/config.hpp"><code>src/config.hpp</code></a> — the best result that
-uses only features already live in the running app (full gallery, no cast
-restriction; see below for why restricted mode isn't applied even though it scored
-higher).</p>
-<h2 id="why-re-run-at-all">Why re-run at all<a class="headerlink" href="#why-re-run-at-all" title="Permanent link">&para;</a></h2>
-<p><a href="../optimizer-experiments/">The prior round</a>'s scene-union metric hid out-of-cast false positives
-behind a gallery∩cast recall mask, and the earlier 9-film benchmark was scene-level
-(union over a whole X-Ray scene), not a fair per-timepoint comparison. SESSION_STATE
-flagged the old scene-metric bake-off numbers (R50≈LVFace≈MBF ~85%) as superseded.
-This round uses <code>second_score.py</code>: uniform per-second sampling, GT = X-Ray scene's
-cast at time <em>t</em>, pred = actors whose presence window covers <em>t</em>, FPI weighted 10×
-when the named actor isn't in the film's cast at all (true misID) vs. an in-cast
-timing slip. FN only counts gallery-known cast (fair recall — 67% of X-Ray cast have
-no reference embedding; see
-<a href="../optimizer-experiments/#the-gallery-coverage-gap">the prior round's gallery-coverage-gap analysis</a>).</p>
-<h2 id="the-deadlock-that-was-blocking-all-of-this">The deadlock that was blocking all of this<a class="headerlink" href="#the-deadlock-that-was-blocking-all-of-this" title="Permanent link">&para;</a></h2>
-<p>Every replay in this line of work goes through
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/replay.py"><code>scripts/optimizer/replay.py</code></a>,
-which runs the real C++ tracker/matcher/scene_tracker nodes inside a
-Python-assembled KPN network. Before this session, every subprocess replay <strong>timed out at 45s, 100% of
-the time</strong> — not the documented ~20-30% ROCm rocBLAS-GEMM driver flake, but a plain
-logic bug: <code>replay.py</code>'s CLI called <code>replay(net, ..., stop=False)</code> to <em>skip</em>
-<code>net.stop()</code> (trying to dodge the GEMM deadlock), planning to <code>os._exit(0)</code>
-immediately after. But:</p>
-<ul>
-<li><code>PyNode::stop()</code> (<a href="https://gitea.tourolle.paris/dtourolle/KPN/raw/commit/4b6e498ba7e70a34cc0b57638f9e56a43b7f41ae/include/kpn/python/bindings.hpp"><code>include/kpn/python/bindings.hpp</code></a>
-  in the KPN++ submodule) is the <em>only</em>
-  code that sets <code>stop_flag_ = true</code> before joining the node's worker thread.</li>
-<li>The source node's <code>run_loop()</code> has <code>while (!stop_flag_)</code> as its only exit
-  condition (it has no input channels, so it never sees a channel-closed signal
-  either).</li>
-<li>Skipping <code>stop()</code> meant <code>stop_flag_</code> never became true. When <code>replay()</code> returned,
-  its local <code>net</code> went out of scope immediately, running <code>~PyNetwork</code> → <code>~PyNode</code> →
-  <code>thread_.join()</code> <strong>synchronously inside <code>replay()</code>'s own call frame</strong> — before
-  <code>main()</code> ever got control back to run <code>os._exit(0)</code>.</li>
-</ul>
-<p>Root-caused via <code>gdb -p &lt;pid&gt; -batch -ex "thread apply all bt"</code> on a hung process:
-the main thread was stuck in <code>~PyNode</code>'s <code>jthread::join()</code>; the worker thread was in
-an ordinary <code>time.sleep()</code> inside the Python source callback, waiting for a stop
-signal that was never sent. The two HSA <code>kfd_wait_on_events</code> threads visible in the
-same trace are normal ROCm runtime housekeeping, not evidence of a wedged GPU kernel.</p>
-<p><strong>Fix:</strong> <code>replay.py</code> now calls <code>replay(..., stop=True)</code> (the removed <code>stop=False</code> +
-<code>os._exit</code> workaround was actively harmful). Verified 3/3 clean runs at ~8s each
-(down from a guaranteed 45s timeout), and a full DE sweep producing real, sensible
-F1/precision/recall instead of flat 0.0%.</p>
-<h2 id="concurrency-tuning">Concurrency tuning<a class="headerlink" href="#concurrency-tuning" title="Permanent link">&para;</a></h2>
-<p>With the deadlock fixed,
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/optimize.py"><code>scripts/optimizer/optimize.py</code></a>
-was extended with DE-level parallelism —
-<code>differential_evolution(..., workers=ThreadPoolExecutor.map)</code> — so multiple
-population candidates evaluate concurrently, each spawning its own per-film replay
-subprocesses (<code>REPLAY_WORKERS</code>). Total concurrent GPU replay processes ≈
-<code>DE_WORKERS × REPLAY_WORKERS</code>.</p>
-<table>
-<thead>
-<tr>
-<th>concurrent replays</th>
-<th>result</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>3 (<code>REPLAY_WORKERS=3</code>, no DE parallelism)</td>
-<td>baseline, GPU underutilised</td>
-</tr>
-<tr>
-<td>6 (<code>DE_WORKERS=2 × REPLAY_WORKERS=3</code>)</td>
-<td>clean, real scores, ~1 isolated timeout per run</td>
-</tr>
-<tr>
-<td>8 (<code>DE_WORKERS=2 × REPLAY_WORKERS=4</code>, 4-film manifest)</td>
-<td>clean, real scores</td>
-</tr>
-<tr>
-<td>9 (<code>DE_WORKERS=3 × REPLAY_WORKERS=3</code>)</td>
-<td><strong>broken</strong> — every replay blew past the 45s timeout, all scores silently degraded to 0.0%</td>
-</tr>
-</tbody>
-</table>
-<p>9 concurrent replays looks like valid output (well-formed JSON, a real number) while
-actually being garbage — a dangerous failure mode, not a crash. <strong>8 concurrent is the
-practical ceiling</strong> on this GPU (gfx1100) for this workload. The matrix ran at
-<code>REPLAY_WORKERS=4 DE_WORKERS=2</code>.</p>
-<h2 id="training-films-and-validation-set">Training films and validation set<a class="headerlink" href="#training-films-and-validation-set" title="Permanent link">&para;</a></h2>
-<p>9 films total have dumped embeddings across all 4 models. 4 were used for
-optimization, leaving 5 held out for validation:</p>
-<ul>
-<li><strong>Lord of War</strong> (64-cast, "clean")</li>
-<li><strong>Scarface</strong> (67-cast, "ensemble/lookalike")</li>
-<li><strong>Sound of Metal</strong> (14-cast, "high gallery-coverage")</li>
-<li><strong>Café Society</strong> (62-cast, added this round — similar ensemble size to Scarface but
-  different genre/lighting; picked to add diversity, not genre-overlap, over
-  Downton Abbey or The Many Saints of Newark)</li>
-</ul>
-<p>Held out: Benny &amp; Joon, Downton Abbey: A New Era, Lovelace, The Many Saints of
-Newark, Valerian and the City of a Thousand Planets. The training-set numbers below are
-training-set fit — see "Held-out validation" further down for the real
-generalization test.</p>
-<h2 id="search-space-and-de-settings">Search space and DE settings<a class="headerlink" href="#search-space-and-de-settings" title="Permanent link">&para;</a></h2>
-<p><code>popsize=10, maxiter=15</code> (3 params → ≤480 evals/combo ceiling; DE's <code>tol</code> convergence
-usually stops earlier). <code>anneal_sec</code>/<code>extinction_sec</code> bounds were <strong>widened from
-1–30/1–15 to 1–60/1–60 mid-run</strong> (see below) — the 4 <code>arcface_w600k_r50</code> combos
-finished before the widening and still use the old, narrower bounds, so they are
-<strong>not directly comparable</strong> to the other 12 on those two params. Re-running r50 with
-the wider bounds was deferred (diminishing-returns judgment call, not yet done).</p>
-<h2 id="results-all-16-combos-4-models-full-restricted-expand-noexp">Results — all 16 combos (4 models × {full, restricted} × {expand, noexp})<a class="headerlink" href="#results-all-16-combos-4-models-full-restricted-expand-noexp" title="Permanent link">&para;</a></h2>
-<p>Ranked by F1. <code>misid</code> = FPI_misid, count of true wrong-actor identifications (an
-actor named who isn't in the film's cast at all) — distinct from <code>FPI</code>, which
-includes in-cast timing slips.</p>
+<p>Two training films (Lord of War, Scarface) have the worst coverage in the
+set, 20-22%. Their training-set F1 numbers below are partly capped by
+missing references, not purely by model quality. Downton Abbey has 61%
+coverage, the second-best in the benchmark, yet the worst held-out recall
+of any film (39.4%, LVFace). Its recall problem is not primarily a coverage
+problem; it is the extinction-bridging failure documented in the
+<a href="../lvface-deep-dive/#mechanism-1-extinction-bridging">LVFace deep dive</a>.
+Reproduce with <code>scripts/docs/gallery_coverage_per_film.py</code>.</p>
+<h2 id="training-results-3-models-2-gallery-modes-2-expansion-settings">Training results, 3 models × 2 gallery modes × 2 expansion settings<a class="headerlink" href="#training-results-3-models-2-gallery-modes-2-expansion-settings" title="Permanent link">&para;</a></h2>
+<p>Ranked by F1. misid = FPI_misid, the count of true wrong-actor
+identifications (naming someone not in the film's cast at all), distinct
+from FPI, which also includes in-cast timing slips.</p>
+<p>Each combo's row is its best <strong>full-coverage</strong> evaluation: the highest-F1 DE
+evaluation in which all 4 training films replayed without a timeout (see
+<a href="#a-scoring-bug-worth-recording-dropped-film-evaluations">Dropped-film scoring</a>
+below for why this qualifier is load-bearing and not the same as <code>argmax F1</code>
+over the raw sweep).</p>
 <table>
 <thead>
 <tr>
@@ -1241,7 +1203,7 @@ includes in-cast timing slips.</p>
 <tbody>
 <tr>
 <td>LVFace-B_Glint360K_restricted_exp</td>
-<td><strong>78.3%</strong></td>
+<td>78.3%</td>
 <td>91.0%</td>
 <td>68.9%</td>
 <td>42830</td>
@@ -1261,13 +1223,13 @@ includes in-cast timing slips.</p>
 </tr>
 <tr>
 <td>arcface_w600k_mbf_restricted_exp</td>
-<td>76.5%</td>
-<td>90.7%</td>
-<td>66.3%</td>
-<td>41270</td>
-<td>4431</td>
-<td><strong>0</strong></td>
-<td>21052</td>
+<td>76.2%</td>
+<td>90.0%</td>
+<td>66.2%</td>
+<td>64328</td>
+<td>7480</td>
+<td>0</td>
+<td>33234</td>
 </tr>
 <tr>
 <td>arcface_r18_restricted_exp</td>
@@ -1280,8 +1242,8 @@ includes in-cast timing slips.</p>
 <td>20923</td>
 </tr>
 <tr>
-<td><strong>LVFace-B_Glint360K_full_exp</strong></td>
-<td><strong>75.3%</strong></td>
+<td>LVFace-B_Glint360K_full_exp</td>
+<td>75.3%</td>
 <td>89.7%</td>
 <td>65.4%</td>
 <td>47757</td>
@@ -1300,16 +1262,6 @@ includes in-cast timing slips.</p>
 <td>22570</td>
 </tr>
 <tr>
-<td>arcface_w600k_mbf_full_noexp</td>
-<td>74.2%</td>
-<td>87.4%</td>
-<td>64.4%</td>
-<td>12645</td>
-<td>1312</td>
-<td>57</td>
-<td>6985</td>
-</tr>
-<tr>
 <td>arcface_r18_restricted_noexp</td>
 <td>73.5%</td>
 <td>91.3%</td>
@@ -1321,13 +1273,13 @@ includes in-cast timing slips.</p>
 </tr>
 <tr>
 <td>LVFace-B_Glint360K_full_noexp</td>
-<td>72.4%</td>
-<td>94.2%</td>
-<td>58.9%</td>
-<td>27077</td>
-<td>1725</td>
-<td><strong>0</strong></td>
-<td>19506</td>
+<td>72.3%</td>
+<td>88.3%</td>
+<td>61.8%</td>
+<td>40363</td>
+<td>3503</td>
+<td>244</td>
+<td>25850</td>
 </tr>
 <tr>
 <td>arcface_w600k_mbf_full_exp</td>
@@ -1340,34 +1292,14 @@ includes in-cast timing slips.</p>
 <td>26338</td>
 </tr>
 <tr>
-<td>arcface_w600k_r50_full_noexp †</td>
-<td>71.6%</td>
-<td>96.7%</td>
-<td>56.9%</td>
-<td>22471</td>
-<td>361</td>
-<td>45</td>
-<td>17012</td>
-</tr>
-<tr>
-<td>arcface_w600k_r50_restricted_exp †</td>
-<td>71.1%</td>
-<td>96.5%</td>
-<td>56.4%</td>
-<td>34954</td>
-<td>1119</td>
-<td>15</td>
-<td>27368</td>
-</tr>
-<tr>
-<td>arcface_w600k_r50_restricted_noexp †</td>
-<td>69.2%</td>
-<td>97.9%</td>
-<td>53.6%</td>
-<td>21146</td>
-<td>327</td>
-<td>15</td>
-<td>18337</td>
+<td>arcface_w600k_mbf_full_noexp</td>
+<td>71.0%</td>
+<td>93.2%</td>
+<td>57.9%</td>
+<td>41699</td>
+<td>2472</td>
+<td>56</td>
+<td>33024</td>
 </tr>
 <tr>
 <td>arcface_r18_full_exp</td>
@@ -1380,16 +1312,6 @@ includes in-cast timing slips.</p>
 <td>28871</td>
 </tr>
 <tr>
-<td>arcface_w600k_r50_full_exp †</td>
-<td>68.5%</td>
-<td>94.0%</td>
-<td>54.1%</td>
-<td>34982</td>
-<td>903</td>
-<td>150</td>
-<td>31231</td>
-</tr>
-<tr>
 <td>arcface_r18_full_noexp</td>
 <td>66.6%</td>
 <td>91.3%</td>
@@ -1401,32 +1323,153 @@ includes in-cast timing slips.</p>
 </tr>
 </tbody>
 </table>
-<p>† old, narrower anneal/extinction bounds (see above) — not directly comparable to
-the other 12 on those two params.</p>
-<p>The same 16 results as a picture — the two headline effects are visible without
-reading a single row: filled (restricted) dots stack the top of the ranking for
-every model color, and yellow (LVFace) leads within both scopes:</p>
-<p><img alt="All 16 bake-off combos ranked by training-set F1" src="../assets/images/rep4_matrix_f1.png" /></p>
-<h2 id="calibration-curves-discriminative-power-independent-of-the-threshold">Calibration curves — discriminative power, independent of the threshold<a class="headerlink" href="#calibration-curves-discriminative-power-independent-of-the-threshold" title="Permanent link">&para;</a></h2>
-<p>Each model's gallery carries a fitted Platt sigmoid <code>P(match | sim) = σ(a·sim + b)</code>
-(embedded directly in the gallery HDF5, see
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/gallery/gallery_calibration.hpp"><code>src/gallery/gallery_calibration.hpp</code></a>).
-Plotting all four side by side shows discriminative power directly, independent of
-whatever <code>prob_threshold</code> a particular run happened to use:</p>
-<p><img alt="Calibrated P(match|similarity) for all four models" src="../assets/images/calibration_curves.png" /></p>
-<p>LVFace-B has both the steepest curve (<code>a=17.7</code>, vs. 15.3–16.2 for the ArcFace
-variants) and the lowest P=0.5 decision boundary (similarity 0.23 vs. 0.27–0.31) —
-it separates same-actor from different-actor pairs more confidently at a lower
-similarity, consistent with it winning the full-gallery F1 comparison below.
-Generated by
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/docs/calibration_chart.py"><code>scripts/docs/calibration_chart.py</code></a>
-(requires each gallery to have been calibrated at least once — run any replay
-against it first).</p>
-<h2 id="two-effects-in-isolation-gallery-scope-and-pose-expansion">Two effects in isolation: gallery scope, and pose expansion<a class="headerlink" href="#two-effects-in-isolation-gallery-scope-and-pose-expansion" title="Permanent link">&para;</a></h2>
-<p>The matrix crosses two independent variables — averaging across all 4 models
-isolates each one from model choice:</p>
-<p><strong>Gallery scope (whole 2418-actor gallery vs. restricted to the film's credited
-cast)</strong> — averaged over both expansion settings and all 4 models:</p>
+<p><img alt="All combos ranked by training-set F1" src="../assets/images/rep4_matrix_f1.png" /></p>
+<p>The two clearest patterns: every model's best-scoring combo uses the
+restricted gallery, and LVFace leads within both gallery modes. <code>full_exp</code>
+(the shipped combination) is the best-scoring option that uses only
+features the running application currently supports; restriction is not
+wired into the application yet (see
+<a href="../gallery-scope/">Whole vs. cast-restricted gallery</a>).</p>
+<h3 id="a-scoring-bug-worth-recording-dropped-film-evaluations">A scoring bug worth recording: dropped-film evaluations<a class="headerlink" href="#a-scoring-bug-worth-recording-dropped-film-evaluations" title="Permanent link">&para;</a></h3>
+<p>The numbers above are corrected ones. The raw <code>rep4_best_*.json</code> files, and an
+earlier version of this table, reported a different <code>arcface_w600k_mbf_full_noexp</code>
+row: <strong>74.2% F1 at TPI 12645</strong>, a third the TPI of every sibling combo. That was
+not a better config; it was an artifact of how the optimizer aggregates.</p>
+<p><code>optimize.py</code> builds each candidate's score from only the films whose replay
+subprocess returned (<code>per_film = [m for m in ex.map(_one, films) if m is not
+None]</code>), then <strong>averages</strong> F1/precision/recall and <strong>sums</strong> TPI/FPI/misID over
+just those survivors. When a film's replay times out (the sweep ran near the
+8-process concurrency ceiling, so this happened intermittently), that film
+silently drops from both. A candidate whose hardest film timed out is therefore
+scored on an easier subset, and differential evolution, maximizing that score,
+will happily converge onto exactly such a candidate. For <code>mbf_full_noexp</code> the
+reported winner was one of 7 evaluations (out of 512) whose TPI had collapsed to
+a partial-film subset; its median-coverage evaluations sit around 51686 TPI.</p>
+<p>The fix here was to re-derive each combo's best row from its DE trajectory
+(<code>experiments/trajectories/rep4_*.jsonl</code>), keeping only evaluations within 30% of
+that combo's median TPI (full 4-film coverage) before taking the best F1. This
+needs no re-running, the honest best configuration was already in the sweep,
+just not the one <code>argmax F1</code> selected. Three combos moved: <code>mbf_full_noexp</code>
+74.2% → <strong>71.0%</strong>, <code>LVFace_full_noexp</code> 72.4% → <strong>72.3%</strong> (and its misID, 0 → 244,
+was itself a dropped-film artifact), <code>mbf_restricted_exp</code> 76.5% → <strong>76.2%</strong>. The
+shipped LVFace <code>full_exp</code> winner was unaffected, its reported evaluation already
+had full coverage (TPI 47757 ≈ median). <code>experiment_charts.py</code> applies the same
+<code>clean_best</code> filter, so every figure on this page matches the corrected table.
+The underlying <code>optimize.py</code> aggregation is also being fixed so a dropped-film
+evaluation can never be selected as a winner again.</p>
+<h3 id="per-film-training-breakdown">Per-film training breakdown<a class="headerlink" href="#per-film-training-breakdown" title="Permanent link">&para;</a></h3>
+<p>The 75.3% LVFace training figure is a macro average across 4 films, not a
+uniform result:</p>
+<table>
+<thead>
+<tr>
+<th>film</th>
+<th>LVFace F1</th>
+<th>mbf F1</th>
+<th>r18 F1</th>
+<th>best model</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Café Society</td>
+<td>68.1%</td>
+<td>62.2%</td>
+<td>60.1%</td>
+<td>LVFace</td>
+</tr>
+<tr>
+<td>Lord of War</td>
+<td>75.6%</td>
+<td>77.2%</td>
+<td>75.6%</td>
+<td>mbf</td>
+</tr>
+<tr>
+<td>Scarface</td>
+<td>71.5%</td>
+<td>68.6%</td>
+<td>64.1%</td>
+<td>LVFace</td>
+</tr>
+<tr>
+<td>Sound of Metal</td>
+<td>78.8%</td>
+<td>76.5%</td>
+<td>71.6%</td>
+<td>LVFace</td>
+</tr>
+</tbody>
+</table>
+<p>LVFace does not win every training film. mbf scores higher on Lord of War
+(77.2% vs 75.6%). LVFace's own training-film range is 68.1% to 78.8%, a
+10.7pp spread, smaller than the 37pp spread seen on held-out films but real.
+Reproduce with <code>scripts/docs/run_holdout_all_models.py --films training</code>.</p>
+<h2 id="held-out-validation-all-3-models">Held-out validation, all 3 models<a class="headerlink" href="#held-out-validation-all-3-models" title="Permanent link">&para;</a></h2>
+<p>The training matrix above is training-set fit. Each model's own tuned
+<code>full_exp</code> config was replayed against the 5 held-out films, scored the
+same way:</p>
+<table>
+<thead>
+<tr>
+<th>film</th>
+<th>LVFace F1</th>
+<th>mbf F1</th>
+<th>r18 F1</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td>Benny &amp; Joon</td>
+<td>83.0%</td>
+<td>78.5%</td>
+<td>77.1%</td>
+</tr>
+<tr>
+<td>Lovelace</td>
+<td>77.5%</td>
+<td>73.7%</td>
+<td>72.2%</td>
+</tr>
+<tr>
+<td>Valerian and the City of a Thousand Planets</td>
+<td>74.1%</td>
+<td>70.2%</td>
+<td>71.0%</td>
+</tr>
+<tr>
+<td>Downton Abbey: A New Era</td>
+<td>56.2%</td>
+<td>55.0%</td>
+<td>53.0%</td>
+</tr>
+<tr>
+<td>The Many Saints of Newark</td>
+<td>46.3%</td>
+<td>44.5%</td>
+<td>42.1%</td>
+</tr>
+<tr>
+<td><strong>macro average</strong></td>
+<td><strong>67.4%</strong></td>
+<td><strong>64.4%</strong></td>
+<td><strong>63.1%</strong></td>
+</tr>
+</tbody>
+</table>
+<p>LVFace scores highest on every one of the 5 held-out films; the ranking
+never flips. Total misIDs across the 5 films: LVFace 1032, mbf 2197, r18
+1224. LVFace has less than half mbf's misID count while also scoring
+higher on every film. This directly confirms the model choice out of
+sample; it is not inferred from the training numbers alone. See the
+<a href="../lvface-deep-dive/">LVFace deep dive</a> for frame-level detail on where and
+why LVFace still fails on the two worst films. Reproduce with
+<code>scripts/docs/run_holdout_all_models.py</code>.</p>
+<h2 id="two-effects-in-isolation-gallery-scope-and-pose-expansion">Two effects in isolation: gallery scope and pose expansion<a class="headerlink" href="#two-effects-in-isolation-gallery-scope-and-pose-expansion" title="Permanent link">&para;</a></h2>
+<p>Averaging across the 3 compared models (r50 excluded) isolates each variable
+from model choice.</p>
+<p><strong>Gallery scope</strong>, averaged over both expansion settings and all 3 models
+(6 evaluations per row):</p>
 <table>
 <thead>
 <tr>
@@ -1434,37 +1477,37 @@ cast)</strong> — averaged over both expansion settings and all 4 models:</p>
 <th>F1</th>
 <th>P</th>
 <th>R</th>
-<th>total misID (16 evals→8 each)</th>
+<th>total misID</th>
 </tr>
 </thead>
 <tbody>
 <tr>
 <td>full</td>
-<td>71.2%</td>
-<td>91.1%</td>
-<td>59.0%</td>
-<td>1073</td>
+<td>71.1%</td>
+<td>89.6%</td>
+<td>59.6%</td>
+<td>1121</td>
 </tr>
 <tr>
-<td><strong>restricted</strong></td>
-<td><strong>74.5%</strong></td>
-<td>92.2%</td>
-<td><strong>62.9%</strong></td>
-<td><strong>329</strong></td>
+<td>restricted</td>
+<td>75.9%</td>
+<td>90.4%</td>
+<td>65.6%</td>
+<td>299</td>
 </tr>
 </tbody>
 </table>
-<p>Restriction wins outright on every axis — not a precision/recall trade, a clean
-win: <strong>+3.3pp F1, +3.9pp recall, and less than a third the total misIDs.</strong> Fewer
+<p>Restriction improves every metric at once. This is not a precision/recall
+trade: +4.8pp F1, +6.0pp recall, and roughly a quarter the misIDs. Fewer
 candidates in the matcher's search space means fewer opportunities for a
-look-alike false match, and (per the recall gain) doesn't cost real detections.
-This is the single cleanest signal in the whole matrix — stronger than the model
-choice itself — which is exactly why cast-restriction becoming a real runtime
-feature (not just an optimizer trick) is the top item in Caveats below.</p>
-<p><strong>Pose expansion (promoting a confidently-identified track's novel-pose views into
-a per-film gallery annex — <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/gallery/track_gallery.hpp"><code>src/gallery/track_gallery.hpp</code></a>)</strong>
-is smaller and interacts with
-scope rather than acting independently:</p>
+lookalike false match, and the recall gain shows this does not cost real
+detections. Restriction is currently an offline optimizer technique, not a
+runtime feature of the application; see
+<a href="../gallery-scope/">Whole vs. cast-restricted gallery</a> for what building it
+into the application would require.</p>
+<p><strong>Pose expansion</strong> (promoting a confidently identified track's novel-pose
+views into a per-film gallery annex,
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/gallery/track_gallery.hpp"><code>src/gallery/track_gallery.hpp</code></a>):</p>
 <table>
 <thead>
 <tr>
@@ -1479,280 +1522,91 @@ scope rather than acting independently:</p>
 <tr>
 <td>full</td>
 <td>off</td>
-<td>71.2%</td>
-<td>58.3%</td>
-<td>209</td>
+<td>70.0%</td>
+<td>57.6%</td>
+<td>407</td>
 </tr>
 <tr>
 <td>full</td>
-<td><strong>on</strong></td>
-<td>71.2%</td>
-<td>59.7%</td>
-<td><strong>864</strong></td>
+<td>on</td>
+<td>72.1%</td>
+<td>61.5%</td>
+<td>714</td>
 </tr>
 <tr>
 <td>restricted</td>
 <td>off</td>
-<td>73.6%</td>
-<td>61.3%</td>
-<td>194</td>
+<td>75.1%</td>
+<td>63.9%</td>
+<td>179</td>
 </tr>
 <tr>
 <td>restricted</td>
-<td><strong>on</strong></td>
-<td><strong>75.4%</strong></td>
-<td><strong>64.5%</strong></td>
-<td>135</td>
+<td>on</td>
+<td>76.7%</td>
+<td>67.2%</td>
+<td>120</td>
 </tr>
 </tbody>
 </table>
-<p>In <strong>restricted</strong> mode, expansion is a clean win (+1.8pp F1, +3.2pp recall, misID
-actually <em>drops</em>) — the annex only ever competes against the film's own ~15-actor
-cast, so a "confidently identified, new pose" view is unlikely to be mistaken for
-someone else. In <strong>full</strong> mode, expansion buys essentially nothing on F1 (71.2% →
-71.2%, recall +1.4pp) while <strong>quadrupling misIDs</strong> (209 → 864): a novel-pose view
-promoted into the annex now competes against the whole 2418-actor gallery, so a
-"confident" identity is confident against the wrong universe of candidates — the
-expansion mechanism is "learning" a pose correctly, but the enlarged evidence pool
-makes it easier for that learned pose to look like a plausible match for a
-different actor. <strong>Practical takeaway: gallery expansion should be paired with
-cast restriction, not used on the full gallery</strong> — the version currently shipped
-as default (<code>full_exp</code>, see TL;DR) sits in the worse of these four cells for this
-specific knob, even though it's the best available combo without cast-restriction
-support in the app yet (see Caveats).</p>
-<h2 id="what-the-data-says">What the data says<a class="headerlink" href="#what-the-data-says" title="Permanent link">&para;</a></h2>
-<ul>
-<li><strong>LVFace was worth its size.</strong> It wins full-gallery mode outright (75.3% vs r50's
-  68.5%, r18's 69.1%, mbf's 72.0%) with the highest recall of any full-mode combo —
-  the earlier scene-union-metric conclusion ("not worth it") doesn't survive the
-  better metric.</li>
-<li><strong>Cast-restriction is a consistent, broad win.</strong> Every model's best combo is
-  <code>restricted</code>. It isn't just precision-safe: <code>arcface_w600k_mbf_restricted_exp</code> and
-  <code>LVFace-B_Glint360K_full_noexp</code> both hit <strong>misid=0</strong> — zero true wrong-actor
-  identifications. But restriction is an <strong>offline optimizer technique, not a live
-  app feature</strong> — it pre-filters each film's gallery to its Jellyfin-credited cast
-  before the matcher ever runs; there's no runtime "restrict to this film's cast"
-  switch in the app today. Implementing it for real is future work, tracked
-  separately from this defaults update.</li>
-<li><strong>Gallery expansion (<code>expand_gallery</code>) is mode-dependent.</strong> It helps on
-  <code>restricted</code> galleries (smaller, so novel-pose promotion adds real signal) and on
-  LVFace's full gallery, but <strong>hurts</strong> r50 and mbf in full mode (compare
-  <code>arcface_w600k_r50_full_exp</code> 68.5% vs <code>full_noexp</code> 71.6%). Don't assume it's a free
-  win — model- and mode-dependent.</li>
-<li><strong>arcface_r18 (smallest/cheapest) is last across all 4 modes</strong> — model capacity
-  matters here, this isn't just parameter-count padding.</li>
-<li><strong><code>anneal_sec</code>/<code>extinction_sec</code> kept pinning at the search ceiling.</strong> With the
-  original 1–30/1–15 bounds, 3 of 4 r50 combos landed at ~93-98% of the upper bound.
-  Widened to 1–60/1–60 mid-run (after the r50 combos had already finished) — every
-  subsequent combo's best config landed at ~90%+ of the <em>new</em> ceiling too (e.g. the
-  LVFace winner: <code>ann=59.2, ext=59.2</code>, both ~99% of 60). The likely mechanism: a
-  strict <code>prob_threshold</code> "earns" a long extinction/anneal window — once false
-  matches are rare, a long window just bridges real presence gaps (occlusion, turned
-  face) instead of smearing false positives into later scenes, which is what made
-  short windows look better under the old, laxer thresholds. <strong>Open question, not
-  resolved</strong>: does this keep climbing past 60s, or does it actually plateau there?
-  Decided not to chase further this round (diminishing-returns judgment call) — flag
-  for a future sweep if it matters.</li>
-</ul>
-<p>The ceiling-pinning is visible in the raw search itself. Every one of the 512
-DE evaluations for the winning combo, plotted over the
-<code>prob_threshold</code> × <code>extinction_sec</code> plane:</p>
+<p>In restricted mode, expansion is a clean win: +1.6pp F1, +3.3pp recall,
+misID drops. The annex only competes against the film's own roughly 15-actor
+cast, so a new pose of a known actor is unlikely to be confused with someone
+else. In full mode, expansion buys +2.1pp F1 and +3.9pp recall but at a real
+cost: misID rises from 407 to 714 as the same new-pose view now competes
+against the full 2418-actor gallery, where a confidently learned pose is more
+likely to match the wrong person. On the full gallery it is a recall-vs-misID
+trade, not a free gain. This training-set effect
+did not reproduce on held-out data; see
+<a href="../pose-expansion/">Does pose expansion help?</a> for the full held-out test
+and the two methodology bugs caught while checking it.</p>
+<h2 id="calibration-curves">Calibration curves<a class="headerlink" href="#calibration-curves" title="Permanent link">&para;</a></h2>
+<p>Each gallery carries a fitted Platt sigmoid <code>P(match | sim) = σ(a·sim + b)</code>,
+stored directly in the gallery HDF5
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/gallery/gallery_calibration.hpp"><code>src/gallery/gallery_calibration.hpp</code></a>).
+This measures discriminative power independent of whatever
+<code>prob_threshold</code> a given run used:</p>
+<p><img alt="Calibrated P(match|similarity) for all four models" src="../assets/images/calibration_curves.png" /></p>
+<p>LVFace has the steepest curve (<code>a=17.7</code> vs 15.3-16.2 for the ArcFace
+variants) and the lowest P=0.5 decision boundary (similarity 0.23 vs
+0.27-0.31), separating same-actor from different-actor pairs more
+confidently at a lower similarity than any ArcFace variant tested,
+including r50. Generated by
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/docs/calibration_chart.py"><code>scripts/docs/calibration_chart.py</code></a>.</p>
+<h2 id="extinction-and-anneal-window-search">Extinction and anneal window search<a class="headerlink" href="#extinction-and-anneal-window-search" title="Permanent link">&para;</a></h2>
+<p>Every one of the 512 DE evaluations for the winning LVFace <code>full_exp</code>
+combo, plotted over the <code>prob_threshold</code> × <code>extinction_sec</code> plane:</p>
 <p><img alt="DE search landscape: 512 evaluations over prob_threshold × extinction_sec" src="../assets/images/de_search_landscape.png" /></p>
-<p>The dark band hugging the top edge <em>is</em> the finding: nearly everything scoring
-well sits at <code>extinction_sec</code> ≥ 50, across a wide range of thresholds, and the
-population converged into a dense cloud around the optimum (threshold ~0.70–0.80,
-extinction pinned at the 60s bound). Short extinction windows (bottom half) are
-uniformly pale — under a strict threshold there is simply no good configuration
-down there. Generated by
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/docs/experiment_charts.py"><code>scripts/docs/experiment_charts.py</code></a>
-from the DE trajectories (<code>experiments/trajectories/*.jsonl</code>, part of the
-<code>experiment-data</code> artifact package).</p>
-<h2 id="held-out-validation-the-number-that-actually-matters">Held-out validation — the number that actually matters<a class="headerlink" href="#held-out-validation-the-number-that-actually-matters" title="Permanent link">&para;</a></h2>
-<p>The 16-combo matrix above is training-set fit. This is the real test: the shipped
-config (<code>LVFace-B_Glint360K_full_exp</code> — <code>prob_threshold=0.754, anneal_sec=35.5,
-extinction_sec=57.4, expand_gallery=true</code>) replayed against the <strong>5 films never seen
-by the optimizer</strong> (Benny &amp; Joon, Downton Abbey: A New Era, Lovelace, The Many
-Saints of Newark, Valerian and the City of a Thousand Planets), scored the same way.</p>
-<table>
-<thead>
-<tr>
-<th>film</th>
-<th>F1</th>
-<th>P</th>
-<th>R</th>
-<th>agree</th>
-<th>TPI</th>
-<th>FPI</th>
-<th>misid</th>
-<th>FN</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td>Benny &amp; Joon</td>
-<td>83.0%</td>
-<td>89.1%</td>
-<td>77.7%</td>
-<td>72.4%</td>
-<td>15125</td>
-<td>1846</td>
-<td>0</td>
-<td>4337</td>
-</tr>
-<tr>
-<td>Lovelace</td>
-<td>77.5%</td>
-<td>90.3%</td>
-<td>67.9%</td>
-<td>72.1%</td>
-<td>14990</td>
-<td>1085</td>
-<td>58</td>
-<td>7085</td>
-</tr>
-<tr>
-<td>Valerian and the City of a Thousand Planets</td>
-<td>74.1%</td>
-<td>97.1%</td>
-<td>60.0%</td>
-<td>58.8%</td>
-<td>18663</td>
-<td>548</td>
-<td>0</td>
-<td>12467</td>
-</tr>
-<tr>
-<td>Downton Abbey: A New Era</td>
-<td>56.2%</td>
-<td>97.8%</td>
-<td>39.4%</td>
-<td>40.6%</td>
-<td>52027</td>
-<td>1173</td>
-<td>0</td>
-<td>80084</td>
-</tr>
-<tr>
-<td><strong>The Many Saints of Newark</strong></td>
-<td><strong>46.3%</strong></td>
-<td><strong>54.7%</strong></td>
-<td>40.1%</td>
-<td>37.0%</td>
-<td>15922</td>
-<td>4394</td>
-<td><strong>974</strong></td>
-<td>23791</td>
-</tr>
-<tr>
-<td><strong>macro average (5 films)</strong></td>
-<td><strong>67.4%</strong></td>
-<td>85.8%</td>
-<td>57.0%</td>
-<td>56.2%</td>
-<td>116727</td>
-<td>9046</td>
-<td>1032</td>
-<td>127764</td>
-</tr>
-</tbody>
-</table>
-<p><img alt="Held-out per-film F1 vs. the training-set fit" src="../assets/images/holdout_f1_by_film.png" /></p>
-<p><strong>67.4% held out vs. 75.3% on training</strong> — an ~8pp drop, and a much more informative
-number than the training-set F1 alone: a <strong>37pp spread between best and worst film</strong>
-(83.0% vs 46.3%). The config does not generalize uniformly.</p>
-<p>Two films are outright failure cases, and rendering bounding boxes + names on the
-extracted frames (<code>replay.py --raw-out</code> +
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/optimizer/dump_error_frames.py"><code>dump_error_frames.py</code></a><code>--raw</code>, see
-Reproduce) turned what looked like a same-scene misidentification into something
-more precise and more damning:</p>
+<p>Nearly everything scoring well sits at <code>extinction_sec</code> above 50, across a
+wide range of thresholds. Short extinction windows are uniformly weaker:
+under a strict threshold, there is no good configuration in that region of
+the search space. The optimizer converged with <code>anneal_sec=59.2,
+extinction_sec=59.2</code>, about 99% of the widened 60s bound, which raises an
+open question not resolved in this round: does performance keep improving
+past 60s, or does it plateau there. Not chased further this pass.</p>
+<h2 id="caveats">Caveats<a class="headerlink" href="#caveats" title="Permanent link">&para;</a></h2>
 <ul>
-<li><strong>The Many Saints of Newark</strong> (mob-family drama, picked as a training-adjacent
-  genre test) has <strong>974 true misIDs</strong> — far more than any training combo saw at any
-  setting. The annotated frame below shows the same mechanism as Downton Abbey,
-  at smaller scale: <strong>"Jon Bernthal 100%", "Joey Diaz 100%", and "Billy Magnussen
-  100%" are all frozen boxes over empty background — a blurred wall, hanging plates —
-  with no face in them at all.</strong> Only one real face in frame has a box, and it
-  carries a <em>second</em>, colliding label ("Leslie Odom Jr." and "Michael Gandolfini"
-  both at high confidence on the same box) — likely two tracks whose frozen bboxes
-  happen to overlap.</li>
-</ul>
-<p><img alt="Frozen ghost boxes over background, The Many Saints of Newark" src="../assets/images/many_saints_ghost_fpi.jpg" />
-  <em>Frame <code>many_saints/fpi/fpi_t03543.jpg</code> from the <code>montage-frames</code> artifact
-  package (<code>scripts/artifacts/pull_artifacts.sh montage-frames
-  Many_Saints_of_Newark</code>).</em></p>
-<ul>
-<li><strong>Downton Abbey: A New Era</strong> (large ensemble, 36-cast) has high precision (97.8%)
-  but recall collapses to 39.4% (FN=80084, by far the largest of the 5). Its
-  starkest failure happens where there is nothing to see at all: the film's hard
-  cut into its closing credits, where <strong>the matcher kept reporting 15 actors —
-  all wrong — for nearly a minute of faceless screen.</strong></li>
-</ul>
-<p>Both are the same mechanism, and it can be <em>measured</em>, not just screenshotted.
-Plotting the dump's own per-second <code>face_count</code> (detector output, independent
-of the tracker) against the number of actors the tracker reports, through
-Downton Abbey's cut to credits:</p>
-<p><img alt="Detector face_count vs. tracker-reported actors through the cut to credits" src="../assets/images/downton_ghost_timeline.png" /></p>
-<p>From the cut onward the detector sees <strong>zero faces</strong> — yet the tracker holds a
-perfectly flat plateau of 15 reported identities for 56 seconds, each with the
-<em>exact same bbox, unchanged to the pixel</em> (verified for Hugh Bonneville:
-<code>(1743.2, 0.0, 171.3, 317.8)</code> at every sampled second from 7222 through 7279+).
-The staircase on the right edge is the extinction window finally expiring,
-actor by actor. That plateau is <code>SceneTrackerFunc</code>'s
-<code>active_[actor_idx].last_bbox</code>
-(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/nodes/scene_tracker_node.hpp"><code>src/nodes/scene_tracker_node.hpp</code></a>)
-being re-emitted
-unchanged — <strong>the extinction state machine working exactly as coded</strong>, not a
-bug in the logic. The film cuts from a packed group shot straight into ~40+
-seconds of blank titles/credits with zero faces, and <code>extinction_sec=57.4</code> is
-comfortably long enough to bridge that entire gap without expiring, so the
-tracker faithfully keeps reporting "last known position" for a cast that is no
-longer on screen at all.</p>
-<p>This reframes the "long extinction window wins" DE-search pattern (see above): it
-isn't unambiguously good. It buys recall by bridging real gaps (occlusion, turned
-face) in some films, but on others — specifically, hard cuts into long faceless
-footage — it manufactures a frozen-bbox ghost the tracker has no way to verify,
-precisely the failure mode the <em>original</em> short-extinction-window default (<code>1.5s</code>)
-was chosen to avoid. The training-set films apparently didn't have a long enough
-faceless stretch after a confirmed identity to expose this; the held-out set did.</p>
-<p>Frames for all three films (<code>benny_joon</code>, <code>many_saints</code>, <code>downton_abbey</code> — one strong
-performer, two failure cases) are under <code>experiments/results/holdout/frames/</code>
-(not committed — pull per film with <code>scripts/artifacts/pull_artifacts.sh
-montage-frames &lt;film-slug&gt;</code>), each
-with a <code>manifest.json</code> listing the bucket (<code>best</code>/<code>fpi</code>/<code>fn</code>), timestamp, and
-predicted vs. ground-truth actors for every dumped frame. Frames are annotated with
-bounding boxes + name/confidence (green = identified, orange = unknown), matching
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/nodes/debug_renderer_node.hpp"><code>src/nodes/debug_renderer_node.hpp</code></a>'s
-colour convention. Generated by
-<code>scripts/optimizer/dump_error_frames.py --raw &lt;replay.py --raw-out output&gt;</code> (see
-Reproduce).</p>
-<p><code>dump_error_frames.py --interval-sec 600</code> also supports a per-N-second sweep
-instead of the fixed best/fpi/fn buckets: one best (highest Jaccard) and one worst
-(lowest Jaccard) frame per 10-minute window across the whole film, e.g.
-<code>experiments/results/holdout/frames/many_saints_intervals/</code> (13 windows × 2 = 26
-frames for the ~2h Many Saints runtime) — a way to sample "how are we doing" evenly
-across a film's runtime rather than only at its most extreme seconds.</p>
-<h2 id="caveats-what-this-is-not">Caveats / what this is not<a class="headerlink" href="#caveats-what-this-is-not" title="Permanent link">&para;</a></h2>
-<ul>
-<li><strong>r50's 4 combos used the old, narrower search bounds</strong> and aren't fully
-  comparable to the other 12 on <code>anneal_sec</code>/<code>extinction_sec</code>.</li>
-<li><strong>The applied defaults use <code>full_exp</code>, not the higher-scoring <code>restricted_exp</code></strong>,
-  because cast-restriction isn't a real runtime feature yet (see above). The
-  78.3% F1 number is not what the shipped defaults will produce — 75.3% is.</li>
-<li><strong><code>full_exp</code> is the best full-gallery combo, but not the safest.</strong> Per the
-  isolated-effects analysis above, <code>expand_gallery=true</code> only cleanly pays off
-  when paired with cast-restriction; on the full gallery it's flat on F1 while
-  ~4x-ing misIDs (209→864, averaged across models). <code>full_noexp</code> scores lower
-  (72.4% vs 75.3% for LVFace) but with <strong>zero</strong> true misIDs and higher precision
-  (94.2% vs 89.7%). Kept <code>full_exp</code> as shipped since it's the highest-F1 option
-  available without cast-restriction, but this is a real F1-vs-safety trade, not
-  a strictly-better choice — worth revisiting if misID rate matters more than
-  the last few points of F1 for a given deployment.</li>
-<li><strong>Switching the default model is an operational change, not just a config tweak</strong>:
-  any existing gallery built from r50 embeddings is incompatible with LVFace
-  embeddings and needs rebuilding.</li>
+<li>r50's 4 combos used the older, narrower search bounds (1-30/1-15 instead
+  of 1-60/1-60) and are further confounded by its thinner gallery. Excluded
+  from all comparisons above except calibration.</li>
+<li>The shipped defaults use <code>full_exp</code> (75.3% training F1), not the
+  higher-scoring <code>restricted_exp</code> (78.3%), because cast restriction is not
+  a runtime feature of the application yet.</li>
+<li><code>expand_gallery</code> is mode-dependent, not a free win. Averaged across models
+  on the full gallery it trades misIDs for recall (see the pose-expansion
+  table). For LVFace specifically, though, <code>full_exp</code> beats <code>full_noexp</code> on
+  every axis at once (F1 75.3 vs 72.3, precision 89.7 vs 88.3, recall 65.4 vs
+  61.8, misID 232 vs 244), so the shipped <code>full_exp</code> is a clean choice for
+  this model, not an F1-vs-safety trade. (An earlier version of this page
+  reported <code>full_noexp</code> at 72.4% with zero misIDs and higher precision, which
+  made it look like the safer option; that was the dropped-film artifact
+  described above, not a real property of the config.)</li>
+<li>Switching the default model is an operational change: any gallery built
+  from a different model's embeddings must be rebuilt before the new
+  default takes effect.</li>
 </ul>
 <h2 id="reproduce">Reproduce<a class="headerlink" href="#reproduce" title="Permanent link">&para;</a></h2>
-<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># 4-film matrix, all 4 models × 2 modes × 2 expansion settings</span>
+<div class="language-bash highlight"><pre><span></span><code><span id="__span-0-1"><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># 4-film training matrix, all 4 models × 2 gallery modes × 2 expansion settings</span>
 </span><span id="__span-0-2"><a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>bash<span class="w"> </span>experiments/run_rep4_subprocess.sh
 </span><span id="__span-0-3"><a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
 </span><span id="__span-0-4"><a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># single combo</span>
@@ -1762,36 +1616,23 @@ across a film's runtime rather than only at its most extreme seconds.</p>
 </span><span id="__span-0-8"><a id="__codelineno-0-8" name="__codelineno-0-8" href="#__codelineno-0-8"></a><span class="w">    </span>--params<span class="w"> </span>prob_threshold:0.5:0.999<span class="w"> </span>anneal_sec:1:60<span class="w"> </span>extinction_sec:1:60<span class="w"> </span><span class="se">\</span>
 </span><span id="__span-0-9"><a id="__codelineno-0-9" name="__codelineno-0-9" href="#__codelineno-0-9"></a><span class="w">    </span>--popsize<span class="w"> </span><span class="m">10</span><span class="w"> </span>--maxiter<span class="w"> </span><span class="m">15</span><span class="w"> </span>--trajectory<span class="w"> </span>traj.jsonl<span class="w"> </span>--out<span class="w"> </span>best.json
 </span><span id="__span-0-10"><a id="__codelineno-0-10" name="__codelineno-0-10" href="#__codelineno-0-10"></a>
-</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># replay the shipped config against a held-out film — --raw-out is needed to draw</span>
-</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a><span class="c1"># bboxes later (the merged pred.json has no per-frame bbox, only actor windows)</span>
-</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a>python3<span class="w"> </span>scripts/optimizer/replay.py<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a><span class="w">    </span>--dump<span class="w"> </span>experiments/dumps/LVFace-B_Glint360K/dump_&lt;slug&gt;.h5<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a><span class="w">    </span>--gallery<span class="w"> </span>experiments/galleries/gallery_LVFace-B_Glint360K.h5<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a><span class="w">    </span>--out<span class="w"> </span>pred.json<span class="w"> </span>--raw-out<span class="w"> </span>raw.jsonl<span class="w"> </span>--prob-threshold<span class="w"> </span><span class="m">0</span>.754<span class="w"> </span>--anneal-sec<span class="w"> </span><span class="m">35</span>.54<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a><span class="w">    </span>--extinction-sec<span class="w"> </span><span class="m">57</span>.43<span class="w"> </span>--expand-gallery
-</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a>
-</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a><span class="c1"># regenerate the report&#39;s charts (16-combo ranking, DE landscape, held-out</span>
-</span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20" href="#__codelineno-0-20"></a><span class="c1"># per-film F1, Downton ghost timeline) from the artifacts under experiments/</span>
+</span><span id="__span-0-11"><a id="__codelineno-0-11" name="__codelineno-0-11" href="#__codelineno-0-11"></a><span class="c1"># held-out validation, all 3 models, 5 films</span>
+</span><span id="__span-0-12"><a id="__codelineno-0-12" name="__codelineno-0-12" href="#__codelineno-0-12"></a>python3<span class="w"> </span>scripts/docs/run_holdout_all_models.py<span class="w"> </span>--out<span class="w"> </span>docs_data/holdout_all_models.json
+</span><span id="__span-0-13"><a id="__codelineno-0-13" name="__codelineno-0-13" href="#__codelineno-0-13"></a>
+</span><span id="__span-0-14"><a id="__codelineno-0-14" name="__codelineno-0-14" href="#__codelineno-0-14"></a><span class="c1"># per-film training breakdown, all 3 models, 4 films</span>
+</span><span id="__span-0-15"><a id="__codelineno-0-15" name="__codelineno-0-15" href="#__codelineno-0-15"></a>python3<span class="w"> </span>scripts/docs/run_holdout_all_models.py<span class="w"> </span>--films<span class="w"> </span>training<span class="w"> </span>--out<span class="w"> </span>docs_data/training_per_film.json
+</span><span id="__span-0-16"><a id="__codelineno-0-16" name="__codelineno-0-16" href="#__codelineno-0-16"></a>
+</span><span id="__span-0-17"><a id="__codelineno-0-17" name="__codelineno-0-17" href="#__codelineno-0-17"></a><span class="c1"># gallery coverage per film</span>
+</span><span id="__span-0-18"><a id="__codelineno-0-18" name="__codelineno-0-18" href="#__codelineno-0-18"></a>python3<span class="w"> </span>scripts/docs/gallery_coverage_per_film.py<span class="w"> </span>--out<span class="w"> </span>docs_data/gallery_coverage_per_film.json
+</span><span id="__span-0-19"><a id="__codelineno-0-19" name="__codelineno-0-19" href="#__codelineno-0-19"></a>
+</span><span id="__span-0-20"><a id="__codelineno-0-20" name="__codelineno-0-20" href="#__codelineno-0-20"></a><span class="c1"># regenerate this page&#39;s charts from experiments/ artifacts</span>
 </span><span id="__span-0-21"><a id="__codelineno-0-21" name="__codelineno-0-21" href="#__codelineno-0-21"></a>python3<span class="w"> </span>scripts/docs/experiment_charts.py<span class="w"> </span>--out-dir<span class="w"> </span>docs/assets/images
 </span><span id="__span-0-22"><a id="__codelineno-0-22" name="__codelineno-0-22" href="#__codelineno-0-22"></a>
-</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23" href="#__codelineno-0-23"></a><span class="c1"># dump example frames (best-agreement / FPI / FN) for visual inspection, annotated</span>
-</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24" href="#__codelineno-0-24"></a><span class="c1"># with bounding boxes + names (--raw is optional; omit for unannotated frames)</span>
-</span><span id="__span-0-25"><a id="__codelineno-0-25" name="__codelineno-0-25" href="#__codelineno-0-25"></a>python3<span class="w"> </span>scripts/optimizer/dump_error_frames.py<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-26"><a id="__codelineno-0-26" name="__codelineno-0-26" href="#__codelineno-0-26"></a><span class="w">    </span>--pred<span class="w"> </span>pred.json<span class="w"> </span>--raw<span class="w"> </span>raw.jsonl<span class="w"> </span>--xray<span class="w"> </span>experiments/xray/.../&lt;xray_dir&gt;<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-27"><a id="__codelineno-0-27" name="__codelineno-0-27" href="#__codelineno-0-27"></a><span class="w">    </span>--movie<span class="w"> </span><span class="s2">&quot;&lt;path to source video&gt;&quot;</span><span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-28"><a id="__codelineno-0-28" name="__codelineno-0-28" href="#__codelineno-0-28"></a><span class="w">    </span>--gallery<span class="w"> </span>experiments/galleries/gallery_LVFace-B_Glint360K.h5<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-29"><a id="__codelineno-0-29" name="__codelineno-0-29" href="#__codelineno-0-29"></a><span class="w">    </span>--out-dir<span class="w"> </span>experiments/results/holdout/frames/&lt;name&gt;<span class="w"> </span>--n-per-bucket<span class="w"> </span><span class="m">4</span>
-</span><span id="__span-0-30"><a id="__codelineno-0-30" name="__codelineno-0-30" href="#__codelineno-0-30"></a>
-</span><span id="__span-0-31"><a id="__codelineno-0-31" name="__codelineno-0-31" href="#__codelineno-0-31"></a><span class="c1"># or: one best + one worst frame per 10-minute window across the whole film</span>
-</span><span id="__span-0-32"><a id="__codelineno-0-32" name="__codelineno-0-32" href="#__codelineno-0-32"></a>python3<span class="w"> </span>scripts/optimizer/dump_error_frames.py<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-33"><a id="__codelineno-0-33" name="__codelineno-0-33" href="#__codelineno-0-33"></a><span class="w">    </span>--pred<span class="w"> </span>pred.json<span class="w"> </span>--raw<span class="w"> </span>raw.jsonl<span class="w"> </span>--xray<span class="w"> </span>experiments/xray/.../&lt;xray_dir&gt;<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-34"><a id="__codelineno-0-34" name="__codelineno-0-34" href="#__codelineno-0-34"></a><span class="w">    </span>--movie<span class="w"> </span><span class="s2">&quot;&lt;path to source video&gt;&quot;</span><span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-35"><a id="__codelineno-0-35" name="__codelineno-0-35" href="#__codelineno-0-35"></a><span class="w">    </span>--gallery<span class="w"> </span>experiments/galleries/gallery_LVFace-B_Glint360K.h5<span class="w"> </span><span class="se">\</span>
-</span><span id="__span-0-36"><a id="__codelineno-0-36" name="__codelineno-0-36" href="#__codelineno-0-36"></a><span class="w">    </span>--out-dir<span class="w"> </span>experiments/results/holdout/frames/&lt;name&gt;_intervals<span class="w"> </span>--interval-sec<span class="w"> </span><span class="m">600</span>
+</span><span id="__span-0-23"><a id="__codelineno-0-23" name="__codelineno-0-23" href="#__codelineno-0-23"></a><span class="c1"># one frame per distinct out-of-cast name across all 9 films (used in the deep dive)</span>
+</span><span id="__span-0-24"><a id="__codelineno-0-24" name="__codelineno-0-24" href="#__codelineno-0-24"></a>python3<span class="w"> </span>scripts/docs/first_fpi_frames.py
 </span></code></pre></div>
-<p>See also: <a href="../optimizer-experiments/">the prior optimizer round</a> (superseded
-metric) and the session log
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/experiments/SESSION_STATE.md"><code>experiments/SESSION_STATE.md</code></a>.</p>
+<p>See also the session log
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/experiments/SESSION_STATE.md"><code>experiments/SESSION_STATE.md</code></a>.</p>
 
 
 
@@ -1844,13 +1685,13 @@ metric) and the session log
         
         
           
-          <a href="../optimizer-experiments/" class="md-footer__link md-footer__link--next" aria-label="Next: Optimizer Experiments (prior round)">
+          <a href="../service-conversion/" class="md-footer__link md-footer__link--next" aria-label="Next: Service Conversion (proposal)">
             <div class="md-footer__title">
               <span class="md-footer__direction">
                 Next
               </span>
               <div class="md-ellipsis">
-                Optimizer Experiments (prior round)
+                Service Conversion (proposal)
               </div>
             </div>
             <div class="md-footer__button md-icon">
diff --git a/pose-expansion/index.html b/pose-expansion/index.html
index 53a3ad2..47769c5 100644
--- a/pose-expansion/index.html
+++ b/pose-expansion/index.html
@@ -80,7 +80,7 @@
     <div data-md-component="skip">
       
         
-        <a href="#pose-expansion-does-learning-new-poses-mid-film-help" class="md-skip">
+        <a href="#pose-expansion-does-promoting-new-poses-mid-film-help" class="md-skip">
           Skip to content
         </a>
       
@@ -242,6 +242,25 @@
   
   
   
+  
+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
     
   
   
@@ -272,26 +291,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -393,6 +393,33 @@
       
   
   
+  
+  
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
     
   
   
@@ -414,10 +441,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" checked>
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" checked>
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="">
             
   
   
@@ -435,8 +462,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="true">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="true">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -569,10 +596,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#the-training-set-signal" class="md-nav__link">
+  <a href="#training-set-signal" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The training-set signal
+        Training-set signal
       
     </span>
   </a>
@@ -580,10 +607,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#held-out-test-does-it-reproduce" class="md-nav__link">
+  <a href="#held-out-test" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Held-out test: does it reproduce?
+        Held-out test
       
     </span>
   </a>
@@ -591,10 +618,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#two-bugs-this-required-catching-this-sections-own-methodology" class="md-nav__link">
+  <a href="#two-methodology-bugs-caught-during-this-check" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Two bugs this required catching (this section's own methodology)
+        Two methodology bugs caught during this check
       
     </span>
   </a>
@@ -602,10 +629,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#what-this-means" class="md-nav__link">
+  <a href="#conclusion" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What this means
+        Conclusion
       
     </span>
   </a>
@@ -670,34 +697,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -764,10 +764,10 @@
     <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix>
       
         <li class="md-nav__item">
-  <a href="#the-training-set-signal" class="md-nav__link">
+  <a href="#training-set-signal" class="md-nav__link">
     <span class="md-ellipsis">
       
-        The training-set signal
+        Training-set signal
       
     </span>
   </a>
@@ -775,10 +775,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#held-out-test-does-it-reproduce" class="md-nav__link">
+  <a href="#held-out-test" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Held-out test: does it reproduce?
+        Held-out test
       
     </span>
   </a>
@@ -786,10 +786,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#two-bugs-this-required-catching-this-sections-own-methodology" class="md-nav__link">
+  <a href="#two-methodology-bugs-caught-during-this-check" class="md-nav__link">
     <span class="md-ellipsis">
       
-        Two bugs this required catching (this section's own methodology)
+        Two methodology bugs caught during this check
       
     </span>
   </a>
@@ -797,10 +797,10 @@
 </li>
       
         <li class="md-nav__item">
-  <a href="#what-this-means" class="md-nav__link">
+  <a href="#conclusion" class="md-nav__link">
     <span class="md-ellipsis">
       
-        What this means
+        Conclusion
       
     </span>
   </a>
@@ -824,16 +824,20 @@
 
 
 
-<h1 id="pose-expansion-does-learning-new-poses-mid-film-help">Pose expansion: does "learning" new poses mid-film help?<a class="headerlink" href="#pose-expansion-does-learning-new-poses-mid-film-help" title="Permanent link">&para;</a></h1>
-<p><code>expand_gallery</code> (<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/gallery/track_gallery.hpp"><code>src/gallery/track_gallery.hpp</code></a>)
-promotes a confidently-identified
-track's novel-pose reference views into a per-film, in-memory gallery annex — the
-idea being that once the pipeline is sure who someone is, a pose it hasn't seen
-before (turned head, different lighting) becomes a free extra reference for
-recognising that actor again later in the same film, without touching the baked
-gallery.</p>
-<h2 id="the-training-set-signal">The training-set signal<a class="headerlink" href="#the-training-set-signal" title="Permanent link">&para;</a></h2>
-<p>Averaged across all 4 models, on the 4 films used for optimization:</p>
+<h1 id="pose-expansion-does-promoting-new-poses-mid-film-help">Pose expansion: does promoting new poses mid-film help?<a class="headerlink" href="#pose-expansion-does-promoting-new-poses-mid-film-help" title="Permanent link">&para;</a></h1>
+<p><code>expand_gallery</code>
+(<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/gallery/track_gallery.hpp"><code>src/gallery/track_gallery.hpp</code></a>)
+promotes a confidently identified track's novel-pose reference views into a
+per-film, in-memory gallery annex. The idea: once the pipeline is confident
+about an identity, a pose it has not seen before (turned head, different
+lighting) becomes an extra reference for recognizing that actor again later
+in the same film, without touching the baked gallery.</p>
+<h2 id="training-set-signal">Training-set signal<a class="headerlink" href="#training-set-signal" title="Permanent link">&para;</a></h2>
+<p>Averaged across the 3 compared models (r50 excluded), on the 4 films used
+for optimization. These are the corrected, full-coverage figures, see the
+<a href="../model-bakeoff/#a-scoring-bug-worth-recording-dropped-film-evaluations">dropped-film note</a>
+in the experiment log for why an earlier version of this table overstated the
+full-mode misID jump (209 → 864) that was itself partly a truncation artifact:</p>
 <table>
 <thead>
 <tr>
@@ -848,54 +852,54 @@ gallery.</p>
 <tr>
 <td>full</td>
 <td>off</td>
-<td>71.2%</td>
-<td>58.3%</td>
-<td>209</td>
+<td>70.0%</td>
+<td>57.6%</td>
+<td>407</td>
 </tr>
 <tr>
 <td>full</td>
-<td><strong>on</strong></td>
-<td>71.2%</td>
-<td>59.7%</td>
-<td><strong>864</strong></td>
+<td>on</td>
+<td>72.1%</td>
+<td>61.5%</td>
+<td>714</td>
 </tr>
 <tr>
 <td>restricted</td>
 <td>off</td>
-<td>73.6%</td>
-<td>61.3%</td>
-<td>194</td>
+<td>75.1%</td>
+<td>63.9%</td>
+<td>179</td>
 </tr>
 <tr>
 <td>restricted</td>
-<td><strong>on</strong></td>
-<td><strong>75.4%</strong></td>
-<td><strong>64.5%</strong></td>
-<td>135</td>
+<td>on</td>
+<td>76.7%</td>
+<td>67.2%</td>
+<td>120</td>
 </tr>
 </tbody>
 </table>
-<p>In <code>restricted</code> mode (matcher's candidate set capped to the film's own credited
-cast) expansion looked like a clean win: +1.8pp F1, +3.2pp recall, misID actually
-lower. In <code>full</code> mode it looked flat-to-costly: ~0 F1 change, recall +1.4pp, but
-misID roughly quadrupled (209 → 864) — see the
-<a href="../model-bakeoff/">bake-off experiment log</a> for the per-model breakdown. That's the number that motivated this page: <strong>does turning
-expansion on actually change what gets recognised, frame by frame, or is the
-aggregate F1 shift something else?</strong></p>
-<h2 id="held-out-test-does-it-reproduce">Held-out test: does it reproduce?<a class="headerlink" href="#held-out-test-does-it-reproduce" title="Permanent link">&para;</a></h2>
-<p>Same model + same tuned config, <code>expand_gallery</code> toggled on vs. off, nothing else
-changed — full gallery mode, per-second scoring against X-Ray. This isolates
-expansion from every other variable (config, model, threshold) that differs
-between the training-set <code>exp</code>/<code>noexp</code> rows above.</p>
-<p><strong>LVFace-B Glint360K, all 5 held-out films</strong> (films never seen by the optimizer):</p>
+<p>In restricted mode, expansion looks like a clean win: +1.6pp F1, +3.3pp
+recall, lower misID. In full mode it looks like a recall-for-misID trade:
++2.1pp F1, +3.9pp recall, but misID rises from 407 to 714. See
+<a href="../model-bakeoff/">the full experiment log</a> for the per-model breakdown.
+This asymmetry motivated the question below: does turning expansion on
+change what gets recognized frame by frame, or is the aggregate F1 shift
+coming from something else.</p>
+<h2 id="held-out-test">Held-out test<a class="headerlink" href="#held-out-test" title="Permanent link">&para;</a></h2>
+<p>Same model, same tuned config, <code>expand_gallery</code> toggled on vs. off, nothing
+else changed, full gallery mode, per-second scoring against X-Ray. This
+isolates expansion from every other variable that differs between the
+training-set rows above.</p>
+<p>LVFace-B Glint360K, all 5 held-out films:</p>
 <table>
 <thead>
 <tr>
 <th>film</th>
 <th>F1 (exp)</th>
 <th>F1 (noexp)</th>
-<th>TPI Δ</th>
-<th>FN Δ</th>
+<th>TPI delta</th>
+<th>FN delta</th>
 </tr>
 </thead>
 <tbody>
@@ -936,54 +940,60 @@ between the training-set <code>exp</code>/<code>noexp</code> rows above.</p>
 </tr>
 </tbody>
 </table>
-<p><strong>ArcFace R18</strong> (Benny &amp; Joon, r18's own tuned config): F1 77.1% for both, TPI/FN
-identical, FPI differs by 2 (noise).</p>
-<p><strong>Every film, both models tested: F1 within 0.1–0.2pp, TPI/FN swings in the tens
-out of tens of thousands.</strong> That's noise, not a signal — expansion made no
-measurable difference to per-second onscreen identification anywhere it was
-tested on unseen data.</p>
-<h2 id="two-bugs-this-required-catching-this-sections-own-methodology">Two bugs this required catching (this section's own methodology)<a class="headerlink" href="#two-bugs-this-required-catching-this-sections-own-methodology" title="Permanent link">&para;</a></h2>
-<p>Getting to the clean table above took two wrong turns, both worth recording
-since they're exactly the kind of error that produces a false positive "look,
-expansion helped!" finding:</p>
+<p>ArcFace R18, Benny &amp; Joon, r18's own tuned config: F1 77.1% for both, TPI
+and FN identical, FPI differs by 2.</p>
+<p>Every film, both models tested: F1 differs by 0.1-0.2pp, TPI/FN swings are
+in the tens out of tens of thousands. This is noise, not a signal.
+Expansion made no measurable difference to per-second on-screen
+identification on any held-out film tested.</p>
+<h2 id="two-methodology-bugs-caught-during-this-check">Two methodology bugs caught during this check<a class="headerlink" href="#two-methodology-bugs-caught-during-this-check" title="Permanent link">&para;</a></h2>
+<p>Getting to the table above required catching two wrong turns, both worth
+recording because they are exactly the kind of error that produces a false
+positive "expansion helped" finding.</p>
 <ol>
-<li><strong>Timeout truncation.</strong> The first Downton Abbey <code>exp</code> replay was cut off by a
-   60s subprocess timeout at ~76% through the film (5589 of 7368 expected
-   seconds) — a genuinely large, silent data loss that showed up as a large,
-   convincing-looking TPI gap (47938 vs 52032) purely because one run had a
-   quarter of the film missing. Caught by comparing <code>n_seconds</code> between runs
-   before trusting any score delta; fixed by re-running with a longer timeout.</li>
-<li><strong>Bbox-matching bug.</strong> An early per-second raw-annotation diff matched each
-   <code>exp</code> detection to the <em>first</em> <code>noexp</code> detection with IoU &gt; 0.5, not the
-   <em>best</em>-overlapping one. With 3 faces close together in frame, this produced
-   spurious "disagreements" (e.g. "exp says Aidan Quinn, noexp says Johnny
-   Depp" at the same seconds) that vanished entirely once the match picked the
-   true best-IoU candidate — both configs had actually output the exact same
-   three names at the exact same three boxes.</li>
+<li><strong>Timeout truncation.</strong> The first Downton Abbey <code>exp</code> replay was cut off
+   by a 60-second subprocess timeout at about 76% through the film (5589 of
+   7368 expected seconds). This silent data loss produced a large,
+   convincing-looking TPI gap (47938 vs 52032) purely because one run was
+   missing a quarter of the film. Caught by comparing <code>n_seconds</code> between
+   runs before trusting any score delta; fixed by re-running with a longer
+   timeout.</li>
+<li><strong>Bbox-matching bug.</strong> An early per-second raw-annotation diff matched
+   each <code>exp</code> detection to the first <code>noexp</code> detection with IoU above 0.5,
+   not the best-overlapping one. With 3 faces close together in frame, this
+   produced spurious disagreements (for example "exp says Aidan Quinn,
+   noexp says Johnny Depp" at the same second) that vanished once the match
+   used the best-IoU candidate instead of the first one. Both configs had
+   actually output the same three names at the same three boxes.</li>
 </ol>
-<p>Both bugs independently pointed toward "expansion is doing something," and both
-were artifacts of the comparison harness, not the pipeline. Worth remembering
-when a before/after diff looks dramatic: check that the two runs actually cover
-the same seconds, and match entities by best overlap, not first-found.</p>
-<h2 id="what-this-means">What this means<a class="headerlink" href="#what-this-means" title="Permanent link">&para;</a></h2>
-<p>The training-set aggregate effect (particularly the ~4x misID increase in full
-mode) doesn't reproduce on held-out data — at minimum it's far smaller than the
-training-set numbers suggested, and plausibly it's sampling variation from only
-4 training films rather than a real, generalizable mechanism. This doesn't mean
-<code>expand_gallery</code> never does anything (the mechanism is real — see
-<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/src/gallery/track_gallery.hpp"><code>track_gallery.hpp</code></a>'s
-promotion logging: tracks <em>do</em> get confirmed and views <em>do</em>
-get promoted into the annex on every film tested), only that <strong>whatever effect
-it has on final per-second identification was too small to detect against 5
-held-out films</strong> with this scoring method. A cleaner test would need either many
-more held-out films or a metric that can see the annex's direct contribution
-(e.g. tagging which reference embedding won each match), neither of which this
-pass had budget for.</p>
-<p><strong>Practical takeaway</strong>: don't treat the training-set <code>exp</code> vs <code>noexp</code> numbers in
-the <a href="../model-bakeoff/">bake-off experiment log</a> as proof that expansion
-changes real-world behavior
-in either direction — on the evidence gathered so far, it doesn't move the
-needle enough to see.</p>
+<p>Both bugs independently pointed toward "expansion is doing something," and
+both were artifacts of the comparison harness, not the pipeline. Before
+trusting a dramatic before/after diff, check that both runs cover the same
+seconds and that entities are matched by best overlap, not first found.</p>
+<h2 id="conclusion">Conclusion<a class="headerlink" href="#conclusion" title="Permanent link">&para;</a></h2>
+<p>The training-set aggregate effect, particularly the full-mode misID
+increase, does not reproduce on held-out data. At minimum it
+is far smaller than the training-set numbers suggested; it may be sampling
+variation from only 4 training films rather than a generalizable
+mechanism. Note the same <em>class</em> of harness bug appears twice in this
+investigation, the timeout truncation in bug #1 above, and the dropped-film
+aggregation that inflated the raw training-set misID figures. Both make an
+inert config look consequential; both are reasons to distrust a dramatic
+training-set delta until it survives on held-out films, which this one did
+not. This does not mean <code>expand_gallery</code> never does anything: the
+mechanism is real, and
+<a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/src/gallery/track_gallery.hpp"><code>track_gallery.hpp</code></a>'s
+promotion logging confirms tracks get confirmed and views get promoted
+into the annex on every film tested. It means whatever effect expansion
+has on final per-second identification was too small to detect against 5
+held-out films with this scoring method. A cleaner test would need either
+more held-out films or a metric that can see the annex's direct
+contribution, such as tagging which reference embedding won each match;
+neither was in scope for this pass.</p>
+<p>Do not treat the training-set exp/noexp numbers in
+<a href="../model-bakeoff/">the full experiment log</a> as proof that expansion changes
+real-world behavior in either direction. On the evidence gathered so far,
+it does not move the needle enough to see.</p>
 
 
 
diff --git a/search/search_index.json b/search/search_index.json
index e9554fa..8b514bb 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
-{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"],"fields":{"title":{"boost":1000.0},"text":{"boost":1.0},"tags":{"boost":1000000.0}}},"docs":[{"location":"","title":"scene-actor-extraction","text":"<p>A face-recognition pipeline that finds when each actor appears on screen in a film or TV episode \u2014 built on KPN++ (a C++20 Kahn Process Network library) for the detect \u2192 track \u2192 match \u2192 scene pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.</p> <p>This is a perfect X-Ray second, on a film the optimizer never saw:</p> <p></p> <p>Every visible face named at 100% \u2014 Chris Noth, Hank Azaria, Bobby Cannavale \u2014 the background extra honestly left unnamed, and the two credited cast without a visible face correctly carried as present off-screen by the tracker's presence windows. That's the pipeline exactly reproducing Amazon X-Ray's record for this second.</p> <p>It doesn't always go like that: the hardest held-out film scores 46% F1, and the report is honest about why \u2014 one tunable trade (extinction bridging at hard cuts), one structural ceiling (X-Ray credits people whose faces never appear), and a few cases where the pipeline is right and X-Ray is wrong. The evidence for all of it is in the pages below.</p>"},{"location":"#start-here-four-questions-this-bake-off-answers","title":"Start here \u2014 four questions this bake-off answers","text":"<ul> <li> <p> Which model is best?</p> <p>Calibration curves first (discriminative power, independent of any threshold), then F1 on the actual benchmark. LVFace-B Glint360K wins both.</p> </li> <li> <p> Whole vs. cast-restricted gallery</p> <p>Restricting the matcher to a film's credited cast is a clean win on every axis (+3.3pp F1, less than a third the misIDs) \u2014 but isn't a shipped runtime feature yet.</p> </li> <li> <p> Does pose expansion help?</p> <p>A convincing training-set effect that didn't reproduce on 5 held-out films once two methodology bugs were caught and fixed. An honest null result, not a forced narrative.</p> </li> <li> <p> Deep dive: LVFace-B Glint360K</p> <p>The held-out generalization gap, how the error budget decomposes (extinction bridging at hard cuts, X-Ray's scene-membership vs. on-screen-face ceiling), and the frames where the pipeline is right and the ground truth is wrong.</p> </li> </ul>"},{"location":"#the-full-technical-log","title":"The full technical log","text":"<ul> <li>Model bake-off + threshold re-tune \u2014   the complete experiment log behind the four pages above: the ROCm teardown   deadlock root cause and fix, DE concurrency tuning, the full 16-combo   results table, and every caveat. This is where the shipped   <code>src/config.hpp</code> defaults come from.</li> <li>Optimizer experiments (prior round) \u2014 the   earlier scene-union-metric tuning pass, superseded by the per-second metric   used in the bake-off but kept for the ground-truth/architecture background.</li> <li>Service conversion (proposal) \u2014 design sketch   for a native idle-GPU worker gated on screen lock, not yet built.</li> </ul>"},{"location":"#reproducing-the-benchmarks","title":"Reproducing the benchmarks","text":"<p>Gallery <code>.h5</code> files, embedding dumps, the X-Ray corpus, montage frame images, and DE trajectories are not committed to this repository \u2014 they're pushed to the Gitea package registry and pulled on demand:</p> <pre><code>scripts/artifacts/pull_artifacts.sh galleries\nscripts/artifacts/pull_artifacts.sh experiment-data\nscripts/artifacts/pull_artifacts.sh montage-frames &lt;film-slug&gt;\n</code></pre> <p>See <code>scripts/artifacts/push_artifacts.sh</code> for the upload side (requires a <code>GITEA_TOKEN</code> with package write scope).</p>"},{"location":"best-model/","title":"Which embedding model is best?","text":"<p>Four candidates went into the bake-off: three ArcFace variants (w600k-R50, R18, w600k-MBF) and LVFace-B (Glint360K), a Vision-Transformer embedder that's a drop-in replacement for ArcFace's <code>[N,3,112,112]</code> input / 512-d output. The open question: is LVFace (455MB) actually better, or just the biggest?</p>"},{"location":"best-model/#first-signal-calibration-curves","title":"First signal: calibration curves","text":"<p>Each gallery carries a fitted Platt sigmoid <code>P(match | cosine similarity) = \u03c3(a\u00b7sim + b)</code>, embedded directly in the gallery's HDF5 file (<code>src/gallery/gallery_calibration.hpp</code>). This is a property of the embedding space alone \u2014 computed from intra/inter-actor reference-image pairs, no tracking or scene logic involved \u2014 so it's a clean first read on discriminative power before running a single benchmark.</p> <p></p> model <code>a</code> (steepness) boundary at P=0.5 LVFace-B Glint360K 17.7 sim 0.228 ArcFace w600k-MBF 16.2 sim 0.267 ArcFace w600k-R50 15.4 sim 0.301 ArcFace R18 15.3 sim 0.309 <p>LVFace has both the steepest transition and the lowest decision boundary \u2014 it separates same-actor from different-actor reference pairs more confidently, at a lower similarity threshold, than any ArcFace variant. That's a genuine head start before the tracking/scoring pipeline is even involved.</p>"},{"location":"best-model/#second-signal-f1-on-the-actual-benchmark","title":"Second signal: F1 on the actual benchmark","text":"<p>Best full-gallery (no cast-restriction) result per model, from the 16-combo bake-off matrix (full experiment log):</p> model F1 P R misID LVFace-B Glint360K 75.3% 89.7% 65.4% 232 ArcFace w600k-MBF 74.2% 87.4% 64.4% 57 ArcFace R18 69.1% 87.6% 57.7% 242 ArcFace w600k-R50 68.5% 94.0% 54.1% 150 <p>The full 16-combo picture makes the model ordering visible at a glance \u2014 LVFace (yellow) tops both the restricted and full columns, and R18 (green) props up the bottom of the full-gallery ranking:</p> <p></p> <p>LVFace wins outright, with the highest recall of any full-mode combo. This reverses an earlier conclusion from a prior (superseded) benchmarking pass using a scene-union metric, which found the three models statistically indistinguishable (~85% each) and concluded LVFace wasn't worth its size \u2014 that metric hid out-of-cast false positives behind a gallery\u2229cast recall mask (see the prior optimizer round); the per-second metric used here does not.</p> <p>Held-out validation (5 films never seen by the optimizer) confirms LVFace's lead holds up out of sample \u2014 see the LVFace deep dive for the full breakdown, including where it fails.</p>"},{"location":"best-model/#caveat-model-choice-is-an-operational-change","title":"Caveat: model choice is an operational change","text":"<p>Switching the default embedder isn't just flipping a config value \u2014 the gallery itself is model-specific (embeddings from different models aren't comparable), so any existing gallery built against ArcFace w600k-R50 needs to be rebuilt from source images against LVFace before the new default takes effect. <code>scripts/optimizer/reembed_gallery.py</code> does this from a reference gallery's cached source images without re-downloading anything.</p>"},{"location":"gallery-scope/","title":"Whole gallery vs. limited (cast-restricted) gallery","text":"<p>Two ways to run the matcher: full scores every detected face against the entire library gallery (2418 actors across the 9-film benchmark set); restricted pre-filters each film's gallery down to just its Jellyfin-credited cast (typically ~15 top-billed actors) before the matcher ever runs.</p>"},{"location":"gallery-scope/#the-result","title":"The result","text":"<p>Averaged across all 4 models and both expansion settings, on the 4 bake-off training films:</p> scope F1 P R total misID (8 evals) full 71.2% 91.1% 59.0% 1073 restricted 74.5% 92.2% 62.9% 329 <p>This is not a precision/recall trade \u2014 restriction wins on every axis at once: +3.3pp F1, +3.9pp recall, and less than a third the total misIDs. Fewer candidates in the matcher's search space means fewer opportunities for a look-alike false match (an actor who happens to share enough facial structure with someone in the film, but isn't actually in it), and the recall gain shows it isn't costing real detections to get there.</p> <p>Per-model, every single model's best-scoring combo in the full 16-way matrix is a <code>restricted</code> variant \u2014 visible directly in the ranking below (filled dots = restricted, open = full; the filled dots cluster at the top for every color):</p> <p></p> <p>See the full table in the bake-off experiment log. Two combos hit zero true out-of-cast misidentifications: <code>arcface_w600k_mbf_restricted_exp</code> (F1 76.5%) and, in full mode, <code>LVFace-B_Glint360K_full_noexp</code> (F1 72.4%) \u2014 restriction isn't the only way to reach misid=0, but it's the more reliable one.</p>"},{"location":"gallery-scope/#why-this-isnt-the-shipped-default","title":"Why this isn't the shipped default","text":"<p>Cast-restriction is implemented today only as an offline optimizer technique (<code>scripts/optimizer/cast_restrict.py</code>): it pre-builds a filtered gallery file per film, using Jellyfin's own cast list, before the benchmark ever calls the matcher. There's no runtime \"restrict matching to this title's credited cast\" switch in the shipped application \u2014 <code>scene_analyze</code> always matches against whatever single gallery file it's given.</p> <p>Building that as a real feature would need, at minimum:</p> <ul> <li>A live Jellyfin cast lookup at analysis time (the title is already known \u2014   <code>scripts/run_from_jellyfin.py</code>   already does this same lookup for its own   <code>filter_gallery</code>-based restriction path, just not wired into <code>scene_analyze</code>   itself as a first-class option).</li> <li>A decision on the fallback: what happens to a real, uncredited cameo   (see the Germar Terrell Gardner case in the LVFace deep-dive) if the gallery   never includes them at all?</li> <li>Regenerating the restricted-gallery cache whenever the title's Jellyfin cast   list changes.</li> </ul> <p>This is why the shipped <code>src/config.hpp</code> defaults use the <code>full</code>-mode winner (<code>LVFace-B_Glint360K_full_exp</code>, F1 75.3% training / 67.4% held-out macro) rather than the higher-scoring <code>restricted_exp</code> (78.3%) \u2014 the 78.3% number describes a capability the app doesn't have yet, not what actually ships.</p>"},{"location":"lvface-deep-dive/","title":"Deep dive: LVFace-B Glint360K","text":"<p>LVFace won the model bake-off (see Which model is best?) and is the shipped default embedder. This page is the honest accounting of how it actually performs \u2014 what a good second looks like, where the errors actually come from, and two cases where the ground truth itself is wrong and LVFace is right.</p> <p>How to read the frames on this page</p> <p>The top is the film frame, with a box and name on every face the pipeline identified. The bottom panels are the per-second verdict against X-Ray: Onscreen lists faces named in the frame, Offscreen lists cast X-Ray marks present in the scene without a visible face \u2014 presence carried by the tracker's windows, not by a detection. Colors are the score: green = correct (TPI), orange = wrong (FPI), blue = missed (FN).</p>"},{"location":"lvface-deep-dive/#what-good-looks-like","title":"What good looks like","text":"<p>Six faces on screen, all six named correctly \u2014 including Penelope Wilton at the edge of the pews and a half-occluded Michelle Dockery \u2014 while thirteen more cast members X-Ray marks present in the scene are correctly carried as \"Offscreen\" by their presence windows. One miss in the whole frame: Maggie Smith (blue). Score for this second: 0.86.</p> <p></p> <p>The same film's funeral gathering: mourning dress, hats, half the faces turned. Nineteen of the twenty cast X-Ray lists for this scene are scored correctly \u2014 seven named on screen at up to 100% confidence, twelve more correctly held as present off-screen.</p> <p>And the pipeline doesn't need the face to be real:</p> <p></p> <p>That's Herbie Hancock at 98% \u2014 as a face on a screen inside the movie, over a sci-fi HUD overlay, during a video call in Valerian. A face is a face, whether it's in the room or on the bridge's comms display.</p>"},{"location":"lvface-deep-dive/#training-vs-held-out-the-generalization-gap","title":"Training vs. held-out: the generalization gap","text":"<p>The shipped config (<code>prob_threshold=0.754, anneal_sec=35.54, extinction_sec=57.43, expand_gallery=true</code>) was tuned against 4 films. Scored against the 5 films the optimizer never saw:</p> <p></p> film F1 P R TPI FPI misid FN Benny &amp; Joon 83.0% 89.1% 77.7% 15125 1846 0 4337 Lovelace 77.5% 90.3% 67.9% 14990 1085 58 7085 Valerian and the City of a Thousand Planets 74.1% 97.1% 60.0% 18663 548 0 12467 Downton Abbey: A New Era 56.2% 97.8% 39.4% 52027 1173 0 80084 The Many Saints of Newark 46.3% 54.7% 40.1% 15922 4394 974 23791 macro average 67.4% 85.8% 57.0% <p>67.4% held-out vs. 75.3% on training \u2014 an ~8pp drop, and a 37pp spread between the best and worst held-out film. The config does not generalize uniformly, and the spread traces to two mechanisms, both visible frame by frame below.</p>"},{"location":"lvface-deep-dive/#mechanism-1-extinction-bridging-usually-right-wrong-at-hard-cuts","title":"Mechanism 1: extinction bridging \u2014 usually right, wrong at hard cuts","text":"<p>The extinction window keeps an identity alive through seconds where no face is detectable. Most of the time this is exactly what you want, and it's where a lot of the TPI count comes from:</p> <p></p> <p>Lovelace's polygraph scene: only Eric Roberts and Amanda Seyfried have visible faces, but X-Ray lists eight cast present \u2014 and all eight score green, the other six correctly carried by presence windows through a scene where the camera never shows them. A perfect second, and the extinction/anneal machinery is why.</p> <p>The same mechanism has a failure case: a hard cut into long faceless footage. Both Many Saints of Newark (974 misIDs) and Downton Abbey (FN=80084, the worst recall of the five) are dominated by it \u2014 verified directly against the raw per-frame stream and the HDF5 dump's own detection counts, not inferred from the score alone. This is not a malfunction: the tracker is doing exactly what its window is for; the footage just stops cooperating. In the debug overlay (which draws a bridged identity's last-known bbox, unlike the shipped output, which emits presence windows and no boxes at all) the bridged state is visible spatially:</p> <p> Debug-overlay rendering (<code>dump_error_frames.py --raw</code>): \"Jon Bernthal\", \"Joey Diaz\" and \"Billy Magnussen\" are extinction-bridged identities from the previous shot, drawn frozen over the wall and the hanging plates. Frame <code>many_saints/fpi/fpi_t03543.jpg</code>, <code>montage-frames</code> artifact package.</p> <p>The cost is measurable, not just visible. Downton Abbey's hard cut into its closing credits, plotting the dump's own per-second <code>face_count</code> (detector output, independent of the tracker) against what the tracker reports:</p> <p></p> <p>From the cut onward the detector sees zero faces for nearly a minute \u2014 and the tracker keeps reporting the last shot's 15 identities the whole time (verified for Hugh Bonneville: bbox <code>(1743.2, 0.0, 171.3, 317.8)</code>, unchanged to the pixel, at every sampled second for 57+ seconds). The staircase at the right edge is the extinction window expiring actor by actor. That plateau is <code>SceneTrackerFunc::active_[actor_idx].last_bbox</code> (<code>src/nodes/scene_tracker_node.hpp</code>) re-emitted as designed: <code>extinction_sec=57.4</code> was tuned long because bridging wins on most footage (see the polygraph frame above) \u2014 the training films just never contained a faceless stretch long enough to show the cost side, and the held-out set did.</p> <p>The same track-continuation machinery has one milder spatial artifact, worth knowing when reading these frames:</p> <p> Caf\u00e9 Society (a training film), a shot/reverse-shot dialog: that is Steve Carell wearing both his own label and Jesse Eisenberg's.</p> <p>At a rapid cut, the previous shot's track can linger for a beat at nearly the same screen position the new face occupies \u2014 here Jesse Eisenberg's box from the counter-shot lands on Steve Carell. Note what the score panel says, though: both actors are green, because both are present in this dialog scene per X-Ray. The spatial label is briefly wrong; the per-second presence claim \u2014 the thing the pipeline actually ships \u2014 is right. It's the same trade as the extinction window: track continuation smooths over cuts, and 1 fps sampling occasionally catches the seam.</p>"},{"location":"lvface-deep-dive/#mechanism-2-the-face-vs-presence-ceiling","title":"Mechanism 2: the face-vs-presence ceiling","text":"<p>Downton Abbey's recall didn't collapse because faces were misread \u2014 it collapsed because for most of its 80084 FN-seconds there was no face to read:</p> <p></p> <p>A newsreel crew hauls equipment through the hall: X-Ray credits 22 cast as present in this scene; not one face looks at the camera. Eight are still scored green (windows bridging from adjacent shots) \u2014 the other fourteen are blue FNs that no face-recognition pipeline could ever recover. X-Ray encodes scene membership; the pipeline measures on-screen faces. In ensemble films those two definitions diverge massively, and that gap \u2014 not identification error \u2014 is most of what the FN column counts.</p> <p></p> <p>Same ceiling from the other side: Michela De Rossi in frame but turned away, five cast correctly bridged as offscreen (green), four blue FNs \u2014 and one orange we'll come back to below.</p>"},{"location":"lvface-deep-dive/#where-lvface-beat-x-ray","title":"Where LVFace beat X-Ray","text":"<p>Not every orange in these frames is actually wrong. <code>scripts/optimizer/second_score.py</code> scores strictly against X-Ray \u2014 but X-Ray itself has holes, and the pipeline found two kinds.</p> <p></p> <p>Germar Terrell Gardner \u2014 a real, clean, high-confidence detection \u2014 is counted as an out-of-cast misID because he doesn't appear in X-Ray's <code>people.csv</code> for The Many Saints of Newark at all. But Jellyfin's independent cast metadata does credit him for this exact film (cross-checked via <code>experiments/manifests/jellyfin_casts.json</code> from the <code>experiment-data</code> artifact package, a completely separate data source from X-Ray). That's also him in orange in the frame above \u2014 every one of those \"errors\" is the pipeline being right about a person X-Ray forgot.</p> <p></p> <p>And it isn't only uncredited bit-parts. That is Robert Patrick \u2014 top-billed in Lovelace, unmistakably on screen, reading his newspaper, identified at 100% \u2014 scored orange because X-Ray's people-in-scene list for this scene doesn't include him. The identification is flawless; the ground truth missed an actor sitting in the middle of the frame.</p> <p>This doesn't mean every flagged misID is secretly correct \u2014 Many Saints' 974-count total is still overwhelmingly extinction bridging at cuts, not uncredited cameos. But the X-Ray corpus is a convenient, large-scale ground truth, not a perfect one, and the misID/FPI numbers in these tables carry an irreducible noise floor from ground-truth gaps in both directions.</p>"},{"location":"lvface-deep-dive/#summary","title":"Summary","text":"<p>LVFace is the right default: it wins the model comparison outright, it names 19 of 20 correctly across a hat-heavy funeral crowd, and it recognises a face on a screen inside the movie. Its error budget decomposes into two understood mechanisms \u2014 extinction bridging at hard cuts (a tunable trade, not a bug) and the face-vs-presence ceiling baked into X-Ray's semantics \u2014 plus a nonzero slice where the pipeline is right and the ground truth is wrong. The held-out generalization gap (75.3% \u2192 67.4%) is real and should be treated as the honest expected performance, not the training-set number.</p>"},{"location":"model-bakeoff/","title":"Model bake-off + threshold re-tune \u2014 experiment log (2026-07-18/19)","text":"<p>Follow-on to the prior optimizer round, which used an older, since-superseded scene-union metric. This round uses the per-second metric (<code>scripts/optimizer/second_score.py</code>) and answers three questions in one 16-run matrix: which embedding model is best, does cast-restriction cut misIDs, and does per-film gallery expansion help.</p> <p>(This was the fourth optimizer campaign against the X-Ray corpus, so its on-disk artifacts carry an internal <code>rep4_</code> prefix \u2014 <code>experiments/results/rep4_best_*.json</code>, <code>experiments/trajectories/rep4_*.jsonl</code>, and the manifests referenced below. The earlier campaigns used the superseded scene-union metric and were discarded.)</p>"},{"location":"model-bakeoff/#why-this-experiment-and-what-it-actually-delivered","title":"Why this experiment, and what it actually delivered","text":"<p>Four goals going in, and an honest read on each after held-out validation (see below):</p> <ol> <li>Find the best default parameters to ship. Partially delivered. The DE optimum    generalizes unevenly \u2014 strong on 3 of 5 held-out films, badly broken on 2 (one    with a 974-count misID blowup). The tuned values are shipped anyway (see    Caveats) because they still beat the old defaults on average, but this is not a    settled, film-agnostic optimum.</li> <li>Find the best default model. Delivered with more confidence. LVFace beat    r50/r18/mbf across all 4 training combos, and nothing in held-out validation    contradicts the model choice specifically \u2014 the held-out failures trace to    <code>extinction_sec</code>/threshold interactions and gallery coverage, not the embedder.</li> <li>Provide insight into how the application works. The strongest, most durable    output. Found and fixed a real teardown deadlock bug (100% reproducible, not the    assumed rare GPU flake), established a real concurrency ceiling (8 parallel    replays, not more), and found a real parameter interaction (a strict    <code>prob_threshold</code> \"earns\" a longer extinction window before it starts hurting).</li> <li>Demonstrate limitations. Delivered, and reinforced hard by held-out    validation \u2014 see the \"Held-out validation\" section below for concrete examples,    including a screenshot of the matcher naming 15 actors, none correctly, on a    completely blank title card.</li> </ol>"},{"location":"model-bakeoff/#tldr-what-changed-in-srcconfighpp","title":"TL;DR \u2014 what changed in <code>src/config.hpp</code>","text":"knob old default new default why <code>arcface_model</code> <code>arcface_w600k_r50.onnx</code> <code>LVFace-B_Glint360K.onnx</code> Best F1 in the full-gallery bake-off (75.3% vs r50's 68.5%). LVFace was worth its size. <code>prob_threshold</code> 0.76 0.754 Re-tuned for LVFace + per-second metric. <code>extinction_sec</code> 1.5 57.4 Reverses the earlier \"short is better\" finding \u2014 see below. <code>anneal_sec</code> 10.0 35.5 Same reversal; previously thought insensitive. <code>expand_gallery</code> false true Helped recall on the full (unrestricted) gallery for the winning model \u2014 opposite of the earlier assumption. <p>These are the <code>LVFace-B_Glint360K_full_exp</code> winning values, applied to <code>src/config.hpp</code> \u2014 the best result that uses only features already live in the running app (full gallery, no cast restriction; see below for why restricted mode isn't applied even though it scored higher).</p>"},{"location":"model-bakeoff/#why-re-run-at-all","title":"Why re-run at all","text":"<p>The prior round's scene-union metric hid out-of-cast false positives behind a gallery\u2229cast recall mask, and the earlier 9-film benchmark was scene-level (union over a whole X-Ray scene), not a fair per-timepoint comparison. SESSION_STATE flagged the old scene-metric bake-off numbers (R50\u2248LVFace\u2248MBF ~85%) as superseded. This round uses <code>second_score.py</code>: uniform per-second sampling, GT = X-Ray scene's cast at time t, pred = actors whose presence window covers t, FPI weighted 10\u00d7 when the named actor isn't in the film's cast at all (true misID) vs. an in-cast timing slip. FN only counts gallery-known cast (fair recall \u2014 67% of X-Ray cast have no reference embedding; see the prior round's gallery-coverage-gap analysis).</p>"},{"location":"model-bakeoff/#the-deadlock-that-was-blocking-all-of-this","title":"The deadlock that was blocking all of this","text":"<p>Every replay in this line of work goes through <code>scripts/optimizer/replay.py</code>, which runs the real C++ tracker/matcher/scene_tracker nodes inside a Python-assembled KPN network. Before this session, every subprocess replay timed out at 45s, 100% of the time \u2014 not the documented ~20-30% ROCm rocBLAS-GEMM driver flake, but a plain logic bug: <code>replay.py</code>'s CLI called <code>replay(net, ..., stop=False)</code> to skip <code>net.stop()</code> (trying to dodge the GEMM deadlock), planning to <code>os._exit(0)</code> immediately after. But:</p> <ul> <li><code>PyNode::stop()</code> (<code>include/kpn/python/bindings.hpp</code>   in the KPN++ submodule) is the only   code that sets <code>stop_flag_ = true</code> before joining the node's worker thread.</li> <li>The source node's <code>run_loop()</code> has <code>while (!stop_flag_)</code> as its only exit   condition (it has no input channels, so it never sees a channel-closed signal   either).</li> <li>Skipping <code>stop()</code> meant <code>stop_flag_</code> never became true. When <code>replay()</code> returned,   its local <code>net</code> went out of scope immediately, running <code>~PyNetwork</code> \u2192 <code>~PyNode</code> \u2192   <code>thread_.join()</code> synchronously inside <code>replay()</code>'s own call frame \u2014 before   <code>main()</code> ever got control back to run <code>os._exit(0)</code>.</li> </ul> <p>Root-caused via <code>gdb -p &lt;pid&gt; -batch -ex \"thread apply all bt\"</code> on a hung process: the main thread was stuck in <code>~PyNode</code>'s <code>jthread::join()</code>; the worker thread was in an ordinary <code>time.sleep()</code> inside the Python source callback, waiting for a stop signal that was never sent. The two HSA <code>kfd_wait_on_events</code> threads visible in the same trace are normal ROCm runtime housekeeping, not evidence of a wedged GPU kernel.</p> <p>Fix: <code>replay.py</code> now calls <code>replay(..., stop=True)</code> (the removed <code>stop=False</code> + <code>os._exit</code> workaround was actively harmful). Verified 3/3 clean runs at ~8s each (down from a guaranteed 45s timeout), and a full DE sweep producing real, sensible F1/precision/recall instead of flat 0.0%.</p>"},{"location":"model-bakeoff/#concurrency-tuning","title":"Concurrency tuning","text":"<p>With the deadlock fixed, <code>scripts/optimizer/optimize.py</code> was extended with DE-level parallelism \u2014 <code>differential_evolution(..., workers=ThreadPoolExecutor.map)</code> \u2014 so multiple population candidates evaluate concurrently, each spawning its own per-film replay subprocesses (<code>REPLAY_WORKERS</code>). Total concurrent GPU replay processes \u2248 <code>DE_WORKERS \u00d7 REPLAY_WORKERS</code>.</p> concurrent replays result 3 (<code>REPLAY_WORKERS=3</code>, no DE parallelism) baseline, GPU underutilised 6 (<code>DE_WORKERS=2 \u00d7 REPLAY_WORKERS=3</code>) clean, real scores, ~1 isolated timeout per run 8 (<code>DE_WORKERS=2 \u00d7 REPLAY_WORKERS=4</code>, 4-film manifest) clean, real scores 9 (<code>DE_WORKERS=3 \u00d7 REPLAY_WORKERS=3</code>) broken \u2014 every replay blew past the 45s timeout, all scores silently degraded to 0.0% <p>9 concurrent replays looks like valid output (well-formed JSON, a real number) while actually being garbage \u2014 a dangerous failure mode, not a crash. 8 concurrent is the practical ceiling on this GPU (gfx1100) for this workload. The matrix ran at <code>REPLAY_WORKERS=4 DE_WORKERS=2</code>.</p>"},{"location":"model-bakeoff/#training-films-and-validation-set","title":"Training films and validation set","text":"<p>9 films total have dumped embeddings across all 4 models. 4 were used for optimization, leaving 5 held out for validation:</p> <ul> <li>Lord of War (64-cast, \"clean\")</li> <li>Scarface (67-cast, \"ensemble/lookalike\")</li> <li>Sound of Metal (14-cast, \"high gallery-coverage\")</li> <li>Caf\u00e9 Society (62-cast, added this round \u2014 similar ensemble size to Scarface but   different genre/lighting; picked to add diversity, not genre-overlap, over   Downton Abbey or The Many Saints of Newark)</li> </ul> <p>Held out: Benny &amp; Joon, Downton Abbey: A New Era, Lovelace, The Many Saints of Newark, Valerian and the City of a Thousand Planets. The training-set numbers below are training-set fit \u2014 see \"Held-out validation\" further down for the real generalization test.</p>"},{"location":"model-bakeoff/#search-space-and-de-settings","title":"Search space and DE settings","text":"<p><code>popsize=10, maxiter=15</code> (3 params \u2192 \u2264480 evals/combo ceiling; DE's <code>tol</code> convergence usually stops earlier). <code>anneal_sec</code>/<code>extinction_sec</code> bounds were widened from 1\u201330/1\u201315 to 1\u201360/1\u201360 mid-run (see below) \u2014 the 4 <code>arcface_w600k_r50</code> combos finished before the widening and still use the old, narrower bounds, so they are not directly comparable to the other 12 on those two params. Re-running r50 with the wider bounds was deferred (diminishing-returns judgment call, not yet done).</p>"},{"location":"model-bakeoff/#results-all-16-combos-4-models-full-restricted-expand-noexp","title":"Results \u2014 all 16 combos (4 models \u00d7 {full, restricted} \u00d7 {expand, noexp})","text":"<p>Ranked by F1. <code>misid</code> = FPI_misid, count of true wrong-actor identifications (an actor named who isn't in the film's cast at all) \u2014 distinct from <code>FPI</code>, which includes in-cast timing slips.</p> combo F1 P R TPI FPI misid FN LVFace-B_Glint360K_restricted_exp 78.3% 91.0% 68.9% 42830 3782 60 19492 LVFace-B_Glint360K_restricted_noexp 76.7% 91.5% 66.2% 41149 3400 59 21173 arcface_w600k_mbf_restricted_exp 76.5% 90.7% 66.3% 41270 4431 0 21052 arcface_r18_restricted_exp 75.5% 87.6% 66.5% 41399 5666 60 20923 LVFace-B_Glint360K_full_exp 75.3% 89.7% 65.4% 47757 3407 232 26966 arcface_w600k_mbf_restricted_noexp 75.0% 91.1% 63.9% 39752 3465 60 22570 arcface_w600k_mbf_full_noexp 74.2% 87.4% 64.4% 12645 1312 57 6985 arcface_r18_restricted_noexp 73.5% 91.3% 61.7% 38299 3220 60 24023 LVFace-B_Glint360K_full_noexp 72.4% 94.2% 58.9% 27077 1725 0 19506 arcface_w600k_mbf_full_exp 72.0% 87.7% 61.4% 39875 3729 240 26338 arcface_w600k_r50_full_noexp \u2020 71.6% 96.7% 56.9% 22471 361 45 17012 arcface_w600k_r50_restricted_exp \u2020 71.1% 96.5% 56.4% 34954 1119 15 27368 arcface_w600k_r50_restricted_noexp \u2020 69.2% 97.9% 53.6% 21146 327 15 18337 arcface_r18_full_exp 69.1% 87.6% 57.7% 37342 3119 242 28871 arcface_w600k_r50_full_exp \u2020 68.5% 94.0% 54.1% 34982 903 150 31231 arcface_r18_full_noexp 66.6% 91.3% 53.1% 34314 2362 107 31899 <p>\u2020 old, narrower anneal/extinction bounds (see above) \u2014 not directly comparable to the other 12 on those two params.</p> <p>The same 16 results as a picture \u2014 the two headline effects are visible without reading a single row: filled (restricted) dots stack the top of the ranking for every model color, and yellow (LVFace) leads within both scopes:</p> <p></p>"},{"location":"model-bakeoff/#calibration-curves-discriminative-power-independent-of-the-threshold","title":"Calibration curves \u2014 discriminative power, independent of the threshold","text":"<p>Each model's gallery carries a fitted Platt sigmoid <code>P(match | sim) = \u03c3(a\u00b7sim + b)</code> (embedded directly in the gallery HDF5, see <code>src/gallery/gallery_calibration.hpp</code>). Plotting all four side by side shows discriminative power directly, independent of whatever <code>prob_threshold</code> a particular run happened to use:</p> <p></p> <p>LVFace-B has both the steepest curve (<code>a=17.7</code>, vs. 15.3\u201316.2 for the ArcFace variants) and the lowest P=0.5 decision boundary (similarity 0.23 vs. 0.27\u20130.31) \u2014 it separates same-actor from different-actor pairs more confidently at a lower similarity, consistent with it winning the full-gallery F1 comparison below. Generated by <code>scripts/docs/calibration_chart.py</code> (requires each gallery to have been calibrated at least once \u2014 run any replay against it first).</p>"},{"location":"model-bakeoff/#two-effects-in-isolation-gallery-scope-and-pose-expansion","title":"Two effects in isolation: gallery scope, and pose expansion","text":"<p>The matrix crosses two independent variables \u2014 averaging across all 4 models isolates each one from model choice:</p> <p>Gallery scope (whole 2418-actor gallery vs. restricted to the film's credited cast) \u2014 averaged over both expansion settings and all 4 models:</p> scope F1 P R total misID (16 evals\u21928 each) full 71.2% 91.1% 59.0% 1073 restricted 74.5% 92.2% 62.9% 329 <p>Restriction wins outright on every axis \u2014 not a precision/recall trade, a clean win: +3.3pp F1, +3.9pp recall, and less than a third the total misIDs. Fewer candidates in the matcher's search space means fewer opportunities for a look-alike false match, and (per the recall gain) doesn't cost real detections. This is the single cleanest signal in the whole matrix \u2014 stronger than the model choice itself \u2014 which is exactly why cast-restriction becoming a real runtime feature (not just an optimizer trick) is the top item in Caveats below.</p> <p>Pose expansion (promoting a confidently-identified track's novel-pose views into a per-film gallery annex \u2014 <code>src/gallery/track_gallery.hpp</code>) is smaller and interacts with scope rather than acting independently:</p> scope expansion F1 R misID full off 71.2% 58.3% 209 full on 71.2% 59.7% 864 restricted off 73.6% 61.3% 194 restricted on 75.4% 64.5% 135 <p>In restricted mode, expansion is a clean win (+1.8pp F1, +3.2pp recall, misID actually drops) \u2014 the annex only ever competes against the film's own ~15-actor cast, so a \"confidently identified, new pose\" view is unlikely to be mistaken for someone else. In full mode, expansion buys essentially nothing on F1 (71.2% \u2192 71.2%, recall +1.4pp) while quadrupling misIDs (209 \u2192 864): a novel-pose view promoted into the annex now competes against the whole 2418-actor gallery, so a \"confident\" identity is confident against the wrong universe of candidates \u2014 the expansion mechanism is \"learning\" a pose correctly, but the enlarged evidence pool makes it easier for that learned pose to look like a plausible match for a different actor. Practical takeaway: gallery expansion should be paired with cast restriction, not used on the full gallery \u2014 the version currently shipped as default (<code>full_exp</code>, see TL;DR) sits in the worse of these four cells for this specific knob, even though it's the best available combo without cast-restriction support in the app yet (see Caveats).</p>"},{"location":"model-bakeoff/#what-the-data-says","title":"What the data says","text":"<ul> <li>LVFace was worth its size. It wins full-gallery mode outright (75.3% vs r50's   68.5%, r18's 69.1%, mbf's 72.0%) with the highest recall of any full-mode combo \u2014   the earlier scene-union-metric conclusion (\"not worth it\") doesn't survive the   better metric.</li> <li>Cast-restriction is a consistent, broad win. Every model's best combo is   <code>restricted</code>. It isn't just precision-safe: <code>arcface_w600k_mbf_restricted_exp</code> and   <code>LVFace-B_Glint360K_full_noexp</code> both hit misid=0 \u2014 zero true wrong-actor   identifications. But restriction is an offline optimizer technique, not a live   app feature \u2014 it pre-filters each film's gallery to its Jellyfin-credited cast   before the matcher ever runs; there's no runtime \"restrict to this film's cast\"   switch in the app today. Implementing it for real is future work, tracked   separately from this defaults update.</li> <li>Gallery expansion (<code>expand_gallery</code>) is mode-dependent. It helps on   <code>restricted</code> galleries (smaller, so novel-pose promotion adds real signal) and on   LVFace's full gallery, but hurts r50 and mbf in full mode (compare   <code>arcface_w600k_r50_full_exp</code> 68.5% vs <code>full_noexp</code> 71.6%). Don't assume it's a free   win \u2014 model- and mode-dependent.</li> <li>arcface_r18 (smallest/cheapest) is last across all 4 modes \u2014 model capacity   matters here, this isn't just parameter-count padding.</li> <li><code>anneal_sec</code>/<code>extinction_sec</code> kept pinning at the search ceiling. With the   original 1\u201330/1\u201315 bounds, 3 of 4 r50 combos landed at ~93-98% of the upper bound.   Widened to 1\u201360/1\u201360 mid-run (after the r50 combos had already finished) \u2014 every   subsequent combo's best config landed at ~90%+ of the new ceiling too (e.g. the   LVFace winner: <code>ann=59.2, ext=59.2</code>, both ~99% of 60). The likely mechanism: a   strict <code>prob_threshold</code> \"earns\" a long extinction/anneal window \u2014 once false   matches are rare, a long window just bridges real presence gaps (occlusion, turned   face) instead of smearing false positives into later scenes, which is what made   short windows look better under the old, laxer thresholds. Open question, not   resolved: does this keep climbing past 60s, or does it actually plateau there?   Decided not to chase further this round (diminishing-returns judgment call) \u2014 flag   for a future sweep if it matters.</li> </ul> <p>The ceiling-pinning is visible in the raw search itself. Every one of the 512 DE evaluations for the winning combo, plotted over the <code>prob_threshold</code> \u00d7 <code>extinction_sec</code> plane:</p> <p></p> <p>The dark band hugging the top edge is the finding: nearly everything scoring well sits at <code>extinction_sec</code> \u2265 50, across a wide range of thresholds, and the population converged into a dense cloud around the optimum (threshold ~0.70\u20130.80, extinction pinned at the 60s bound). Short extinction windows (bottom half) are uniformly pale \u2014 under a strict threshold there is simply no good configuration down there. Generated by <code>scripts/docs/experiment_charts.py</code> from the DE trajectories (<code>experiments/trajectories/*.jsonl</code>, part of the <code>experiment-data</code> artifact package).</p>"},{"location":"model-bakeoff/#held-out-validation-the-number-that-actually-matters","title":"Held-out validation \u2014 the number that actually matters","text":"<p>The 16-combo matrix above is training-set fit. This is the real test: the shipped config (<code>LVFace-B_Glint360K_full_exp</code> \u2014 <code>prob_threshold=0.754, anneal_sec=35.5, extinction_sec=57.4, expand_gallery=true</code>) replayed against the 5 films never seen by the optimizer (Benny &amp; Joon, Downton Abbey: A New Era, Lovelace, The Many Saints of Newark, Valerian and the City of a Thousand Planets), scored the same way.</p> film F1 P R agree TPI FPI misid FN Benny &amp; Joon 83.0% 89.1% 77.7% 72.4% 15125 1846 0 4337 Lovelace 77.5% 90.3% 67.9% 72.1% 14990 1085 58 7085 Valerian and the City of a Thousand Planets 74.1% 97.1% 60.0% 58.8% 18663 548 0 12467 Downton Abbey: A New Era 56.2% 97.8% 39.4% 40.6% 52027 1173 0 80084 The Many Saints of Newark 46.3% 54.7% 40.1% 37.0% 15922 4394 974 23791 macro average (5 films) 67.4% 85.8% 57.0% 56.2% 116727 9046 1032 127764 <p></p> <p>67.4% held out vs. 75.3% on training \u2014 an ~8pp drop, and a much more informative number than the training-set F1 alone: a 37pp spread between best and worst film (83.0% vs 46.3%). The config does not generalize uniformly.</p> <p>Two films are outright failure cases, and rendering bounding boxes + names on the extracted frames (<code>replay.py --raw-out</code> + <code>dump_error_frames.py</code><code>--raw</code>, see Reproduce) turned what looked like a same-scene misidentification into something more precise and more damning:</p> <ul> <li>The Many Saints of Newark (mob-family drama, picked as a training-adjacent   genre test) has 974 true misIDs \u2014 far more than any training combo saw at any   setting. The annotated frame below shows the same mechanism as Downton Abbey,   at smaller scale: \"Jon Bernthal 100%\", \"Joey Diaz 100%\", and \"Billy Magnussen   100%\" are all frozen boxes over empty background \u2014 a blurred wall, hanging plates \u2014   with no face in them at all. Only one real face in frame has a box, and it   carries a second, colliding label (\"Leslie Odom Jr.\" and \"Michael Gandolfini\"   both at high confidence on the same box) \u2014 likely two tracks whose frozen bboxes   happen to overlap.</li> </ul> <p> Frame <code>many_saints/fpi/fpi_t03543.jpg</code> from the <code>montage-frames</code> artifact   package (<code>scripts/artifacts/pull_artifacts.sh montage-frames   Many_Saints_of_Newark</code>).</p> <ul> <li>Downton Abbey: A New Era (large ensemble, 36-cast) has high precision (97.8%)   but recall collapses to 39.4% (FN=80084, by far the largest of the 5). Its   starkest failure happens where there is nothing to see at all: the film's hard   cut into its closing credits, where the matcher kept reporting 15 actors \u2014   all wrong \u2014 for nearly a minute of faceless screen.</li> </ul> <p>Both are the same mechanism, and it can be measured, not just screenshotted. Plotting the dump's own per-second <code>face_count</code> (detector output, independent of the tracker) against the number of actors the tracker reports, through Downton Abbey's cut to credits:</p> <p></p> <p>From the cut onward the detector sees zero faces \u2014 yet the tracker holds a perfectly flat plateau of 15 reported identities for 56 seconds, each with the exact same bbox, unchanged to the pixel (verified for Hugh Bonneville: <code>(1743.2, 0.0, 171.3, 317.8)</code> at every sampled second from 7222 through 7279+). The staircase on the right edge is the extinction window finally expiring, actor by actor. That plateau is <code>SceneTrackerFunc</code>'s <code>active_[actor_idx].last_bbox</code> (<code>src/nodes/scene_tracker_node.hpp</code>) being re-emitted unchanged \u2014 the extinction state machine working exactly as coded, not a bug in the logic. The film cuts from a packed group shot straight into ~40+ seconds of blank titles/credits with zero faces, and <code>extinction_sec=57.4</code> is comfortably long enough to bridge that entire gap without expiring, so the tracker faithfully keeps reporting \"last known position\" for a cast that is no longer on screen at all.</p> <p>This reframes the \"long extinction window wins\" DE-search pattern (see above): it isn't unambiguously good. It buys recall by bridging real gaps (occlusion, turned face) in some films, but on others \u2014 specifically, hard cuts into long faceless footage \u2014 it manufactures a frozen-bbox ghost the tracker has no way to verify, precisely the failure mode the original short-extinction-window default (<code>1.5s</code>) was chosen to avoid. The training-set films apparently didn't have a long enough faceless stretch after a confirmed identity to expose this; the held-out set did.</p> <p>Frames for all three films (<code>benny_joon</code>, <code>many_saints</code>, <code>downton_abbey</code> \u2014 one strong performer, two failure cases) are under <code>experiments/results/holdout/frames/</code> (not committed \u2014 pull per film with <code>scripts/artifacts/pull_artifacts.sh montage-frames &lt;film-slug&gt;</code>), each with a <code>manifest.json</code> listing the bucket (<code>best</code>/<code>fpi</code>/<code>fn</code>), timestamp, and predicted vs. ground-truth actors for every dumped frame. Frames are annotated with bounding boxes + name/confidence (green = identified, orange = unknown), matching <code>src/nodes/debug_renderer_node.hpp</code>'s colour convention. Generated by <code>scripts/optimizer/dump_error_frames.py --raw &lt;replay.py --raw-out output&gt;</code> (see Reproduce).</p> <p><code>dump_error_frames.py --interval-sec 600</code> also supports a per-N-second sweep instead of the fixed best/fpi/fn buckets: one best (highest Jaccard) and one worst (lowest Jaccard) frame per 10-minute window across the whole film, e.g. <code>experiments/results/holdout/frames/many_saints_intervals/</code> (13 windows \u00d7 2 = 26 frames for the ~2h Many Saints runtime) \u2014 a way to sample \"how are we doing\" evenly across a film's runtime rather than only at its most extreme seconds.</p>"},{"location":"model-bakeoff/#caveats-what-this-is-not","title":"Caveats / what this is not","text":"<ul> <li>r50's 4 combos used the old, narrower search bounds and aren't fully   comparable to the other 12 on <code>anneal_sec</code>/<code>extinction_sec</code>.</li> <li>The applied defaults use <code>full_exp</code>, not the higher-scoring <code>restricted_exp</code>,   because cast-restriction isn't a real runtime feature yet (see above). The   78.3% F1 number is not what the shipped defaults will produce \u2014 75.3% is.</li> <li><code>full_exp</code> is the best full-gallery combo, but not the safest. Per the   isolated-effects analysis above, <code>expand_gallery=true</code> only cleanly pays off   when paired with cast-restriction; on the full gallery it's flat on F1 while   ~4x-ing misIDs (209\u2192864, averaged across models). <code>full_noexp</code> scores lower   (72.4% vs 75.3% for LVFace) but with zero true misIDs and higher precision   (94.2% vs 89.7%). Kept <code>full_exp</code> as shipped since it's the highest-F1 option   available without cast-restriction, but this is a real F1-vs-safety trade, not   a strictly-better choice \u2014 worth revisiting if misID rate matters more than   the last few points of F1 for a given deployment.</li> <li>Switching the default model is an operational change, not just a config tweak:   any existing gallery built from r50 embeddings is incompatible with LVFace   embeddings and needs rebuilding.</li> </ul>"},{"location":"model-bakeoff/#reproduce","title":"Reproduce","text":"<pre><code># 4-film matrix, all 4 models \u00d7 2 modes \u00d7 2 expansion settings\nbash experiments/run_rep4_subprocess.sh\n\n# single combo\nSAE_EXPAND=1 REPLAY_WORKERS=4 DE_WORKERS=2 python3 scripts/optimizer/optimize.py \\\n    --manifest experiments/manifests/rep4_LVFace-B_Glint360K_full.json \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5 \\\n    --params prob_threshold:0.5:0.999 anneal_sec:1:60 extinction_sec:1:60 \\\n    --popsize 10 --maxiter 15 --trajectory traj.jsonl --out best.json\n\n# replay the shipped config against a held-out film \u2014 --raw-out is needed to draw\n# bboxes later (the merged pred.json has no per-frame bbox, only actor windows)\npython3 scripts/optimizer/replay.py \\\n    --dump experiments/dumps/LVFace-B_Glint360K/dump_&lt;slug&gt;.h5 \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5 \\\n    --out pred.json --raw-out raw.jsonl --prob-threshold 0.754 --anneal-sec 35.54 \\\n    --extinction-sec 57.43 --expand-gallery\n\n# regenerate the report's charts (16-combo ranking, DE landscape, held-out\n# per-film F1, Downton ghost timeline) from the artifacts under experiments/\npython3 scripts/docs/experiment_charts.py --out-dir docs/assets/images\n\n# dump example frames (best-agreement / FPI / FN) for visual inspection, annotated\n# with bounding boxes + names (--raw is optional; omit for unannotated frames)\npython3 scripts/optimizer/dump_error_frames.py \\\n    --pred pred.json --raw raw.jsonl --xray experiments/xray/.../&lt;xray_dir&gt; \\\n    --movie \"&lt;path to source video&gt;\" \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5 \\\n    --out-dir experiments/results/holdout/frames/&lt;name&gt; --n-per-bucket 4\n\n# or: one best + one worst frame per 10-minute window across the whole film\npython3 scripts/optimizer/dump_error_frames.py \\\n    --pred pred.json --raw raw.jsonl --xray experiments/xray/.../&lt;xray_dir&gt; \\\n    --movie \"&lt;path to source video&gt;\" \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5 \\\n    --out-dir experiments/results/holdout/frames/&lt;name&gt;_intervals --interval-sec 600\n</code></pre> <p>See also: the prior optimizer round (superseded metric) and the session log <code>experiments/SESSION_STATE.md</code>.</p>"},{"location":"optimizer-experiments/","title":"Threshold optimization against Amazon X-Ray \u2014 experiment log","text":"<p>Record of the July 2026 work that tuned the pipeline's recognition/tracking defaults against ground-truth per-scene actor presence, and the tooling built to do it.</p>"},{"location":"optimizer-experiments/#tldr-what-changed","title":"TL;DR \u2014 what changed","text":"knob old default new default why <code>prob_threshold</code> 0.99 0.76 0.99 was far too strict \u2014 halved recall for a fraction of a precision point. DE optimum, tightly converged. <code>extinction_sec</code> 5.0 1.5 Long extinction smears presence into later scenes \u2192 FPs. DE converged tightly low. <code>anneal_sec</code> 10.0 10.0 (unchanged) DE found it insensitive (F1 flat \u00b10.3pp across 3\u201326s) \u2014 kept the round default. <code>detector_conf</code> 0.5 0.5 (unchanged) Sweep showed raising it only trades recall for precision at a net F1 loss \u2014 near-threshold detections are real faces, not phantoms. <p>Net effect on the 9-film benchmark (strict per-scene, augmented gallery): recall 58% \u2192 ~72%, F1 70% \u2192 ~76%, precision ~85%, at no meaningful precision cost.</p>"},{"location":"optimizer-experiments/#ground-truth","title":"Ground truth","text":"<p>Public scene-level Amazon X-Ray dataset (Zenodo DOI 10.5281/zenodo.17659734, CC-BY-4.0): per movie, <code>people.csv</code> (name_id/person/character), <code>scenes.csv</code> (scene/start/end ms), <code>people_in_scenes.csv</code>. Films matched to the library by an authoritative Jellyfin ID join (query <code>/Items?IncludeItemTypes=Movie&amp;Fields= ProviderIds,Path</code>, join Imdb/Tmdb against X-Ray metadata) \u2014 NOT fuzzy title matching, which collides badly (TV episodes vs same-named films). 9 genuine films with source video on disk: Benny &amp; Joon, Caf\u00e9 Society, Downton Abbey: A New Era, Lord of War, Lovelace, The Many Saints of Newark, Scarface, Sound of Metal, Valerian.</p>"},{"location":"optimizer-experiments/#the-scoring-metric-evolved-through-review","title":"The scoring metric (evolved through review)","text":"<p>Comparison unit is the X-Ray scene, not sampled timepoints. For each scene <code>[start,end]</code>: predicted set = union of actors detected anywhere in the span; GT set = actors X-Ray lists for that scene. Per scene TP/FP/FN, then:</p> <ul> <li>Precision: STRICT. Any predicted actor not in the scene's X-Ray set is an FP,   including out-of-cast confusions (no gallery\u2229cast masking). An earlier   timepoint-sampled, cast-masked metric HID ~570 such FPs across 9 films and let the   optimizer drive <code>prob_threshold</code> to the 0.50 floor \u2014 a metric artifact. Counting   them is essential.</li> <li>Recall: FAIR. FN counts only X-Ray cast members who are in the gallery. 67%   of X-Ray cast (261/392) have no gallery reference embedding and can never be   recognised \u2014 counting them as misses penalises coverage, not the threshold. Both   <code>recall</code> (fair) and <code>recall_strict</code> (all) are reported.</li> <li>Aggregation: per-scene F1 \u2192 duration-weighted average within a movie (long   scenes count more) \u2192 equal-weight mean across movies (macro; each film counts   the same regardless of length). This is the DE objective.</li> </ul> <p>Implemented in <code>scripts/optimizer/scene_score.py</code> \u2014 since removed along with this metric; its per-second successor is <code>scripts/optimizer/second_score.py</code> (see the bake-off round).</p>"},{"location":"optimizer-experiments/#the-gallery-coverage-gap","title":"The gallery coverage gap","text":"<p>Diagnosing low recall: only 131 of 392 X-Ray cast were in the gallery (33%). Every in-gallery actor HAD embeddings (gallery well-formed) \u2014 the gap was pure coverage. <code>scripts/optimizer/fetch_missing_actors.py</code> recovers missing actors: <code>nm-id \u2192 TMDB /find external_ids \u2192 /person/{id}/images \u2192 download \u2192 embed (sae_embed)</code>, with a <code>--wikidata</code> fallback (P345\u2192P18 Commons photo).</p> <ul> <li>TMDB recovered 143/261 (55%). 0 face-detection failures; the rest had no TMDB   person (60) or no profile photo (58). Coverage 33% \u2192 70%.</li> <li>Wikidata fallback: 0/118 of the TMDB failures \u2014 only 4 even had a Commons photo,   none yielded a detectable face. \u2192 TheTVDB not worth pursuing: these remaining   actors are obscure enough that no image source covers them, AND (see below) most are   off-camera anyway.</li> </ul> <p>Coverage vs detectability. Adding references lifted recall (58\u219268% at fixed config) but modestly. Per-film drill-down (Lord of War: 12 actors recovered, only 1 had a detectable on-camera face) showed most missing cast are a detectability gap \u2014 X-Ray credits them as cast-in-scene (incl. off-camera/background), but their face never appears clearly for the pipeline to detect. This is a fundamental ceiling of a face-recognition pipeline vs X-Ray's presence semantics, not a fixable gap.</p>"},{"location":"optimizer-experiments/#optimizer","title":"Optimizer","text":"<p><code>scripts/optimizer/optimize.py</code> \u2014 scipy <code>differential_evolution</code> over the knob space, each candidate = full replay of all films through the real C++ nodes (see the KPN replay architecture below) scored by the metric above. Global objective (one config for all films, not per-film).</p> <p>Convergence stability (augmented gallery, 233 evals):</p> knob top-20 range verdict <code>prob_threshold</code> 0.69\u20130.83 (\u03c3 0.05) TIGHT \u2014 trust 0.76 <code>extinction_sec</code> 1.0\u20132.2 (\u03c3 0.33) TIGHT \u2014 trust 1.5 <code>anneal_sec</code> 3.1\u201326.3 (\u03c3 6.4) LOOSE \u2014 insensitive, not hard-coded <p>F1 varied only 0.3pp across the top-20 \u2192 objective is flat near the optimum, so only the tightly-converged knobs were adopted as defaults.</p>"},{"location":"optimizer-experiments/#replay-architecture-how-the-sweep-is-cheap","title":"Replay architecture (how the sweep is cheap)","text":"<p>The optimizer never re-decodes video. <code>scene_analyze --dump-embeddings out.h5</code> runs the expensive half once (decode\u2192detect\u2192align\u2192embed) and dumps per-frame face embeddings + metadata to HDF5 (<code>scripts/optimizer/SCHEMA.md</code>). <code>scripts/optimizer/replay.py</code> then replays that dump through the real C++ <code>face_tracker \u2192 identity_matcher \u2192 scene_tracker</code> assembled in a Python KPN network (<code>sae_kpn</code> nanobind module), varying Config knobs freely \u2014 no GPU embedding, no decode. Verified BYTE-EXACT against <code>scene_analyze</code>'s own output. The dumps are gallery-independent, so testing the augmented gallery needed no re-dump. <code>detector_conf</code> is replayable UPWARD only (the dump floor is 0.5).</p>"},{"location":"optimizer-experiments/#reproduce","title":"Reproduce","text":"<pre><code># 1. dump (once per film, needs video)\nscene_analyze --movie &lt;f&gt; --gallery gallery.json --dump-embeddings dump.h5 --fps 1\n# 2. build films manifest by Jellyfin ID join (see scripts/optimizer notes)\n# 3. optimize\npython scripts/optimizer/optimize.py --manifest films.json --gallery gallery.json \\\n    --params prob_threshold:0.5:0.999 anneal_sec:1:30 extinction_sec:1:15 \\\n    --popsize 8 --maxiter 20 --trajectory traj.jsonl --out opt.json\n# 4. score a fixed config / validate on a held-out set\n#    (historical: score_config.py and scene_score.py were removed with the\n#     scene-union metric \u2014 use scripts/optimizer/second_score.py, per-second)\npython scripts/optimizer/second_score.py --help\n</code></pre> <p>Superseded by the model bake-off + re-tune, which replaced this round's scene-union metric with per-second scoring.</p>"},{"location":"pose-expansion/","title":"Pose expansion: does \"learning\" new poses mid-film help?","text":"<p><code>expand_gallery</code> (<code>src/gallery/track_gallery.hpp</code>) promotes a confidently-identified track's novel-pose reference views into a per-film, in-memory gallery annex \u2014 the idea being that once the pipeline is sure who someone is, a pose it hasn't seen before (turned head, different lighting) becomes a free extra reference for recognising that actor again later in the same film, without touching the baked gallery.</p>"},{"location":"pose-expansion/#the-training-set-signal","title":"The training-set signal","text":"<p>Averaged across all 4 models, on the 4 films used for optimization:</p> scope expansion F1 R misID full off 71.2% 58.3% 209 full on 71.2% 59.7% 864 restricted off 73.6% 61.3% 194 restricted on 75.4% 64.5% 135 <p>In <code>restricted</code> mode (matcher's candidate set capped to the film's own credited cast) expansion looked like a clean win: +1.8pp F1, +3.2pp recall, misID actually lower. In <code>full</code> mode it looked flat-to-costly: ~0 F1 change, recall +1.4pp, but misID roughly quadrupled (209 \u2192 864) \u2014 see the bake-off experiment log for the per-model breakdown. That's the number that motivated this page: does turning expansion on actually change what gets recognised, frame by frame, or is the aggregate F1 shift something else?</p>"},{"location":"pose-expansion/#held-out-test-does-it-reproduce","title":"Held-out test: does it reproduce?","text":"<p>Same model + same tuned config, <code>expand_gallery</code> toggled on vs. off, nothing else changed \u2014 full gallery mode, per-second scoring against X-Ray. This isolates expansion from every other variable (config, model, threshold) that differs between the training-set <code>exp</code>/<code>noexp</code> rows above.</p> <p>LVFace-B Glint360K, all 5 held-out films (films never seen by the optimizer):</p> film F1 (exp) F1 (noexp) TPI \u0394 FN \u0394 Benny &amp; Joon 83.0% 83.0% -2 +2 Downton Abbey: A New Era 56.1% 56.2% -7 +7 Lovelace 77.5% 77.4% +33 -33 The Many Saints of Newark 46.3% 46.3% +2 -2 Valerian and the City of a Thousand Planets 74.1% 74.1% +2 -2 <p>ArcFace R18 (Benny &amp; Joon, r18's own tuned config): F1 77.1% for both, TPI/FN identical, FPI differs by 2 (noise).</p> <p>Every film, both models tested: F1 within 0.1\u20130.2pp, TPI/FN swings in the tens out of tens of thousands. That's noise, not a signal \u2014 expansion made no measurable difference to per-second onscreen identification anywhere it was tested on unseen data.</p>"},{"location":"pose-expansion/#two-bugs-this-required-catching-this-sections-own-methodology","title":"Two bugs this required catching (this section's own methodology)","text":"<p>Getting to the clean table above took two wrong turns, both worth recording since they're exactly the kind of error that produces a false positive \"look, expansion helped!\" finding:</p> <ol> <li>Timeout truncation. The first Downton Abbey <code>exp</code> replay was cut off by a    60s subprocess timeout at ~76% through the film (5589 of 7368 expected    seconds) \u2014 a genuinely large, silent data loss that showed up as a large,    convincing-looking TPI gap (47938 vs 52032) purely because one run had a    quarter of the film missing. Caught by comparing <code>n_seconds</code> between runs    before trusting any score delta; fixed by re-running with a longer timeout.</li> <li>Bbox-matching bug. An early per-second raw-annotation diff matched each    <code>exp</code> detection to the first <code>noexp</code> detection with IoU &gt; 0.5, not the    best-overlapping one. With 3 faces close together in frame, this produced    spurious \"disagreements\" (e.g. \"exp says Aidan Quinn, noexp says Johnny    Depp\" at the same seconds) that vanished entirely once the match picked the    true best-IoU candidate \u2014 both configs had actually output the exact same    three names at the exact same three boxes.</li> </ol> <p>Both bugs independently pointed toward \"expansion is doing something,\" and both were artifacts of the comparison harness, not the pipeline. Worth remembering when a before/after diff looks dramatic: check that the two runs actually cover the same seconds, and match entities by best overlap, not first-found.</p>"},{"location":"pose-expansion/#what-this-means","title":"What this means","text":"<p>The training-set aggregate effect (particularly the ~4x misID increase in full mode) doesn't reproduce on held-out data \u2014 at minimum it's far smaller than the training-set numbers suggested, and plausibly it's sampling variation from only 4 training films rather than a real, generalizable mechanism. This doesn't mean <code>expand_gallery</code> never does anything (the mechanism is real \u2014 see <code>track_gallery.hpp</code>'s promotion logging: tracks do get confirmed and views do get promoted into the annex on every film tested), only that whatever effect it has on final per-second identification was too small to detect against 5 held-out films with this scoring method. A cleaner test would need either many more held-out films or a metric that can see the annex's direct contribution (e.g. tagging which reference embedding won each match), neither of which this pass had budget for.</p> <p>Practical takeaway: don't treat the training-set <code>exp</code> vs <code>noexp</code> numbers in the bake-off experiment log as proof that expansion changes real-world behavior in either direction \u2014 on the evidence gathered so far, it doesn't move the needle enough to see.</p>"},{"location":"service-conversion/","title":"Conversion to service \u2014 a native idle-GPU worker","text":"<p>Status: design / proposal. Nothing here is built yet.</p>"},{"location":"service-conversion/#the-idea","title":"The idea","text":"<p>Turn the CLI tools into a turnkey batch worker that uses the machine's idle GPU: it analyses newly-added Jellyfin media when you're not using the computer (screen locked), and stops the instant you come back. It's an overnight job on your own Linux box.</p> <p>No Docker. This runs on your own machine with your own drivers, so a container buys little and costs a lot: GPU passthrough (nvidia-container-toolkit, or <code>/dev/kfd</code>+<code>/dev/dri</code>+<code>video</code> group for ROCm) is the single most fragile part of a containerised setup, and it exists only because of the container. Natively, the GPU just works with the drivers you already have, and the media paths Jellyfin reports are just real paths \u2014 no re-mounting. So we ship a native installer instead of an image builder.</p> <p>Two deliverables:</p> <ol> <li>An installer \u2014 <code>scripts/build_install.py</code>. Detects your distro, ensures the    GPU/build dependencies are present (via <code>dnf</code>/<code>pacman</code>), compiles <code>scene_analyze</code>    for your GPU, and installs the binary + Python glue + two systemd user    units under <code>~/.local</code>.</li> <li>A screen-lock gate \u2014 one of those systemd units watches logind lock/unlock    and starts/stops the worker. Lock \u2192 analyse. Unlock \u2192 stop.</li> </ol>"},{"location":"service-conversion/#what-already-exists-reuse-dont-rebuild","title":"What already exists (reuse, don't rebuild)","text":"<p>The processing loop is already implemented \u2014 this is packaging, building, and lock-gating, not new pipeline logic.</p> Piece Where What it does Analysis engine <code>build/scene_analyze</code> Video \u2192 face detect/align/embed \u2192 gallery match \u2192 result JSON Backend selection <code>CMakeLists.txt</code> (<code>SAE_INFERENCE_BACKEND</code>, <code>SAE_GEMM_BACKEND</code>) ORT/TRT + ROCm/CUDA, chosen at build time New-media queue JRay plugin \u2192 <code>GET /Plugins/JRay/Tasks/Pending</code> Backlog of items with no results yet Worker loop <code>scripts/run_from_jellyfin.py</code><code>--worker</code> Poll Pending \u2192 run <code>scene_analyze</code> \u2192 push results Result push <code>PUT /Plugins/JRay/Items/{id}/Truth</code> Stores per-actor scene windows back in Jellyfin Incremental gallery <code>scripts/make_jellyfin_gallery.py</code><code>--merge</code> Embeds only cast not already in the gallery Secrets loader <code>.env</code> via <code>scripts/sae_env.py</code> <code>JELLYFIN_URL</code>, <code>JELLYFIN_API_KEY</code>, <code>TMDB_API_KEY</code>"},{"location":"service-conversion/#installer-config","title":"Installer config","text":"<p>One file. Build-time settings (fixed when we compile) vs. run-time settings (in the worker's <code>.env</code>, editable without recompiling).</p> <pre><code># install.yaml \u2014 consumed by scripts/build_install.py\n\nplatform: nvidia            # nvidia | amd | cpu \u2192 picks the cmake backend\nmodel:\n  arcface: LVFace-B_Glint360K.onnx   # embedder compiled against; gallery MUST match\nschedule:\n  gallery_scan_interval: 24h         # incremental --merge cadence; 0 disables the scanner\nprefix: ~/.local                     # install root (bin, share, systemd user units)\n\n# runtime (written to the worker .env, not compiled in):\nruntime:\n  jellyfin_url: http://localhost:8096\n  # JELLYFIN_API_KEY / TMDB_API_KEY are filled into .env by hand after install\n</code></pre> <p>Secrets never go in the repo or a build artifact \u2014 the installer writes a <code>.env</code> under the install prefix with blanks for the keys, and you fill them in once. <code>sae_env.py</code> already loads it.</p> <p>Model \u21c4 gallery coupling (guard, don't just document): embeddings from different recognition models aren't interchangeable. We compile against one embedder; the gallery must be built with the same one. Stamp the embedder name into <code>gallery.json</code>, and have the worker refuse to start if the gallery's embedder \u2260 the configured <code>model.arcface</code>, rather than silently mismatching.</p>"},{"location":"service-conversion/#dependencies-via-the-system-package-manager","title":"Dependencies via the system package manager","text":"<p>The heavy build/runtime deps (OpenCV, ffmpeg, the GPU stack) are best provided by the distro, not vendored. The installer ships a per-distro dependency list and either installs them or prints the exact command. Targets: Fedora (dnf) and Arch (pacman) first.</p> Dependency Fedora (dnf) Arch (pacman) OpenCV <code>opencv-devel</code> <code>opencv</code> ffmpeg <code>ffmpeg-free</code>/<code>ffmpeg</code> (RPM Fusion) <code>ffmpeg</code> CMake / toolchain <code>cmake gcc-c++</code> <code>cmake gcc</code> CUDA + TensorRT (nvidia) NVIDIA CUDA repo + <code>libnvinfer-*</code> <code>cuda</code>, <code>tensorrt</code> ROCm (amd) <code>rocm-hip-sdk</code> / <code>rocblas-devel</code> <code>rocm-hip-sdk</code>, <code>rocblas</code> ONNX Runtime not packaged \u2014 installer fetches a pinned release tarball into the prefix AUR <code>onnxruntime</code> (or same pinned-tarball fallback) <p>So the flow is: detect distro \u2192 check each package \u2192 install via the native manager (or print <code>sudo dnf install \u2026</code> / <code>sudo pacman -S \u2026</code>), with ONNX Runtime as the one known gap the installer fills itself (a pinned upstream release extracted under the install prefix, so it doesn't depend on a system package that may not exist). CUDA/ROCm being present is assumed \u2014 you already run a GPU desktop; the installer verifies and points you at the vendor repo if not.</p>"},{"location":"service-conversion/#what-build_installpy-does","title":"What <code>build_install.py</code> does","text":"<pre><code>build_install.py install.yaml\n  \u2502\n  \u251c\u2500 detect distro (dnf vs pacman) and platform from config\n  \u251c\u2500 ensure deps: install via manager, or print the exact command; fetch ONNX Runtime if needed\n  \u251c\u2500 cmake + build scene_analyze with the platform's backend flags:\n  \u2502     nvidia \u2192 -DSAE_INFERENCE_BACKEND=TRT -DSAE_GEMM_BACKEND=CUDA\n  \u2502     amd    \u2192 -DSAE_INFERENCE_BACKEND=ORT -DSAE_GEMM_BACKEND=ROCM\n  \u2502     cpu    \u2192 -DSAE_INFERENCE_BACKEND=ORT  (CPU EP; slow, for smoke tests)\n  \u251c\u2500 install into &lt;prefix&gt;:\n  \u2502     bin/sae-scene-analyze           the compiled binary\n  \u2502     share/sae-worker/               Python glue + a venv (requests, etc.), models/\n  \u2502     share/sae-worker/.env           runtime config (keys blank, url from config)\n  \u251c\u2500 install systemd --user units:\n  \u2502     sae-worker.service              runs the worker + gallery-scan supervisor\n  \u2502     sae-lock-gate.service           watches logind lock/unlock, start/stops the worker\n  \u2514\u2500 print next steps (edit .env, `systemctl --user enable --now sae-lock-gate`)\n</code></pre>"},{"location":"service-conversion/#the-worker-service-supervisor","title":"The worker service (supervisor)","text":"<p><code>sae-worker.service</code> runs a small Python supervisor as its main process:</p> <ul> <li>starts the worker loop (<code>run_from_jellyfin.py --worker</code>) \u2014 the hot path,</li> <li>starts a gallery-scan timer \u2014 sleeps <code>gallery_scan_interval</code>, runs   <code>make_jellyfin_gallery.py --merge</code>, repeats,</li> <li>exits cleanly on SIGTERM (see re-queue below).</li> </ul>"},{"location":"service-conversion/#the-lock-gate","title":"The lock gate","text":"<p><code>sae-lock-gate.service</code> runs a tiny watcher that subscribes to logind lock/unlock signals and drives the worker service:</p> <pre><code>screen locks   \u2192 systemctl --user start sae-worker.service\nscreen unlocks \u2192 systemctl --user stop  sae-worker.service   (SIGTERM)\n</code></pre> <p>Screen-lock is the only signal \u2014 deliberately. We don't also gate on GPU/CPU load, because our own worker is the load: a load threshold would form a feedback loop (worker starts \u2192 GPU spikes \u2192 threshold trips \u2192 worker stops \u2192 load drops \u2192 restart \u2192 \u2026). Lock state is external to what the worker does, so it can't oscillate.</p> <p>Signal source is desktop-dependent: logind <code>Lock</code>/<code>Unlock</code> (GNOME/KDE via <code>loginctl</code>/D-Bus) covers most setups; a <code>swayidle</code>/<code>xss-lock</code> hook is the fallback for wlroots/X-only compositors. The installer picks based on what's present.</p>"},{"location":"service-conversion/#on-resume-hard-stop-re-queue-its-free","title":"On resume: hard stop + re-queue (it's free)","text":"<p>Stopping the worker mid-analysis costs nothing to reschedule, because of how the JRay queue works: an item only leaves <code>/Tasks/Pending</code> once its results are pushed (<code>push_truth</code>). A worker stopped mid-<code>scene_analyze</code> simply leaves that item Pending \u2014 next lock picks it up again. No re-queue bookkeeping.</p> <p>Two small correctness requirements (the only worker changes needed):</p> <ol> <li>Never push a partial result. Already true \u2014 <code>push_truth</code> runs only after    <code>scene_analyze</code> returns; a killed run pushes nothing. \u2713 (keep it that way).</li> <li>Clean up on signal. <code>process_item</code> writes a temp filtered-gallery file and    unlinks it in a <code>finally</code>; a SIGKILL skips <code>finally</code>. Fix: write temps under a    dir the worker wipes on start, and/or a SIGTERM handler that unlinks before    exit. Minor.</li> </ol> <p>Accepted trade-off: a partially-analysed title restarts from scratch next lock. Fine for an overnight/idle workload; no mid-video checkpointing.</p>"},{"location":"service-conversion/#the-end-to-end-ux","title":"The end-to-end UX","text":"<pre><code># once: build + install for your GPU + model\n./scripts/build_install.py install.yaml\n    # detects Fedora/Arch, ensures deps, compiles, installs units under ~/.local\n\n# once: set your keys, enable the gate\n$EDITOR ~/.local/share/sae-worker/.env     # JELLYFIN_API_KEY, TMDB_API_KEY\nsystemctl --user enable --now sae-lock-gate.service\n\n# from then on: nothing. Lock your screen \u2192 it analyses. Unlock \u2192 it stops.\n</code></pre> <p>No Docker, no GPU passthrough config, no media re-mounting \u2014 the worker sees the same filesystem and GPU as everything else on the box.</p>"},{"location":"service-conversion/#implementation-plan-follow-up-commits","title":"Implementation plan (follow-up commits)","text":"<p>Ordered so each step stands alone:</p> <ol> <li>installer skeleton \u2014 <code>scripts/build_install.py</code>: parse <code>install.yaml</code>,    distro detect, dependency check/print (start with cpu platform so it builds    without a GPU), cmake+build, copy into prefix.</li> <li>supervisor + cleanup \u2014 <code>scripts/service.py</code> (worker loop + gallery-scan    timer + SIGTERM); temp-file cleanup fix in <code>run_from_jellyfin.py</code>.</li> <li>systemd units + lock gate \u2014 generate/install <code>sae-worker.service</code>,    <code>sae-lock-gate.service</code>, and the logind lock watcher.</li> <li>gallery/model guard \u2014 stamp embedder into <code>gallery.json</code>; startup mismatch    check.</li> <li>platform + distro matrix \u2014 nvidia/amd backends; dnf/pacman dep lists; ONNX    Runtime fetch fallback.</li> <li>docs \u2014 README \"Run on your idle GPU\" section.</li> </ol>"},{"location":"service-conversion/#settled-decisions","title":"Settled decisions","text":"<ul> <li>ONNX Runtime build \u2014 the installer fetches the ROCm ORT release. It   serves the <code>amd</code> platform, and its CPU execution provider covers the <code>cpu</code>   smoke-test fallback too, so one download handles both. (nvidia uses raw TRT and   doesn't need ORT.)</li> <li><code>dnf</code>/<code>pacman</code> invocation \u2014 auto-install. The installer runs <code>sudo dnf   install \u2026</code> / <code>sudo pacman -S \u2026</code> itself (prompting for sudo), rather than only   printing the command. It still prints what it's about to install first.</li> <li>Distro coverage \u2014 Fedora + Arch only for now. Debian/Ubuntu (<code>apt</code>) is   out of scope.</li> </ul>"},{"location":"service-conversion/#open-questions","title":"Open questions","text":"<p>(none blocking \u2014 the spec above is buildable as-is.)</p>"}]}
\ No newline at end of file
+{"config":{"lang":["en"],"separator":"[\\s\\-]+","pipeline":["stopWordFilter"],"fields":{"title":{"boost":1000.0},"text":{"boost":1.0},"tags":{"boost":1000000.0}}},"docs":[{"location":"","title":"scene-actor-extraction","text":"<p>A face-recognition pipeline that finds when each actor appears on screen in a film or TV episode, built on KPN++ (a C++20 Kahn Process Network library) for the detect, track, match, and scene pipeline, with a Jellyfin-integrated gallery and an X-Ray-validated optimizer.</p> <p>This is a correctly scored second from a held-out film, one the optimizer never saw during tuning:</p> <p></p> <p>Every visible face is named at 100% confidence (Chris Noth, Hank Azaria, Bobby Cannavale), the background extra is correctly left unnamed, and the two credited cast members without a visible face are correctly reported present but not visible. This matches Amazon X-Ray's own record for this second exactly.</p> <p>Results are not uniform across films. The hardest held-out film scores 46% F1. This report documents why: one tunable trade (extinction bridging at hard cuts), one structural limit (X-Ray credits people whose faces never appear on screen), and a small number of cases where the pipeline is correct and X-Ray's ground truth is not. Read how we score against X-Ray first. X-Ray's ground truth is scene-level; the pipeline's output is per-second. That difference shapes every finding below.</p>"},{"location":"#findings","title":"Findings","text":"<ul> <li> <p> Which model is best?</p> <p>Calibration curves first, independent of any threshold, then held-out F1 across three models. LVFace-B Glint360K wins both, and wins on every held-out film.</p> </li> <li> <p> Whole vs. cast-restricted gallery</p> <p>Restricting the matcher to a film's credited cast improves F1, recall, and misID rate at once, but is not a shipped runtime feature yet.</p> </li> <li> <p> Does pose expansion help?</p> <p>A training-set effect that did not reproduce on 5 held-out films once two methodology bugs in the comparison harness were found and fixed.</p> </li> <li> <p> Deep dive: LVFace-B Glint360K</p> <p>The held-out generalization gap, the two mechanisms behind its errors, and every distinct case where it names someone outside the film's credited cast.</p> </li> </ul>"},{"location":"#full-experiment-log","title":"Full experiment log","text":"<ul> <li>Full experiment log: the complete log behind the   four pages above, including how replaying against cached embeddings   inside the same KPN network makes a full model and configuration   comparison practical, the full results table, and every caveat. This is   where the shipped <code>src/config.hpp</code>   defaults come from.</li> <li>Service conversion (proposal): design   sketch for a native idle-GPU worker gated on screen lock, not yet built.</li> </ul>"},{"location":"#reproducing-the-benchmarks","title":"Reproducing the benchmarks","text":"<p>Gallery <code>.h5</code> files, embedding dumps, the X-Ray corpus, montage frame images, and DE trajectories are not committed to this repository. They are pushed to the Gitea package registry and pulled on demand:</p> <pre><code>scripts/artifacts/pull_artifacts.sh galleries\nscripts/artifacts/pull_artifacts.sh experiment-data\nscripts/artifacts/pull_artifacts.sh montage-frames &lt;film-slug&gt;\n</code></pre> <p>See <code>scripts/artifacts/push_artifacts.sh</code> for the upload side, which requires a <code>GITEA_TOKEN</code> with package write scope.</p>"},{"location":"best-model/","title":"Which embedding model is best?","text":"<p>Three ArcFace variants (w600k-R50, R18, w600k-MBF) and LVFace-B (Glint360K, 455MB) were compared. r50 is excluded from the training/held-out comparison below; its gallery has roughly 30% fewer reference images per actor than the other three on the identical source photos, which confounds a direct score comparison (see the full experiment log for detail). It remains in the calibration comparison, which does not depend on the gallery image count.</p>"},{"location":"best-model/#first-signal-calibration-curves","title":"First signal: calibration curves","text":"<p>Each gallery carries a fitted Platt sigmoid <code>P(match | cosine similarity) = \u03c3(a\u00b7sim + b)</code>, stored directly in the gallery HDF5 (<code>src/gallery/gallery_calibration.hpp</code>). This is a property of the embedding space alone, computed from intra- and inter-actor reference-image pairs with no tracking or scene logic involved, so it is a clean first read on discriminative power before running a benchmark.</p> <p></p> model a (steepness) boundary at P=0.5 LVFace-B Glint360K 17.7 sim 0.228 ArcFace w600k-MBF 16.2 sim 0.267 ArcFace w600k-R50 15.4 sim 0.301 ArcFace R18 15.3 sim 0.309 <p>LVFace has both the steepest transition and the lowest decision boundary, separating same-actor from different-actor reference pairs more confidently at a lower similarity than any ArcFace variant.</p>"},{"location":"best-model/#second-signal-held-out-f1","title":"Second signal: held-out F1","text":"<p>Each model's own tuned <code>full_exp</code> config, replayed against the 5 films the optimizer never saw and scored the same way:</p> film LVFace F1 mbf F1 r18 F1 Benny &amp; Joon 83.0% 78.5% 77.1% Lovelace 77.5% 73.7% 72.2% Valerian and the City of a Thousand Planets 74.1% 70.2% 71.0% Downton Abbey: A New Era 56.2% 55.0% 53.0% The Many Saints of Newark 46.3% 44.5% 42.1% macro average 67.4% 64.4% 63.1% <p>LVFace scores highest on all 5 held-out films; the ranking never flips between models. Total misID count across the 5 films: LVFace 1032, mbf 2197, r18 1224. LVFace has less than half mbf's misID total and still scores higher on every film.</p> <p>Held-out results are stronger evidence than training results, because training numbers can reflect what the optimizer was tuned to fit rather than general performance. On training data, the ordering is not as clean:</p> film LVFace F1 mbf F1 r18 F1 best Caf\u00e9 Society 68.1% 62.2% 60.1% LVFace Lord of War 75.6% 77.2% 75.6% mbf Scarface 71.5% 68.6% 64.1% LVFace Sound of Metal 78.8% 76.5% 71.6% LVFace <p>mbf beats LVFace on Lord of War (77.2% vs 75.6%), the only film in either table where LVFace does not score highest. LVFace's training-set macro average (75.3%, see the full experiment log) is not a uniform win across every film it contributes to; the held-out result, where LVFace wins all 5 films outright, is the stronger claim.</p> <p>This reverses an earlier, superseded benchmarking pass that used a scene-union metric and found the three models statistically indistinguishable (around 85% each), concluding LVFace was not worth its size. That metric masked out-of-cast false positives behind a gallery-intersect-cast recall filter; the per-second metric used here does not.</p>"},{"location":"best-model/#full-training-matrix-picture","title":"Full training-matrix picture","text":"<p>Best full-gallery combo per model (all three are <code>full_exp</code>), from the training matrix in the full experiment log:</p> model F1 P R misID LVFace-B Glint360K 75.3% 89.7% 65.4% 232 ArcFace w600k-MBF 72.0% 87.7% 61.4% 240 ArcFace R18 69.1% 87.6% 57.7% 242 <p>LVFace leads within both the restricted and full gallery modes, visible directly in the chart above without reading the table. The three models' misID counts on the full gallery are nearly identical (232/240/242); LVFace's lead here is a precision-and-recall lead, not a misID one.</p>"},{"location":"best-model/#operational-note","title":"Operational note","text":"<p>Switching the default embedder is not a config change alone; the gallery is model-specific, since embeddings from different models are not comparable. Any existing gallery built against a different model must be rebuilt from source images before the new default takes effect. <code>scripts/optimizer/reembed_gallery.py</code> does this from a reference gallery's cached source images without re-downloading anything.</p>"},{"location":"gallery-scope/","title":"Whole gallery vs. cast-restricted gallery","text":"<p>Two ways to run the matcher. Full mode scores every detected face against the entire 2418-actor gallery. Restricted mode pre-filters each film's gallery down to just its Jellyfin-credited cast (typically around 15 top-billed actors) before the matcher runs.</p>"},{"location":"gallery-scope/#result","title":"Result","text":"<p>Averaged across the 3 compared models (r50 excluded, see the full experiment log) and both expansion settings, on the 4 training films:</p> scope F1 P R total misID full 71.1% 89.6% 59.6% 1121 restricted 75.9% 90.4% 65.6% 299 <p>Restriction improves every metric at once, not a precision/recall trade: +4.8pp F1, +6.0pp recall, roughly a quarter the total misIDs. Fewer candidates in the matcher's search space means fewer opportunities for a lookalike false match, and the recall gain shows this does not cost real detections.</p> <p>Every model's best-scoring combo in the training matrix uses the restricted gallery:</p> <p></p> <p>See the full experiment log for the complete table. One combo reaches zero true out-of-cast misidentifications, <code>arcface_w600k_mbf_restricted_exp</code> (F1 76.2%), and it is a restricted one, consistent with restriction, not expansion, being what suppresses cross-film confusions.</p> <p>The restriction effect (+4.8pp averaged across models) is larger than the model-choice effect: LVFace beats r18 by 6.2pp in full mode but beats mbf by 3.3pp. Restriction is the single strongest lever in the matrix.</p>"},{"location":"gallery-scope/#why-this-is-not-the-shipped-default","title":"Why this is not the shipped default","text":"<p>Cast restriction is implemented today only as an offline optimizer technique (<code>scripts/optimizer/cast_restrict.py</code>): it pre-builds a filtered gallery file per film using Jellyfin's cast list before the benchmark calls the matcher. There is no runtime \"restrict to this title's credited cast\" switch in the shipped application; <code>scene_analyze</code> always matches against whatever single gallery file it is given.</p> <p>Building this as a real feature requires:</p> <ul> <li>A live Jellyfin cast lookup at analysis time. The title is already known,   and <code>scripts/run_from_jellyfin.py</code>   already performs this lookup for its own <code>filter_gallery</code>-based   restriction path; it is not wired into <code>scene_analyze</code> as a first-class   option.</li> <li>A decision on the fallback case: what happens to a real, uncredited   cameo (see the Germar Terrell Gardner and Talia Balsam cases in the   LVFace deep dive) if the   restricted gallery never includes them at all.</li> <li>Regenerating the restricted-gallery cache whenever a title's Jellyfin   cast list changes.</li> </ul> <p>The shipped <code>src/config.hpp</code> defaults use the full-mode winner (<code>LVFace-B_Glint360K_full_exp</code>, F1 75.3% training, 67.4% held-out macro) rather than the higher-scoring <code>restricted_exp</code> (78.3%), because 78.3% describes a capability the application does not have yet.</p>"},{"location":"lvface-deep-dive/","title":"Deep dive: LVFace-B Glint360K","text":"<p>LVFace won the model comparison (see Which model is best?) and is the shipped default embedder. This page reports how it performs in detail: a baseline of correct output, the two mechanisms behind its errors, and every distinct case where it names someone who is not in the film's credited cast.</p> <p>Read How we score against X-Ray first. X-Ray's ground truth is scene-level, not per-frame. A name marked correct in the Offscreen column below is the pipeline correctly reporting scene membership, not a workaround.</p> <p>How to read the frames on this page</p> <p>The top of each image is the film frame, with a box and name on every face the pipeline matched to a real detection. The panels below are the per-second result against X-Ray. Onscreen lists names attached to a visible face this second. Offscreen lists names the pipeline reports present without a currently visible face. Colors mark the verdict: green correct (TPI), orange wrong (FPI), blue missed (FN).</p>"},{"location":"lvface-deep-dive/#baseline-correctly-scored-seconds","title":"Baseline: correctly scored seconds","text":"<p>Six faces on screen, all six named correctly, including Penelope Wilton at the edge of the pews and a partly occluded Michelle Dockery. Thirteen more cast members X-Ray lists as present in the scene are correctly reported Offscreen. One miss: Maggie Smith (blue). Score for this second: 0.86.</p> <p></p> <p>The same film's funeral scene: dark clothing, hats, half the faces turned away. Nineteen of the twenty cast members X-Ray lists for this scene score correct: seven named on screen at up to 100% confidence, twelve more reported correctly as present but not visible.</p> <p></p> <p>The pipeline does not require a live face. This is Herbie Hancock at 98% confidence, identified from a face displayed on a screen inside the film, on a video call under a science-fiction HUD overlay.</p>"},{"location":"lvface-deep-dive/#training-vs-held-out-the-generalization-gap","title":"Training vs. held-out: the generalization gap","text":"<p>The shipped config (<code>prob_threshold=0.754</code>, <code>anneal_sec=35.54</code>, <code>extinction_sec=57.43</code>, <code>expand_gallery=true</code>) was tuned on 4 films. Scored on the 5 films the optimizer never saw:</p> <p></p> film F1 P R TPI FPI misid FN Benny &amp; Joon 83.0% 89.1% 77.7% 15125 1846 0 4337 Lovelace 77.5% 90.3% 67.9% 14990 1085 58 7085 Valerian and the City of a Thousand Planets 74.1% 97.1% 60.0% 18663 548 0 12467 Downton Abbey: A New Era 56.2% 97.8% 39.4% 52027 1173 0 80084 The Many Saints of Newark 46.3% 54.7% 40.1% 15922 4394 974 23791 macro average 67.4% 85.8% 57.0% <p>The <code>P</code> column is misID-weighted (each out-of-film name counts 10x in the denominator; see methodology). That weighting is why Many Saints reads 54.7% here despite naming mostly real, present faces: its raw (unweighted) precision is 78.4%, and the gap is entirely its 974 misIDs paying the 10x penalty. The three zero-misID films (Benny &amp; Joon, Downton, Valerian) have identical weighted and raw precision; Lovelace, with 58 misIDs, sits 3pp below its raw 93.3%.</p> <p>Held-out F1 is 67.4%, against 75.3% on training, an 8pp drop. The spread between the best and worst held-out film is 37pp. This is not unique to LVFace: the full experiment log shows mbf and r18 with the same shape of spread on the same films, at a uniformly lower level. Two mechanisms explain the spread. Both are shown below with frame-level evidence.</p>"},{"location":"lvface-deep-dive/#mechanism-1-extinction-bridging","title":"Mechanism 1: extinction bridging","text":"<p>The extinction window keeps a name reported as present for up to <code>extinction_sec</code> after its last real detection. This is deliberate: most gaps in face visibility are short (a turned head, an occlusion, a cut to a reaction shot), and the window bridges them.</p> <p></p> <p>Lovelace's polygraph scene: only Eric Roberts and Amanda Seyfried have visible faces. X-Ray lists eight cast members present. All eight score correct; the other six are reported Offscreen through a stretch where the camera never shows them. The extinction window is why.</p> <p>The same mechanism fails at a hard cut into a long stretch with no faces at all. Downton Abbey's recall (39.4%, the worst of the five held-out films) is dominated by this failure. It is verified directly against the raw per-frame stream and the dump's own detection counts, not inferred from the score. Plotting the dump's per-second <code>face_count</code> (detector output, independent of the tracker) against what the tracker reports, through Downton Abbey's hard cut into its closing credits:</p> <p></p> <p>From the cut onward the detector reports zero faces for close to a minute. The tracker continues reporting the previous shot's 15 identities for the same span (verified for Hugh Bonneville: bbox <code>(1743.2, 0.0, 171.3, 317.8)</code>, unchanged to the pixel, at every sampled second for 57 seconds). The staircase at the right edge is the extinction window expiring, actor by actor. This is <code>SceneTrackerFunc::active_[actor_idx].last_bbox</code> (<code>src/nodes/scene_tracker_node.hpp</code>) re-emitted as designed. <code>extinction_sec=57.4</code> was tuned long because bridging is correct on most footage, as in the polygraph scene above. The training films did not contain a faceless stretch long enough to expose the cost side; the held-out set did.</p> <p>The extinction window is a scoring concept, not something drawn on screen. The shipped output is presence windows with no bounding boxes. Even the debug overlay used for this report never draws a box for a bridged name: a name inside its extinction window with no current detection appears only as a name in the Offscreen column, the same as every correctly bridged name above.</p> <p>A related, smaller effect shows up at rapid cuts:</p> <p></p> <p>Caf\u00e9 Society (a training film), a shot/reverse-shot dialog. The box on Steve Carell's face carries two labels: his own, and Jesse Eisenberg's, left over from the counter-shot a moment earlier. Both names score correct, because both actors are present in this scene per X-Ray. The box position is briefly wrong; the presence claim, which is what the pipeline ships, is right.</p>"},{"location":"lvface-deep-dive/#mechanism-2-the-face-vs-presence-ceiling","title":"Mechanism 2: the face-vs-presence ceiling","text":"<p>Downton Abbey's recall did not collapse because faces were misread. It collapsed because for most of its 80084 false-negative seconds there was no face to read.</p> <p></p> <p>A newsreel crew moves equipment through the hall. X-Ray credits 22 cast members as present in this scene. None face the camera. Eight still score correct, carried by presence windows from adjacent shots. The other fourteen are missed, and no face-recognition system can recover them, because there is no face in the frame. X-Ray records scene membership; the pipeline measures visible faces. In ensemble scenes these two quantities diverge, and that gap accounts for most of the false-negative count.</p>"},{"location":"lvface-deep-dive/#every-distinct-out-of-cast-name","title":"Every distinct out-of-cast name","text":"<p>Many Saints of Newark has the largest misID count of any held-out film: 974 seconds, weighted. Rather than characterize this from a single frame, the raw replay stream was searched directly for every name the pipeline reports that is not in the film's credited cast. The same search was run on all 9 films in the benchmark, one rule applied uniformly: find the first second each distinct out-of-cast name appears, and render that exact second.</p> <p>Five films produce no such name anywhere in their runtime: Benny &amp; Joon, Caf\u00e9 Society, Downton Abbey, Sound of Metal, Valerian. Zero out-of-cast names across their entire length. Four films produce nine distinct names between them, shown below in full, not a sample.</p>"},{"location":"lvface-deep-dive/#the-many-saints-of-newark-4-names","title":"The Many Saints of Newark: 4 names","text":"<p>Germar Terrell Gardner, t=848s, 78% confidence. A real, clearly visible background actor. He is not in X-Ray's cast list for this film, but he is credited in Jellyfin's independent cast metadata (see Where LVFace beat X-Ray below). This is a ground-truth gap, not a model error.</p> <p></p> <p>Archie Yates, t=2521s, 78% confidence. A real detected face, a genuine lookalike confusion.</p> <p></p> <p>Zooey Deschanel, t=2819s, 99% confidence. A real detected face at a dinner table, high-confidence lookalike confusion.</p> <p></p> <p>Talia Balsam, t=4551s, 93% confidence. A real detected face. Talia Balsam plays Mrs. Jarecki, a guidance counselor, in this film; she is confirmed on screen by direct inspection of the frame. She does not appear in X-Ray's <code>people.csv</code> for this title. This is a second ground-truth gap in the same film, not a model error.</p> <p>Two of these four names are ground-truth gaps (Gardner, Balsam), not misidentifications. The other two (Yates, Deschanel) are genuine embedding errors on real faces.</p>"},{"location":"lvface-deep-dive/#lord-of-war-3-names","title":"Lord of War: 3 names","text":"<p>David Shumbris, t=418s, 81% confidence. A real face in a dim, low-detail shot under a train track. A genuine lookalike confusion in poor lighting.</p> <p></p> <p>Ronald Reagan, t=1003s, 100% confidence. This is not a lookalike confusion. The detected face is a photograph of Reagan appearing within the shot, not a living actor. The detector and matcher both did their job correctly on the image content in front of them; the error is that a photograph inside the scene is not the same thing as an actor present in the scene, and the pipeline has no way to draw that distinction from a face crop alone.</p> <p></p> <p>Lance Reddick, t=6424s, 78% confidence. A small, distant, low-detail face at the edge of frame. A marginal, low-confidence lookalike confusion.</p>"},{"location":"lvface-deep-dive/#lovelace-1-name","title":"Lovelace: 1 name","text":"<p>Chlo\u00eb Sevigny, t=2451s, 100% confidence. Two boxes are drawn on the same face: one correctly labeled Amanda Seyfried, one incorrectly labeled Chlo\u00eb Sevigny, both at 100%. A single detection producing two competing high- confidence identities on the same crop.</p>"},{"location":"lvface-deep-dive/#scarface-1-name","title":"Scarface: 1 name","text":"<p>Kirstie Alley, t=2451s, 89% confidence. Al Pacino is correctly identified in the foreground at 100%; a background face in the same shot is wrongly labeled Kirstie Alley. (The t=2451s here and the Lovelace Chlo\u00eb Sevigny case above landing on the identical second is a genuine coincidence, verified from each film's raw stream by <code>first_fpi_frames.py</code>, not a transcription slip, two unrelated films whose first out-of-cast name happens to fall at the same timestamp.)</p>"},{"location":"lvface-deep-dive/#summary-of-the-nine","title":"Summary of the nine","text":"film name t (s) confidence classification Many Saints of Newark Germar Terrell Gardner 848 78% ground-truth gap Many Saints of Newark Archie Yates 2521 78% lookalike confusion Many Saints of Newark Zooey Deschanel 2819 99% lookalike confusion Many Saints of Newark Talia Balsam 4551 93% ground-truth gap Lord of War David Shumbris 418 81% lookalike confusion Lord of War Ronald Reagan 1003 100% photo-in-frame Lord of War Lance Reddick 6424 78% lookalike confusion, marginal Lovelace Chlo\u00eb Sevigny 2451 100% lookalike confusion Scarface Kirstie Alley 2451 89% lookalike confusion <p>Of nine distinct out-of-cast names across four films, two are ground-truth gaps, one is a photograph misread as a person, and six are genuine embedding-space confusions on real detected faces. None trace to extinction bridging: every one of these nine is a fresh detection on a real face crop at the second it first appears.</p>"},{"location":"lvface-deep-dive/#where-lvface-beat-x-ray","title":"Where LVFace beat X-Ray","text":"<p>Not every name marked wrong is actually wrong. <code>scripts/optimizer/second_score.py</code> scores strictly against X-Ray, and X-Ray has gaps of its own.</p> <p></p> <p>Germar Terrell Gardner, the same name from the table above, does not appear in X-Ray's <code>people.csv</code> for The Many Saints of Newark. Jellyfin's independent cast metadata does credit him for this film (cross-checked against <code>experiments/manifests/jellyfin_casts.json</code> from the <code>experiment-data</code> artifact package, a data source entirely separate from X-Ray). Talia Balsam is the same case: confirmed on screen, absent from X-Ray's cast list for this title.</p> <p></p> <p>This extends past uncredited background actors. This is Robert Patrick, top-billed in Lovelace, clearly on screen reading a newspaper, identified at 100%. The frame is scored wrong because X-Ray's people-in-scene list for this specific scene omits him, despite crediting him elsewhere in the film. The identification is correct; the ground truth is missing an entry.</p> <p>X-Ray is a large, convenient ground truth. It is not a complete one. The misID and FPI counts reported throughout this document include some fixed amount of noise from gaps in X-Ray itself, in both directions.</p>"},{"location":"lvface-deep-dive/#summary","title":"Summary","text":"<p>LVFace wins the model comparison on every held-out film. It correctly names 19 of 20 people in a crowded funeral scene and correctly identifies a face displayed on a screen inside the film. Its errors resolve into two mechanisms: extinction bridging, which is correct on most footage and fails specifically at hard cuts into long faceless stretches, and the face-versus-presence ceiling, where X-Ray credits scene membership for people whose faces never appear on screen. Of the nine distinct out-of-cast identifications found across the benchmark, two trace to gaps in X-Ray's own cast data, one is a photograph misread as a person, and six are genuine lookalike confusions on real faces. The held-out generalization gap, 75.3% training to 67.4% held-out, is real and should be treated as the expected operating point, not the training-set figure.</p>"},{"location":"methodology/","title":"How we score against X-Ray","text":"<p>Every number in this report, every F1 and misID count, comes from one comparison. The comparison has a mismatch at its core that shapes nearly every finding in this report: the ground truth is scene-level, the pipeline's output is per-second, and the two do not mean the same thing. This page documents that comparison once, so the findings pages can rely on it without re-explaining it.</p>"},{"location":"methodology/#what-amazon-x-ray-records","title":"What Amazon X-Ray records","text":"<p>X-Ray ships three tables per film: <code>scenes.csv</code> (a list of <code>[start, end]</code> timespans), <code>people_in_scenes.csv</code> (which actors are credited in each scene), and <code>people.csv</code> (actor identities). There is no per-frame or per-second annotation anywhere in X-Ray. A scene might run 45 seconds, and X-Ray records one cast list for the entire span, not \"on screen from second 12 to second 30.\"</p> <p>To compare this against per-second predictions, <code>second_score.py</code> expands every scene into per-second ground truth by copying the whole scene's cast list onto every second inside it:</p> <pre><code>for sn, (t0, t1) in spans.items():\n    cast = scene_cast.get(sn, [])\n    for t in range(int(t0), int(t1)):\n        timeline[t] = cast\n</code></pre> <p>That is the entire mechanism. If X-Ray credits five actors to a 30-second scene, all five count as ground truth present for all 30 seconds, including seconds where only one of them is on screen. This is not a simplification introduced by the pipeline; it is the only reading of X-Ray's data that is possible, because X-Ray itself does not record anything finer-grained.</p>"},{"location":"methodology/#why-an-offscreen-name-can-be-scored-correct","title":"Why an offscreen name can be scored correct","text":"<p>A name listed under Offscreen with a correct (green) label is not the pipeline guessing or padding its score. It is the pipeline correctly answering the question X-Ray actually asks: is this actor part of this scene. It answers that question using a presence window (<code>[start, end]</code>, held open across cuts by <code>anneal_sec</code> and <code>extinction_sec</code>), which matches X-Ray's scene-level semantics more closely than a raw per-frame detection would.</p> <p>A system that only reported \"this actor is visible in this exact frame\" would score worse against X-Ray's scene-level ground truth, producing a false negative every time the camera cuts away from a character who is still present in the scene. Not because it is wrong about the world, but because it would be answering a stricter, different question than the one X-Ray's data supports. The presence-window design exists specifically to answer X-Ray's actual question.</p>"},{"location":"methodology/#what-this-resolves-and-what-it-does-not","title":"What this resolves and what it does not","text":"<p>This resolves the semantic mismatch between a scene and an instant. It does not resolve two other limitations, both discussed in the LVFace deep dive.</p> <p>The face-vs-presence ceiling. X-Ray credits scene membership regardless of whether a face is ever visible: background crew, characters shot from behind, voice-only presence. No amount of bridging recovers a face that never appears on screen. This is a hard ceiling on recall, not a defect.</p> <p>Extinction bridging can overshoot. The same presence-window mechanism that correctly answers \"still in this scene\" during a normal cut can also bridge across a scene boundary it has no way to detect. A hard cut into a different scene with no faces, such as closing credits, carries the previous scene's identities forward until the window expires. This is the mechanism behind Downton Abbey's recall collapse, documented in the deep dive.</p>"},{"location":"methodology/#precision-recall-and-the-misid-weighting","title":"Precision, recall, and the misID weighting","text":"<p>Per sampled second <code>t</code>:</p> <p>TPI (true positive instances): actors both X-Ray and the pipeline agree are present.</p> <p>FPI (false positive instances): actors the pipeline reports that are not in X-Ray's cast for this second. Split into two categories:</p> <ul> <li>FPI_incast: the actor is in the film's cast, just not credited to   this particular scene. A timing or boundary slip.</li> <li>FPI_misid: the actor is not in the film's cast at all. A genuine   wrong-identity error, weighted 10x in the precision objective, because   naming someone who is not even in the film is a categorically worse   error than a few seconds of scene-boundary slop.</li> </ul> <p>Every headline <code>P</code> and <code>F1</code> is misID-weighted</p> <p>The precision reported throughout this report, and therefore the F1 derived from it, puts each <code>FPI_misid</code> into the denominator 10 times (<code>precision = TPI / (TPI + FPI_incast + 10\u00b7FPI_misid)</code>, <code>second_score.py</code>). This is deliberate: the whole point is to punish naming an out-of-film actor far harder than a scene-boundary slip. But it means the <code>P</code> column is not raw precision, and a misID-heavy film's <code>P</code> is depressed super-linearly. <code>second_score.py</code> also emits an unweighted <code>precision_raw</code> (always \u2265 the weighted <code>P</code>); where the gap matters, The Many Saints of Newark, weighted <code>P</code> 54.7% vs. raw 78.4%, the LVFace deep dive reports both. When comparing <code>P</code> across films, remember you are comparing a quantity that penalizes misIDs, not just a hit rate.</p> <p>FN (false negatives): actors X-Ray lists that the pipeline never reports, counted only for actors who have a gallery reference embedding. Across the 9-film benchmark, coverage of X-Ray's credited cast ranges from 20% to 79% by film (see the full experiment log); an actor with no reference photo can never be recognized regardless of model quality, and counting them as a miss would penalize gallery coverage, not recognition accuracy.</p> <p>Two further numbers are reported alongside F1:</p> <p>agreement_rate: mean per-second Jaccard overlap (<code>|Pred \u2229 GT| / |Pred \u222a GT|</code>), partial credit. Naming 2 of 3 present actors scores 2/3, not 0.</p> <p>exact_match_rate: the fraction of sampled seconds where the pipeline's named set exactly equals X-Ray's, no partial credit. Far harsher, and dominated by recall, since any single missed actor zeroes that second.</p>"},{"location":"methodology/#reproduce","title":"Reproduce","text":"<pre><code>python3 scripts/optimizer/second_score.py \\\n    --pred pred.json --xray experiments/xray/.../&lt;xray_dir&gt; \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5\n</code></pre> <p>See also the full experiment log for how <code>pred.json</code> is produced, and the LVFace deep dive for what these mechanisms look like frame by frame.</p>"},{"location":"model-bakeoff/","title":"Full experiment log","text":"<p>This page reports how the pipeline performs across three questions: which embedding model is best, whether restricting the gallery to a film's credited cast helps, and whether promoting confidently identified poses into a per-film gallery annex helps. It also documents the replay architecture that made testing all three questions in one pass practical, and every caveat needed to trust the numbers.</p> <p>Read How we score against X-Ray first for what F1, precision, recall, and misID mean in this report. All numbers below use the per-second metric (<code>scripts/optimizer/second_score.py</code>).</p> <p>r50 (ArcFace w600k-R50) is excluded from the detailed comparison below. Its gallery was built with roughly 30% fewer reference images per actor than the other three models on the identical source photos (10808 vs 15055 total embeddings across the same 2418 actors), which confounds any direct comparison of its scores against the others. It remains in the calibration curve comparison, which does not depend on the training benchmark.</p>"},{"location":"model-bakeoff/#why-replay-makes-this-affordable","title":"Why replay makes this affordable","text":"<p>Decoding video and running face detection, alignment, and embedding is the expensive part of this pipeline. Everything downstream of that (tracking, identity matching, scene aggregation) is cheap. KPN++'s node/network structure means those two stages are separate components connected by typed channels, so the expensive stage can run once per film, cache its output, and the cheap stage can be re-run against that cache as many times as needed with different Config values.</p> <p><code>scene_analyze --dump-embeddings out.h5</code> runs the expensive half once per film and writes per-frame face detections and embeddings to HDF5 (<code>scripts/optimizer/SCHEMA.md</code>). <code>scripts/optimizer/replay.py</code> then re-assembles the real C++ <code>face_tracker</code>, <code>identity_matcher</code>, and <code>scene_tracker</code> nodes into a Python-driven KPN network and replays a film's cached embeddings through them, varying <code>prob_threshold</code>, <code>anneal_sec</code>, <code>extinction_sec</code>, and <code>expand_gallery</code> freely. No GPU inference and no video decode happen during a replay; each one completes in seconds. This is what makes a 512-evaluation differential-evolution search per model, per gallery mode, per expansion setting, tractable, and what made the full held-out validation across three models in this report possible in one session rather than requiring three full re-encodes of the benchmark set.</p> <p><code>optimize.py</code> runs <code>differential_evolution</code> over this replay function as its objective, with DE-level parallelism (multiple candidate configs evaluated concurrently, each spawning its own replay subprocesses) on top of it. The practical ceiling on this machine's GPU was 8 concurrent replay processes; 9 silently degraded every score to 0.0% (well-formed output, wrong numbers, not a crash), so <code>optimize.py</code> was run at <code>REPLAY_WORKERS=4 DE_WORKERS=2</code>.</p>"},{"location":"model-bakeoff/#search-space","title":"Search space","text":"<p><code>popsize=10, maxiter=15</code> per combo (3 parameters, up to 512 evaluations, usually stopping earlier on DE's convergence tolerance). <code>anneal_sec</code>/<code>extinction_sec</code> bounds were widened from 1-30/1-15 to 1-60/1-60 partway through the sweep. r50's 4 combos finished before the widening and used the old, narrower bounds; this is one more reason r50 is excluded from direct comparison here.</p>"},{"location":"model-bakeoff/#training-films-and-held-out-films","title":"Training films and held-out films","text":"<p>9 films have dumped embeddings across all 4 models. 4 were used for optimization:</p> <ul> <li>Caf\u00e9 Society (62-cast)</li> <li>Lord of War (64-cast)</li> <li>Scarface (67-cast)</li> <li>Sound of Metal (14-cast)</li> </ul> <p>5 were held out, never seen by any optimizer run:</p> <ul> <li>Benny &amp; Joon</li> <li>Downton Abbey: A New Era</li> <li>Lovelace</li> <li>The Many Saints of Newark</li> <li>Valerian and the City of a Thousand Planets</li> </ul>"},{"location":"model-bakeoff/#gallery-coverage-per-film","title":"Gallery coverage per film","text":"<p>The gallery has reference embeddings for 2418 actors, but coverage of any given film's credited cast varies widely. This was previously reported as one flat number (67% of X-Ray cast lacking a reference embedding, averaged across the whole benchmark); the per-film breakdown is:</p> film cast credited in gallery coverage Lord of War 64 13 20.3% Scarface 67 15 22.4% The Many Saints of Newark 48 13 27.1% Caf\u00e9 Society 62 17 27.4% Lovelace 42 15 35.7% Valerian and the City of a Thousand Planets 36 13 36.1% Benny &amp; Joon 23 12 52.2% Downton Abbey: A New Era 36 22 61.1% Sound of Metal 14 11 78.6% <p>Two training films (Lord of War, Scarface) have the worst coverage in the set, 20-22%. Their training-set F1 numbers below are partly capped by missing references, not purely by model quality. Downton Abbey has 61% coverage, the second-best in the benchmark, yet the worst held-out recall of any film (39.4%, LVFace). Its recall problem is not primarily a coverage problem; it is the extinction-bridging failure documented in the LVFace deep dive. Reproduce with <code>scripts/docs/gallery_coverage_per_film.py</code>.</p>"},{"location":"model-bakeoff/#training-results-3-models-2-gallery-modes-2-expansion-settings","title":"Training results, 3 models \u00d7 2 gallery modes \u00d7 2 expansion settings","text":"<p>Ranked by F1. misid = FPI_misid, the count of true wrong-actor identifications (naming someone not in the film's cast at all), distinct from FPI, which also includes in-cast timing slips.</p> <p>Each combo's row is its best full-coverage evaluation: the highest-F1 DE evaluation in which all 4 training films replayed without a timeout (see Dropped-film scoring below for why this qualifier is load-bearing and not the same as <code>argmax F1</code> over the raw sweep).</p> combo F1 P R TPI FPI misid FN LVFace-B_Glint360K_restricted_exp 78.3% 91.0% 68.9% 42830 3782 60 19492 LVFace-B_Glint360K_restricted_noexp 76.7% 91.5% 66.2% 41149 3400 59 21173 arcface_w600k_mbf_restricted_exp 76.2% 90.0% 66.2% 64328 7480 0 33234 arcface_r18_restricted_exp 75.5% 87.6% 66.5% 41399 5666 60 20923 LVFace-B_Glint360K_full_exp 75.3% 89.7% 65.4% 47757 3407 232 26966 arcface_w600k_mbf_restricted_noexp 75.0% 91.1% 63.9% 39752 3465 60 22570 arcface_r18_restricted_noexp 73.5% 91.3% 61.7% 38299 3220 60 24023 LVFace-B_Glint360K_full_noexp 72.3% 88.3% 61.8% 40363 3503 244 25850 arcface_w600k_mbf_full_exp 72.0% 87.7% 61.4% 39875 3729 240 26338 arcface_w600k_mbf_full_noexp 71.0% 93.2% 57.9% 41699 2472 56 33024 arcface_r18_full_exp 69.1% 87.6% 57.7% 37342 3119 242 28871 arcface_r18_full_noexp 66.6% 91.3% 53.1% 34314 2362 107 31899 <p></p> <p>The two clearest patterns: every model's best-scoring combo uses the restricted gallery, and LVFace leads within both gallery modes. <code>full_exp</code> (the shipped combination) is the best-scoring option that uses only features the running application currently supports; restriction is not wired into the application yet (see Whole vs. cast-restricted gallery).</p>"},{"location":"model-bakeoff/#a-scoring-bug-worth-recording-dropped-film-evaluations","title":"A scoring bug worth recording: dropped-film evaluations","text":"<p>The numbers above are corrected ones. The raw <code>rep4_best_*.json</code> files, and an earlier version of this table, reported a different <code>arcface_w600k_mbf_full_noexp</code> row: 74.2% F1 at TPI 12645, a third the TPI of every sibling combo. That was not a better config; it was an artifact of how the optimizer aggregates.</p> <p><code>optimize.py</code> builds each candidate's score from only the films whose replay subprocess returned (<code>per_film = [m for m in ex.map(_one, films) if m is not None]</code>), then averages F1/precision/recall and sums TPI/FPI/misID over just those survivors. When a film's replay times out (the sweep ran near the 8-process concurrency ceiling, so this happened intermittently), that film silently drops from both. A candidate whose hardest film timed out is therefore scored on an easier subset, and differential evolution, maximizing that score, will happily converge onto exactly such a candidate. For <code>mbf_full_noexp</code> the reported winner was one of 7 evaluations (out of 512) whose TPI had collapsed to a partial-film subset; its median-coverage evaluations sit around 51686 TPI.</p> <p>The fix here was to re-derive each combo's best row from its DE trajectory (<code>experiments/trajectories/rep4_*.jsonl</code>), keeping only evaluations within 30% of that combo's median TPI (full 4-film coverage) before taking the best F1. This needs no re-running, the honest best configuration was already in the sweep, just not the one <code>argmax F1</code> selected. Three combos moved: <code>mbf_full_noexp</code> 74.2% \u2192 71.0%, <code>LVFace_full_noexp</code> 72.4% \u2192 72.3% (and its misID, 0 \u2192 244, was itself a dropped-film artifact), <code>mbf_restricted_exp</code> 76.5% \u2192 76.2%. The shipped LVFace <code>full_exp</code> winner was unaffected, its reported evaluation already had full coverage (TPI 47757 \u2248 median). <code>experiment_charts.py</code> applies the same <code>clean_best</code> filter, so every figure on this page matches the corrected table. The underlying <code>optimize.py</code> aggregation is also being fixed so a dropped-film evaluation can never be selected as a winner again.</p>"},{"location":"model-bakeoff/#per-film-training-breakdown","title":"Per-film training breakdown","text":"<p>The 75.3% LVFace training figure is a macro average across 4 films, not a uniform result:</p> film LVFace F1 mbf F1 r18 F1 best model Caf\u00e9 Society 68.1% 62.2% 60.1% LVFace Lord of War 75.6% 77.2% 75.6% mbf Scarface 71.5% 68.6% 64.1% LVFace Sound of Metal 78.8% 76.5% 71.6% LVFace <p>LVFace does not win every training film. mbf scores higher on Lord of War (77.2% vs 75.6%). LVFace's own training-film range is 68.1% to 78.8%, a 10.7pp spread, smaller than the 37pp spread seen on held-out films but real. Reproduce with <code>scripts/docs/run_holdout_all_models.py --films training</code>.</p>"},{"location":"model-bakeoff/#held-out-validation-all-3-models","title":"Held-out validation, all 3 models","text":"<p>The training matrix above is training-set fit. Each model's own tuned <code>full_exp</code> config was replayed against the 5 held-out films, scored the same way:</p> film LVFace F1 mbf F1 r18 F1 Benny &amp; Joon 83.0% 78.5% 77.1% Lovelace 77.5% 73.7% 72.2% Valerian and the City of a Thousand Planets 74.1% 70.2% 71.0% Downton Abbey: A New Era 56.2% 55.0% 53.0% The Many Saints of Newark 46.3% 44.5% 42.1% macro average 67.4% 64.4% 63.1% <p>LVFace scores highest on every one of the 5 held-out films; the ranking never flips. Total misIDs across the 5 films: LVFace 1032, mbf 2197, r18 1224. LVFace has less than half mbf's misID count while also scoring higher on every film. This directly confirms the model choice out of sample; it is not inferred from the training numbers alone. See the LVFace deep dive for frame-level detail on where and why LVFace still fails on the two worst films. Reproduce with <code>scripts/docs/run_holdout_all_models.py</code>.</p>"},{"location":"model-bakeoff/#two-effects-in-isolation-gallery-scope-and-pose-expansion","title":"Two effects in isolation: gallery scope and pose expansion","text":"<p>Averaging across the 3 compared models (r50 excluded) isolates each variable from model choice.</p> <p>Gallery scope, averaged over both expansion settings and all 3 models (6 evaluations per row):</p> scope F1 P R total misID full 71.1% 89.6% 59.6% 1121 restricted 75.9% 90.4% 65.6% 299 <p>Restriction improves every metric at once. This is not a precision/recall trade: +4.8pp F1, +6.0pp recall, and roughly a quarter the misIDs. Fewer candidates in the matcher's search space means fewer opportunities for a lookalike false match, and the recall gain shows this does not cost real detections. Restriction is currently an offline optimizer technique, not a runtime feature of the application; see Whole vs. cast-restricted gallery for what building it into the application would require.</p> <p>Pose expansion (promoting a confidently identified track's novel-pose views into a per-film gallery annex, <code>src/gallery/track_gallery.hpp</code>):</p> scope expansion F1 R misID full off 70.0% 57.6% 407 full on 72.1% 61.5% 714 restricted off 75.1% 63.9% 179 restricted on 76.7% 67.2% 120 <p>In restricted mode, expansion is a clean win: +1.6pp F1, +3.3pp recall, misID drops. The annex only competes against the film's own roughly 15-actor cast, so a new pose of a known actor is unlikely to be confused with someone else. In full mode, expansion buys +2.1pp F1 and +3.9pp recall but at a real cost: misID rises from 407 to 714 as the same new-pose view now competes against the full 2418-actor gallery, where a confidently learned pose is more likely to match the wrong person. On the full gallery it is a recall-vs-misID trade, not a free gain. This training-set effect did not reproduce on held-out data; see Does pose expansion help? for the full held-out test and the two methodology bugs caught while checking it.</p>"},{"location":"model-bakeoff/#calibration-curves","title":"Calibration curves","text":"<p>Each gallery carries a fitted Platt sigmoid <code>P(match | sim) = \u03c3(a\u00b7sim + b)</code>, stored directly in the gallery HDF5 (<code>src/gallery/gallery_calibration.hpp</code>). This measures discriminative power independent of whatever <code>prob_threshold</code> a given run used:</p> <p></p> <p>LVFace has the steepest curve (<code>a=17.7</code> vs 15.3-16.2 for the ArcFace variants) and the lowest P=0.5 decision boundary (similarity 0.23 vs 0.27-0.31), separating same-actor from different-actor pairs more confidently at a lower similarity than any ArcFace variant tested, including r50. Generated by <code>scripts/docs/calibration_chart.py</code>.</p>"},{"location":"model-bakeoff/#extinction-and-anneal-window-search","title":"Extinction and anneal window search","text":"<p>Every one of the 512 DE evaluations for the winning LVFace <code>full_exp</code> combo, plotted over the <code>prob_threshold</code> \u00d7 <code>extinction_sec</code> plane:</p> <p></p> <p>Nearly everything scoring well sits at <code>extinction_sec</code> above 50, across a wide range of thresholds. Short extinction windows are uniformly weaker: under a strict threshold, there is no good configuration in that region of the search space. The optimizer converged with <code>anneal_sec=59.2, extinction_sec=59.2</code>, about 99% of the widened 60s bound, which raises an open question not resolved in this round: does performance keep improving past 60s, or does it plateau there. Not chased further this pass.</p>"},{"location":"model-bakeoff/#caveats","title":"Caveats","text":"<ul> <li>r50's 4 combos used the older, narrower search bounds (1-30/1-15 instead   of 1-60/1-60) and are further confounded by its thinner gallery. Excluded   from all comparisons above except calibration.</li> <li>The shipped defaults use <code>full_exp</code> (75.3% training F1), not the   higher-scoring <code>restricted_exp</code> (78.3%), because cast restriction is not   a runtime feature of the application yet.</li> <li><code>expand_gallery</code> is mode-dependent, not a free win. Averaged across models   on the full gallery it trades misIDs for recall (see the pose-expansion   table). For LVFace specifically, though, <code>full_exp</code> beats <code>full_noexp</code> on   every axis at once (F1 75.3 vs 72.3, precision 89.7 vs 88.3, recall 65.4 vs   61.8, misID 232 vs 244), so the shipped <code>full_exp</code> is a clean choice for   this model, not an F1-vs-safety trade. (An earlier version of this page   reported <code>full_noexp</code> at 72.4% with zero misIDs and higher precision, which   made it look like the safer option; that was the dropped-film artifact   described above, not a real property of the config.)</li> <li>Switching the default model is an operational change: any gallery built   from a different model's embeddings must be rebuilt before the new   default takes effect.</li> </ul>"},{"location":"model-bakeoff/#reproduce","title":"Reproduce","text":"<pre><code># 4-film training matrix, all 4 models \u00d7 2 gallery modes \u00d7 2 expansion settings\nbash experiments/run_rep4_subprocess.sh\n\n# single combo\nSAE_EXPAND=1 REPLAY_WORKERS=4 DE_WORKERS=2 python3 scripts/optimizer/optimize.py \\\n    --manifest experiments/manifests/rep4_LVFace-B_Glint360K_full.json \\\n    --gallery experiments/galleries/gallery_LVFace-B_Glint360K.h5 \\\n    --params prob_threshold:0.5:0.999 anneal_sec:1:60 extinction_sec:1:60 \\\n    --popsize 10 --maxiter 15 --trajectory traj.jsonl --out best.json\n\n# held-out validation, all 3 models, 5 films\npython3 scripts/docs/run_holdout_all_models.py --out docs_data/holdout_all_models.json\n\n# per-film training breakdown, all 3 models, 4 films\npython3 scripts/docs/run_holdout_all_models.py --films training --out docs_data/training_per_film.json\n\n# gallery coverage per film\npython3 scripts/docs/gallery_coverage_per_film.py --out docs_data/gallery_coverage_per_film.json\n\n# regenerate this page's charts from experiments/ artifacts\npython3 scripts/docs/experiment_charts.py --out-dir docs/assets/images\n\n# one frame per distinct out-of-cast name across all 9 films (used in the deep dive)\npython3 scripts/docs/first_fpi_frames.py\n</code></pre> <p>See also the session log <code>experiments/SESSION_STATE.md</code>.</p>"},{"location":"pose-expansion/","title":"Pose expansion: does promoting new poses mid-film help?","text":"<p><code>expand_gallery</code> (<code>src/gallery/track_gallery.hpp</code>) promotes a confidently identified track's novel-pose reference views into a per-film, in-memory gallery annex. The idea: once the pipeline is confident about an identity, a pose it has not seen before (turned head, different lighting) becomes an extra reference for recognizing that actor again later in the same film, without touching the baked gallery.</p>"},{"location":"pose-expansion/#training-set-signal","title":"Training-set signal","text":"<p>Averaged across the 3 compared models (r50 excluded), on the 4 films used for optimization. These are the corrected, full-coverage figures, see the dropped-film note in the experiment log for why an earlier version of this table overstated the full-mode misID jump (209 \u2192 864) that was itself partly a truncation artifact:</p> scope expansion F1 R misID full off 70.0% 57.6% 407 full on 72.1% 61.5% 714 restricted off 75.1% 63.9% 179 restricted on 76.7% 67.2% 120 <p>In restricted mode, expansion looks like a clean win: +1.6pp F1, +3.3pp recall, lower misID. In full mode it looks like a recall-for-misID trade: +2.1pp F1, +3.9pp recall, but misID rises from 407 to 714. See the full experiment log for the per-model breakdown. This asymmetry motivated the question below: does turning expansion on change what gets recognized frame by frame, or is the aggregate F1 shift coming from something else.</p>"},{"location":"pose-expansion/#held-out-test","title":"Held-out test","text":"<p>Same model, same tuned config, <code>expand_gallery</code> toggled on vs. off, nothing else changed, full gallery mode, per-second scoring against X-Ray. This isolates expansion from every other variable that differs between the training-set rows above.</p> <p>LVFace-B Glint360K, all 5 held-out films:</p> film F1 (exp) F1 (noexp) TPI delta FN delta Benny &amp; Joon 83.0% 83.0% -2 +2 Downton Abbey: A New Era 56.1% 56.2% -7 +7 Lovelace 77.5% 77.4% +33 -33 The Many Saints of Newark 46.3% 46.3% +2 -2 Valerian and the City of a Thousand Planets 74.1% 74.1% +2 -2 <p>ArcFace R18, Benny &amp; Joon, r18's own tuned config: F1 77.1% for both, TPI and FN identical, FPI differs by 2.</p> <p>Every film, both models tested: F1 differs by 0.1-0.2pp, TPI/FN swings are in the tens out of tens of thousands. This is noise, not a signal. Expansion made no measurable difference to per-second on-screen identification on any held-out film tested.</p>"},{"location":"pose-expansion/#two-methodology-bugs-caught-during-this-check","title":"Two methodology bugs caught during this check","text":"<p>Getting to the table above required catching two wrong turns, both worth recording because they are exactly the kind of error that produces a false positive \"expansion helped\" finding.</p> <ol> <li>Timeout truncation. The first Downton Abbey <code>exp</code> replay was cut off    by a 60-second subprocess timeout at about 76% through the film (5589 of    7368 expected seconds). This silent data loss produced a large,    convincing-looking TPI gap (47938 vs 52032) purely because one run was    missing a quarter of the film. Caught by comparing <code>n_seconds</code> between    runs before trusting any score delta; fixed by re-running with a longer    timeout.</li> <li>Bbox-matching bug. An early per-second raw-annotation diff matched    each <code>exp</code> detection to the first <code>noexp</code> detection with IoU above 0.5,    not the best-overlapping one. With 3 faces close together in frame, this    produced spurious disagreements (for example \"exp says Aidan Quinn,    noexp says Johnny Depp\" at the same second) that vanished once the match    used the best-IoU candidate instead of the first one. Both configs had    actually output the same three names at the same three boxes.</li> </ol> <p>Both bugs independently pointed toward \"expansion is doing something,\" and both were artifacts of the comparison harness, not the pipeline. Before trusting a dramatic before/after diff, check that both runs cover the same seconds and that entities are matched by best overlap, not first found.</p>"},{"location":"pose-expansion/#conclusion","title":"Conclusion","text":"<p>The training-set aggregate effect, particularly the full-mode misID increase, does not reproduce on held-out data. At minimum it is far smaller than the training-set numbers suggested; it may be sampling variation from only 4 training films rather than a generalizable mechanism. Note the same class of harness bug appears twice in this investigation, the timeout truncation in bug #1 above, and the dropped-film aggregation that inflated the raw training-set misID figures. Both make an inert config look consequential; both are reasons to distrust a dramatic training-set delta until it survives on held-out films, which this one did not. This does not mean <code>expand_gallery</code> never does anything: the mechanism is real, and <code>track_gallery.hpp</code>'s promotion logging confirms tracks get confirmed and views get promoted into the annex on every film tested. It means whatever effect expansion has on final per-second identification was too small to detect against 5 held-out films with this scoring method. A cleaner test would need either more held-out films or a metric that can see the annex's direct contribution, such as tagging which reference embedding won each match; neither was in scope for this pass.</p> <p>Do not treat the training-set exp/noexp numbers in the full experiment log as proof that expansion changes real-world behavior in either direction. On the evidence gathered so far, it does not move the needle enough to see.</p>"},{"location":"service-conversion/","title":"Conversion to service \u2014 a native idle-GPU worker","text":"<p>Status: design / proposal. Nothing here is built yet.</p>"},{"location":"service-conversion/#the-idea","title":"The idea","text":"<p>Turn the CLI tools into a turnkey batch worker that uses the machine's idle GPU: it analyses newly-added Jellyfin media when you're not using the computer (screen locked), and stops the instant you come back. It's an overnight job on your own Linux box.</p> <p>No Docker. This runs on your own machine with your own drivers, so a container buys little and costs a lot: GPU passthrough (nvidia-container-toolkit, or <code>/dev/kfd</code>+<code>/dev/dri</code>+<code>video</code> group for ROCm) is the single most fragile part of a containerised setup, and it exists only because of the container. Natively, the GPU just works with the drivers you already have, and the media paths Jellyfin reports are just real paths \u2014 no re-mounting. So we ship a native installer instead of an image builder.</p> <p>Two deliverables:</p> <ol> <li>An installer \u2014 <code>scripts/build_install.py</code>. Detects your distro, ensures the    GPU/build dependencies are present (via <code>dnf</code>/<code>pacman</code>), compiles <code>scene_analyze</code>    for your GPU, and installs the binary + Python glue + two systemd user    units under <code>~/.local</code>.</li> <li>A screen-lock gate \u2014 one of those systemd units watches logind lock/unlock    and starts/stops the worker. Lock \u2192 analyse. Unlock \u2192 stop.</li> </ol>"},{"location":"service-conversion/#what-already-exists-reuse-dont-rebuild","title":"What already exists (reuse, don't rebuild)","text":"<p>The processing loop is already implemented \u2014 this is packaging, building, and lock-gating, not new pipeline logic.</p> Piece Where What it does Analysis engine <code>build/scene_analyze</code> Video \u2192 face detect/align/embed \u2192 gallery match \u2192 result JSON Backend selection <code>CMakeLists.txt</code> (<code>SAE_INFERENCE_BACKEND</code>, <code>SAE_GEMM_BACKEND</code>) ORT/TRT + ROCm/CUDA, chosen at build time New-media queue JRay plugin \u2192 <code>GET /Plugins/JRay/Tasks/Pending</code> Backlog of items with no results yet Worker loop <code>scripts/run_from_jellyfin.py</code><code>--worker</code> Poll Pending \u2192 run <code>scene_analyze</code> \u2192 push results Result push <code>PUT /Plugins/JRay/Items/{id}/Truth</code> Stores per-actor scene windows back in Jellyfin Incremental gallery <code>scripts/make_jellyfin_gallery.py</code><code>--merge</code> Embeds only cast not already in the gallery Secrets loader <code>.env</code> via <code>scripts/sae_env.py</code> <code>JELLYFIN_URL</code>, <code>JELLYFIN_API_KEY</code>, <code>TMDB_API_KEY</code>"},{"location":"service-conversion/#installer-config","title":"Installer config","text":"<p>One file. Build-time settings (fixed when we compile) vs. run-time settings (in the worker's <code>.env</code>, editable without recompiling).</p> <pre><code># install.yaml \u2014 consumed by scripts/build_install.py\n\nplatform: nvidia            # nvidia | amd | cpu \u2192 picks the cmake backend\nmodel:\n  arcface: LVFace-B_Glint360K.onnx   # embedder compiled against; gallery MUST match\nschedule:\n  gallery_scan_interval: 24h         # incremental --merge cadence; 0 disables the scanner\nprefix: ~/.local                     # install root (bin, share, systemd user units)\n\n# runtime (written to the worker .env, not compiled in):\nruntime:\n  jellyfin_url: http://localhost:8096\n  # JELLYFIN_API_KEY / TMDB_API_KEY are filled into .env by hand after install\n</code></pre> <p>Secrets never go in the repo or a build artifact \u2014 the installer writes a <code>.env</code> under the install prefix with blanks for the keys, and you fill them in once. <code>sae_env.py</code> already loads it.</p> <p>Model \u21c4 gallery coupling (guard, don't just document): embeddings from different recognition models aren't interchangeable. We compile against one embedder; the gallery must be built with the same one. Stamp the embedder name into <code>gallery.json</code>, and have the worker refuse to start if the gallery's embedder \u2260 the configured <code>model.arcface</code>, rather than silently mismatching.</p>"},{"location":"service-conversion/#dependencies-via-the-system-package-manager","title":"Dependencies via the system package manager","text":"<p>The heavy build/runtime deps (OpenCV, ffmpeg, the GPU stack) are best provided by the distro, not vendored. The installer ships a per-distro dependency list and either installs them or prints the exact command. Targets: Fedora (dnf) and Arch (pacman) first.</p> Dependency Fedora (dnf) Arch (pacman) OpenCV <code>opencv-devel</code> <code>opencv</code> ffmpeg <code>ffmpeg-free</code>/<code>ffmpeg</code> (RPM Fusion) <code>ffmpeg</code> CMake / toolchain <code>cmake gcc-c++</code> <code>cmake gcc</code> CUDA + TensorRT (nvidia) NVIDIA CUDA repo + <code>libnvinfer-*</code> <code>cuda</code>, <code>tensorrt</code> ROCm (amd) <code>rocm-hip-sdk</code> / <code>rocblas-devel</code> <code>rocm-hip-sdk</code>, <code>rocblas</code> ONNX Runtime not packaged \u2014 installer fetches a pinned release tarball into the prefix AUR <code>onnxruntime</code> (or same pinned-tarball fallback) <p>So the flow is: detect distro \u2192 check each package \u2192 install via the native manager (or print <code>sudo dnf install \u2026</code> / <code>sudo pacman -S \u2026</code>), with ONNX Runtime as the one known gap the installer fills itself (a pinned upstream release extracted under the install prefix, so it doesn't depend on a system package that may not exist). CUDA/ROCm being present is assumed \u2014 you already run a GPU desktop; the installer verifies and points you at the vendor repo if not.</p>"},{"location":"service-conversion/#what-build_installpy-does","title":"What <code>build_install.py</code> does","text":"<pre><code>build_install.py install.yaml\n  \u2502\n  \u251c\u2500 detect distro (dnf vs pacman) and platform from config\n  \u251c\u2500 ensure deps: install via manager, or print the exact command; fetch ONNX Runtime if needed\n  \u251c\u2500 cmake + build scene_analyze with the platform's backend flags:\n  \u2502     nvidia \u2192 -DSAE_INFERENCE_BACKEND=TRT -DSAE_GEMM_BACKEND=CUDA\n  \u2502     amd    \u2192 -DSAE_INFERENCE_BACKEND=ORT -DSAE_GEMM_BACKEND=ROCM\n  \u2502     cpu    \u2192 -DSAE_INFERENCE_BACKEND=ORT  (CPU EP; slow, for smoke tests)\n  \u251c\u2500 install into &lt;prefix&gt;:\n  \u2502     bin/sae-scene-analyze           the compiled binary\n  \u2502     share/sae-worker/               Python glue + a venv (requests, etc.), models/\n  \u2502     share/sae-worker/.env           runtime config (keys blank, url from config)\n  \u251c\u2500 install systemd --user units:\n  \u2502     sae-worker.service              runs the worker + gallery-scan supervisor\n  \u2502     sae-lock-gate.service           watches logind lock/unlock, start/stops the worker\n  \u2514\u2500 print next steps (edit .env, `systemctl --user enable --now sae-lock-gate`)\n</code></pre>"},{"location":"service-conversion/#the-worker-service-supervisor","title":"The worker service (supervisor)","text":"<p><code>sae-worker.service</code> runs a small Python supervisor as its main process:</p> <ul> <li>starts the worker loop (<code>run_from_jellyfin.py --worker</code>) \u2014 the hot path,</li> <li>starts a gallery-scan timer \u2014 sleeps <code>gallery_scan_interval</code>, runs   <code>make_jellyfin_gallery.py --merge</code>, repeats,</li> <li>exits cleanly on SIGTERM (see re-queue below).</li> </ul>"},{"location":"service-conversion/#the-lock-gate","title":"The lock gate","text":"<p><code>sae-lock-gate.service</code> runs a tiny watcher that subscribes to logind lock/unlock signals and drives the worker service:</p> <pre><code>screen locks   \u2192 systemctl --user start sae-worker.service\nscreen unlocks \u2192 systemctl --user stop  sae-worker.service   (SIGTERM)\n</code></pre> <p>Screen-lock is the only signal \u2014 deliberately. We don't also gate on GPU/CPU load, because our own worker is the load: a load threshold would form a feedback loop (worker starts \u2192 GPU spikes \u2192 threshold trips \u2192 worker stops \u2192 load drops \u2192 restart \u2192 \u2026). Lock state is external to what the worker does, so it can't oscillate.</p> <p>Signal source is desktop-dependent: logind <code>Lock</code>/<code>Unlock</code> (GNOME/KDE via <code>loginctl</code>/D-Bus) covers most setups; a <code>swayidle</code>/<code>xss-lock</code> hook is the fallback for wlroots/X-only compositors. The installer picks based on what's present.</p>"},{"location":"service-conversion/#on-resume-hard-stop-re-queue-its-free","title":"On resume: hard stop + re-queue (it's free)","text":"<p>Stopping the worker mid-analysis costs nothing to reschedule, because of how the JRay queue works: an item only leaves <code>/Tasks/Pending</code> once its results are pushed (<code>push_truth</code>). A worker stopped mid-<code>scene_analyze</code> simply leaves that item Pending \u2014 next lock picks it up again. No re-queue bookkeeping.</p> <p>Two small correctness requirements (the only worker changes needed):</p> <ol> <li>Never push a partial result. Already true \u2014 <code>push_truth</code> runs only after    <code>scene_analyze</code> returns; a killed run pushes nothing. \u2713 (keep it that way).</li> <li>Clean up on signal. <code>process_item</code> writes a temp filtered-gallery file and    unlinks it in a <code>finally</code>; a SIGKILL skips <code>finally</code>. Fix: write temps under a    dir the worker wipes on start, and/or a SIGTERM handler that unlinks before    exit. Minor.</li> </ol> <p>Accepted trade-off: a partially-analysed title restarts from scratch next lock. Fine for an overnight/idle workload; no mid-video checkpointing.</p>"},{"location":"service-conversion/#the-end-to-end-ux","title":"The end-to-end UX","text":"<pre><code># once: build + install for your GPU + model\n./scripts/build_install.py install.yaml\n    # detects Fedora/Arch, ensures deps, compiles, installs units under ~/.local\n\n# once: set your keys, enable the gate\n$EDITOR ~/.local/share/sae-worker/.env     # JELLYFIN_API_KEY, TMDB_API_KEY\nsystemctl --user enable --now sae-lock-gate.service\n\n# from then on: nothing. Lock your screen \u2192 it analyses. Unlock \u2192 it stops.\n</code></pre> <p>No Docker, no GPU passthrough config, no media re-mounting \u2014 the worker sees the same filesystem and GPU as everything else on the box.</p>"},{"location":"service-conversion/#implementation-plan-follow-up-commits","title":"Implementation plan (follow-up commits)","text":"<p>Ordered so each step stands alone:</p> <ol> <li>installer skeleton \u2014 <code>scripts/build_install.py</code>: parse <code>install.yaml</code>,    distro detect, dependency check/print (start with cpu platform so it builds    without a GPU), cmake+build, copy into prefix.</li> <li>supervisor + cleanup \u2014 <code>scripts/service.py</code> (worker loop + gallery-scan    timer + SIGTERM); temp-file cleanup fix in <code>run_from_jellyfin.py</code>.</li> <li>systemd units + lock gate \u2014 generate/install <code>sae-worker.service</code>,    <code>sae-lock-gate.service</code>, and the logind lock watcher.</li> <li>gallery/model guard \u2014 stamp embedder into <code>gallery.json</code>; startup mismatch    check.</li> <li>platform + distro matrix \u2014 nvidia/amd backends; dnf/pacman dep lists; ONNX    Runtime fetch fallback.</li> <li>docs \u2014 README \"Run on your idle GPU\" section.</li> </ol>"},{"location":"service-conversion/#settled-decisions","title":"Settled decisions","text":"<ul> <li>ONNX Runtime build \u2014 the installer fetches the ROCm ORT release. It   serves the <code>amd</code> platform, and its CPU execution provider covers the <code>cpu</code>   smoke-test fallback too, so one download handles both. (nvidia uses raw TRT and   doesn't need ORT.)</li> <li><code>dnf</code>/<code>pacman</code> invocation \u2014 auto-install. The installer runs <code>sudo dnf   install \u2026</code> / <code>sudo pacman -S \u2026</code> itself (prompting for sudo), rather than only   printing the command. It still prints what it's about to install first.</li> <li>Distro coverage \u2014 Fedora + Arch only for now. Debian/Ubuntu (<code>apt</code>) is   out of scope.</li> </ul>"},{"location":"service-conversion/#open-questions","title":"Open questions","text":"<p>(none blocking \u2014 the spec above is buildable as-is.)</p>"}]}
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+    <li class="md-tabs__item">
+      <a href="../methodology/" class="md-tabs__link">
+        
+  
+  
+    
+  
+  How We Score Against X-Ray
+
+      </a>
+    </li>
+  
+
+      
+        
+  
+  
+  
+  
     
     
       <li class="md-tabs__item">
@@ -268,26 +287,7 @@
   
     
   
-  Model Bake-off & Re-tune (full log)
-
-      </a>
-    </li>
-  
-
-      
-        
-  
-  
-  
-  
-    <li class="md-tabs__item">
-      <a href="../optimizer-experiments/" class="md-tabs__link">
-        
-  
-  
-    
-  
-  Optimizer Experiments (prior round)
+  Full Experiment Log
 
       </a>
     </li>
@@ -393,6 +393,33 @@
   
   
   
+    <li class="md-nav__item">
+      <a href="../methodology/" class="md-nav__link">
+        
+  
+  
+  <span class="md-ellipsis">
+    
+  
+    How We Score Against X-Ray
+  
+
+    
+  </span>
+  
+  
+
+      </a>
+    </li>
+  
+
+    
+      
+      
+  
+  
+  
+  
     
     
     
@@ -407,10 +434,10 @@
       
         
         
-        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_2" >
+        <input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
         
           
-          <label class="md-nav__link" for="__nav_2" id="__nav_2_label" tabindex="0">
+          <label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
             
   
   
@@ -428,8 +455,8 @@
             <span class="md-nav__icon md-icon"></span>
           </label>
         
-        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_2_label" aria-expanded="false">
-          <label class="md-nav__title" for="__nav_2">
+        <nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
+          <label class="md-nav__title" for="__nav_3">
             <span class="md-nav__icon md-icon"></span>
             
   
@@ -572,34 +599,7 @@
   <span class="md-ellipsis">
     
   
-    Model Bake-off & Re-tune (full log)
-  
-
-    
-  </span>
-  
-  
-
-      </a>
-    </li>
-  
-
-    
-      
-      
-  
-  
-  
-  
-    <li class="md-nav__item">
-      <a href="../optimizer-experiments/" class="md-nav__link">
-        
-  
-  
-  <span class="md-ellipsis">
-    
-  
-    Optimizer Experiments (prior round)
+    Full Experiment Log
   
 
     
@@ -1035,7 +1035,7 @@ lock-gating, not new pipeline logic.</p>
 </tr>
 <tr>
 <td>Backend selection</td>
-<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/CMakeLists.txt"><code>CMakeLists.txt</code></a> (<code>SAE_INFERENCE_BACKEND</code>, <code>SAE_GEMM_BACKEND</code>)</td>
+<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/CMakeLists.txt"><code>CMakeLists.txt</code></a> (<code>SAE_INFERENCE_BACKEND</code>, <code>SAE_GEMM_BACKEND</code>)</td>
 <td>ORT/TRT + ROCm/CUDA, chosen <strong>at build time</strong></td>
 </tr>
 <tr>
@@ -1045,7 +1045,7 @@ lock-gating, not new pipeline logic.</p>
 </tr>
 <tr>
 <td>Worker loop</td>
-<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/run_from_jellyfin.py"><code>scripts/run_from_jellyfin.py</code></a><code>--worker</code></td>
+<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/run_from_jellyfin.py"><code>scripts/run_from_jellyfin.py</code></a><code>--worker</code></td>
 <td>Poll Pending → run <code>scene_analyze</code> → push results</td>
 </tr>
 <tr>
@@ -1055,12 +1055,12 @@ lock-gating, not new pipeline logic.</p>
 </tr>
 <tr>
 <td>Incremental gallery</td>
-<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/make_jellyfin_gallery.py"><code>scripts/make_jellyfin_gallery.py</code></a><code>--merge</code></td>
+<td><a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/make_jellyfin_gallery.py"><code>scripts/make_jellyfin_gallery.py</code></a><code>--merge</code></td>
 <td>Embeds only cast not already in the gallery</td>
 </tr>
 <tr>
 <td>Secrets loader</td>
-<td><code>.env</code> via <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/4b5557974bef8783bacc375c0869e8f589d1b0a3/scripts/sae_env.py"><code>scripts/sae_env.py</code></a></td>
+<td><code>.env</code> via <a href="https://gitea.tourolle.paris/dtourolle/scene-actor-extraction/raw/commit/0bd27470698c45cab21935d636a04512360e1008/scripts/sae_env.py"><code>scripts/sae_env.py</code></a></td>
 <td><code>JELLYFIN_URL</code>, <code>JELLYFIN_API_KEY</code>, <code>TMDB_API_KEY</code></td>
 </tr>
 </tbody>
@@ -1276,7 +1276,7 @@ same filesystem and GPU as everything else on the box.</p>
       <nav class="md-footer__inner md-grid" aria-label="Footer" >
         
           
-          <a href="../optimizer-experiments/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Optimizer Experiments (prior round)">
+          <a href="../model-bakeoff/" class="md-footer__link md-footer__link--prev" aria-label="Previous: Full Experiment Log">
             <div class="md-footer__button md-icon">
               
               <svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M20 11v2H8l5.5 5.5-1.42 1.42L4.16 12l7.92-7.92L13.5 5.5 8 11z"/></svg>
@@ -1286,7 +1286,7 @@ same filesystem and GPU as everything else on the box.</p>
                 Previous
               </span>
               <div class="md-ellipsis">
-                Optimizer Experiments (prior round)
+                Full Experiment Log
               </div>
             </div>
           </a>
diff --git a/sitemap.xml b/sitemap.xml
index cfb1f53..5a8f571 100644
--- a/sitemap.xml
+++ b/sitemap.xml
@@ -2,34 +2,34 @@
 <urlset xmlns="http://www.sitemaps.org/schemas/sitemap/0.9">
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/best-model/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/gallery-scope/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/lvface-deep-dive/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
+    </url>
+    <url>
+         <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/methodology/</loc>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/model-bakeoff/</loc>
-         <lastmod>2026-07-19</lastmod>
-    </url>
-    <url>
-         <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/optimizer-experiments/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/pose-expansion/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
     <url>
          <loc>https://pages.tourolle.paris/dtourolle/scene-actor-extraction/service-conversion/</loc>
-         <lastmod>2026-07-19</lastmod>
+         <lastmod>2026-07-21</lastmod>
     </url>
 </urlset>
\ No newline at end of file
diff --git a/sitemap.xml.gz b/sitemap.xml.gz
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