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PROJECT_README.md

@@ -37,7 +37,7 @@
 
 **Ropedia Xperience-10M Task Suite** turns the public Xperience-10M sample into a readable embodied-AI benchmark surface. It keeps the evidence trail explicit: what is derived from the one public sample episode, what is evaluated on selected 128-episode held-out splits, what is mirrored to Hugging Face, and what still requires gated raw data or new model-specific evaluators.
 
-**Updated:** 2026-06-18.
+**Updated:** 2026-06-21.
 
 **Scope:** one public sample episode for the fully reproducible task suite; selected 128-episode public-safe artifacts for Qwen3-Omni, Cosmos3, metadata baselines, and raw-feature baselines. Raw Xperience-10M MP4/HDF5/RRD files, full Qwen weights, and gated data are not redistributed here.
 
@@ -45,6 +45,7 @@
 
 - [How To Read This Project](#how-to-read-this-project)
 - [At A Glance](#at-a-glance)
+- [Two Evidence Lines](#two-evidence-lines)
 - [Fast Reader Map](#fast-reader-map)
 - [Why This Project Exists](#why-this-project-exists)
 - [Start Here](#start-here)
@@ -97,6 +98,87 @@ The multilingual README files are reader guides. The canonical technical evidenc
   </tbody>
 </table>
 
+## Two Evidence Lines
+
+The public suite is organized around two result lines. Keep them separate when
+reading metrics.
+
+<table>
+  <thead>
+    <tr>
+      <th width="20%">Line</th>
+      <th width="26%">Data unit</th>
+      <th width="24%">Methods</th>
+      <th>Primary use</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1 sample episode</strong></td>
+      <td>One public Xperience-10M sample episode: 5,821 frames, 1,161 aligned 20-frame windows, 8,546 feature dimensions.</td>
+      <td>Minimal heads and Neural MLP heads on all 20 tasks: 40/40 scored method-task records.</td>
+      <td>Inspect raw sample files, understand task definitions, rerun local baselines, and debug whether each task is well-posed.</td>
+    </tr>
+    <tr>
+      <td><strong>128 selected episodes</strong></td>
+      <td>Selected held-out 96/16/16 split: 34,269 exported windows with public-safe processed features linked to official gated episode paths.</td>
+      <td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano: 140/140 scored 128-line records.</td>
+      <td>Compare same-split baselines and model branches; use proxy flags where the public export lacks a direct raw target.</td>
+    </tr>
+  </tbody>
+</table>
+
+### Result Ledger
+
+<table>
+  <thead>
+    <tr>
+      <th width="20%">Line</th>
+      <th width="14%">Methods</th>
+      <th width="14%">Tasks</th>
+      <th width="18%">Scored records</th>
+      <th width="16%">Direct scores</th>
+      <th>Proxy scores</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1 sample episode</strong></td>
+      <td>2</td>
+      <td>20</td>
+      <td>40/40</td>
+      <td>40</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <td><strong>128 selected episodes</strong></td>
+      <td>7</td>
+      <td>20</td>
+      <td>140/140</td>
+      <td>134</td>
+      <td>6 compact-proxy scores, each source-linked and reasoned.</td>
+    </tr>
+    <tr>
+      <td><strong>Total public matrix</strong></td>
+      <td>9</td>
+      <td>20</td>
+      <td>180/180</td>
+      <td>174</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+
+Result entry points:
+[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md),
+[`two_evidence_lines.json`](docs/data/two_evidence_lines.json),
+[`TWO_EVIDENCE_LINE_RESULT_SUMMARY.md`](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
+[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json),
+[`single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json),
+[`episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json),
+[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), and
+[`xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json).
+
 ## Fast Reader Map
 
 <table>
@@ -126,7 +208,7 @@ The multilingual README files are reader guides. The canonical technical evidenc
     <tr>
       <td><strong>Compare results</strong></td>
       <td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
-      <td><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">gap audit</a></td>
+      <td><a href="docs/data/two_evidence_line_result_summary.json">two-line result summary</a><br><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">score/proxy audit</a></td>
     </tr>
     <tr>
       <td><strong>Understand one sample</strong></td>
@@ -614,7 +696,7 @@ task metrics. The machine-readable copies are
 [`docs/data/unified_task_model_radar.json`](docs/data/unified_task_model_radar.json)
 and
 [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json);
-the explicit score-gap ledger is
+the explicit score/proxy ledger is
 [`docs/data/task_method_20_gap_audit.json`](docs/data/task_method_20_gap_audit.json)
 and [`TASK_METHOD_20_GAP_AUDIT.md`](TASK_METHOD_20_GAP_AUDIT.md);
 the reader-facing matrix is

README.de.md

@@ -46,10 +46,10 @@ Dieses Repository macht aus dem öffentlichen Xperience-10M-Sample eine prüfbar
 
 | Linie | Dateneinheit | Methoden und Ergebnisse | Zweck |
 | --- | --- | --- | --- |
-| 1 Sample-Episode | 5,821 Frames; 1,161 ausgerichtete 20-Frame-Fenster; 8,546 Dimensionen. | Minimal + Neural MLP auf 20 Aufgaben; 40/40 gescorte Einträge. | Sample-Dateien, Aufgaben, reproduzierbare Baselines und Aufgabenqualität prüfen. |
-| 128 ausgewählte Episoden | 96/16/16 Split; 34,269 exportierte Fenster; public-safe Features mit offiziellen gated Episode-Pfaden. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super und Cosmos3-Nano; 140/140 gescorte Einträge. | Baselines und Modellzweige auf demselben Split vergleichen; Proxy-Targets bleiben sichtbar. |
+| 1 Sample-Episode | 5,821 Frames; 1,161 ausgerichtete 20-Frame-Fenster; 8,546 Dimensionen. | Minimal + Neural MLP auf 20 Aufgaben; 40/40 gescorte Einträge; alle sind direct scores. | Sample-Dateien, Aufgaben, reproduzierbare Baselines und Aufgabenqualität prüfen. |
+| 128 ausgewählte Episoden | 96/16/16 Split; 34,269 exportierte Fenster; public-safe Features mit offiziellen gated Episode-Pfaden. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super und Cosmos3-Nano; 140/140 gescorte Einträge; 134 direct + 6 compact proxy. | Baselines und Modellzweige auf demselben Split vergleichen; Proxy-Targets bleiben sichtbar. |
 
-Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
+Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
 
 ## Schneller Einstieg
 
@@ -67,7 +67,7 @@ Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
 
 - Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
 - Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
-- Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos; die öffentliche Matrix steht bei 180/180 gescorten Einträgen mit sichtbaren Proxy-Targets.
+- Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos; die öffentliche Matrix steht bei 180/180 gescorten Einträgen: 174 direct und 6 compact proxy, mit sichtbaren Proxy-Targets.
 - Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert.
 
 ## Öffentliche Grenze

README.es.md

@@ -46,10 +46,10 @@ Este repositorio convierte el episodio público de muestra de Xperience-10M en u
 
 | Línea | Unidad de datos | Métodos y resultados | Uso |
 | --- | --- | --- | --- |
-| 1 episodio de muestra | 5,821 frames; 1,161 ventanas alineadas de 20 frames; 8,546 dimensiones. | Minimal + Neural MLP en 20 tareas; 40/40 registros con score. | Inspeccionar archivos de muestra, definiciones de tarea, baselines reproducibles y validez de tareas. |
-| 128 episodios seleccionados | Split 96/16/16; 34,269 ventanas exportadas; features public-safe ligadas a episode paths oficiales gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super y Cosmos3-Nano; 140/140 registros con score. | Comparar baselines y ramas de modelo en el mismo split; los proxy targets permanecen visibles. |
+| 1 episodio de muestra | 5,821 frames; 1,161 ventanas alineadas de 20 frames; 8,546 dimensiones. | Minimal + Neural MLP en 20 tareas; 40/40 registros con score; todos son direct scores. | Inspeccionar archivos de muestra, definiciones de tarea, baselines reproducibles y validez de tareas. |
+| 128 episodios seleccionados | Split 96/16/16; 34,269 ventanas exportadas; features public-safe ligadas a episode paths oficiales gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super y Cosmos3-Nano; 140/140 registros con score; 134 direct + 6 compact proxy. | Comparar baselines y ramas de modelo en el mismo split; los proxy targets permanecen visibles. |
 
-Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
+Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
 
 ## Ruta Rápida
 
@@ -67,7 +67,7 @@ Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
 
 - Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
 - Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
-- Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos; la matriz pública está en 180/180 registros con score y mantiene visibles los proxy targets.
+- Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos; la matriz pública está en 180/180 registros con score: 174 direct y 6 compact proxy, con proxy targets visibles.
 - Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia.
 
 ## Límite Público

README.fr.md

@@ -46,10 +46,10 @@ Ce dépôt transforme l'épisode public d'exemple Xperience-10M en laboratoire d
 
 | Ligne | Unité de données | Méthodes et résultats | Usage |
 | --- | --- | --- | --- |
-| 1 épisode d'exemple | 5,821 frames; 1,161 fenêtres alignées de 20 frames; 8,546 dimensions. | Minimal + Neural MLP sur 20 tâches; 40/40 enregistrements scorés. | Inspecter les fichiers sample, les définitions de tâches, les baselines reproductibles et la validité des tâches. |
-| 128 épisodes sélectionnés | Split 96/16/16; 34,269 fenêtres exportées; features public-safe liées aux chemins gated officiels. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super et Cosmos3-Nano; 140/140 enregistrements scorés. | Comparer les baselines et branches de modèles sur le même split; les proxy targets restent visibles. |
+| 1 épisode d'exemple | 5,821 frames; 1,161 fenêtres alignées de 20 frames; 8,546 dimensions. | Minimal + Neural MLP sur 20 tâches; 40/40 enregistrements scorés; tous sont des direct scores. | Inspecter les fichiers sample, les définitions de tâches, les baselines reproductibles et la validité des tâches. |
+| 128 épisodes sélectionnés | Split 96/16/16; 34,269 fenêtres exportées; features public-safe liées aux chemins gated officiels. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super et Cosmos3-Nano; 140/140 enregistrements scorés; 134 direct + 6 compact proxy. | Comparer les baselines et branches de modèles sur le même split; les proxy targets restent visibles. |
 
-Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
+Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
 
 ## Parcours Rapide
 
@@ -67,7 +67,7 @@ Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_line
 
 - Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
 - Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
-- Résultats : minimal/NN sur l'épisode public couvrent 20/20; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos; la matrice publique atteint 180/180 enregistrements scorés avec proxy targets visibles.
+- Résultats : minimal/NN sur l'épisode public couvrent 20/20; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
 - Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires.
 
 ## Frontière Publique

README.ja.md

@@ -46,10 +46,10 @@
 
 | ライン | データ単位 | 手法と結果 | 用途 |
 | --- | --- | --- | --- |
-| 1 sample episode | 5,821 frames、1,161 aligned 20-frame windows、8,546 dimensions。 | Minimal + Neural MLP が 20 tasks を覆盖; 40/40 scored records。 | Raw sample files、task definitions、reproducible baselines、task validity を確認。 |
-| 128 selected episodes | 96/16/16 split、34,269 exported windows、public-safe features が official gated episode paths に対応。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano; 140/140 scored records。 | 同一 split の baselines と model branches を比較; proxy targets は明示。 |
+| 1 sample episode | 5,821 frames、1,161 aligned 20-frame windows、8,546 dimensions。 | Minimal + Neural MLP が 20 tasks を覆盖; 40/40 scored records; すべて direct scores。 | Raw sample files、task definitions、reproducible baselines、task validity を確認。 |
+| 128 selected episodes | 96/16/16 split、34,269 exported windows、public-safe features が official gated episode paths に対応。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano; 140/140 scored records; 134 direct + 6 compact proxy。 | 同一 split の baselines と model branches を比較; proxy targets は明示。 |
 
-入口: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)。
+入口: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)、[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json)。
 
 ## クイックルート
 
@@ -67,7 +67,7 @@
 
 - データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
 - タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
-- 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、proxy targets は明示します。
+- 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、174 direct と 6 compact proxy を分離し、proxy targets は明示します。
 - 方向: spatial intelligence、human-video world model、vision-language-action に対して、タスク対応と必要証拠を記録しています。
 
 ## 公開境界

README.ko.md

@@ -46,10 +46,10 @@
 
 | 라인 | 데이터 단위 | 방법과 결과 | 용도 |
 | --- | --- | --- | --- |
-| 1 sample episode | 5,821 frames, 1,161 aligned 20-frame windows, 8,546 dimensions. | Minimal + Neural MLP가 20 tasks 전체를 평가; 40/40 scored records. | Raw sample files, task definitions, reproducible baselines, task validity 확인. |
-| 128 selected episodes | 96/16/16 split, 34,269 exported windows, public-safe features가 official gated episode paths에 연결됨. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored records. | 같은 split에서 baselines와 model branches 비교; proxy targets는 명시 유지. |
+| 1 sample episode | 5,821 frames, 1,161 aligned 20-frame windows, 8,546 dimensions. | Minimal + Neural MLP가 20 tasks 전체를 평가; 40/40 scored records; 모두 direct scores. | Raw sample files, task definitions, reproducible baselines, task validity 확인. |
+| 128 selected episodes | 96/16/16 split, 34,269 exported windows, public-safe features가 official gated episode paths에 연결됨. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored records; 134 direct + 6 compact proxy. | 같은 split에서 baselines와 model branches 비교; proxy targets는 명시 유지. |
 
-입구: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
+입구: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
 
 ## 빠른 경로
 
@@ -67,7 +67,7 @@
 
 - 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
 - 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
-- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 proxy targets를 명시합니다.
+- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 174 direct와 6 compact proxy를 분리하고 proxy targets를 명시합니다.
 - 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
 
 ## 공개 경계

README.pt.md

@@ -46,10 +46,10 @@ Este repositório transforma o episódio público de amostra do Xperience-10M em
 
 | Linha | Unidade de dados | Métodos e resultados | Uso |
 | --- | --- | --- | --- |
-| 1 episódio de amostra | 5,821 frames; 1,161 janelas alinhadas de 20 frames; 8,546 dimensões. | Minimal + Neural MLP em 20 tarefas; 40/40 registros com score. | Inspecionar arquivos da amostra, definições de tarefas, baselines reproduzíveis e validade das tarefas. |
-| 128 episódios selecionados | Split 96/16/16; 34,269 janelas exportadas; features public-safe ligadas aos caminhos oficiais gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super e Cosmos3-Nano; 140/140 registros com score. | Comparar baselines e ramos de modelo no mesmo split; proxy targets permanecem visíveis. |
+| 1 episódio de amostra | 5,821 frames; 1,161 janelas alinhadas de 20 frames; 8,546 dimensões. | Minimal + Neural MLP em 20 tarefas; 40/40 registros com score; todos são direct scores. | Inspecionar arquivos da amostra, definições de tarefas, baselines reproduzíveis e validade das tarefas. |
+| 128 episódios selecionados | Split 96/16/16; 34,269 janelas exportadas; features public-safe ligadas aos caminhos oficiais gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super e Cosmos3-Nano; 140/140 registros com score; 134 direct + 6 compact proxy. | Comparar baselines e ramos de modelo no mesmo split; proxy targets permanecem visíveis. |
 
-Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
+Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
 
 ## Rota Rápida
 
@@ -67,7 +67,7 @@ Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
 
 - Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
 - Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
-- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score e mantém proxy targets visíveis.
+- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score: 174 direct e 6 compact proxy, com proxy targets visíveis.
 - Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
 
 ## Fronteira Pública

README.zh.md

@@ -46,10 +46,10 @@
 
 | 线 | 数据单元 | 方法与结果 | 用途 |
 | --- | --- | --- | --- |
-| 1 sample episode | 5,821 帧；1,161 个 20-frame 对齐窗口；8,546 维特征。 | Minimal + Neural MLP；20 个任务全覆盖；40/40 scored records。 | 检查原始 sample 文件、任务定义、可复现基线和每个任务是否成立。 |
-| 128 selected episodes | 96/16/16 split；34,269 个导出窗口；public-safe 特征链接到官方 gated episode path。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano；140/140 scored records。 | 比较同一 split 上的基线和模型分支；proxy target 会显式标注。 |
+| 1 sample episode | 5,821 帧；1,161 个 20-frame 对齐窗口；8,546 维特征。 | Minimal + Neural MLP；20 个任务全覆盖；40/40 scored records；全部为 direct scores。 | 检查原始 sample 文件、任务定义、可复现基线和每个任务是否成立。 |
+| 128 selected episodes | 96/16/16 split；34,269 个导出窗口；public-safe 特征链接到官方 gated episode path。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano；140/140 scored records；134 direct + 6 compact proxy。 | 比较同一 split 上的基线和模型分支；proxy target 会显式标注。 |
 
-入口：[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)。
+入口：[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)、[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json)。
 
 ## 快速入口
 
@@ -67,7 +67,7 @@
 
 - 数据层：公开 sample episode 被切成 20-frame 窗口，并连接视频、音频、深度、pose/SLAM、mocap、IMU、calibration 和语言标注。
 - 任务层：20 个统一任务覆盖识别、预测、检索、重建、同步、长时预测、action-object 关系和 sensor bridge。
-- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注；当前公开矩阵为 180/180 scored records，proxy target 显式保留。
+- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注；当前公开矩阵为 180/180 scored records，其中 174 direct、6 compact proxy，proxy target 显式保留。
 - 训练方向：spatial intelligence、human-video world model、vision-language-action 三条 pipeline 已经有任务映射和需要的证据清单。
 
 ## 公开边界

TASK_METHOD_20_GAP_AUDIT.md

@@ -1,6 +1,6 @@
 # Task Method 20-Result Completion Audit
 
-Generated: `2026-06-20T20:38:59+00:00`
+Generated: `2026-06-21T07:36:39+00:00`
 
 This audit is the explicit completion ledger for the 9-method x 20-task result
 matrix. The current public matrix is complete at 180/180 scored records while

TWO_EVIDENCE_LINES.md

@@ -5,13 +5,22 @@ The public Xperience-10M task suite has two result lines. Read them separately.
 | Line | Data unit | Methods | Best use |
 | --- | --- | --- | --- |
 | 1 sample episode | One public sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546 feature dimensions. | Minimal heads and Neural MLP heads on all 20 tasks; 40/40 scored method-task records. | Inspect raw files, understand each task, rerun local baselines, and debug task quality. |
-| 128 selected episodes | Selected held-out 96/16/16 split; 34,269 exported windows; public-safe processed features linked to official gated episode paths. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored 128-line records. | Compare same-split baselines and model branches; keep proxy flags visible when direct raw targets are unavailable. |
+| 128 selected episodes | Selected held-out 96/16/16 split; 34,269 exported windows; public-safe processed features linked to official gated episode paths. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored 128-line records: 134 direct + 6 compact-proxy scores. | Compare same-split baselines and model branches; keep proxy flags visible when direct raw targets are unavailable. |
+
+## Result Ledger
+
+| Line | Methods | Tasks | Scored records | Direct scores | Proxy scores |
+| --- | --- | --- | --- | --- | --- |
+| 1 sample episode | 2 | 20 | 40/40 | 40 | 0 |
+| 128 selected episodes | 7 | 20 | 140/140 | 134 | 6 compact-proxy scores |
+| Total public matrix | 9 | 20 | 180/180 | 174 | 6 |
 
 ## Result Files
 
 | Purpose | Artifact |
 | --- | --- |
 | Unified 9-method x 20-task matrix | [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json) |
+| Two-line result summary | [`docs/data/two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json) |
 | 1-episode radar data | [`docs/data/single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json) |
 | 128-episode radar data | [`docs/data/episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json) |
 | 128-episode feature index | [`docs/data/xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json) |

TWO_EVIDENCE_LINE_RESULT_SUMMARY.md

@@ -0,0 +1,40 @@
+# Two Evidence-Line Result Summary
+
+Generated: `2026-06-21T07:36:39+00:00`.
+
+Source matrix: [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)
+
+Interpretation rule: Use the 1-episode line for task construction and reproducibility claims. Use the 128-episode line for held-out comparison and model-branch claims.
+
+## Public Score Totals
+
+- Lines: 2
+- Tasks per method: 20
+- Methods: 9
+- Scored records: 180/180
+- Direct scores: 174
+- Compact-proxy scores: 6
+
+## Line Ledger
+
+| Line | Methods | Tasks | Scored records | Direct scores | Proxy scores | Method families |
+| --- | --- | --- | --- | --- | --- | --- |
+| 1 sample episode | 2 | 20 | 40/40 | 40 | 0 | Minimal, Neural MLP |
+| 128 selected episodes | 7 | 20 | 140/140 | 134 | 6 | 128ep Aligned Simple, 128ep Aligned NN, 128ep Raw Simple, 128ep Raw NN, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, Cosmos3-Nano Future Window |
+
+## Proxy-Scored Cells
+
+| Task | Task label | Method | Metric | Reason |
+| --- | --- | --- | --- | --- |
+| 15 | Interaction Text Prediction | 128ep Raw Simple | macro_f1 | documented compact proxy completion for this raw128 task axis |
+| 15 | Interaction Text Prediction | 128ep Raw NN | macro_f1 | documented compact proxy completion for this raw128 task axis |
+| 19 | Camera-View Synchronization Retrieval | 128ep Aligned Simple | mrr | paired camera-view embeddings are absent from the 128 JSONL/feature export; metadata features retrieve the synchronized same-window depth/audio block as a documented compact synchronization proxy |
+| 19 | Camera-View Synchronization Retrieval | 128ep Aligned NN | mrr | paired camera-view embeddings are absent from the 128 JSONL/feature export; metadata features retrieve the synchronized same-window depth/audio block as a documented compact synchronization proxy |
+| 19 | Camera-View Synchronization Retrieval | 128ep Raw Simple | mrr | documented compact proxy completion for this raw128 task axis |
+| 19 | Camera-View Synchronization Retrieval | 128ep Raw NN | mrr | documented compact proxy completion for this raw128 task axis |
+
+## Reader Policy
+
+- 1 sample episode: Use for task construction, raw-file inspection, local reproducibility, and controlled Minimal-vs-Neural baseline behavior.
+- 128 selected episodes: Use for held-out comparison, metadata/raw-feature baselines, Qwen3/Cosmos branches, and scale-up decisions.
+- Proxy scores: Proxy-scored cells stay numeric only when the source artifact and reason are attached; they should not be read as direct raw-target measurements.

data/mirror_parity.json

@@ -1,9 +1,9 @@
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@@ -60,6 +60,7 @@
     "EVALUATION_PROTOCOL.md": true,
     "TASK_SUITE_20.md": true,
     "TASK_METHOD_20_SOURCE_AUDIT.md": true,
+    "TWO_EVIDENCE_LINE_RESULT_SUMMARY.md": true,
     "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md": true,
     "FIGURE_INDEX.md": true,
     "SOURCE_ALIGNMENT_AUDIT.md": true,
@@ -99,6 +100,8 @@
     "docs/data/rendered_site_check.json": true,
     "docs/data/scope_claims_audit.json": true,
     "docs/data/task_surface_integrity.json": true,
+    "docs/data/two_evidence_lines.json": true,
+    "docs/data/two_evidence_line_result_summary.json": true,
     "docs/data/website_integrity.json": true,
     "docs/data/summary_metrics.json": true,
     "docs/data/task_suite_20.json": true,
@@ -229,8 +232,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1515,
-      "text_file_count": 1254,
+      "file_count": 1518,
+      "text_file_count": 1257,
       "largest_file": {
         "path": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv",
         "bytes": 73057076
@@ -240,8 +243,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 564,
-      "text_file_count": 417,
+      "file_count": 567,
+      "text_file_count": 420,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.jsonl",
         "bytes": 10221085
@@ -251,8 +254,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 4486,
-      "text_file_count": 1274,
+      "file_count": 4489,
+      "text_file_count": 1277,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
         "bytes": 135591061
@@ -262,8 +265,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 5240,
-      "text_file_count": 1444,
+      "file_count": 5244,
+      "text_file_count": 1448,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
         "bytes": 135591061

data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T06:55:35+00:00",
+  "generated_at_utc": "2026-06-21T08:13:59+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T06:55:08+00:00",
+  "generated_at_utc": "2026-06-21T08:14:02+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-21T06:55:00+00:00",
+  "generated_at_utc": "2026-06-21T08:14:17+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_method_20_gap_audit.json

@@ -1,5 +1,5 @@
 {
-  "generated_at_utc": "2026-06-20T20:38:59+00:00",
+  "generated_at_utc": "2026-06-21T07:36:39+00:00",
   "immediate_actions": [
     {
       "artifact": "docs/data/task_method_20_gap_audit.json",

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T06:55:35+00:00",
+  "generated_at_utc": "2026-06-21T08:13:59+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

data/two_evidence_line_result_summary.json

@@ -0,0 +1,255 @@
+{
+  "generated_at_utc": "2026-06-21T07:36:39+00:00",
+  "interpretation_rule": "Use the 1-episode line for task construction and reproducibility claims. Use the 128-episode line for held-out comparison and model-branch claims.",
+  "lines": [
+    {
+      "artifact_entry_points": [
+        "docs/data/single_episode_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
+        "results/episode_task_suite/summary_report.json",
+        "results/episode_task_suite/feature_manifest.json",
+        "docs/single_episode_explorer.html"
+      ],
+      "data_unit": "One public Xperience-10M sample episode",
+      "direct_scored_method_task_count": 40,
+      "id": "single_public_sample_episode",
+      "label": "1 sample episode",
+      "method_count": 2,
+      "method_task_record_count": 40,
+      "methods": [
+        {
+          "direct_scored_task_count": 20,
+          "id": "minimal",
+          "label": "Minimal",
+          "method_detail": "Single-episode simple heads over the public sample split.",
+          "proxy_scored_task_count": 0,
+          "result_record_count": 20,
+          "scope": "1 public sample episode",
+          "scored_task_count": 20,
+          "status_counts": {
+            "scored": 20
+          }
+        },
+        {
+          "direct_scored_task_count": 20,
+          "id": "neural_mlp",
+          "label": "Neural MLP",
+          "method_detail": "Single-episode compact PyTorch MLP heads on the same 20 task contracts.",
+          "proxy_scored_task_count": 0,
+          "result_record_count": 20,
+          "scope": "1 public sample episode",
+          "scored_task_count": 20,
+          "status_counts": {
+            "scored": 20
+          }
+        }
+      ],
+      "primary_use": "Inspect raw files, understand each task, rerun local baselines, and debug task quality.",
+      "proxy_scored_method_task_count": 0,
+      "scored_method_task_count": 40,
+      "task_count": 20
+    },
+    {
+      "artifact_entry_points": [
+        "docs/data/episode128_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
+        "docs/data/xperience10m_128_episode_feature_index.json",
+        "docs/data/omni_model_comparison.json",
+        "docs/data/task_method_20_gap_audit.json"
+      ],
+      "data_unit": "Selected held-out 96/16/16 split with public-safe processed features linked to official gated episode paths",
+      "direct_scored_method_task_count": 134,
+      "id": "selected_128_episode_surface",
+      "label": "128 selected episodes",
+      "method_count": 7,
+      "method_task_record_count": 140,
+      "methods": [
+        {
+          "direct_scored_task_count": 19,
+          "id": "metadata128_simple",
+          "label": "128ep Aligned Simple",
+          "method_detail": "128-episode aligned simple baselines: JSONL metadata/text tasks plus staged sensor-block tasks where the processed target exists.",
+          "proxy_scored_task_count": 1,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, JSONL metadata/text plus staged sensor-block targets where available",
+          "scored_task_count": 20,
+          "status_counts": {
+            "proxy_scored": 1,
+            "scored": 19
+          }
+        },
+        {
+          "direct_scored_task_count": 19,
+          "id": "metadata128_neural_mlp",
+          "label": "128ep Aligned NN",
+          "method_detail": "128-episode aligned MLP baselines: JSONL metadata/text tasks plus staged sensor-block tasks where the processed target exists.",
+          "proxy_scored_task_count": 1,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, JSONL metadata/text plus staged sensor-block targets where available",
+          "scored_task_count": 20,
+          "status_counts": {
+            "proxy_scored": 1,
+            "scored": 19
+          }
+        },
+        {
+          "direct_scored_task_count": 18,
+          "id": "raw128_simple",
+          "label": "128ep Raw Simple",
+          "method_detail": "128-episode 4430-dim sensor NPZ simple heads; tasks 15/19 use compact proxies.",
+          "proxy_scored_task_count": 2,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
+          "scored_task_count": 20,
+          "status_counts": {
+            "proxy_scored": 2,
+            "scored": 18
+          }
+        },
+        {
+          "direct_scored_task_count": 18,
+          "id": "raw128_neural_mlp",
+          "label": "128ep Raw NN",
+          "method_detail": "128-episode 4430-dim sensor NPZ MLP heads; tasks 15/19 use compact proxies.",
+          "proxy_scored_task_count": 2,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
+          "scored_task_count": 20,
+          "status_counts": {
+            "proxy_scored": 2,
+            "scored": 18
+          }
+        },
+        {
+          "direct_scored_task_count": 20,
+          "id": "qwen3_omni_v6_lora",
+          "label": "Qwen3-Omni v6 LoRA",
+          "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
+          "proxy_scored_task_count": 0,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, held-out test",
+          "scored_task_count": 20,
+          "status_counts": {
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+          }
+        },
+        {
+          "direct_scored_task_count": 20,
+          "id": "cosmos3_super_reasoner",
+          "label": "Cosmos3-Super Reasoner",
+          "method_detail": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation, plus task 5/8/9/10/11/12/13/14/16/17/18/19/20 probes where public metrics exist.",
+          "proxy_scored_task_count": 0,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, held-out test",
+          "scored_task_count": 20,
+          "status_counts": {
+            "scored": 20
+          }
+        },
+        {
+          "direct_scored_task_count": 20,
+          "id": "cosmos3_nano_future_window",
+          "label": "Cosmos3-Nano Future Window",
+          "method_detail": "Verified Cosmos3-Nano future-window compatibility metrics, plus model-output probes for tasks 2/5/7/8/10/11/12/13/14/15/16/17/18/19 and a derived task-20 boundary timing probe scored from held-out future-window artifacts.",
+          "proxy_scored_task_count": 0,
+          "result_record_count": 20,
+          "scope": "128 selected episodes, held-out test",
+          "scored_task_count": 20,
+          "status_counts": {
+            "scored": 20
+          }
+        }
+      ],
+      "primary_use": "Compare same-split baselines and model branches while keeping evidence type explicit.",
+      "proxy_scored_method_task_count": 6,
+      "scored_method_task_count": 140,
+      "task_count": 20
+    }
+  ],
+  "proxy_records": [
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw Simple",
+      "metric_key": "macro_f1",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_simple",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/simple_raw128/interaction_text_prediction/metrics.json",
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "task_number": 15
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw NN",
+      "metric_key": "macro_f1",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_neural_mlp",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/neural_mlp_raw128/interaction_text_prediction/metrics.json",
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "task_number": 15
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Aligned Simple",
+      "metric_key": "mrr",
+      "reason": "paired camera-view embeddings are absent from the 128 JSONL/feature export; metadata features retrieve the synchronized same-window depth/audio block as a documented compact synchronization proxy",
+      "series_id": "metadata128_simple",
+      "source": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Aligned NN",
+      "metric_key": "mrr",
+      "reason": "paired camera-view embeddings are absent from the 128 JSONL/feature export; metadata features retrieve the synchronized same-window depth/audio block as a documented compact synchronization proxy",
+      "series_id": "metadata128_neural_mlp",
+      "source": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/neural_mlp/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw Simple",
+      "metric_key": "mrr",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_simple",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/simple_raw128/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw NN",
+      "metric_key": "mrr",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_neural_mlp",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/neural_mlp_raw128/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    }
+  ],
+  "reader_policy": {
+    "proxy_policy": "Proxy-scored cells stay numeric only when the source artifact and reason are attached; they should not be read as direct raw-target measurements.",
+    "selected_128_episode_surface": "Use for held-out comparison, metadata/raw-feature baselines, Qwen3/Cosmos branches, and scale-up decisions.",
+    "single_public_sample_episode": "Use for task construction, raw-file inspection, local reproducibility, and controlled Minimal-vs-Neural baseline behavior."
+  },
+  "source_lines": "docs/data/two_evidence_lines.json",
+  "source_matrix": "docs/data/task_method_20_result_matrix.json",
+  "status": "pass",
+  "summary": {
+    "direct_scored_method_task_count": 174,
+    "line_count": 2,
+    "method_count": 9,
+    "method_task_record_count": 180,
+    "proxy_scored_method_task_count": 6,
+    "scored_method_task_count": 180,
+    "task_count": 20
+  },
+  "title": "Two Evidence-Line Result Summary"
+}

data/two_evidence_lines.json

@@ -18,9 +18,12 @@
       "task_axes": 20,
       "method_task_records": 40,
       "scored_records": 40,
+      "direct_scored_records": 40,
+      "proxy_scored_records": 0,
       "best_use": "Inspect raw files, understand each task, rerun local baselines, and debug task quality.",
       "primary_artifacts": [
         "docs/data/single_episode_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
         "results/episode_task_suite/summary_report.json",
         "results/episode_task_suite/feature_manifest.json",
         "docs/single_episode_explorer.html"
@@ -49,10 +52,13 @@
       "task_axes": 20,
       "method_task_records": 140,
       "scored_records": 140,
+      "direct_scored_records": 134,
+      "proxy_scored_records": 6,
       "proxy_policy": "Proxy flags remain visible where the public export lacks a direct raw target.",
       "best_use": "Compare same-split baselines and model branches while keeping evidence type explicit.",
       "primary_artifacts": [
         "docs/data/episode128_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
         "docs/data/xperience10m_128_episode_feature_index.json",
         "docs/data/omni_model_comparison.json",
         "docs/data/task_method_20_gap_audit.json"
@@ -64,6 +70,9 @@
     "methods": 9,
     "method_task_records": 180,
     "scored_records": 180,
+    "direct_scored_records": 174,
+    "proxy_scored_records": 6,
+    "summary_artifact": "docs/data/two_evidence_line_result_summary.json",
     "artifact": "docs/data/task_method_20_result_matrix.json"
   }
 }

data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T07:14:14+00:00",
+  "generated_at_utc": "2026-06-21T08:12:52+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 235,
+    "local_references": 240,
     "external_reference_count": 152,
-    "json_files": 52,
+    "json_files": 53,
     "image_assets_referenced": 28,
     "failure_count": 0
   },
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 110785,
-      "evidence_index": 150703
+      "overview_index": 112836,
+      "evidence_index": 152914
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 110785,
-      "protocol_index": 146884,
-      "evidence_index": 150703
+      "overview_index": 112836,
+      "protocol_index": 149095,
+      "evidence_index": 152914
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -180,7 +180,7 @@
       "status": "pass",
       "reason": "The Suite anchor should show the task-suite map before the modality atlas.",
       "first_marker_index": 471,
-      "second_marker_index": 3805
+      "second_marker_index": 3762
     },
     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,7 +277,7 @@
     {
       "path": "index.html",
       "id_count": 95,
-      "reference_count": 207,
+      "reference_count": 212,
       "image_count": 34
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     {
@@ -351,7 +351,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1410035,
+      "bytes": 1413010,
       "top_level_type": "dict"
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     {
@@ -401,7 +401,7 @@
     },
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+      "bytes": 10819,
       "top_level_type": "dict"
     },
     {
@@ -529,9 +529,14 @@
       "bytes": 33402,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/two_evidence_line_result_summary.json",
+      "bytes": 11383,
+      "top_level_type": "dict"
+    },
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-      "bytes": 2421,
+      "bytes": 2816,
       "top_level_type": "dict"
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@@ -541,7 +546,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20254,
+      "bytes": 20380,
       "top_level_type": "dict"
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     {

docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T07:17:33+00:00",
+  "generated_at_utc": "2026-06-21T08:16:26+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 1250,
+    "group_count": 1252,
     "failure_count": 0,
     "failures_by_surface": {}
   },
@@ -922,45 +922,45 @@
       "local": {
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
-        "bytes": 10662,
-        "sha256": "7c19e3e13f4c2986404ab855d8459bd4e30378ab2757861952f88a3183fc6fc5"
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+        "sha256": "6d76c7452b7c47e10a8c933c69b8a98402d3fd3db0d44608f73b9b67e4d3f58c"
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       "mirrors": {
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-          "sha256": "7c19e3e13f4c2986404ab855d8459bd4e30378ab2757861952f88a3183fc6fc5"
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-          "bytes": 10662,
-          "sha256": "7c19e3e13f4c2986404ab855d8459bd4e30378ab2757861952f88a3183fc6fc5"
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 {
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+      "metric_key": "macro_f1",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_simple",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/simple_raw128/interaction_text_prediction/metrics.json",
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "task_number": 15
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw NN",
+      "metric_key": "macro_f1",
+      "reason": "documented compact proxy completion for this raw128 task axis",
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+      "task_number": 15
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+      "method": "128ep Aligned Simple",
+      "metric_key": "mrr",
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+      "series_id": "metadata128_simple",
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+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
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+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Aligned NN",
+      "metric_key": "mrr",
+      "reason": "paired camera-view embeddings are absent from the 128 JSONL/feature export; metadata features retrieve the synchronized same-window depth/audio block as a documented compact synchronization proxy",
+      "series_id": "metadata128_neural_mlp",
+      "source": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/neural_mlp/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw Simple",
+      "metric_key": "mrr",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_simple",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/simple_raw128/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    },
+    {
+      "line_id": "selected_128_episode_surface",
+      "method": "128ep Raw NN",
+      "metric_key": "mrr",
+      "reason": "documented compact proxy completion for this raw128 task axis",
+      "series_id": "raw128_neural_mlp",
+      "source": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/neural_mlp_raw128/camera_view_sync_retrieval/metrics.json",
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "task_number": 19
+    }
+  ],
+  "reader_policy": {
+    "proxy_policy": "Proxy-scored cells stay numeric only when the source artifact and reason are attached; they should not be read as direct raw-target measurements.",
+    "selected_128_episode_surface": "Use for held-out comparison, metadata/raw-feature baselines, Qwen3/Cosmos branches, and scale-up decisions.",
+    "single_public_sample_episode": "Use for task construction, raw-file inspection, local reproducibility, and controlled Minimal-vs-Neural baseline behavior."
+  },
+  "source_lines": "docs/data/two_evidence_lines.json",
+  "source_matrix": "docs/data/task_method_20_result_matrix.json",
+  "status": "pass",
+  "summary": {
+    "direct_scored_method_task_count": 174,
+    "line_count": 2,
+    "method_count": 9,
+    "method_task_record_count": 180,
+    "proxy_scored_method_task_count": 6,
+    "scored_method_task_count": 180,
+    "task_count": 20
+  },
+  "title": "Two Evidence-Line Result Summary"
+}

docs/data/two_evidence_lines.json

@@ -18,9 +18,12 @@
       "task_axes": 20,
       "method_task_records": 40,
       "scored_records": 40,
+      "direct_scored_records": 40,
+      "proxy_scored_records": 0,
       "best_use": "Inspect raw files, understand each task, rerun local baselines, and debug task quality.",
       "primary_artifacts": [
         "docs/data/single_episode_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
         "results/episode_task_suite/summary_report.json",
         "results/episode_task_suite/feature_manifest.json",
         "docs/single_episode_explorer.html"
@@ -49,10 +52,13 @@
       "task_axes": 20,
       "method_task_records": 140,
       "scored_records": 140,
+      "direct_scored_records": 134,
+      "proxy_scored_records": 6,
       "proxy_policy": "Proxy flags remain visible where the public export lacks a direct raw target.",
       "best_use": "Compare same-split baselines and model branches while keeping evidence type explicit.",
       "primary_artifacts": [
         "docs/data/episode128_task_model_radar.json",
+        "docs/data/two_evidence_line_result_summary.json",
         "docs/data/xperience10m_128_episode_feature_index.json",
         "docs/data/omni_model_comparison.json",
         "docs/data/task_method_20_gap_audit.json"
@@ -64,6 +70,9 @@
     "methods": 9,
     "method_task_records": 180,
     "scored_records": 180,
+    "direct_scored_records": 174,
+    "proxy_scored_records": 6,
+    "summary_artifact": "docs/data/two_evidence_line_result_summary.json",
     "artifact": "docs/data/task_method_20_result_matrix.json"
   }
 }

docs/data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-21T07:14:14+00:00",
+  "generated_at_utc": "2026-06-21T08:12:52+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 235,
+    "local_references": 240,
     "external_reference_count": 152,
-    "json_files": 52,
+    "json_files": 53,
     "image_assets_referenced": 28,
     "failure_count": 0
   },
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 110785,
-      "evidence_index": 150703
+      "overview_index": 112836,
+      "evidence_index": 152914
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 110785,
-      "protocol_index": 146884,
-      "evidence_index": 150703
+      "overview_index": 112836,
+      "protocol_index": 149095,
+      "evidence_index": 152914
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -180,7 +180,7 @@
       "status": "pass",
       "reason": "The Suite anchor should show the task-suite map before the modality atlas.",
       "first_marker_index": 471,
-      "second_marker_index": 3805
+      "second_marker_index": 3762
     },
     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,7 +277,7 @@
     {
       "path": "index.html",
       "id_count": 95,
-      "reference_count": 207,
+      "reference_count": 212,
       "image_count": 34
     },
     {
@@ -351,7 +351,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1410035,
+      "bytes": 1413010,
       "top_level_type": "dict"
     },
     {
@@ -401,7 +401,7 @@
     },
     {
       "path": "data/publication_audit.json",
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+      "bytes": 10819,
       "top_level_type": "dict"
     },
     {
@@ -529,9 +529,14 @@
       "bytes": 33402,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/two_evidence_line_result_summary.json",
+      "bytes": 11383,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/two_evidence_lines.json",
-      "bytes": 2421,
+      "bytes": 2816,
       "top_level_type": "dict"
     },
     {
@@ -541,7 +546,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20254,
+      "bytes": 20380,
       "top_level_type": "dict"
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docs/index.html

@@ -189,44 +189,69 @@
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       outline-offset: 3px;
     }
-    .nav-action {
-      border: 1px solid rgba(204, 255, 160, 0.46);
+    .nav-external-actions {
+      display: inline-flex;
+      align-items: center;
+      gap: 4px;
+      padding: 4px;
+      border: 1px solid rgba(204, 255, 160, 0.24);
+      border-radius: 999px;
       background:
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-        rgba(6, 16, 6, 0.86);
-      color: #f4f8ef;
-      height: 42px;
-      min-width: 82px;
-      padding: 0 14px;
+        linear-gradient(180deg, rgba(255, 255, 255, 0.055), rgba(255, 255, 255, 0.025)),
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+      box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.018), 0 14px 38px rgba(0, 0, 0, 0.18);
+      flex: 0 0 auto;
+      white-space: nowrap;
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+    .nav-action {
+      border: 0;
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+      color: #dfeadc;
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+      padding: 0 11px 0 6px;
       display: inline-flex;
       align-items: center;
       justify-content: center;
+      gap: 7px;
       text-decoration: none;
       font-family: var(--font-btn);
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       line-height: 1;
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       border-radius: 999px;
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     .nav-action::before {
-      content: none;
+      content: attr(data-mark);
+      width: 24px;
+      height: 24px;
+      display: inline-flex;
+      align-items: center;
+      justify-content: center;
+      border: 1px solid rgba(204, 255, 160, 0.28);
+      border-radius: 999px;
+      background: rgba(204, 255, 160, 0.08);
+      color: var(--green);
+      font-size: 9.5px;
+      font-weight: 800;
+      line-height: 1;
+      letter-spacing: 0;
+      flex: 0 0 auto;
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     .nav-action-hf {
-      min-width: 74px;
+      min-width: 76px;
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     .nav-action-repo {
-      min-width: 88px;
-      background: var(--green);
-      border-color: rgba(204, 255, 160, 0.92);
-      color: #020502;
-      box-shadow: 0 10px 30px rgba(204, 255, 160, 0.12);
+      min-width: 86px;
+      background: rgba(204, 255, 160, 0.14);
+      color: var(--green);
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     .nav-action-repo::before {
-      background: rgba(2, 5, 2, 0.10);
-      border-color: rgba(2, 5, 2, 0.18);
+      background: var(--green);
+      border-color: var(--green);
       color: #020502;
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     .nav-action-text-short {
@@ -234,28 +259,14 @@
     }
     .nav-action:hover {
       transform: translateY(-1px);
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-      background:
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-        rgba(6, 16, 6, 0.88);
+      background: rgba(204, 255, 160, 0.11);
       color: var(--green);
-      box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.04), 0 12px 32px rgba(204, 255, 160, 0.08);
+      box-shadow: inset 0 0 0 1px rgba(204, 255, 160, 0.08);
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     .nav-action-repo:hover {
-      background: #eaffd5;
-      border-color: #eaffd5;
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-    .nav-links a.nav-action {
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       background: var(--green);
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-    .nav-links a.nav-action:hover {
-      border-color: var(--green);
-      background: #f4f8ef;
-      color: #020502;
-    }
     .nav-tools {
       display: flex;
       align-items: center;
@@ -285,6 +296,12 @@
       flex: 0 1 auto;
       max-width: 128px;
       overflow: hidden;
+      position: relative;
+    }
+    .site-language::before {
+      content: attr(data-short);
+      display: none;
+      pointer-events: none;
     }
     .site-language label {
       position: absolute;
@@ -3233,16 +3250,20 @@
       .site-language select {
         max-width: 92px;
       }
+      .nav-external-actions {
+        gap: 3px;
+        padding: 3px;
+      }
       .nav-action {
-        height: 42px;
+        height: 34px;
         min-width: 58px;
-        padding: 0 12px;
+        padding: 0 10px 0 5px;
       }
       .nav-action-hf {
-        min-width: 48px;
+        min-width: 56px;
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       .nav-action-repo {
-        min-width: 64px;
+        min-width: 68px;
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       .nav-action-text-full {
         display: none;
@@ -3316,10 +3337,16 @@
       .project-tabs-shell .wrap {
         width: min(100% - 28px, var(--max));
       }
+      .nav-inner {
+        position: relative;
+      }
       .brand {
         font-size: 16px;
         gap: 9px;
       }
+      .brand span {
+        display: none;
+      }
       .brand-logo {
         width: 38px;
         height: 38px;
@@ -3328,25 +3355,72 @@
         min-height: 38px;
         padding: 0 8px;
         font-size: 12px;
+        width: 58px;
+        max-width: 58px;
+        justify-content: center;
+        position: absolute;
+        left: 146px;
+        right: auto;
+        top: 50%;
+        transform: translateY(-50%);
+      }
+      .site-language::before {
+        display: block;
+        position: absolute;
+        left: 11px;
+        top: 50%;
+        transform: translateY(-50%);
+        color: var(--ink);
+        font-weight: 780;
+        line-height: 1;
       }
       .site-language select {
-        max-width: 84px;
+        width: 100%;
+        max-width: none;
+        color: transparent;
+        padding-right: 17px;
+        background-position:
+          calc(100% - 9px) 52%,
+          calc(100% - 4px) 52%;
       }
       .nav-tools {
         gap: 6px;
         padding-left: 0;
         border-left: 0;
+        flex: 0 0 auto;
+        width: 0;
+        justify-content: flex-end;
+        overflow: visible;
+        position: static;
+      }
+      .nav-external-actions {
+        padding: 3px;
+        flex: 0 0 auto;
+        position: absolute;
+        left: 58px;
+        right: auto;
+        top: 50%;
+        transform: translateY(-50%);
       }
       .nav-action {
-        min-width: 42px;
-        height: 38px;
-        padding: 0 9px;
+        min-width: 28px;
+        height: 32px;
+        padding: 0 4px;
         font-size: 11.5px;
+        gap: 5px;
+      }
+      .nav-action::before {
+        width: 22px;
+        height: 22px;
+        font-size: 9px;
       }
       .nav-action-repo {
         display: inline-flex;
-        min-width: 52px;
-        padding: 0 10px;
+        min-width: 28px;
+        padding: 0 4px;
+      }
+      .nav-action-text-short {
+        display: none;
       }
       .project-tabs-shell { top: var(--nav-height); padding: 10px 0; }
       .project-tabs {
@@ -3502,26 +3576,44 @@
       }
     }
     @media (max-width: 460px) {
-      .brand span {
-        max-width: 138px;
+      .nav-inner {
+        gap: 7px;
+      }
+      .brand {
+        gap: 7px;
       }
       .site-language {
-        max-width: 82px;
-        padding: 0 7px;
+        width: 54px;
+        max-width: 54px;
+        padding: 0 6px;
       }
       .site-language select {
-        max-width: 68px;
+        max-width: none;
+        padding-right: 17px;
       }
       .nav-tools {
-        gap: 5px;
+        gap: 4px;
+      }
+      .nav-external-actions {
+        gap: 2px;
+        padding: 2px;
       }
       .nav-action {
-        min-width: 38px;
-        padding: 0 7px;
+        min-width: 24px;
+        height: 30px;
+        padding: 0 3px;
+      }
+      .nav-action::before {
+        width: 20px;
+        height: 20px;
+        font-size: 8.5px;
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+      .nav-action-text-short {
+        display: none;
       }
       .nav-action-repo {
-        min-width: 46px;
-        padding: 0 8px;
+        min-width: 24px;
+        padding: 0 3px;
       }
     }
     @media (prefers-reduced-motion: reduce) {
@@ -3557,7 +3649,7 @@
         <a class="nav-optional" href="#artifacts">Files</a>
       </div>
       <div class="nav-tools">
-        <div class="site-language notranslate" translate="no">
+        <div class="site-language notranslate" translate="no" data-short="EN">
           <label for="siteLanguage">Language</label>
           <select id="siteLanguage" aria-label="Translate this website">
             <option value="en">English</option>
@@ -3571,14 +3663,16 @@
           </select>
           <span id="siteLanguageStatus" class="site-language-status" aria-live="polite"></span>
         </div>
-        <a class="nav-action nav-action-hf" data-mark="HF" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite" aria-label="Open Hugging Face Space">
-          <span class="nav-action-text-full">HF Space</span>
-          <span class="nav-action-text-short">HF</span>
-        </a>
-        <a class="nav-action nav-action-repo" data-mark="GH" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite" aria-label="Open GitHub repository">
-          <span class="nav-action-text-full">GitHub</span>
-          <span class="nav-action-text-short">Repo</span>
-        </a>
+        <div class="nav-external-actions" aria-label="External project links">
+          <a class="nav-action nav-action-hf" data-mark="HF" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite" aria-label="Open Hugging Face Space">
+            <span class="nav-action-text-full">HF Space</span>
+            <span class="nav-action-text-short">HF</span>
+          </a>
+          <a class="nav-action nav-action-repo" data-mark="GH" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite" aria-label="Open GitHub repository">
+            <span class="nav-action-text-full">GitHub</span>
+            <span class="nav-action-text-short">Repo</span>
+          </a>
+        </div>
       </div>
       <div id="google_translate_element" aria-hidden="true"></div>
     </div>
@@ -3695,7 +3789,7 @@
               <a href="assets/charts/episode128_task_model_radar.svg">128ep radar</a>
               <a href="data/unified_task_model_radar.json">Open radar JSON</a>
               <a href="data/task_method_20_result_matrix.json">Open 20-result matrix</a>
-              <a href="data/task_method_20_gap_audit.json">Open gap audit</a>
+              <a href="data/task_method_20_gap_audit.json">Open score/proxy audit</a>
             </div>
           </div>
         </div>
@@ -3761,14 +3855,14 @@
               <td>One public Xperience-10M sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546-dimensional feature contract.</td>
               <td>Minimal heads and Neural MLP heads. Both cover all 20 task contracts, for 40/40 scored method-task records.</td>
               <td>Raw sample inspection, file organization, task definitions, local reproducibility, and controlled baseline behavior.</td>
-              <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar JSON</a><br><a href="data/two_evidence_lines.json">line JSON</a></td>
+              <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="data/two_evidence_lines.json">line JSON</a></td>
             </tr>
             <tr>
               <td>128 selected episodes</td>
               <td>Selected held-out 96/16/16 split; 34,269 exported windows; public-safe metadata/raw-feature artifacts linked to official gated episode paths.</td>
               <td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano. Together they form 140/140 scored 128-line method-task records.</td>
               <td>Same-split comparison, model-branch diagnostics, Qwen/Cosmos evidence, and the next scale-up decisions.</td>
-              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
             </tr>
           </tbody>
         </table>
@@ -4590,8 +4684,8 @@
             <p>Higher-is-better metrics are plotted directly on 0-1 axes. Lower-is-better metrics are converted to best/value within the task, while raw values, status reasons, sources, and the two raw128 compact proxy notes remain in the JSON mirrors.</p>
           </article>
           <article class="figure-brief-card">
-            <h3>Score gap audit</h3>
-            <p>The matrix has 180 method-task records and 180 numeric scores. The gap audit remains published as the evidence ledger for which artifacts support each score, including documented proxy axes where raw targets are absent.</p>
+            <h3>Score/proxy audit</h3>
+            <p>The matrix has 180 method-task records and 180 numeric scores. The audit records which artifacts support each score and marks compact-proxy axes where raw targets are absent.</p>
           </article>
         </div>
         <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v6" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, scored-axis counts, and proxy notes">
@@ -4707,7 +4801,7 @@
           <article class="suite-line-card">
             <small>1 episode results</small>
             <h3>Task-lab evidence</h3>
-            <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. This is the cleanest place to inspect whether each task is well-posed.</p>
+            <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. All 40 scores are direct task-target metrics.</p>
             <div class="suite-line-facts">
               <span><strong>2</strong>methods</span>
               <span><strong>20</strong>task axes</span>
@@ -4718,7 +4812,7 @@
           <article class="suite-line-card">
             <small>128 episode results</small>
             <h3>Scale-up evidence</h3>
-            <p>Metadata/raw baselines and Qwen/Cosmos branches use the aligned 128-episode surface. Compact proxy scores remain marked where a direct raw target is absent.</p>
+            <p>Metadata/raw baselines and Qwen/Cosmos branches use the aligned 128-episode surface. It has 134 direct scores plus 6 compact-proxy scores.</p>
             <div class="suite-line-facts">
               <span><strong>7</strong>methods</span>
               <span><strong>20</strong>task axes</span>
@@ -4727,6 +4821,48 @@
             <a href="assets/charts/episode128_task_model_radar.svg">Open 128-episode radar</a>
           </article>
         </div>
+        <table class="line-table" aria-label="Direct and proxy score ledger by evidence line">
+          <thead>
+            <tr>
+              <th>Line</th>
+              <th>Methods</th>
+              <th>Tasks</th>
+              <th>Scored records</th>
+              <th>Direct scores</th>
+              <th>Proxy scores</th>
+              <th>Machine-readable source</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>1 sample episode</td>
+              <td>2</td>
+              <td>20</td>
+              <td>40/40</td>
+              <td>40</td>
+              <td>0</td>
+              <td><a href="data/single_episode_task_model_radar.json">single-episode radar JSON</a></td>
+            </tr>
+            <tr>
+              <td>128 selected episodes</td>
+              <td>7</td>
+              <td>20</td>
+              <td>140/140</td>
+              <td>134</td>
+              <td>6 compact-proxy scores, each source-linked and reasoned.</td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a></td>
+            </tr>
+            <tr>
+              <td>Total public matrix</td>
+              <td>9</td>
+              <td>20</td>
+              <td>180/180</td>
+              <td>174</td>
+              <td>6</td>
+              <td><a href="data/two_evidence_line_result_summary.json">two-line result summary JSON</a></td>
+            </tr>
+          </tbody>
+        </table>
         <div class="artifact-grid">
           <article class="artifact primary-artifact">
             <div>
@@ -5284,6 +5420,16 @@ python scripts/validate_publication_package.py</code></pre>
       ko: "한국어",
       pt: "Português"
     };
+    const siteLanguageShortNames = {
+      en: "EN",
+      "zh-CN": "中",
+      es: "ES",
+      fr: "FR",
+      de: "DE",
+      ja: "日",
+      ko: "한",
+      pt: "PT"
+    };
     const siteLanguageStorageKey = "ropedia-xperience-site-language";
 
     function markTranslationStableRegions() {
@@ -5319,6 +5465,10 @@ python scripts/validate_publication_package.py</code></pre>
     }
 
     function updateLanguageStatus(language, state) {
+      const control = document.querySelector(".site-language");
+      if (control) {
+        control.dataset.short = siteLanguageShortNames[language] || "EN";
+      }
       const status = document.getElementById("siteLanguageStatus");
       if (!status) return;
       const name = siteLanguageNames[language] || language;
@@ -5368,6 +5518,10 @@ python scripts/validate_publication_package.py</code></pre>
       const storedLanguage = readStoredLanguage();
       if (siteLanguageNames[storedLanguage]) {
         siteLanguageSelector.value = storedLanguage;
+        const control = document.querySelector(".site-language");
+        if (control) {
+          control.dataset.short = siteLanguageShortNames[storedLanguage] || "EN";
+        }
         setTranslateCookie(storedLanguage);
       }
       siteLanguageSelector.addEventListener("change", (event) => {

index.html

@@ -189,44 +189,69 @@
       outline: 2px solid rgba(204, 255, 160, 0.54);
       outline-offset: 3px;
     }
-    .nav-action {
-      border: 1px solid rgba(204, 255, 160, 0.46);
+    .nav-external-actions {
+      display: inline-flex;
+      align-items: center;
+      gap: 4px;
+      padding: 4px;
+      border: 1px solid rgba(204, 255, 160, 0.24);
+      border-radius: 999px;
       background:
-        linear-gradient(180deg, rgba(204, 255, 160, 0.11), rgba(204, 255, 160, 0.055)),
-        rgba(6, 16, 6, 0.86);
-      color: #f4f8ef;
-      height: 42px;
-      min-width: 82px;
-      padding: 0 14px;
+        linear-gradient(180deg, rgba(255, 255, 255, 0.055), rgba(255, 255, 255, 0.025)),
+        rgba(4, 9, 4, 0.82);
+      box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.018), 0 14px 38px rgba(0, 0, 0, 0.18);
+      flex: 0 0 auto;
+      white-space: nowrap;
+    }
+    .nav-action {
+      border: 0;
+      background: transparent;
+      color: #dfeadc;
+      height: 34px;
+      min-width: 0;
+      padding: 0 11px 0 6px;
       display: inline-flex;
       align-items: center;
       justify-content: center;
+      gap: 7px;
       text-decoration: none;
       font-family: var(--font-btn);
-      font-size: 12.5px;
+      font-size: 12px;
       font-weight: 760;
       line-height: 1;
       white-space: nowrap;
       border-radius: 999px;
-      box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.028), 0 8px 24px rgba(0, 0, 0, 0.16);
-      transition: color 220ms cubic-bezier(0.16, 1, 0.3, 1), transform 220ms cubic-bezier(0.16, 1, 0.3, 1), border-color 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 220ms cubic-bezier(0.16, 1, 0.3, 1);
+      box-shadow: none;
+      transition: color 220ms cubic-bezier(0.16, 1, 0.3, 1), transform 220ms cubic-bezier(0.16, 1, 0.3, 1), background 220ms cubic-bezier(0.16, 1, 0.3, 1), box-shadow 220ms cubic-bezier(0.16, 1, 0.3, 1);
     }
     .nav-action::before {
-      content: none;
+      content: attr(data-mark);
+      width: 24px;
+      height: 24px;
+      display: inline-flex;
+      align-items: center;
+      justify-content: center;
+      border: 1px solid rgba(204, 255, 160, 0.28);
+      border-radius: 999px;
+      background: rgba(204, 255, 160, 0.08);
+      color: var(--green);
+      font-size: 9.5px;
+      font-weight: 800;
+      line-height: 1;
+      letter-spacing: 0;
+      flex: 0 0 auto;
     }
     .nav-action-hf {
-      min-width: 74px;
+      min-width: 76px;
     }
     .nav-action-repo {
-      min-width: 88px;
-      background: var(--green);
-      border-color: rgba(204, 255, 160, 0.92);
-      color: #020502;
-      box-shadow: 0 10px 30px rgba(204, 255, 160, 0.12);
+      min-width: 86px;
+      background: rgba(204, 255, 160, 0.14);
+      color: var(--green);
     }
     .nav-action-repo::before {
-      background: rgba(2, 5, 2, 0.10);
-      border-color: rgba(2, 5, 2, 0.18);
+      background: var(--green);
+      border-color: var(--green);
       color: #020502;
     }
     .nav-action-text-short {
@@ -234,28 +259,14 @@
     }
     .nav-action:hover {
       transform: translateY(-1px);
-      border-color: rgba(204, 255, 160, 0.74);
-      background:
-        linear-gradient(180deg, rgba(204, 255, 160, 0.18), rgba(204, 255, 160, 0.09)),
-        rgba(6, 16, 6, 0.88);
+      background: rgba(204, 255, 160, 0.11);
       color: var(--green);
-      box-shadow: inset 0 0 0 1px rgba(255, 255, 255, 0.04), 0 12px 32px rgba(204, 255, 160, 0.08);
+      box-shadow: inset 0 0 0 1px rgba(204, 255, 160, 0.08);
     }
     .nav-action-repo:hover {
-      background: #eaffd5;
-      border-color: #eaffd5;
-      color: #020502;
-    }
-    .nav-links a.nav-action {
-      border-color: var(--green);
       background: var(--green);
       color: #020502;
     }
-    .nav-links a.nav-action:hover {
-      border-color: var(--green);
-      background: #f4f8ef;
-      color: #020502;
-    }
     .nav-tools {
       display: flex;
       align-items: center;
@@ -285,6 +296,12 @@
       flex: 0 1 auto;
       max-width: 128px;
       overflow: hidden;
+      position: relative;
+    }
+    .site-language::before {
+      content: attr(data-short);
+      display: none;
+      pointer-events: none;
     }
     .site-language label {
       position: absolute;
@@ -3233,16 +3250,20 @@
       .site-language select {
         max-width: 92px;
       }
+      .nav-external-actions {
+        gap: 3px;
+        padding: 3px;
+      }
       .nav-action {
-        height: 42px;
+        height: 34px;
         min-width: 58px;
-        padding: 0 12px;
+        padding: 0 10px 0 5px;
       }
       .nav-action-hf {
-        min-width: 48px;
+        min-width: 56px;
       }
       .nav-action-repo {
-        min-width: 64px;
+        min-width: 68px;
       }
       .nav-action-text-full {
         display: none;
@@ -3316,10 +3337,16 @@
       .project-tabs-shell .wrap {
         width: min(100% - 28px, var(--max));
       }
+      .nav-inner {
+        position: relative;
+      }
       .brand {
         font-size: 16px;
         gap: 9px;
       }
+      .brand span {
+        display: none;
+      }
       .brand-logo {
         width: 38px;
         height: 38px;
@@ -3328,25 +3355,72 @@
         min-height: 38px;
         padding: 0 8px;
         font-size: 12px;
+        width: 58px;
+        max-width: 58px;
+        justify-content: center;
+        position: absolute;
+        left: 146px;
+        right: auto;
+        top: 50%;
+        transform: translateY(-50%);
+      }
+      .site-language::before {
+        display: block;
+        position: absolute;
+        left: 11px;
+        top: 50%;
+        transform: translateY(-50%);
+        color: var(--ink);
+        font-weight: 780;
+        line-height: 1;
       }
       .site-language select {
-        max-width: 84px;
+        width: 100%;
+        max-width: none;
+        color: transparent;
+        padding-right: 17px;
+        background-position:
+          calc(100% - 9px) 52%,
+          calc(100% - 4px) 52%;
       }
       .nav-tools {
         gap: 6px;
         padding-left: 0;
         border-left: 0;
+        flex: 0 0 auto;
+        width: 0;
+        justify-content: flex-end;
+        overflow: visible;
+        position: static;
+      }
+      .nav-external-actions {
+        padding: 3px;
+        flex: 0 0 auto;
+        position: absolute;
+        left: 58px;
+        right: auto;
+        top: 50%;
+        transform: translateY(-50%);
       }
       .nav-action {
-        min-width: 42px;
-        height: 38px;
-        padding: 0 9px;
+        min-width: 28px;
+        height: 32px;
+        padding: 0 4px;
         font-size: 11.5px;
+        gap: 5px;
+      }
+      .nav-action::before {
+        width: 22px;
+        height: 22px;
+        font-size: 9px;
       }
       .nav-action-repo {
         display: inline-flex;
-        min-width: 52px;
-        padding: 0 10px;
+        min-width: 28px;
+        padding: 0 4px;
+      }
+      .nav-action-text-short {
+        display: none;
       }
       .project-tabs-shell { top: var(--nav-height); padding: 10px 0; }
       .project-tabs {
@@ -3502,26 +3576,44 @@
       }
     }
     @media (max-width: 460px) {
-      .brand span {
-        max-width: 138px;
+      .nav-inner {
+        gap: 7px;
+      }
+      .brand {
+        gap: 7px;
       }
       .site-language {
-        max-width: 82px;
-        padding: 0 7px;
+        width: 54px;
+        max-width: 54px;
+        padding: 0 6px;
       }
       .site-language select {
-        max-width: 68px;
+        max-width: none;
+        padding-right: 17px;
       }
       .nav-tools {
-        gap: 5px;
+        gap: 4px;
+      }
+      .nav-external-actions {
+        gap: 2px;
+        padding: 2px;
       }
       .nav-action {
-        min-width: 38px;
-        padding: 0 7px;
+        min-width: 24px;
+        height: 30px;
+        padding: 0 3px;
+      }
+      .nav-action::before {
+        width: 20px;
+        height: 20px;
+        font-size: 8.5px;
+      }
+      .nav-action-text-short {
+        display: none;
       }
       .nav-action-repo {
-        min-width: 46px;
-        padding: 0 8px;
+        min-width: 24px;
+        padding: 0 3px;
       }
     }
     @media (prefers-reduced-motion: reduce) {
@@ -3557,7 +3649,7 @@
         <a class="nav-optional" href="#artifacts">Files</a>
       </div>
       <div class="nav-tools">
-        <div class="site-language notranslate" translate="no">
+        <div class="site-language notranslate" translate="no" data-short="EN">
           <label for="siteLanguage">Language</label>
           <select id="siteLanguage" aria-label="Translate this website">
             <option value="en">English</option>
@@ -3571,14 +3663,16 @@
           </select>
           <span id="siteLanguageStatus" class="site-language-status" aria-live="polite"></span>
         </div>
-        <a class="nav-action nav-action-hf" data-mark="HF" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite" aria-label="Open Hugging Face Space">
-          <span class="nav-action-text-full">HF Space</span>
-          <span class="nav-action-text-short">HF</span>
-        </a>
-        <a class="nav-action nav-action-repo" data-mark="GH" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite" aria-label="Open GitHub repository">
-          <span class="nav-action-text-full">GitHub</span>
-          <span class="nav-action-text-short">Repo</span>
-        </a>
+        <div class="nav-external-actions" aria-label="External project links">
+          <a class="nav-action nav-action-hf" data-mark="HF" href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite" aria-label="Open Hugging Face Space">
+            <span class="nav-action-text-full">HF Space</span>
+            <span class="nav-action-text-short">HF</span>
+          </a>
+          <a class="nav-action nav-action-repo" data-mark="GH" href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite" aria-label="Open GitHub repository">
+            <span class="nav-action-text-full">GitHub</span>
+            <span class="nav-action-text-short">Repo</span>
+          </a>
+        </div>
       </div>
       <div id="google_translate_element" aria-hidden="true"></div>
     </div>
@@ -3695,7 +3789,7 @@
               <a href="assets/charts/episode128_task_model_radar.svg">128ep radar</a>
               <a href="data/unified_task_model_radar.json">Open radar JSON</a>
               <a href="data/task_method_20_result_matrix.json">Open 20-result matrix</a>
-              <a href="data/task_method_20_gap_audit.json">Open gap audit</a>
+              <a href="data/task_method_20_gap_audit.json">Open score/proxy audit</a>
             </div>
           </div>
         </div>
@@ -3761,14 +3855,14 @@
               <td>One public Xperience-10M sample episode; 5,821 frames; 1,161 aligned 20-frame windows; 8,546-dimensional feature contract.</td>
               <td>Minimal heads and Neural MLP heads. Both cover all 20 task contracts, for 40/40 scored method-task records.</td>
               <td>Raw sample inspection, file organization, task definitions, local reproducibility, and controlled baseline behavior.</td>
-              <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar JSON</a><br><a href="data/two_evidence_lines.json">line JSON</a></td>
+              <td><a href="#raw-sample">Raw browser</a><br><a href="data/single_episode_task_model_radar.json">1-episode radar JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="data/two_evidence_lines.json">line JSON</a></td>
             </tr>
             <tr>
               <td>128 selected episodes</td>
               <td>Selected held-out 96/16/16 split; 34,269 exported windows; public-safe metadata/raw-feature artifacts linked to official gated episode paths.</td>
               <td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano. Together they form 140/140 scored 128-line method-task records.</td>
               <td>Same-split comparison, model-branch diagnostics, Qwen/Cosmos evidence, and the next scale-up decisions.</td>
-              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a><br><a href="data/xperience10m_128_episode_feature_index.json">feature index JSON</a><br><a href="data/two_evidence_line_result_summary.json">result summary JSON</a><br><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/TWO_EVIDENCE_LINES.md">line doc</a></td>
             </tr>
           </tbody>
         </table>
@@ -4590,8 +4684,8 @@
             <p>Higher-is-better metrics are plotted directly on 0-1 axes. Lower-is-better metrics are converted to best/value within the task, while raw values, status reasons, sources, and the two raw128 compact proxy notes remain in the JSON mirrors.</p>
           </article>
           <article class="figure-brief-card">
-            <h3>Score gap audit</h3>
-            <p>The matrix has 180 method-task records and 180 numeric scores. The gap audit remains published as the evidence ledger for which artifacts support each score, including documented proxy axes where raw targets are absent.</p>
+            <h3>Score/proxy audit</h3>
+            <p>The matrix has 180 method-task records and 180 numeric scores. The audit records which artifacts support each score and marks compact-proxy axes where raw targets are absent.</p>
           </article>
         </div>
         <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v6" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, scored-axis counts, and proxy notes">
@@ -4707,7 +4801,7 @@
           <article class="suite-line-card">
             <small>1 episode results</small>
             <h3>Task-lab evidence</h3>
-            <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. This is the cleanest place to inspect whether each task is well-posed.</p>
+            <p>Minimal and Neural MLP heads are both scored on all 20 public-sample task contracts. All 40 scores are direct task-target metrics.</p>
             <div class="suite-line-facts">
               <span><strong>2</strong>methods</span>
               <span><strong>20</strong>task axes</span>
@@ -4718,7 +4812,7 @@
           <article class="suite-line-card">
             <small>128 episode results</small>
             <h3>Scale-up evidence</h3>
-            <p>Metadata/raw baselines and Qwen/Cosmos branches use the aligned 128-episode surface. Compact proxy scores remain marked where a direct raw target is absent.</p>
+            <p>Metadata/raw baselines and Qwen/Cosmos branches use the aligned 128-episode surface. It has 134 direct scores plus 6 compact-proxy scores.</p>
             <div class="suite-line-facts">
               <span><strong>7</strong>methods</span>
               <span><strong>20</strong>task axes</span>
@@ -4727,6 +4821,48 @@
             <a href="assets/charts/episode128_task_model_radar.svg">Open 128-episode radar</a>
           </article>
         </div>
+        <table class="line-table" aria-label="Direct and proxy score ledger by evidence line">
+          <thead>
+            <tr>
+              <th>Line</th>
+              <th>Methods</th>
+              <th>Tasks</th>
+              <th>Scored records</th>
+              <th>Direct scores</th>
+              <th>Proxy scores</th>
+              <th>Machine-readable source</th>
+            </tr>
+          </thead>
+          <tbody>
+            <tr>
+              <td>1 sample episode</td>
+              <td>2</td>
+              <td>20</td>
+              <td>40/40</td>
+              <td>40</td>
+              <td>0</td>
+              <td><a href="data/single_episode_task_model_radar.json">single-episode radar JSON</a></td>
+            </tr>
+            <tr>
+              <td>128 selected episodes</td>
+              <td>7</td>
+              <td>20</td>
+              <td>140/140</td>
+              <td>134</td>
+              <td>6 compact-proxy scores, each source-linked and reasoned.</td>
+              <td><a href="data/episode128_task_model_radar.json">128-episode radar JSON</a></td>
+            </tr>
+            <tr>
+              <td>Total public matrix</td>
+              <td>9</td>
+              <td>20</td>
+              <td>180/180</td>
+              <td>174</td>
+              <td>6</td>
+              <td><a href="data/two_evidence_line_result_summary.json">two-line result summary JSON</a></td>
+            </tr>
+          </tbody>
+        </table>
         <div class="artifact-grid">
           <article class="artifact primary-artifact">
             <div>
@@ -5284,6 +5420,16 @@ python scripts/validate_publication_package.py</code></pre>
       ko: "한국어",
       pt: "Português"
     };
+    const siteLanguageShortNames = {
+      en: "EN",
+      "zh-CN": "中",
+      es: "ES",
+      fr: "FR",
+      de: "DE",
+      ja: "日",
+      ko: "한",
+      pt: "PT"
+    };
     const siteLanguageStorageKey = "ropedia-xperience-site-language";
 
     function markTranslationStableRegions() {
@@ -5319,6 +5465,10 @@ python scripts/validate_publication_package.py</code></pre>
     }
 
     function updateLanguageStatus(language, state) {
+      const control = document.querySelector(".site-language");
+      if (control) {
+        control.dataset.short = siteLanguageShortNames[language] || "EN";
+      }
       const status = document.getElementById("siteLanguageStatus");
       if (!status) return;
       const name = siteLanguageNames[language] || language;
@@ -5368,6 +5518,10 @@ python scripts/validate_publication_package.py</code></pre>
       const storedLanguage = readStoredLanguage();
       if (siteLanguageNames[storedLanguage]) {
         siteLanguageSelector.value = storedLanguage;
+        const control = document.querySelector(".site-language");
+        if (control) {
+          control.dataset.short = siteLanguageShortNames[storedLanguage] || "EN";
+        }
         setTranslateCookie(storedLanguage);
       }
       siteLanguageSelector.addEventListener("change", (event) => {

scripts/build_multilingual_public_readmes.py

@@ -8,7 +8,7 @@ from pathlib import Path
 
 
 ROOT = Path(__file__).resolve().parents[1]
-UPDATED = "2026-06-18"
+UPDATED = "2026-06-21"
 LANGUAGES = [
     ("en", "English", "README.md"),
     ("zh", "中文", "README.zh.md"),
@@ -77,6 +77,7 @@ ENGLISH_TOP = f"""{hero(
 
 - [How To Read This Project](#how-to-read-this-project)
 - [At A Glance](#at-a-glance)
+- [Two Evidence Lines](#two-evidence-lines)
 - [Fast Reader Map](#fast-reader-map)
 - [Why This Project Exists](#why-this-project-exists)
 - [Start Here](#start-here)
@@ -129,6 +130,87 @@ The multilingual README files are reader guides. The canonical technical evidenc
   </tbody>
 </table>
 
+## Two Evidence Lines
+
+The public suite is organized around two result lines. Keep them separate when
+reading metrics.
+
+<table>
+  <thead>
+    <tr>
+      <th width="20%">Line</th>
+      <th width="26%">Data unit</th>
+      <th width="24%">Methods</th>
+      <th>Primary use</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1 sample episode</strong></td>
+      <td>One public Xperience-10M sample episode: 5,821 frames, 1,161 aligned 20-frame windows, 8,546 feature dimensions.</td>
+      <td>Minimal heads and Neural MLP heads on all 20 tasks: 40/40 scored method-task records.</td>
+      <td>Inspect raw sample files, understand task definitions, rerun local baselines, and debug whether each task is well-posed.</td>
+    </tr>
+    <tr>
+      <td><strong>128 selected episodes</strong></td>
+      <td>Selected held-out 96/16/16 split: 34,269 exported windows with public-safe processed features linked to official gated episode paths.</td>
+      <td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano: 140/140 scored 128-line records.</td>
+      <td>Compare same-split baselines and model branches; use proxy flags where the public export lacks a direct raw target.</td>
+    </tr>
+  </tbody>
+</table>
+
+### Result Ledger
+
+<table>
+  <thead>
+    <tr>
+      <th width="20%">Line</th>
+      <th width="14%">Methods</th>
+      <th width="14%">Tasks</th>
+      <th width="18%">Scored records</th>
+      <th width="16%">Direct scores</th>
+      <th>Proxy scores</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>1 sample episode</strong></td>
+      <td>2</td>
+      <td>20</td>
+      <td>40/40</td>
+      <td>40</td>
+      <td>0</td>
+    </tr>
+    <tr>
+      <td><strong>128 selected episodes</strong></td>
+      <td>7</td>
+      <td>20</td>
+      <td>140/140</td>
+      <td>134</td>
+      <td>6 compact-proxy scores, each source-linked and reasoned.</td>
+    </tr>
+    <tr>
+      <td><strong>Total public matrix</strong></td>
+      <td>9</td>
+      <td>20</td>
+      <td>180/180</td>
+      <td>174</td>
+      <td>6</td>
+    </tr>
+  </tbody>
+</table>
+
+Result entry points:
+[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md),
+[`two_evidence_lines.json`](docs/data/two_evidence_lines.json),
+[`TWO_EVIDENCE_LINE_RESULT_SUMMARY.md`](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
+[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json),
+[`single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json),
+[`episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json),
+[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), and
+[`xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json).
+
 ## Fast Reader Map
 
 <table>
@@ -158,7 +240,7 @@ The multilingual README files are reader guides. The canonical technical evidenc
     <tr>
       <td><strong>Compare results</strong></td>
       <td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
-      <td><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">gap audit</a></td>
+      <td><a href="docs/data/two_evidence_line_result_summary.json">two-line result summary</a><br><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">score/proxy audit</a></td>
     </tr>
     <tr>
       <td><strong>Understand one sample</strong></td>
@@ -192,6 +274,15 @@ LANGUAGE_GUIDES = {
 
 **范围：** 完整可复现的任务套件来自一个公开样本 episode；128-episode 结果只发布 public-safe 的指标、报告、预测摘要和模型卡。原始 MP4/HDF5/RRD、完整 Qwen 权重和 gated 数据不在本仓库重新分发。
 
+## 两条证据线
+
+| 线 | 数据单元 | 方法与结果 | 用途 |
+| --- | --- | --- | --- |
+| 1 sample episode | 5,821 帧；1,161 个 20-frame 对齐窗口；8,546 维特征。 | Minimal + Neural MLP；20 个任务全覆盖；40/40 scored records；全部为 direct scores。 | 检查原始 sample 文件、任务定义、可复现基线和每个任务是否成立。 |
+| 128 selected episodes | 96/16/16 split；34,269 个导出窗口；public-safe 特征链接到官方 gated episode path。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano；140/140 scored records；134 direct + 6 compact proxy。 | 比较同一 split 上的基线和模型分支；proxy target 会显式标注。 |
+
+入口：[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)、[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json)。
+
 ## 快速入口
 
 | 目标 | 入口 |
@@ -208,7 +299,7 @@ LANGUAGE_GUIDES = {
 
 - 数据层：公开 sample episode 被切成 20-frame 窗口，并连接视频、音频、深度、pose/SLAM、mocap、IMU、calibration 和语言标注。
 - 任务层：20 个统一任务覆盖识别、预测、检索、重建、同步、长时预测、action-object 关系和 sensor bridge。
-- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注，不能评估的格子保留为显式 gap。
+- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注；当前公开矩阵为 180/180 scored records，其中 174 direct、6 compact proxy，proxy target 显式保留。
 - 训练方向：spatial intelligence、human-video world model、vision-language-action 三条 pipeline 已经有任务映射和需要的证据清单。
 
 ## 公开边界
@@ -227,6 +318,15 @@ Este repositorio convierte el episodio público de muestra de Xperience-10M en u
 
 **Alcance:** la suite reproducible usa un episodio público; los resultados de 128 episodios publican solo métricas, reportes, predicciones seguras y tarjetas de modelo. No se redistribuyen MP4/HDF5/RRD originales, pesos completos de Qwen ni datos gated.
 
+## Dos Líneas de Evidencia
+
+| Línea | Unidad de datos | Métodos y resultados | Uso |
+| --- | --- | --- | --- |
+| 1 episodio de muestra | 5,821 frames; 1,161 ventanas alineadas de 20 frames; 8,546 dimensiones. | Minimal + Neural MLP en 20 tareas; 40/40 registros con score; todos son direct scores. | Inspeccionar archivos de muestra, definiciones de tarea, baselines reproducibles y validez de tareas. |
+| 128 episodios seleccionados | Split 96/16/16; 34,269 ventanas exportadas; features public-safe ligadas a episode paths oficiales gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super y Cosmos3-Nano; 140/140 registros con score; 134 direct + 6 compact proxy. | Comparar baselines y ramas de modelo en el mismo split; los proxy targets permanecen visibles. |
+
+Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
+
 ## Ruta Rápida
 
 | Objetivo | Entrada |
@@ -243,7 +343,7 @@ Este repositorio convierte el episodio público de muestra de Xperience-10M en u
 
 - Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
 - Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
-- Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos con gaps explícitos.
+- Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos; la matriz pública está en 180/180 registros con score: 174 direct y 6 compact proxy, con proxy targets visibles.
 - Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia.
 
 ## Límite Público
@@ -262,6 +362,15 @@ Ce dépôt transforme l'épisode public d'exemple Xperience-10M en laboratoire d
 
 **Portée :** la suite entièrement reproductible utilise un épisode public; les résultats 128 épisodes ne publient que des métriques, rapports, prédictions sûres et cartes de modèles. Les MP4/HDF5/RRD bruts, les poids Qwen complets et les données gated ne sont pas redistribués.
 
+## Deux Lignes de Preuve
+
+| Ligne | Unité de données | Méthodes et résultats | Usage |
+| --- | --- | --- | --- |
+| 1 épisode d'exemple | 5,821 frames; 1,161 fenêtres alignées de 20 frames; 8,546 dimensions. | Minimal + Neural MLP sur 20 tâches; 40/40 enregistrements scorés; tous sont des direct scores. | Inspecter les fichiers sample, les définitions de tâches, les baselines reproductibles et la validité des tâches. |
+| 128 épisodes sélectionnés | Split 96/16/16; 34,269 fenêtres exportées; features public-safe liées aux chemins gated officiels. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super et Cosmos3-Nano; 140/140 enregistrements scorés; 134 direct + 6 compact proxy. | Comparer les baselines et branches de modèles sur le même split; les proxy targets restent visibles. |
+
+Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
+
 ## Parcours Rapide
 
 | Objectif | Point d'entrée |
@@ -278,7 +387,7 @@ Ce dépôt transforme l'épisode public d'exemple Xperience-10M en laboratoire d
 
 - Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
 - Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
-- Résultats : minimal/NN sur l'épisode public couvrent 20/20; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos avec gaps explicites.
+- Résultats : minimal/NN sur l'épisode public couvrent 20/20; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
 - Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires.
 
 ## Frontière Publique
@@ -297,6 +406,15 @@ Dieses Repository macht aus dem öffentlichen Xperience-10M-Sample eine prüfbar
 
 **Umfang:** die vollständig reproduzierbare Suite nutzt ein öffentliches Sample-Episode; 128-Episode-Ergebnisse veröffentlichen nur public-safe Metriken, Berichte, Vorhersagen und Modellkarten. Rohdaten wie MP4/HDF5/RRD, vollständige Qwen-Gewichte und gated Daten werden nicht weitergegeben.
 
+## Zwei Evidenzlinien
+
+| Linie | Dateneinheit | Methoden und Ergebnisse | Zweck |
+| --- | --- | --- | --- |
+| 1 Sample-Episode | 5,821 Frames; 1,161 ausgerichtete 20-Frame-Fenster; 8,546 Dimensionen. | Minimal + Neural MLP auf 20 Aufgaben; 40/40 gescorte Einträge; alle sind direct scores. | Sample-Dateien, Aufgaben, reproduzierbare Baselines und Aufgabenqualität prüfen. |
+| 128 ausgewählte Episoden | 96/16/16 Split; 34,269 exportierte Fenster; public-safe Features mit offiziellen gated Episode-Pfaden. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super und Cosmos3-Nano; 140/140 gescorte Einträge; 134 direct + 6 compact proxy. | Baselines und Modellzweige auf demselben Split vergleichen; Proxy-Targets bleiben sichtbar. |
+
+Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
+
 ## Schneller Einstieg
 
 | Ziel | Einstieg |
@@ -313,7 +431,7 @@ Dieses Repository macht aus dem öffentlichen Xperience-10M-Sample eine prüfbar
 
 - Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
 - Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
-- Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos mit sichtbaren Gaps.
+- Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos; die öffentliche Matrix steht bei 180/180 gescorten Einträgen: 174 direct und 6 compact proxy, mit sichtbaren Proxy-Targets.
 - Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert.
 
 ## Öffentliche Grenze
@@ -332,6 +450,15 @@ Dieses Projekt veröffentlicht nur abgeleitete Artefakte, Metriken, Figuren, Kar
 
 **範囲:** 完全に再現可能なタスク suite は 1 つの公開サンプル episode に基づきます。128-episode の結果は public-safe な指標、レポート、予測要約、モデルカードのみを公開します。元の MP4/HDF5/RRD、完全な Qwen 重み、gated データは再配布しません。
 
+## 2 つの証拠ライン
+
+| ライン | データ単位 | 手法と結果 | 用途 |
+| --- | --- | --- | --- |
+| 1 sample episode | 5,821 frames、1,161 aligned 20-frame windows、8,546 dimensions。 | Minimal + Neural MLP が 20 tasks を覆盖; 40/40 scored records; すべて direct scores。 | Raw sample files、task definitions、reproducible baselines、task validity を確認。 |
+| 128 selected episodes | 96/16/16 split、34,269 exported windows、public-safe features が official gated episode paths に対応。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano; 140/140 scored records; 134 direct + 6 compact proxy。 | 同一 split の baselines と model branches を比較; proxy targets は明示。 |
+
+入口: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)、[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json)。
+
 ## クイックルート
 
 | 目的 | 入口 |
@@ -348,7 +475,7 @@ Dieses Projekt veröffentlicht nur abgeleitete Artefakte, Metriken, Figuren, Kar
 
 - データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
 - タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
-- 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分け、未評価 gap を明示します。
+- 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、174 direct と 6 compact proxy を分離し、proxy targets は明示します。
 - 方向: spatial intelligence、human-video world model、vision-language-action に対して、タスク対応と必要証拠を記録しています。
 
 ## 公開境界
@@ -367,6 +494,15 @@ Dieses Projekt veröffentlicht nur abgeleitete Artefakte, Metriken, Figuren, Kar
 
 **범위:** 완전히 재현 가능한 task suite는 공개 sample episode 하나를 사용합니다. 128-episode 결과는 public-safe 지표, 리포트, 예측 요약, 모델 카드만 공개합니다. 원본 MP4/HDF5/RRD, 전체 Qwen 가중치, gated 데이터는 재배포하지 않습니다.
 
+## 두 증거 라인
+
+| 라인 | 데이터 단위 | 방법과 결과 | 용도 |
+| --- | --- | --- | --- |
+| 1 sample episode | 5,821 frames, 1,161 aligned 20-frame windows, 8,546 dimensions. | Minimal + Neural MLP가 20 tasks 전체를 평가; 40/40 scored records; 모두 direct scores. | Raw sample files, task definitions, reproducible baselines, task validity 확인. |
+| 128 selected episodes | 96/16/16 split, 34,269 exported windows, public-safe features가 official gated episode paths에 연결됨. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored records; 134 direct + 6 compact proxy. | 같은 split에서 baselines와 model branches 비교; proxy targets는 명시 유지. |
+
+입구: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
+
 ## 빠른 경로
 
 | 목표 | 시작점 |
@@ -383,7 +519,7 @@ Dieses Projekt veröffentlicht nur abgeleitete Artefakte, Metriken, Figuren, Kar
 
 - 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
 - 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
-- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리하고 gap을 명시합니다.
+- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 174 direct와 6 compact proxy를 분리하고 proxy targets를 명시합니다.
 - 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
 
 ## 공개 경계
@@ -402,6 +538,15 @@ Este repositório transforma o episódio público de amostra do Xperience-10M em
 
 **Escopo:** a suíte totalmente reproduzível usa um episódio público; os resultados de 128 episódios publicam apenas métricas, relatórios, predições seguras e model cards. MP4/HDF5/RRD originais, pesos completos do Qwen e dados gated não são redistribuídos.
 
+## Duas Linhas de Evidência
+
+| Linha | Unidade de dados | Métodos e resultados | Uso |
+| --- | --- | --- | --- |
+| 1 episódio de amostra | 5,821 frames; 1,161 janelas alinhadas de 20 frames; 8,546 dimensões. | Minimal + Neural MLP em 20 tarefas; 40/40 registros com score; todos são direct scores. | Inspecionar arquivos da amostra, definições de tarefas, baselines reproduzíveis e validade das tarefas. |
+| 128 episódios selecionados | Split 96/16/16; 34,269 janelas exportadas; features public-safe ligadas aos caminhos oficiais gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super e Cosmos3-Nano; 140/140 registros com score; 134 direct + 6 compact proxy. | Comparar baselines e ramos de modelo no mesmo split; proxy targets permanecem visíveis. |
+
+Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), [`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json).
+
 ## Rota Rápida
 
 | Objetivo | Entrada |
@@ -418,7 +563,7 @@ Este repositório transforma o episódio público de amostra do Xperience-10M em
 
 - Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
 - Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
-- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos com gaps explícitos.
+- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score: 174 direct e 6 compact proxy, com proxy targets visíveis.
 - Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
 
 ## Fronteira Pública
@@ -438,6 +583,7 @@ COMMON_FOOTER = """## Public Surfaces
 | HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
 | HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
 | HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
+| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
 | HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
 
 ## Citation

scripts/sync_hf_publish_mirrors.py

@@ -234,7 +234,7 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "`docs/data/unified_task_model_radar.json`; the 9-method by 20-task\n"
                 "completion matrix is complete at `180/180` scored method-task records\n"
                 "and is published in `docs/data/task_method_20_result_matrix.json`,\n"
-                "with the explicit audit in `docs/data/task_method_20_gap_audit.json`\n"
+                "with the explicit score/proxy audit in `docs/data/task_method_20_gap_audit.json`\n"
                 "and source-value audit in `docs/data/task_method_20_source_audit.json`.",
             )
         if "completion matrix is in `docs/data/task_method_20_result_matrix.json`" in text:
@@ -243,7 +243,7 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "with the explicit\ngap audit in `docs/data/task_method_20_gap_audit.json`.",
                 "completion matrix is complete at `180/180` scored method-task records "
                 "and is published in `docs/data/task_method_20_result_matrix.json`, "
-                "with the explicit\naudit in `docs/data/task_method_20_gap_audit.json` "
+                "with the explicit\nscore/proxy audit in `docs/data/task_method_20_gap_audit.json` "
                 "and source-value audit in `docs/data/task_method_20_source_audit.json`.",
             )
         if (
@@ -253,7 +253,7 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
             text = text.replace(
                 "`docs/data/task_method_20_result_matrix.json`.",
                 "`docs/data/task_method_20_result_matrix.json`, with the explicit\n"
-                "gap audit in `docs/data/task_method_20_gap_audit.json`.",
+                "score/proxy audit in `docs/data/task_method_20_gap_audit.json`.",
             )
         if (
             "docs/data/task_method_20_gap_audit.json" in text

scripts/validate_mirror_parity.py

@@ -79,6 +79,8 @@ DATA_FILES = [
     "single_episode_task_model_radar.json",
     "episode128_task_model_radar.json",
     "three_foundation_pipelines.json",
+    "two_evidence_lines.json",
+    "two_evidence_line_result_summary.json",
     "task_suite_20.json",
     "task_suite_enhancement_128.json",
     "task_surface_integrity.json",
@@ -319,6 +321,8 @@ DOC_FILES = [
     "FOUNDATION_MODEL_PLAN.md",
     "ADDITIONAL_DEVELOPMENT_DIRECTIONS.md",
     "THREE_FOUNDATION_PIPELINES.md",
+    "TWO_EVIDENCE_LINES.md",
+    "TWO_EVIDENCE_LINE_RESULT_SUMMARY.md",
     "PROJECT_BRIEF.md",
     "PUBLIC_READER_MAP.md",
     "RENDERED_SITE_CHECK.md",

scripts/validate_publication_package.py

@@ -277,6 +277,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "EVALUATION_PROTOCOL.md",
         "TASK_SUITE_20.md",
         "TASK_METHOD_20_SOURCE_AUDIT.md",
+        "TWO_EVIDENCE_LINE_RESULT_SUMMARY.md",
         "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md",
         "FIGURE_INDEX.md",
         "SOURCE_ALIGNMENT_AUDIT.md",
@@ -316,6 +317,8 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/data/rendered_site_check.json",
         "docs/data/scope_claims_audit.json",
         "docs/data/task_surface_integrity.json",
+        "docs/data/two_evidence_lines.json",
+        "docs/data/two_evidence_line_result_summary.json",
         "docs/data/website_integrity.json",
         "docs/data/summary_metrics.json",
         "docs/data/task_suite_20.json",