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Browse files- PROJECT_README.md +20 -18
- README.de.md +2 -2
- README.es.md +2 -2
- README.fr.md +2 -2
- README.ja.md +2 -2
- README.ko.md +2 -2
- README.md +20 -18
- README.pt.md +2 -2
- README.zh.md +2 -2
- data/artifact_index.json +3 -3
- data/mirror_parity.json +124 -124
- data/public_surface_qa.json +6 -6
- data/publication_audit.json +1 -1
- data/quality_gates.json +1 -1
- data/source_alignment_audit.json +1 -1
- data/task_surface_integrity.json +1 -1
- data/website_integrity.json +6 -6
- docs/data/artifact_index.json +3 -3
- docs/data/mirror_parity.json +124 -124
- docs/data/public_surface_qa.json +6 -6
- docs/data/publication_audit.json +1 -1
- docs/data/quality_gates.json +1 -1
- docs/data/source_alignment_audit.json +1 -1
- docs/data/task_surface_integrity.json +1 -1
- docs/data/website_integrity.json +6 -6
- docs/index.html +139 -45
- index.html +139 -45
- metrics/artifact_index.json +3 -3
- metrics/mirror_parity.json +124 -124
- metrics/public_surface_qa.json +6 -6
- metrics/publication_audit.json +1 -1
- metrics/quality_gates.json +1 -1
- metrics/source_alignment_audit.json +1 -1
- metrics/task_surface_integrity.json +1 -1
- metrics/website_integrity.json +6 -6
- scripts/build_multilingual_public_readmes.py +29 -2
PROJECT_README.md
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@@ -117,6 +117,8 @@ The multilingual README files are reader guides. The canonical technical evidenc
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Read the project as three layers. The **20 tasks** are the scored benchmark contracts. The **4 directions** are reader-facing research groupings over those same tasks. The **3 foundation pipelines** are training recipes that reuse the same modalities, windows, and task targets. They are related, but they are not three competing task sets.
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<p align="center">
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<img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, and 3 foundation-model pipeline tracks" width="100%">
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</p>
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</table>
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Detailed lineage:
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Result entry points:
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## Fast Reader Map
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<tr>
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<td><strong>Choose the public surface</strong></td>
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<td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
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<td><a href="docs/data/public_reader_map.json">
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</tr>
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<tr>
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<td><strong>Decode project terms</strong></td>
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<td><a href="GLOSSARY.md">Glossary</a></td>
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<td><a href="docs/data/glossary.json">glossary
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</tr>
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<tr>
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<td><strong>Inspect the 20 tasks</strong></td>
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<td><a href="TASK_SUITE_20.md">
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<td><a href="docs/data/task_suite_20.json">
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</tr>
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<tr>
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<td><strong>Compare results</strong></td>
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<td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
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<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
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</tr>
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<tr>
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<td><strong>Understand one sample</strong></td>
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<tr>
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<td><strong>Read foundation directions</strong></td>
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<td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
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<td><a href="docs/data/three_foundation_pipelines.json">
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</tr>
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<tr>
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<td><strong>Reproduce or audit</strong></td>
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Read the project as three layers. The **20 tasks** are the scored benchmark contracts. The **4 directions** are reader-facing research groupings over those same tasks. The **3 foundation pipelines** are training recipes that reuse the same modalities, windows, and task targets. They are related, but they are not three competing task sets.
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Reader rule: if it has a metric, it is a **task**; if it explains what the evidence studies, it is a **direction**; if it describes model inputs and training targets, it is a **pipeline**.
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<p align="center">
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<img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, and 3 foundation-model pipeline tracks" width="100%">
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</p>
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</table>
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Detailed lineage:
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[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md) and
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[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json).
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Result entry points:
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[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md),
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[`two_evidence_lines.json`](docs/data/two_evidence_lines.json),
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[`TWO_EVIDENCE_LINE_RESULT_SUMMARY.md`](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
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[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json),
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[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md),
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[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json),
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[`single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json),
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[`episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json),
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[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), and
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[`xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json).
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## Fast Reader Map
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<tr>
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<td><strong>Choose the public surface</strong></td>
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<td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
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<td><a href="docs/data/public_reader_map.json">public_reader_map.json</a></td>
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</tr>
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<tr>
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<td><strong>Decode project terms</strong></td>
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<td><a href="GLOSSARY.md">Glossary</a></td>
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<td><a href="docs/data/glossary.json">glossary.json</a></td>
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</tr>
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<tr>
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<td><strong>Inspect the 20 tasks</strong></td>
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<td><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a></td>
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<td><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a></td>
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</tr>
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<tr>
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<td><strong>Compare results</strong></td>
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<td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
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<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>
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</tr>
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<tr>
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<td><strong>Understand one sample</strong></td>
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<tr>
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<td><strong>Read foundation directions</strong></td>
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<td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
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<td><a href="docs/data/three_foundation_pipelines.json">three_foundation_pipelines.json</a><br><a href="FOUNDATION_MODEL_PLAN.md">foundation model plan</a></td>
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</tr>
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<td><strong>Reproduce or audit</strong></td>
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README.de.md
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## Struktur
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- Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
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- Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
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- Struktur: 20 Aufgaben / 4 research directions / 3 foundation pipelines. Die 20 Aufgaben sind die Score-Achsen; die 4 directions gruppieren dieselben Aufgaben für Lesen und Forschung; die 3 pipelines sind Trainingspfade, keine neuen Aufgaben-Tiers.
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- 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.
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## Öffentliche Grenze
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## Struktur
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- Leseregel: Hat es eine Metrik, gehört es zu den 20 Aufgaben; erklärt es, was die Evidenz untersucht, gehört es zu den 4 research directions; beschreibt es Trainings-Inputs und Targets, gehört es zu den 3 foundation pipelines.
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- Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
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- Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
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- 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.
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- Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert.
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## Öffentliche Grenze
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README.es.md
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## Estructura
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- Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
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- Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
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- Estructura: 20 tareas / 4 research directions / 3 foundation pipelines. Las 20 tareas son ejes de puntuación; las 4 directions agrupan esas mismas tareas para lectura e investigación; las 3 pipelines son rutas de entrenamiento, no nuevos tiers de tareas.
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- 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.
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## Límite Público
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## Estructura
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- Regla de lectura: si tiene una métrica, es una tarea de las 20; si explica qué estudia la evidencia, es una de las 4 research directions; si define inputs/outputs de entrenamiento, es una de las 3 foundation pipelines.
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- Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
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- Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
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- 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.
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- Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia.
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## Límite Público
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README.fr.md
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## Structure
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- Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
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- Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
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- Structure : 20 tâches / 4 research directions / 3 foundation pipelines. Les 20 tâches sont les axes de score; les 4 directions regroupent ces mêmes tâches pour la lecture et la recherche; les 3 pipelines sont des pistes d'entraînement, pas de nouveaux tiers de tâches.
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- Résultats : minimal/NN sur l'épisode public couvrent 20/20; la ligne 128 épisodes sépare metadata, raw features, Qwen3-Omni et Cosmos3; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
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## Frontière Publique
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## Structure
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- Règle de lecture : avec une métrique, c'est l'une des 20 tâches; si cela explique ce que les preuves étudient, c'est l'une des 4 research directions; si cela définit des inputs/outputs d'entraînement, c'est l'une des 3 foundation pipelines.
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- Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
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- Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
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- Résultats : minimal/NN sur l'épisode public couvrent 20/20; la ligne 128 épisodes sépare metadata, raw features, Qwen3-Omni et Cosmos3; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
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- Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires.
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## Frontière Publique
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README.ja.md
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## 構造
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- データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
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- タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
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- 構造: 20 tasks / 4 research directions / 3 foundation pipelines。20 tasks は scoring axes、4 directions は同じタスクを読むための研究グループ、3 pipelines は大規模モデルの training tracks であり、新しい task tier ではありません。
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- 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。
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## 公開境界
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## 構造
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- 読み方のルール: metric があるものは 20 tasks、evidence が何を調べるかを説明するものは 4 research directions、training input/output を定義するものは 3 foundation pipelines です。
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- データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
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- タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
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- 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。
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- 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。
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## 公開境界
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README.ko.md
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## 구조
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- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
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- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
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- 구조: 20 tasks / 4 research directions / 3 foundation pipelines. 20 tasks는 scoring axes이고, 4 directions는 같은 tasks를 읽기/연구 관점으로 grouping하며, 3 pipelines는 더 큰 모델을 위한 training tracks이지 새로운 task tier가 아닙니다.
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- 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.
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## 공개 경계
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## 구조
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- 읽기 규칙: metric이 있으면 20개 task layer이고, evidence가 무엇을 연구하는지 설명하면 4개 research direction layer이며, model input/output과 training target을 설명하면 3개 foundation pipeline layer입니다.
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- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
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- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
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- 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.
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- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
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## 공개 경계
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README.md
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Read the project as three layers. The **20 tasks** are the scored benchmark contracts. The **4 directions** are reader-facing research groupings over those same tasks. The **3 foundation pipelines** are training recipes that reuse the same modalities, windows, and task targets. They are related, but they are not three competing task sets.
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<p align="center">
|
| 143 |
<img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, and 3 foundation-model pipeline tracks" width="100%">
|
| 144 |
</p>
|
|
@@ -297,20 +299,20 @@ These are Qwen3-Omni run versions inside **Line 2: selected 128 episodes**. They
|
|
| 297 |
</table>
|
| 298 |
|
| 299 |
Detailed lineage:
|
| 300 |
-
[
|
| 301 |
-
[
|
| 302 |
|
| 303 |
Result entry points:
|
| 304 |
-
[
|
| 305 |
-
[
|
| 306 |
-
[
|
| 307 |
-
[
|
| 308 |
-
[
|
| 309 |
-
[
|
| 310 |
-
[
|
| 311 |
-
[
|
| 312 |
-
[
|
| 313 |
-
[
|
| 314 |
|
| 315 |
## Fast Reader Map
|
| 316 |
|
|
@@ -331,22 +333,22 @@ Result entry points:
|
|
| 331 |
<tr>
|
| 332 |
<td><strong>Choose the public surface</strong></td>
|
| 333 |
<td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
|
| 334 |
-
<td><a href="docs/data/public_reader_map.json">
|
| 335 |
</tr>
|
| 336 |
<tr>
|
| 337 |
<td><strong>Decode project terms</strong></td>
|
| 338 |
<td><a href="GLOSSARY.md">Glossary</a></td>
|
| 339 |
-
<td><a href="docs/data/glossary.json">glossary
|
| 340 |
</tr>
|
| 341 |
<tr>
|
| 342 |
<td><strong>Inspect the 20 tasks</strong></td>
|
| 343 |
-
<td><a href="TASK_SUITE_20.md">
|
| 344 |
-
<td><a href="docs/data/task_suite_20.json">
|
| 345 |
</tr>
|
| 346 |
<tr>
|
| 347 |
<td><strong>Compare results</strong></td>
|
| 348 |
<td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
|
| 349 |
-
<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
|
| 350 |
</tr>
|
| 351 |
<tr>
|
| 352 |
<td><strong>Understand one sample</strong></td>
|
|
@@ -356,7 +358,7 @@ Result entry points:
|
|
| 356 |
<tr>
|
| 357 |
<td><strong>Read foundation directions</strong></td>
|
| 358 |
<td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
|
| 359 |
-
<td><a href="docs/data/three_foundation_pipelines.json">
|
| 360 |
</tr>
|
| 361 |
<tr>
|
| 362 |
<td><strong>Reproduce or audit</strong></td>
|
|
|
|
| 139 |
|
| 140 |
Read the project as three layers. The **20 tasks** are the scored benchmark contracts. The **4 directions** are reader-facing research groupings over those same tasks. The **3 foundation pipelines** are training recipes that reuse the same modalities, windows, and task targets. They are related, but they are not three competing task sets.
|
| 141 |
|
| 142 |
+
Reader rule: if it has a metric, it is a **task**; if it explains what the evidence studies, it is a **direction**; if it describes model inputs and training targets, it is a **pipeline**.
|
| 143 |
+
|
| 144 |
<p align="center">
|
| 145 |
<img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, and 3 foundation-model pipeline tracks" width="100%">
|
| 146 |
</p>
|
|
|
|
| 299 |
</table>
|
| 300 |
|
| 301 |
Detailed lineage:
|
| 302 |
+
[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md) and
|
| 303 |
+
[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json).
|
| 304 |
|
| 305 |
Result entry points:
|
| 306 |
+
[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md),
|
| 307 |
+
[`two_evidence_lines.json`](docs/data/two_evidence_lines.json),
|
| 308 |
+
[`TWO_EVIDENCE_LINE_RESULT_SUMMARY.md`](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
|
| 309 |
+
[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json),
|
| 310 |
+
[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md),
|
| 311 |
+
[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json),
|
| 312 |
+
[`single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json),
|
| 313 |
+
[`episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json),
|
| 314 |
+
[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), and
|
| 315 |
+
[`xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json).
|
| 316 |
|
| 317 |
## Fast Reader Map
|
| 318 |
|
|
|
|
| 333 |
<tr>
|
| 334 |
<td><strong>Choose the public surface</strong></td>
|
| 335 |
<td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
|
| 336 |
+
<td><a href="docs/data/public_reader_map.json">public_reader_map.json</a></td>
|
| 337 |
</tr>
|
| 338 |
<tr>
|
| 339 |
<td><strong>Decode project terms</strong></td>
|
| 340 |
<td><a href="GLOSSARY.md">Glossary</a></td>
|
| 341 |
+
<td><a href="docs/data/glossary.json">glossary.json</a></td>
|
| 342 |
</tr>
|
| 343 |
<tr>
|
| 344 |
<td><strong>Inspect the 20 tasks</strong></td>
|
| 345 |
+
<td><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a></td>
|
| 346 |
+
<td><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a></td>
|
| 347 |
</tr>
|
| 348 |
<tr>
|
| 349 |
<td><strong>Compare results</strong></td>
|
| 350 |
<td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
|
| 351 |
+
<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>
|
| 352 |
</tr>
|
| 353 |
<tr>
|
| 354 |
<td><strong>Understand one sample</strong></td>
|
|
|
|
| 358 |
<tr>
|
| 359 |
<td><strong>Read foundation directions</strong></td>
|
| 360 |
<td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
|
| 361 |
+
<td><a href="docs/data/three_foundation_pipelines.json">three_foundation_pipelines.json</a><br><a href="FOUNDATION_MODEL_PLAN.md">foundation model plan</a></td>
|
| 362 |
</tr>
|
| 363 |
<tr>
|
| 364 |
<td><strong>Reproduce or audit</strong></td>
|
README.pt.md
CHANGED
|
@@ -69,11 +69,11 @@ Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
|
|
| 69 |
|
| 70 |
## Estrutura
|
| 71 |
|
|
|
|
| 72 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
|
| 73 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
|
| 74 |
-
- Estrutura: 20 tarefas / 4 research directions / 3 foundation pipelines. As 20 tarefas são eixos de pontuação; as 4 directions agrupam as mesmas tarefas para leitura e pesquisa; as 3 pipelines são rotas de treinamento, não novos tiers de tarefas.
|
| 75 |
- 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.
|
| 76 |
-
-
|
| 77 |
|
| 78 |
## Fronteira Pública
|
| 79 |
|
|
|
|
| 69 |
|
| 70 |
## Estrutura
|
| 71 |
|
| 72 |
+
- Regra de leitura: se tem uma métrica, pertence às 20 tarefas; se explica o que a evidência estuda, pertence às 4 research directions; se define inputs/outputs de treino, pertence às 3 foundation pipelines.
|
| 73 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
|
| 74 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
|
|
|
|
| 75 |
- 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.
|
| 76 |
+
- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
|
| 77 |
|
| 78 |
## Fronteira Pública
|
| 79 |
|
README.zh.md
CHANGED
|
@@ -69,11 +69,11 @@
|
|
| 69 |
|
| 70 |
## 核心结构
|
| 71 |
|
|
|
|
| 72 |
- 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。
|
| 73 |
- 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。
|
| 74 |
-
- 结构层：20 个任务 / 4 个 research directions / 3 个 foundation pipelines。20 个任务是评分轴；4 个 directions 是同一批任务上的阅读和研究分组；3 个 pipelines 是更大模型的训练路线，不是新的任务 tier。
|
| 75 |
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-Omni v6 LoRA、Cosmos3-Super Reasoner、Cosmos3-Nano Future Window 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。
|
| 76 |
-
- 训练
|
| 77 |
|
| 78 |
## 公开边界
|
| 79 |
|
|
|
|
| 69 |
|
| 70 |
## 核心结构
|
| 71 |
|
| 72 |
+
- 识别规则：有 metric 的是 20 个任务层；解释这些 evidence 研究什么的是 4 个 research directions；描述模型 input/output 和训练目标的是 3 条 foundation pipelines。
|
| 73 |
- 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。
|
| 74 |
- 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。
|
|
|
|
| 75 |
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-Omni v6 LoRA、Cosmos3-Super Reasoner、Cosmos3-Nano Future Window 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。
|
| 76 |
+
- 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。
|
| 77 |
|
| 78 |
## 公开边界
|
| 79 |
|
data/artifact_index.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
@@ -632,7 +632,7 @@
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
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"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
-
"sha256": "
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
@@ -1182,7 +1182,7 @@
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
-
"sha256": "
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:56+00:00",
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
| 633 |
"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
+
"sha256": "3d4d51f1d18623217bfadd89f9aeaa3497f99d5e60b5743262249559a020179e"
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
+
"sha256": "cf6694f3ffb8e5fef261f32e63503121a3837d6a49375be410b3057263c21b99"
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
data/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1308,
|
|
@@ -139,44 +139,44 @@
|
|
| 139 |
"path": "repo:docs/data/artifact_index.json",
|
| 140 |
"exists": true,
|
| 141 |
"bytes": 124477,
|
| 142 |
-
"sha256": "
|
| 143 |
},
|
| 144 |
"mirrors": {
|
| 145 |
"hf_space": {
|
| 146 |
"path": "hf_space:data/artifact_index.json",
|
| 147 |
"exists": true,
|
| 148 |
"bytes": 124477,
|
| 149 |
-
"sha256": "
|
| 150 |
},
|
| 151 |
"hf_artifacts_data": {
|
| 152 |
"path": "hf_artifacts:data/artifact_index.json",
|
| 153 |
"exists": true,
|
| 154 |
"bytes": 124477,
|
| 155 |
-
"sha256": "
|
| 156 |
},
|
| 157 |
"hf_artifacts": {
|
| 158 |
"path": "hf_artifacts:docs/data/artifact_index.json",
|
| 159 |
"exists": true,
|
| 160 |
"bytes": 124477,
|
| 161 |
-
"sha256": "
|
| 162 |
},
|
| 163 |
"hf_model_data": {
|
| 164 |
"path": "hf_model:data/artifact_index.json",
|
| 165 |
"exists": true,
|
| 166 |
"bytes": 124477,
|
| 167 |
-
"sha256": "
|
| 168 |
},
|
| 169 |
"hf_model_docs_data": {
|
| 170 |
"path": "hf_model:docs/data/artifact_index.json",
|
| 171 |
"exists": true,
|
| 172 |
"bytes": 124477,
|
| 173 |
-
"sha256": "
|
| 174 |
},
|
| 175 |
"hf_model": {
|
| 176 |
"path": "hf_model:metrics/artifact_index.json",
|
| 177 |
"exists": true,
|
| 178 |
"bytes": 124477,
|
| 179 |
-
"sha256": "
|
| 180 |
}
|
| 181 |
},
|
| 182 |
"failures": []
|
|
@@ -972,44 +972,44 @@
|
|
| 972 |
"path": "repo:docs/data/publication_audit.json",
|
| 973 |
"exists": true,
|
| 974 |
"bytes": 10940,
|
| 975 |
-
"sha256": "
|
| 976 |
},
|
| 977 |
"mirrors": {
|
| 978 |
"hf_space": {
|
| 979 |
"path": "hf_space:data/publication_audit.json",
|
| 980 |
"exists": true,
|
| 981 |
"bytes": 10940,
|
| 982 |
-
"sha256": "
|
| 983 |
},
|
| 984 |
"hf_artifacts_data": {
|
| 985 |
"path": "hf_artifacts:data/publication_audit.json",
|
| 986 |
"exists": true,
|
| 987 |
"bytes": 10940,
|
| 988 |
-
"sha256": "
|
| 989 |
},
|
| 990 |
"hf_artifacts": {
|
| 991 |
"path": "hf_artifacts:docs/data/publication_audit.json",
|
| 992 |
"exists": true,
|
| 993 |
"bytes": 10940,
|
| 994 |
-
"sha256": "
|
| 995 |
},
|
| 996 |
"hf_model_data": {
|
| 997 |
"path": "hf_model:data/publication_audit.json",
|
| 998 |
"exists": true,
|
| 999 |
"bytes": 10940,
|
| 1000 |
-
"sha256": "
|
| 1001 |
},
|
| 1002 |
"hf_model_docs_data": {
|
| 1003 |
"path": "hf_model:docs/data/publication_audit.json",
|
| 1004 |
"exists": true,
|
| 1005 |
"bytes": 10940,
|
| 1006 |
-
"sha256": "
|
| 1007 |
},
|
| 1008 |
"hf_model": {
|
| 1009 |
"path": "hf_model:metrics/publication_audit.json",
|
| 1010 |
"exists": true,
|
| 1011 |
"bytes": 10940,
|
| 1012 |
-
"sha256": "
|
| 1013 |
}
|
| 1014 |
},
|
| 1015 |
"failures": []
|
|
@@ -1021,44 +1021,44 @@
|
|
| 1021 |
"path": "repo:docs/data/public_surface_qa.json",
|
| 1022 |
"exists": true,
|
| 1023 |
"bytes": 7690,
|
| 1024 |
-
"sha256": "
|
| 1025 |
},
|
| 1026 |
"mirrors": {
|
| 1027 |
"hf_space": {
|
| 1028 |
"path": "hf_space:data/public_surface_qa.json",
|
| 1029 |
"exists": true,
|
| 1030 |
"bytes": 7690,
|
| 1031 |
-
"sha256": "
|
| 1032 |
},
|
| 1033 |
"hf_artifacts_data": {
|
| 1034 |
"path": "hf_artifacts:data/public_surface_qa.json",
|
| 1035 |
"exists": true,
|
| 1036 |
"bytes": 7690,
|
| 1037 |
-
"sha256": "
|
| 1038 |
},
|
| 1039 |
"hf_artifacts": {
|
| 1040 |
"path": "hf_artifacts:docs/data/public_surface_qa.json",
|
| 1041 |
"exists": true,
|
| 1042 |
"bytes": 7690,
|
| 1043 |
-
"sha256": "
|
| 1044 |
},
|
| 1045 |
"hf_model_data": {
|
| 1046 |
"path": "hf_model:data/public_surface_qa.json",
|
| 1047 |
"exists": true,
|
| 1048 |
"bytes": 7690,
|
| 1049 |
-
"sha256": "
|
| 1050 |
},
|
| 1051 |
"hf_model_docs_data": {
|
| 1052 |
"path": "hf_model:docs/data/public_surface_qa.json",
|
| 1053 |
"exists": true,
|
| 1054 |
"bytes": 7690,
|
| 1055 |
-
"sha256": "
|
| 1056 |
},
|
| 1057 |
"hf_model": {
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"path": "hf_model:README.de.md",
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| 32409 |
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"path": "hf_model:README.ko.md",
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"path": "repo:README.pt.md",
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"path": "hf_space:README.pt.md",
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|
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|
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"path": "hf_artifacts:README.pt.md",
|
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},
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| 32459 |
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"path": "hf_model:README.pt.md",
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|
data/public_surface_qa.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
@@ -18,7 +18,7 @@
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
-
"generated_at_utc": "2026-06-
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
@@ -28,12 +28,12 @@
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
-
"generated_at_utc": "2026-06-
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
@@ -43,12 +43,12 @@
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-22T22:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:20+00:00",
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00"
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-22T22:57:51+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
data/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00",
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
data/quality_gates.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:38+00:00",
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
data/source_alignment_audit.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00",
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
data/task_surface_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00",
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
data/website_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
@@ -80,8 +80,8 @@
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
-
"overview_index":
|
| 84 |
-
"evidence_index":
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
@@ -159,9 +159,9 @@
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
-
"overview_index":
|
| 163 |
-
"protocol_index":
|
| 164 |
-
"evidence_index":
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
+
"overview_index": 184628,
|
| 84 |
+
"evidence_index": 243036
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
+
"overview_index": 184628,
|
| 163 |
+
"protocol_index": 239241,
|
| 164 |
+
"evidence_index": 243036
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
docs/data/artifact_index.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
@@ -632,7 +632,7 @@
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
| 633 |
"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
-
"sha256": "
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
@@ -1182,7 +1182,7 @@
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
-
"sha256": "
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:56+00:00",
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
| 633 |
"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
+
"sha256": "3d4d51f1d18623217bfadd89f9aeaa3497f99d5e60b5743262249559a020179e"
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
+
"sha256": "cf6694f3ffb8e5fef261f32e63503121a3837d6a49375be410b3057263c21b99"
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
docs/data/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1308,
|
|
@@ -139,44 +139,44 @@
|
|
| 139 |
"path": "repo:docs/data/artifact_index.json",
|
| 140 |
"exists": true,
|
| 141 |
"bytes": 124477,
|
| 142 |
-
"sha256": "
|
| 143 |
},
|
| 144 |
"mirrors": {
|
| 145 |
"hf_space": {
|
| 146 |
"path": "hf_space:data/artifact_index.json",
|
| 147 |
"exists": true,
|
| 148 |
"bytes": 124477,
|
| 149 |
-
"sha256": "
|
| 150 |
},
|
| 151 |
"hf_artifacts_data": {
|
| 152 |
"path": "hf_artifacts:data/artifact_index.json",
|
| 153 |
"exists": true,
|
| 154 |
"bytes": 124477,
|
| 155 |
-
"sha256": "
|
| 156 |
},
|
| 157 |
"hf_artifacts": {
|
| 158 |
"path": "hf_artifacts:docs/data/artifact_index.json",
|
| 159 |
"exists": true,
|
| 160 |
"bytes": 124477,
|
| 161 |
-
"sha256": "
|
| 162 |
},
|
| 163 |
"hf_model_data": {
|
| 164 |
"path": "hf_model:data/artifact_index.json",
|
| 165 |
"exists": true,
|
| 166 |
"bytes": 124477,
|
| 167 |
-
"sha256": "
|
| 168 |
},
|
| 169 |
"hf_model_docs_data": {
|
| 170 |
"path": "hf_model:docs/data/artifact_index.json",
|
| 171 |
"exists": true,
|
| 172 |
"bytes": 124477,
|
| 173 |
-
"sha256": "
|
| 174 |
},
|
| 175 |
"hf_model": {
|
| 176 |
"path": "hf_model:metrics/artifact_index.json",
|
| 177 |
"exists": true,
|
| 178 |
"bytes": 124477,
|
| 179 |
-
"sha256": "
|
| 180 |
}
|
| 181 |
},
|
| 182 |
"failures": []
|
|
@@ -972,44 +972,44 @@
|
|
| 972 |
"path": "repo:docs/data/publication_audit.json",
|
| 973 |
"exists": true,
|
| 974 |
"bytes": 10940,
|
| 975 |
-
"sha256": "
|
| 976 |
},
|
| 977 |
"mirrors": {
|
| 978 |
"hf_space": {
|
| 979 |
"path": "hf_space:data/publication_audit.json",
|
| 980 |
"exists": true,
|
| 981 |
"bytes": 10940,
|
| 982 |
-
"sha256": "
|
| 983 |
},
|
| 984 |
"hf_artifacts_data": {
|
| 985 |
"path": "hf_artifacts:data/publication_audit.json",
|
| 986 |
"exists": true,
|
| 987 |
"bytes": 10940,
|
| 988 |
-
"sha256": "
|
| 989 |
},
|
| 990 |
"hf_artifacts": {
|
| 991 |
"path": "hf_artifacts:docs/data/publication_audit.json",
|
| 992 |
"exists": true,
|
| 993 |
"bytes": 10940,
|
| 994 |
-
"sha256": "
|
| 995 |
},
|
| 996 |
"hf_model_data": {
|
| 997 |
"path": "hf_model:data/publication_audit.json",
|
| 998 |
"exists": true,
|
| 999 |
"bytes": 10940,
|
| 1000 |
-
"sha256": "
|
| 1001 |
},
|
| 1002 |
"hf_model_docs_data": {
|
| 1003 |
"path": "hf_model:docs/data/publication_audit.json",
|
| 1004 |
"exists": true,
|
| 1005 |
"bytes": 10940,
|
| 1006 |
-
"sha256": "
|
| 1007 |
},
|
| 1008 |
"hf_model": {
|
| 1009 |
"path": "hf_model:metrics/publication_audit.json",
|
| 1010 |
"exists": true,
|
| 1011 |
"bytes": 10940,
|
| 1012 |
-
"sha256": "
|
| 1013 |
}
|
| 1014 |
},
|
| 1015 |
"failures": []
|
|
@@ -1021,44 +1021,44 @@
|
|
| 1021 |
"path": "repo:docs/data/public_surface_qa.json",
|
| 1022 |
"exists": true,
|
| 1023 |
"bytes": 7690,
|
| 1024 |
-
"sha256": "
|
| 1025 |
},
|
| 1026 |
"mirrors": {
|
| 1027 |
"hf_space": {
|
| 1028 |
"path": "hf_space:data/public_surface_qa.json",
|
| 1029 |
"exists": true,
|
| 1030 |
"bytes": 7690,
|
| 1031 |
-
"sha256": "
|
| 1032 |
},
|
| 1033 |
"hf_artifacts_data": {
|
| 1034 |
"path": "hf_artifacts:data/public_surface_qa.json",
|
| 1035 |
"exists": true,
|
| 1036 |
"bytes": 7690,
|
| 1037 |
-
"sha256": "
|
| 1038 |
},
|
| 1039 |
"hf_artifacts": {
|
| 1040 |
"path": "hf_artifacts:docs/data/public_surface_qa.json",
|
| 1041 |
"exists": true,
|
| 1042 |
"bytes": 7690,
|
| 1043 |
-
"sha256": "
|
| 1044 |
},
|
| 1045 |
"hf_model_data": {
|
| 1046 |
"path": "hf_model:data/public_surface_qa.json",
|
| 1047 |
"exists": true,
|
| 1048 |
"bytes": 7690,
|
| 1049 |
-
"sha256": "
|
| 1050 |
},
|
| 1051 |
"hf_model_docs_data": {
|
| 1052 |
"path": "hf_model:docs/data/public_surface_qa.json",
|
| 1053 |
"exists": true,
|
| 1054 |
"bytes": 7690,
|
| 1055 |
-
"sha256": "
|
| 1056 |
},
|
| 1057 |
"hf_model": {
|
| 1058 |
"path": "hf_model:metrics/public_surface_qa.json",
|
| 1059 |
"exists": true,
|
| 1060 |
"bytes": 7690,
|
| 1061 |
-
"sha256": "
|
| 1062 |
}
|
| 1063 |
},
|
| 1064 |
"failures": []
|
|
@@ -1217,44 +1217,44 @@
|
|
| 1217 |
"path": "repo:docs/data/quality_gates.json",
|
| 1218 |
"exists": true,
|
| 1219 |
"bytes": 8640,
|
| 1220 |
-
"sha256": "
|
| 1221 |
},
|
| 1222 |
"mirrors": {
|
| 1223 |
"hf_space": {
|
| 1224 |
"path": "hf_space:data/quality_gates.json",
|
| 1225 |
"exists": true,
|
| 1226 |
"bytes": 8640,
|
| 1227 |
-
"sha256": "
|
| 1228 |
},
|
| 1229 |
"hf_artifacts_data": {
|
| 1230 |
"path": "hf_artifacts:data/quality_gates.json",
|
| 1231 |
"exists": true,
|
| 1232 |
"bytes": 8640,
|
| 1233 |
-
"sha256": "
|
| 1234 |
},
|
| 1235 |
"hf_artifacts": {
|
| 1236 |
"path": "hf_artifacts:docs/data/quality_gates.json",
|
| 1237 |
"exists": true,
|
| 1238 |
"bytes": 8640,
|
| 1239 |
-
"sha256": "
|
| 1240 |
},
|
| 1241 |
"hf_model_data": {
|
| 1242 |
"path": "hf_model:data/quality_gates.json",
|
| 1243 |
"exists": true,
|
| 1244 |
"bytes": 8640,
|
| 1245 |
-
"sha256": "
|
| 1246 |
},
|
| 1247 |
"hf_model_docs_data": {
|
| 1248 |
"path": "hf_model:docs/data/quality_gates.json",
|
| 1249 |
"exists": true,
|
| 1250 |
"bytes": 8640,
|
| 1251 |
-
"sha256": "
|
| 1252 |
},
|
| 1253 |
"hf_model": {
|
| 1254 |
"path": "hf_model:metrics/quality_gates.json",
|
| 1255 |
"exists": true,
|
| 1256 |
"bytes": 8640,
|
| 1257 |
-
"sha256": "
|
| 1258 |
}
|
| 1259 |
},
|
| 1260 |
"failures": []
|
|
@@ -1756,44 +1756,44 @@
|
|
| 1756 |
"path": "repo:docs/data/source_alignment_audit.json",
|
| 1757 |
"exists": true,
|
| 1758 |
"bytes": 4432,
|
| 1759 |
-
"sha256": "
|
| 1760 |
},
|
| 1761 |
"mirrors": {
|
| 1762 |
"hf_space": {
|
| 1763 |
"path": "hf_space:data/source_alignment_audit.json",
|
| 1764 |
"exists": true,
|
| 1765 |
"bytes": 4432,
|
| 1766 |
-
"sha256": "
|
| 1767 |
},
|
| 1768 |
"hf_artifacts_data": {
|
| 1769 |
"path": "hf_artifacts:data/source_alignment_audit.json",
|
| 1770 |
"exists": true,
|
| 1771 |
"bytes": 4432,
|
| 1772 |
-
"sha256": "
|
| 1773 |
},
|
| 1774 |
"hf_artifacts": {
|
| 1775 |
"path": "hf_artifacts:docs/data/source_alignment_audit.json",
|
| 1776 |
"exists": true,
|
| 1777 |
"bytes": 4432,
|
| 1778 |
-
"sha256": "
|
| 1779 |
},
|
| 1780 |
"hf_model_data": {
|
| 1781 |
"path": "hf_model:data/source_alignment_audit.json",
|
| 1782 |
"exists": true,
|
| 1783 |
"bytes": 4432,
|
| 1784 |
-
"sha256": "
|
| 1785 |
},
|
| 1786 |
"hf_model_docs_data": {
|
| 1787 |
"path": "hf_model:docs/data/source_alignment_audit.json",
|
| 1788 |
"exists": true,
|
| 1789 |
"bytes": 4432,
|
| 1790 |
-
"sha256": "
|
| 1791 |
},
|
| 1792 |
"hf_model": {
|
| 1793 |
"path": "hf_model:metrics/source_alignment_audit.json",
|
| 1794 |
"exists": true,
|
| 1795 |
"bytes": 4432,
|
| 1796 |
-
"sha256": "
|
| 1797 |
}
|
| 1798 |
},
|
| 1799 |
"failures": []
|
|
@@ -2197,44 +2197,44 @@
|
|
| 2197 |
"path": "repo:docs/data/task_surface_integrity.json",
|
| 2198 |
"exists": true,
|
| 2199 |
"bytes": 46399,
|
| 2200 |
-
"sha256": "
|
| 2201 |
},
|
| 2202 |
"mirrors": {
|
| 2203 |
"hf_space": {
|
| 2204 |
"path": "hf_space:data/task_surface_integrity.json",
|
| 2205 |
"exists": true,
|
| 2206 |
"bytes": 46399,
|
| 2207 |
-
"sha256": "
|
| 2208 |
},
|
| 2209 |
"hf_artifacts_data": {
|
| 2210 |
"path": "hf_artifacts:data/task_surface_integrity.json",
|
| 2211 |
"exists": true,
|
| 2212 |
"bytes": 46399,
|
| 2213 |
-
"sha256": "
|
| 2214 |
},
|
| 2215 |
"hf_artifacts": {
|
| 2216 |
"path": "hf_artifacts:docs/data/task_surface_integrity.json",
|
| 2217 |
"exists": true,
|
| 2218 |
"bytes": 46399,
|
| 2219 |
-
"sha256": "
|
| 2220 |
},
|
| 2221 |
"hf_model_data": {
|
| 2222 |
"path": "hf_model:data/task_surface_integrity.json",
|
| 2223 |
"exists": true,
|
| 2224 |
"bytes": 46399,
|
| 2225 |
-
"sha256": "
|
| 2226 |
},
|
| 2227 |
"hf_model_docs_data": {
|
| 2228 |
"path": "hf_model:docs/data/task_surface_integrity.json",
|
| 2229 |
"exists": true,
|
| 2230 |
"bytes": 46399,
|
| 2231 |
-
"sha256": "
|
| 2232 |
},
|
| 2233 |
"hf_model": {
|
| 2234 |
"path": "hf_model:metrics/task_surface_integrity.json",
|
| 2235 |
"exists": true,
|
| 2236 |
"bytes": 46399,
|
| 2237 |
-
"sha256": "
|
| 2238 |
}
|
| 2239 |
},
|
| 2240 |
"failures": []
|
|
@@ -2589,44 +2589,44 @@
|
|
| 2589 |
"path": "repo:docs/data/website_integrity.json",
|
| 2590 |
"exists": true,
|
| 2591 |
"bytes": 25144,
|
| 2592 |
-
"sha256": "
|
| 2593 |
},
|
| 2594 |
"mirrors": {
|
| 2595 |
"hf_space": {
|
| 2596 |
"path": "hf_space:data/website_integrity.json",
|
| 2597 |
"exists": true,
|
| 2598 |
"bytes": 25144,
|
| 2599 |
-
"sha256": "
|
| 2600 |
},
|
| 2601 |
"hf_artifacts_data": {
|
| 2602 |
"path": "hf_artifacts:data/website_integrity.json",
|
| 2603 |
"exists": true,
|
| 2604 |
"bytes": 25144,
|
| 2605 |
-
"sha256": "
|
| 2606 |
},
|
| 2607 |
"hf_artifacts": {
|
| 2608 |
"path": "hf_artifacts:docs/data/website_integrity.json",
|
| 2609 |
"exists": true,
|
| 2610 |
"bytes": 25144,
|
| 2611 |
-
"sha256": "
|
| 2612 |
},
|
| 2613 |
"hf_model_data": {
|
| 2614 |
"path": "hf_model:data/website_integrity.json",
|
| 2615 |
"exists": true,
|
| 2616 |
"bytes": 25144,
|
| 2617 |
-
"sha256": "
|
| 2618 |
},
|
| 2619 |
"hf_model_docs_data": {
|
| 2620 |
"path": "hf_model:docs/data/website_integrity.json",
|
| 2621 |
"exists": true,
|
| 2622 |
"bytes": 25144,
|
| 2623 |
-
"sha256": "
|
| 2624 |
},
|
| 2625 |
"hf_model": {
|
| 2626 |
"path": "hf_model:metrics/website_integrity.json",
|
| 2627 |
"exists": true,
|
| 2628 |
"bytes": 25144,
|
| 2629 |
-
"sha256": "
|
| 2630 |
}
|
| 2631 |
},
|
| 2632 |
"failures": []
|
|
@@ -6699,21 +6699,21 @@
|
|
| 6699 |
"local": {
|
| 6700 |
"path": "repo:scripts/build_multilingual_public_readmes.py",
|
| 6701 |
"exists": true,
|
| 6702 |
-
"bytes":
|
| 6703 |
-
"sha256": "
|
| 6704 |
},
|
| 6705 |
"mirrors": {
|
| 6706 |
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+
"sha256": "7653dafb12c0ede82a51e0d64c7010350e71d0510a8120c85bf90a8c29b261dd"
|
| 32383 |
},
|
| 32384 |
"mirrors": {
|
| 32385 |
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|
| 32386 |
"path": "hf_space:README.ja.md",
|
| 32387 |
"exists": true,
|
| 32388 |
+
"bytes": 7766,
|
| 32389 |
+
"sha256": "7653dafb12c0ede82a51e0d64c7010350e71d0510a8120c85bf90a8c29b261dd"
|
| 32390 |
},
|
| 32391 |
"hf_artifacts": {
|
| 32392 |
"path": "hf_artifacts:README.ja.md",
|
| 32393 |
"exists": true,
|
| 32394 |
+
"bytes": 7766,
|
| 32395 |
+
"sha256": "7653dafb12c0ede82a51e0d64c7010350e71d0510a8120c85bf90a8c29b261dd"
|
| 32396 |
},
|
| 32397 |
"hf_model": {
|
| 32398 |
"path": "hf_model:README.ja.md",
|
| 32399 |
"exists": true,
|
| 32400 |
+
"bytes": 7766,
|
| 32401 |
+
"sha256": "7653dafb12c0ede82a51e0d64c7010350e71d0510a8120c85bf90a8c29b261dd"
|
| 32402 |
}
|
| 32403 |
},
|
| 32404 |
"failures": []
|
|
|
|
| 32409 |
"local": {
|
| 32410 |
"path": "repo:README.ko.md",
|
| 32411 |
"exists": true,
|
| 32412 |
+
"bytes": 7598,
|
| 32413 |
+
"sha256": "9781111a6179bf495e93b6f3b16fc06a5e2105c87a66c6e650ba2494263ff44c"
|
| 32414 |
},
|
| 32415 |
"mirrors": {
|
| 32416 |
"hf_space": {
|
| 32417 |
"path": "hf_space:README.ko.md",
|
| 32418 |
"exists": true,
|
| 32419 |
+
"bytes": 7598,
|
| 32420 |
+
"sha256": "9781111a6179bf495e93b6f3b16fc06a5e2105c87a66c6e650ba2494263ff44c"
|
| 32421 |
},
|
| 32422 |
"hf_artifacts": {
|
| 32423 |
"path": "hf_artifacts:README.ko.md",
|
| 32424 |
"exists": true,
|
| 32425 |
+
"bytes": 7598,
|
| 32426 |
+
"sha256": "9781111a6179bf495e93b6f3b16fc06a5e2105c87a66c6e650ba2494263ff44c"
|
| 32427 |
},
|
| 32428 |
"hf_model": {
|
| 32429 |
"path": "hf_model:README.ko.md",
|
| 32430 |
"exists": true,
|
| 32431 |
+
"bytes": 7598,
|
| 32432 |
+
"sha256": "9781111a6179bf495e93b6f3b16fc06a5e2105c87a66c6e650ba2494263ff44c"
|
| 32433 |
}
|
| 32434 |
},
|
| 32435 |
"failures": []
|
|
|
|
| 32440 |
"local": {
|
| 32441 |
"path": "repo:README.pt.md",
|
| 32442 |
"exists": true,
|
| 32443 |
+
"bytes": 7539,
|
| 32444 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32445 |
},
|
| 32446 |
"mirrors": {
|
| 32447 |
"hf_space": {
|
| 32448 |
"path": "hf_space:README.pt.md",
|
| 32449 |
"exists": true,
|
| 32450 |
+
"bytes": 7539,
|
| 32451 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32452 |
},
|
| 32453 |
"hf_artifacts": {
|
| 32454 |
"path": "hf_artifacts:README.pt.md",
|
| 32455 |
"exists": true,
|
| 32456 |
+
"bytes": 7539,
|
| 32457 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32458 |
},
|
| 32459 |
"hf_model": {
|
| 32460 |
"path": "hf_model:README.pt.md",
|
| 32461 |
"exists": true,
|
| 32462 |
+
"bytes": 7539,
|
| 32463 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32464 |
}
|
| 32465 |
},
|
| 32466 |
"failures": []
|
docs/data/public_surface_qa.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
@@ -18,7 +18,7 @@
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
-
"generated_at_utc": "2026-06-
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
@@ -28,12 +28,12 @@
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
-
"generated_at_utc": "2026-06-
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
@@ -43,12 +43,12 @@
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-22T22:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:20+00:00",
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00"
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-22T22:57:51+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
docs/data/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00",
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
docs/data/quality_gates.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:38+00:00",
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
docs/data/source_alignment_audit.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00",
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
docs/data/task_surface_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00",
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
docs/data/website_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
@@ -80,8 +80,8 @@
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
-
"overview_index":
|
| 84 |
-
"evidence_index":
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
@@ -159,9 +159,9 @@
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
-
"overview_index":
|
| 163 |
-
"protocol_index":
|
| 164 |
-
"evidence_index":
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
+
"overview_index": 184628,
|
| 84 |
+
"evidence_index": 243036
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
+
"overview_index": 184628,
|
| 163 |
+
"protocol_index": 239241,
|
| 164 |
+
"evidence_index": 243036
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
docs/index.html
CHANGED
|
@@ -1875,6 +1875,43 @@
|
|
| 1875 |
font-size: 12px;
|
| 1876 |
line-height: 1.48;
|
| 1877 |
}
|
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|
| 1878 |
.task-axis-key {
|
| 1879 |
display: grid;
|
| 1880 |
grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
|
|
@@ -2264,6 +2301,40 @@
|
|
| 2264 |
font-size: 12px;
|
| 2265 |
line-height: 1.3;
|
| 2266 |
}
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|
| 2267 |
.result-matrix-body {
|
| 2268 |
padding: 22px 24px 24px;
|
| 2269 |
}
|
|
@@ -5525,6 +5596,7 @@
|
|
| 5525 |
.reader-route-list,
|
| 5526 |
.task-axis-grid,
|
| 5527 |
.task-axis-summary,
|
|
|
|
| 5528 |
.task-axis-key,
|
| 5529 |
.task-axis-visual figcaption,
|
| 5530 |
.method-flow-steps {
|
|
@@ -5820,7 +5892,7 @@
|
|
| 5820 |
width: 100%;
|
| 5821 |
justify-content: center;
|
| 5822 |
}
|
| 5823 |
-
.hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .hero-paths, .reader-journey, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order, .reader-priority-grid, .result-rules-panel, .reader-route-list, .task-axis-grid, .task-axis-summary, .task-axis-key, .task-axis-task-list, .task-axis-visual figcaption, .method-flow-steps, .suite-task-index-head { grid-template-columns: 1fr; }
|
| 5824 |
.task-axis-summary-card {
|
| 5825 |
grid-template-columns: auto 1fr;
|
| 5826 |
}
|
|
@@ -6378,6 +6450,23 @@
|
|
| 6378 |
</div>
|
| 6379 |
</article>
|
| 6380 |
</div>
|
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|
| 6381 |
<figure class="task-axis-visual">
|
| 6382 |
<img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v1" alt="Text-free visual relationship map: twenty task nodes flow into four research grouping sectors, then into three foundation-model training pipelines.">
|
| 6383 |
<figcaption aria-label="Relationship map legend">
|
|
@@ -7758,43 +7847,49 @@
|
|
| 7758 |
<div class="result-matrix-stat"><strong>174</strong><span>direct scores</span></div>
|
| 7759 |
<div class="result-matrix-stat"><strong>6</strong><span>compact-proxy scores</span></div>
|
| 7760 |
</div>
|
| 7761 |
-
<
|
| 7762 |
-
<
|
| 7763 |
-
<span
|
| 7764 |
-
<
|
| 7765 |
-
|
| 7766 |
-
|
| 7767 |
-
|
| 7768 |
-
|
| 7769 |
-
|
| 7770 |
-
<
|
| 7771 |
-
|
| 7772 |
-
|
| 7773 |
-
|
| 7774 |
-
|
| 7775 |
-
|
| 7776 |
-
<
|
| 7777 |
-
|
| 7778 |
-
|
| 7779 |
-
|
| 7780 |
-
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| 7781 |
-
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-
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-
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| 7784 |
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-
<
|
| 7789 |
-
|
| 7790 |
-
|
| 7791 |
-
<
|
| 7792 |
-
<
|
| 7793 |
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| 7794 |
-
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|
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|
| 7795 |
</div>
|
| 7796 |
-
|
| 7797 |
-
</div>
|
| 7798 |
</div>
|
| 7799 |
<div class="result-subsection" id="models">
|
| 7800 |
<div class="result-subsection-head">
|
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@@ -8174,30 +8269,30 @@
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| 8174 |
<section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
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| 8175 |
<div class="wrap">
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| 8176 |
<div class="section-head">
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| 8177 |
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<h2>
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| 8178 |
-
<p>
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</div>
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| 8180 |
<div class="resource-mode-grid" aria-label="Resource entry modes">
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| 8181 |
<article class="resource-mode">
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| 8182 |
-
<small>
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| 8183 |
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<strong>
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| 8184 |
<p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p>
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| 8185 |
<a href="#artifact-panel-public-surfaces">Open public surfaces</a>
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| 8186 |
</article>
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| 8187 |
<article class="resource-mode">
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| 8188 |
-
<small>
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| 8189 |
<strong>Check result parity</strong>
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| 8190 |
<p>Use validators, source alignment, mirror parity, and live URL/hash checks before trusting a number.</p>
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| 8191 |
<a href="#artifact-panel-checks">Open checks</a>
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| 8192 |
</article>
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| 8193 |
<article class="resource-mode">
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| 8194 |
-
<small>
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| 8195 |
<strong>Run the task pipeline</strong>
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| 8196 |
<p>Start from scripts, windows, feature manifests, task contracts, and minimal/neural result outputs.</p>
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| 8197 |
<a href="#run">Open commands</a>
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| 8198 |
</article>
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| 8199 |
<article class="resource-mode">
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| 8200 |
-
<small>
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| 8201 |
<strong>Continue model work</strong>
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| 8202 |
<p>Use Qwen3-Omni v6, Cosmos3-Super/Nano packages, the 128-episode feature index, and foundation-model plans for the next runs.</p>
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| 8203 |
<a href="#omni-scale-up">Open scale-up</a>
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@@ -8509,7 +8604,6 @@ python scripts/validate_publication_package.py</code></pre>
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| 8509 |
".result-matrix-panel",
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| 8510 |
".result-matrix-stat",
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| 8511 |
".direction-card",
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| 8512 |
-
".artifact",
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| 8513 |
".artifact-group",
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| 8514 |
".glossary-summary article"
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| 8515 |
].join(",");
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| 1875 |
font-size: 12px;
|
| 1876 |
line-height: 1.48;
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| 1877 |
}
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| 1878 |
+
.task-axis-reader-rule {
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| 1879 |
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display: grid;
|
| 1880 |
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grid-template-columns: repeat(3, minmax(0, 1fr));
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| 1881 |
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gap: 12px;
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| 1882 |
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margin: 0 0 16px;
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| 1883 |
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}
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| 1884 |
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.task-axis-reader-rule article {
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| 1885 |
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min-width: 0;
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| 1886 |
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border: 1px solid rgba(204, 255, 160, 0.18);
|
| 1887 |
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border-radius: 10px;
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| 1888 |
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background:
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| 1889 |
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linear-gradient(180deg, rgba(204, 255, 160, 0.075), rgba(2, 5, 2, 0.62)),
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| 1890 |
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rgba(2, 5, 2, 0.56);
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| 1891 |
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padding: 14px;
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| 1892 |
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}
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| 1893 |
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.task-axis-reader-rule span {
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| 1894 |
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color: var(--cyan);
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| 1895 |
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font-family: var(--font-mono);
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| 1896 |
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font-size: 10px;
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| 1897 |
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font-weight: 850;
|
| 1898 |
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letter-spacing: 0.08em;
|
| 1899 |
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text-transform: uppercase;
|
| 1900 |
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}
|
| 1901 |
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.task-axis-reader-rule strong {
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| 1902 |
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display: block;
|
| 1903 |
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margin-top: 6px;
|
| 1904 |
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color: var(--ink);
|
| 1905 |
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font-family: var(--font-ui);
|
| 1906 |
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font-size: 17px;
|
| 1907 |
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line-height: 1.12;
|
| 1908 |
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}
|
| 1909 |
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.task-axis-reader-rule p {
|
| 1910 |
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margin: 7px 0 0;
|
| 1911 |
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color: var(--muted);
|
| 1912 |
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font-size: 12.5px;
|
| 1913 |
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line-height: 1.45;
|
| 1914 |
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}
|
| 1915 |
.task-axis-key {
|
| 1916 |
display: grid;
|
| 1917 |
grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
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| 2301 |
font-size: 12px;
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| 2302 |
line-height: 1.3;
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| 2303 |
}
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| 2304 |
+
.result-matrix-details {
|
| 2305 |
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border-top: 1px solid rgba(204, 255, 160, 0.14);
|
| 2306 |
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background: rgba(2, 5, 2, 0.26);
|
| 2307 |
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}
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| 2308 |
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.result-matrix-details summary {
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| 2309 |
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list-style: none;
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| 2310 |
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cursor: pointer;
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| 2311 |
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display: grid;
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| 2312 |
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grid-template-columns: minmax(0, 0.34fr) minmax(260px, 1fr);
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| 2313 |
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gap: 16px;
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| 2314 |
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align-items: center;
|
| 2315 |
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padding: 18px 24px;
|
| 2316 |
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color: var(--muted);
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| 2317 |
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}
|
| 2318 |
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.result-matrix-details summary::-webkit-details-marker {
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| 2319 |
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display: none;
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| 2320 |
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}
|
| 2321 |
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.result-matrix-details summary span {
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| 2322 |
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color: var(--green);
|
| 2323 |
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font-family: var(--font-mono);
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| 2324 |
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font-size: 11px;
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| 2325 |
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font-weight: 820;
|
| 2326 |
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letter-spacing: 0.08em;
|
| 2327 |
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text-transform: uppercase;
|
| 2328 |
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}
|
| 2329 |
+
.result-matrix-details summary strong {
|
| 2330 |
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color: rgba(245, 247, 240, 0.76);
|
| 2331 |
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font-size: 13px;
|
| 2332 |
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line-height: 1.45;
|
| 2333 |
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}
|
| 2334 |
+
.result-matrix-details[open] summary {
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| 2335 |
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border-bottom: 1px solid rgba(204, 255, 160, 0.12);
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| 2336 |
+
background: rgba(204, 255, 160, 0.06);
|
| 2337 |
+
}
|
| 2338 |
.result-matrix-body {
|
| 2339 |
padding: 22px 24px 24px;
|
| 2340 |
}
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| 5596 |
.reader-route-list,
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| 5597 |
.task-axis-grid,
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| 5598 |
.task-axis-summary,
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| 5599 |
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.task-axis-reader-rule,
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| 5600 |
.task-axis-key,
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| 5601 |
.task-axis-visual figcaption,
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| 5602 |
.method-flow-steps {
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| 5892 |
width: 100%;
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| 5893 |
justify-content: center;
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| 5894 |
}
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| 5895 |
+
.hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .hero-paths, .reader-journey, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order, .reader-priority-grid, .result-rules-panel, .reader-route-list, .task-axis-grid, .task-axis-summary, .task-axis-reader-rule, .task-axis-key, .task-axis-task-list, .task-axis-visual figcaption, .method-flow-steps, .suite-task-index-head { grid-template-columns: 1fr; }
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| 5896 |
.task-axis-summary-card {
|
| 5897 |
grid-template-columns: auto 1fr;
|
| 5898 |
}
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|
| 6450 |
</div>
|
| 6451 |
</article>
|
| 6452 |
</div>
|
| 6453 |
+
<div class="task-axis-reader-rule" aria-label="How to identify the three public layers">
|
| 6454 |
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<article>
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| 6455 |
+
<span>score question</span>
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| 6456 |
+
<strong>If it has a metric, it belongs to the 20-task layer.</strong>
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| 6457 |
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<p>Task cards, radars, and the 180-record table all use the same numbered task IDs.</p>
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| 6458 |
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</article>
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| 6459 |
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<article>
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| 6460 |
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<span>research question</span>
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| 6461 |
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<strong>If it explains what the evidence studies, it belongs to the 4-direction layer.</strong>
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| 6462 |
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<p>Directions A-D group the same 20 tasks. They are not extra tasks or a second tier.</p>
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| 6463 |
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</article>
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| 6464 |
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<article>
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| 6465 |
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<span>training question</span>
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| 6466 |
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<strong>If it describes model inputs and targets, it belongs to the 3-pipeline layer.</strong>
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| 6467 |
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<p>Spatial, world-model, and VLA pipelines are recipes for future scale-up and task-grounded model training.</p>
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| 6468 |
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</article>
|
| 6469 |
+
</div>
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| 6470 |
<figure class="task-axis-visual">
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| 6471 |
<img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v1" alt="Text-free visual relationship map: twenty task nodes flow into four research grouping sectors, then into three foundation-model training pipelines.">
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| 6472 |
<figcaption aria-label="Relationship map legend">
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|
| 7847 |
<div class="result-matrix-stat"><strong>174</strong><span>direct scores</span></div>
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| 7848 |
<div class="result-matrix-stat"><strong>6</strong><span>compact-proxy scores</span></div>
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| 7849 |
</div>
|
| 7850 |
+
<details class="result-matrix-details">
|
| 7851 |
+
<summary>
|
| 7852 |
+
<span>open full 180-score audit table</span>
|
| 7853 |
+
<strong>Use this after the citation ledger. It contains the method summary, every method-task cell, direct/proxy badges, raw metric values, normalized radar values, and source-aligned evidence notes.</strong>
|
| 7854 |
+
</summary>
|
| 7855 |
+
<div class="result-matrix-body">
|
| 7856 |
+
<div class="result-matrix-legend" aria-label="Result table legend">
|
| 7857 |
+
<span class="matrix-legend-pill" style="--pill-color:#ccffa0"><span class="term-with-help"><span>direct task score</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></span>
|
| 7858 |
+
<span class="matrix-legend-pill" style="--pill-color:#f472b6"><span class="term-with-help"><span>compact proxy score</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></span>
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| 7859 |
+
<span class="matrix-legend-pill" style="--pill-color:#9bb8ff"><span class="term-with-help"><span>raw value is citeable</span><span class="term-help" tabindex="0" aria-label="Raw metric value: The original metric value emitted by the runner or verified package." title="The original metric value emitted by the runner or verified package.">i<span class="term-tooltip" role="tooltip">The original metric value emitted by the runner or verified package. This is the value to cite.</span></span></span></span>
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| 7860 |
+
<span class="matrix-legend-pill" style="--pill-color:#67e8d1"><span class="term-with-help"><span>normalized value is radar-only</span><span class="term-help" tabindex="0" aria-label="Normalized radar value: A 0-1 plotting value used only to draw comparable radar polygons." title="A 0-1 plotting value used only to draw comparable radar polygons.">i<span class="term-tooltip" role="tooltip">A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.</span></span></span></span>
|
| 7861 |
+
</div>
|
| 7862 |
+
<div class="method-summary-scroll" aria-label="Method summary table container">
|
| 7863 |
+
<table class="method-summary-table" id="resultMethodSummary">
|
| 7864 |
+
<thead>
|
| 7865 |
+
<tr>
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| 7866 |
+
<th><span class="term-with-help"><span>Method</span><span class="term-help" tabindex="0" aria-label="Method row: One named method family in the matrix." title="One named method family in the matrix.">i<span class="term-tooltip" role="tooltip">One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super.</span></span></span></th>
|
| 7867 |
+
<th><span class="term-with-help"><span>Line</span><span class="term-help" tabindex="0" aria-label="Evidence line: A reading lane for a group of results." title="A reading lane for a group of results.">i<span class="term-tooltip" role="tooltip">A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.</span></span></span></th>
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| 7868 |
+
<th><span class="term-with-help"><span>Records</span><span class="term-help" tabindex="0" aria-label="Task-method record: One method evaluated on one task." title="One method evaluated on one task.">i<span class="term-tooltip" role="tooltip">One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.</span></span></span></th>
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| 7869 |
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<th><span class="term-with-help"><span>Direct</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></th>
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| 7870 |
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<th><span class="term-with-help"><span>Proxy</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></th>
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| 7871 |
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<th>Scope</th>
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| 7872 |
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</tr>
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| 7873 |
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</thead>
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| 7874 |
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<tbody>
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| 7875 |
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<tr><td colspan="6">Loading result summary...</td></tr>
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| 7876 |
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</tbody>
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| 7877 |
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</table>
|
| 7878 |
+
</div>
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| 7879 |
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<div class="result-matrix-scroll" aria-label="Scrollable 180-result score table">
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| 7880 |
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<table class="result-score-table" id="resultScoreTable">
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| 7881 |
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<caption class="sr-only">Raw and normalized scores for 9 methods across 20 Xperience-10M tasks.</caption>
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| 7882 |
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<thead>
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| 7883 |
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<tr><th>Method</th><th>Loading tasks...</th></tr>
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| 7884 |
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</thead>
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| 7885 |
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<tbody>
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| 7886 |
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<tr><td colspan="2">Loading 180-result matrix...</td></tr>
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| 7887 |
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</tbody>
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| 7888 |
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</table>
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| 7889 |
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</div>
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| 7890 |
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<p class="result-matrix-foot">Best-practice reading rule: compare methods within the same evidence line first, then use the proxy badges before interpreting cross-method totals. Six compact-proxy cells are intentionally visible rather than blended into direct raw-target scores.</p>
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| 7891 |
</div>
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| 7892 |
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</details>
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| 7893 |
</div>
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| 7894 |
<div class="result-subsection" id="models">
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| 7895 |
<div class="result-subsection-head">
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| 8269 |
<section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
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| 8270 |
<div class="wrap">
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| 8271 |
<div class="section-head">
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| 8272 |
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<h2>Choose the artifact by the question you have.</h2>
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| 8273 |
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<p>If you want a public surface, a score check, a reproducible run, or the next model-training package, start with the route cards below. The full artifact library remains available after that first choice. Raw Xperience-10M data and Qwen weights are not redistributed.</p>
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| 8274 |
</div>
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| 8275 |
<div class="resource-mode-grid" aria-label="Resource entry modes">
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| 8276 |
<article class="resource-mode">
|
| 8277 |
+
<small>if you want public links</small>
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| 8278 |
+
<strong>Open the right public surface</strong>
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| 8279 |
<p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p>
|
| 8280 |
<a href="#artifact-panel-public-surfaces">Open public surfaces</a>
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| 8281 |
</article>
|
| 8282 |
<article class="resource-mode">
|
| 8283 |
+
<small>if you want to trust a score</small>
|
| 8284 |
<strong>Check result parity</strong>
|
| 8285 |
<p>Use validators, source alignment, mirror parity, and live URL/hash checks before trusting a number.</p>
|
| 8286 |
<a href="#artifact-panel-checks">Open checks</a>
|
| 8287 |
</article>
|
| 8288 |
<article class="resource-mode">
|
| 8289 |
+
<small>if you want to rerun</small>
|
| 8290 |
<strong>Run the task pipeline</strong>
|
| 8291 |
<p>Start from scripts, windows, feature manifests, task contracts, and minimal/neural result outputs.</p>
|
| 8292 |
<a href="#run">Open commands</a>
|
| 8293 |
</article>
|
| 8294 |
<article class="resource-mode">
|
| 8295 |
+
<small>if you want next models</small>
|
| 8296 |
<strong>Continue model work</strong>
|
| 8297 |
<p>Use Qwen3-Omni v6, Cosmos3-Super/Nano packages, the 128-episode feature index, and foundation-model plans for the next runs.</p>
|
| 8298 |
<a href="#omni-scale-up">Open scale-up</a>
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|
| 8604 |
".result-matrix-panel",
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| 8605 |
".result-matrix-stat",
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| 8606 |
".direction-card",
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| 8607 |
".artifact-group",
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| 8608 |
".glossary-summary article"
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| 8609 |
].join(",");
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index.html
CHANGED
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@@ -1875,6 +1875,43 @@
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font-size: 12px;
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| 1876 |
line-height: 1.48;
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}
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| 1878 |
.task-axis-key {
|
| 1879 |
display: grid;
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| 1880 |
grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
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@@ -2264,6 +2301,40 @@
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font-size: 12px;
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line-height: 1.3;
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}
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| 2267 |
.result-matrix-body {
|
| 2268 |
padding: 22px 24px 24px;
|
| 2269 |
}
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|
@@ -5525,6 +5596,7 @@
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|
| 5525 |
.reader-route-list,
|
| 5526 |
.task-axis-grid,
|
| 5527 |
.task-axis-summary,
|
|
|
|
| 5528 |
.task-axis-key,
|
| 5529 |
.task-axis-visual figcaption,
|
| 5530 |
.method-flow-steps {
|
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@@ -5820,7 +5892,7 @@
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|
| 5820 |
width: 100%;
|
| 5821 |
justify-content: center;
|
| 5822 |
}
|
| 5823 |
-
.hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .hero-paths, .reader-journey, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order, .reader-priority-grid, .result-rules-panel, .reader-route-list, .task-axis-grid, .task-axis-summary, .task-axis-key, .task-axis-task-list, .task-axis-visual figcaption, .method-flow-steps, .suite-task-index-head { grid-template-columns: 1fr; }
|
| 5824 |
.task-axis-summary-card {
|
| 5825 |
grid-template-columns: auto 1fr;
|
| 5826 |
}
|
|
@@ -6378,6 +6450,23 @@
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|
| 6378 |
</div>
|
| 6379 |
</article>
|
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</div>
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| 6381 |
<figure class="task-axis-visual">
|
| 6382 |
<img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v1" alt="Text-free visual relationship map: twenty task nodes flow into four research grouping sectors, then into three foundation-model training pipelines.">
|
| 6383 |
<figcaption aria-label="Relationship map legend">
|
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@@ -7758,43 +7847,49 @@
|
|
| 7758 |
<div class="result-matrix-stat"><strong>174</strong><span>direct scores</span></div>
|
| 7759 |
<div class="result-matrix-stat"><strong>6</strong><span>compact-proxy scores</span></div>
|
| 7760 |
</div>
|
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-
<
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<
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<span
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<
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<
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<
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</div>
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-
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-
</div>
|
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</div>
|
| 7799 |
<div class="result-subsection" id="models">
|
| 7800 |
<div class="result-subsection-head">
|
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@@ -8174,30 +8269,30 @@
|
|
| 8174 |
<section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
|
| 8175 |
<div class="wrap">
|
| 8176 |
<div class="section-head">
|
| 8177 |
-
<h2>
|
| 8178 |
-
<p>
|
| 8179 |
</div>
|
| 8180 |
<div class="resource-mode-grid" aria-label="Resource entry modes">
|
| 8181 |
<article class="resource-mode">
|
| 8182 |
-
<small>
|
| 8183 |
-
<strong>
|
| 8184 |
<p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p>
|
| 8185 |
<a href="#artifact-panel-public-surfaces">Open public surfaces</a>
|
| 8186 |
</article>
|
| 8187 |
<article class="resource-mode">
|
| 8188 |
-
<small>
|
| 8189 |
<strong>Check result parity</strong>
|
| 8190 |
<p>Use validators, source alignment, mirror parity, and live URL/hash checks before trusting a number.</p>
|
| 8191 |
<a href="#artifact-panel-checks">Open checks</a>
|
| 8192 |
</article>
|
| 8193 |
<article class="resource-mode">
|
| 8194 |
-
<small>
|
| 8195 |
<strong>Run the task pipeline</strong>
|
| 8196 |
<p>Start from scripts, windows, feature manifests, task contracts, and minimal/neural result outputs.</p>
|
| 8197 |
<a href="#run">Open commands</a>
|
| 8198 |
</article>
|
| 8199 |
<article class="resource-mode">
|
| 8200 |
-
<small>
|
| 8201 |
<strong>Continue model work</strong>
|
| 8202 |
<p>Use Qwen3-Omni v6, Cosmos3-Super/Nano packages, the 128-episode feature index, and foundation-model plans for the next runs.</p>
|
| 8203 |
<a href="#omni-scale-up">Open scale-up</a>
|
|
@@ -8509,7 +8604,6 @@ python scripts/validate_publication_package.py</code></pre>
|
|
| 8509 |
".result-matrix-panel",
|
| 8510 |
".result-matrix-stat",
|
| 8511 |
".direction-card",
|
| 8512 |
-
".artifact",
|
| 8513 |
".artifact-group",
|
| 8514 |
".glossary-summary article"
|
| 8515 |
].join(",");
|
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|
| 1875 |
font-size: 12px;
|
| 1876 |
line-height: 1.48;
|
| 1877 |
}
|
| 1878 |
+
.task-axis-reader-rule {
|
| 1879 |
+
display: grid;
|
| 1880 |
+
grid-template-columns: repeat(3, minmax(0, 1fr));
|
| 1881 |
+
gap: 12px;
|
| 1882 |
+
margin: 0 0 16px;
|
| 1883 |
+
}
|
| 1884 |
+
.task-axis-reader-rule article {
|
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+
min-width: 0;
|
| 1886 |
+
border: 1px solid rgba(204, 255, 160, 0.18);
|
| 1887 |
+
border-radius: 10px;
|
| 1888 |
+
background:
|
| 1889 |
+
linear-gradient(180deg, rgba(204, 255, 160, 0.075), rgba(2, 5, 2, 0.62)),
|
| 1890 |
+
rgba(2, 5, 2, 0.56);
|
| 1891 |
+
padding: 14px;
|
| 1892 |
+
}
|
| 1893 |
+
.task-axis-reader-rule span {
|
| 1894 |
+
color: var(--cyan);
|
| 1895 |
+
font-family: var(--font-mono);
|
| 1896 |
+
font-size: 10px;
|
| 1897 |
+
font-weight: 850;
|
| 1898 |
+
letter-spacing: 0.08em;
|
| 1899 |
+
text-transform: uppercase;
|
| 1900 |
+
}
|
| 1901 |
+
.task-axis-reader-rule strong {
|
| 1902 |
+
display: block;
|
| 1903 |
+
margin-top: 6px;
|
| 1904 |
+
color: var(--ink);
|
| 1905 |
+
font-family: var(--font-ui);
|
| 1906 |
+
font-size: 17px;
|
| 1907 |
+
line-height: 1.12;
|
| 1908 |
+
}
|
| 1909 |
+
.task-axis-reader-rule p {
|
| 1910 |
+
margin: 7px 0 0;
|
| 1911 |
+
color: var(--muted);
|
| 1912 |
+
font-size: 12.5px;
|
| 1913 |
+
line-height: 1.45;
|
| 1914 |
+
}
|
| 1915 |
.task-axis-key {
|
| 1916 |
display: grid;
|
| 1917 |
grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
|
|
|
|
| 2301 |
font-size: 12px;
|
| 2302 |
line-height: 1.3;
|
| 2303 |
}
|
| 2304 |
+
.result-matrix-details {
|
| 2305 |
+
border-top: 1px solid rgba(204, 255, 160, 0.14);
|
| 2306 |
+
background: rgba(2, 5, 2, 0.26);
|
| 2307 |
+
}
|
| 2308 |
+
.result-matrix-details summary {
|
| 2309 |
+
list-style: none;
|
| 2310 |
+
cursor: pointer;
|
| 2311 |
+
display: grid;
|
| 2312 |
+
grid-template-columns: minmax(0, 0.34fr) minmax(260px, 1fr);
|
| 2313 |
+
gap: 16px;
|
| 2314 |
+
align-items: center;
|
| 2315 |
+
padding: 18px 24px;
|
| 2316 |
+
color: var(--muted);
|
| 2317 |
+
}
|
| 2318 |
+
.result-matrix-details summary::-webkit-details-marker {
|
| 2319 |
+
display: none;
|
| 2320 |
+
}
|
| 2321 |
+
.result-matrix-details summary span {
|
| 2322 |
+
color: var(--green);
|
| 2323 |
+
font-family: var(--font-mono);
|
| 2324 |
+
font-size: 11px;
|
| 2325 |
+
font-weight: 820;
|
| 2326 |
+
letter-spacing: 0.08em;
|
| 2327 |
+
text-transform: uppercase;
|
| 2328 |
+
}
|
| 2329 |
+
.result-matrix-details summary strong {
|
| 2330 |
+
color: rgba(245, 247, 240, 0.76);
|
| 2331 |
+
font-size: 13px;
|
| 2332 |
+
line-height: 1.45;
|
| 2333 |
+
}
|
| 2334 |
+
.result-matrix-details[open] summary {
|
| 2335 |
+
border-bottom: 1px solid rgba(204, 255, 160, 0.12);
|
| 2336 |
+
background: rgba(204, 255, 160, 0.06);
|
| 2337 |
+
}
|
| 2338 |
.result-matrix-body {
|
| 2339 |
padding: 22px 24px 24px;
|
| 2340 |
}
|
|
|
|
| 5596 |
.reader-route-list,
|
| 5597 |
.task-axis-grid,
|
| 5598 |
.task-axis-summary,
|
| 5599 |
+
.task-axis-reader-rule,
|
| 5600 |
.task-axis-key,
|
| 5601 |
.task-axis-visual figcaption,
|
| 5602 |
.method-flow-steps {
|
|
|
|
| 5892 |
width: 100%;
|
| 5893 |
justify-content: center;
|
| 5894 |
}
|
| 5895 |
+
.hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .surface-map, .boundary-strip, .chart-grid, .callout-row, .direction-grid, .baseline-strip, .extension-grid, .walk-flow, .flow-steps, .storyboard-steps, .task-selector, .hero-paths, .reader-journey, .resource-mode-grid, .suite-lines, .suite-line-facts, .result-reading-order, .reader-priority-grid, .result-rules-panel, .reader-route-list, .task-axis-grid, .task-axis-summary, .task-axis-reader-rule, .task-axis-key, .task-axis-task-list, .task-axis-visual figcaption, .method-flow-steps, .suite-task-index-head { grid-template-columns: 1fr; }
|
| 5896 |
.task-axis-summary-card {
|
| 5897 |
grid-template-columns: auto 1fr;
|
| 5898 |
}
|
|
|
|
| 6450 |
</div>
|
| 6451 |
</article>
|
| 6452 |
</div>
|
| 6453 |
+
<div class="task-axis-reader-rule" aria-label="How to identify the three public layers">
|
| 6454 |
+
<article>
|
| 6455 |
+
<span>score question</span>
|
| 6456 |
+
<strong>If it has a metric, it belongs to the 20-task layer.</strong>
|
| 6457 |
+
<p>Task cards, radars, and the 180-record table all use the same numbered task IDs.</p>
|
| 6458 |
+
</article>
|
| 6459 |
+
<article>
|
| 6460 |
+
<span>research question</span>
|
| 6461 |
+
<strong>If it explains what the evidence studies, it belongs to the 4-direction layer.</strong>
|
| 6462 |
+
<p>Directions A-D group the same 20 tasks. They are not extra tasks or a second tier.</p>
|
| 6463 |
+
</article>
|
| 6464 |
+
<article>
|
| 6465 |
+
<span>training question</span>
|
| 6466 |
+
<strong>If it describes model inputs and targets, it belongs to the 3-pipeline layer.</strong>
|
| 6467 |
+
<p>Spatial, world-model, and VLA pipelines are recipes for future scale-up and task-grounded model training.</p>
|
| 6468 |
+
</article>
|
| 6469 |
+
</div>
|
| 6470 |
<figure class="task-axis-visual">
|
| 6471 |
<img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v1" alt="Text-free visual relationship map: twenty task nodes flow into four research grouping sectors, then into three foundation-model training pipelines.">
|
| 6472 |
<figcaption aria-label="Relationship map legend">
|
|
|
|
| 7847 |
<div class="result-matrix-stat"><strong>174</strong><span>direct scores</span></div>
|
| 7848 |
<div class="result-matrix-stat"><strong>6</strong><span>compact-proxy scores</span></div>
|
| 7849 |
</div>
|
| 7850 |
+
<details class="result-matrix-details">
|
| 7851 |
+
<summary>
|
| 7852 |
+
<span>open full 180-score audit table</span>
|
| 7853 |
+
<strong>Use this after the citation ledger. It contains the method summary, every method-task cell, direct/proxy badges, raw metric values, normalized radar values, and source-aligned evidence notes.</strong>
|
| 7854 |
+
</summary>
|
| 7855 |
+
<div class="result-matrix-body">
|
| 7856 |
+
<div class="result-matrix-legend" aria-label="Result table legend">
|
| 7857 |
+
<span class="matrix-legend-pill" style="--pill-color:#ccffa0"><span class="term-with-help"><span>direct task score</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></span>
|
| 7858 |
+
<span class="matrix-legend-pill" style="--pill-color:#f472b6"><span class="term-with-help"><span>compact proxy score</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></span>
|
| 7859 |
+
<span class="matrix-legend-pill" style="--pill-color:#9bb8ff"><span class="term-with-help"><span>raw value is citeable</span><span class="term-help" tabindex="0" aria-label="Raw metric value: The original metric value emitted by the runner or verified package." title="The original metric value emitted by the runner or verified package.">i<span class="term-tooltip" role="tooltip">The original metric value emitted by the runner or verified package. This is the value to cite.</span></span></span></span>
|
| 7860 |
+
<span class="matrix-legend-pill" style="--pill-color:#67e8d1"><span class="term-with-help"><span>normalized value is radar-only</span><span class="term-help" tabindex="0" aria-label="Normalized radar value: A 0-1 plotting value used only to draw comparable radar polygons." title="A 0-1 plotting value used only to draw comparable radar polygons.">i<span class="term-tooltip" role="tooltip">A 0-1 plotting value used only to draw comparable radar polygons across metrics with different scales.</span></span></span></span>
|
| 7861 |
+
</div>
|
| 7862 |
+
<div class="method-summary-scroll" aria-label="Method summary table container">
|
| 7863 |
+
<table class="method-summary-table" id="resultMethodSummary">
|
| 7864 |
+
<thead>
|
| 7865 |
+
<tr>
|
| 7866 |
+
<th><span class="term-with-help"><span>Method</span><span class="term-help" tabindex="0" aria-label="Method row: One named method family in the matrix." title="One named method family in the matrix.">i<span class="term-tooltip" role="tooltip">One named method family in the matrix, such as Minimal, 128ep Raw NN, Qwen3-Omni v6, or Cosmos3-Super.</span></span></span></th>
|
| 7867 |
+
<th><span class="term-with-help"><span>Line</span><span class="term-help" tabindex="0" aria-label="Evidence line: A reading lane for a group of results." title="A reading lane for a group of results.">i<span class="term-tooltip" role="tooltip">A reading lane for a group of results: Line 1 is one public sample episode; Line 2 is selected-128 held-out comparison.</span></span></span></th>
|
| 7868 |
+
<th><span class="term-with-help"><span>Records</span><span class="term-help" tabindex="0" aria-label="Task-method record: One method evaluated on one task." title="One method evaluated on one task.">i<span class="term-tooltip" role="tooltip">One method evaluated on one task. 9 methods x 20 tasks gives 180 public result records.</span></span></span></th>
|
| 7869 |
+
<th><span class="term-with-help"><span>Direct</span><span class="term-help" tabindex="0" aria-label="Direct score: A metric computed against the task target directly." title="A metric computed against the task target directly.">i<span class="term-tooltip" role="tooltip">A metric computed against the task target directly. This is the preferred score type in the 20-task matrix.</span></span></span></th>
|
| 7870 |
+
<th><span class="term-with-help"><span>Proxy</span><span class="term-help" tabindex="0" aria-label="Compact-proxy score: A bounded proxy metric when a direct raw target is not publicly available." title="A bounded proxy metric when a direct raw target is not publicly available.">i<span class="term-tooltip" role="tooltip">A bounded proxy metric when a direct raw target is not publicly available. It stays explicit so readers do not over-read it.</span></span></span></th>
|
| 7871 |
+
<th>Scope</th>
|
| 7872 |
+
</tr>
|
| 7873 |
+
</thead>
|
| 7874 |
+
<tbody>
|
| 7875 |
+
<tr><td colspan="6">Loading result summary...</td></tr>
|
| 7876 |
+
</tbody>
|
| 7877 |
+
</table>
|
| 7878 |
+
</div>
|
| 7879 |
+
<div class="result-matrix-scroll" aria-label="Scrollable 180-result score table">
|
| 7880 |
+
<table class="result-score-table" id="resultScoreTable">
|
| 7881 |
+
<caption class="sr-only">Raw and normalized scores for 9 methods across 20 Xperience-10M tasks.</caption>
|
| 7882 |
+
<thead>
|
| 7883 |
+
<tr><th>Method</th><th>Loading tasks...</th></tr>
|
| 7884 |
+
</thead>
|
| 7885 |
+
<tbody>
|
| 7886 |
+
<tr><td colspan="2">Loading 180-result matrix...</td></tr>
|
| 7887 |
+
</tbody>
|
| 7888 |
+
</table>
|
| 7889 |
+
</div>
|
| 7890 |
+
<p class="result-matrix-foot">Best-practice reading rule: compare methods within the same evidence line first, then use the proxy badges before interpreting cross-method totals. Six compact-proxy cells are intentionally visible rather than blended into direct raw-target scores.</p>
|
| 7891 |
</div>
|
| 7892 |
+
</details>
|
|
|
|
| 7893 |
</div>
|
| 7894 |
<div class="result-subsection" id="models">
|
| 7895 |
<div class="result-subsection-head">
|
|
|
|
| 8269 |
<section id="artifacts" data-project-tab="resources" role="tabpanel" aria-labelledby="tab-resources" tabindex="-1">
|
| 8270 |
<div class="wrap">
|
| 8271 |
<div class="section-head">
|
| 8272 |
+
<h2>Choose the artifact by the question you have.</h2>
|
| 8273 |
+
<p>If you want a public surface, a score check, a reproducible run, or the next model-training package, start with the route cards below. The full artifact library remains available after that first choice. Raw Xperience-10M data and Qwen weights are not redistributed.</p>
|
| 8274 |
</div>
|
| 8275 |
<div class="resource-mode-grid" aria-label="Resource entry modes">
|
| 8276 |
<article class="resource-mode">
|
| 8277 |
+
<small>if you want public links</small>
|
| 8278 |
+
<strong>Open the right public surface</strong>
|
| 8279 |
<p>Open GitHub, HF Space, artifact dataset, baseline models, or consolidated weights/results without guessing.</p>
|
| 8280 |
<a href="#artifact-panel-public-surfaces">Open public surfaces</a>
|
| 8281 |
</article>
|
| 8282 |
<article class="resource-mode">
|
| 8283 |
+
<small>if you want to trust a score</small>
|
| 8284 |
<strong>Check result parity</strong>
|
| 8285 |
<p>Use validators, source alignment, mirror parity, and live URL/hash checks before trusting a number.</p>
|
| 8286 |
<a href="#artifact-panel-checks">Open checks</a>
|
| 8287 |
</article>
|
| 8288 |
<article class="resource-mode">
|
| 8289 |
+
<small>if you want to rerun</small>
|
| 8290 |
<strong>Run the task pipeline</strong>
|
| 8291 |
<p>Start from scripts, windows, feature manifests, task contracts, and minimal/neural result outputs.</p>
|
| 8292 |
<a href="#run">Open commands</a>
|
| 8293 |
</article>
|
| 8294 |
<article class="resource-mode">
|
| 8295 |
+
<small>if you want next models</small>
|
| 8296 |
<strong>Continue model work</strong>
|
| 8297 |
<p>Use Qwen3-Omni v6, Cosmos3-Super/Nano packages, the 128-episode feature index, and foundation-model plans for the next runs.</p>
|
| 8298 |
<a href="#omni-scale-up">Open scale-up</a>
|
|
|
|
| 8604 |
".result-matrix-panel",
|
| 8605 |
".result-matrix-stat",
|
| 8606 |
".direction-card",
|
|
|
|
| 8607 |
".artifact-group",
|
| 8608 |
".glossary-summary article"
|
| 8609 |
].join(",");
|
metrics/artifact_index.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
@@ -632,7 +632,7 @@
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
| 633 |
"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
-
"sha256": "
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
@@ -1182,7 +1182,7 @@
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
-
"sha256": "
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Artifact Index",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:56+00:00",
|
| 4 |
"status": "pass",
|
| 5 |
"artifact_count": 228,
|
| 6 |
"missing": [],
|
|
|
|
| 632 |
"shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
|
| 633 |
"exists": true,
|
| 634 |
"bytes": 4432,
|
| 635 |
+
"sha256": "3d4d51f1d18623217bfadd89f9aeaa3497f99d5e60b5743262249559a020179e"
|
| 636 |
},
|
| 637 |
{
|
| 638 |
"id": "source_alignment_validator",
|
|
|
|
| 1182 |
"shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
|
| 1183 |
"exists": true,
|
| 1184 |
"bytes": 8640,
|
| 1185 |
+
"sha256": "cf6694f3ffb8e5fef261f32e63503121a3837d6a49375be410b3057263c21b99"
|
| 1186 |
},
|
| 1187 |
{
|
| 1188 |
"id": "public_surface_qa",
|
metrics/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1308,
|
|
@@ -139,44 +139,44 @@
|
|
| 139 |
"path": "repo:docs/data/artifact_index.json",
|
| 140 |
"exists": true,
|
| 141 |
"bytes": 124477,
|
| 142 |
-
"sha256": "
|
| 143 |
},
|
| 144 |
"mirrors": {
|
| 145 |
"hf_space": {
|
| 146 |
"path": "hf_space:data/artifact_index.json",
|
| 147 |
"exists": true,
|
| 148 |
"bytes": 124477,
|
| 149 |
-
"sha256": "
|
| 150 |
},
|
| 151 |
"hf_artifacts_data": {
|
| 152 |
"path": "hf_artifacts:data/artifact_index.json",
|
| 153 |
"exists": true,
|
| 154 |
"bytes": 124477,
|
| 155 |
-
"sha256": "
|
| 156 |
},
|
| 157 |
"hf_artifacts": {
|
| 158 |
"path": "hf_artifacts:docs/data/artifact_index.json",
|
| 159 |
"exists": true,
|
| 160 |
"bytes": 124477,
|
| 161 |
-
"sha256": "
|
| 162 |
},
|
| 163 |
"hf_model_data": {
|
| 164 |
"path": "hf_model:data/artifact_index.json",
|
| 165 |
"exists": true,
|
| 166 |
"bytes": 124477,
|
| 167 |
-
"sha256": "
|
| 168 |
},
|
| 169 |
"hf_model_docs_data": {
|
| 170 |
"path": "hf_model:docs/data/artifact_index.json",
|
| 171 |
"exists": true,
|
| 172 |
"bytes": 124477,
|
| 173 |
-
"sha256": "
|
| 174 |
},
|
| 175 |
"hf_model": {
|
| 176 |
"path": "hf_model:metrics/artifact_index.json",
|
| 177 |
"exists": true,
|
| 178 |
"bytes": 124477,
|
| 179 |
-
"sha256": "
|
| 180 |
}
|
| 181 |
},
|
| 182 |
"failures": []
|
|
@@ -972,44 +972,44 @@
|
|
| 972 |
"path": "repo:docs/data/publication_audit.json",
|
| 973 |
"exists": true,
|
| 974 |
"bytes": 10940,
|
| 975 |
-
"sha256": "
|
| 976 |
},
|
| 977 |
"mirrors": {
|
| 978 |
"hf_space": {
|
| 979 |
"path": "hf_space:data/publication_audit.json",
|
| 980 |
"exists": true,
|
| 981 |
"bytes": 10940,
|
| 982 |
-
"sha256": "
|
| 983 |
},
|
| 984 |
"hf_artifacts_data": {
|
| 985 |
"path": "hf_artifacts:data/publication_audit.json",
|
| 986 |
"exists": true,
|
| 987 |
"bytes": 10940,
|
| 988 |
-
"sha256": "
|
| 989 |
},
|
| 990 |
"hf_artifacts": {
|
| 991 |
"path": "hf_artifacts:docs/data/publication_audit.json",
|
| 992 |
"exists": true,
|
| 993 |
"bytes": 10940,
|
| 994 |
-
"sha256": "
|
| 995 |
},
|
| 996 |
"hf_model_data": {
|
| 997 |
"path": "hf_model:data/publication_audit.json",
|
| 998 |
"exists": true,
|
| 999 |
"bytes": 10940,
|
| 1000 |
-
"sha256": "
|
| 1001 |
},
|
| 1002 |
"hf_model_docs_data": {
|
| 1003 |
"path": "hf_model:docs/data/publication_audit.json",
|
| 1004 |
"exists": true,
|
| 1005 |
"bytes": 10940,
|
| 1006 |
-
"sha256": "
|
| 1007 |
},
|
| 1008 |
"hf_model": {
|
| 1009 |
"path": "hf_model:metrics/publication_audit.json",
|
| 1010 |
"exists": true,
|
| 1011 |
"bytes": 10940,
|
| 1012 |
-
"sha256": "
|
| 1013 |
}
|
| 1014 |
},
|
| 1015 |
"failures": []
|
|
@@ -1021,44 +1021,44 @@
|
|
| 1021 |
"path": "repo:docs/data/public_surface_qa.json",
|
| 1022 |
"exists": true,
|
| 1023 |
"bytes": 7690,
|
| 1024 |
-
"sha256": "
|
| 1025 |
},
|
| 1026 |
"mirrors": {
|
| 1027 |
"hf_space": {
|
| 1028 |
"path": "hf_space:data/public_surface_qa.json",
|
| 1029 |
"exists": true,
|
| 1030 |
"bytes": 7690,
|
| 1031 |
-
"sha256": "
|
| 1032 |
},
|
| 1033 |
"hf_artifacts_data": {
|
| 1034 |
"path": "hf_artifacts:data/public_surface_qa.json",
|
| 1035 |
"exists": true,
|
| 1036 |
"bytes": 7690,
|
| 1037 |
-
"sha256": "
|
| 1038 |
},
|
| 1039 |
"hf_artifacts": {
|
| 1040 |
"path": "hf_artifacts:docs/data/public_surface_qa.json",
|
| 1041 |
"exists": true,
|
| 1042 |
"bytes": 7690,
|
| 1043 |
-
"sha256": "
|
| 1044 |
},
|
| 1045 |
"hf_model_data": {
|
| 1046 |
"path": "hf_model:data/public_surface_qa.json",
|
| 1047 |
"exists": true,
|
| 1048 |
"bytes": 7690,
|
| 1049 |
-
"sha256": "
|
| 1050 |
},
|
| 1051 |
"hf_model_docs_data": {
|
| 1052 |
"path": "hf_model:docs/data/public_surface_qa.json",
|
| 1053 |
"exists": true,
|
| 1054 |
"bytes": 7690,
|
| 1055 |
-
"sha256": "
|
| 1056 |
},
|
| 1057 |
"hf_model": {
|
| 1058 |
"path": "hf_model:metrics/public_surface_qa.json",
|
| 1059 |
"exists": true,
|
| 1060 |
"bytes": 7690,
|
| 1061 |
-
"sha256": "
|
| 1062 |
}
|
| 1063 |
},
|
| 1064 |
"failures": []
|
|
@@ -1217,44 +1217,44 @@
|
|
| 1217 |
"path": "repo:docs/data/quality_gates.json",
|
| 1218 |
"exists": true,
|
| 1219 |
"bytes": 8640,
|
| 1220 |
-
"sha256": "
|
| 1221 |
},
|
| 1222 |
"mirrors": {
|
| 1223 |
"hf_space": {
|
| 1224 |
"path": "hf_space:data/quality_gates.json",
|
| 1225 |
"exists": true,
|
| 1226 |
"bytes": 8640,
|
| 1227 |
-
"sha256": "
|
| 1228 |
},
|
| 1229 |
"hf_artifacts_data": {
|
| 1230 |
"path": "hf_artifacts:data/quality_gates.json",
|
| 1231 |
"exists": true,
|
| 1232 |
"bytes": 8640,
|
| 1233 |
-
"sha256": "
|
| 1234 |
},
|
| 1235 |
"hf_artifacts": {
|
| 1236 |
"path": "hf_artifacts:docs/data/quality_gates.json",
|
| 1237 |
"exists": true,
|
| 1238 |
"bytes": 8640,
|
| 1239 |
-
"sha256": "
|
| 1240 |
},
|
| 1241 |
"hf_model_data": {
|
| 1242 |
"path": "hf_model:data/quality_gates.json",
|
| 1243 |
"exists": true,
|
| 1244 |
"bytes": 8640,
|
| 1245 |
-
"sha256": "
|
| 1246 |
},
|
| 1247 |
"hf_model_docs_data": {
|
| 1248 |
"path": "hf_model:docs/data/quality_gates.json",
|
| 1249 |
"exists": true,
|
| 1250 |
"bytes": 8640,
|
| 1251 |
-
"sha256": "
|
| 1252 |
},
|
| 1253 |
"hf_model": {
|
| 1254 |
"path": "hf_model:metrics/quality_gates.json",
|
| 1255 |
"exists": true,
|
| 1256 |
"bytes": 8640,
|
| 1257 |
-
"sha256": "
|
| 1258 |
}
|
| 1259 |
},
|
| 1260 |
"failures": []
|
|
@@ -1756,44 +1756,44 @@
|
|
| 1756 |
"path": "repo:docs/data/source_alignment_audit.json",
|
| 1757 |
"exists": true,
|
| 1758 |
"bytes": 4432,
|
| 1759 |
-
"sha256": "
|
| 1760 |
},
|
| 1761 |
"mirrors": {
|
| 1762 |
"hf_space": {
|
| 1763 |
"path": "hf_space:data/source_alignment_audit.json",
|
| 1764 |
"exists": true,
|
| 1765 |
"bytes": 4432,
|
| 1766 |
-
"sha256": "
|
| 1767 |
},
|
| 1768 |
"hf_artifacts_data": {
|
| 1769 |
"path": "hf_artifacts:data/source_alignment_audit.json",
|
| 1770 |
"exists": true,
|
| 1771 |
"bytes": 4432,
|
| 1772 |
-
"sha256": "
|
| 1773 |
},
|
| 1774 |
"hf_artifacts": {
|
| 1775 |
"path": "hf_artifacts:docs/data/source_alignment_audit.json",
|
| 1776 |
"exists": true,
|
| 1777 |
"bytes": 4432,
|
| 1778 |
-
"sha256": "
|
| 1779 |
},
|
| 1780 |
"hf_model_data": {
|
| 1781 |
"path": "hf_model:data/source_alignment_audit.json",
|
| 1782 |
"exists": true,
|
| 1783 |
"bytes": 4432,
|
| 1784 |
-
"sha256": "
|
| 1785 |
},
|
| 1786 |
"hf_model_docs_data": {
|
| 1787 |
"path": "hf_model:docs/data/source_alignment_audit.json",
|
| 1788 |
"exists": true,
|
| 1789 |
"bytes": 4432,
|
| 1790 |
-
"sha256": "
|
| 1791 |
},
|
| 1792 |
"hf_model": {
|
| 1793 |
"path": "hf_model:metrics/source_alignment_audit.json",
|
| 1794 |
"exists": true,
|
| 1795 |
"bytes": 4432,
|
| 1796 |
-
"sha256": "
|
| 1797 |
}
|
| 1798 |
},
|
| 1799 |
"failures": []
|
|
@@ -2197,44 +2197,44 @@
|
|
| 2197 |
"path": "repo:docs/data/task_surface_integrity.json",
|
| 2198 |
"exists": true,
|
| 2199 |
"bytes": 46399,
|
| 2200 |
-
"sha256": "
|
| 2201 |
},
|
| 2202 |
"mirrors": {
|
| 2203 |
"hf_space": {
|
| 2204 |
"path": "hf_space:data/task_surface_integrity.json",
|
| 2205 |
"exists": true,
|
| 2206 |
"bytes": 46399,
|
| 2207 |
-
"sha256": "
|
| 2208 |
},
|
| 2209 |
"hf_artifacts_data": {
|
| 2210 |
"path": "hf_artifacts:data/task_surface_integrity.json",
|
| 2211 |
"exists": true,
|
| 2212 |
"bytes": 46399,
|
| 2213 |
-
"sha256": "
|
| 2214 |
},
|
| 2215 |
"hf_artifacts": {
|
| 2216 |
"path": "hf_artifacts:docs/data/task_surface_integrity.json",
|
| 2217 |
"exists": true,
|
| 2218 |
"bytes": 46399,
|
| 2219 |
-
"sha256": "
|
| 2220 |
},
|
| 2221 |
"hf_model_data": {
|
| 2222 |
"path": "hf_model:data/task_surface_integrity.json",
|
| 2223 |
"exists": true,
|
| 2224 |
"bytes": 46399,
|
| 2225 |
-
"sha256": "
|
| 2226 |
},
|
| 2227 |
"hf_model_docs_data": {
|
| 2228 |
"path": "hf_model:docs/data/task_surface_integrity.json",
|
| 2229 |
"exists": true,
|
| 2230 |
"bytes": 46399,
|
| 2231 |
-
"sha256": "
|
| 2232 |
},
|
| 2233 |
"hf_model": {
|
| 2234 |
"path": "hf_model:metrics/task_surface_integrity.json",
|
| 2235 |
"exists": true,
|
| 2236 |
"bytes": 46399,
|
| 2237 |
-
"sha256": "
|
| 2238 |
}
|
| 2239 |
},
|
| 2240 |
"failures": []
|
|
@@ -2589,44 +2589,44 @@
|
|
| 2589 |
"path": "repo:docs/data/website_integrity.json",
|
| 2590 |
"exists": true,
|
| 2591 |
"bytes": 25144,
|
| 2592 |
-
"sha256": "
|
| 2593 |
},
|
| 2594 |
"mirrors": {
|
| 2595 |
"hf_space": {
|
| 2596 |
"path": "hf_space:data/website_integrity.json",
|
| 2597 |
"exists": true,
|
| 2598 |
"bytes": 25144,
|
| 2599 |
-
"sha256": "
|
| 2600 |
},
|
| 2601 |
"hf_artifacts_data": {
|
| 2602 |
"path": "hf_artifacts:data/website_integrity.json",
|
| 2603 |
"exists": true,
|
| 2604 |
"bytes": 25144,
|
| 2605 |
-
"sha256": "
|
| 2606 |
},
|
| 2607 |
"hf_artifacts": {
|
| 2608 |
"path": "hf_artifacts:docs/data/website_integrity.json",
|
| 2609 |
"exists": true,
|
| 2610 |
"bytes": 25144,
|
| 2611 |
-
"sha256": "
|
| 2612 |
},
|
| 2613 |
"hf_model_data": {
|
| 2614 |
"path": "hf_model:data/website_integrity.json",
|
| 2615 |
"exists": true,
|
| 2616 |
"bytes": 25144,
|
| 2617 |
-
"sha256": "
|
| 2618 |
},
|
| 2619 |
"hf_model_docs_data": {
|
| 2620 |
"path": "hf_model:docs/data/website_integrity.json",
|
| 2621 |
"exists": true,
|
| 2622 |
"bytes": 25144,
|
| 2623 |
-
"sha256": "
|
| 2624 |
},
|
| 2625 |
"hf_model": {
|
| 2626 |
"path": "hf_model:metrics/website_integrity.json",
|
| 2627 |
"exists": true,
|
| 2628 |
"bytes": 25144,
|
| 2629 |
-
"sha256": "
|
| 2630 |
}
|
| 2631 |
},
|
| 2632 |
"failures": []
|
|
@@ -6699,21 +6699,21 @@
|
|
| 6699 |
"local": {
|
| 6700 |
"path": "repo:scripts/build_multilingual_public_readmes.py",
|
| 6701 |
"exists": true,
|
| 6702 |
-
"bytes":
|
| 6703 |
-
"sha256": "
|
| 6704 |
},
|
| 6705 |
"mirrors": {
|
| 6706 |
"hf_artifacts": {
|
| 6707 |
"path": "hf_artifacts:scripts/build_multilingual_public_readmes.py",
|
| 6708 |
"exists": true,
|
| 6709 |
-
"bytes":
|
| 6710 |
-
"sha256": "
|
| 6711 |
},
|
| 6712 |
"hf_model": {
|
| 6713 |
"path": "hf_model:scripts/build_multilingual_public_readmes.py",
|
| 6714 |
"exists": true,
|
| 6715 |
-
"bytes":
|
| 6716 |
-
"sha256": "
|
| 6717 |
}
|
| 6718 |
},
|
| 6719 |
"failures": []
|
|
@@ -7503,39 +7503,39 @@
|
|
| 7503 |
"local": {
|
| 7504 |
"path": "repo:docs/index.html",
|
| 7505 |
"exists": true,
|
| 7506 |
-
"bytes":
|
| 7507 |
-
"sha256": "
|
| 7508 |
},
|
| 7509 |
"mirrors": {
|
| 7510 |
"hf_space": {
|
| 7511 |
"path": "hf_space:index.html",
|
| 7512 |
"exists": true,
|
| 7513 |
-
"bytes":
|
| 7514 |
-
"sha256": "
|
| 7515 |
},
|
| 7516 |
"hf_artifacts_root": {
|
| 7517 |
"path": "hf_artifacts:index.html",
|
| 7518 |
"exists": true,
|
| 7519 |
-
"bytes":
|
| 7520 |
-
"sha256": "
|
| 7521 |
},
|
| 7522 |
"hf_artifacts_docs": {
|
| 7523 |
"path": "hf_artifacts:docs/index.html",
|
| 7524 |
"exists": true,
|
| 7525 |
-
"bytes":
|
| 7526 |
-
"sha256": "
|
| 7527 |
},
|
| 7528 |
"hf_model": {
|
| 7529 |
"path": "hf_model:index.html",
|
| 7530 |
"exists": true,
|
| 7531 |
-
"bytes":
|
| 7532 |
-
"sha256": "
|
| 7533 |
},
|
| 7534 |
"hf_model_docs": {
|
| 7535 |
"path": "hf_model:docs/index.html",
|
| 7536 |
"exists": true,
|
| 7537 |
-
"bytes":
|
| 7538 |
-
"sha256": "
|
| 7539 |
}
|
| 7540 |
},
|
| 7541 |
"failures": []
|
|
@@ -32254,27 +32254,27 @@
|
|
| 32254 |
"local": {
|
| 32255 |
"path": "repo:README.zh.md",
|
| 32256 |
"exists": true,
|
| 32257 |
-
"bytes":
|
| 32258 |
-
"sha256": "
|
| 32259 |
},
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| 32431 |
+
"bytes": 7598,
|
| 32432 |
+
"sha256": "9781111a6179bf495e93b6f3b16fc06a5e2105c87a66c6e650ba2494263ff44c"
|
| 32433 |
}
|
| 32434 |
},
|
| 32435 |
"failures": []
|
|
|
|
| 32440 |
"local": {
|
| 32441 |
"path": "repo:README.pt.md",
|
| 32442 |
"exists": true,
|
| 32443 |
+
"bytes": 7539,
|
| 32444 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32445 |
},
|
| 32446 |
"mirrors": {
|
| 32447 |
"hf_space": {
|
| 32448 |
"path": "hf_space:README.pt.md",
|
| 32449 |
"exists": true,
|
| 32450 |
+
"bytes": 7539,
|
| 32451 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32452 |
},
|
| 32453 |
"hf_artifacts": {
|
| 32454 |
"path": "hf_artifacts:README.pt.md",
|
| 32455 |
"exists": true,
|
| 32456 |
+
"bytes": 7539,
|
| 32457 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32458 |
},
|
| 32459 |
"hf_model": {
|
| 32460 |
"path": "hf_model:README.pt.md",
|
| 32461 |
"exists": true,
|
| 32462 |
+
"bytes": 7539,
|
| 32463 |
+
"sha256": "4e42f71d7ca95b0fabb76442c6fb66476716bd3d32416c335bda36a6da68a61a"
|
| 32464 |
}
|
| 32465 |
},
|
| 32466 |
"failures": []
|
metrics/public_surface_qa.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
@@ -18,7 +18,7 @@
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
-
"generated_at_utc": "2026-06-
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
@@ -28,12 +28,12 @@
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
-
"generated_at_utc": "2026-06-
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
@@ -43,12 +43,12 @@
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-22T22:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:20+00:00",
|
| 5 |
"scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
|
| 6 |
"checks": [
|
| 7 |
{
|
|
|
|
| 18 |
"website_integrity": {
|
| 19 |
"exists": true,
|
| 20 |
"status": "pass",
|
| 21 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 28 |
"task_surface_integrity": {
|
| 29 |
"exists": true,
|
| 30 |
"status": "pass",
|
| 31 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00"
|
| 32 |
},
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
|
|
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-22T22:57:51+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
metrics/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:23:03+00:00",
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
metrics/quality_gates.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Release Checks",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:23:38+00:00",
|
| 5 |
"rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
|
| 6 |
"automated_gates": [
|
| 7 |
{
|
metrics/source_alignment_audit.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
-
"generated_at_utc": "2026-06-
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Source Alignment Note",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-22T23:22:08+00:00",
|
| 5 |
"alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
|
| 6 |
"alignment_summary": {
|
| 7 |
"full_dataset_repo": "ropedia-ai/xperience-10m",
|
metrics/task_surface_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:22:25+00:00",
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
metrics/website_integrity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
@@ -80,8 +80,8 @@
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
-
"overview_index":
|
| 84 |
-
"evidence_index":
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
@@ -159,9 +159,9 @@
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
-
"overview_index":
|
| 163 |
-
"protocol_index":
|
| 164 |
-
"evidence_index":
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-22T23:21:49+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 80 |
"name": "project_overview_precedes_progress_ledger",
|
| 81 |
"status": "pass",
|
| 82 |
"reason": "The project overview should appear before the deeper progress ledger.",
|
| 83 |
+
"overview_index": 184628,
|
| 84 |
+
"evidence_index": 243036
|
| 85 |
},
|
| 86 |
{
|
| 87 |
"name": "project_status_links_json",
|
|
|
|
| 159 |
"name": "evaluation_protocol_between_overview_and_progress",
|
| 160 |
"status": "pass",
|
| 161 |
"reason": "The evaluation protocol should appear before the deeper evidence ledger.",
|
| 162 |
+
"overview_index": 184628,
|
| 163 |
+
"protocol_index": 239241,
|
| 164 |
+
"evidence_index": 243036
|
| 165 |
},
|
| 166 |
{
|
| 167 |
"name": "evaluation_protocol_links_json",
|
scripts/build_multilingual_public_readmes.py
CHANGED
|
@@ -122,6 +122,10 @@ The multilingual README files are reader guides. The canonical technical evidenc
|
|
| 122 |
<td><strong>20 task contracts</strong></td>
|
| 123 |
<td>Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.</td>
|
| 124 |
</tr>
|
|
|
|
|
|
|
|
|
|
|
|
|
| 125 |
<tr>
|
| 126 |
<td><strong>Line 1 methods</strong></td>
|
| 127 |
<td>Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode: 40/40 direct scores.</td>
|
|
@@ -131,8 +135,8 @@ The multilingual README files are reader guides. The canonical technical evidenc
|
|
| 131 |
<td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window cover all 20 selected-128 task axes: 140/140 scores.</td>
|
| 132 |
</tr>
|
| 133 |
<tr>
|
| 134 |
-
<td><strong>
|
| 135 |
-
<td>Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as
|
| 136 |
</tr>
|
| 137 |
<tr>
|
| 138 |
<td><strong>Public mirrors</strong></td>
|
|
@@ -141,6 +145,22 @@ The multilingual README files are reader guides. The canonical technical evidenc
|
|
| 141 |
</tbody>
|
| 142 |
</table>
|
| 143 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 144 |
## Two Evidence Lines
|
| 145 |
|
| 146 |
The public suite is organized around two evidence lines. Keep them separate when reading metrics.
|
|
@@ -399,6 +419,7 @@ LANGUAGE_GUIDES = {
|
|
| 399 |
|
| 400 |
## 核心结构
|
| 401 |
|
|
|
|
| 402 |
- 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。
|
| 403 |
- 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。
|
| 404 |
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-Omni v6 LoRA、Cosmos3-Super Reasoner、Cosmos3-Nano Future Window 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。
|
|
@@ -447,6 +468,7 @@ Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
|
|
| 447 |
|
| 448 |
## Estructura
|
| 449 |
|
|
|
|
| 450 |
- Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
|
| 451 |
- Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
|
| 452 |
- 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.
|
|
@@ -495,6 +517,7 @@ Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_line
|
|
| 495 |
|
| 496 |
## Structure
|
| 497 |
|
|
|
|
| 498 |
- Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
|
| 499 |
- Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
|
| 500 |
- Résultats : minimal/NN sur l'épisode public couvrent 20/20; la ligne 128 épisodes sépare metadata, raw features, Qwen3-Omni et Cosmos3; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
|
|
@@ -543,6 +566,7 @@ Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
|
|
| 543 |
|
| 544 |
## Struktur
|
| 545 |
|
|
|
|
| 546 |
- Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
|
| 547 |
- Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
|
| 548 |
- 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.
|
|
@@ -591,6 +615,7 @@ Method blocks: Line 1 は task-head baselines（Minimal、Neural MLP）。Line 2
|
|
| 591 |
|
| 592 |
## 構造
|
| 593 |
|
|
|
|
| 594 |
- データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
|
| 595 |
- タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
|
| 596 |
- 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。
|
|
@@ -639,6 +664,7 @@ Method blocks: Line 1 は task-head baselines（Minimal、Neural MLP）。Line 2
|
|
| 639 |
|
| 640 |
## 구조
|
| 641 |
|
|
|
|
| 642 |
- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
|
| 643 |
- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
|
| 644 |
- 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.
|
|
@@ -687,6 +713,7 @@ Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines
|
|
| 687 |
|
| 688 |
## Estrutura
|
| 689 |
|
|
|
|
| 690 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
|
| 691 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
|
| 692 |
- 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.
|
|
|
|
| 122 |
<td><strong>20 task contracts</strong></td>
|
| 123 |
<td>Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.</td>
|
| 124 |
</tr>
|
| 125 |
+
<tr>
|
| 126 |
+
<td><strong>4 research directions</strong></td>
|
| 127 |
+
<td>Human Modeling & Motion Understanding; 3D/4D Reconstruction & Neural Rendering; Egocentric Vision & Interaction; Scene Reconstruction & World Modeling. These are reading groups over the same 20 tasks, not separate benchmark tiers.</td>
|
| 128 |
+
</tr>
|
| 129 |
<tr>
|
| 130 |
<td><strong>Line 1 methods</strong></td>
|
| 131 |
<td>Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode: 40/40 direct scores.</td>
|
|
|
|
| 135 |
<td>Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni v6 LoRA, Cosmos3-Super Reasoner, and Cosmos3-Nano Future Window cover all 20 selected-128 task axes: 140/140 scores.</td>
|
| 136 |
</tr>
|
| 137 |
<tr>
|
| 138 |
+
<td><strong>3 foundation pipelines</strong></td>
|
| 139 |
+
<td>Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as training recipes with task mappings, input-output contracts, and model-evidence requirements.</td>
|
| 140 |
</tr>
|
| 141 |
<tr>
|
| 142 |
<td><strong>Public mirrors</strong></td>
|
|
|
|
| 145 |
</tbody>
|
| 146 |
</table>
|
| 147 |
|
| 148 |
+
## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines
|
| 149 |
+
|
| 150 |
+
Read the project as three layers. The **20 tasks** are the scored benchmark contracts. The **4 directions** are reader-facing research groupings over those same tasks. The **3 foundation pipelines** are training recipes that reuse the same modalities, windows, and task targets. They are related, but they are not three competing task sets.
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+
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Reader rule: if it has a metric, it is a **task**; if it explains what the evidence studies, it is a **direction**; if it describes model inputs and training targets, it is a **pipeline**.
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+
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+
<p align="center">
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<img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, and 3 foundation-model pipeline tracks" width="100%">
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</p>
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+
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+
| Layer | Count | Reader role | Exact public labels |
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| 159 |
+
| --- | ---: | --- | --- |
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| 160 |
+
| Task contracts | 20 | Score axes used by the matrix, radars, task cards, and method rows. | Action Recognition; Procedure Step Recognition; Action Boundary Detection; Next-Action Prediction; Hand Trajectory Forecasting; Contact State Prediction; Object Relevance Prediction; Language Grounding; Cross-Modal Retrieval; Cross-Modal Reconstruction; Temporal Order Verification; Multimodal Synchronization Detection; Long-Horizon Next-Action Forecasting; Long-Horizon Next-Subtask Forecasting; Interaction Text Prediction; Action-Object Relation Prediction; Future Object-Set Forecasting; IMU-to-Hand Pose Reconstruction; Camera-View Synchronization Retrieval; Time-to-Next-Transition Regression. |
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| Research directions | 4 | Ways to interpret what the 20 tasks study; not separate benchmark tiers. | Human Modeling & Motion Understanding; 3D/4D Reconstruction & Neural Rendering; Egocentric Vision & Interaction; Scene Reconstruction & World Modeling. |
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| Foundation pipelines | 3 | Larger-model training tracks with separate input-output recipes and result gates. | Spatial intelligence models; Human-video world models; Vision-language-action models. |
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+
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## Two Evidence Lines
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| 166 |
The public suite is organized around two evidence lines. Keep them separate when reading metrics.
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| 419 |
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## 核心结构
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| 421 |
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| 422 |
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- 识别规则：有 metric 的是 20 个任务层；解释这些 evidence 研究什么的是 4 个 research directions；描述模型 input/output 和训练目标的是 3 条 foundation pipelines。
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| 423 |
- 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。
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| 424 |
- 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。
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| 425 |
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-Omni v6 LoRA、Cosmos3-Super Reasoner、Cosmos3-Nano Future Window 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。
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| 468 |
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| 469 |
## Estructura
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| 470 |
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| 471 |
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- Regla de lectura: si tiene una métrica, es una tarea de las 20; si explica qué estudia la evidencia, es una de las 4 research directions; si define inputs/outputs de entrenamiento, es una de las 3 foundation pipelines.
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| 472 |
- Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
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| 473 |
- Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
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| 474 |
- 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.
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| 517 |
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| 518 |
## Structure
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| 519 |
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| 520 |
+
- Règle de lecture : avec une métrique, c'est l'une des 20 tâches; si cela explique ce que les preuves étudient, c'est l'une des 4 research directions; si cela définit des inputs/outputs d'entraînement, c'est l'une des 3 foundation pipelines.
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| 521 |
- Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
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| 522 |
- Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
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| 523 |
- Résultats : minimal/NN sur l'épisode public couvrent 20/20; la ligne 128 épisodes sépare metadata, raw features, Qwen3-Omni et Cosmos3; la matrice publique atteint 180/180 enregistrements scorés: 174 direct et 6 compact proxy, avec proxy targets visibles.
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| 566 |
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| 567 |
## Struktur
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| 568 |
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| 569 |
+
- Leseregel: Hat es eine Metrik, gehört es zu den 20 Aufgaben; erklärt es, was die Evidenz untersucht, gehört es zu den 4 research directions; beschreibt es Trainings-Inputs und Targets, gehört es zu den 3 foundation pipelines.
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| 570 |
- Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
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| 571 |
- Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
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| 572 |
- 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.
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| 615 |
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| 616 |
## 構造
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| 617 |
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| 618 |
+
- 読み方のルール: metric があるものは 20 tasks、evidence が何を調べるかを説明するものは 4 research directions、training input/output を定義するものは 3 foundation pipelines です。
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| 619 |
- データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
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| 620 |
- タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
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| 621 |
- 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。
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|
| 664 |
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| 665 |
## 구조
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| 666 |
|
| 667 |
+
- 읽기 규칙: metric이 있으면 20개 task layer이고, evidence가 무엇을 연구하는지 설명하면 4개 research direction layer이며, model input/output과 training target을 설명하면 3개 foundation pipeline layer입니다.
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| 668 |
- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
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| 669 |
- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
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| 670 |
- 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.
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| 713 |
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| 714 |
## Estrutura
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| 715 |
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| 716 |
+
- Regra de leitura: se tem uma métrica, pertence às 20 tarefas; se explica o que a evidência estuda, pertence às 4 research directions; se define inputs/outputs de treino, pertence às 3 foundation pipelines.
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| 717 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
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| 718 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
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| 719 |
- 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.
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