Align multilingual evidence-line documentation
Browse files- README.de.md +12 -2
- README.es.md +12 -2
- README.fr.md +12 -2
- README.ja.md +12 -2
- README.ko.md +12 -2
- README.pt.md +12 -2
- README.zh.md +12 -2
- data/language_versions.json +1 -1
- data/mirror_parity.json +119 -119
- data/public_surface_qa.json +6 -6
- data/publication_audit.json +1 -1
- data/quality_gates.json +1 -1
- data/scope_claims_audit.json +1 -1
- data/source_alignment_audit.json +1 -1
- data/task_surface_integrity.json +1 -1
- data/website_integrity.json +1 -1
- docs/data/language_versions.json +1 -1
- docs/data/mirror_parity.json +119 -119
- docs/data/public_surface_qa.json +6 -6
- docs/data/publication_audit.json +1 -1
- docs/data/quality_gates.json +1 -1
- docs/data/scope_claims_audit.json +1 -1
- docs/data/source_alignment_audit.json +1 -1
- docs/data/task_surface_integrity.json +1 -1
- docs/data/website_integrity.json +1 -1
- metrics/language_versions.json +1 -1
- metrics/mirror_parity.json +119 -119
- metrics/public_surface_qa.json +6 -6
- metrics/publication_audit.json +1 -1
- metrics/quality_gates.json +1 -1
- metrics/scope_claims_audit.json +1 -1
- metrics/source_alignment_audit.json +1 -1
- metrics/task_surface_integrity.json +1 -1
- metrics/website_integrity.json +1 -1
README.de.md
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Dieses Repository macht aus dem öffentlichen Xperience-10M-Sample eine prüfbare Aufgabenoberfläche für Embodied AI. Beginnen Sie mit Dashboard und Projektstatus, danach mit Aufgabenverträgen, Ergebnismatrizen und Hugging-Face-Spiegeln.
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**Aktualisiert:** 2026-06-
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**Umfang:** die vollständig reproduzierbare Suite nutzt ein öffentliches Sample-Episode; 128-Episode-Ergebnisse veröffentlichen nur public-safe Metriken, Berichte, Vorhersagen und Modellkarten. Rohdaten wie MP4/HDF5/RRD, vollständige Qwen-Gewichte und gated Daten werden nicht weitergegeben.
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## Schneller Einstieg
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| Ziel | Einstieg |
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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 mit sichtbaren
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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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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Dieses Repository macht aus dem öffentlichen Xperience-10M-Sample eine prüfbare Aufgabenoberfläche für Embodied AI. Beginnen Sie mit Dashboard und Projektstatus, danach mit Aufgabenverträgen, Ergebnismatrizen und Hugging-Face-Spiegeln.
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**Aktualisiert:** 2026-06-21.
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**Umfang:** die vollständig reproduzierbare Suite nutzt ein öffentliches Sample-Episode; 128-Episode-Ergebnisse veröffentlichen nur public-safe Metriken, Berichte, Vorhersagen und Modellkarten. Rohdaten wie MP4/HDF5/RRD, vollständige Qwen-Gewichte und gated Daten werden nicht weitergegeben.
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## Zwei Evidenzlinien
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| Linie | Dateneinheit | Methoden und Ergebnisse | Zweck |
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| --- | --- | --- | --- |
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| 1 Sample-Episode | 5,821 Frames; 1,161 ausgerichtete 20-Frame-Fenster; 8,546 Dimensionen. | Minimal + Neural MLP auf 20 Aufgaben; 40/40 gescorte Einträge. | Sample-Dateien, Aufgaben, reproduzierbare Baselines und Aufgabenqualität prüfen. |
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| 128 ausgewählte Episoden | 96/16/16 Split; 34,269 exportierte Fenster; public-safe Features mit offiziellen gated Episode-Pfaden. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super und Cosmos3-Nano; 140/140 gescorte Einträge. | Baselines und Modellzweige auf demselben Split vergleichen; Proxy-Targets bleiben sichtbar. |
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Einstieg: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
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## Schneller Einstieg
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| Ziel | Einstieg |
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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 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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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README.es.md
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Este repositorio convierte el episodio público de muestra de Xperience-10M en un laboratorio verificable de tareas para embodied AI. Empieza por el panel visual y el estado del proyecto; después entra en las tareas, matrices de resultados y espejos de Hugging Face.
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**Actualizado:** 2026-06-
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**Alcance:** la suite reproducible usa un episodio público; los resultados de 128 episodios publican solo métricas, reportes, predicciones seguras y tarjetas de modelo. No se redistribuyen MP4/HDF5/RRD originales, pesos completos de Qwen ni datos gated.
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## Ruta Rápida
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| Objetivo | Entrada |
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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 con
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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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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Este repositorio convierte el episodio público de muestra de Xperience-10M en un laboratorio verificable de tareas para embodied AI. Empieza por el panel visual y el estado del proyecto; después entra en las tareas, matrices de resultados y espejos de Hugging Face.
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**Actualizado:** 2026-06-21.
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**Alcance:** la suite reproducible usa un episodio público; los resultados de 128 episodios publican solo métricas, reportes, predicciones seguras y tarjetas de modelo. No se redistribuyen MP4/HDF5/RRD originales, pesos completos de Qwen ni datos gated.
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## Dos Líneas de Evidencia
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| Línea | Unidad de datos | Métodos y resultados | Uso |
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| --- | --- | --- | --- |
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| 1 episodio de muestra | 5,821 frames; 1,161 ventanas alineadas de 20 frames; 8,546 dimensiones. | Minimal + Neural MLP en 20 tareas; 40/40 registros con score. | Inspeccionar archivos de muestra, definiciones de tarea, baselines reproducibles y validez de tareas. |
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| 128 episodios seleccionados | Split 96/16/16; 34,269 ventanas exportadas; features public-safe ligadas a episode paths oficiales gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super y Cosmos3-Nano; 140/140 registros con score. | Comparar baselines y ramas de modelo en el mismo split; los proxy targets permanecen visibles. |
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Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
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## Ruta Rápida
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| Objetivo | Entrada |
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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 y mantiene visibles los proxy targets.
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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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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README.fr.md
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Ce dépôt transforme l'épisode public d'exemple Xperience-10M en laboratoire de tâches vérifiable pour l'IA incarnée. Commencez par le tableau de bord et le statut du projet, puis ouvrez les contrats de tâches, les matrices de résultats et les miroirs Hugging Face.
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**Mise à jour :** 2026-06-
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**Portée :** la suite entièrement reproductible utilise un épisode public; les résultats 128 épisodes ne publient que des métriques, rapports, prédictions sûres et cartes de modèles. Les MP4/HDF5/RRD bruts, les poids Qwen complets et les données gated ne sont pas redistribués.
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## Parcours Rapide
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| Objectif | Point d'entrée |
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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; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos avec
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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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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Ce dépôt transforme l'épisode public d'exemple Xperience-10M en laboratoire de tâches vérifiable pour l'IA incarnée. Commencez par le tableau de bord et le statut du projet, puis ouvrez les contrats de tâches, les matrices de résultats et les miroirs Hugging Face.
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**Mise à jour :** 2026-06-21.
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**Portée :** la suite entièrement reproductible utilise un épisode public; les résultats 128 épisodes ne publient que des métriques, rapports, prédictions sûres et cartes de modèles. Les MP4/HDF5/RRD bruts, les poids Qwen complets et les données gated ne sont pas redistribués.
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## Deux Lignes de Preuve
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| Ligne | Unité de données | Méthodes et résultats | Usage |
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| --- | --- | --- | --- |
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| 1 épisode d'exemple | 5,821 frames; 1,161 fenêtres alignées de 20 frames; 8,546 dimensions. | Minimal + Neural MLP sur 20 tâches; 40/40 enregistrements scorés. | Inspecter les fichiers sample, les définitions de tâches, les baselines reproductibles et la validité des tâches. |
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| 128 épisodes sélectionnés | Split 96/16/16; 34,269 fenêtres exportées; features public-safe liées aux chemins gated officiels. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super et Cosmos3-Nano; 140/140 enregistrements scorés. | Comparer les baselines et branches de modèles sur le même split; les proxy targets restent visibles. |
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Entrées : [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
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## Parcours Rapide
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| Objectif | Point d'entrée |
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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; les branches 128 épisodes séparent metadata, raw features, Qwen3 et Cosmos; la matrice publique atteint 180/180 enregistrements scorés 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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
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README.ja.md
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このリポジトリは、公開 Xperience-10M サンプル episode を、検証可能な embodied AI タスク実験面に変換します。まずダッシュボードとプロジェクト状態を見て、その後 20 タスク、結果行列、Hugging Face ミラーを確認してください。
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**更新日:** 2026-06-
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**範囲:** 完全に再現可能なタスク suite は 1 つの公開サンプル episode に基づきます。128-episode の結果は public-safe な指標、レポート、予測要約、モデルカードのみを公開します。元の MP4/HDF5/RRD、完全な Qwen 重み、gated データは再配布しません。
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## クイックルート
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| 目的 | 入口 |
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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、
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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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| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
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| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
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| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
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| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
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## Citation
|
|
|
|
| 38 |
|
| 39 |
このリポジトリは、公開 Xperience-10M サンプル episode を、検証可能な embodied AI タスク実験面に変換します。まずダッシュボードとプロジェクト状態を見て、その後 20 タスク、結果行列、Hugging Face ミラーを確認してください。
|
| 40 |
|
| 41 |
+
**更新日:** 2026-06-21。
|
| 42 |
|
| 43 |
**範囲:** 完全に再現可能なタスク suite は 1 つの公開サンプル episode に基づきます。128-episode の結果は public-safe な指標、レポート、予測要約、モデルカードのみを公開します。元の MP4/HDF5/RRD、完全な Qwen 重み、gated データは再配布しません。
|
| 44 |
|
| 45 |
+
## 2 つの証拠ライン
|
| 46 |
+
|
| 47 |
+
| ライン | データ単位 | 手法と結果 | 用途 |
|
| 48 |
+
| --- | --- | --- | --- |
|
| 49 |
+
| 1 sample episode | 5,821 frames、1,161 aligned 20-frame windows、8,546 dimensions。 | Minimal + Neural MLP が 20 tasks を覆盖; 40/40 scored records。 | Raw sample files、task definitions、reproducible baselines、task validity を確認。 |
|
| 50 |
+
| 128 selected episodes | 96/16/16 split、34,269 exported windows、public-safe features が official gated episode paths に対応。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano; 140/140 scored records。 | 同一 split の baselines と model branches を比較; proxy targets は明示。 |
|
| 51 |
+
|
| 52 |
+
入口: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)。
|
| 53 |
+
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| 54 |
## クイックルート
|
| 55 |
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| 56 |
| 目的 | 入口 |
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| 67 |
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| 68 |
- 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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| 69 |
- タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
|
| 70 |
+
- 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、proxy targets は明示します。
|
| 71 |
- 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。
|
| 72 |
|
| 73 |
## 公開境界
|
|
|
|
| 83 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 84 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 85 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
| 86 |
+
| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
|
| 87 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 88 |
|
| 89 |
## Citation
|
README.ko.md
CHANGED
|
@@ -38,10 +38,19 @@
|
|
| 38 |
|
| 39 |
이 저장소는 공개 Xperience-10M sample episode를 검증 가능한 embodied AI 과제 실험 표면으로 정리합니다. 먼저 대시보드와 프로젝트 상태를 보고, 이후 20개 과제, 결과 행렬, Hugging Face 미러를 확인하세요.
|
| 40 |
|
| 41 |
-
**업데이트:** 2026-06-
|
| 42 |
|
| 43 |
**범위:** 완전히 재현 가능한 task suite는 공개 sample episode 하나를 사용합니다. 128-episode 결과는 public-safe 지표, 리포트, 예측 요약, 모델 카드만 공개합니다. 원본 MP4/HDF5/RRD, 전체 Qwen 가중치, gated 데이터는 재배포하지 않습니다.
|
| 44 |
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| 45 |
## 빠른 경로
|
| 46 |
|
| 47 |
| 목표 | 시작점 |
|
|
@@ -58,7 +67,7 @@
|
|
| 58 |
|
| 59 |
- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
|
| 60 |
- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
|
| 61 |
-
- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리
|
| 62 |
- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
|
| 63 |
|
| 64 |
## 공개 경계
|
|
@@ -74,6 +83,7 @@
|
|
| 74 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 75 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 76 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
|
|
|
| 77 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 78 |
|
| 79 |
## Citation
|
|
|
|
| 38 |
|
| 39 |
이 저장소는 공개 Xperience-10M sample episode를 검증 가능한 embodied AI 과제 실험 표면으로 정리합니다. 먼저 대시보드와 프로젝트 상태를 보고, 이후 20개 과제, 결과 행렬, Hugging Face 미러를 확인하세요.
|
| 40 |
|
| 41 |
+
**업데이트:** 2026-06-21.
|
| 42 |
|
| 43 |
**범위:** 완전히 재현 가능한 task suite는 공개 sample episode 하나를 사용합니다. 128-episode 결과는 public-safe 지표, 리포트, 예측 요약, 모델 카드만 공개합니다. 원본 MP4/HDF5/RRD, 전체 Qwen 가중치, gated 데이터는 재배포하지 않습니다.
|
| 44 |
|
| 45 |
+
## 두 증거 라인
|
| 46 |
+
|
| 47 |
+
| 라인 | 데이터 단위 | 방법과 결과 | 용도 |
|
| 48 |
+
| --- | --- | --- | --- |
|
| 49 |
+
| 1 sample episode | 5,821 frames, 1,161 aligned 20-frame windows, 8,546 dimensions. | Minimal + Neural MLP가 20 tasks 전체를 평가; 40/40 scored records. | Raw sample files, task definitions, reproducible baselines, task validity 확인. |
|
| 50 |
+
| 128 selected episodes | 96/16/16 split, 34,269 exported windows, public-safe features가 official gated episode paths에 연결됨. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super, Cosmos3-Nano; 140/140 scored records. | 같은 split에서 baselines와 model branches 비교; proxy targets는 명시 유지. |
|
| 51 |
+
|
| 52 |
+
입구: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
|
| 53 |
+
|
| 54 |
## 빠른 경로
|
| 55 |
|
| 56 |
| 목표 | 시작점 |
|
|
|
|
| 67 |
|
| 68 |
- 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
|
| 69 |
- 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
|
| 70 |
+
- 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 proxy targets를 명시합니다.
|
| 71 |
- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
|
| 72 |
|
| 73 |
## 공개 경계
|
|
|
|
| 83 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 84 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 85 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
| 86 |
+
| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
|
| 87 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 88 |
|
| 89 |
## Citation
|
README.pt.md
CHANGED
|
@@ -38,10 +38,19 @@
|
|
| 38 |
|
| 39 |
Este repositório transforma o episódio público de amostra do Xperience-10M em um laboratório verificável de tarefas para embodied AI. Comece pelo painel visual e pelo status do projeto; depois abra os contratos de tarefas, matrizes de resultados e espelhos no Hugging Face.
|
| 40 |
|
| 41 |
-
**Atualizado:** 2026-06-
|
| 42 |
|
| 43 |
**Escopo:** a suíte totalmente reproduzível usa um episódio público; os resultados de 128 episódios publicam apenas métricas, relatórios, predições seguras e model cards. MP4/HDF5/RRD originais, pesos completos do Qwen e dados gated não são redistribuídos.
|
| 44 |
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| 45 |
## Rota Rápida
|
| 46 |
|
| 47 |
| Objetivo | Entrada |
|
|
@@ -58,7 +67,7 @@ Este repositório transforma o episódio público de amostra do Xperience-10M em
|
|
| 58 |
|
| 59 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
|
| 60 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
|
| 61 |
-
- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos com
|
| 62 |
- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
|
| 63 |
|
| 64 |
## Fronteira Pública
|
|
@@ -74,6 +83,7 @@ O projeto publica apenas artifacts derivados, métricas, figuras, cards e resumo
|
|
| 74 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 75 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 76 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
|
|
|
| 77 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 78 |
|
| 79 |
## Citation
|
|
|
|
| 38 |
|
| 39 |
Este repositório transforma o episódio público de amostra do Xperience-10M em um laboratório verificável de tarefas para embodied AI. Comece pelo painel visual e pelo status do projeto; depois abra os contratos de tarefas, matrizes de resultados e espelhos no Hugging Face.
|
| 40 |
|
| 41 |
+
**Atualizado:** 2026-06-21.
|
| 42 |
|
| 43 |
**Escopo:** a suíte totalmente reproduzível usa um episódio público; os resultados de 128 episódios publicam apenas métricas, relatórios, predições seguras e model cards. MP4/HDF5/RRD originais, pesos completos do Qwen e dados gated não são redistribuídos.
|
| 44 |
|
| 45 |
+
## Duas Linhas de Evidência
|
| 46 |
+
|
| 47 |
+
| Linha | Unidade de dados | Métodos e resultados | Uso |
|
| 48 |
+
| --- | --- | --- | --- |
|
| 49 |
+
| 1 episódio de amostra | 5,821 frames; 1,161 janelas alinhadas de 20 frames; 8,546 dimensões. | Minimal + Neural MLP em 20 tarefas; 40/40 registros com score. | Inspecionar arquivos da amostra, definições de tarefas, baselines reproduzíveis e validade das tarefas. |
|
| 50 |
+
| 128 episódios selecionados | Split 96/16/16; 34,269 janelas exportadas; features public-safe ligadas aos caminhos oficiais gated. | Metadata simple/NN, raw-feature simple/NN, Qwen3-Omni, Cosmos3-Super e Cosmos3-Nano; 140/140 registros com score. | Comparar baselines e ramos de modelo no mesmo split; proxy targets permanecem visíveis. |
|
| 51 |
+
|
| 52 |
+
Entradas: [`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md), [`two_evidence_lines.json`](docs/data/two_evidence_lines.json), [`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json).
|
| 53 |
+
|
| 54 |
## Rota Rápida
|
| 55 |
|
| 56 |
| Objetivo | Entrada |
|
|
|
|
| 67 |
|
| 68 |
- Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
|
| 69 |
- Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
|
| 70 |
+
- Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score e mantém proxy targets visíveis.
|
| 71 |
- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
|
| 72 |
|
| 73 |
## Fronteira Pública
|
|
|
|
| 83 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 84 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 85 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
| 86 |
+
| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
|
| 87 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 88 |
|
| 89 |
## Citation
|
README.zh.md
CHANGED
|
@@ -38,10 +38,19 @@
|
|
| 38 |
|
| 39 |
这个仓库把 Ropedia 公开的 Xperience-10M sample episode 变成一个可检查的具身智能任务实验室。请先看仪表盘和项目状态，再进入 20 个任务、结果矩阵和 Hugging Face 镜像。
|
| 40 |
|
| 41 |
-
**更新时间：** 2026-06-
|
| 42 |
|
| 43 |
**范围：** 完整可复现的任务套件来自一个公开样本 episode；128-episode 结果只发布 public-safe 的指标、报告、预测摘要和模型卡。原始 MP4/HDF5/RRD、完整 Qwen 权重和 gated 数据不在本仓库重新分发。
|
| 44 |
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|
| 45 |
## 快速入口
|
| 46 |
|
| 47 |
| 目标 | 入口 |
|
|
@@ -58,7 +67,7 @@
|
|
| 58 |
|
| 59 |
- 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。
|
| 60 |
- 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。
|
| 61 |
-
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注
|
| 62 |
- 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。
|
| 63 |
|
| 64 |
## 公开边界
|
|
@@ -74,6 +83,7 @@
|
|
| 74 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 75 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 76 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
|
|
|
| 77 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 78 |
|
| 79 |
## Citation
|
|
|
|
| 38 |
|
| 39 |
这个仓库把 Ropedia 公开的 Xperience-10M sample episode 变成一个可检查的具身智能任务实验室。请先看仪表盘和项目状态，再进入 20 个任务、结果矩阵和 Hugging Face 镜像。
|
| 40 |
|
| 41 |
+
**更新时间：** 2026-06-21。
|
| 42 |
|
| 43 |
**范围：** 完整可复现的任务套件来自一个公开样本 episode；128-episode 结果只发布 public-safe 的指标、报告、预测摘要和模型卡。原始 MP4/HDF5/RRD、完整 Qwen 权重和 gated 数据不在本仓库重新分发。
|
| 44 |
|
| 45 |
+
## 两条证据线
|
| 46 |
+
|
| 47 |
+
| 线 | 数据单元 | 方法与结果 | 用途 |
|
| 48 |
+
| --- | --- | --- | --- |
|
| 49 |
+
| 1 sample episode | 5,821 帧；1,161 个 20-frame 对齐窗口；8,546 维特征。 | Minimal + Neural MLP；20 个任务全覆盖；40/40 scored records。 | 检查原始 sample 文件、任务定义、可复现基线和每个任务是否成立。 |
|
| 50 |
+
| 128 selected episodes | 96/16/16 split；34,269 个导出窗口；public-safe 特征链接到官方 gated episode path。 | Metadata simple/NN、raw-feature simple/NN、Qwen3-Omni、Cosmos3-Super、Cosmos3-Nano；140/140 scored records。 | 比较同一 split 上的基线和模型分支；proxy target 会显式标注。 |
|
| 51 |
+
|
| 52 |
+
入口：[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md)、[`two_evidence_lines.json`](docs/data/two_evidence_lines.json)、[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)。
|
| 53 |
+
|
| 54 |
## 快速入口
|
| 55 |
|
| 56 |
| 目标 | 入口 |
|
|
|
|
| 67 |
|
| 68 |
- 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。
|
| 69 |
- 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。
|
| 70 |
+
- 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw/Qwen3/Cosmos 分开标注；当前公开矩阵为 180/180 scored records，proxy target 显式保留。
|
| 71 |
- 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。
|
| 72 |
|
| 73 |
## 公开边界
|
|
|
|
| 83 |
| HF Space | https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite |
|
| 84 |
| HF artifacts | https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts |
|
| 85 |
| HF baselines | https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines |
|
| 86 |
+
| HF weights/results | https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results |
|
| 87 |
| HF collection | https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite |
|
| 88 |
|
| 89 |
## Citation
|
data/language_versions.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
-
"updated": "2026-06-
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
+
"updated": "2026-06-21",
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
data/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1250,
|
|
@@ -482,44 +482,44 @@
|
|
| 482 |
"path": "repo:docs/data/language_versions.json",
|
| 483 |
"exists": true,
|
| 484 |
"bytes": 1762,
|
| 485 |
-
"sha256": "
|
| 486 |
},
|
| 487 |
"mirrors": {
|
| 488 |
"hf_space": {
|
| 489 |
"path": "hf_space:data/language_versions.json",
|
| 490 |
"exists": true,
|
| 491 |
"bytes": 1762,
|
| 492 |
-
"sha256": "
|
| 493 |
},
|
| 494 |
"hf_artifacts_data": {
|
| 495 |
"path": "hf_artifacts:data/language_versions.json",
|
| 496 |
"exists": true,
|
| 497 |
"bytes": 1762,
|
| 498 |
-
"sha256": "
|
| 499 |
},
|
| 500 |
"hf_artifacts": {
|
| 501 |
"path": "hf_artifacts:docs/data/language_versions.json",
|
| 502 |
"exists": true,
|
| 503 |
"bytes": 1762,
|
| 504 |
-
"sha256": "
|
| 505 |
},
|
| 506 |
"hf_model_data": {
|
| 507 |
"path": "hf_model:data/language_versions.json",
|
| 508 |
"exists": true,
|
| 509 |
"bytes": 1762,
|
| 510 |
-
"sha256": "
|
| 511 |
},
|
| 512 |
"hf_model_docs_data": {
|
| 513 |
"path": "hf_model:docs/data/language_versions.json",
|
| 514 |
"exists": true,
|
| 515 |
"bytes": 1762,
|
| 516 |
-
"sha256": "
|
| 517 |
},
|
| 518 |
"hf_model": {
|
| 519 |
"path": "hf_model:metrics/language_versions.json",
|
| 520 |
"exists": true,
|
| 521 |
"bytes": 1762,
|
| 522 |
-
"sha256": "
|
| 523 |
}
|
| 524 |
},
|
| 525 |
"failures": []
|
|
@@ -923,44 +923,44 @@
|
|
| 923 |
"path": "repo:docs/data/publication_audit.json",
|
| 924 |
"exists": true,
|
| 925 |
"bytes": 10662,
|
| 926 |
-
"sha256": "
|
| 927 |
},
|
| 928 |
"mirrors": {
|
| 929 |
"hf_space": {
|
| 930 |
"path": "hf_space:data/publication_audit.json",
|
| 931 |
"exists": true,
|
| 932 |
"bytes": 10662,
|
| 933 |
-
"sha256": "
|
| 934 |
},
|
| 935 |
"hf_artifacts_data": {
|
| 936 |
"path": "hf_artifacts:data/publication_audit.json",
|
| 937 |
"exists": true,
|
| 938 |
"bytes": 10662,
|
| 939 |
-
"sha256": "
|
| 940 |
},
|
| 941 |
"hf_artifacts": {
|
| 942 |
"path": "hf_artifacts:docs/data/publication_audit.json",
|
| 943 |
"exists": true,
|
| 944 |
"bytes": 10662,
|
| 945 |
-
"sha256": "
|
| 946 |
},
|
| 947 |
"hf_model_data": {
|
| 948 |
"path": "hf_model:data/publication_audit.json",
|
| 949 |
"exists": true,
|
| 950 |
"bytes": 10662,
|
| 951 |
-
"sha256": "
|
| 952 |
},
|
| 953 |
"hf_model_docs_data": {
|
| 954 |
"path": "hf_model:docs/data/publication_audit.json",
|
| 955 |
"exists": true,
|
| 956 |
"bytes": 10662,
|
| 957 |
-
"sha256": "
|
| 958 |
},
|
| 959 |
"hf_model": {
|
| 960 |
"path": "hf_model:metrics/publication_audit.json",
|
| 961 |
"exists": true,
|
| 962 |
"bytes": 10662,
|
| 963 |
-
"sha256": "
|
| 964 |
}
|
| 965 |
},
|
| 966 |
"failures": []
|
|
@@ -972,44 +972,44 @@
|
|
| 972 |
"path": "repo:docs/data/public_surface_qa.json",
|
| 973 |
"exists": true,
|
| 974 |
"bytes": 7208,
|
| 975 |
-
"sha256": "
|
| 976 |
},
|
| 977 |
"mirrors": {
|
| 978 |
"hf_space": {
|
| 979 |
"path": "hf_space:data/public_surface_qa.json",
|
| 980 |
"exists": true,
|
| 981 |
"bytes": 7208,
|
| 982 |
-
"sha256": "
|
| 983 |
},
|
| 984 |
"hf_artifacts_data": {
|
| 985 |
"path": "hf_artifacts:data/public_surface_qa.json",
|
| 986 |
"exists": true,
|
| 987 |
"bytes": 7208,
|
| 988 |
-
"sha256": "
|
| 989 |
},
|
| 990 |
"hf_artifacts": {
|
| 991 |
"path": "hf_artifacts:docs/data/public_surface_qa.json",
|
| 992 |
"exists": true,
|
| 993 |
"bytes": 7208,
|
| 994 |
-
"sha256": "
|
| 995 |
},
|
| 996 |
"hf_model_data": {
|
| 997 |
"path": "hf_model:data/public_surface_qa.json",
|
| 998 |
"exists": true,
|
| 999 |
"bytes": 7208,
|
| 1000 |
-
"sha256": "
|
| 1001 |
},
|
| 1002 |
"hf_model_docs_data": {
|
| 1003 |
"path": "hf_model:docs/data/public_surface_qa.json",
|
| 1004 |
"exists": true,
|
| 1005 |
"bytes": 7208,
|
| 1006 |
-
"sha256": "
|
| 1007 |
},
|
| 1008 |
"hf_model": {
|
| 1009 |
"path": "hf_model:metrics/public_surface_qa.json",
|
| 1010 |
"exists": true,
|
| 1011 |
"bytes": 7208,
|
| 1012 |
-
"sha256": "
|
| 1013 |
}
|
| 1014 |
},
|
| 1015 |
"failures": []
|
|
@@ -1119,44 +1119,44 @@
|
|
| 1119 |
"path": "repo:docs/data/quality_gates.json",
|
| 1120 |
"exists": true,
|
| 1121 |
"bytes": 8640,
|
| 1122 |
-
"sha256": "
|
| 1123 |
},
|
| 1124 |
"mirrors": {
|
| 1125 |
"hf_space": {
|
| 1126 |
"path": "hf_space:data/quality_gates.json",
|
| 1127 |
"exists": true,
|
| 1128 |
"bytes": 8640,
|
| 1129 |
-
"sha256": "
|
| 1130 |
},
|
| 1131 |
"hf_artifacts_data": {
|
| 1132 |
"path": "hf_artifacts:data/quality_gates.json",
|
| 1133 |
"exists": true,
|
| 1134 |
"bytes": 8640,
|
| 1135 |
-
"sha256": "
|
| 1136 |
},
|
| 1137 |
"hf_artifacts": {
|
| 1138 |
"path": "hf_artifacts:docs/data/quality_gates.json",
|
| 1139 |
"exists": true,
|
| 1140 |
"bytes": 8640,
|
| 1141 |
-
"sha256": "
|
| 1142 |
},
|
| 1143 |
"hf_model_data": {
|
| 1144 |
"path": "hf_model:data/quality_gates.json",
|
| 1145 |
"exists": true,
|
| 1146 |
"bytes": 8640,
|
| 1147 |
-
"sha256": "
|
| 1148 |
},
|
| 1149 |
"hf_model_docs_data": {
|
| 1150 |
"path": "hf_model:docs/data/quality_gates.json",
|
| 1151 |
"exists": true,
|
| 1152 |
"bytes": 8640,
|
| 1153 |
-
"sha256": "
|
| 1154 |
},
|
| 1155 |
"hf_model": {
|
| 1156 |
"path": "hf_model:metrics/quality_gates.json",
|
| 1157 |
"exists": true,
|
| 1158 |
"bytes": 8640,
|
| 1159 |
-
"sha256": "
|
| 1160 |
}
|
| 1161 |
},
|
| 1162 |
"failures": []
|
|
@@ -1560,44 +1560,44 @@
|
|
| 1560 |
"path": "repo:docs/data/scope_claims_audit.json",
|
| 1561 |
"exists": true,
|
| 1562 |
"bytes": 21313,
|
| 1563 |
-
"sha256": "
|
| 1564 |
},
|
| 1565 |
"mirrors": {
|
| 1566 |
"hf_space": {
|
| 1567 |
"path": "hf_space:data/scope_claims_audit.json",
|
| 1568 |
"exists": true,
|
| 1569 |
"bytes": 21313,
|
| 1570 |
-
"sha256": "
|
| 1571 |
},
|
| 1572 |
"hf_artifacts_data": {
|
| 1573 |
"path": "hf_artifacts:data/scope_claims_audit.json",
|
| 1574 |
"exists": true,
|
| 1575 |
"bytes": 21313,
|
| 1576 |
-
"sha256": "
|
| 1577 |
},
|
| 1578 |
"hf_artifacts": {
|
| 1579 |
"path": "hf_artifacts:docs/data/scope_claims_audit.json",
|
| 1580 |
"exists": true,
|
| 1581 |
"bytes": 21313,
|
| 1582 |
-
"sha256": "
|
| 1583 |
},
|
| 1584 |
"hf_model_data": {
|
| 1585 |
"path": "hf_model:data/scope_claims_audit.json",
|
| 1586 |
"exists": true,
|
| 1587 |
"bytes": 21313,
|
| 1588 |
-
"sha256": "
|
| 1589 |
},
|
| 1590 |
"hf_model_docs_data": {
|
| 1591 |
"path": "hf_model:docs/data/scope_claims_audit.json",
|
| 1592 |
"exists": true,
|
| 1593 |
"bytes": 21313,
|
| 1594 |
-
"sha256": "
|
| 1595 |
},
|
| 1596 |
"hf_model": {
|
| 1597 |
"path": "hf_model:metrics/scope_claims_audit.json",
|
| 1598 |
"exists": true,
|
| 1599 |
"bytes": 21313,
|
| 1600 |
-
"sha256": "
|
| 1601 |
}
|
| 1602 |
},
|
| 1603 |
"failures": []
|
|
@@ -1658,44 +1658,44 @@
|
|
| 1658 |
"path": "repo:docs/data/source_alignment_audit.json",
|
| 1659 |
"exists": true,
|
| 1660 |
"bytes": 4432,
|
| 1661 |
-
"sha256": "
|
| 1662 |
},
|
| 1663 |
"mirrors": {
|
| 1664 |
"hf_space": {
|
| 1665 |
"path": "hf_space:data/source_alignment_audit.json",
|
| 1666 |
"exists": true,
|
| 1667 |
"bytes": 4432,
|
| 1668 |
-
"sha256": "
|
| 1669 |
},
|
| 1670 |
"hf_artifacts_data": {
|
| 1671 |
"path": "hf_artifacts:data/source_alignment_audit.json",
|
| 1672 |
"exists": true,
|
| 1673 |
"bytes": 4432,
|
| 1674 |
-
"sha256": "
|
| 1675 |
},
|
| 1676 |
"hf_artifacts": {
|
| 1677 |
"path": "hf_artifacts:docs/data/source_alignment_audit.json",
|
| 1678 |
"exists": true,
|
| 1679 |
"bytes": 4432,
|
| 1680 |
-
"sha256": "
|
| 1681 |
},
|
| 1682 |
"hf_model_data": {
|
| 1683 |
"path": "hf_model:data/source_alignment_audit.json",
|
| 1684 |
"exists": true,
|
| 1685 |
"bytes": 4432,
|
| 1686 |
-
"sha256": "
|
| 1687 |
},
|
| 1688 |
"hf_model_docs_data": {
|
| 1689 |
"path": "hf_model:docs/data/source_alignment_audit.json",
|
| 1690 |
"exists": true,
|
| 1691 |
"bytes": 4432,
|
| 1692 |
-
"sha256": "
|
| 1693 |
},
|
| 1694 |
"hf_model": {
|
| 1695 |
"path": "hf_model:metrics/source_alignment_audit.json",
|
| 1696 |
"exists": true,
|
| 1697 |
"bytes": 4432,
|
| 1698 |
-
"sha256": "
|
| 1699 |
}
|
| 1700 |
},
|
| 1701 |
"failures": []
|
|
@@ -2050,44 +2050,44 @@
|
|
| 2050 |
"path": "repo:docs/data/task_surface_integrity.json",
|
| 2051 |
"exists": true,
|
| 2052 |
"bytes": 46246,
|
| 2053 |
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"sha256": "
|
| 2054 |
},
|
| 2055 |
"mirrors": {
|
| 2056 |
"hf_space": {
|
| 2057 |
"path": "hf_space:data/task_surface_integrity.json",
|
| 2058 |
"exists": true,
|
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"bytes": 46246,
|
| 2060 |
-
"sha256": "
|
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},
|
| 2062 |
"hf_artifacts_data": {
|
| 2063 |
"path": "hf_artifacts:data/task_surface_integrity.json",
|
| 2064 |
"exists": true,
|
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"bytes": 46246,
|
| 2066 |
-
"sha256": "
|
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},
|
| 2068 |
"hf_artifacts": {
|
| 2069 |
"path": "hf_artifacts:docs/data/task_surface_integrity.json",
|
| 2070 |
"exists": true,
|
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"bytes": 46246,
|
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-
"sha256": "
|
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},
|
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"hf_model_data": {
|
| 2075 |
"path": "hf_model:data/task_surface_integrity.json",
|
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"exists": true,
|
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"bytes": 46246,
|
| 2078 |
-
"sha256": "
|
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},
|
| 2080 |
"hf_model_docs_data": {
|
| 2081 |
"path": "hf_model:docs/data/task_surface_integrity.json",
|
| 2082 |
"exists": true,
|
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"bytes": 46246,
|
| 2084 |
-
"sha256": "
|
| 2085 |
},
|
| 2086 |
"hf_model": {
|
| 2087 |
"path": "hf_model:metrics/task_surface_integrity.json",
|
| 2088 |
"exists": true,
|
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"bytes": 46246,
|
| 2090 |
-
"sha256": "
|
| 2091 |
}
|
| 2092 |
},
|
| 2093 |
"failures": []
|
|
@@ -2393,44 +2393,44 @@
|
|
| 2393 |
"path": "repo:docs/data/website_integrity.json",
|
| 2394 |
"exists": true,
|
| 2395 |
"bytes": 20254,
|
| 2396 |
-
"sha256": "
|
| 2397 |
},
|
| 2398 |
"mirrors": {
|
| 2399 |
"hf_space": {
|
| 2400 |
"path": "hf_space:data/website_integrity.json",
|
| 2401 |
"exists": true,
|
| 2402 |
"bytes": 20254,
|
| 2403 |
-
"sha256": "
|
| 2404 |
},
|
| 2405 |
"hf_artifacts_data": {
|
| 2406 |
"path": "hf_artifacts:data/website_integrity.json",
|
| 2407 |
"exists": true,
|
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"bytes": 20254,
|
| 2409 |
-
"sha256": "
|
| 2410 |
},
|
| 2411 |
"hf_artifacts": {
|
| 2412 |
"path": "hf_artifacts:docs/data/website_integrity.json",
|
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"exists": true,
|
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"bytes": 20254,
|
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-
"sha256": "
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},
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"hf_model_data": {
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"path": "hf_model:data/website_integrity.json",
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"exists": true,
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"bytes": 20254,
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"sha256": "
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},
|
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"hf_model_docs_data": {
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"path": "hf_model:docs/data/website_integrity.json",
|
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"exists": true,
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"bytes": 20254,
|
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-
"sha256": "
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},
|
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"hf_model": {
|
| 2430 |
"path": "hf_model:metrics/website_integrity.json",
|
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"exists": true,
|
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"bytes": 20254,
|
| 2433 |
-
"sha256": "
|
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}
|
| 2435 |
},
|
| 2436 |
"failures": []
|
|
@@ -5343,21 +5343,21 @@
|
|
| 5343 |
"local": {
|
| 5344 |
"path": "repo:scripts/build_multilingual_public_readmes.py",
|
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"exists": true,
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-
"bytes":
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"sha256": "
|
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},
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"mirrors": {
|
| 5350 |
"hf_artifacts": {
|
| 5351 |
"path": "hf_artifacts:scripts/build_multilingual_public_readmes.py",
|
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"exists": true,
|
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-
"bytes":
|
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-
"sha256": "
|
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},
|
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"hf_model": {
|
| 5357 |
"path": "hf_model:scripts/build_multilingual_public_readmes.py",
|
| 5358 |
"exists": true,
|
| 5359 |
-
"bytes":
|
| 5360 |
-
"sha256": "
|
| 5361 |
}
|
| 5362 |
},
|
| 5363 |
"failures": []
|
|
@@ -30477,27 +30477,27 @@
|
|
| 30477 |
"local": {
|
| 30478 |
"path": "repo:README.zh.md",
|
| 30479 |
"exists": true,
|
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-
"bytes":
|
| 30481 |
-
"sha256": "
|
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},
|
| 30483 |
"mirrors": {
|
| 30484 |
"hf_space": {
|
| 30485 |
"path": "hf_space:README.zh.md",
|
| 30486 |
"exists": true,
|
| 30487 |
-
"bytes":
|
| 30488 |
-
"sha256": "
|
| 30489 |
},
|
| 30490 |
"hf_artifacts": {
|
| 30491 |
"path": "hf_artifacts:README.zh.md",
|
| 30492 |
"exists": true,
|
| 30493 |
-
"bytes":
|
| 30494 |
-
"sha256": "
|
| 30495 |
},
|
| 30496 |
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|
| 30666 |
+
"bytes": 6176,
|
| 30667 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30668 |
},
|
| 30669 |
"mirrors": {
|
| 30670 |
"hf_space": {
|
| 30671 |
"path": "hf_space:README.pt.md",
|
| 30672 |
"exists": true,
|
| 30673 |
+
"bytes": 6176,
|
| 30674 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30675 |
},
|
| 30676 |
"hf_artifacts": {
|
| 30677 |
"path": "hf_artifacts:README.pt.md",
|
| 30678 |
"exists": true,
|
| 30679 |
+
"bytes": 6176,
|
| 30680 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30681 |
},
|
| 30682 |
"hf_model": {
|
| 30683 |
"path": "hf_model:README.pt.md",
|
| 30684 |
"exists": true,
|
| 30685 |
+
"bytes": 6176,
|
| 30686 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30687 |
}
|
| 30688 |
},
|
| 30689 |
"failures": []
|
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": "2026-06-21T06:
|
| 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": "2026-06-21T06:
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
@@ -33,22 +33,22 @@
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-21T06:
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
-
"generated_at_utc": "2026-06-21T06:
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-21T06:
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-21T06:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:55:02+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-21T06:55:00+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00"
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-21T06:28:19+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-21T06:28:04+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
data/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:56:26+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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:35+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/scope_claims_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00",
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:00+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": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:02+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
docs/data/language_versions.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
-
"updated": "2026-06-
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
+
"updated": "2026-06-21",
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
docs/data/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1250,
|
|
@@ -482,44 +482,44 @@
|
|
| 482 |
"path": "repo:docs/data/language_versions.json",
|
| 483 |
"exists": true,
|
| 484 |
"bytes": 1762,
|
| 485 |
-
"sha256": "
|
| 486 |
},
|
| 487 |
"mirrors": {
|
| 488 |
"hf_space": {
|
| 489 |
"path": "hf_space:data/language_versions.json",
|
| 490 |
"exists": true,
|
| 491 |
"bytes": 1762,
|
| 492 |
-
"sha256": "
|
| 493 |
},
|
| 494 |
"hf_artifacts_data": {
|
| 495 |
"path": "hf_artifacts:data/language_versions.json",
|
| 496 |
"exists": true,
|
| 497 |
"bytes": 1762,
|
| 498 |
-
"sha256": "
|
| 499 |
},
|
| 500 |
"hf_artifacts": {
|
| 501 |
"path": "hf_artifacts:docs/data/language_versions.json",
|
| 502 |
"exists": true,
|
| 503 |
"bytes": 1762,
|
| 504 |
-
"sha256": "
|
| 505 |
},
|
| 506 |
"hf_model_data": {
|
| 507 |
"path": "hf_model:data/language_versions.json",
|
| 508 |
"exists": true,
|
| 509 |
"bytes": 1762,
|
| 510 |
-
"sha256": "
|
| 511 |
},
|
| 512 |
"hf_model_docs_data": {
|
| 513 |
"path": "hf_model:docs/data/language_versions.json",
|
| 514 |
"exists": true,
|
| 515 |
"bytes": 1762,
|
| 516 |
-
"sha256": "
|
| 517 |
},
|
| 518 |
"hf_model": {
|
| 519 |
"path": "hf_model:metrics/language_versions.json",
|
| 520 |
"exists": true,
|
| 521 |
"bytes": 1762,
|
| 522 |
-
"sha256": "
|
| 523 |
}
|
| 524 |
},
|
| 525 |
"failures": []
|
|
@@ -923,44 +923,44 @@
|
|
| 923 |
"path": "repo:docs/data/publication_audit.json",
|
| 924 |
"exists": true,
|
| 925 |
"bytes": 10662,
|
| 926 |
-
"sha256": "
|
| 927 |
},
|
| 928 |
"mirrors": {
|
| 929 |
"hf_space": {
|
| 930 |
"path": "hf_space:data/publication_audit.json",
|
| 931 |
"exists": true,
|
| 932 |
"bytes": 10662,
|
| 933 |
-
"sha256": "
|
| 934 |
},
|
| 935 |
"hf_artifacts_data": {
|
| 936 |
"path": "hf_artifacts:data/publication_audit.json",
|
| 937 |
"exists": true,
|
| 938 |
"bytes": 10662,
|
| 939 |
-
"sha256": "
|
| 940 |
},
|
| 941 |
"hf_artifacts": {
|
| 942 |
"path": "hf_artifacts:docs/data/publication_audit.json",
|
| 943 |
"exists": true,
|
| 944 |
"bytes": 10662,
|
| 945 |
-
"sha256": "
|
| 946 |
},
|
| 947 |
"hf_model_data": {
|
| 948 |
"path": "hf_model:data/publication_audit.json",
|
| 949 |
"exists": true,
|
| 950 |
"bytes": 10662,
|
| 951 |
-
"sha256": "
|
| 952 |
},
|
| 953 |
"hf_model_docs_data": {
|
| 954 |
"path": "hf_model:docs/data/publication_audit.json",
|
| 955 |
"exists": true,
|
| 956 |
"bytes": 10662,
|
| 957 |
-
"sha256": "
|
| 958 |
},
|
| 959 |
"hf_model": {
|
| 960 |
"path": "hf_model:metrics/publication_audit.json",
|
| 961 |
"exists": true,
|
| 962 |
"bytes": 10662,
|
| 963 |
-
"sha256": "
|
| 964 |
}
|
| 965 |
},
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| 966 |
"failures": []
|
|
@@ -972,44 +972,44 @@
|
|
| 972 |
"path": "repo:docs/data/public_surface_qa.json",
|
| 973 |
"exists": true,
|
| 974 |
"bytes": 7208,
|
| 975 |
-
"sha256": "
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| 976 |
},
|
| 977 |
"mirrors": {
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| 978 |
"hf_space": {
|
| 979 |
"path": "hf_space:data/public_surface_qa.json",
|
| 980 |
"exists": true,
|
| 981 |
"bytes": 7208,
|
| 982 |
-
"sha256": "
|
| 983 |
},
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| 984 |
"hf_artifacts_data": {
|
| 985 |
"path": "hf_artifacts:data/public_surface_qa.json",
|
| 986 |
"exists": true,
|
| 987 |
"bytes": 7208,
|
| 988 |
-
"sha256": "
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| 989 |
},
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| 990 |
"hf_artifacts": {
|
| 991 |
"path": "hf_artifacts:docs/data/public_surface_qa.json",
|
| 992 |
"exists": true,
|
| 993 |
"bytes": 7208,
|
| 994 |
-
"sha256": "
|
| 995 |
},
|
| 996 |
"hf_model_data": {
|
| 997 |
"path": "hf_model:data/public_surface_qa.json",
|
| 998 |
"exists": true,
|
| 999 |
"bytes": 7208,
|
| 1000 |
-
"sha256": "
|
| 1001 |
},
|
| 1002 |
"hf_model_docs_data": {
|
| 1003 |
"path": "hf_model:docs/data/public_surface_qa.json",
|
| 1004 |
"exists": true,
|
| 1005 |
"bytes": 7208,
|
| 1006 |
-
"sha256": "
|
| 1007 |
},
|
| 1008 |
"hf_model": {
|
| 1009 |
"path": "hf_model:metrics/public_surface_qa.json",
|
| 1010 |
"exists": true,
|
| 1011 |
"bytes": 7208,
|
| 1012 |
-
"sha256": "
|
| 1013 |
}
|
| 1014 |
},
|
| 1015 |
"failures": []
|
|
@@ -1119,44 +1119,44 @@
|
|
| 1119 |
"path": "repo:docs/data/quality_gates.json",
|
| 1120 |
"exists": true,
|
| 1121 |
"bytes": 8640,
|
| 1122 |
-
"sha256": "
|
| 1123 |
},
|
| 1124 |
"mirrors": {
|
| 1125 |
"hf_space": {
|
| 1126 |
"path": "hf_space:data/quality_gates.json",
|
| 1127 |
"exists": true,
|
| 1128 |
"bytes": 8640,
|
| 1129 |
-
"sha256": "
|
| 1130 |
},
|
| 1131 |
"hf_artifacts_data": {
|
| 1132 |
"path": "hf_artifacts:data/quality_gates.json",
|
| 1133 |
"exists": true,
|
| 1134 |
"bytes": 8640,
|
| 1135 |
-
"sha256": "
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| 1136 |
},
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| 1137 |
"hf_artifacts": {
|
| 1138 |
"path": "hf_artifacts:docs/data/quality_gates.json",
|
| 1139 |
"exists": true,
|
| 1140 |
"bytes": 8640,
|
| 1141 |
-
"sha256": "
|
| 1142 |
},
|
| 1143 |
"hf_model_data": {
|
| 1144 |
"path": "hf_model:data/quality_gates.json",
|
| 1145 |
"exists": true,
|
| 1146 |
"bytes": 8640,
|
| 1147 |
-
"sha256": "
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| 1148 |
},
|
| 1149 |
"hf_model_docs_data": {
|
| 1150 |
"path": "hf_model:docs/data/quality_gates.json",
|
| 1151 |
"exists": true,
|
| 1152 |
"bytes": 8640,
|
| 1153 |
-
"sha256": "
|
| 1154 |
},
|
| 1155 |
"hf_model": {
|
| 1156 |
"path": "hf_model:metrics/quality_gates.json",
|
| 1157 |
"exists": true,
|
| 1158 |
"bytes": 8640,
|
| 1159 |
-
"sha256": "
|
| 1160 |
}
|
| 1161 |
},
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| 1162 |
"failures": []
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|
@@ -1560,44 +1560,44 @@
|
|
| 1560 |
"path": "repo:docs/data/scope_claims_audit.json",
|
| 1561 |
"exists": true,
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| 1562 |
"bytes": 21313,
|
| 1563 |
-
"sha256": "
|
| 1564 |
},
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| 1565 |
"mirrors": {
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| 1566 |
"hf_space": {
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| 1567 |
"path": "hf_space:data/scope_claims_audit.json",
|
| 1568 |
"exists": true,
|
| 1569 |
"bytes": 21313,
|
| 1570 |
-
"sha256": "
|
| 1571 |
},
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| 1572 |
"hf_artifacts_data": {
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| 1573 |
"path": "hf_artifacts:data/scope_claims_audit.json",
|
| 1574 |
"exists": true,
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| 1575 |
"bytes": 21313,
|
| 1576 |
-
"sha256": "
|
| 1577 |
},
|
| 1578 |
"hf_artifacts": {
|
| 1579 |
"path": "hf_artifacts:docs/data/scope_claims_audit.json",
|
| 1580 |
"exists": true,
|
| 1581 |
"bytes": 21313,
|
| 1582 |
-
"sha256": "
|
| 1583 |
},
|
| 1584 |
"hf_model_data": {
|
| 1585 |
"path": "hf_model:data/scope_claims_audit.json",
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},
|
| 30490 |
"hf_artifacts": {
|
| 30491 |
"path": "hf_artifacts:README.zh.md",
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"exists": true,
|
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"bytes": 6084,
|
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"sha256": "dce36c1ab8230f451c29869da96747601d642f3e39b8faad737723fccdeba090"
|
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},
|
| 30496 |
"hf_model": {
|
| 30497 |
"path": "hf_model:README.zh.md",
|
| 30498 |
"exists": true,
|
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"bytes": 6084,
|
| 30500 |
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"sha256": "dce36c1ab8230f451c29869da96747601d642f3e39b8faad737723fccdeba090"
|
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}
|
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},
|
| 30503 |
"failures": []
|
|
|
|
| 30508 |
"local": {
|
| 30509 |
"path": "repo:README.es.md",
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"exists": true,
|
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"bytes": 6151,
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"sha256": "05dca04da902da48d3c38b96003261783409db82c6b7a4927550c4fdd729f856"
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},
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| 30514 |
"mirrors": {
|
| 30515 |
"hf_space": {
|
| 30516 |
"path": "hf_space:README.es.md",
|
| 30517 |
"exists": true,
|
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"bytes": 6151,
|
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},
|
| 30521 |
"hf_artifacts": {
|
| 30522 |
"path": "hf_artifacts:README.es.md",
|
| 30523 |
"exists": true,
|
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"bytes": 6151,
|
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"sha256": "05dca04da902da48d3c38b96003261783409db82c6b7a4927550c4fdd729f856"
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},
|
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"hf_model": {
|
| 30528 |
"path": "hf_model:README.es.md",
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| 30529 |
"exists": true,
|
| 30530 |
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"bytes": 6151,
|
| 30531 |
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"sha256": "05dca04da902da48d3c38b96003261783409db82c6b7a4927550c4fdd729f856"
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}
|
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},
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| 30534 |
"failures": []
|
|
|
|
| 30539 |
"local": {
|
| 30540 |
"path": "repo:README.fr.md",
|
| 30541 |
"exists": true,
|
| 30542 |
+
"bytes": 6259,
|
| 30543 |
+
"sha256": "d3c7c604dbdcfc3cbe5184136d8a3d7639c1560ef642639f1af52a2950517dfb"
|
| 30544 |
},
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| 30545 |
"mirrors": {
|
| 30546 |
"hf_space": {
|
| 30547 |
"path": "hf_space:README.fr.md",
|
| 30548 |
"exists": true,
|
| 30549 |
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"bytes": 6259,
|
| 30550 |
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"sha256": "d3c7c604dbdcfc3cbe5184136d8a3d7639c1560ef642639f1af52a2950517dfb"
|
| 30551 |
},
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| 30552 |
"hf_artifacts": {
|
| 30553 |
"path": "hf_artifacts:README.fr.md",
|
| 30554 |
"exists": true,
|
| 30555 |
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"bytes": 6259,
|
| 30556 |
+
"sha256": "d3c7c604dbdcfc3cbe5184136d8a3d7639c1560ef642639f1af52a2950517dfb"
|
| 30557 |
},
|
| 30558 |
"hf_model": {
|
| 30559 |
"path": "hf_model:README.fr.md",
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| 30560 |
"exists": true,
|
| 30561 |
+
"bytes": 6259,
|
| 30562 |
+
"sha256": "d3c7c604dbdcfc3cbe5184136d8a3d7639c1560ef642639f1af52a2950517dfb"
|
| 30563 |
}
|
| 30564 |
},
|
| 30565 |
"failures": []
|
|
|
|
| 30570 |
"local": {
|
| 30571 |
"path": "repo:README.de.md",
|
| 30572 |
"exists": true,
|
| 30573 |
+
"bytes": 6173,
|
| 30574 |
+
"sha256": "a3c83c90e82eca7c07f191f648f43a4b68bc16417fbff39ed30285bb205e083a"
|
| 30575 |
},
|
| 30576 |
"mirrors": {
|
| 30577 |
"hf_space": {
|
| 30578 |
"path": "hf_space:README.de.md",
|
| 30579 |
"exists": true,
|
| 30580 |
+
"bytes": 6173,
|
| 30581 |
+
"sha256": "a3c83c90e82eca7c07f191f648f43a4b68bc16417fbff39ed30285bb205e083a"
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},
|
| 30583 |
"hf_artifacts": {
|
| 30584 |
"path": "hf_artifacts:README.de.md",
|
| 30585 |
"exists": true,
|
| 30586 |
+
"bytes": 6173,
|
| 30587 |
+
"sha256": "a3c83c90e82eca7c07f191f648f43a4b68bc16417fbff39ed30285bb205e083a"
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| 30588 |
},
|
| 30589 |
"hf_model": {
|
| 30590 |
"path": "hf_model:README.de.md",
|
| 30591 |
"exists": true,
|
| 30592 |
+
"bytes": 6173,
|
| 30593 |
+
"sha256": "a3c83c90e82eca7c07f191f648f43a4b68bc16417fbff39ed30285bb205e083a"
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| 30594 |
}
|
| 30595 |
},
|
| 30596 |
"failures": []
|
|
|
|
| 30601 |
"local": {
|
| 30602 |
"path": "repo:README.ja.md",
|
| 30603 |
"exists": true,
|
| 30604 |
+
"bytes": 6314,
|
| 30605 |
+
"sha256": "47cb46ff33864d59b0745a67f76ff0b7118dd212ade6e331c382f905596c0ba7"
|
| 30606 |
},
|
| 30607 |
"mirrors": {
|
| 30608 |
"hf_space": {
|
| 30609 |
"path": "hf_space:README.ja.md",
|
| 30610 |
"exists": true,
|
| 30611 |
+
"bytes": 6314,
|
| 30612 |
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"sha256": "47cb46ff33864d59b0745a67f76ff0b7118dd212ade6e331c382f905596c0ba7"
|
| 30613 |
},
|
| 30614 |
"hf_artifacts": {
|
| 30615 |
"path": "hf_artifacts:README.ja.md",
|
| 30616 |
"exists": true,
|
| 30617 |
+
"bytes": 6314,
|
| 30618 |
+
"sha256": "47cb46ff33864d59b0745a67f76ff0b7118dd212ade6e331c382f905596c0ba7"
|
| 30619 |
},
|
| 30620 |
"hf_model": {
|
| 30621 |
"path": "hf_model:README.ja.md",
|
| 30622 |
"exists": true,
|
| 30623 |
+
"bytes": 6314,
|
| 30624 |
+
"sha256": "47cb46ff33864d59b0745a67f76ff0b7118dd212ade6e331c382f905596c0ba7"
|
| 30625 |
}
|
| 30626 |
},
|
| 30627 |
"failures": []
|
|
|
|
| 30632 |
"local": {
|
| 30633 |
"path": "repo:README.ko.md",
|
| 30634 |
"exists": true,
|
| 30635 |
+
"bytes": 6141,
|
| 30636 |
+
"sha256": "eaecd5fabe52ac42482cd2df17a20268d93d056f69cce04985e764c0a652e923"
|
| 30637 |
},
|
| 30638 |
"mirrors": {
|
| 30639 |
"hf_space": {
|
| 30640 |
"path": "hf_space:README.ko.md",
|
| 30641 |
"exists": true,
|
| 30642 |
+
"bytes": 6141,
|
| 30643 |
+
"sha256": "eaecd5fabe52ac42482cd2df17a20268d93d056f69cce04985e764c0a652e923"
|
| 30644 |
},
|
| 30645 |
"hf_artifacts": {
|
| 30646 |
"path": "hf_artifacts:README.ko.md",
|
| 30647 |
"exists": true,
|
| 30648 |
+
"bytes": 6141,
|
| 30649 |
+
"sha256": "eaecd5fabe52ac42482cd2df17a20268d93d056f69cce04985e764c0a652e923"
|
| 30650 |
},
|
| 30651 |
"hf_model": {
|
| 30652 |
"path": "hf_model:README.ko.md",
|
| 30653 |
"exists": true,
|
| 30654 |
+
"bytes": 6141,
|
| 30655 |
+
"sha256": "eaecd5fabe52ac42482cd2df17a20268d93d056f69cce04985e764c0a652e923"
|
| 30656 |
}
|
| 30657 |
},
|
| 30658 |
"failures": []
|
|
|
|
| 30663 |
"local": {
|
| 30664 |
"path": "repo:README.pt.md",
|
| 30665 |
"exists": true,
|
| 30666 |
+
"bytes": 6176,
|
| 30667 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30668 |
},
|
| 30669 |
"mirrors": {
|
| 30670 |
"hf_space": {
|
| 30671 |
"path": "hf_space:README.pt.md",
|
| 30672 |
"exists": true,
|
| 30673 |
+
"bytes": 6176,
|
| 30674 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30675 |
},
|
| 30676 |
"hf_artifacts": {
|
| 30677 |
"path": "hf_artifacts:README.pt.md",
|
| 30678 |
"exists": true,
|
| 30679 |
+
"bytes": 6176,
|
| 30680 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30681 |
},
|
| 30682 |
"hf_model": {
|
| 30683 |
"path": "hf_model:README.pt.md",
|
| 30684 |
"exists": true,
|
| 30685 |
+
"bytes": 6176,
|
| 30686 |
+
"sha256": "4160433cd69561591a44334676df42bb6220475e97e2397b3808e4678bf200b3"
|
| 30687 |
}
|
| 30688 |
},
|
| 30689 |
"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": "2026-06-21T06:
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| 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": "2026-06-21T06:
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
@@ -33,22 +33,22 @@
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-21T06:
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
-
"generated_at_utc": "2026-06-21T06:
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-21T06:
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-21T06:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:55:02+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-21T06:55:00+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00"
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-21T06:28:19+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-21T06:28:04+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
docs/data/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:56:26+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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:35+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/scope_claims_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00",
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:00+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": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:02+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
metrics/language_versions.json
CHANGED
|
@@ -1,7 +1,7 @@
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
-
"updated": "2026-06-
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Task Suite Language Versions",
|
| 3 |
"status": "pass",
|
| 4 |
+
"updated": "2026-06-21",
|
| 5 |
"language_count": 8,
|
| 6 |
"languages": [
|
| 7 |
{
|
metrics/mirror_parity.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"hf_root": "hf_publish",
|
| 5 |
"summary": {
|
| 6 |
"group_count": 1250,
|
|
@@ -482,44 +482,44 @@
|
|
| 482 |
"path": "repo:docs/data/language_versions.json",
|
| 483 |
"exists": true,
|
| 484 |
"bytes": 1762,
|
| 485 |
-
"sha256": "
|
| 486 |
},
|
| 487 |
"mirrors": {
|
| 488 |
"hf_space": {
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| 489 |
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|
|
@@ -33,22 +33,22 @@
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
-
"generated_at_utc": "2026-06-21T06:
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
-
"generated_at_utc": "2026-06-21T06:
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
-
"generated_at_utc": "2026-06-21T06:
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
-
"generated_at_utc": "2026-06-21T06:
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
|
|
|
| 1 |
{
|
| 2 |
"title": "Ropedia Xperience-10M Public Project Surface",
|
| 3 |
"status": "pass",
|
| 4 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:55:02+00:00"
|
| 22 |
},
|
| 23 |
"rendered_site_check": {
|
| 24 |
"exists": true,
|
|
|
|
| 33 |
"source_alignment": {
|
| 34 |
"exists": true,
|
| 35 |
"status": "pass",
|
| 36 |
+
"generated_at_utc": "2026-06-21T06:55:00+00:00"
|
| 37 |
},
|
| 38 |
"scale_up_status": {
|
| 39 |
"exists": true,
|
| 40 |
"status": "pass",
|
| 41 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00"
|
| 42 |
},
|
| 43 |
"publication_package": {
|
| 44 |
"exists": true,
|
| 45 |
"status": "pass",
|
| 46 |
+
"generated_at_utc": "2026-06-21T06:28:19+00:00"
|
| 47 |
},
|
| 48 |
"mirror_parity": {
|
| 49 |
"exists": true,
|
| 50 |
"status": "pass",
|
| 51 |
+
"generated_at_utc": "2026-06-21T06:28:04+00:00"
|
| 52 |
}
|
| 53 |
},
|
| 54 |
"failures": {}
|
metrics/publication_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"checks": [
|
| 5 |
{
|
| 6 |
"name": "required_publication_assets_present",
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:56:26+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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:35+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/scope_claims_audit.json
CHANGED
|
@@ -1,6 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
-
"generated_at_utc": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:08+00:00",
|
| 4 |
"summary": {
|
| 5 |
"qwen3_omni_verified_diagnostic_pilot": true,
|
| 6 |
"dataset_manifest_num_episodes": 119,
|
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": "2026-06-21T06:
|
| 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": "2026-06-21T06:55:00+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": "2026-06-21T06:
|
| 4 |
"summary": {
|
| 5 |
"original_walkthrough_task_count": 12,
|
| 6 |
"expected_original_walkthrough_task_count": 12,
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:35+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": "2026-06-21T06:
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|
|
|
|
| 1 |
{
|
| 2 |
"status": "pass",
|
| 3 |
+
"generated_at_utc": "2026-06-21T06:55:02+00:00",
|
| 4 |
"docs_root": "docs",
|
| 5 |
"site_base": "/ropedia-xperience-10m-task-suite/",
|
| 6 |
"summary": {
|