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FIGURE_INDEX.md

@@ -17,7 +17,7 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | Original task-suite infographic | `docs/assets/task_suite_infographic.png` | 1800 x 5000 | `scripts/render_task_suite_infographic.py` | Primary visual map of the walkthrough-backed task families, verified metrics, and sample modalities; the unified public suite is documented as 20 tasks. |
 | Episode-to-task pipeline diagram | `docs/assets/pipeline_diagram.png` | 1800 x 1120 | `scripts/generate_visualizations.py` | End-to-end data processing and evaluation pipeline overview. |
 | Qwen3-Omni LoRA training pipeline | `docs/assets/qwen3_omni_lora_pipeline.png` | 1536 x 1024 | `docs/assets/qwen3_omni_lora_pipeline.prompt.md` | Detailed raw-data-to-adapter flow for staged Xperience-10M Qwen3-Omni LoRA training. |
-| 20-task / 4-direction / 3-pipeline relationship map | `docs/assets/charts/task_direction_pipeline_relationship.png` | 1672 x 941 | `docs/assets/charts/task_direction_pipeline_relationship.prompt.md` | Overview map showing the exact 20 task tiles, four research-direction groups, and three foundation-pipeline columns used by the public reader flow. |
+| 20-task / 4-direction / 3-pipeline / unified-model relationship map | `docs/assets/charts/task_direction_pipeline_relationship.png` | 1672 x 941 | `docs/assets/charts/task_direction_pipeline_relationship.prompt.md` | Overview map showing the exact 20 task tiles, four research-direction groups, three foundation-pipeline columns, and the unified embodied model target used by the public reader flow. |
 | Spatial intelligence slide diagram | `docs/assets/foundation-pipelines/spatial-intelligence-pipeline.png` | 2560 x 1920 | `scripts/render_foundation_pipeline_diagrams.py` | High-resolution slide diagram for the spatial intelligence pipeline track. |
 | Human-video world model slide diagram | `docs/assets/foundation-pipelines/human-video-world-model-pipeline.png` | 2560 x 1920 | `scripts/render_foundation_pipeline_diagrams.py` | High-resolution slide diagram for the human-video world-model pipeline track. |
 | Vision-language-action slide diagram | `docs/assets/foundation-pipelines/vision-language-action-pipeline.png` | 2560 x 1920 | `scripts/render_foundation_pipeline_diagrams.py` | High-resolution slide diagram for the VLA/action-policy pipeline track. |

PROJECT_README.md

@@ -106,6 +106,10 @@ The multilingual README files are reader guides. The canonical technical evidenc
       <td><strong>3 foundation pipelines</strong></td>
       <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>
     </tr>
+    <tr>
+      <td><strong>1 unified target</strong></td>
+      <td>The long-term embodied foundation-model target connects perception, 3D memory, language-grounded reasoning, action, and planning without adding a new score axis.</td>
+    </tr>
     <tr>
       <td><strong>Public mirrors</strong></td>
       <td>GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3-Omni and Cosmos3 model repos, and HF collection.</td>
@@ -113,14 +117,14 @@ The multilingual README files are reader guides. The canonical technical evidenc
   </tbody>
 </table>
 
-## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines
+## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines / 1 Unified Target
 
-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. Use them in that order when reading the project.
+Read the project as four connected 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. The **1 unified embodied model target** is the long-term integration goal after those pipelines mature. Use them in that order when reading the project.
 
-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**.
+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**; if it combines perception, 3D memory, language, action, and planning, it is the **unified target** rather than an extra score axis.
 
 <p align="center">
-  <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%">
+  <img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, 3 foundation-model pipeline tracks, and 1 unified embodied model target" width="100%">
 </p>
 
 | Layer | Count | Reader role | Exact public labels |
@@ -128,6 +132,7 @@ Reader rule: if it has a metric, it is a **task**; if it explains what the evide
 | 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. |
 | 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. |
 | 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. |
+| Unified embodied model target | 1 | Long-term integration target, not a task/method row in the 180-result matrix. | Perception; 3D memory; language-grounded reasoning; action; planning. |
 
 ## Two Evidence Lines
 

README.de.md

@@ -69,11 +69,11 @@ Einstieg: [Leitfaden zu zwei Evidenzlinien](TWO_EVIDENCE_LINES.md), [Daten der z
 
 ## Struktur
 
-- 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.
+- 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; verbindet es Wahrnehmung, 3D-Gedächtnis, Sprache, Aktion und Planung, ist es das unified embodied model target, keine zusätzliche Score-Achse.
 - Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
 - Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
 - Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos; die öffentliche Matrix steht bei 180/180 gescorten Einträgen: 174 direct und 6 compact proxy, mit sichtbaren Proxy-Targets.
-- Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert.
+- Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert; das langfristige Ziel ist ein unified embodied foundation model.
 
 ## Öffentliche Grenze
 

README.es.md

@@ -69,11 +69,11 @@ Entradas: [guia de dos lineas de evidencia](TWO_EVIDENCE_LINES.md), [datos de do
 
 ## Estructura
 
-- 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.
+- 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; si combina percepción, memoria 3D, lenguaje, acción y planificación, es el unified embodied model target, no otro eje de score.
 - Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
 - Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
 - Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos; la matriz pública está en 180/180 registros con score: 174 direct y 6 compact proxy, con proxy targets visibles.
-- Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia.
+- Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia; el objetivo largo plazo es un unified embodied foundation model.
 
 ## Límite Público
 

README.fr.md

@@ -69,11 +69,11 @@ Entrées : [guide des deux lignes de preuve](TWO_EVIDENCE_LINES.md), [données d
 
 ## Structure
 
-- 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.
+- 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; si cela combine perception, mémoire 3D, langage, action et planification, c'est la cible unified embodied model, pas un nouvel axe de score.
 - Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
 - Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
 - Résultats : minimal/NN sur l'épisode public couvrent 20/20; 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.
-- Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires.
+- Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires; l'objectif à long terme est un unified embodied foundation model.
 
 ## Frontière Publique
 

README.ja.md

@@ -69,11 +69,11 @@ Method blocks: Line 1 は task-head baselines（Minimal、Neural MLP）。Line 2
 
 ## 構造
 
-- 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。
+- 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。perception、3D memory、language reasoning、action、planning を統合するものは unified embodied model target であり、新しい score axis ではありません。
 - データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
 - タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
 - 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、174 direct と 6 compact proxy を分離し、proxy targets は明示します。
-- 方向: spatial intelligence、human-video world model、vision-language-action に対して、タスク対応と必要証拠を記録しています。
+- 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。長期目標は unified embodied foundation model です。
 
 ## 公開境界
 

README.ko.md

@@ -69,11 +69,11 @@
 
 ## 구조
 
-- 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.
+- 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. perception, 3D memory, language reasoning, action, planning을 합치는 것은 unified embodied model target이며 새 score axis가 아닙니다.
 - 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
 - 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
 - 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 174 direct와 6 compact proxy를 분리하고 proxy targets를 명시합니다.
-- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
+- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다. 장기 목표는 unified embodied foundation model입니다.
 
 ## 공개 경계
 

README.md

@@ -128,6 +128,10 @@ The multilingual README files are reader guides. The canonical technical evidenc
       <td><strong>3 foundation pipelines</strong></td>
       <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>
     </tr>
+    <tr>
+      <td><strong>1 unified target</strong></td>
+      <td>The long-term embodied foundation-model target connects perception, 3D memory, language-grounded reasoning, action, and planning without adding a new score axis.</td>
+    </tr>
     <tr>
       <td><strong>Public mirrors</strong></td>
       <td>GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3-Omni and Cosmos3 model repos, and HF collection.</td>
@@ -135,14 +139,14 @@ The multilingual README files are reader guides. The canonical technical evidenc
   </tbody>
 </table>
 
-## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines
+## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines / 1 Unified Target
 
-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. Use them in that order when reading the project.
+Read the project as four connected 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. The **1 unified embodied model target** is the long-term integration goal after those pipelines mature. Use them in that order when reading the project.
 
-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**.
+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**; if it combines perception, 3D memory, language, action, and planning, it is the **unified target** rather than an extra score axis.
 
 <p align="center">
-  <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%">
+  <img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, 3 foundation-model pipeline tracks, and 1 unified embodied model target" width="100%">
 </p>
 
 | Layer | Count | Reader role | Exact public labels |
@@ -150,6 +154,7 @@ Reader rule: if it has a metric, it is a **task**; if it explains what the evide
 | 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. |
 | 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. |
 | 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. |
+| Unified embodied model target | 1 | Long-term integration target, not a task/method row in the 180-result matrix. | Perception; 3D memory; language-grounded reasoning; action; planning. |
 
 ## Two Evidence Lines
 

README.pt.md

@@ -69,11 +69,11 @@ Entradas: [guia de duas linhas de evidencia](TWO_EVIDENCE_LINES.md), [dados das
 
 ## Estrutura
 
-- 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.
+- 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; se combina percepção, memória 3D, linguagem, ação e planejamento, pertence ao unified embodied model target, não a um novo eixo de score.
 - Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
 - Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
 - Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score: 174 direct e 6 compact proxy, com proxy targets visíveis.
-- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
+- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência; o objetivo de longo prazo é um unified embodied foundation model.
 
 ## Fronteira Pública
 

README.zh.md

@@ -69,11 +69,11 @@
 
 ## 核心结构
 
-- 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。
+- 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；把感知、3D 记忆、语言推理、action 和 planning 合并起来的是 unified embodied model target，不是新的评分轴。
 - 数据层：公开 sample episode 被切成 20-frame 窗口，并连接视频、音频、深度、pose/SLAM、mocap、IMU、calibration 和语言标注。
 - 任务层：20 个统一任务覆盖识别、预测、检索、重建、同步、长时预测、action-object 关系和 sensor bridge。
 - 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-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。
-- 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。
+- 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；长期目标是一个 unified embodied foundation model。
 
 ## 公开边界
 

assets/charts/task_direction_pipeline_relationship.prompt.md

@@ -14,13 +14,17 @@ Asset type: high-resolution website figure for Ropedia Xperience-10M.
 
 Primary request: generate a polished, readable, dark neon Ropedia-style
 infographic that clearly explains the current expandable structure:
-20 task contracts -> 4 research directions -> 3 foundation training pipelines.
+20 task contracts -> 4 research directions -> 3 foundation training pipelines
+-> 1 unified embodied foundation model target.
 
 Critical text accuracy and layout:
 
-- Make the numbers 20, 4, and 3 unmistakable. Use large section headings:
-  `20 TASKS`, `4 RESEARCH DIRECTIONS`, `3 FOUNDATION PIPELINES`.
-- Add the subtitle: `Current public release, open to expand`.
+- Use the exact title: `Ropedia Xperience-10M Task Suite`.
+- Make the numbers 20, 4, 3, and 1 unmistakable. Use large section headings:
+  `20 TASKS`, `4 RESEARCH DIRECTIONS`, `3 FOUNDATION PIPELINES`,
+  and `1 UNIFIED EMBODIED FOUNDATION MODEL`.
+- Add the subtitle:
+  `20 tasks -> 4 directions -> 3 pipelines -> 1 unified embodied foundation model, open to expand`.
 - Left side: exactly twenty task tiles in a 5 x 4 grid. Use large numbers
   `01` through `20` plus simple icons. Keep task labels short and readable:
   Action, Step, Boundary, Next Action, Hand Path, Contact, Object, Grounding,
@@ -45,9 +49,13 @@ Critical text accuracy and layout:
   - Spatial Intelligence: RGB + depth + pose -> 3D geometry + spatial reasoning
   - Human-Video World Model: video + action + motion -> future frame + future action
   - Vision-Language-Action: video + language + motion -> robot action chunk
+- Far right: exactly one final synthesis card labeled
+  `1 Unified Embodied Foundation Model`. Show it as the convergence target for
+  perception, 3D memory, language-grounded reasoning, action, and planning.
 - Draw clean arrows from the 20-task grid into the 4 direction panels, then
-  arrows into the 3 pipeline cards.
-- Add a footer line: `Tasks, directions, and pipelines can expand as new evidence is added.`
+  arrows into the 3 pipeline cards, then arrows into the unified model target.
+- Add a footer line:
+  `Tasks, directions, pipelines, and unified model targets can expand as new evidence is added.`
 
 Visual style:
 
@@ -58,15 +66,10 @@ Visual style:
 - No photorealistic presenter, no white page background, no watermark.
 - Avoid tiny decorative clutter; prioritize readability at website size.
 
-## Post-Processing
-
-The generated image output was used as the visual base. The top title band was
-locally corrected with an exact text overlay to avoid generated-text spelling
-drift while preserving the generated diagram content.
-
 ## Verification
 
-The committed asset was visually checked after post-processing. The page also
-includes an HTML key that lists the exact 20 task names, four canonical research
-directions, and three foundation-pipeline tracks, so reader-facing counts do
-not depend only on the image.
+The committed asset was visually checked at its native 1672 x 941 resolution.
+The page also includes an HTML key that lists the exact 20 task names, four
+canonical research directions, three foundation-pipeline tracks, and one
+unified embodied foundation-model target, so reader-facing counts do not depend
+only on the image.

data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:16:44+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 30,
   "figures": [
@@ -109,14 +109,14 @@
     },
     {
       "id": "task_direction_pipeline_relationship",
-      "title": "20-task / 4-direction / 3-pipeline relationship map",
+      "title": "20-task / 4-direction / 3-pipeline / unified-model relationship map",
       "path": "docs/assets/charts/task_direction_pipeline_relationship.png",
-      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, and three foundation-pipeline columns used by the public reader flow.",
+      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, three foundation-pipeline columns, and the unified embodied model target used by the public reader flow.",
       "source_script": "docs/assets/charts/task_direction_pipeline_relationship.prompt.md",
       "surface": "website overview, HF Space, artifact dataset, model card",
       "exists": true,
-      "bytes": 1541517,
-      "sha256": "0e70d267ada316447cd8ade3181f5037af9805721476ca02dde7070697d94ac0",
+      "bytes": 1795186,
+      "sha256": "901448d1ec9ce2ab563a5f90fd1b6450de6c3dce4c236d342f98f5927416131b",
       "dimensions": {
         "format": "PNG",
         "width": 1672,

data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:24:37+00:00",
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+          "sha256": "8ca437514dc45f5a77d13e3933907cc047992a67bac43c3f96a3161ea069c71d"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:README.de.md",
           "exists": true,
-          "bytes": 7565,
-          "sha256": "c7d8b5a23a48ab03bf559979c0edfab466f987c3264e69ef01800025653d483f"
+          "bytes": 7775,
+          "sha256": "8ca437514dc45f5a77d13e3933907cc047992a67bac43c3f96a3161ea069c71d"
         },
         "hf_model": {
           "path": "hf_model:README.de.md",
           "exists": true,
-          "bytes": 7565,
-          "sha256": "c7d8b5a23a48ab03bf559979c0edfab466f987c3264e69ef01800025653d483f"
+          "bytes": 7775,
+          "sha256": "8ca437514dc45f5a77d13e3933907cc047992a67bac43c3f96a3161ea069c71d"
         }
       },
       "failures": []
@@ -32378,27 +32378,27 @@
       "local": {
         "path": "repo:README.ja.md",
         "exists": true,
-        "bytes": 7748,
-        "sha256": "75666b8f00b7cb54899c1015de450a010aa32de6637790f1ac1f6eaad28619f4"
+        "bytes": 7983,
+        "sha256": "05c65af68e324d0fa25e5fe41ee2987f691b49753d7e337a3af5839b6f18eb88"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:README.ja.md",
           "exists": true,
-          "bytes": 7748,
-          "sha256": "75666b8f00b7cb54899c1015de450a010aa32de6637790f1ac1f6eaad28619f4"
+          "bytes": 7983,
+          "sha256": "05c65af68e324d0fa25e5fe41ee2987f691b49753d7e337a3af5839b6f18eb88"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:README.ja.md",
           "exists": true,
-          "bytes": 7748,
-          "sha256": "75666b8f00b7cb54899c1015de450a010aa32de6637790f1ac1f6eaad28619f4"
+          "bytes": 7983,
+          "sha256": "05c65af68e324d0fa25e5fe41ee2987f691b49753d7e337a3af5839b6f18eb88"
         },
         "hf_model": {
           "path": "hf_model:README.ja.md",
           "exists": true,
-          "bytes": 7748,
-          "sha256": "75666b8f00b7cb54899c1015de450a010aa32de6637790f1ac1f6eaad28619f4"
+          "bytes": 7983,
+          "sha256": "05c65af68e324d0fa25e5fe41ee2987f691b49753d7e337a3af5839b6f18eb88"
         }
       },
       "failures": []
@@ -32409,27 +32409,27 @@
       "local": {
         "path": "repo:README.ko.md",
         "exists": true,
-        "bytes": 7537,
-        "sha256": "fdf2557cb593df4bd2639feab884580314c7ce5c07ca12b1a52893f47af7f5bf"
+        "bytes": 7746,
+        "sha256": "68ffe45ae456ec735fc207acbbebe635e65668e6692c630cb5273831bc33754a"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:README.ko.md",
           "exists": true,
-          "bytes": 7537,
-          "sha256": "fdf2557cb593df4bd2639feab884580314c7ce5c07ca12b1a52893f47af7f5bf"
+          "bytes": 7746,
+          "sha256": "68ffe45ae456ec735fc207acbbebe635e65668e6692c630cb5273831bc33754a"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:README.ko.md",
           "exists": true,
-          "bytes": 7537,
-          "sha256": "fdf2557cb593df4bd2639feab884580314c7ce5c07ca12b1a52893f47af7f5bf"
+          "bytes": 7746,
+          "sha256": "68ffe45ae456ec735fc207acbbebe635e65668e6692c630cb5273831bc33754a"
         },
         "hf_model": {
           "path": "hf_model:README.ko.md",
           "exists": true,
-          "bytes": 7537,
-          "sha256": "fdf2557cb593df4bd2639feab884580314c7ce5c07ca12b1a52893f47af7f5bf"
+          "bytes": 7746,
+          "sha256": "68ffe45ae456ec735fc207acbbebe635e65668e6692c630cb5273831bc33754a"
         }
       },
       "failures": []
@@ -32440,27 +32440,27 @@
       "local": {
         "path": "repo:README.pt.md",
         "exists": true,
-        "bytes": 7551,
-        "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
+        "bytes": 7762,
+        "sha256": "47cb1f5d8b0e19e31ea64e4d5b8f0b8f3d12bc63b2012a9afb91af9b55de902d"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:README.pt.md",
           "exists": true,
-          "bytes": 7551,
-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
+          "bytes": 7762,
+          "sha256": "47cb1f5d8b0e19e31ea64e4d5b8f0b8f3d12bc63b2012a9afb91af9b55de902d"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:README.pt.md",
           "exists": true,
-          "bytes": 7551,
-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
+          "bytes": 7762,
+          "sha256": "47cb1f5d8b0e19e31ea64e4d5b8f0b8f3d12bc63b2012a9afb91af9b55de902d"
         },
         "hf_model": {
           "path": "hf_model:README.pt.md",
           "exists": true,
-          "bytes": 7551,
-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
+          "bytes": 7762,
+          "sha256": "47cb1f5d8b0e19e31ea64e4d5b8f0b8f3d12bc63b2012a9afb91af9b55de902d"
         }
       },
       "failures": []
@@ -32595,27 +32595,27 @@
       "local": {
         "path": "repo:FIGURE_INDEX.md",
         "exists": true,
-        "bytes": 7421,
-        "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+        "bytes": 7472,
+        "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 7421,
-          "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+          "bytes": 7472,
+          "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 7421,
-          "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+          "bytes": 7472,
+          "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
         },
         "hf_model": {
           "path": "hf_model:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 7421,
-          "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+          "bytes": 7472,
+          "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
         }
       },
       "failures": []

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:25:44+00:00",
+  "generated_at_utc": "2026-06-23T07:26:14+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -28,17 +28,17 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T04:12:55+00:00"
+          "generated_at_utc": "2026-06-23T07:24:36+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T04:12:55+00:00"
+          "generated_at_utc": "2026-06-23T07:24:36+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T21:02:07+00:00"
+          "generated_at_utc": "2026-06-23T07:24:48+00:00"
         },
         "publication_package": {
           "exists": true,
@@ -48,7 +48,7 @@
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T06:24:37+00:00"
+          "generated_at_utc": "2026-06-23T06:27:20+00:00"
         }
       },
       "failures": {}
@@ -95,8 +95,8 @@
       "status": "pass",
       "reason": "Public copy should consistently present the project as Ropedia Xperience-10M, with the Qwen3-Omni scale-up status.",
       "marker_counts": {
-        "Ropedia Xperience-10M Task Suite": 22,
-        "Xperience-10M": 173,
+        "Ropedia Xperience-10M Task Suite": 23,
+        "Xperience-10M": 174,
         "20-task": 126,
         "Qwen3-Omni": 232,
         "128-episode pilot": 1

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:24:46+00:00",
+  "generated_at_utc": "2026-06-23T07:44:56+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T21:02:07+00:00",
+  "generated_at_utc": "2026-06-23T07:24:48+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
+  "generated_at_utc": "2026-06-23T07:24:36+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
+  "generated_at_utc": "2026-06-23T07:24:36+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:26:19+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 192813,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "evidence_index": 257878
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 192813,
-      "protocol_index": 251984,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "protocol_index": 254083,
+      "evidence_index": 257878
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -345,7 +345,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 20339,
+      "bytes": 20390,
       "top_level_type": "dict"
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 14235,
+      "bytes": 10940,
       "top_level_type": "dict"
     },
     {
@@ -678,7 +678,7 @@
     {
       "path": "assets/charts/task_direction_pipeline_relationship.png",
       "exists": true,
-      "bytes": 1541517,
+      "bytes": 1795186,
       "width": 1672,
       "height": 941,
       "format": "PNG"

docs/assets/charts/task_direction_pipeline_relationship.prompt.md

@@ -14,13 +14,17 @@ Asset type: high-resolution website figure for Ropedia Xperience-10M.
 
 Primary request: generate a polished, readable, dark neon Ropedia-style
 infographic that clearly explains the current expandable structure:
-20 task contracts -> 4 research directions -> 3 foundation training pipelines.
+20 task contracts -> 4 research directions -> 3 foundation training pipelines
+-> 1 unified embodied foundation model target.
 
 Critical text accuracy and layout:
 
-- Make the numbers 20, 4, and 3 unmistakable. Use large section headings:
-  `20 TASKS`, `4 RESEARCH DIRECTIONS`, `3 FOUNDATION PIPELINES`.
-- Add the subtitle: `Current public release, open to expand`.
+- Use the exact title: `Ropedia Xperience-10M Task Suite`.
+- Make the numbers 20, 4, 3, and 1 unmistakable. Use large section headings:
+  `20 TASKS`, `4 RESEARCH DIRECTIONS`, `3 FOUNDATION PIPELINES`,
+  and `1 UNIFIED EMBODIED FOUNDATION MODEL`.
+- Add the subtitle:
+  `20 tasks -> 4 directions -> 3 pipelines -> 1 unified embodied foundation model, open to expand`.
 - Left side: exactly twenty task tiles in a 5 x 4 grid. Use large numbers
   `01` through `20` plus simple icons. Keep task labels short and readable:
   Action, Step, Boundary, Next Action, Hand Path, Contact, Object, Grounding,
@@ -45,9 +49,13 @@ Critical text accuracy and layout:
   - Spatial Intelligence: RGB + depth + pose -> 3D geometry + spatial reasoning
   - Human-Video World Model: video + action + motion -> future frame + future action
   - Vision-Language-Action: video + language + motion -> robot action chunk
+- Far right: exactly one final synthesis card labeled
+  `1 Unified Embodied Foundation Model`. Show it as the convergence target for
+  perception, 3D memory, language-grounded reasoning, action, and planning.
 - Draw clean arrows from the 20-task grid into the 4 direction panels, then
-  arrows into the 3 pipeline cards.
-- Add a footer line: `Tasks, directions, and pipelines can expand as new evidence is added.`
+  arrows into the 3 pipeline cards, then arrows into the unified model target.
+- Add a footer line:
+  `Tasks, directions, pipelines, and unified model targets can expand as new evidence is added.`
 
 Visual style:
 
@@ -58,15 +66,10 @@ Visual style:
 - No photorealistic presenter, no white page background, no watermark.
 - Avoid tiny decorative clutter; prioritize readability at website size.
 
-## Post-Processing
-
-The generated image output was used as the visual base. The top title band was
-locally corrected with an exact text overlay to avoid generated-text spelling
-drift while preserving the generated diagram content.
-
 ## Verification
 
-The committed asset was visually checked after post-processing. The page also
-includes an HTML key that lists the exact 20 task names, four canonical research
-directions, and three foundation-pipeline tracks, so reader-facing counts do
-not depend only on the image.
+The committed asset was visually checked at its native 1672 x 941 resolution.
+The page also includes an HTML key that lists the exact 20 task names, four
+canonical research directions, three foundation-pipeline tracks, and one
+unified embodied foundation-model target, so reader-facing counts do not depend
+only on the image.

docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:24:42+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -632,7 +632,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "e8706007477657bf1d8155006462101d355b8c9bf0d8ab155fc74a577ccfec46"
+      "sha256": "9da89d3ebcdd8494d26ad63c00064b757c2fbefbb6bf3708a64c5e3d2aa5a1cf"
     },
     {
       "id": "source_alignment_validator",
@@ -1093,8 +1093,8 @@
       "surface": "repo_hf",
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
       "exists": true,
-      "bytes": 7421,
-      "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+      "bytes": 7472,
+      "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
     },
     {
       "id": "figure_index_json",
@@ -1104,8 +1104,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 20339,
-      "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
+      "bytes": 20390,
+      "sha256": "78d850db3f9b627e754139a2316f359ca6c61b142d43d87d1c51326364e31d38"
     },
     {
       "id": "figure_index_builder",
@@ -1115,8 +1115,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 17444,
-      "sha256": "521f69760ba5f7d2993222495833f56f883aca6b832e71dd36c410a757bc1e20"
+      "bytes": 17495,
+      "sha256": "cab9aa79378e5851e1bb80b01bc0c1fb2bb53342201d7a34caf7629926108dd7"
     },
     {
       "id": "brand_assets_json",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "dedc34fa478d0d55198dd775205f0fe76032a964f70b6eb34e7d5941bcb3ab03"
+      "sha256": "812203f1e12b211770f2a0771ecc2cf27d350250ea8181867e41e9c5f9ff47fc"
     },
     {
       "id": "public_surface_qa",
@@ -1318,8 +1318,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 69811,
-      "sha256": "4d44fb7c634d84735a9c0ec2b3c1ab56c75fc65cce6137aae981f81de9121040"
+      "bytes": 69839,
+      "sha256": "9bfe428736e073ca99ddd5a7e4aab1644028866c3501d46568c313efb5c155bf"
     },
     {
       "id": "reproducibility_contract",
@@ -1363,7 +1363,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 14235,
+      "bytes": 10940,
       "hash_policy": "existence_and_size_only"
     },
     {

docs/data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:16:44+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 30,
   "figures": [
@@ -109,14 +109,14 @@
     },
     {
       "id": "task_direction_pipeline_relationship",
-      "title": "20-task / 4-direction / 3-pipeline relationship map",
+      "title": "20-task / 4-direction / 3-pipeline / unified-model relationship map",
       "path": "docs/assets/charts/task_direction_pipeline_relationship.png",
-      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, and three foundation-pipeline columns used by the public reader flow.",
+      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, three foundation-pipeline columns, and the unified embodied model target used by the public reader flow.",
       "source_script": "docs/assets/charts/task_direction_pipeline_relationship.prompt.md",
       "surface": "website overview, HF Space, artifact dataset, model card",
       "exists": true,
-      "bytes": 1541517,
-      "sha256": "0e70d267ada316447cd8ade3181f5037af9805721476ca02dde7070697d94ac0",
+      "bytes": 1795186,
+      "sha256": "901448d1ec9ce2ab563a5f90fd1b6450de6c3dce4c236d342f98f5927416131b",
       "dimensions": {
         "format": "PNG",
         "width": 1672,

docs/data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:24:37+00:00",
+  "generated_at_utc": "2026-06-23T07:44:41+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1308,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+        "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
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@@ -32378,27 +32378,27 @@
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@@ -32409,27 +32409,27 @@
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@@ -32440,27 +32440,27 @@
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-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
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-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
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-          "sha256": "bed488d4e66baa7c28643dd27acaf5b3b037452f895a7a70acdce04a8e807fa4"
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@@ -32595,27 +32595,27 @@
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-          "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
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         "hf_artifacts": {
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-          "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
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docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:25:44+00:00",
+  "generated_at_utc": "2026-06-23T07:26:14+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -28,17 +28,17 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T04:12:55+00:00"
+          "generated_at_utc": "2026-06-23T07:24:36+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T04:12:55+00:00"
+          "generated_at_utc": "2026-06-23T07:24:36+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T21:02:07+00:00"
+          "generated_at_utc": "2026-06-23T07:24:48+00:00"
         },
         "publication_package": {
           "exists": true,
@@ -48,7 +48,7 @@
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-23T06:24:37+00:00"
+          "generated_at_utc": "2026-06-23T06:27:20+00:00"
         }
       },
       "failures": {}
@@ -95,8 +95,8 @@
       "status": "pass",
       "reason": "Public copy should consistently present the project as Ropedia Xperience-10M, with the Qwen3-Omni scale-up status.",
       "marker_counts": {
-        "Ropedia Xperience-10M Task Suite": 22,
-        "Xperience-10M": 173,
+        "Ropedia Xperience-10M Task Suite": 23,
+        "Xperience-10M": 174,
         "20-task": 126,
         "Qwen3-Omni": 232,
         "128-episode pilot": 1

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:24:46+00:00",
+  "generated_at_utc": "2026-06-23T07:44:56+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:25:44+00:00",
+  "generated_at_utc": "2026-06-23T07:26:14+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T21:02:07+00:00",
+  "generated_at_utc": "2026-06-23T07:24:48+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
+  "generated_at_utc": "2026-06-23T07:24:36+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
+  "generated_at_utc": "2026-06-23T07:24:36+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:26:19+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 192813,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "evidence_index": 257878
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 192813,
-      "protocol_index": 251984,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "protocol_index": 254083,
+      "evidence_index": 257878
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -345,7 +345,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 20339,
+      "bytes": 20390,
       "top_level_type": "dict"
     },
     {
@@ -420,7 +420,7 @@
     },
     {
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+      "bytes": 10940,
       "top_level_type": "dict"
     },
     {
@@ -678,7 +678,7 @@
     {
       "path": "assets/charts/task_direction_pipeline_relationship.png",
       "exists": true,
-      "bytes": 1541517,
+      "bytes": 1795186,
       "width": 1672,
       "height": 941,
       "format": "PNG"

docs/index.html

@@ -1766,12 +1766,12 @@
     }
     .task-axis-grid {
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 12px;
     }
     .task-axis-visual {
       position: relative;
-      margin: 0 0 16px;
+      margin: 22px 0 18px;
       border: 1px solid rgba(204, 255, 160, 0.18);
       border-radius: var(--radius);
       overflow: hidden;
@@ -1787,18 +1787,17 @@
       background: #020502;
     }
     .task-axis-visual figcaption {
-      position: absolute;
-      left: 14px;
-      right: 14px;
-      bottom: 14px;
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 8px;
+      padding: 12px;
+      border-top: 1px solid rgba(204, 255, 160, 0.16);
+      background: rgba(2, 5, 2, 0.92);
     }
     .task-axis-visual figcaption span {
       border: 1px solid rgba(204, 255, 160, 0.22);
       border-radius: 8px;
-      background: rgba(2, 5, 2, 0.72);
+      background: rgba(204, 255, 160, 0.055);
       backdrop-filter: blur(10px);
       color: rgba(245, 247, 240, 0.84);
       font-family: var(--font-ui);
@@ -1819,7 +1818,7 @@
     }
     .task-axis-summary {
       display: grid;
-      grid-template-columns: 1.08fr 1fr 0.92fr;
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 12px;
       margin: 0 0 16px;
     }
@@ -1877,7 +1876,7 @@
     }
     .task-axis-reader-rule {
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 12px;
       margin: 0 0 16px;
     }
@@ -1914,7 +1913,7 @@
     }
     .task-axis-key {
       display: grid;
-      grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
+      grid-template-columns: minmax(0, 1.35fr) repeat(3, minmax(0, 0.82fr));
       gap: 12px;
       margin: 0 0 16px;
     }
@@ -2048,7 +2047,7 @@
     }
     .task-axis-flow {
       display: grid;
-      grid-template-columns: 1fr auto 1fr auto 1fr;
+      grid-template-columns: 1fr auto 1fr auto 1fr auto 1fr;
       align-items: center;
       gap: 10px;
       margin-top: 14px;
@@ -6709,13 +6708,13 @@
             <a href="#artifacts"><span>04 reuse</span><strong>Artifacts and mirrors</strong><em>GitHub, GitHub Pages, HF Space, datasets, weights, and validation outputs.</em></a>
           </div>
         </div>
-        <div class="task-axis-panel" aria-label="Relationship between tasks, research directions, and training pipelines">
+        <div class="task-axis-panel" aria-label="Relationship between tasks, research directions, training pipelines, and unified embodied model target">
           <div class="task-axis-head">
             <div>
               <small>relationship map</small>
-              <strong>20 tasks / 4 directions / 3 foundation pipelines.</strong>
+              <strong>20 tasks / 4 directions / 3 pipelines / 1 unified model target.</strong>
             </div>
-            <p>Read the project in that order. The 20 tasks are the scored contracts; the four directions are reader-facing research groupings over those tasks; the three foundation pipelines are model-training recipes that reuse the same episode files.</p>
+            <p>Read the project in that order. The 20 tasks are scored contracts; the four directions group what those scores study; the three pipelines define training recipes; the unified embodied model is the long-term integration target.</p>
           </div>
           <div class="task-axis-summary" aria-label="Reader key for the public structure">
             <article class="task-axis-summary-card">
@@ -6742,8 +6741,16 @@
                 <p>Spatial intelligence, human-video world models, and vision-language-action models reuse the same files with different input-output recipes.</p>
               </div>
             </article>
+            <article class="task-axis-summary-card">
+              <span class="task-axis-count">1</span>
+              <div>
+                <small>unified target</small>
+                <strong>One embodied foundation model goal.</strong>
+                <p>The pipeline outputs converge toward perception, 3D memory, language, action, and planning in one model family.</p>
+              </div>
+            </article>
           </div>
-          <div class="task-axis-reader-rule" aria-label="How to identify the three public layers">
+          <div class="task-axis-reader-rule" aria-label="How to identify the public layers">
             <article>
               <span>score question</span>
               <strong>If it has a metric, it belongs to the 20-task layer.</strong>
@@ -6759,13 +6766,19 @@
               <strong>If it describes model inputs and targets, it belongs to the 3-pipeline layer.</strong>
               <p>Spatial, world-model, and VLA pipelines are recipes for future scale-up and task-grounded model training.</p>
             </article>
+            <article>
+              <span>synthesis question</span>
+              <strong>If it combines pipeline abilities, it belongs to the unified-model target.</strong>
+              <p>This is the expandable integration goal, not an extra scored task axis in the 180-result matrix.</p>
+            </article>
           </div>
           <figure class="task-axis-visual">
-            <img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v2-chatgpt" alt="Labeled relationship map showing 20 task contracts flowing into four named research directions and three foundation-model training pipelines, with the current structure marked as expandable.">
+            <img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v4-unified" alt="Labeled Ropedia Xperience-10M Task Suite relationship map showing 20 task contracts flowing into four named research directions, three foundation-model training pipelines, and one unified embodied foundation model target, with each layer open to expand.">
             <figcaption aria-label="Relationship map legend">
               <span><strong>20 tasks</strong>Score axes used by every method row.</span>
               <span><strong>4 directions</strong>Research questions for reading those scores.</span>
               <span><strong>3 pipelines</strong>Training recipes for larger foundation models.</span>
+              <span><strong>1 unified model</strong>Future integration target, not a new score axis.</span>
             </figcaption>
           </figure>
           <div class="task-axis-key" aria-label="Explicit relationship key for the map">
@@ -6817,6 +6830,17 @@
                 <li><b>3</b>Vision-language-action models</li>
               </ol>
             </article>
+            <article class="task-axis-key-card">
+              <span>1 unified target</span>
+              <strong>The pipelines converge into one embodied model goal.</strong>
+              <p>This target integrates perception, 3D memory, language, action, and planning. It is a research direction for scale-up, not a tenth method or twenty-first task.</p>
+              <ol class="task-axis-mini-list">
+                <li><b>P</b>Perception and multimodal input</li>
+                <li><b>M</b>3D memory and scene state</li>
+                <li><b>L</b>Language-grounded reasoning</li>
+                <li><b>A</b>Action and planning</li>
+              </ol>
+            </article>
           </div>
           <div class="task-axis-grid">
             <article class="task-axis-card">
@@ -6834,6 +6858,11 @@
               <strong>3 foundation pipelines</strong>
               <p>Spatial intelligence models; Human-video world models; Vision-language-action models.</p>
             </article>
+            <article class="task-axis-card">
+              <span>synthesis target</span>
+              <strong>1 unified embodied model</strong>
+              <p>A long-term model target where perception, spatial memory, language, action, and planning are trained against the same evidence structure.</p>
+            </article>
           </div>
           <div class="task-axis-flow" aria-label="Tasks directions and pipelines reading formula">
             <strong>20 tasks are the score axes.</strong>
@@ -6841,6 +6870,8 @@
             <strong>4 directions organize what the scores study.</strong>
             <span>-></span>
             <strong>3 pipelines describe how to train larger models.</strong>
+            <span>-></span>
+            <strong>1 unified model is the integration target.</strong>
           </div>
         </div>
         <figure class="line-map-figure">

index.html

@@ -1766,12 +1766,12 @@
     }
     .task-axis-grid {
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 12px;
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     .task-axis-visual {
       position: relative;
-      margin: 0 0 16px;
+      margin: 22px 0 18px;
       border: 1px solid rgba(204, 255, 160, 0.18);
       border-radius: var(--radius);
       overflow: hidden;
@@ -1787,18 +1787,17 @@
       background: #020502;
     }
     .task-axis-visual figcaption {
-      position: absolute;
-      left: 14px;
-      right: 14px;
-      bottom: 14px;
       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
       gap: 8px;
+      padding: 12px;
+      border-top: 1px solid rgba(204, 255, 160, 0.16);
+      background: rgba(2, 5, 2, 0.92);
     }
     .task-axis-visual figcaption span {
       border: 1px solid rgba(204, 255, 160, 0.22);
       border-radius: 8px;
-      background: rgba(2, 5, 2, 0.72);
+      background: rgba(204, 255, 160, 0.055);
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       font-family: var(--font-ui);
@@ -1819,7 +1818,7 @@
     }
     .task-axis-summary {
       display: grid;
-      grid-template-columns: 1.08fr 1fr 0.92fr;
+      grid-template-columns: repeat(4, minmax(0, 1fr));
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@@ -1877,7 +1876,7 @@
     }
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       display: grid;
-      grid-template-columns: repeat(3, minmax(0, 1fr));
+      grid-template-columns: repeat(4, minmax(0, 1fr));
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       margin: 0 0 16px;
     }
@@ -1914,7 +1913,7 @@
     }
     .task-axis-key {
       display: grid;
-      grid-template-columns: minmax(0, 1.24fr) minmax(0, 0.92fr) minmax(0, 0.84fr);
+      grid-template-columns: minmax(0, 1.35fr) repeat(3, minmax(0, 0.82fr));
       gap: 12px;
       margin: 0 0 16px;
     }
@@ -2048,7 +2047,7 @@
     }
     .task-axis-flow {
       display: grid;
-      grid-template-columns: 1fr auto 1fr auto 1fr;
+      grid-template-columns: 1fr auto 1fr auto 1fr auto 1fr;
       align-items: center;
       gap: 10px;
       margin-top: 14px;
@@ -6709,13 +6708,13 @@
             <a href="#artifacts"><span>04 reuse</span><strong>Artifacts and mirrors</strong><em>GitHub, GitHub Pages, HF Space, datasets, weights, and validation outputs.</em></a>
           </div>
         </div>
-        <div class="task-axis-panel" aria-label="Relationship between tasks, research directions, and training pipelines">
+        <div class="task-axis-panel" aria-label="Relationship between tasks, research directions, training pipelines, and unified embodied model target">
           <div class="task-axis-head">
             <div>
               <small>relationship map</small>
-              <strong>20 tasks / 4 directions / 3 foundation pipelines.</strong>
+              <strong>20 tasks / 4 directions / 3 pipelines / 1 unified model target.</strong>
             </div>
-            <p>Read the project in that order. The 20 tasks are the scored contracts; the four directions are reader-facing research groupings over those tasks; the three foundation pipelines are model-training recipes that reuse the same episode files.</p>
+            <p>Read the project in that order. The 20 tasks are scored contracts; the four directions group what those scores study; the three pipelines define training recipes; the unified embodied model is the long-term integration target.</p>
           </div>
           <div class="task-axis-summary" aria-label="Reader key for the public structure">
             <article class="task-axis-summary-card">
@@ -6742,8 +6741,16 @@
                 <p>Spatial intelligence, human-video world models, and vision-language-action models reuse the same files with different input-output recipes.</p>
               </div>
             </article>
+            <article class="task-axis-summary-card">
+              <span class="task-axis-count">1</span>
+              <div>
+                <small>unified target</small>
+                <strong>One embodied foundation model goal.</strong>
+                <p>The pipeline outputs converge toward perception, 3D memory, language, action, and planning in one model family.</p>
+              </div>
+            </article>
           </div>
-          <div class="task-axis-reader-rule" aria-label="How to identify the three public layers">
+          <div class="task-axis-reader-rule" aria-label="How to identify the public layers">
             <article>
               <span>score question</span>
               <strong>If it has a metric, it belongs to the 20-task layer.</strong>
@@ -6759,13 +6766,19 @@
               <strong>If it describes model inputs and targets, it belongs to the 3-pipeline layer.</strong>
               <p>Spatial, world-model, and VLA pipelines are recipes for future scale-up and task-grounded model training.</p>
             </article>
+            <article>
+              <span>synthesis question</span>
+              <strong>If it combines pipeline abilities, it belongs to the unified-model target.</strong>
+              <p>This is the expandable integration goal, not an extra scored task axis in the 180-result matrix.</p>
+            </article>
           </div>
           <figure class="task-axis-visual">
-            <img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v2-chatgpt" alt="Labeled relationship map showing 20 task contracts flowing into four named research directions and three foundation-model training pipelines, with the current structure marked as expandable.">
+            <img src="assets/charts/task_direction_pipeline_relationship.png?v=relationship-map-v4-unified" alt="Labeled Ropedia Xperience-10M Task Suite relationship map showing 20 task contracts flowing into four named research directions, three foundation-model training pipelines, and one unified embodied foundation model target, with each layer open to expand.">
             <figcaption aria-label="Relationship map legend">
               <span><strong>20 tasks</strong>Score axes used by every method row.</span>
               <span><strong>4 directions</strong>Research questions for reading those scores.</span>
               <span><strong>3 pipelines</strong>Training recipes for larger foundation models.</span>
+              <span><strong>1 unified model</strong>Future integration target, not a new score axis.</span>
             </figcaption>
           </figure>
           <div class="task-axis-key" aria-label="Explicit relationship key for the map">
@@ -6817,6 +6830,17 @@
                 <li><b>3</b>Vision-language-action models</li>
               </ol>
             </article>
+            <article class="task-axis-key-card">
+              <span>1 unified target</span>
+              <strong>The pipelines converge into one embodied model goal.</strong>
+              <p>This target integrates perception, 3D memory, language, action, and planning. It is a research direction for scale-up, not a tenth method or twenty-first task.</p>
+              <ol class="task-axis-mini-list">
+                <li><b>P</b>Perception and multimodal input</li>
+                <li><b>M</b>3D memory and scene state</li>
+                <li><b>L</b>Language-grounded reasoning</li>
+                <li><b>A</b>Action and planning</li>
+              </ol>
+            </article>
           </div>
           <div class="task-axis-grid">
             <article class="task-axis-card">
@@ -6834,6 +6858,11 @@
               <strong>3 foundation pipelines</strong>
               <p>Spatial intelligence models; Human-video world models; Vision-language-action models.</p>
             </article>
+            <article class="task-axis-card">
+              <span>synthesis target</span>
+              <strong>1 unified embodied model</strong>
+              <p>A long-term model target where perception, spatial memory, language, action, and planning are trained against the same evidence structure.</p>
+            </article>
           </div>
           <div class="task-axis-flow" aria-label="Tasks directions and pipelines reading formula">
             <strong>20 tasks are the score axes.</strong>
@@ -6841,6 +6870,8 @@
             <strong>4 directions organize what the scores study.</strong>
             <span>-></span>
             <strong>3 pipelines describe how to train larger models.</strong>
+            <span>-></span>
+            <strong>1 unified model is the integration target.</strong>
           </div>
         </div>
         <figure class="line-map-figure">

metrics/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:24:42+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -632,7 +632,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "e8706007477657bf1d8155006462101d355b8c9bf0d8ab155fc74a577ccfec46"
+      "sha256": "9da89d3ebcdd8494d26ad63c00064b757c2fbefbb6bf3708a64c5e3d2aa5a1cf"
     },
     {
       "id": "source_alignment_validator",
@@ -1093,8 +1093,8 @@
       "surface": "repo_hf",
       "shows": "Catalogs public figures, charts, modality thumbnails, dimensions, hashes, roles, and source scripts.",
       "exists": true,
-      "bytes": 7421,
-      "sha256": "76306997bd59ee0219d5f990fb92358a25ec0d15a70d4eed213215abf6fd152c"
+      "bytes": 7472,
+      "sha256": "cb574f637e457a47b6b36bfa8b905886007e122064f4e12afd0143cca0ede76b"
     },
     {
       "id": "figure_index_json",
@@ -1104,8 +1104,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
-      "bytes": 20339,
-      "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
+      "bytes": 20390,
+      "sha256": "78d850db3f9b627e754139a2316f359ca6c61b142d43d87d1c51326364e31d38"
     },
     {
       "id": "figure_index_builder",
@@ -1115,8 +1115,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 17444,
-      "sha256": "521f69760ba5f7d2993222495833f56f883aca6b832e71dd36c410a757bc1e20"
+      "bytes": 17495,
+      "sha256": "cab9aa79378e5851e1bb80b01bc0c1fb2bb53342201d7a34caf7629926108dd7"
     },
     {
       "id": "brand_assets_json",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "dedc34fa478d0d55198dd775205f0fe76032a964f70b6eb34e7d5941bcb3ab03"
+      "sha256": "812203f1e12b211770f2a0771ecc2cf27d350250ea8181867e41e9c5f9ff47fc"
     },
     {
       "id": "public_surface_qa",
@@ -1318,8 +1318,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 69811,
-      "sha256": "4d44fb7c634d84735a9c0ec2b3c1ab56c75fc65cce6137aae981f81de9121040"
+      "bytes": 69839,
+      "sha256": "9bfe428736e073ca99ddd5a7e4aab1644028866c3501d46568c313efb5c155bf"
     },
     {
       "id": "reproducibility_contract",
@@ -1363,7 +1363,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 14235,
+      "bytes": 10940,
       "hash_policy": "existence_and_size_only"
     },
     {

metrics/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:16:44+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 30,
   "figures": [
@@ -109,14 +109,14 @@
     },
     {
       "id": "task_direction_pipeline_relationship",
-      "title": "20-task / 4-direction / 3-pipeline relationship map",
+      "title": "20-task / 4-direction / 3-pipeline / unified-model relationship map",
       "path": "docs/assets/charts/task_direction_pipeline_relationship.png",
-      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, and three foundation-pipeline columns used by the public reader flow.",
+      "role": "Overview map showing the exact 20 task tiles, four research-direction groups, three foundation-pipeline columns, and the unified embodied model target used by the public reader flow.",
       "source_script": "docs/assets/charts/task_direction_pipeline_relationship.prompt.md",
       "surface": "website overview, HF Space, artifact dataset, model card",
       "exists": true,
-      "bytes": 1541517,
-      "sha256": "0e70d267ada316447cd8ade3181f5037af9805721476ca02dde7070697d94ac0",
+      "bytes": 1795186,
+      "sha256": "901448d1ec9ce2ab563a5f90fd1b6450de6c3dce4c236d342f98f5927416131b",
       "dimensions": {
         "format": "PNG",
         "width": 1672,

metrics/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:24:37+00:00",
+  "generated_at_utc": "2026-06-23T07:44:41+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1308,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+        "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
       },
       "mirrors": {
         "hf_space": {
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           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "aa842a9b46089d1b6091841a2d7fc5ed854ebac3ffffbe6f4b13eb3a8387d534"
+          "sha256": "dec694dd4cf58a5f09028821b4c3165e03a4835649f5821b05f60b8a879e7354"
         }
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@@ -334,45 +334,45 @@
       "local": {
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-        "bytes": 20339,
-        "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
+        "bytes": 20390,
+        "sha256": "78d850db3f9b627e754139a2316f359ca6c61b142d43d87d1c51326364e31d38"
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       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/figure_index.json",
           "exists": true,
-          "bytes": 20339,
-          "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
+          "bytes": 20390,
+          "sha256": "78d850db3f9b627e754139a2316f359ca6c61b142d43d87d1c51326364e31d38"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/figure_index.json",
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-          "bytes": 20339,
-          "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
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           "path": "hf_model:docs/data/figure_index.json",
           "exists": true,
-          "bytes": 20339,
-          "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
+          "bytes": 20390,
+          "sha256": "78d850db3f9b627e754139a2316f359ca6c61b142d43d87d1c51326364e31d38"
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           "path": "hf_model:metrics/figure_index.json",
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-          "bytes": 20339,
-          "sha256": "15bb5dad98b9e53cb88c74e20e0a2b7e8f0080bf6d8301262b3df3e97216fe33"
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@@ -972,44 +972,44 @@
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         "bytes": 10940,
-        "sha256": "a32ab09cf1ab4aa3b434eb6409be36b7c60d2060f6268060e560478ba826ff62"
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           "bytes": 10940,
-          "sha256": "a32ab09cf1ab4aa3b434eb6409be36b7c60d2060f6268060e560478ba826ff62"
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           "bytes": 10940,
-          "sha256": "a32ab09cf1ab4aa3b434eb6409be36b7c60d2060f6268060e560478ba826ff62"
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@@ -1021,44 +1021,44 @@
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@@ -32595,27 +32595,27 @@
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metrics/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:25:44+00:00",
+  "generated_at_utc": "2026-06-23T07:26:14+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
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@@ -28,17 +28,17 @@
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-          "generated_at_utc": "2026-06-23T04:12:55+00:00"
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@@ -48,7 +48,7 @@
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@@ -95,8 +95,8 @@
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metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
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+  "generated_at_utc": "2026-06-23T07:44:56+00:00",
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metrics/quality_gates.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:25:44+00:00",
+  "generated_at_utc": "2026-06-23T07:26:14+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
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metrics/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
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+  "generated_at_utc": "2026-06-23T07:24:48+00:00",
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metrics/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
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   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

metrics/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T04:12:55+00:00",
+  "generated_at_utc": "2026-06-23T07:24:36+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,

metrics/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-23T06:16:03+00:00",
+  "generated_at_utc": "2026-06-23T07:26:19+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
-      "overview_index": 192813,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "evidence_index": 257878
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 192813,
-      "protocol_index": 251984,
-      "evidence_index": 255779
+      "overview_index": 192865,
+      "protocol_index": 254083,
+      "evidence_index": 257878
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -345,7 +345,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 20339,
+      "bytes": 20390,
       "top_level_type": "dict"
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 14235,
+      "bytes": 10940,
       "top_level_type": "dict"
     },
     {
@@ -678,7 +678,7 @@
     {
       "path": "assets/charts/task_direction_pipeline_relationship.png",
       "exists": true,
-      "bytes": 1541517,
+      "bytes": 1795186,
       "width": 1672,
       "height": 941,
       "format": "PNG"

scripts/build_figure_index.py

@@ -68,9 +68,9 @@ FIGURES = [
     },
     {
         "id": "task_direction_pipeline_relationship",
-        "title": "20-task / 4-direction / 3-pipeline relationship map",
+        "title": "20-task / 4-direction / 3-pipeline / unified-model relationship map",
         "path": "docs/assets/charts/task_direction_pipeline_relationship.png",
-        "role": "Overview map showing the exact 20 task tiles, four research-direction groups, and three foundation-pipeline columns used by the public reader flow.",
+        "role": "Overview map showing the exact 20 task tiles, four research-direction groups, three foundation-pipeline columns, and the unified embodied model target used by the public reader flow.",
         "source_script": "docs/assets/charts/task_direction_pipeline_relationship.prompt.md",
         "surface": "website overview, HF Space, artifact dataset, model card",
     },

scripts/build_multilingual_public_readmes.py

@@ -138,6 +138,10 @@ The multilingual README files are reader guides. The canonical technical evidenc
       <td><strong>3 foundation pipelines</strong></td>
       <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>
     </tr>
+    <tr>
+      <td><strong>1 unified target</strong></td>
+      <td>The long-term embodied foundation-model target connects perception, 3D memory, language-grounded reasoning, action, and planning without adding a new score axis.</td>
+    </tr>
     <tr>
       <td><strong>Public mirrors</strong></td>
       <td>GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3-Omni and Cosmos3 model repos, and HF collection.</td>
@@ -145,14 +149,14 @@ The multilingual README files are reader guides. The canonical technical evidenc
   </tbody>
 </table>
 
-## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines
+## Public Structure: 20 Tasks / 4 Directions / 3 Pipelines / 1 Unified Target
 
-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. Use them in that order when reading the project.
+Read the project as four connected 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. The **1 unified embodied model target** is the long-term integration goal after those pipelines mature. Use them in that order when reading the project.
 
-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**.
+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**; if it combines perception, 3D memory, language, action, and planning, it is the **unified target** rather than an extra score axis.
 
 <p align="center">
-  <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%">
+  <img src="docs/assets/charts/task_direction_pipeline_relationship.png" alt="Relationship map showing 20 task contracts, 4 research directions, 3 foundation-model pipeline tracks, and 1 unified embodied model target" width="100%">
 </p>
 
 | Layer | Count | Reader role | Exact public labels |
@@ -160,6 +164,7 @@ Reader rule: if it has a metric, it is a **task**; if it explains what the evide
 | 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. |
 | 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. |
 | 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. |
+| Unified embodied model target | 1 | Long-term integration target, not a task/method row in the 180-result matrix. | Perception; 3D memory; language-grounded reasoning; action; planning. |
 
 ## Two Evidence Lines
 
@@ -419,11 +424,11 @@ LANGUAGE_GUIDES = {
 
 ## 核心结构
 
-- 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。
+- 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；把感知、3D 记忆、语言推理、action 和 planning 合并起来的是 unified embodied model target，不是新的评分轴。
 - 数据层：公开 sample episode 被切成 20-frame 窗口，并连接视频、音频、深度、pose/SLAM、mocap、IMU、calibration 和语言标注。
 - 任务层：20 个统一任务覆盖识别、预测、检索、重建、同步、长时预测、action-object 关系和 sensor bridge。
 - 结果层：单 episode minimal/NN 覆盖 20/20；128-episode metadata/raw、Qwen3-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。
-- 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。
+- 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；长期目标是一个 unified embodied foundation model。
 
 ## 公开边界
 
@@ -468,11 +473,11 @@ Entradas: [guia de dos lineas de evidencia](TWO_EVIDENCE_LINES.md), [datos de do
 
 ## Estructura
 
-- 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.
+- 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; si combina percepción, memoria 3D, lenguaje, acción y planificación, es el unified embodied model target, no otro eje de score.
 - Datos: ventanas de 20 frames con video, audio, profundidad, pose/SLAM, mocap, IMU, calibración y lenguaje.
 - Tareas: 20 contratos para reconocimiento, predicción, recuperación, reconstrucción, sincronización, horizonte largo, relación acción-objeto y puentes de sensores.
 - Resultados: minimal/NN de un episodio cubren 20/20; las ramas de 128 episodios separan metadata, raw features, Qwen3 y Cosmos; la matriz pública está en 180/180 registros con score: 174 direct y 6 compact proxy, con proxy targets visibles.
-- Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia.
+- Direcciones: spatial intelligence, human-video world model y vision-language-action tienen mapeo de tareas y requisitos de evidencia; el objetivo largo plazo es un unified embodied foundation model.
 
 ## Límite Público
 
@@ -517,11 +522,11 @@ Entrées : [guide des deux lignes de preuve](TWO_EVIDENCE_LINES.md), [données d
 
 ## Structure
 
-- 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.
+- 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; si cela combine perception, mémoire 3D, langage, action et planification, c'est la cible unified embodied model, pas un nouvel axe de score.
 - Données : fenêtres de 20 frames reliant vidéo, audio, profondeur, pose/SLAM, mocap, IMU, calibration et annotations de langage.
 - Tâches : 20 contrats couvrant reconnaissance, prévision, retrieval, reconstruction, ordre, synchronisation, horizon long, relations action-objet et sensor bridge.
 - Résultats : minimal/NN sur l'épisode public couvrent 20/20; 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.
-- Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires.
+- Directions : spatial intelligence, human-video world model et vision-language-action sont documentés avec tâches et preuves nécessaires; l'objectif à long terme est un unified embodied foundation model.
 
 ## Frontière Publique
 
@@ -566,11 +571,11 @@ Einstieg: [Leitfaden zu zwei Evidenzlinien](TWO_EVIDENCE_LINES.md), [Daten der z
 
 ## Struktur
 
-- 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.
+- 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; verbindet es Wahrnehmung, 3D-Gedächtnis, Sprache, Aktion und Planung, ist es das unified embodied model target, keine zusätzliche Score-Achse.
 - Daten: 20-Frame-Fenster über Video, Audio, Tiefe, Pose/SLAM, Mocap, IMU, Kalibrierung und Sprachannotation.
 - Aufgaben: 20 Verträge für Erkennung, Vorhersage, Retrieval, Rekonstruktion, Ordnung, Synchronisierung, Langhorizont-Prognose, Aktion-Objekt-Bindung und Sensor-Brücken.
 - Ergebnisse: Single-Episode minimal/NN decken 20/20 ab; 128-Episode-Zweige trennen Metadata, Raw Features, Qwen3 und Cosmos; die öffentliche Matrix steht bei 180/180 gescorten Einträgen: 174 direct und 6 compact proxy, mit sichtbaren Proxy-Targets.
-- Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert.
+- Richtungen: spatial intelligence, human-video world model und vision-language-action sind mit Aufgaben und Evidenzanforderungen dokumentiert; das langfristige Ziel ist ein unified embodied foundation model.
 
 ## Öffentliche Grenze
 
@@ -615,11 +620,11 @@ Method blocks: Line 1 は task-head baselines（Minimal、Neural MLP）。Line 2
 
 ## 構造
 
-- 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。
+- 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。perception、3D memory、language reasoning、action、planning を統合するものは unified embodied model target であり、新しい score axis ではありません。
 - データ: 20-frame window が video、audio、depth、pose/SLAM、mocap、IMU、calibration、language annotation を結びます。
 - タスク: 認識、予測、retrieval、reconstruction、order、sync、long-horizon、action-object、sensor bridge など 20 契約。
 - 結果: single-episode minimal/NN は 20/20。128-episode 側は metadata、raw feature、Qwen3、Cosmos を証拠タイプ別に分けます。公開 matrix は 180/180 scored records で、174 direct と 6 compact proxy を分離し、proxy targets は明示します。
-- 方向: spatial intelligence、human-video world model、vision-language-action に対して、タスク対応と必要証拠を記録しています。
+- 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。長期目標は unified embodied foundation model です。
 
 ## 公開境界
 
@@ -664,11 +669,11 @@ Method blocks: Line 1 は task-head baselines（Minimal、Neural MLP）。Line 2
 
 ## 구조
 
-- 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.
+- 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. perception, 3D memory, language reasoning, action, planning을 합치는 것은 unified embodied model target이며 새 score axis가 아닙니다.
 - 데이터: 20-frame window가 video, audio, depth, pose/SLAM, mocap, IMU, calibration, language annotation을 연결합니다.
 - 과제: 인식, 예측, retrieval, reconstruction, order, sync, long-horizon, action-object binding, sensor bridge 등 20개 계약.
 - 결과: single-episode minimal/NN은 20/20; 128-episode 레이어는 metadata, raw feature, Qwen3, Cosmos를 증거 유형별로 분리합니다. 공개 matrix는 180/180 scored records이며 174 direct와 6 compact proxy를 분리하고 proxy targets를 명시합니다.
-- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다.
+- 방향: spatial intelligence, human-video world model, vision-language-action에 대해 과제 매핑과 필요한 증거를 기록합니다. 장기 목표는 unified embodied foundation model입니다.
 
 ## 공개 경계
 
@@ -713,11 +718,11 @@ Entradas: [guia de duas linhas de evidencia](TWO_EVIDENCE_LINES.md), [dados das
 
 ## Estrutura
 
-- 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.
+- 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; se combina percepção, memória 3D, linguagem, ação e planejamento, pertence ao unified embodied model target, não a um novo eixo de score.
 - Dados: janelas de 20 frames ligam vídeo, áudio, profundidade, pose/SLAM, mocap, IMU, calibração e anotações de linguagem.
 - Tarefas: 20 contratos cobrem reconhecimento, previsão, retrieval, reconstrução, ordem, sincronização, horizonte longo, relação ação-objeto e pontes de sensores.
 - Resultados: minimal/NN de um episódio cobrem 20/20; a camada de 128 episódios separa metadata, raw features, Qwen3 e Cosmos; a matriz pública está em 180/180 registros com score: 174 direct e 6 compact proxy, com proxy targets visíveis.
-- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência.
+- Direções: spatial intelligence, human-video world model e vision-language-action têm mapeamento de tarefas e requisitos de evidência; o objetivo de longo prazo é um unified embodied foundation model.
 
 ## Fronteira Pública
 

scripts/verify_live_publication.py

@@ -682,7 +682,7 @@ MARKER_CHECKS = [
             "Ropedia Xperience-10M 20-task suite.",
             "9 public method scores",
             "unified_task_model_radar.svg",
-            "20 tasks / 4 directions / 3 foundation pipelines.",
+            "20 tasks / 4 directions / 3 pipelines / 1 unified model target.",
             "If it has a metric, it belongs to the 20-task layer.",
             "Cite raw metric values; use radar values only as visual summaries.",
             "open full 180-score audit table",
@@ -732,7 +732,7 @@ MARKER_CHECKS = [
             "Ropedia Xperience-10M 20-task suite.",
             "9 public method scores",
             "unified_task_model_radar.svg",
-            "20 tasks / 4 directions / 3 foundation pipelines.",
+            "20 tasks / 4 directions / 3 pipelines / 1 unified model target.",
             "If it has a metric, it belongs to the 20-task layer.",
             "Cite raw metric values; use radar values only as visual summaries.",
             "open full 180-score audit table",