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PROJECT_README.md

@@ -62,26 +62,89 @@ The multilingual README files are reader guides. The canonical technical evidenc
 
 ## At A Glance
 
-| Signal | Current public state |
-| --- | --- |
-| 20 task contracts | Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. |
-| 180 method-task records | 9 methods x 20 tasks. Numeric scores appear only where a real task target and source artifact exist; unsupported and not-yet-evaluated cells stay visible. |
-| Public-sample baselines | Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode. |
-| 128-episode comparison layer | Metadata/simple, metadata/NN, raw-feature simple, raw-feature NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano branches are separated by evidence type. |
-| Foundation directions | Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as trainable directions with task mappings and model-evidence requirements. |
-| Public mirrors | GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3/Cosmos model repos, and HF collection. |
+<table>
+  <thead>
+    <tr>
+      <th width="24%">Signal</th>
+      <th>Current public state</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>20 task contracts</strong></td>
+      <td>Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.</td>
+    </tr>
+    <tr>
+      <td><strong>180 method-task records</strong></td>
+      <td>9 methods x 20 tasks. Numeric scores appear only where a real task target and source artifact exist; unsupported and not-yet-evaluated cells stay visible.</td>
+    </tr>
+    <tr>
+      <td><strong>Public-sample baselines</strong></td>
+      <td>Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode.</td>
+    </tr>
+    <tr>
+      <td><strong>128-episode comparison layer</strong></td>
+      <td>Metadata/simple, metadata/NN, raw-feature simple, raw-feature NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano branches are separated by evidence type.</td>
+    </tr>
+    <tr>
+      <td><strong>Foundation directions</strong></td>
+      <td>Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as trainable directions with task mappings and model-evidence requirements.</td>
+    </tr>
+    <tr>
+      <td><strong>Public mirrors</strong></td>
+      <td>GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3/Cosmos model repos, and HF collection.</td>
+    </tr>
+  </tbody>
+</table>
 
 ## Fast Reader Map
 
-| Reader goal | Start here | Then inspect |
-| --- | --- | --- |
-| Understand the project quickly | [Project brief](PROJECT_BRIEF.md), [project status](PROJECT_STATUS.md) | [Dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
-| Choose the right public surface | [Public reader map](PUBLIC_READER_MAP.md) | [public_reader_map.json](docs/data/public_reader_map.json) |
-| Inspect the 20 task contracts | [TASK_SUITE_20.md](TASK_SUITE_20.md) | [task_suite_20.json](docs/data/task_suite_20.json), [task walkthroughs](results/episode_task_suite/task_walkthroughs/) |
-| Compare results | [Research takeaways](RESEARCH_TAKEAWAYS.md) | [20-result matrix](docs/data/task_method_20_result_matrix.json), [radar JSON](docs/data/unified_task_model_radar.json), [gap audit](docs/data/task_method_20_gap_audit.json) |
-| Understand one data sample | [Single-episode explorer](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/single_episode_explorer.html) | [raw sample file map](docs/data/raw_sample_files.json), [feature manifest](results/episode_task_suite/feature_manifest.json) |
-| Read foundation training directions | [THREE_FOUNDATION_PIPELINES.md](THREE_FOUNDATION_PIPELINES.md) | [three_foundation_pipelines.json](docs/data/three_foundation_pipelines.json), [foundation model plan](FOUNDATION_MODEL_PLAN.md) |
-| Reproduce or audit | [REPRODUCIBILITY.md](REPRODUCIBILITY.md), [EVIDENCE_CONTRACT.md](EVIDENCE_CONTRACT.md) | [quality gates](docs/data/quality_gates.json), [publication audit](docs/data/publication_audit.json), [mirror parity](docs/data/mirror_parity.json) |
+<table>
+  <thead>
+    <tr>
+      <th width="26%">Reader goal</th>
+      <th width="32%">Start here</th>
+      <th>Then inspect</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Understand quickly</strong></td>
+      <td><a href="PROJECT_BRIEF.md">Project brief</a><br><a href="PROJECT_STATUS.md">Project status</a></td>
+      <td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">Dashboard</a></td>
+    </tr>
+    <tr>
+      <td><strong>Choose the public surface</strong></td>
+      <td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
+      <td><a href="docs/data/public_reader_map.json">public_reader_map.json</a></td>
+    </tr>
+    <tr>
+      <td><strong>Inspect the 20 tasks</strong></td>
+      <td><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a></td>
+      <td><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a></td>
+    </tr>
+    <tr>
+      <td><strong>Compare results</strong></td>
+      <td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
+      <td><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">gap audit</a></td>
+    </tr>
+    <tr>
+      <td><strong>Understand one sample</strong></td>
+      <td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/single_episode_explorer.html">Single-episode explorer</a></td>
+      <td><a href="docs/data/raw_sample_files.json">raw sample file map</a><br><a href="results/episode_task_suite/feature_manifest.json">feature manifest</a></td>
+    </tr>
+    <tr>
+      <td><strong>Read foundation directions</strong></td>
+      <td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
+      <td><a href="docs/data/three_foundation_pipelines.json">three_foundation_pipelines.json</a><br><a href="FOUNDATION_MODEL_PLAN.md">foundation model plan</a></td>
+    </tr>
+    <tr>
+      <td><strong>Reproduce or audit</strong></td>
+      <td><a href="REPRODUCIBILITY.md">Reproducibility</a><br><a href="EVIDENCE_CONTRACT.md">Evidence contract</a></td>
+      <td><a href="docs/data/quality_gates.json">quality gates</a><br><a href="docs/data/publication_audit.json">publication audit</a><br><a href="docs/data/mirror_parity.json">mirror parity</a></td>
+    </tr>
+  </tbody>
+</table>
 
 ## Why This Project Exists
 
@@ -95,12 +158,20 @@ cannot show, and what evidence should exist before claiming model quality.
 The work is designed to demonstrate four capabilities that matter for
 embodied-AI research infrastructure:
 
-| Capability | What this project shows |
-| --- | --- |
-| Multimodal data understanding | Parses the public sample into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals |
-| Task design | Defines 20 human-readable tasks in one unified public-sample suite, plus four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs |
-| Model and evaluation discipline | Runs minimal and compact neural baselines, records predictions/metrics, keeps chronological split boundaries explicit, and separates sample evidence from held-out claims |
-| Scale-up planning | Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model branches, policy-model branches, and the future Xperience-native foundation-model pretraining goal |
+<table>
+  <thead>
+    <tr>
+      <th width="26%">Capability</th>
+      <th>What this project shows</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>Multimodal data understanding</strong></td><td>Parses the public sample into synchronized windows across video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.</td></tr>
+    <tr><td><strong>Task design</strong></td><td>Defines 20 human-readable tasks in one unified public-sample suite, plus four direction-extension probes with inputs, outputs, process modules, metrics, and case-study walkthroughs.</td></tr>
+    <tr><td><strong>Model and evaluation discipline</strong></td><td>Runs minimal and compact neural baselines, records predictions/metrics, keeps chronological split boundaries explicit, and separates sample evidence from held-out claims.</td></tr>
+    <tr><td><strong>Scale-up planning</strong></td><td>Connects the public-sample pipeline to 32/128-episode held-out pilots, Qwen3-Omni LoRA, Cosmos-style world-model branches, policy-model branches, and the future Xperience-native foundation-model pretraining goal.</td></tr>
+  </tbody>
+</table>
 
 ## Start Here
 
@@ -111,29 +182,45 @@ route through those surfaces, or use the machine-readable companion
 For the one-page project summary, use [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md)
 and [`docs/data/project_brief.json`](docs/data/project_brief.json).
 
-| Reader goal | Best entry point |
-| --- | --- |
-| Choose the right public surface | [`PUBLIC_READER_MAP.md`](PUBLIC_READER_MAP.md), [`docs/data/public_reader_map.json`](docs/data/public_reader_map.json) |
-| Understand the whole project quickly | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md) |
-| See the visual research dashboard | [GitHub Pages dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
-| Navigate the unified 20 tasks, four tracks, and scale-up plan | [Interactive research roadmap](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/research_roadmap.html), [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`docs/data/research_roadmap_interactive.json`](docs/data/research_roadmap_interactive.json) |
-| Compare current task metrics | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) |
-| Compare possible foundation backbones | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json) |
-| Understand the future native pretraining goal | [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) |
-| See additional concrete project directions | [`ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`](ADDITIONAL_DEVELOPMENT_DIRECTIONS.md), [`docs/data/additional_development_directions.json`](docs/data/additional_development_directions.json) |
-| Understand one model input | [`results/episode_task_suite/feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`results/episode_task_suite/windows.csv`](results/episode_task_suite/windows.csv) |
-| Check multi-episode data status | [`results/omni_finetune/DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md) |
+<table>
+  <thead>
+    <tr>
+      <th width="32%">Reader goal</th>
+      <th>Best entry point</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>Choose the right public surface</strong></td><td><a href="PUBLIC_READER_MAP.md">PUBLIC_READER_MAP.md</a><br><a href="docs/data/public_reader_map.json">public_reader_map.json</a></td></tr>
+    <tr><td><strong>Understand the whole project quickly</strong></td><td><a href="PROJECT_BRIEF.md">PROJECT_BRIEF.md</a></td></tr>
+    <tr><td><strong>See the visual research dashboard</strong></td><td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">GitHub Pages dashboard</a></td></tr>
+    <tr><td><strong>Navigate the unified 20 tasks, four tracks, and scale-up plan</strong></td><td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/research_roadmap.html">Interactive research roadmap</a><br><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a><br><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="docs/data/research_roadmap_interactive.json">research_roadmap_interactive.json</a></td></tr>
+    <tr><td><strong>Compare current task metrics</strong></td><td><a href="RESEARCH_TAKEAWAYS.md">RESEARCH_TAKEAWAYS.md</a><br><a href="docs/data/summary_metrics.json">summary_metrics.json</a></td></tr>
+    <tr><td><strong>Compare possible foundation backbones</strong></td><td><a href="FOUNDATION_MODEL_PLAN.md">FOUNDATION_MODEL_PLAN.md</a><br><a href="docs/data/foundation_model_plan.json">foundation_model_plan.json</a></td></tr>
+    <tr><td><strong>Understand the future native pretraining goal</strong></td><td><a href="XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md</a></td></tr>
+    <tr><td><strong>See additional concrete project directions</strong></td><td><a href="ADDITIONAL_DEVELOPMENT_DIRECTIONS.md">ADDITIONAL_DEVELOPMENT_DIRECTIONS.md</a><br><a href="docs/data/additional_development_directions.json">additional_development_directions.json</a></td></tr>
+    <tr><td><strong>Understand one model input</strong></td><td><a href="results/episode_task_suite/feature_manifest.json">feature_manifest.json</a><br><a href="results/episode_task_suite/windows.csv">windows.csv</a></td></tr>
+    <tr><td><strong>Check multi-episode data status</strong></td><td><a href="results/omni_finetune/DATA_ACCESS_STATUS.md">DATA_ACCESS_STATUS.md</a></td></tr>
+  </tbody>
+</table>
 
 ## Public Surface Map
 
-| Surface | What it is for |
-| --- | --- |
-| GitHub repo | Source of truth for docs, scripts, generated JSON, validators, and commit history |
-| GitHub Pages dashboard | Best visual overview of the sample, 20 tasks, radar results, foundation directions, and resources |
-| Hugging Face Space | Hub-hosted copy of the dashboard and static app assets |
-| HF artifact dataset | Public-safe metrics, reports, website JSON, result packages, and derived evidence files |
-| HF baseline model repo | Minimal/neural baseline weights, figures, metrics, and mirrored task artifacts |
-| Qwen3/Cosmos model repos | Adapter-specific public weights or package cards when a model branch is verified and publishable |
+<table>
+  <thead>
+    <tr>
+      <th width="28%">Surface</th>
+      <th>What it is for</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>GitHub repo</strong></td><td>Source of truth for docs, scripts, generated JSON, validators, and commit history.</td></tr>
+    <tr><td><strong>GitHub Pages dashboard</strong></td><td>Best visual overview of the sample, 20 tasks, radar results, foundation directions, and resources.</td></tr>
+    <tr><td><strong>Hugging Face Space</strong></td><td>Hub-hosted copy of the dashboard and static app assets.</td></tr>
+    <tr><td><strong>HF artifact dataset</strong></td><td>Public-safe metrics, reports, website JSON, result packages, and derived evidence files.</td></tr>
+    <tr><td><strong>HF baseline model repo</strong></td><td>Minimal/neural baseline weights, figures, metrics, and mirrored task artifacts.</td></tr>
+    <tr><td><strong>Qwen3/Cosmos model repos</strong></td><td>Adapter-specific public weights or package cards when a model branch is verified and publishable.</td></tr>
+  </tbody>
+</table>
 
 Public release checks are exposed as JSON for mirrors and dashboards:
 [`docs/data/website_integrity.json`](docs/data/website_integrity.json),
@@ -146,15 +233,34 @@ Public release checks are exposed as JSON for mirrors and dashboards:
 
 ## Research Project Overview
 
-| Theme | Current implementation |
-| --- | --- |
-| Dataset slice | One public Xperience-10M sample episode, 5,821 frames, 1,161 windows, and an 8,546-dimensional representation |
-| Modalities | Video, audio, depth, camera pose/SLAM, hand/body mocap, IMU, calibration, and language annotations |
-| Task suite | 20 human-readable tasks form one embodied-AI public-sample suite; tasks 1-12 are the original contracts and tasks 13-20 reuse the same windows, split discipline, and minimal/neural head pattern |
-| Baselines | Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split; companion simple/NN metadata baselines are also aligned to the selected 128-episode 96/16/16 split |
-| Research directions | Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling |
-| Scale-up path | The selected-episode Qwen3-Omni LoRA final diagnostic result is verified on the 96/16/16 split; same-split simple/NN metadata baselines now cover the 12 task ids as a companion comparison. The Qwen result proves the multi-episode export/train/eval/package loop and meets the strict-JSON target, but weak action/subtask metrics make it a baseline for error analysis rather than a strong model. Cosmos3 now has three verified diagnostics: Nano future-window compatibility, Super base-weight Reasoner evaluation, and Super forward-dynamics LoRA fine-tuning over camera-pose proxy targets. |
-| Public surfaces | GitHub repo, GitHub Pages dashboard, GHCR static-site package, HF Space, HF artifact dataset, HF baseline-model repo, and HF collection |
+<table>
+  <thead>
+    <tr>
+      <th width="22%">Theme</th>
+      <th>Current implementation</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>Dataset slice</strong></td><td>One public Xperience-10M sample episode, 5,821 frames, 1,161 windows, and an 8,546-dimensional representation.</td></tr>
+    <tr><td><strong>Modalities</strong></td><td>Video, audio, depth, camera pose/SLAM, hand/body mocap, IMU, calibration, and language annotations.</td></tr>
+    <tr><td><strong>Task suite</strong></td><td>20 human-readable tasks form one embodied-AI public-sample suite; tasks 1-12 are the original contracts and tasks 13-20 reuse the same windows, split discipline, and minimal/neural head pattern.</td></tr>
+    <tr><td><strong>Baselines</strong></td><td>Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split; companion simple/NN metadata baselines are also aligned to the selected 128-episode 96/16/16 split.</td></tr>
+    <tr><td><strong>Research directions</strong></td><td>Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</td></tr>
+    <tr>
+      <td><strong>Scale-up path</strong></td>
+      <td>
+        <ul>
+          <li>The selected-episode Qwen3-Omni LoRA v6 diagnostic package is verified on the 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions.</li>
+          <li>v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row where it is stronger on other metrics.</li>
+          <li>Same-split simple/NN metadata baselines cover the 12 JSON-supported task ids, while the raw-feature simple/NN run covers 20/20 task axes with compact-proxy notes for tasks 15 and 19.</li>
+          <li>The Qwen result proves the multi-episode export/train/eval/package loop and meets the strict-JSON target, but weak action/subtask metrics make it a baseline for error analysis rather than a strong model.</li>
+          <li>Cosmos3 has three verified diagnostics: Nano future-window compatibility, Super base-weight Reasoner evaluation, and Super forward-dynamics LoRA fine-tuning over camera-pose proxy targets.</li>
+        </ul>
+      </td>
+    </tr>
+    <tr><td><strong>Public surfaces</strong></td><td>GitHub repo, GitHub Pages dashboard, GHCR static-site package, HF Space, HF artifact dataset, HF baseline-model repo, and HF collection.</td></tr>
+  </tbody>
+</table>
 
 For the fastest interpretation of the current metrics, start with
 [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md) and
@@ -185,13 +291,81 @@ Current contributions:
 
 This project is best read as a staged embodied-AI research study:
 
-| Layer | Current scope | Where to start |
-| --- | --- | --- |
-| Data understanding | One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned windows, and an 8,546-dimensional multimodal representation. | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md) |
-| Task suite | Twenty human-readable tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, synchronization, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. Tasks 13-20 live under the historical `tier2_task_suite` artifact path for link stability, but they are part of the same suite. | [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md) |
-| Baselines | Minimal heads and compact PyTorch MLP heads provide a first controlled comparison on the same chronological split; the selected 128-episode setup also has same-split simple/NN metadata baselines for JSON-supported tasks and raw-feature simple/NN baselines on all 20 task axes, with tasks 15 and 19 explicitly marked as compact-proxy completions. | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/), [`results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`](results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md), [`results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary_all.json`](results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary_all.json) |
-| Diagnostics | Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard. | [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md), [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) |
-| Scale-up | The selected 128-episode Qwen3-Omni LoRA diagnostic path now has a latest verified v6 held-out package: 96/16/16 selected episodes, 34,269 exported windows, 4,032 held-out test predictions, and public-safe metrics/predictions. v6 improves action macro-F1/contact accuracy versus v5, while v5 remains the pinned prior release row because it is stronger on several other metrics. Same-split simple/NN metadata baselines are published for JSON-supported axes, and the raw-feature run now adds simple/NN baselines on 20/20 task axes; tasks 15 and 19 are documented compact proxies because raw interaction strings and paired video-view embeddings are absent from the 128 export. Cosmos3-Nano has a verified future-window compatibility package. Cosmos3-Super now has two verified branches: a 448-window base-weight JSON-task Reasoner evaluation and a fine-tuned forward-dynamics LoRA package over camera-pose proxy targets with 2,848 train rows, 512 val rows, and 448 test rows. The 128-episode enhancement pack records the no-new-episode path: dense-window sizing, hierarchical action/subtask targets, task bottlenecks, and experiment cards for the next Qwen/Cosmos/policy pushes without overwriting existing results. | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`TASK_SUITE_ENHANCEMENT_128.md`](TASK_SUITE_ENHANCEMENT_128.md), [`docs/data/task_suite_enhancement_128.json`](docs/data/task_suite_enhancement_128.json), [`docs/data/omni_model_comparison.json`](docs/data/omni_model_comparison.json), [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`docs/data/qwen3_v5_v6_comparison.json`](docs/data/qwen3_v5_v6_comparison.json), [`results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md`](results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md), [`results/omni_finetune/OMNI_MODEL_COMPARISON.md`](results/omni_finetune/OMNI_MODEL_COMPARISON.md), [`results/omni_finetune/verified_public/`](results/omni_finetune/verified_public/), [`results/omni_finetune/task_suite_enhancement_128_v1_20260608/`](results/omni_finetune/task_suite_enhancement_128_v1_20260608/) |
+<table>
+  <thead>
+    <tr>
+      <th width="17%">Layer</th>
+      <th width="53%">Current scope</th>
+      <th width="30%">Where to start</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Data understanding</strong></td>
+      <td>One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned windows, and an 8,546-dimensional multimodal representation.</td>
+      <td><a href="PROJECT_BRIEF.md">PROJECT_BRIEF.md</a><br><a href="PROJECT_STATUS.md">PROJECT_STATUS.md</a></td>
+    </tr>
+    <tr>
+      <td><strong>Task suite</strong></td>
+      <td>
+        Twenty human-readable tasks cover recognition, prediction, retrieval, reconstruction, synchronization, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.
+        Tasks 13-20 keep the historical <code>tier2_task_suite</code> artifact path for link stability, but they are part of the same suite.
+      </td>
+      <td>
+        <a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a><br>
+        <a href="docs/data/task_suite_20.json">task_suite_20.json</a><br>
+        <a href="RESEARCH_TAKEAWAYS.md">RESEARCH_TAKEAWAYS.md</a><br>
+        <a href="results/episode_task_suite/summary_report.json">summary_report.json</a><br>
+        <a href="results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md">TIER2_TASK_BASELINES.md</a>
+      </td>
+    </tr>
+    <tr>
+      <td><strong>Baselines</strong></td>
+      <td>
+        Minimal heads and compact PyTorch MLP heads provide a controlled single-episode comparison on the same chronological split.
+        The selected 128-episode setup adds same-split metadata simple/NN baselines for JSON-supported tasks and raw-feature simple/NN baselines on all 20 task axes.
+        Tasks 15 and 19 are explicitly marked as compact-proxy completions.
+      </td>
+      <td>
+        <a href="results/episode_task_suite/neural_mlp/">neural_mlp/</a><br>
+        <a href="results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md">BASELINE_ALIGNMENT_REPORT.md</a><br>
+        <a href="results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary_all.json">raw20 run summary</a>
+      </td>
+    </tr>
+    <tr>
+      <td><strong>Diagnostics</strong></td>
+      <td>Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard.</td>
+      <td><a href="results/audio_ablation/AUDIO_ABLATION_SUMMARY.md">AUDIO_ABLATION_SUMMARY.md</a><br><a href="docs/single_episode_explorer.html">single_episode_explorer.html</a></td>
+    </tr>
+    <tr>
+      <td><strong>Scale-up</strong></td>
+      <td>
+        <ul>
+          <li>Qwen3-Omni LoRA v6 is verified on the selected 96/16/16 split with 34,269 exported windows and 4,032 held-out test predictions.</li>
+          <li>v6 improves action macro-F1/contact accuracy versus v5; v5 remains a pinned prior-release row because it is stronger on several other metrics.</li>
+          <li>Same-split simple/NN metadata baselines are published for JSON-supported axes, and the raw-feature run adds simple/NN baselines on 20/20 task axes.</li>
+          <li>Tasks 15 and 19 are documented compact proxies because raw interaction strings and paired video-view embeddings are absent from the 128 export.</li>
+          <li>Cosmos3-Nano has a verified future-window compatibility package; Cosmos3-Super has a 448-window base-weight JSON-task Reasoner evaluation.</li>
+          <li>Cosmos3-Super also has a fine-tuned forward-dynamics LoRA package over camera-pose proxy targets with 2,848 train rows, 512 validation rows, and 448 test rows.</li>
+          <li>The 128-episode enhancement pack records dense-window sizing, hierarchical action/subtask targets, task bottlenecks, and next experiment cards without overwriting existing results.</li>
+        </ul>
+      </td>
+      <td>
+        <a href="RESEARCH_ROADMAP.md">RESEARCH_ROADMAP.md</a><br>
+        <a href="FOUNDATION_MODEL_PLAN.md">FOUNDATION_MODEL_PLAN.md</a><br>
+        <a href="TASK_SUITE_ENHANCEMENT_128.md">TASK_SUITE_ENHANCEMENT_128.md</a><br>
+        <a href="docs/data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a><br>
+        <a href="docs/data/omni_model_comparison.json">omni_model_comparison.json</a><br>
+        <a href="docs/data/omni_finetune_verified_result.json">omni_finetune_verified_result.json</a><br>
+        <a href="docs/data/qwen3_v5_v6_comparison.json">qwen3_v5_v6_comparison.json</a><br>
+        <a href="results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md">QWEN3_V5_V6_COMPARISON_20260614.md</a><br>
+        <a href="results/omni_finetune/OMNI_MODEL_COMPARISON.md">OMNI_MODEL_COMPARISON.md</a><br>
+        <a href="results/omni_finetune/verified_public/">verified_public/</a><br>
+        <a href="results/omni_finetune/task_suite_enhancement_128_v1_20260608/">task_suite_enhancement_128_v1_20260608/</a>
+      </td>
+    </tr>
+  </tbody>
+</table>
 
 Detailed dataset notes, reproduction checks, and generated JSON reports are
 included for readers who want to inspect the implementation, but they are
@@ -216,35 +390,53 @@ If you only have one minute, use
 [`docs/data/project_status.json`](docs/data/project_status.json).
 They give the current research state in one compact table:
 
-| Area | Current decision |
-| --- | --- |
-| Public-sample pipeline | Verified on one public sample episode: 5,821 frames, 1,161 windows, 8,546 dimensions |
-| 20-task suite | Verified minimal baselines with committed metrics, predictions, and manifests |
-| Neural heads | Verified compact PyTorch MLP heads over the same task contracts and chronological splits |
-| Dataset context | Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented |
-| Evaluation protocol | Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics |
-| Website and Hub pages | Public dashboard, Hugging Face Space, artifact dataset, baseline model repo, and collection use the same project framing and links |
-| Qwen3-Omni multi-episode pilot | Final verified diagnostic result package exists for the selected 96/16/16 episode split; JSON validity meets the target, while action/subtask metrics remain weak |
-| Raw Xperience-10M data / full Qwen weights | Not redistributed |
+<table>
+  <thead>
+    <tr>
+      <th width="28%">Area</th>
+      <th>Current decision</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>Public-sample pipeline</strong></td><td>Verified on one public sample episode: 5,821 frames, 1,161 windows, 8,546 dimensions.</td></tr>
+    <tr><td><strong>20-task suite</strong></td><td>Verified minimal baselines with committed metrics, predictions, and manifests.</td></tr>
+    <tr><td><strong>Neural heads</strong></td><td>Verified compact PyTorch MLP heads over the same task contracts and chronological splits.</td></tr>
+    <tr><td><strong>Dataset context</strong></td><td>Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented.</td></tr>
+    <tr><td><strong>Evaluation protocol</strong></td><td>Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics.</td></tr>
+    <tr><td><strong>Website and Hub pages</strong></td><td>Public dashboard, Hugging Face Space, artifact dataset, baseline model repo, and collection use the same project framing and links.</td></tr>
+    <tr><td><strong>Qwen3-Omni multi-episode pilot</strong></td><td>Final verified diagnostic result package exists for the selected 96/16/16 episode split; JSON validity meets the target, while action/subtask metrics remain weak.</td></tr>
+    <tr><td><strong>Raw data / full Qwen weights</strong></td><td>Raw Xperience-10M data and full Qwen weights are not redistributed.</td></tr>
+  </tbody>
+</table>
 
 ## 90-Second Research Project Path
 
 If you are reading the project cold, open these in order:
 
-| Step | Question | Primary artifacts | What should be true |
-| --- | --- | --- | --- |
-| 1 | What is this project? | [`PROJECT_BRIEF.md`](PROJECT_BRIEF.md), [`PROJECT_STATUS.md`](PROJECT_STATUS.md), [dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) | A public-sample Xperience-10M research project with 20 tasks, baselines, and a scale-up plan. |
-| 2 | What data is used? | [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md), [official HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m), [sample HF dataset](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) | The implemented suite uses one public sample episode; the gated dataset is reserved for selected multi-episode training. |
-| 3 | What does one model input contain? | [`windows.csv`](results/episode_task_suite/windows.csv), [`feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`available_modalities.json`](results/episode_task_suite/available_modalities.json) | Each window is an aligned multimodal unit with video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals. |
-| 4 | What are the 20 tasks? | [`TASK_SUITE_20.md`](TASK_SUITE_20.md), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json), [`results/episode_task_suite/task_walkthroughs/`](results/episode_task_suite/task_walkthroughs/), [`docs/data/task_walkthroughs.json`](docs/data/task_walkthroughs.json) | Every task has a human-readable name, input, output, metric, baseline scores, and an explicit artifact path. |
-| 5 | How are tasks evaluated? | [`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md), [`docs/data/evaluation_protocol.json`](docs/data/evaluation_protocol.json) | The window unit, chronological split, leakage controls, task metrics, and current limitations are explicit. |
-| 6 | What do the current results mean? | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`docs/data/research_takeaways.json`](docs/data/research_takeaways.json), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) | Current metrics describe sample-level task behavior and identify which signals need larger held-out experiments. |
-| 7 | Which models are implemented? | [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json), [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/), [HF baseline repo](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) | Each task has minimal and neural-head evidence over the same feature windows. |
-| 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json), [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json) | The unified tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
-| 9 | Which foundation model comes next? | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 is now represented by Nano future-window compatibility and Super forward-dynamics LoRA; policy models wait for robot-compatible action targets; Xperience-native pretraining is the full-corpus future goal. |
-| 10 | How can the 128-episode suite be pushed without more data? | [`TASK_SUITE_ENHANCEMENT_128.md`](TASK_SUITE_ENHANCEMENT_128.md), [`docs/data/task_suite_enhancement_128.json`](docs/data/task_suite_enhancement_128.json) | The enhancement pack proposes dense windows, hierarchical action/subtask labels, raw-feature shard priorities, and `multiscale_20s10_40s20_80s40` as the next export target. |
-| 11 | How do I reproduce it? | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md), [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) | Public commands and expected outputs are documented for the sample-episode task suite. |
-| 12 | What is still pending? | [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md), [`MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | The final held-out diagnostic Qwen pass is verified and JSON-validity target is met; strong action/subtask model quality remains pending. |
+<table>
+  <thead>
+    <tr>
+      <th width="6%">Step</th>
+      <th width="24%">Question</th>
+      <th width="34%">Primary artifacts</th>
+      <th>What should be true</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>1</strong></td><td>What is this project?</td><td><a href="PROJECT_BRIEF.md">PROJECT_BRIEF.md</a><br><a href="PROJECT_STATUS.md">PROJECT_STATUS.md</a><br><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">Dashboard</a></td><td>A public-sample Xperience-10M research project with 20 tasks, baselines, and a scale-up plan.</td></tr>
+    <tr><td><strong>2</strong></td><td>What data is used?</td><td><a href="XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">Dataset-card alignment</a><br><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">Official HF dataset</a><br><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">Sample HF dataset</a></td><td>The implemented suite uses one public sample episode; the gated dataset is reserved for selected multi-episode training.</td></tr>
+    <tr><td><strong>3</strong></td><td>What does one model input contain?</td><td><a href="results/episode_task_suite/windows.csv">windows.csv</a><br><a href="results/episode_task_suite/feature_manifest.json">feature_manifest.json</a><br><a href="results/episode_task_suite/available_modalities.json">available_modalities.json</a></td><td>Each window is an aligned multimodal unit with video, audio, depth, pose/SLAM, mocap, IMU, calibration, and language-derived signals.</td></tr>
+    <tr><td><strong>4</strong></td><td>What are the 20 tasks?</td><td><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a><br><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a><br><a href="docs/data/task_walkthroughs.json">task_walkthroughs.json</a></td><td>Every task has a human-readable name, input, output, metric, baseline scores, and an explicit artifact path.</td></tr>
+    <tr><td><strong>5</strong></td><td>How are tasks evaluated?</td><td><a href="EVALUATION_PROTOCOL.md">EVALUATION_PROTOCOL.md</a><br><a href="docs/data/evaluation_protocol.json">evaluation_protocol.json</a></td><td>The window unit, chronological split, leakage controls, task metrics, and current limitations are explicit.</td></tr>
+    <tr><td><strong>6</strong></td><td>What do current results mean?</td><td><a href="RESEARCH_TAKEAWAYS.md">RESEARCH_TAKEAWAYS.md</a><br><a href="docs/data/research_takeaways.json">research_takeaways.json</a><br><a href="docs/data/summary_metrics.json">summary_metrics.json</a></td><td>Current metrics describe sample-level task behavior and identify which signals need larger held-out experiments.</td></tr>
+    <tr><td><strong>7</strong></td><td>Which models are implemented?</td><td><a href="results/episode_task_suite/summary_report.json">summary_report.json</a><br><a href="results/episode_task_suite/neural_mlp/">neural_mlp/</a><br><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">HF baseline repo</a></td><td>Each task has minimal and neural-head evidence over the same feature windows.</td></tr>
+    <tr><td><strong>8</strong></td><td>What research directions does this support?</td><td><a href="RESEARCH_ROADMAP.md">RESEARCH_ROADMAP.md</a><br><a href="docs/data/research_directions.json">research_directions.json</a><br><a href="docs/data/research_direction_extensions.json">research_direction_extensions.json</a><br><a href="docs/data/task_suite_20.json">task_suite_20.json</a></td><td>The unified tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling.</td></tr>
+    <tr><td><strong>9</strong></td><td>Which foundation model comes next?</td><td><a href="FOUNDATION_MODEL_PLAN.md">FOUNDATION_MODEL_PLAN.md</a><br><a href="docs/data/foundation_model_plan.json">foundation_model_plan.json</a><br><a href="XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">Native pretraining plan</a></td><td>Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 has Nano compatibility and Super forward-dynamics LoRA; policy models wait for robot-compatible action targets.</td></tr>
+    <tr><td><strong>10</strong></td><td>How can the 128-episode suite be pushed without more data?</td><td><a href="TASK_SUITE_ENHANCEMENT_128.md">TASK_SUITE_ENHANCEMENT_128.md</a><br><a href="docs/data/task_suite_enhancement_128.json">task_suite_enhancement_128.json</a></td><td>The enhancement pack proposes dense windows, hierarchical action/subtask labels, raw-feature shard priorities, and <code>multiscale_20s10_40s20_80s40</code> as the next export target.</td></tr>
+    <tr><td><strong>11</strong></td><td>How do I reproduce it?</td><td><a href="REPRODUCIBILITY.md">REPRODUCIBILITY.md</a><br><a href="notes/reproducibility_audit.md">reproducibility_audit.md</a></td><td>Public commands and expected outputs are documented for the sample-episode task suite.</td></tr>
+    <tr><td><strong>12</strong></td><td>What is still pending?</td><td><a href="docs/data/omni_finetune_verified_result.json">omni_finetune_verified_result.json</a><br><a href="results/omni_finetune/DATA_ACCESS_STATUS.md">DATA_ACCESS_STATUS.md</a><br><a href="results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">MULTI_EPISODE_ACCESS_STATUS.md</a></td><td>The final held-out diagnostic Qwen pass is verified and JSON-validity target is met; strong action/subtask model quality remains pending.</td></tr>
+  </tbody>
+</table>
 
 A compact reader-path summary is available at
 [`docs/data/project_packet.json`](docs/data/project_packet.json).
@@ -315,44 +507,61 @@ Hugging Face Space app:
 
 ## Read This Project In Three Layers
 
-| Layer | What to inspect | Why it matters |
-| --- | --- | --- |
-| Project status | `PROJECT_STATUS.md`, `docs/data/project_status.json` | Gives a one-table current project summary before reading the full artifact trail |
-| Data contract | `windows.csv`, `feature_manifest.json`, modality manifests | Confirms what each sample window contains before modeling |
-| Dataset context | `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`, official dataset links | Explains the official dataset, public sample, modalities, access boundary, and what this repo uses |
-| Visual assets | `FIGURE_INDEX.md`, `docs/assets/` | Shows the task-suite graphic, modality thumbnails, pipeline diagrams, charts, and logo assets |
-| Evaluation protocol | `EVALUATION_PROTOCOL.md`, `docs/data/evaluation_protocol.json` | Defines the task unit, split, metrics, leakage controls, and current limitations |
-| Research roadmap | `RESEARCH_ROADMAP.md`, `docs/data/research_roadmap.json` | Shows the path from sample-level task development to multi-episode work, larger model branches, and the future native-pretraining goal |
-| Additional development directions | `ADDITIONAL_DEVELOPMENT_DIRECTIONS.md`, `docs/data/additional_development_directions.json` | Records concrete non-backbone tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer |
-| Xperience Embodied Foundation Model plan | `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` | Describes the long-term full-corpus pretraining goal, target modules, objectives, staged scale-up, hardware ranges, and evaluation protocol |
-| Minimal heads | softmax, ridge projection/regression, multi-label logistic heads | Keeps every input/output contract visible and inspectable |
-| Neural heads | PyTorch MLP classifiers/regressors under `neural_mlp/` | Checks whether nonlinear heads improve each task without changing features |
-| Evidence | metrics, predictions, confusion matrices, diagrams, dashboard | Makes the single-episode task development inspectable without rerunning first |
-| Artifact guide | `ARTIFACT_GUIDE.md` | Groups the public evidence into research-project layers after the first-pass overview |
-| Reproducibility contract | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json` | States public commands, expected outputs, exact-match reproduction evidence, and non-reproducible boundaries |
-| Citation metadata | `CITATION.cff`, `codemeta.json`, `LICENSE` | Makes the repo easier to cite, index, and reuse without confusing code license and dataset terms |
+<table>
+  <thead>
+    <tr>
+      <th width="24%">Layer</th>
+      <th width="34%">What to inspect</th>
+      <th>Why it matters</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>Project status</strong></td><td><a href="PROJECT_STATUS.md">PROJECT_STATUS.md</a><br><a href="docs/data/project_status.json">project_status.json</a></td><td>Gives a one-table current project summary before reading the full artifact trail.</td></tr>
+    <tr><td><strong>Data contract</strong></td><td><a href="results/episode_task_suite/windows.csv">windows.csv</a><br><a href="results/episode_task_suite/feature_manifest.json">feature_manifest.json</a><br>modality manifests</td><td>Confirms what each sample window contains before modeling.</td></tr>
+    <tr><td><strong>Dataset context</strong></td><td><a href="XPERIENCE10M_DATASET_CARD_ALIGNMENT.md">XPERIENCE10M_DATASET_CARD_ALIGNMENT.md</a><br>official dataset links</td><td>Explains the official dataset, public sample, modalities, access boundary, and what this repo uses.</td></tr>
+    <tr><td><strong>Visual assets</strong></td><td><a href="FIGURE_INDEX.md">FIGURE_INDEX.md</a><br><a href="docs/assets/">docs/assets/</a></td><td>Shows the task-suite graphic, modality thumbnails, pipeline diagrams, charts, and logo assets.</td></tr>
+    <tr><td><strong>Evaluation protocol</strong></td><td><a href="EVALUATION_PROTOCOL.md">EVALUATION_PROTOCOL.md</a><br><a href="docs/data/evaluation_protocol.json">evaluation_protocol.json</a></td><td>Defines the task unit, split, metrics, leakage controls, and current limitations.</td></tr>
+    <tr><td><strong>Research roadmap</strong></td><td><a href="RESEARCH_ROADMAP.md">RESEARCH_ROADMAP.md</a><br><a href="docs/data/research_roadmap.json">research_roadmap.json</a></td><td>Shows the path from sample-level task development to multi-episode work, larger model branches, and the future native-pretraining goal.</td></tr>
+    <tr><td><strong>Additional development directions</strong></td><td><a href="ADDITIONAL_DEVELOPMENT_DIRECTIONS.md">ADDITIONAL_DEVELOPMENT_DIRECTIONS.md</a><br><a href="docs/data/additional_development_directions.json">additional_development_directions.json</a></td><td>Records concrete non-backbone tracks: taxonomy, benchmark protocol, representation learning, skill graphs, affordances, 3D/4D memory, QA, and policy transfer.</td></tr>
+    <tr><td><strong>Xperience Embodied Foundation Model plan</strong></td><td><a href="XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md</a></td><td>Describes the long-term full-corpus pretraining goal, target modules, objectives, staged scale-up, hardware ranges, and evaluation protocol.</td></tr>
+    <tr><td><strong>Minimal heads</strong></td><td>softmax<br>ridge projection/regression<br>multi-label logistic heads</td><td>Keeps every input/output contract visible and inspectable.</td></tr>
+    <tr><td><strong>Neural heads</strong></td><td>PyTorch MLP classifiers/regressors under <a href="results/episode_task_suite/neural_mlp/">neural_mlp/</a></td><td>Checks whether nonlinear heads improve each task without changing features.</td></tr>
+    <tr><td><strong>Evidence</strong></td><td>metrics<br>predictions<br>confusion matrices<br>diagrams<br>dashboard</td><td>Makes the single-episode task development inspectable without rerunning first.</td></tr>
+    <tr><td><strong>Artifact guide</strong></td><td><a href="ARTIFACT_GUIDE.md">ARTIFACT_GUIDE.md</a></td><td>Groups the public evidence into research-project layers after the first-pass overview.</td></tr>
+    <tr><td><strong>Reproducibility contract</strong></td><td><a href="REPRODUCIBILITY.md">REPRODUCIBILITY.md</a><br><a href="docs/data/reproducibility_matrix.json">reproducibility_matrix.json</a></td><td>States public commands, expected outputs, exact-match reproduction evidence, and non-reproducible boundaries.</td></tr>
+    <tr><td><strong>Citation metadata</strong></td><td><a href="CITATION.cff">CITATION.cff</a><br><a href="codemeta.json">codemeta.json</a><br><a href="LICENSE">LICENSE</a></td><td>Makes the repo easier to cite, index, and reuse without confusing code license and dataset terms.</td></tr>
+  </tbody>
+</table>
 
 ## Links
 
-| Resource | Link |
-| --- | --- |
-| This GitHub repo | [github.com/ChaoYue0307/ropedia-xperience-10m-task-suite](https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite) |
-| This project website | [chaoyue0307.github.io/ropedia-xperience-10m-task-suite](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
-| This Hugging Face Space | [huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite](https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite) |
-| Live Hugging Face static app | [cy0307-ropedia-xperience-10m-task-suite.static.hf.space](https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/) |
-| GitHub Container package | [ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite](https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/pkgs/container/ropedia-xperience-10m-task-suite) |
-| Derived artifacts on Hugging Face | [huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts](https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts) |
-| Minimal and neural task baselines on Hugging Face | [huggingface.co/cy0307/ropedia-xperience-10m-task-baselines](https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines) |
-| Qwen3-Omni 128-episode LoRA adapter | [huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep](https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep) |
-| Cosmos3-Super forward-dynamics LoRA adapter | [huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep](https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep) |
-| Hugging Face collection | [huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite](https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite) |
-| Xperience-10M dataset website | [ropedia.com/dataset](https://ropedia.com/dataset) |
-| Xperience-10M release page | [ropedia.com/blog/20260316_xperience_10m](https://ropedia.com/blog/20260316_xperience_10m) |
-| Ropedia GitHub organization | [github.com/Ropedia](https://github.com/Ropedia) |
-| HOMIE Toolkit | [github.com/Ropedia/HOMIE-toolkit](https://github.com/Ropedia/HOMIE-toolkit) |
-| Xperience-10M Hugging Face dataset | [huggingface.co/datasets/ropedia-ai/xperience-10m](https://huggingface.co/datasets/ropedia-ai/xperience-10m) |
-| Xperience-10M sample on Hugging Face | [huggingface.co/datasets/ropedia-ai/xperience-10m-sample](https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample) |
-| Ropedia Hugging Face organization | [huggingface.co/ropedia-ai](https://huggingface.co/ropedia-ai) |
+<table>
+  <thead>
+    <tr>
+      <th width="34%">Resource</th>
+      <th>Link</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr><td><strong>This GitHub repo</strong></td><td><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite">github.com/ChaoYue0307/ropedia-xperience-10m-task-suite</a></td></tr>
+    <tr><td><strong>This project website</strong></td><td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">chaoyue0307.github.io/ropedia-xperience-10m-task-suite</a></td></tr>
+    <tr><td><strong>This Hugging Face Space</strong></td><td><a href="https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite">huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite</a></td></tr>
+    <tr><td><strong>Live Hugging Face static app</strong></td><td><a href="https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/">cy0307-ropedia-xperience-10m-task-suite.static.hf.space</a></td></tr>
+    <tr><td><strong>GitHub Container package</strong></td><td><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/pkgs/container/ropedia-xperience-10m-task-suite">ghcr.io/chaoyue0307/ropedia-xperience-10m-task-suite</a></td></tr>
+    <tr><td><strong>Derived artifacts on Hugging Face</strong></td><td><a href="https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts">huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts</a></td></tr>
+    <tr><td><strong>Minimal and neural task baselines on Hugging Face</strong></td><td><a href="https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines">huggingface.co/cy0307/ropedia-xperience-10m-task-baselines</a></td></tr>
+    <tr><td><strong>Qwen3-Omni 128-episode LoRA adapter</strong></td><td><a href="https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep">huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep</a></td></tr>
+    <tr><td><strong>Cosmos3-Super forward-dynamics LoRA adapter</strong></td><td><a href="https://huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep">huggingface.co/cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep</a></td></tr>
+    <tr><td><strong>Hugging Face collection</strong></td><td><a href="https://huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite">huggingface.co/collections/cy0307/ropedia-xperience-10m-task-suite</a></td></tr>
+    <tr><td><strong>Xperience-10M dataset website</strong></td><td><a href="https://ropedia.com/dataset">ropedia.com/dataset</a></td></tr>
+    <tr><td><strong>Xperience-10M release page</strong></td><td><a href="https://ropedia.com/blog/20260316_xperience_10m">ropedia.com/blog/20260316_xperience_10m</a></td></tr>
+    <tr><td><strong>Ropedia GitHub organization</strong></td><td><a href="https://github.com/Ropedia">github.com/Ropedia</a></td></tr>
+    <tr><td><strong>HOMIE Toolkit</strong></td><td><a href="https://github.com/Ropedia/HOMIE-toolkit">github.com/Ropedia/HOMIE-toolkit</a></td></tr>
+    <tr><td><strong>Xperience-10M Hugging Face dataset</strong></td><td><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m">huggingface.co/datasets/ropedia-ai/xperience-10m</a></td></tr>
+    <tr><td><strong>Xperience-10M sample on Hugging Face</strong></td><td><a href="https://huggingface.co/datasets/ropedia-ai/xperience-10m-sample">huggingface.co/datasets/ropedia-ai/xperience-10m-sample</a></td></tr>
+    <tr><td><strong>Ropedia Hugging Face organization</strong></td><td><a href="https://huggingface.co/ropedia-ai">huggingface.co/ropedia-ai</a></td></tr>
+  </tbody>
+</table>
 
 ## Citation, License, And Metadata
 

data/mirror_parity.json

@@ -1,6 +1,6 @@
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@@ -923,44 +923,44 @@
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@@ -972,44 +972,44 @@
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@@ -2295,44 +2295,44 @@
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@@ -4563,21 +4563,21 @@
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@@ -4938,21 +4938,21 @@
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data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T20:45:10+00:00",
+  "generated_at_utc": "2026-06-17T21:08:52+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
     {
@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:21:52+00:00"
+          "generated_at_utc": "2026-06-17T21:06:30+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:20:08+00:00"
+          "generated_at_utc": "2026-06-17T20:46:02+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:19:10+00:00"
+          "generated_at_utc": "2026-06-17T20:46:03+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:20:10+00:00"
+          "generated_at_utc": "2026-06-17T20:45:37+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:22:04+00:00"
+          "generated_at_utc": "2026-06-17T21:07:59+00:00"
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         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:23:22+00:00"
+          "generated_at_utc": "2026-06-17T21:07:55+00:00"
         }
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       "failures": {}
@@ -121,7 +121,7 @@
       "reason": "Readers should be able to find website reference, release package, mirror, and public presentation files from public copy.",
       "marker_counts": {
         "data/project_brief.json": 8,
-        "data/public_reader_map.json": 20,
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@@ -129,8 +129,8 @@
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         "data/research_roadmap.json": 15,
-        "data/task_suite_enhancement_128.json": 28,
-        "data/task_suite_20.json": 46,
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         "data/unified_task_model_radar.json": 21,
         "data/single_episode_task_model_radar.json": 11,
         "data/episode128_task_model_radar.json": 11,
@@ -149,8 +149,8 @@
       "reason": "The public surfaces should expose the shared reader map in both Markdown and JSON form.",
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-        "docs/data/public_reader_map.json": 17,
-        "data/public_reader_map.json": 20
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data/publication_audit.json

@@ -1,6 +1,6 @@
 {
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+  "generated_at_utc": "2026-06-17T21:09:18+00:00",
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@@ -228,7 +228,7 @@
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-      "file_count": 2446,
+      "file_count": 2447,
       "text_file_count": 1058,
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data/quality_gates.json

@@ -1,7 +1,7 @@
 {
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-  "generated_at_utc": "2026-06-17T20:46:57+00:00",
+  "generated_at_utc": "2026-06-17T21:08:52+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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data/website_integrity.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-17T20:45:10+00:00",
+  "generated_at_utc": "2026-06-17T21:08:53+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
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@@ -531,7 +531,7 @@
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+      "bytes": 19885,
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docs/data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
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   "hf_root": "hf_publish",
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@@ -923,44 +923,44 @@
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@@ -972,44 +972,44 @@
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@@ -1119,44 +1119,44 @@
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@@ -2295,44 +2295,44 @@
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         "bytes": 19885,
-        "sha256": "32cd4ab200055a3d2e5164af343783f4d4449bf097cb604204db207c209e580a"
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           "bytes": 19885,
-          "sha256": "32cd4ab200055a3d2e5164af343783f4d4449bf097cb604204db207c209e580a"
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           "bytes": 19885,
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         "hf_model_docs_data": {
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           "bytes": 19885,
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@@ -4563,21 +4563,21 @@
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-        "bytes": 28694,
-        "sha256": "80d8a106027a6cf9ef4fb242650c9ffd4096cc5bc5ebe36d7bfa0f638d48a0d8"
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@@ -4938,21 +4938,21 @@
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-        "sha256": "874867e5d587a9649be14e9f9977bfdf7c1b95308d7523c0ed7bb92b36c4ec30"
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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": "2026-06-17T20:45:10+00:00",
+  "generated_at_utc": "2026-06-17T21:08:52+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
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@@ -18,7 +18,7 @@
         "website_integrity": {
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           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:21:52+00:00"
+          "generated_at_utc": "2026-06-17T21:06:30+00:00"
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         "rendered_site_check": {
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@@ -28,27 +28,27 @@
         "task_surface_integrity": {
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           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:20:08+00:00"
+          "generated_at_utc": "2026-06-17T20:46:02+00:00"
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         "source_alignment": {
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           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:19:10+00:00"
+          "generated_at_utc": "2026-06-17T20:46:03+00:00"
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         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-17T18:20:10+00:00"
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-          "generated_at_utc": "2026-06-17T18:22:04+00:00"
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+          "generated_at_utc": "2026-06-17T21:07:55+00:00"
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       },
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@@ -121,7 +121,7 @@
       "reason": "Readers should be able to find website reference, release package, mirror, and public presentation files from public copy.",
       "marker_counts": {
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-        "data/public_reader_map.json": 20,
+        "data/public_reader_map.json": 17,
         "data/website_integrity.json": 6,
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         "data/task_surface_integrity.json": 15,
@@ -129,8 +129,8 @@
         "data/mirror_parity.json": 9,
         "data/public_surface_qa.json": 7,
         "data/research_roadmap.json": 15,
-        "data/task_suite_enhancement_128.json": 28,
-        "data/task_suite_20.json": 46,
+        "data/task_suite_enhancement_128.json": 22,
+        "data/task_suite_20.json": 34,
         "data/unified_task_model_radar.json": 21,
         "data/single_episode_task_model_radar.json": 11,
         "data/episode128_task_model_radar.json": 11,
@@ -149,8 +149,8 @@
       "reason": "The public surfaces should expose the shared reader map in both Markdown and JSON form.",
       "marker_counts": {
         "PUBLIC_READER_MAP.md": 18,
-        "docs/data/public_reader_map.json": 17,
-        "data/public_reader_map.json": 20
+        "docs/data/public_reader_map.json": 14,
+        "data/public_reader_map.json": 17
       }
     },
     {

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T20:45:47+00:00",
+  "generated_at_utc": "2026-06-17T21:09:18+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -228,7 +228,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2446,
+      "file_count": 2447,
       "text_file_count": 1058,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-17T20:46:57+00:00",
+  "generated_at_utc": "2026-06-17T21:08:52+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/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-17T20:45:10+00:00",
+  "generated_at_utc": "2026-06-17T21:08:53+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -531,7 +531,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 19884,
+      "bytes": 19885,
       "top_level_type": "dict"
     },
     {

scripts/build_multilingual_public_readmes.py

@@ -94,26 +94,89 @@ The multilingual README files are reader guides. The canonical technical evidenc
 
 ## At A Glance
 
-| Signal | Current public state |
-| --- | --- |
-| 20 task contracts | Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing. |
-| 180 method-task records | 9 methods x 20 tasks. Numeric scores appear only where a real task target and source artifact exist; unsupported and not-yet-evaluated cells stay visible. |
-| Public-sample baselines | Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode. |
-| 128-episode comparison layer | Metadata/simple, metadata/NN, raw-feature simple, raw-feature NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano branches are separated by evidence type. |
-| Foundation directions | Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as trainable directions with task mappings and model-evidence requirements. |
-| Public mirrors | GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3/Cosmos model repos, and HF collection. |
+<table>
+  <thead>
+    <tr>
+      <th width="24%">Signal</th>
+      <th>Current public state</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>20 task contracts</strong></td>
+      <td>Action, procedure, transition, trajectory, contact, objects, language, retrieval, reconstruction, order, sync, long-horizon forecasting, interaction text, action-object binding, sensor bridging, camera sync, and transition timing.</td>
+    </tr>
+    <tr>
+      <td><strong>180 method-task records</strong></td>
+      <td>9 methods x 20 tasks. Numeric scores appear only where a real task target and source artifact exist; unsupported and not-yet-evaluated cells stay visible.</td>
+    </tr>
+    <tr>
+      <td><strong>Public-sample baselines</strong></td>
+      <td>Minimal and Neural MLP baselines cover all 20 tasks on the one public sample episode.</td>
+    </tr>
+    <tr>
+      <td><strong>128-episode comparison layer</strong></td>
+      <td>Metadata/simple, metadata/NN, raw-feature simple, raw-feature NN, Qwen3-Omni, Cosmos3-Super, and Cosmos3-Nano branches are separated by evidence type.</td>
+    </tr>
+    <tr>
+      <td><strong>Foundation directions</strong></td>
+      <td>Spatial intelligence, human-video world modeling, and vision-language-action pipelines are documented as trainable directions with task mappings and model-evidence requirements.</td>
+    </tr>
+    <tr>
+      <td><strong>Public mirrors</strong></td>
+      <td>GitHub, GitHub Pages, HF Space, HF artifact dataset, HF baseline model repo, Qwen3/Cosmos model repos, and HF collection.</td>
+    </tr>
+  </tbody>
+</table>
 
 ## Fast Reader Map
 
-| Reader goal | Start here | Then inspect |
-| --- | --- | --- |
-| Understand the project quickly | [Project brief](PROJECT_BRIEF.md), [project status](PROJECT_STATUS.md) | [Dashboard](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/) |
-| Choose the right public surface | [Public reader map](PUBLIC_READER_MAP.md) | [public_reader_map.json](docs/data/public_reader_map.json) |
-| Inspect the 20 task contracts | [TASK_SUITE_20.md](TASK_SUITE_20.md) | [task_suite_20.json](docs/data/task_suite_20.json), [task walkthroughs](results/episode_task_suite/task_walkthroughs/) |
-| Compare results | [Research takeaways](RESEARCH_TAKEAWAYS.md) | [20-result matrix](docs/data/task_method_20_result_matrix.json), [radar JSON](docs/data/unified_task_model_radar.json), [gap audit](docs/data/task_method_20_gap_audit.json) |
-| Understand one data sample | [Single-episode explorer](https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/single_episode_explorer.html) | [raw sample file map](docs/data/raw_sample_files.json), [feature manifest](results/episode_task_suite/feature_manifest.json) |
-| Read foundation training directions | [THREE_FOUNDATION_PIPELINES.md](THREE_FOUNDATION_PIPELINES.md) | [three_foundation_pipelines.json](docs/data/three_foundation_pipelines.json), [foundation model plan](FOUNDATION_MODEL_PLAN.md) |
-| Reproduce or audit | [REPRODUCIBILITY.md](REPRODUCIBILITY.md), [EVIDENCE_CONTRACT.md](EVIDENCE_CONTRACT.md) | [quality gates](docs/data/quality_gates.json), [publication audit](docs/data/publication_audit.json), [mirror parity](docs/data/mirror_parity.json) |
+<table>
+  <thead>
+    <tr>
+      <th width="26%">Reader goal</th>
+      <th width="32%">Start here</th>
+      <th>Then inspect</th>
+    </tr>
+  </thead>
+  <tbody>
+    <tr>
+      <td><strong>Understand quickly</strong></td>
+      <td><a href="PROJECT_BRIEF.md">Project brief</a><br><a href="PROJECT_STATUS.md">Project status</a></td>
+      <td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">Dashboard</a></td>
+    </tr>
+    <tr>
+      <td><strong>Choose the public surface</strong></td>
+      <td><a href="PUBLIC_READER_MAP.md">Public reader map</a></td>
+      <td><a href="docs/data/public_reader_map.json">public_reader_map.json</a></td>
+    </tr>
+    <tr>
+      <td><strong>Inspect the 20 tasks</strong></td>
+      <td><a href="TASK_SUITE_20.md">TASK_SUITE_20.md</a></td>
+      <td><a href="docs/data/task_suite_20.json">task_suite_20.json</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a></td>
+    </tr>
+    <tr>
+      <td><strong>Compare results</strong></td>
+      <td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a></td>
+      <td><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">gap audit</a></td>
+    </tr>
+    <tr>
+      <td><strong>Understand one sample</strong></td>
+      <td><a href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/single_episode_explorer.html">Single-episode explorer</a></td>
+      <td><a href="docs/data/raw_sample_files.json">raw sample file map</a><br><a href="results/episode_task_suite/feature_manifest.json">feature manifest</a></td>
+    </tr>
+    <tr>
+      <td><strong>Read foundation directions</strong></td>
+      <td><a href="THREE_FOUNDATION_PIPELINES.md">Three foundation pipelines</a></td>
+      <td><a href="docs/data/three_foundation_pipelines.json">three_foundation_pipelines.json</a><br><a href="FOUNDATION_MODEL_PLAN.md">foundation model plan</a></td>
+    </tr>
+    <tr>
+      <td><strong>Reproduce or audit</strong></td>
+      <td><a href="REPRODUCIBILITY.md">Reproducibility</a><br><a href="EVIDENCE_CONTRACT.md">Evidence contract</a></td>
+      <td><a href="docs/data/quality_gates.json">quality gates</a><br><a href="docs/data/publication_audit.json">publication audit</a><br><a href="docs/data/mirror_parity.json">mirror parity</a></td>
+    </tr>
+  </tbody>
+</table>
 """
 
 

scripts/sync_hf_publish_mirrors.py

@@ -331,11 +331,18 @@ def refresh_project_readme_cards(hf_root: Path, *, dry_run: bool) -> list[str]:
     return updated
 
 
-def read_current_scaleup_line() -> str:
+def read_current_scaleup_line() -> str | None:
+    """Return the legacy Markdown scale-up row when the README still has one.
+
+    The current public README uses an HTML table for the research overview, so
+    mirrored full project cards no longer need a standalone Markdown row. Keep
+    this compatibility hook for older compact cards only.
+    """
+
     for line in (ROOT / "README.md").read_text(encoding="utf-8").splitlines():
         if line.startswith("| Scale-up |"):
             return line
-    raise SystemExit("Could not find current Scale-up row in README.md")
+    return None
 
 
 def ensure_current_qwen_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
@@ -368,17 +375,15 @@ def ensure_current_qwen_card_links(hf_root: Path, *, dry_run: bool) -> list[str]
         text = original
         if README_QWEN_OLD_PARAGRAPH in text:
             text = text.replace(README_QWEN_OLD_PARAGRAPH, README_QWEN_CURRENT_PARAGRAPH, 1)
-        lines = text.splitlines()
         changed = text != original
-        for idx, line in enumerate(lines):
-            if line.startswith("| Scale-up |"):
-                if line != scaleup_line:
-                    lines[idx] = scaleup_line
-                    changed = True
-                break
-        else:
-            lines.append(scaleup_line)
-            changed = True
+        lines = text.splitlines()
+        if scaleup_line:
+            for idx, line in enumerate(lines):
+                if line.startswith("| Scale-up |"):
+                    if line != scaleup_line:
+                        lines[idx] = scaleup_line
+                        changed = True
+                    break
         if changed:
             updated.append(relative_path)
             if not dry_run: