Add files using upload-large-folder tool

PROJECT_README.md

@@ -257,20 +257,20 @@ These are Qwen3-Omni run versions inside **Line 2: selected 128 episodes**. They
 </table>
 
 Detailed lineage:
-[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md) and
-[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json).
+[Qwen lineage note](QWEN3_OMNI_RUN_LINEAGE.md) and
+[Qwen lineage data](docs/data/qwen3_omni_run_lineage.json).
 
 Result entry points:
-[`TWO_EVIDENCE_LINES.md`](TWO_EVIDENCE_LINES.md),
-[`two_evidence_lines.json`](docs/data/two_evidence_lines.json),
-[`TWO_EVIDENCE_LINE_RESULT_SUMMARY.md`](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
-[`two_evidence_line_result_summary.json`](docs/data/two_evidence_line_result_summary.json),
-[`QWEN3_OMNI_RUN_LINEAGE.md`](QWEN3_OMNI_RUN_LINEAGE.md),
-[`qwen3_omni_run_lineage.json`](docs/data/qwen3_omni_run_lineage.json),
-[`single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json),
-[`episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json),
-[`task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json), and
-[`xperience10m_128_episode_feature_index.json`](docs/data/xperience10m_128_episode_feature_index.json).
+[two evidence-line note](TWO_EVIDENCE_LINES.md),
+[two evidence-line data](docs/data/two_evidence_lines.json),
+[result summary note](TWO_EVIDENCE_LINE_RESULT_SUMMARY.md),
+[result summary data](docs/data/two_evidence_line_result_summary.json),
+[Qwen lineage note](QWEN3_OMNI_RUN_LINEAGE.md),
+[Qwen lineage data](docs/data/qwen3_omni_run_lineage.json),
+[1-episode radar data](docs/data/single_episode_task_model_radar.json),
+[128-episode radar data](docs/data/episode128_task_model_radar.json),
+[180-result matrix data](docs/data/task_method_20_result_matrix.json), and
+[selected-128 source and feature index](docs/data/xperience10m_128_episode_feature_index.json).
 
 ## Fast Reader Map
 
@@ -291,22 +291,22 @@ Result entry points:
     <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>
+      <td><a href="docs/data/public_reader_map.json">reader-map data</a></td>
     </tr>
     <tr>
       <td><strong>Decode project terms</strong></td>
       <td><a href="GLOSSARY.md">Glossary</a></td>
-      <td><a href="docs/data/glossary.json">glossary.json</a></td>
+      <td><a href="docs/data/glossary.json">glossary data</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>
+      <td><a href="TASK_SUITE_20.md">20-task suite note</a></td>
+      <td><a href="docs/data/task_suite_20.json">task contract data</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/two_evidence_line_result_summary.json">two-line result summary</a><br><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar JSON</a><br><a href="docs/data/task_method_20_gap_audit.json">score/proxy audit</a></td>
+      <td><a href="docs/data/two_evidence_line_result_summary.json">two-line result summary</a><br><a href="docs/data/task_method_20_result_matrix.json">20-result matrix</a><br><a href="docs/data/unified_task_model_radar.json">radar data</a><br><a href="docs/data/task_method_20_gap_audit.json">score/proxy audit</a></td>
     </tr>
     <tr>
       <td><strong>Understand one sample</strong></td>
@@ -316,7 +316,7 @@ Result entry points:
     <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>
+      <td><a href="docs/data/three_foundation_pipelines.json">pipeline data</a><br><a href="FOUNDATION_MODEL_PLAN.md">foundation model plan</a></td>
     </tr>
     <tr>
       <td><strong>Reproduce or audit</strong></td>
@@ -357,11 +357,11 @@ embodied-AI research infrastructure:
 ## Start Here
 
 The public release is split across GitHub, the website, and Hugging Face. Use
-[`PUBLIC_READER_MAP.md`](PUBLIC_READER_MAP.md) first if you want the shortest
+[the public reader map](PUBLIC_READER_MAP.md) first if you want the shortest
 route through those surfaces, or use the machine-readable companion
-[`docs/data/public_reader_map.json`](docs/data/public_reader_map.json).
-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-map data](docs/data/public_reader_map.json).
+For the one-page project summary, use [the project brief](PROJECT_BRIEF.md)
+and [project-summary data](docs/data/project_brief.json).
 
 <table>
   <thead>
@@ -371,27 +371,27 @@ and [`docs/data/project_brief.json`](docs/data/project_brief.json).
     </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>Resolve confusing terms and abbreviations</strong></td><td><a href="GLOSSARY.md">GLOSSARY.md</a><br><a href="docs/data/glossary.json">glossary.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>Choose the right public surface</strong></td><td><a href="PUBLIC_READER_MAP.md">Public reader map</a><br><a href="docs/data/public_reader_map.json">reader-map data</a></td></tr>
+    <tr><td><strong>Resolve confusing terms and abbreviations</strong></td><td><a href="GLOSSARY.md">Glossary</a><br><a href="docs/data/glossary.json">glossary data</a></td></tr>
+    <tr><td><strong>Understand the whole project quickly</strong></td><td><a href="PROJECT_BRIEF.md">Project brief</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>
+    <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">20-task suite note</a><br><a href="docs/data/task_suite_20.json">task contract data</a><br><a href="docs/data/research_roadmap_interactive.json">interactive roadmap data</a></td></tr>
+    <tr><td><strong>Compare current task metrics</strong></td><td><a href="RESEARCH_TAKEAWAYS.md">Research takeaways</a><br><a href="docs/data/summary_metrics.json">summary metrics</a></td></tr>
+    <tr><td><strong>Compare possible foundation backbones</strong></td><td><a href="FOUNDATION_MODEL_PLAN.md">Foundation-model plan</a><br><a href="docs/data/foundation_model_plan.json">foundation-model data</a></td></tr>
+    <tr><td><strong>Understand the future native pretraining goal</strong></td><td><a href="XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">Native pretraining plan</a></td></tr>
+    <tr><td><strong>See additional concrete project directions</strong></td><td><a href="ADDITIONAL_DEVELOPMENT_DIRECTIONS.md">Additional development directions</a><br><a href="docs/data/additional_development_directions.json">direction data</a></td></tr>
+    <tr><td><strong>Understand one model input</strong></td><td><a href="results/episode_task_suite/feature_manifest.json">feature manifest</a><br><a href="results/episode_task_suite/windows.csv">window table</a></td></tr>
+    <tr><td><strong>Check multi-episode data status</strong></td><td><a href="results/omni_finetune/DATA_ACCESS_STATUS.md">multi-episode data status</a></td></tr>
   </tbody>
 </table>
 
 ## Glossary
 
-Use [`GLOSSARY.md`](GLOSSARY.md) when a term such as evidence line,
+Use [the glossary](GLOSSARY.md) when a term such as evidence line,
 20-frame window, direct score, compact-proxy score, raw metric value,
 normalized radar value, minimal/minimum baseline, simple baseline, Qwen v1-v6,
 Cosmos3-Super, LoRA adapter, or HF artifact dataset is unclear. The same definitions are mirrored as
-[`docs/data/glossary.json`](docs/data/glossary.json) for the website and
+[glossary data](docs/data/glossary.json) for the website and
 Hugging Face repos.
 
 ## Public Surface Map
@@ -404,23 +404,23 @@ Hugging Face repos.
     </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 repo</strong></td><td>Source of truth for docs, scripts, generated data, 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 artifact dataset</strong></td><td>Public-safe metrics, reports, website data, 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-Omni and Cosmos3 model repos</strong></td><td>Adapter-specific public weights or package cards when Qwen3-Omni v6, Cosmos3-Super, or Cosmos3-Nano runs are 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),
-[`docs/data/rendered_site_check.json`](docs/data/rendered_site_check.json),
-[`docs/data/task_surface_integrity.json`](docs/data/task_surface_integrity.json),
-[`docs/data/publication_audit.json`](docs/data/publication_audit.json),
-[`docs/data/mirror_parity.json`](docs/data/mirror_parity.json),
-[`docs/data/public_surface_qa.json`](docs/data/public_surface_qa.json), and
-[`docs/data/research_roadmap.json`](docs/data/research_roadmap.json).
+Public release checks are exposed as structured records for mirrors and dashboards:
+[website integrity](docs/data/website_integrity.json),
+[rendered-site check](docs/data/rendered_site_check.json),
+[task-surface integrity](docs/data/task_surface_integrity.json),
+[publication audit](docs/data/publication_audit.json),
+[mirror parity](docs/data/mirror_parity.json),
+[public-surface QA](docs/data/public_surface_qa.json), and
+[roadmap data](docs/data/research_roadmap.json).
 
 ## Research Project Overview
 
@@ -454,8 +454,8 @@ Public release checks are exposed as JSON for mirrors and dashboards:
 </table>
 
 For the fastest interpretation of the current metrics, start with
-[`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md) and
-[`docs/data/research_takeaways.json`](docs/data/research_takeaways.json).
+[the research takeaways](RESEARCH_TAKEAWAYS.md) and
+[takeaway data](docs/data/research_takeaways.json).
 They summarize what the public sample results actually show: class shift under
 chronological splits, neural gains on dynamics/order/alignment, harder
 retrieval/reconstruction probes, and why the next model-quality step needs
@@ -503,11 +503,11 @@ This project is best read as a staged embodied-AI research study:
         Historical <code>tier2_task_suite</code> artifact paths are kept for link stability, but they are provenance paths inside 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>
+        <a href="TASK_SUITE_20.md">20-task suite note</a><br>
+        <a href="docs/data/task_suite_20.json">task contract data</a><br>
+        <a href="RESEARCH_TAKEAWAYS.md">research takeaways</a><br>
+        <a href="results/episode_task_suite/summary_report.json">summary report</a><br>
+        <a href="results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md">historical provenance baselines</a>
       </td>
     </tr>
     <tr>
@@ -518,15 +518,15 @@ This project is best read as a staged embodied-AI research study:
         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/episode_task_suite/neural_mlp/">neural MLP outputs</a><br>
+        <a href="results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md">baseline alignment report</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>
+      <td><a href="results/audio_ablation/AUDIO_ABLATION_SUMMARY.md">audio ablation summary</a><br><a href="docs/single_episode_explorer.html">single-episode explorer</a></td>
     </tr>
     <tr>
       <td><strong>Scale-up</strong></td>
@@ -542,31 +542,31 @@ This project is best read as a staged embodied-AI research study:
         </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="XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md">XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md</a><br>
-        <a href="docs/data/xperience10m_128_episode_feature_index.json">xperience10m_128_episode_feature_index.json</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>
+        <a href="RESEARCH_ROADMAP.md">research roadmap</a><br>
+        <a href="FOUNDATION_MODEL_PLAN.md">foundation-model plan</a><br>
+        <a href="XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md">selected-128 feature index note</a><br>
+        <a href="docs/data/xperience10m_128_episode_feature_index.json">selected-128 feature-index data</a><br>
+        <a href="TASK_SUITE_ENHANCEMENT_128.md">selected-128 enhancement note</a><br>
+        <a href="docs/data/task_suite_enhancement_128.json">selected-128 enhancement data</a><br>
+        <a href="docs/data/omni_model_comparison.json">model comparison data</a><br>
+        <a href="docs/data/omni_finetune_verified_result.json">verified Omni result data</a><br>
+        <a href="docs/data/qwen3_v5_v6_comparison.json">Qwen v5/v6 comparison data</a><br>
+        <a href="results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md">Qwen v5/v6 comparison note</a><br>
+        <a href="results/omni_finetune/OMNI_MODEL_COMPARISON.md">Omni model comparison note</a><br>
+        <a href="results/omni_finetune/verified_public/">verified public package</a><br>
+        <a href="results/omni_finetune/task_suite_enhancement_128_v1_20260608/">selected-128 enhancement run</a>
       </td>
     </tr>
   </tbody>
 </table>
 
-Detailed dataset notes, reproduction checks, and generated JSON reports are
+Detailed dataset notes, reproduction checks, and generated data reports are
 included for readers who want to inspect the implementation, but they are
 supporting materials rather than the main reading path. Use
-[`ARTIFACT_GUIDE.md`](ARTIFACT_GUIDE.md) when you want the full file map.
+[the artifact guide](ARTIFACT_GUIDE.md) when you want the full file map.
 
-Source alignment is tracked in [`SOURCE_ALIGNMENT_AUDIT.md`](SOURCE_ALIGNMENT_AUDIT.md)
-and [`docs/data/source_alignment_audit.json`](docs/data/source_alignment_audit.json).
+Source alignment is tracked in the [source-alignment note](SOURCE_ALIGNMENT_AUDIT.md)
+and [source-alignment data](docs/data/source_alignment_audit.json).
 The official gated `ropedia-ai/xperience-10m` card reports `31.9 TB` on the
 live HF surface and an `about-1PB` full-scale storage statement; the committed
 API-listing snapshot records `12,103 episode folders` as upstream `metadata only`,
@@ -579,8 +579,8 @@ that the data is `limited in diversity` is preserved.
 ## Project Status
 
 If you only have one minute, use
-[`PROJECT_STATUS.md`](PROJECT_STATUS.md) and
-[`docs/data/project_status.json`](docs/data/project_status.json).
+[the project status note](PROJECT_STATUS.md) and
+[project-status data](docs/data/project_status.json).
 They give the current research state in one compact table:
 
 <table>
@@ -616,39 +616,39 @@ If you are reading the project cold, open these in order:
     </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>1</strong></td><td>What is this project?</td><td><a href="PROJECT_BRIEF.md">Project brief</a><br><a href="PROJECT_STATUS.md">Project status</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>
+    <tr><td><strong>3</strong></td><td>What does one model input contain?</td><td><a href="results/episode_task_suite/windows.csv">window table</a><br><a href="results/episode_task_suite/feature_manifest.json">feature manifest</a><br><a href="results/episode_task_suite/available_modalities.json">available-modality data</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">20-task suite note</a><br><a href="docs/data/task_suite_20.json">task contract data</a><br><a href="results/episode_task_suite/task_walkthroughs/">task walkthroughs</a><br><a href="docs/data/task_walkthroughs.json">walkthrough data</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 note</a><br><a href="docs/data/evaluation_protocol.json">evaluation-protocol data</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</a><br><a href="docs/data/research_takeaways.json">takeaway data</a><br><a href="docs/data/summary_metrics.json">summary metrics</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</a><br><a href="results/episode_task_suite/neural_mlp/">neural MLP outputs</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</a><br><a href="docs/data/research_directions.json">direction data</a><br><a href="docs/data/research_direction_extensions.json">extension-probe data</a><br><a href="docs/data/task_suite_20.json">task contract data</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</a><br><a href="docs/data/foundation_model_plan.json">foundation-model data</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">selected-128 enhancement note</a><br><a href="docs/data/task_suite_enhancement_128.json">selected-128 enhancement data</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 guide</a><br><a href="notes/reproducibility_audit.md">reproduction audit</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">verified Omni result data</a><br><a href="results/omni_finetune/DATA_ACCESS_STATUS.md">data access status</a><br><a href="results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">multi-episode access status</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).
+[project-packet data](docs/data/project_packet.json).
 
 ## Supporting Files
 
-[`ARTIFACT_GUIDE.md`](ARTIFACT_GUIDE.md) is the human-readable map for readers
+[The artifact guide](ARTIFACT_GUIDE.md) is the human-readable map for readers
 who want to inspect the project files after the first pass. It groups the main
 briefs, task outputs, baseline results, visual assets, data notes, and
 scale-up documents.
 
-[`docs/data/artifact_index.json`](docs/data/artifact_index.json) is the compact
+[The artifact index](docs/data/artifact_index.json) is the compact
 machine-readable companion used by the website and Hugging Face artifact
 dataset.
 
 ## Evaluation Protocol
 
-[`EVALUATION_PROTOCOL.md`](EVALUATION_PROTOCOL.md) and
-[`docs/data/evaluation_protocol.json`](docs/data/evaluation_protocol.json) are
+[The evaluation protocol](EVALUATION_PROTOCOL.md) and
+[evaluation-protocol data](docs/data/evaluation_protocol.json) are
 generated from committed metric artifacts. They define:
 
 - the 20-frame window unit, stride, feature dimension, and raw-data policy,
@@ -685,8 +685,8 @@ The current verified public-sample subset is:
   pose/SLAM, mocap, IMU, calibration, and language-derived signals.
 
 Detailed dataset notes are available in
-[`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
-and [`docs/data/xperience10m_dataset_card_alignment.json`](docs/data/xperience10m_dataset_card_alignment.json)
+[the dataset-card alignment note](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
+and [dataset-alignment data](docs/data/xperience10m_dataset_card_alignment.json)
 for readers who need the full upstream-card and access-term context. The
 practical reading rule is simple: Line 1 is the task lab, Line 2 is the
 selected-128 comparison surface, and compact-proxy cells stay explicitly marked
@@ -711,20 +711,20 @@ Hugging Face Space app:
     </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 tracks, 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>Project status</strong></td><td><a href="PROJECT_STATUS.md">project status</a><br><a href="docs/data/project_status.json">project-status data</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">window table</a><br><a href="results/episode_task_suite/feature_manifest.json">feature manifest</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">dataset-card alignment</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</a><br><a href="docs/assets/">site 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 note</a><br><a href="docs/data/evaluation_protocol.json">evaluation-protocol data</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</a><br><a href="docs/data/research_roadmap.json">roadmap data</a></td><td>Shows the path from sample-level task development to multi-episode work, larger model tracks, 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</a><br><a href="docs/data/additional_development_directions.json">direction data</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">native pretraining plan</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 reader-facing views 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>
+    <tr><td><strong>Artifact guide</strong></td><td><a href="ARTIFACT_GUIDE.md">artifact guide</a></td><td>Groups the public evidence into reader-facing views after the first-pass overview.</td></tr>
+    <tr><td><strong>Reproducibility contract</strong></td><td><a href="REPRODUCIBILITY.md">reproducibility guide</a><br><a href="docs/data/reproducibility_matrix.json">reproducibility matrix</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 metadata</a><br><a href="codemeta.json">software metadata</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>
 

data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T16:47:26+00:00",
+  "generated_at_utc": "2026-06-22T17:49:11+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1306,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+        "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         }
       },
       "failures": []
@@ -972,44 +972,44 @@
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
         "bytes": 10940,
-        "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+        "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
         },
         "hf_model_data": {
           "path": "hf_model:data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/publication_audit.json",
           "exists": true,
           "bytes": 10940,
-          "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
+          "sha256": "cf090fe53be3f85923674af08ad982e12635db7e44e87182982461ce27e858d3"
         },
         "hf_model": {
           "path": "hf_model:metrics/publication_audit.json",
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data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
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-  "generated_at_utc": "2026-06-22T17:03:49+00:00",
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@@ -28,12 +28,12 @@
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data/quality_gates.json

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data/task_surface_integrity.json

@@ -1,6 +1,6 @@
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-  "generated_at_utc": "2026-06-22T15:07:48+00:00",
+  "generated_at_utc": "2026-06-22T17:45:19+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,
@@ -1579,9 +1579,14 @@
       "marker": "class=\"task-card\""
     },
     {
-      "name": "website_marker_present:class=\"task-card-media\"",
+      "name": "website_marker_present:class=\"task-card-icon\"",
       "status": "pass",
-      "marker": "class=\"task-card-media\""
+      "marker": "class=\"task-card-icon\""
+    },
+    {
+      "name": "website_marker_present:class=\"task-modality-chips\"",
+      "status": "pass",
+      "marker": "class=\"task-modality-chips\""
     },
     {
       "name": "website_marker_present:class=\"story-button",
@@ -1625,7 +1630,7 @@
       "status": "pass"
     },
     {
-      "name": "task_cards_use_representative_modality_thumbnail",
+      "name": "task_cards_use_assigned_icons_and_modality_chips",
       "status": "pass"
     },
     {

docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-22T17:03:53+00:00",
+  "generated_at_utc": "2026-06-22T17:35:20+00:00",
   "status": "pass",
   "artifact_count": 228,
   "missing": [],
@@ -632,7 +632,7 @@
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       "bytes": 4432,
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@@ -1182,7 +1182,7 @@
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       "bytes": 8640,
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     {
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@@ -1249,7 +1249,7 @@
       "volatile": true,
       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
       "exists": true,
-      "bytes": 46246,
+      "bytes": 46497,
       "hash_policy": "existence_and_size_only"
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docs/data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T16:47:26+00:00",
+  "generated_at_utc": "2026-06-22T17:49:11+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1306,
@@ -139,44 +139,44 @@
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@@ -972,44 +972,44 @@
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@@ -1021,44 +1021,44 @@
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           "bytes": 7690,
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@@ -1217,44 +1217,44 @@
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@@ -1756,44 +1756,44 @@
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@@ -2196,45 +2196,45 @@
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@@ -2589,44 +2589,44 @@
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           "bytes": 24948,
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           "bytes": 24948,
-          "sha256": "2b372c787eacb812c102570d562e3931f44dbf9c4d7b7edbe0ccf81fda1570c2"
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           "bytes": 24948,
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@@ -7175,21 +7175,21 @@
       "local": {
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         "exists": true,
-        "bytes": 17305,
-        "sha256": "9689a0bffcf301d3e7e0da8cc90d40eda4b39eceb0859608072c23d5bde3d836"
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+        "sha256": "3094cdd1642f38a04685cf27cfca6b8181f365c1dead6e64cf066f0fd9b7d3a7"
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       "mirrors": {
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-          "bytes": 17305,
-          "sha256": "9689a0bffcf301d3e7e0da8cc90d40eda4b39eceb0859608072c23d5bde3d836"
+          "bytes": 17467,
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         "hf_model": {
           "path": "hf_model:scripts/validate_task_surface.py",
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-          "bytes": 17305,
-          "sha256": "9689a0bffcf301d3e7e0da8cc90d40eda4b39eceb0859608072c23d5bde3d836"
+          "bytes": 17467,
+          "sha256": "3094cdd1642f38a04685cf27cfca6b8181f365c1dead6e64cf066f0fd9b7d3a7"
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       "failures": []
@@ -7429,39 +7429,39 @@
       "local": {
         "path": "repo:docs/index.html",
         "exists": true,
-        "bytes": 377623,
-        "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
+        "bytes": 379760,
+        "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
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       "mirrors": {
         "hf_space": {
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           "exists": true,
-          "bytes": 377623,
-          "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
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         },
         "hf_artifacts_root": {
           "path": "hf_artifacts:index.html",
           "exists": true,
-          "bytes": 377623,
-          "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
+          "bytes": 379760,
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         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/index.html",
           "exists": true,
-          "bytes": 377623,
-          "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
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         "hf_model": {
           "path": "hf_model:index.html",
           "exists": true,
-          "bytes": 377623,
-          "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
+          "bytes": 379760,
+          "sha256": "5ab185f3c9022a2eb5efa0d00efd23a010b16776a069c307f953aef0d7657272"
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         "hf_model_docs": {
           "path": "hf_model:docs/index.html",
           "exists": true,
-          "bytes": 377623,
-          "sha256": "10052aa09444caaacc1306368be495a44f356f239dc3e614ef6f5fde6196072e"
+          "bytes": 379760,
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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": "2026-06-22T17:03:49+00:00",
+  "generated_at_utc": "2026-06-22T17:41:00+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": "2026-06-22T17:01:55+00:00"
+          "generated_at_utc": "2026-06-22T17:38:14+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,12 +28,12 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T15:07:48+00:00"
+          "generated_at_utc": "2026-06-22T17:37:13+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T15:07:48+00:00"
+          "generated_at_utc": "2026-06-22T17:35:19+00:00"
         },
         "scale_up_status": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:02:31+00:00"
+          "generated_at_utc": "2026-06-22T17:40:04+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T16:47:26+00:00"
+          "generated_at_utc": "2026-06-22T17:05:46+00:00"
         }
       },
       "failures": {}
@@ -76,7 +76,7 @@
       "marker_counts": {
         "role=\"tablist\"": 3,
         "role=\"tab\"": 10,
-        "role=\"tabpanel\"": 28,
+        "role=\"tabpanel\"": 27,
         "aria-selected": 13,
         "aria-controls": 11,
         "moveProjectTabFocus": 2,
@@ -97,7 +97,7 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 22,
         "Xperience-10M": 170,
-        "20-task": 117,
+        "20-task": 120,
         "Qwen3-Omni": 233,
         "128-episode pilot": 1
       }

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:02:31+00:00",
+  "generated_at_utc": "2026-06-22T17:46:27+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -235,7 +235,7 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1556,
+      "file_count": 1557,
       "text_file_count": 1287,
       "largest_file": {
         "path": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv",

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:03:47+00:00",
+  "generated_at_utc": "2026-06-22T17:40:55+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/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T15:07:48+00:00",
+  "generated_at_utc": "2026-06-22T17:35:19+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": "2026-06-22T15:07:48+00:00",
+  "generated_at_utc": "2026-06-22T17:45:19+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,
@@ -1579,9 +1579,14 @@
       "marker": "class=\"task-card\""
     },
     {
-      "name": "website_marker_present:class=\"task-card-media\"",
+      "name": "website_marker_present:class=\"task-card-icon\"",
       "status": "pass",
-      "marker": "class=\"task-card-media\""
+      "marker": "class=\"task-card-icon\""
+    },
+    {
+      "name": "website_marker_present:class=\"task-modality-chips\"",
+      "status": "pass",
+      "marker": "class=\"task-modality-chips\""
     },
     {
       "name": "website_marker_present:class=\"story-button",
@@ -1625,7 +1630,7 @@
       "status": "pass"
     },
     {
-      "name": "task_cards_use_representative_modality_thumbnail",
+      "name": "task_cards_use_assigned_icons_and_modality_chips",
       "status": "pass"
     },
     {

docs/data/website_integrity.json

@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:01:55+00:00",
+  "generated_at_utc": "2026-06-22T17:45:23+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 282,
+    "local_references": 286,
     "external_reference_count": 151,
     "json_files": 56,
     "image_assets_referenced": 49,
@@ -30,7 +30,7 @@
       "name": "project_sections_are_assigned_to_tabs",
       "status": "pass",
       "reason": "Every major research section should be assigned to a tab group.",
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     },
     {
       "name": "project_hash_router_preserves_deep_links",
@@ -58,8 +58,8 @@
       "name": "project_sections_are_labeled_tabpanels",
       "status": "pass",
       "reason": "Every tabbed research section should expose a labeled panel role.",
-      "panel_count": 28,
-      "labeled_panel_count": 24
+      "panel_count": 27,
+      "labeled_panel_count": 23
     },
     {
       "name": "project_tabs_update_selected_state",
@@ -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": 156111,
-      "evidence_index": 203925
+      "overview_index": 157884,
+      "evidence_index": 205698
     },
     {
       "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": 156111,
-      "protocol_index": 200130,
-      "evidence_index": 203925
+      "overview_index": 157884,
+      "protocol_index": 201903,
+      "evidence_index": 205698
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -186,8 +186,8 @@
       "name": "suite_task_map_precedes_radar_surface",
       "status": "pass",
       "reason": "The Suite anchor should show the task-suite map before the radar/results surface.",
-      "first_marker_index": 468,
-      "second_marker_index": 2016
+      "first_marker_index": 760,
+      "second_marker_index": 2368
     },
     {
       "name": "raw_sample_stream_ledger_contains_seven_modalities",
@@ -290,8 +290,8 @@
     },
     {
       "path": "index.html",
-      "id_count": 102,
-      "reference_count": 254,
+      "id_count": 103,
+      "reference_count": 258,
       "image_count": 54
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10940,
+      "bytes": 10844,
       "top_level_type": "dict"
     },
     {
@@ -540,7 +540,7 @@
     },
     {
       "path": "data/task_surface_integrity.json",
-      "bytes": 46246,
+      "bytes": 46399,
       "top_level_type": "dict"
     },
     {

docs/index.html

@@ -833,7 +833,6 @@
     #extensions { order: 13; }
     #architectures { order: 14; }
     #walkthroughs { order: 15; }
-    #tasks { order: 16; }
     #features { order: 17; }
     #diagnostics { order: 18; }
     #evidence { order: 19; }
@@ -844,6 +843,68 @@
     #suite { padding: 62px 0 76px; }
     #suite .wrap { width: min(1680px, calc(100% - 48px)); }
     #suite .section-head { max-width: var(--max); margin-inline: auto; }
+    .suite-jump-row {
+      max-width: var(--max);
+      margin: -8px auto 24px;
+      display: flex;
+      flex-wrap: wrap;
+      gap: 10px;
+    }
+    .suite-jump-row a {
+      display: inline-flex;
+      align-items: center;
+      min-height: 38px;
+      border: 1px solid rgba(204, 255, 160, 0.22);
+      border-radius: 999px;
+      padding: 0 14px;
+      color: var(--accent-2);
+      background: rgba(6, 14, 7, 0.74);
+      font-size: 12px;
+      font-weight: 800;
+      letter-spacing: 0.04em;
+      text-decoration: none;
+      text-transform: uppercase;
+    }
+    .suite-jump-row a:hover {
+      border-color: rgba(204, 255, 160, 0.72);
+      color: var(--ink);
+      background: rgba(204, 255, 160, 0.10);
+    }
+    .suite-radar-block {
+      scroll-margin-top: var(--tab-stack-offset);
+    }
+    .suite-task-cards-block {
+      scroll-margin-top: var(--tab-stack-offset);
+      margin-top: clamp(28px, 4vw, 48px);
+      border: 1px solid rgba(204, 255, 160, 0.22);
+      border-radius: var(--radius);
+      background:
+        radial-gradient(circle at top left, rgba(204, 255, 160, 0.10), transparent 34%),
+        linear-gradient(180deg, rgba(6, 14, 7, 0.86), rgba(2, 5, 2, 0.88));
+      padding: clamp(20px, 3vw, 32px);
+    }
+    .task-suite-subhead {
+      display: grid;
+      grid-template-columns: minmax(0, 0.9fr) minmax(280px, 0.62fr);
+      gap: 22px;
+      align-items: end;
+      margin-bottom: 20px;
+    }
+    .task-suite-subhead h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: clamp(26px, 3vw, 40px);
+      line-height: 1.04;
+      letter-spacing: 0;
+      text-wrap: balance;
+    }
+    .task-suite-subhead p {
+      margin: 0;
+      color: var(--muted);
+      font-size: 15px;
+      line-height: 1.6;
+      text-wrap: pretty;
+    }
     .section-head {
       display: flex;
       justify-content: space-between;
@@ -5287,7 +5348,7 @@
           <strong>Start</strong>
           <span>project overview and roadmap</span>
         </button>
-        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card suite walkthroughs tasks" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs extensions" tabindex="-1">
           <strong>Data & Tasks</strong>
           <span>dataset sample and task suite</span>
         </button>
@@ -6240,9 +6301,15 @@
     <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Ropedia Xperience-10M Unified 20-Task Suite.</h2>
-          <p>The suite connects synchronized multimodal windows to 20 task contracts in one table, one radar surface, and one source-linked result matrix. Historical filenames remain only for stable artifact links.</p>
+          <h2>Ropedia Xperience-10M 20-task suite.</h2>
+          <p>Task map, radar comparisons, task cards, and the 180-result table are kept in one reading flow. Start with the map, inspect the score surfaces, then open each task card for its input, process, output, and metric.</p>
         </div>
+        <nav class="suite-jump-row" aria-label="20-task suite quick links">
+          <a href="#task-suite-map">Task map</a>
+          <a href="#suite-radars">Radars</a>
+          <a href="#tasks">Task cards</a>
+          <a href="#result-matrix-table">180-result table</a>
+        </nav>
         <div class="figure-pan" id="task-suite-map">
           <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v14-modality-compact" alt="Infographic showing Ropedia Xperience-10M task families with compact modality cards and visible thumbnails">
         </div>
@@ -6260,27 +6327,50 @@
             <p>The matrix has 180/180 scored method-task records: 174 direct scores and 6 compact-proxy scores. The audit records the source artifact, metric key, and proxy reason for each marked cell.</p>
           </article>
         </div>
-        <img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified grouped 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, score counts, and proxy notes">
-        <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
-          <article class="split-radar-card">
-            <h3>1-Episode 20-Task Radar</h3>
-            <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts in one enlarged panel without 128-episode methods competing for attention.</p>
-            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
-            <div class="split-radar-links">
-              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
-              <a href="data/single_episode_task_model_radar.json">Open chart data</a>
-            </div>
-          </article>
-          <article class="split-radar-card">
-            <h3>128-Episode 20-Task Radar</h3>
-            <p>Seven aligned 128-episode methods cover all 20 axes across metadata/text, raw-feature, and foundation-model panels. Proxy axes stay labeled in the chart and source data.</p>
-            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="128-episode grouped 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts">
-            <div class="split-radar-links">
-              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
-              <a href="data/episode128_task_model_radar.json">Open chart data</a>
-              <a href="data/task_method_20_gap_audit.json">Gap audit</a>
+        <div class="suite-radar-block" id="suite-radars">
+          <img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified grouped 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, score counts, and proxy notes">
+          <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
+            <article class="split-radar-card">
+              <h3>1-Episode 20-Task Radar</h3>
+              <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts in one enlarged panel without 128-episode methods competing for attention.</p>
+              <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+              <div class="split-radar-links">
+                <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+                <a href="data/single_episode_task_model_radar.json">Open chart data</a>
+              </div>
+            </article>
+            <article class="split-radar-card">
+              <h3>128-Episode 20-Task Radar</h3>
+              <p>Seven aligned 128-episode methods cover all 20 axes across metadata/text, raw-feature, and foundation-model panels. Proxy axes stay labeled in the chart and source data.</p>
+              <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="128-episode grouped 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts">
+              <div class="split-radar-links">
+                <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+                <a href="data/episode128_task_model_radar.json">Open chart data</a>
+                <a href="data/task_method_20_gap_audit.json">Gap audit</a>
+              </div>
+            </article>
+          </div>
+        </div>
+        <div class="suite-task-cards-block" id="tasks">
+          <div class="task-suite-subhead">
+            <h3>All 20 task cards in the same suite.</h3>
+            <p>Each card uses its assigned icon and shows the task name, input sources, process, output target, metric, current Minimal score, and Neural MLP score. Use the filters for scanning; the cards stay tied to the task map and radar axes above.</p>
+          </div>
+          <article class="task-icon-atlas">
+            <div>
+              <h3>Assigned visual language for the 20 tasks.</h3>
+              <p>The overall generated atlas keeps the icon family visible, while each task card below uses its own crisp assigned SVG for reliable loading and public mirrors.</p>
             </div>
+            <img src="assets/task-icons/task-icon-atlas.png" alt="Generated 4 by 5 atlas of the 20 Ropedia Xperience-10M task icons" loading="lazy">
           </article>
+          <div class="task-toolbar" aria-label="Task filters">
+            <button class="filter active" data-filter="all">All tasks</button>
+            <button class="filter" data-filter="supervised">Supervised</button>
+            <button class="filter" data-filter="forecast">Forecast</button>
+            <button class="filter" data-filter="retrieval">Retrieval</button>
+            <button class="filter" data-filter="diagnostic">Diagnostic</button>
+          </div>
+          <div class="task-grid" id="taskGrid" aria-live="polite"></div>
         </div>
       </div>
     </section>
@@ -6764,30 +6854,6 @@
       </div>
     </section>
 
-    <section id="tasks" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Task cards and metrics.</h2>
-          <p>All 20 task contracts are shown together with readable research names, assigned task icons, compact modality chips, explicit input-process-output contracts, and verified public-sample Minimal versus Neural MLP primary metrics. The full 180-record method matrix is separate; these cards are the quick single-episode task-head read.</p>
-        </div>
-        <article class="task-icon-atlas">
-          <div>
-            <h3>Assigned visual language for the 20 tasks.</h3>
-            <p>The overall generated atlas keeps the icon family visible, while each task card below uses its own crisp assigned SVG for reliable loading and public mirrors.</p>
-          </div>
-          <img src="assets/task-icons/task-icon-atlas.png" alt="Generated 4 by 5 atlas of the 20 Ropedia Xperience-10M task icons" loading="lazy">
-        </article>
-        <div class="task-toolbar" aria-label="Task filters">
-          <button class="filter active" data-filter="all">All tasks</button>
-          <button class="filter" data-filter="supervised">Supervised</button>
-          <button class="filter" data-filter="forecast">Forecast</button>
-          <button class="filter" data-filter="retrieval">Retrieval</button>
-          <button class="filter" data-filter="diagnostic">Diagnostic</button>
-        </div>
-        <div class="task-grid" id="taskGrid" aria-live="polite"></div>
-      </div>
-    </section>
-
     <section id="features" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
@@ -7249,8 +7315,7 @@ python scripts/validate_publication_package.py</code></pre>
       ".direction-card",
       ".artifact",
       ".artifact-group",
-      ".glossary-summary article",
-      ".task-card"
+      ".glossary-summary article"
     ].join(",");
 
     function updatePageProgress() {
@@ -7530,9 +7595,9 @@ python scripts/validate_publication_package.py</code></pre>
       "reading-path": "Best for choosing an order through the repo, website, and HF surfaces.",
       "dataset-card": "Best for source alignment and public-sample boundaries.",
       "raw-sample": "Best for inspecting the sample files, media previews, and file relationships.",
-      suite: "Best for the unified 20-task contracts, radar, and score matrix.",
+      suite: "Best for the unified 20-task map, radar comparisons, task cards, and score matrix.",
       walkthroughs: "Best for case-study style task explanations.",
-      tasks: "Best for task-by-task input, output, and metric cards.",
+      tasks: "Best for the integrated task-card grid inside the 20-task suite.",
       pipeline: "Best for understanding how raw episode data becomes features and results.",
       protocol: "Best for splits, leakage controls, metrics, and evaluation rules.",
       architectures: "Best for how task heads and model tracks are organized.",
@@ -7550,6 +7615,7 @@ python scripts/validate_publication_package.py</code></pre>
       run: "Best for reproduction commands."
     };
     const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab]));
+    if (document.getElementById("tasks")) sectionTabMap.tasks = "data";
     const tabLabels = Object.fromEntries(
       tabButtons.map((button) => [button.dataset.tabKey, button.querySelector("strong")?.textContent?.trim() || button.dataset.tabKey])
     );

index.html

@@ -833,7 +833,6 @@
     #extensions { order: 13; }
     #architectures { order: 14; }
     #walkthroughs { order: 15; }
-    #tasks { order: 16; }
     #features { order: 17; }
     #diagnostics { order: 18; }
     #evidence { order: 19; }
@@ -844,6 +843,68 @@
     #suite { padding: 62px 0 76px; }
     #suite .wrap { width: min(1680px, calc(100% - 48px)); }
     #suite .section-head { max-width: var(--max); margin-inline: auto; }
+    .suite-jump-row {
+      max-width: var(--max);
+      margin: -8px auto 24px;
+      display: flex;
+      flex-wrap: wrap;
+      gap: 10px;
+    }
+    .suite-jump-row a {
+      display: inline-flex;
+      align-items: center;
+      min-height: 38px;
+      border: 1px solid rgba(204, 255, 160, 0.22);
+      border-radius: 999px;
+      padding: 0 14px;
+      color: var(--accent-2);
+      background: rgba(6, 14, 7, 0.74);
+      font-size: 12px;
+      font-weight: 800;
+      letter-spacing: 0.04em;
+      text-decoration: none;
+      text-transform: uppercase;
+    }
+    .suite-jump-row a:hover {
+      border-color: rgba(204, 255, 160, 0.72);
+      color: var(--ink);
+      background: rgba(204, 255, 160, 0.10);
+    }
+    .suite-radar-block {
+      scroll-margin-top: var(--tab-stack-offset);
+    }
+    .suite-task-cards-block {
+      scroll-margin-top: var(--tab-stack-offset);
+      margin-top: clamp(28px, 4vw, 48px);
+      border: 1px solid rgba(204, 255, 160, 0.22);
+      border-radius: var(--radius);
+      background:
+        radial-gradient(circle at top left, rgba(204, 255, 160, 0.10), transparent 34%),
+        linear-gradient(180deg, rgba(6, 14, 7, 0.86), rgba(2, 5, 2, 0.88));
+      padding: clamp(20px, 3vw, 32px);
+    }
+    .task-suite-subhead {
+      display: grid;
+      grid-template-columns: minmax(0, 0.9fr) minmax(280px, 0.62fr);
+      gap: 22px;
+      align-items: end;
+      margin-bottom: 20px;
+    }
+    .task-suite-subhead h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: clamp(26px, 3vw, 40px);
+      line-height: 1.04;
+      letter-spacing: 0;
+      text-wrap: balance;
+    }
+    .task-suite-subhead p {
+      margin: 0;
+      color: var(--muted);
+      font-size: 15px;
+      line-height: 1.6;
+      text-wrap: pretty;
+    }
     .section-head {
       display: flex;
       justify-content: space-between;
@@ -5287,7 +5348,7 @@
           <strong>Start</strong>
           <span>project overview and roadmap</span>
         </button>
-        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card suite walkthroughs tasks" tabindex="-1">
+        <button type="button" class="project-tab" id="tab-data" role="tab" data-tab-key="data" data-default-section="dataset-card" aria-selected="false" aria-pressed="false" aria-controls="dataset-card raw-sample suite walkthroughs extensions" tabindex="-1">
           <strong>Data & Tasks</strong>
           <span>dataset sample and task suite</span>
         </button>
@@ -6240,9 +6301,15 @@
     <section id="suite" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
-          <h2>Ropedia Xperience-10M Unified 20-Task Suite.</h2>
-          <p>The suite connects synchronized multimodal windows to 20 task contracts in one table, one radar surface, and one source-linked result matrix. Historical filenames remain only for stable artifact links.</p>
+          <h2>Ropedia Xperience-10M 20-task suite.</h2>
+          <p>Task map, radar comparisons, task cards, and the 180-result table are kept in one reading flow. Start with the map, inspect the score surfaces, then open each task card for its input, process, output, and metric.</p>
         </div>
+        <nav class="suite-jump-row" aria-label="20-task suite quick links">
+          <a href="#task-suite-map">Task map</a>
+          <a href="#suite-radars">Radars</a>
+          <a href="#tasks">Task cards</a>
+          <a href="#result-matrix-table">180-result table</a>
+        </nav>
         <div class="figure-pan" id="task-suite-map">
           <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v14-modality-compact" alt="Infographic showing Ropedia Xperience-10M task families with compact modality cards and visible thumbnails">
         </div>
@@ -6260,27 +6327,50 @@
             <p>The matrix has 180/180 scored method-task records: 174 direct scores and 6 compact-proxy scores. The audit records the source artifact, metric key, and proxy reason for each marked cell.</p>
           </article>
         </div>
-        <img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified grouped 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, score counts, and proxy notes">
-        <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
-          <article class="split-radar-card">
-            <h3>1-Episode 20-Task Radar</h3>
-            <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts in one enlarged panel without 128-episode methods competing for attention.</p>
-            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
-            <div class="split-radar-links">
-              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
-              <a href="data/single_episode_task_model_radar.json">Open chart data</a>
-            </div>
-          </article>
-          <article class="split-radar-card">
-            <h3>128-Episode 20-Task Radar</h3>
-            <p>Seven aligned 128-episode methods cover all 20 axes across metadata/text, raw-feature, and foundation-model panels. Proxy axes stay labeled in the chart and source data.</p>
-            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="128-episode grouped 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts">
-            <div class="split-radar-links">
-              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
-              <a href="data/episode128_task_model_radar.json">Open chart data</a>
-              <a href="data/task_method_20_gap_audit.json">Gap audit</a>
+        <div class="suite-radar-block" id="suite-radars">
+          <img class="chart radar-chart unified-radar-chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v8-readable" alt="Unified grouped 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, score counts, and proxy notes">
+          <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
+            <article class="split-radar-card">
+              <h3>1-Episode 20-Task Radar</h3>
+              <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts in one enlarged panel without 128-episode methods competing for attention.</p>
+              <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+              <div class="split-radar-links">
+                <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+                <a href="data/single_episode_task_model_radar.json">Open chart data</a>
+              </div>
+            </article>
+            <article class="split-radar-card">
+              <h3>128-Episode 20-Task Radar</h3>
+              <p>Seven aligned 128-episode methods cover all 20 axes across metadata/text, raw-feature, and foundation-model panels. Proxy axes stay labeled in the chart and source data.</p>
+              <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v3-readable" alt="128-episode grouped 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 series with explicit score counts">
+              <div class="split-radar-links">
+                <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+                <a href="data/episode128_task_model_radar.json">Open chart data</a>
+                <a href="data/task_method_20_gap_audit.json">Gap audit</a>
+              </div>
+            </article>
+          </div>
+        </div>
+        <div class="suite-task-cards-block" id="tasks">
+          <div class="task-suite-subhead">
+            <h3>All 20 task cards in the same suite.</h3>
+            <p>Each card uses its assigned icon and shows the task name, input sources, process, output target, metric, current Minimal score, and Neural MLP score. Use the filters for scanning; the cards stay tied to the task map and radar axes above.</p>
+          </div>
+          <article class="task-icon-atlas">
+            <div>
+              <h3>Assigned visual language for the 20 tasks.</h3>
+              <p>The overall generated atlas keeps the icon family visible, while each task card below uses its own crisp assigned SVG for reliable loading and public mirrors.</p>
             </div>
+            <img src="assets/task-icons/task-icon-atlas.png" alt="Generated 4 by 5 atlas of the 20 Ropedia Xperience-10M task icons" loading="lazy">
           </article>
+          <div class="task-toolbar" aria-label="Task filters">
+            <button class="filter active" data-filter="all">All tasks</button>
+            <button class="filter" data-filter="supervised">Supervised</button>
+            <button class="filter" data-filter="forecast">Forecast</button>
+            <button class="filter" data-filter="retrieval">Retrieval</button>
+            <button class="filter" data-filter="diagnostic">Diagnostic</button>
+          </div>
+          <div class="task-grid" id="taskGrid" aria-live="polite"></div>
         </div>
       </div>
     </section>
@@ -6764,30 +6854,6 @@
       </div>
     </section>
 
-    <section id="tasks" data-project-tab="data" role="tabpanel" aria-labelledby="tab-data" tabindex="-1">
-      <div class="wrap">
-        <div class="section-head">
-          <h2>Task cards and metrics.</h2>
-          <p>All 20 task contracts are shown together with readable research names, assigned task icons, compact modality chips, explicit input-process-output contracts, and verified public-sample Minimal versus Neural MLP primary metrics. The full 180-record method matrix is separate; these cards are the quick single-episode task-head read.</p>
-        </div>
-        <article class="task-icon-atlas">
-          <div>
-            <h3>Assigned visual language for the 20 tasks.</h3>
-            <p>The overall generated atlas keeps the icon family visible, while each task card below uses its own crisp assigned SVG for reliable loading and public mirrors.</p>
-          </div>
-          <img src="assets/task-icons/task-icon-atlas.png" alt="Generated 4 by 5 atlas of the 20 Ropedia Xperience-10M task icons" loading="lazy">
-        </article>
-        <div class="task-toolbar" aria-label="Task filters">
-          <button class="filter active" data-filter="all">All tasks</button>
-          <button class="filter" data-filter="supervised">Supervised</button>
-          <button class="filter" data-filter="forecast">Forecast</button>
-          <button class="filter" data-filter="retrieval">Retrieval</button>
-          <button class="filter" data-filter="diagnostic">Diagnostic</button>
-        </div>
-        <div class="task-grid" id="taskGrid" aria-live="polite"></div>
-      </div>
-    </section>
-
     <section id="features" data-project-tab="method" role="tabpanel" aria-labelledby="tab-method" tabindex="-1">
       <div class="wrap">
         <div class="section-head">
@@ -7249,8 +7315,7 @@ python scripts/validate_publication_package.py</code></pre>
       ".direction-card",
       ".artifact",
       ".artifact-group",
-      ".glossary-summary article",
-      ".task-card"
+      ".glossary-summary article"
     ].join(",");
 
     function updatePageProgress() {
@@ -7530,9 +7595,9 @@ python scripts/validate_publication_package.py</code></pre>
       "reading-path": "Best for choosing an order through the repo, website, and HF surfaces.",
       "dataset-card": "Best for source alignment and public-sample boundaries.",
       "raw-sample": "Best for inspecting the sample files, media previews, and file relationships.",
-      suite: "Best for the unified 20-task contracts, radar, and score matrix.",
+      suite: "Best for the unified 20-task map, radar comparisons, task cards, and score matrix.",
       walkthroughs: "Best for case-study style task explanations.",
-      tasks: "Best for task-by-task input, output, and metric cards.",
+      tasks: "Best for the integrated task-card grid inside the 20-task suite.",
       pipeline: "Best for understanding how raw episode data becomes features and results.",
       protocol: "Best for splits, leakage controls, metrics, and evaluation rules.",
       architectures: "Best for how task heads and model tracks are organized.",
@@ -7550,6 +7615,7 @@ python scripts/validate_publication_package.py</code></pre>
       run: "Best for reproduction commands."
     };
     const sectionTabMap = Object.fromEntries(tabSections.map((section) => [section.id, section.dataset.projectTab]));
+    if (document.getElementById("tasks")) sectionTabMap.tasks = "data";
     const tabLabels = Object.fromEntries(
       tabButtons.map((button) => [button.dataset.tabKey, button.querySelector("strong")?.textContent?.trim() || button.dataset.tabKey])
     );

metrics/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-22T17:03:53+00:00",
+  "generated_at_utc": "2026-06-22T17:35:20+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": "c00a89e8694e08a6bb844924da00ba78bcf6c5da96690d548d670bf0baa4fa9f"
+      "sha256": "fd964392bdc7397f24b463964226a10cadbd5c12459df067baf1c56782a829e4"
     },
     {
       "id": "source_alignment_validator",
@@ -1182,7 +1182,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8640,
-      "sha256": "2b4c81d8a9ab9ffc980d148dabcd5b09f1f6dba89e7b878214d1dbd122a573b4"
+      "sha256": "12334406b10e1fb6dd04434d25aaa617b2b4f6144752afa9a27a91f7273e3994"
     },
     {
       "id": "public_surface_qa",
@@ -1249,7 +1249,7 @@
       "volatile": true,
       "shows": "Confirms the public original-task cards use human-readable research names, representative modality thumbnails, and the interactive walkthrough/player JSON contract.",
       "exists": true,
-      "bytes": 46246,
+      "bytes": 46497,
       "hash_policy": "existence_and_size_only"
     },
     {

metrics/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T16:47:26+00:00",
+  "generated_at_utc": "2026-06-22T17:49:11+00:00",
   "hf_root": "hf_publish",
   "summary": {
     "group_count": 1306,
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 124477,
-        "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+        "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 124477,
-          "sha256": "051df495c20b5b3c7f6f573bb4528b7066ff2f4b17e228035abd2d1c84ee509e"
+          "sha256": "7d1e562631e816077efb8a4f4f3df8d9ea84803df8355df66365227a089e8e11"
         }
       },
       "failures": []
@@ -972,44 +972,44 @@
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
         "bytes": 10940,
-        "sha256": "6f0932d82892b3b8c1fa12a7d92f608d6aa9c3d28331c06a246c85a02ad355b5"
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metrics/public_surface_qa.json

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           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:01:55+00:00"
+          "generated_at_utc": "2026-06-22T17:38:14+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,12 +28,12 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T15:07:48+00:00"
+          "generated_at_utc": "2026-06-22T17:37:13+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T15:07:48+00:00"
+          "generated_at_utc": "2026-06-22T17:35:19+00:00"
         },
         "scale_up_status": {
           "exists": true,
@@ -43,12 +43,12 @@
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T17:02:31+00:00"
+          "generated_at_utc": "2026-06-22T17:40:04+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-22T16:47:26+00:00"
+          "generated_at_utc": "2026-06-22T17:05:46+00:00"
         }
       },
       "failures": {}
@@ -76,7 +76,7 @@
       "marker_counts": {
         "role=\"tablist\"": 3,
         "role=\"tab\"": 10,
-        "role=\"tabpanel\"": 28,
+        "role=\"tabpanel\"": 27,
         "aria-selected": 13,
         "aria-controls": 11,
         "moveProjectTabFocus": 2,
@@ -97,7 +97,7 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 22,
         "Xperience-10M": 170,
-        "20-task": 117,
+        "20-task": 120,
         "Qwen3-Omni": 233,
         "128-episode pilot": 1
       }

metrics/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:02:31+00:00",
+  "generated_at_utc": "2026-06-22T17:46:27+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -235,7 +235,7 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1556,
+      "file_count": 1557,
       "text_file_count": 1287,
       "largest_file": {
         "path": "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2/interaction_text_prediction/confusion_matrix.csv",

metrics/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:03:47+00:00",
+  "generated_at_utc": "2026-06-22T17:40:55+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": [
     {

metrics/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-22T15:07:48+00:00",
+  "generated_at_utc": "2026-06-22T17:35:19+00:00",
   "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": "2026-06-22T15:07:48+00:00",
+  "generated_at_utc": "2026-06-22T17:45:19+00:00",
   "summary": {
     "original_walkthrough_task_count": 12,
     "expected_original_walkthrough_task_count": 12,
@@ -1579,9 +1579,14 @@
       "marker": "class=\"task-card\""
     },
     {
-      "name": "website_marker_present:class=\"task-card-media\"",
+      "name": "website_marker_present:class=\"task-card-icon\"",
       "status": "pass",
-      "marker": "class=\"task-card-media\""
+      "marker": "class=\"task-card-icon\""
+    },
+    {
+      "name": "website_marker_present:class=\"task-modality-chips\"",
+      "status": "pass",
+      "marker": "class=\"task-modality-chips\""
     },
     {
       "name": "website_marker_present:class=\"story-button",
@@ -1625,7 +1630,7 @@
       "status": "pass"
     },
     {
-      "name": "task_cards_use_representative_modality_thumbnail",
+      "name": "task_cards_use_assigned_icons_and_modality_chips",
       "status": "pass"
     },
     {

metrics/website_integrity.json

@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-22T17:01:55+00:00",
+  "generated_at_utc": "2026-06-22T17:45:23+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 282,
+    "local_references": 286,
     "external_reference_count": 151,
     "json_files": 56,
     "image_assets_referenced": 49,
@@ -30,7 +30,7 @@
       "name": "project_sections_are_assigned_to_tabs",
       "status": "pass",
       "reason": "Every major research section should be assigned to a tab group.",
-      "section_count": 24
+      "section_count": 23
     },
     {
       "name": "project_hash_router_preserves_deep_links",
@@ -58,8 +58,8 @@
       "name": "project_sections_are_labeled_tabpanels",
       "status": "pass",
       "reason": "Every tabbed research section should expose a labeled panel role.",
-      "panel_count": 28,
-      "labeled_panel_count": 24
+      "panel_count": 27,
+      "labeled_panel_count": 23
     },
     {
       "name": "project_tabs_update_selected_state",
@@ -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": 156111,
-      "evidence_index": 203925
+      "overview_index": 157884,
+      "evidence_index": 205698
     },
     {
       "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": 156111,
-      "protocol_index": 200130,
-      "evidence_index": 203925
+      "overview_index": 157884,
+      "protocol_index": 201903,
+      "evidence_index": 205698
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -186,8 +186,8 @@
       "name": "suite_task_map_precedes_radar_surface",
       "status": "pass",
       "reason": "The Suite anchor should show the task-suite map before the radar/results surface.",
-      "first_marker_index": 468,
-      "second_marker_index": 2016
+      "first_marker_index": 760,
+      "second_marker_index": 2368
     },
     {
       "name": "raw_sample_stream_ledger_contains_seven_modalities",
@@ -290,8 +290,8 @@
     },
     {
       "path": "index.html",
-      "id_count": 102,
-      "reference_count": 254,
+      "id_count": 103,
+      "reference_count": 258,
       "image_count": 54
     },
     {
@@ -420,7 +420,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 10940,
+      "bytes": 10844,
       "top_level_type": "dict"
     },
     {
@@ -540,7 +540,7 @@
     },
     {
       "path": "data/task_surface_integrity.json",
-      "bytes": 46246,
+      "bytes": 46399,
       "top_level_type": "dict"
     },
     {

scripts/build_unified_task_model_radar.py

@@ -247,6 +247,8 @@ OUTPUT_MATRIX_MD = ROOT / "TASK_METHOD_20_RESULT_MATRIX.md"
 OUTPUT_SVG = ROOT / "docs/assets/charts/unified_task_model_radar.svg"
 OUTPUT_SINGLE_SVG = ROOT / "docs/assets/charts/single_episode_task_model_radar.svg"
 OUTPUT_128_SVG = ROOT / "docs/assets/charts/episode128_task_model_radar.svg"
+RADAR_SVG_WIDTH = 2600
+RADAR_SVG_HEIGHT = 2300
 
 
 SERIES = {
@@ -1260,14 +1262,14 @@ def draw_radar_grid(
                 stroke_width=1.0,
             )
         )
-        parts.append(svg_text(cx + 8, cy - ring_radius + 4, f"{value:.2g}", size=max(9, label_size - 2), fill="#a5afa2", weight=620, opacity=0.72))
+        parts.append(svg_text(cx + 10, cy - ring_radius + 5, f"{value:.2g}", size=max(12, label_size - 2), fill="#a5afa2", weight=680, opacity=0.78))
     for task, angle in zip(tasks, angles):
         x, y = point(cx, cy, radius, angle)
-        parts.append(f'<line x1="{cx:.1f}" y1="{cy:.1f}" x2="{x:.1f}" y2="{y:.1f}" stroke="#ccffa0" stroke-opacity="0.11" stroke-width="1"/>')
-        lx, ly = point(cx, cy, radius + 28, angle)
+        parts.append(f'<line x1="{cx:.1f}" y1="{cy:.1f}" x2="{x:.1f}" y2="{y:.1f}" stroke="#ccffa0" stroke-opacity="0.12" stroke-width="1.2"/>')
+        lx, ly = point(cx, cy, radius + 38, angle)
         proxy = task["task_id"] in PROXY_TASK_IDS
         color = "#f472b6" if proxy else "#ccffa0"
-        parts.append(f'<circle cx="{lx:.1f}" cy="{ly:.1f}" r="{label_size + 2:.1f}" fill="{color}" fill-opacity="0.12" stroke="{color}" stroke-opacity="0.34"/>')
+        parts.append(f'<circle cx="{lx:.1f}" cy="{ly:.1f}" r="{label_size + 5:.1f}" fill="{color}" fill-opacity="0.14" stroke="{color}" stroke-opacity="0.48" stroke-width="1.4"/>')
         parts.append(svg_text(lx, ly + label_size * 0.33, f"{task['task_number']:02d}", size=label_size, fill=color, anchor="middle", weight=850, opacity=0.98))
 
 
@@ -1326,9 +1328,9 @@ def draw_radar_series(
         px, py = point(cx, cy, plotted_radius, angle)
         proxy = value.get("status") == "proxy_scored"
         parts.append(
-            f'<circle cx="{px:.1f}" cy="{py:.1f}" r="{5.5 if proxy else 4.4:.1f}" '
+            f'<circle cx="{px:.1f}" cy="{py:.1f}" r="{7.2 if proxy else 5.8:.1f}" '
             f'fill="{spec["color"]}" fill-opacity="0.95" stroke="{"#f4f8ef" if proxy else "#020502"}" '
-            f'stroke-width="{2.1 if proxy else 1.3:.1f}"/>'
+            f'stroke-width="{2.4 if proxy else 1.7:.1f}"/>'
         )
 
 
@@ -1347,24 +1349,24 @@ def draw_radar_panel(
     tasks = payload["tasks"]
     angles = [-math.pi / 2 + 2 * math.pi * i / len(tasks) for i in range(len(tasks))]
     panel_bg = "#071007"
-    parts.append(f'<rect x="{x:.1f}" y="{y:.1f}" width="{width:.1f}" height="{height:.1f}" rx="18" fill="{panel_bg}" fill-opacity="0.90" stroke="#ccffa0" stroke-opacity="0.22"/>')
-    parts.append(svg_text(x + 28, y + 44, str(group["title"]), size=26 if large else 20, weight=850))
-    parts.append(svg_text(x + 28, y + 74, str(group["subtitle"]), size=14 if large else 12, fill="#a5afa2", weight=600))
+    parts.append(f'<rect x="{x:.1f}" y="{y:.1f}" width="{width:.1f}" height="{height:.1f}" rx="20" fill="{panel_bg}" fill-opacity="0.90" stroke="#ccffa0" stroke-opacity="0.22"/>')
+    parts.append(svg_text(x + 30, y + 50, str(group["title"]), size=32 if large else 24, weight=850))
+    parts.append(svg_text(x + 30, y + 84, str(group["subtitle"]), size=17 if large else 15, fill="#a5afa2", weight=620))
 
     if large:
-        cx = x + width * 0.39
+        cx = x + width * 0.38
         cy = y + height * 0.56
-        radius = min(width * 0.20, height * 0.34)
+        radius = min(width * 0.215, height * 0.36)
         legend_x = x + width * 0.68
-        legend_y = y + 160
-        label_size = 12
+        legend_y = y + 172
+        label_size = 17
     else:
         cx = x + width * 0.38
-        cy = y + height * 0.57
-        radius = min(width * 0.18, height * 0.30)
-        legend_x = x + width * 0.67
-        legend_y = y + 122
-        label_size = 8
+        cy = y + height * 0.58
+        radius = min(width * 0.205, height * 0.325)
+        legend_x = x + width * 0.60
+        legend_y = y + 146
+        label_size = 13
 
     draw_radar_grid(parts, cx=cx, cy=cy, radius=radius, tasks=tasks, angles=angles, label_size=label_size)
 
@@ -1379,72 +1381,72 @@ def draw_radar_panel(
             tasks=tasks,
             angles=angles,
             series_id=series_id,
-            stroke_width=4.3 if large else 3.2,
+            stroke_width=5.4 if large else 4.4,
             fill_opacity=max(0.026, fill_opacity - idx * 0.010),
         )
 
-    parts.append(svg_text(legend_x, legend_y - 34, "Methods", size=17 if large else 14, fill="#ccffa0", weight=850))
+    parts.append(svg_text(legend_x, legend_y - 38, "Methods", size=20 if large else 17, fill="#ccffa0", weight=850))
     for idx, series_id in enumerate(series_ids):
         record = series_record_by_id[series_id]
         color = record["color"]
-        row_y = legend_y + idx * (92 if large else 74)
-        parts.append(f'<line x1="{legend_x:.1f}" y1="{row_y:.1f}" x2="{legend_x + 58:.1f}" y2="{row_y:.1f}" stroke="{color}" stroke-width="{6 if large else 5}" stroke-linecap="round" stroke-dasharray="{record.get("stroke_dasharray") or ""}"/>')
-        parts.append(f'<circle cx="{legend_x + 29:.1f}" cy="{row_y:.1f}" r="{6 if large else 5}" fill="{color}" stroke="#020502" stroke-width="1.5"/>')
-        parts.append(svg_text(legend_x + 74, row_y + 5, record["label"], size=15 if large else 12, weight=850))
+        row_y = legend_y + idx * (114 if large else 96)
+        parts.append(f'<line x1="{legend_x:.1f}" y1="{row_y:.1f}" x2="{legend_x + 68:.1f}" y2="{row_y:.1f}" stroke="{color}" stroke-width="{7 if large else 6}" stroke-linecap="round" stroke-dasharray="{record.get("stroke_dasharray") or ""}"/>')
+        parts.append(f'<circle cx="{legend_x + 34:.1f}" cy="{row_y:.1f}" r="{7 if large else 6}" fill="{color}" stroke="#020502" stroke-width="1.8"/>')
+        parts.append(svg_text(legend_x + 86, row_y + 6, record["label"], size=18 if large else 15, weight=850))
         coverage = f"{record['scored_task_count']}/20 scored"
         proxy = record.get("proxy_scored_task_count", 0)
         if proxy:
             coverage += f" · {proxy} proxy"
-        parts.append(svg_text(legend_x + 74, row_y + (28 if large else 22), coverage, size=12 if large else 10, fill=color, weight=800))
-        detail = split_text(METHOD_DETAILS.get(series_id, record["scope"]), 50 if large else 44)[:2]
-        parts.extend(svg_text_lines(legend_x + 74, row_y + (49 if large else 40), detail, size=10 if large else 8, fill="#a5afa2", weight=560, line_height=13 if large else 10))
+        parts.append(svg_text(legend_x + 86, row_y + (34 if large else 28), coverage, size=14 if large else 12, fill=color, weight=800))
+        detail = split_text(METHOD_DETAILS.get(series_id, record["scope"]), 48 if large else 34)[:2]
+        parts.extend(svg_text_lines(legend_x + 86, row_y + (58 if large else 50), detail, size=12 if large else 11, fill="#a5afa2", weight=580, line_height=16 if large else 14))
 
-    parts.append(svg_text(x + 28, y + height - 30, "Radius = sqrt(normalized score); exact raw and normalized values are in the matrix.", size=11 if large else 9, fill="#a5afa2", weight=600, opacity=0.88))
+    parts.append(svg_text(x + 30, y + height - 32, "Radius = sqrt(normalized score); exact raw and normalized values are in the matrix.", size=13 if large else 12, fill="#a5afa2", weight=620, opacity=0.90))
 
 
 def draw_task_key(parts: list[str], *, x: float, y: float, width: float, tasks: list[dict[str, Any]], compact: bool = False) -> None:
-    height = 292 if not compact else 250
+    height = 356 if not compact else 320
     parts.append(f'<rect x="{x:.1f}" y="{y:.1f}" width="{width:.1f}" height="{height:.1f}" rx="16" fill="#020502" fill-opacity="0.62" stroke="#ccffa0" stroke-opacity="0.18"/>')
-    parts.append(svg_text(x + 28, y + 42, "20-task axis key", size=20, weight=850))
-    parts.append(svg_text(x + 250, y + 42, "Task numbers stay on the radar; full names and proxy axes stay here.", size=13, fill="#a5afa2", weight=600))
+    parts.append(svg_text(x + 30, y + 48, "20-task axis key", size=24, weight=850))
+    parts.append(svg_text(x + 308, y + 48, "Task numbers stay on the radar; full names and proxy axes stay here.", size=16, fill="#a5afa2", weight=620))
     col_count = 4
-    col_w = (width - 56) / col_count
-    row_h = 42 if not compact else 36
+    col_w = (width - 60) / col_count
+    row_h = 52 if not compact else 46
     for idx, task in enumerate(tasks):
         col = idx // 5
         row = idx % 5
-        x0 = x + 28 + col * col_w
-        y0 = y + 84 + row * row_h
+        x0 = x + 30 + col * col_w
+        y0 = y + 96 + row * row_h
         proxy = task["task_id"] in PROXY_TASK_IDS
         color = "#f472b6" if proxy else "#ccffa0"
-        parts.append(f'<rect x="{x0:.1f}" y="{y0 - 17:.1f}" width="35" height="25" rx="6" fill="{color}" fill-opacity="0.13" stroke="{color}" stroke-opacity="0.40"/>')
-        parts.append(svg_text(x0 + 17.5, y0 + 1, f"{task['task_number']:02d}", size=10, fill=color, anchor="middle", weight=850))
+        parts.append(f'<rect x="{x0:.1f}" y="{y0 - 21:.1f}" width="43" height="31" rx="8" fill="{color}" fill-opacity="0.13" stroke="{color}" stroke-opacity="0.44" stroke-width="1.2"/>')
+        parts.append(svg_text(x0 + 21.5, y0 + 2, f"{task['task_number']:02d}", size=13, fill=color, anchor="middle", weight=850))
         task_name = str(task["label"])
-        if len(task_name) > 34:
-            task_name = task_name[:31].rstrip() + "..."
-        parts.append(svg_text(x0 + 46, y0 - 2, task_name, size=11 if not compact else 10, fill="#f4f8ef", weight=800))
+        if len(task_name) > 42:
+            task_name = task_name[:39].rstrip() + "..."
+        parts.append(svg_text(x0 + 56, y0 - 4, task_name, size=14 if not compact else 13, fill="#f4f8ef", weight=820))
         metric = str(task.get("metric_name") or task.get("metric_key") or "")
         direction = "lower" if task.get("metric_direction") == "lower" else "higher"
         metric_text = f"{metric}; {direction} better"
         if proxy:
             metric_text += "; proxy axis"
-        if len(metric_text) > 43:
-            metric_text = metric_text[:40].rstrip() + "..."
-        parts.append(svg_text(x0 + 46, y0 + 16, metric_text, size=9, fill="#a5afa2", weight=560))
+        if len(metric_text) > 50:
+            metric_text = metric_text[:47].rstrip() + "..."
+        parts.append(svg_text(x0 + 56, y0 + 18, metric_text, size=11, fill="#a5afa2", weight=580))
 
 
 def draw_reading_rules(parts: list[str], *, y: float, reading_rules: tuple[str, str, str] | None) -> None:
     if reading_rules is None:
         reading_rules = (
-            "Use the panels for shape and coverage; use docs/data/task_method_20_result_matrix.json for exact ranks, raw values, direct/proxy flags, and sources.",
+            "Use the panels for shape and coverage; use the companion result matrix for exact ranks, raw values, direct/proxy flags, and sources.",
             "The old nine-method overlay was replaced by grouped small multiples so each radar compares only related methods.",
-            "SVG radius uses sqrt(normalized_score) for readable area; JSON normalized_score remains linear and unchanged.",
+            "SVG radius uses sqrt(normalized score) for readable area; the stored normalized score remains linear and unchanged.",
         )
-    parts.append(f'<rect x="70" y="{y:.1f}" width="2260" height="118" rx="14" fill="#020502" fill-opacity="0.62" stroke="#ccffa0" stroke-opacity="0.16"/>')
-    parts.append(svg_text(100, y + 33, "Reading rules", size=16, fill="#ccffa0", weight=850))
-    parts.append(svg_text(230, y + 33, reading_rules[0], size=13, fill="#dce8d7", weight=650))
-    parts.append(svg_text(230, y + 61, reading_rules[1], size=12, fill="#a5afa2", weight=560))
-    parts.append(svg_text(230, y + 87, reading_rules[2], size=12, fill="#a5afa2", weight=560))
+    parts.append(f'<rect x="70" y="{y:.1f}" width="2460" height="166" rx="16" fill="#020502" fill-opacity="0.62" stroke="#ccffa0" stroke-opacity="0.16"/>')
+    parts.append(svg_text(102, y + 42, "Reading rules", size=20, fill="#ccffa0", weight=850))
+    parts.append(svg_text(288, y + 42, reading_rules[0], size=15, fill="#dce8d7", weight=680))
+    parts.append(svg_text(288, y + 78, reading_rules[1], size=14, fill="#a5afa2", weight=580))
+    parts.append(svg_text(288, y + 112, reading_rules[2], size=14, fill="#a5afa2", weight=580))
 
 
 def render_svg(
@@ -1459,7 +1461,7 @@ def render_svg(
     reading_rules: tuple[str, str, str] | None = None,
 ) -> str:
     del polygon_series_ids
-    width, height = 2400, 1900
+    width, height = RADAR_SVG_WIDTH, RADAR_SVG_HEIGHT
     tasks = payload["tasks"]
     if series_ids is None:
         series_ids = tuple(record["id"] for record in payload["series"])
@@ -1473,22 +1475,22 @@ def render_svg(
         "</defs>",
         '<rect width="100%" height="100%" fill="#020502"/>',
         '<rect width="100%" height="100%" fill="url(#dots)" opacity="0.40"/>',
-        '<rect x="28" y="28" width="2344" height="1844" rx="22" fill="#061006" fill-opacity="0.90" stroke="#ccffa0" stroke-opacity="0.22"/>',
-        svg_text(70, 86, title or payload.get("title", "20-Task Model Radar"), size=36, weight=850),
+        '<rect x="28" y="28" width="2544" height="2244" rx="24" fill="#061006" fill-opacity="0.90" stroke="#ccffa0" stroke-opacity="0.22"/>',
+        svg_text(70, 92, title or payload.get("title", "20-Task Model Radar"), size=46, weight=850),
         svg_text(
             70,
-            122,
+            136,
             subtitle or "Grouped small-multiple radars for the nine-method, 180-result comparison.",
-            size=18,
+            size=22,
             fill="#dce8d7",
             weight=650,
         ),
         svg_text(
             70,
-            150,
+            170,
             context_line
             or "Related methods are compared in separate panels to avoid the unreadable nine-polygon overlay.",
-            size=15,
+            size=17,
             fill="#a5afa2",
             weight=560,
         ),
@@ -1504,28 +1506,28 @@ def render_svg(
         ]
     chip_x = 70
     for label, color in chip_specs:
-        chip_w = max(128, min(280, 18 + len(label) * 8.3))
-        parts.append(f'<rect x="{chip_x:.1f}" y="174" width="{chip_w:.1f}" height="34" rx="17" fill="{color}" fill-opacity="0.10" stroke="{color}" stroke-opacity="0.38"/>')
-        parts.append(svg_text(chip_x + 16, 197, label, size=13, fill=color, weight=780))
+        chip_w = max(152, min(340, 24 + len(label) * 9.4))
+        parts.append(f'<rect x="{chip_x:.1f}" y="198" width="{chip_w:.1f}" height="42" rx="21" fill="{color}" fill-opacity="0.10" stroke="{color}" stroke-opacity="0.38"/>')
+        parts.append(svg_text(chip_x + 18, 226, label, size=15, fill=color, weight=780))
         chip_x += chip_w + 12
 
     if len(groups) == 1:
         draw_radar_panel(
             parts,
             x=70,
-            y=242,
-            width=2260,
-            height=1040,
+            y=278,
+            width=2460,
+            height=1165,
             group=groups[0],
             payload=payload,
             series_record_by_id=series_record_by_id,
             large=True,
         )
-        key_y = 1322
+        key_y = 1490
     elif len(groups) == 3:
-        panel_w, panel_h = 1100, 545
-        start_x, start_y = 70, 248
-        gap_x, gap_y = 30, 34
+        panel_w, panel_h = 1198, 625
+        start_x, start_y = 70, 278
+        gap_x, gap_y = 34, 42
         for idx, group in enumerate(groups[:2]):
             draw_radar_panel(
                 parts,
@@ -1548,11 +1550,11 @@ def render_svg(
             series_record_by_id=series_record_by_id,
             large=True,
         )
-        key_y = 1438
+        key_y = 1616
     else:
-        panel_w, panel_h = 1100, 545
-        start_x, start_y = 70, 248
-        gap_x, gap_y = 30, 34
+        panel_w, panel_h = 1198, 625
+        start_x, start_y = 70, 278
+        gap_x, gap_y = 34, 42
         for idx, group in enumerate(groups):
             col = idx % 2
             row = idx // 2
@@ -1566,10 +1568,10 @@ def render_svg(
                 payload=payload,
                 series_record_by_id=series_record_by_id,
             )
-        key_y = 1438
+        key_y = 1616
 
-    draw_task_key(parts, x=70, y=key_y, width=2260, tasks=tasks, compact=len(groups) == 1)
-    draw_reading_rules(parts, y=1750 if len(groups) > 1 else 1632, reading_rules=reading_rules)
+    draw_task_key(parts, x=70, y=key_y, width=2460, tasks=tasks, compact=len(groups) == 1)
+    draw_reading_rules(parts, y=2030 if len(groups) > 1 else 1850, reading_rules=reading_rules)
     parts.append("</svg>")
     return "\n".join(parts) + "\n"
 
@@ -1629,7 +1631,7 @@ def main() -> int:
             reading_rules=(
                 "Both single-episode methods have numeric scores on every one of the 20 task contracts.",
                 "This radar is the cleanest view of public-sample Minimal vs Neural MLP behavior before any 128-episode scale-up.",
-                "Raw metric values and sources remain in docs/data/single_episode_task_model_radar.json and docs/data/task_method_20_result_matrix.json.",
+                "Raw metric values and evidence sources remain in the companion radar data and 180-result matrix.",
             ),
         ),
         encoding="utf-8",
@@ -1652,7 +1654,7 @@ def main() -> int:
             reading_rules=(
                 "Every 128-episode method has 20 result records and all 140 rows are scored in this split radar.",
                 "Raw128 Simple and Raw128 NN are complete 20/20 scored multi-episode baselines; tasks 15/19 are documented compact proxies and are marked in the task key.",
-                "Qwen3-Omni and Cosmos3 rows use verified held-out outputs or derived probe artifacts; source paths stay in the matrix JSON.",
+                "Qwen3-Omni and Cosmos3 rows use verified held-out outputs or derived probe artifacts; evidence sources stay in the matrix data.",
             ),
         ),
         encoding="utf-8",

scripts/validate_task_surface.py

@@ -318,7 +318,8 @@ def validate_website(source: str, failures: list[dict[str, Any]]) -> list[dict[s
         'id="playerScrub"',
         'fetch("data/task_walkthroughs.json"',
         'class="task-card"',
-        'class="task-card-media"',
+        'class="task-card-icon"',
+        'class="task-modality-chips"',
         'class="story-button',
         'class="flow-step',
         'id="playerPlay"',
@@ -362,8 +363,11 @@ def validate_website(source: str, failures: list[dict[str, Any]]) -> list[dict[s
     )
     checks.append(
         check(
-            "task.poster_modality" in task_card_renderer and "task-card-media" in task_card_renderer,
-            "task_cards_use_representative_modality_thumbnail",
+            "taskIconFor(task)" in task_card_renderer
+            and "task-card-icon" in task_card_renderer
+            and "task-modality-chips" in task_card_renderer
+            and "task.modalities" in task_card_renderer,
+            "task_cards_use_assigned_icons_and_modality_chips",
             failures,
         )
     )