Add files using upload-large-folder tool

FIGURE_INDEX.md

@@ -31,6 +31,7 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | Research direction coverage chart | `docs/assets/charts/research_direction_coverage.svg` | 1180 x 700 | `scripts/generate_visualizations.py` | Four-track coverage map for Ropedia research directions. |
 | Research direction extension chart | `docs/assets/charts/research_direction_extension_tasks.svg` | 1420 x 920 | `scripts/generate_visualizations.py` | Four coded extension probes, one per Ropedia research direction. |
 | Tasks 13-20 baseline chart | `docs/assets/charts/tier2_task_suite.svg` | 1440 x 832 | `scripts/tier2_task_suite.py` | Eight additional sample-supported tasks in the unified 20-task suite with aligned minimal and neural baseline metrics. |
+| Unified 20-task model radar | `docs/assets/charts/unified_task_model_radar.svg` | 1720 x 1220 | `scripts/build_unified_task_model_radar.py` | Twenty-axis direction-aware comparison of minimal and neural MLP baselines, with Qwen3/Cosmos task-aligned overlay points and branch notes. |
 | Feature block chart | `docs/assets/charts/feature_blocks.svg` | 1100 x 760 | `scripts/generate_visualizations.py` | Feature allocation by modality block. |
 | Minimal task score chart | `docs/assets/charts/episode_task_scores.svg` | 1100 x 556 | `scripts/generate_visualizations.py` | Minimal baseline metric snapshot across the task suite. |
 | Cross-modal retrieval chart | `docs/assets/charts/cross_modal_retrieval.svg` | 1100 x 284 | `scripts/generate_visualizations.py` | Retrieval behavior chart for the cross-modal task. |

PROJECT_README.md

@@ -312,6 +312,15 @@ and [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json). Tasks 13-20
 also have a compact chart and result bundle under the historical
 `tier2_task_suite` path for stable public links.
 
+![Unified 20-task model radar](docs/assets/charts/unified_task_model_radar.svg)
+
+The unified radar compares all 20 task axes with two filled colors for the
+minimal and neural MLP baselines. Qwen3-Omni and Cosmos3 overlays are plotted
+only where their verified 128-episode public metrics map to the same task
+semantics; Cosmos3-Super forward-dynamics LoRA remains a branch card because
+its camera-pose proxy MSE is not one of the 20 task metrics. The machine-readable
+copy is [`docs/data/unified_task_model_radar.json`](docs/data/unified_task_model_radar.json).
+
 The website also includes a responsive native modality atlas backed by
 [`docs/data/modality_atlas.json`](docs/data/modality_atlas.json) and
 [`docs/assets/modalities/`](docs/assets/modalities/). Those assets are small
@@ -346,6 +355,7 @@ scripts/
   research_direction_extension_tasks.py # one extra data-backed probe per track
   tier2_task_suite.py              # historical-name builder for tasks 13-20
   build_unified_task_suite.py       # builds TASK_SUITE_20.md and task_suite_20.json
+  build_unified_task_model_radar.py # builds the unified 20-axis model comparison chart
   task_walkthroughs.py              # human-readable task-card and walkthrough-storyboard metadata
   generate_visualizations.py        # refreshes SVG charts + summary JSON
   render_task_suite_infographic.py  # renders the task-suite presentation PNG
@@ -385,6 +395,7 @@ docs/
   data/additional_development_directions.json # concrete non-backbone project directions
   data/summary_metrics.json         # website-readable metrics bundle
   data/task_suite_20.json           # unified 20-task suite bundle
+  data/unified_task_model_radar.json # 20-task radar values and model-branch overlays
   data/evidence_contract.json       # machine-readable project scope
   data/artifact_index.json          # compact project-artifact catalog
   data/live_publication_status.json # live GitHub/HF publication verification
@@ -405,6 +416,7 @@ docs/
   assets/pipeline_diagram.png       # verified episode pipeline graphic
   assets/qwen3_omni_lora_pipeline.png # Qwen3-Omni LoRA training-flow figure
   assets/task_architectures.png     # verified task-head architecture map
+  assets/charts/unified_task_model_radar.svg # 20-task minimal/NN/Qwen/Cosmos radar
   assets/charts/*.svg               # regenerated visualizations
 
 notes/
@@ -963,11 +975,15 @@ stable artifact links. They should be read as the result bundle for tasks
 
 - [`TASK_SUITE_20.md`](TASK_SUITE_20.md)
 - [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json)
+- [`docs/data/unified_task_model_radar.json`](docs/data/unified_task_model_radar.json)
 - [`TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md)
 - [`tier2_task_suite_results.json`](results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json)
 - [`docs/data/tier2_task_suite.json`](docs/data/tier2_task_suite.json)
+- [`unified_task_model_radar.svg`](docs/assets/charts/unified_task_model_radar.svg)
 - [`tier2_task_suite.svg`](docs/assets/charts/tier2_task_suite.svg)
 
+![Unified 20-task model radar](docs/assets/charts/unified_task_model_radar.svg)
+
 ![Tasks 13-20 baseline chart](docs/assets/charts/tier2_task_suite.svg)
 
 | # | Task | Input | Output | Minimal | Neural MLP | Meaning |

README.md

@@ -60,7 +60,9 @@ The public-sample task surface is now one unified 20-task suite in
 original sample tasks; Tasks 13-20 reuse the same 20-frame windows, 5-frame
 stride, feature manifest, chronological split, and minimal/neural head pattern.
 The historical `tier2_task_suite` path is retained only for stable artifact
-links to tasks 13-20.
+links to tasks 13-20. The unified radar chart is published as
+`docs/assets/charts/unified_task_model_radar.svg` with values in
+`docs/data/unified_task_model_radar.json`.
 
 ## Dataset Boundary
 

REPRODUCIBILITY.md

@@ -77,6 +77,7 @@ python scripts/research_direction_taxonomy.py
 python scripts/research_direction_extension_tasks.py
 python scripts/tier2_task_suite.py
 python scripts/build_unified_task_suite.py
+python scripts/build_unified_task_model_radar.py
 python scripts/task_walkthroughs.py
 python scripts/validate_source_alignment.py
 python scripts/build_evaluation_protocol.py
@@ -159,6 +160,7 @@ Verified staged-GPU smoke evidence from 2026-06-14:
 | --- | --- |
 | Minimal baselines | `results/min_action_model/`, `results/min_all_modalities_action_model/`, metrics and model weights |
 | Unified 20-task suite | `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`, `results/episode_task_suite/summary_report.json`, per-task `metrics.json`, predictions, confusion matrices, and the tasks 13-20 historical `tier2_task_suite` result bundle |
+| Unified 20-task model radar | `docs/data/unified_task_model_radar.json`, `docs/assets/charts/unified_task_model_radar.svg` |
 | Neural heads | `results/episode_task_suite/neural_mlp/**/metrics.json`, histories, model checkpoints |
 | Research directions | `results/episode_task_suite/research_directions/`, `docs/data/research_directions.json` |
 | Direction probes | `results/episode_task_suite/research_direction_extensions/`, `docs/data/research_direction_extensions.json` |

data/artifact_index.json

@@ -1,8 +1,8 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-16T04:56:20+00:00",
+  "generated_at_utc": "2026-06-16T05:26:50+00:00",
   "status": "pass",
-  "artifact_count": 172,
+  "artifact_count": 175,
   "missing": [],
   "by_kind": {
     "project_path": 14,
@@ -13,7 +13,9 @@
     "project_scope": 1,
     "source_alignment": 5,
     "evaluation_protocol": 6,
-    "website_data": 5,
+    "website_data": 6,
+    "generated_figure": 5,
+    "visualization_builder": 1,
     "result_interpretation": 5,
     "metrics_source": 27,
     "visual_evidence": 7,
@@ -27,7 +29,6 @@
     "data_contract": 3,
     "result_directory": 1,
     "taxonomy": 1,
-    "generated_figure": 4,
     "onboarding_doc": 1,
     "generated_figure_assets": 1,
     "citation": 1,
@@ -463,7 +464,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "c5401a313bcc68152e590cf06fa7c5219d42617566dff9cf82cc80905a4c288e"
+      "sha256": "80aa58d6d2025ba77465889df31081662baa131296e5f39d7e212b9539625e72"
     },
     {
       "id": "source_alignment_validator",
@@ -575,6 +576,39 @@
       "bytes": 12322,
       "sha256": "ad2ae2f918fb8362e47d271ae8f1c1f3807b009a7241fa7df94f84fbddccffe8"
     },
+    {
+      "id": "unified_task_model_radar_json",
+      "title": "Unified 20-task model radar JSON",
+      "path": "docs/data/unified_task_model_radar.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, and branch-card caveats.",
+      "exists": true,
+      "bytes": 31157,
+      "sha256": "f4d96879daf1fc7566b9f85e7ce26b46dc36d97e40a16b56348e591cdfe701ae"
+    },
+    {
+      "id": "unified_task_model_radar_chart",
+      "title": "Unified 20-task model radar",
+      "path": "docs/assets/charts/unified_task_model_radar.svg",
+      "kind": "generated_figure",
+      "surface": "website_hf",
+      "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
+      "exists": true,
+      "bytes": 26681,
+      "sha256": "418ab49da39a034a5aa6d465b1e8cb9b733d6eabfe98adb4e4df82f519b85e9d"
+    },
+    {
+      "id": "unified_task_model_radar_builder",
+      "title": "Unified 20-task model radar builder",
+      "path": "scripts/build_unified_task_model_radar.py",
+      "kind": "visualization_builder",
+      "surface": "repo_hf",
+      "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
+      "exists": true,
+      "bytes": 20521,
+      "sha256": "3dee4cc45daf62a8217123c31b30c5fbb8910d34ad82fb1da98f0ae10344b020"
+    },
     {
       "id": "research_takeaways",
       "title": "Research takeaways",
@@ -683,7 +717,7 @@
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
       "bytes": 14939,
-      "sha256": "fdf8d2d06d580908cc7fe70974cbd28c7490180cb314f5d532ba55985edcea6d"
+      "sha256": "07ad9e56fd0102800ca66fbe4a4d134088aff664d02a8583f99a692b98d78724"
     },
     {
       "id": "figure_index_builder",
@@ -693,8 +727,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 13934,
-      "sha256": "a10e699097c86b645ecb03e42ac4fc2409e10170431a5bccbc38abad403ef209"
+      "bytes": 14404,
+      "sha256": "c7d8c538cc3ae23ee90d2da41213c92c2bcd271bca3266e8f45847aebe439e44"
     },
     {
       "id": "brand_assets_json",
@@ -793,8 +827,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the public presentation report before release.",
       "exists": true,
-      "bytes": 12235,
-      "sha256": "fc5742789d882ceca8b7ce8a21ed401c2731fa22036c406847167199a724f09d"
+      "bytes": 12335,
+      "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
     },
     {
       "id": "task_surface_integrity",
@@ -863,7 +897,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 123218,
+      "bytes": 125973,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -874,8 +908,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 49719,
-      "sha256": "db6769f5ed43301f123950e0e2d3d1e8564115dbc6b43ea7a9080da4c9d4c3aa"
+      "bytes": 51736,
+      "sha256": "9e5a9507bf6a8b1de06b2b1555c96411a6f420df3152c73ae65ce4e4f982aa59"
     },
     {
       "id": "reproducibility_contract",
@@ -885,8 +919,8 @@
       "surface": "repo_hf",
       "shows": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
       "exists": true,
-      "bytes": 9878,
-      "sha256": "3d0525785f9a1eaad399aeed249fc899d0b141efd778d0d2f3f229dd9b6594a3"
+      "bytes": 10054,
+      "sha256": "d12c020fd00c6a7b907300c9c5f20d613a0f033e32f7a62cbed3f8dfbbe95216"
     },
     {
       "id": "reproducibility_matrix",
@@ -896,8 +930,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable reproduction steps with expected artifacts and public boundaries.",
       "exists": true,
-      "bytes": 6673,
-      "sha256": "585c4e7c67b0bfca80628267b71c7fd5f0ac0c534bdc964c6509d8c53a0e0e83"
+      "bytes": 6815,
+      "sha256": "ff44893cac56c229d6eb5d20d8cb261ea38e0358e6444615406affd692d8d98e"
     },
     {
       "id": "artifact_index_builder",
@@ -907,8 +941,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 46648,
-      "sha256": "86feb8e47b9766762cd683c94bac88a195e36c877e0a470604ff708da738fd00"
+      "bytes": 47751,
+      "sha256": "0fe1eefff336f5fc6b12e7e49668973e45a12b7354e91a513dda90e2a5c9f582"
     },
     {
       "id": "publication_audit",

data/figure_index.json

@@ -1,9 +1,9 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:21+00:00",
+  "generated_at_utc": "2026-06-16T05:26:50+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
-  "figure_count": 23,
+  "figure_count": 24,
   "figures": [
     {
       "id": "brand_logo_mark",
@@ -351,6 +351,24 @@
       },
       "source_script_exists": true
     },
+    {
+      "id": "unified_task_model_radar",
+      "title": "Unified 20-task model radar",
+      "path": "docs/assets/charts/unified_task_model_radar.svg",
+      "role": "Twenty-axis direction-aware comparison of minimal and neural MLP baselines, with Qwen3/Cosmos task-aligned overlay points and branch notes.",
+      "source_script": "scripts/build_unified_task_model_radar.py",
+      "surface": "website unified task section, README, HF mirrors",
+      "exists": true,
+      "bytes": 26681,
+      "sha256": "418ab49da39a034a5aa6d465b1e8cb9b733d6eabfe98adb4e4df82f519b85e9d",
+      "dimensions": {
+        "format": "SVG",
+        "width": 1720,
+        "height": 1220,
+        "view_box": "0 0 1720 1220"
+      },
+      "source_script_exists": true
+    },
     {
       "id": "feature_blocks_chart",
       "title": "Feature block chart",

data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T05:07:00+00:00",
+  "generated_at_utc": "2026-06-16T05:27:08+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 426,
+    "group_count": 429,
     "failure_count": 0,
     "failures_by_surface": {}
   },
@@ -138,45 +138,45 @@
       "local": {
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-        "bytes": 93924,
-        "sha256": "5268802976cd77456b38ebfc81f889b863ac96f0a4176bcddb2e6a0b7f1d532d"
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-          "bytes": 93924,
-          "sha256": "5268802976cd77456b38ebfc81f889b863ac96f0a4176bcddb2e6a0b7f1d532d"
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-          "bytes": 93924,
-          "sha256": "5268802976cd77456b38ebfc81f889b863ac96f0a4176bcddb2e6a0b7f1d532d"
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           "path": "hf_model:docs/data/artifact_index.json",
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-          "bytes": 93924,
-          "sha256": "5268802976cd77456b38ebfc81f889b863ac96f0a4176bcddb2e6a0b7f1d532d"
+          "bytes": 95405,
+          "sha256": "a80ceb51913c429b0e2a87bfea9a30f861a030b77a55550fccb0748291b40eb3"
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           "path": "hf_model:metrics/artifact_index.json",
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-          "bytes": 93924,
-          "sha256": "5268802976cd77456b38ebfc81f889b863ac96f0a4176bcddb2e6a0b7f1d532d"
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@@ -334,45 +334,45 @@
       "local": {
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-        "bytes": 14939,
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-          "sha256": "07ad9e56fd0102800ca66fbe4a4d134088aff664d02a8583f99a692b98d78724"
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-          "bytes": 14939,
-          "sha256": "07ad9e56fd0102800ca66fbe4a4d134088aff664d02a8583f99a692b98d78724"
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+          "sha256": "5257f8f07f2c5913ce629f834dc70b81745d45e1c4978bfeea57fdea135ca882"
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-          "bytes": 14939,
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@@ -432,45 +432,45 @@
       "local": {
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         "exists": true,
-        "bytes": 126966,
-        "sha256": "f712c255c05c5ebb40c34bc2c12812854470f4c513308f8790bf655dfa6e6ed1"
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+        "sha256": "2b66f6fa9c789eaffa3ad43052e8656ad4ec7ef941cbac4c50b90a8ba9e4ef91"
       },
       "mirrors": {
         "hf_space": {
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@@ -3669,6 +3755,31 @@
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@@ -13737,27 +13848,27 @@
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data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:20+00:00",
+  "generated_at_utc": "2026-06-16T05:27:29+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
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@@ -18,7 +18,7 @@
         "website_integrity": {
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           "status": "pass",
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+          "generated_at_utc": "2026-06-16T05:27:06+00:00"
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@@ -28,27 +28,27 @@
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-          "generated_at_utc": "2026-06-16T04:49:21+00:00"
+          "generated_at_utc": "2026-06-16T05:27:19+00:00"
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         "source_alignment": {
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           "status": "pass",
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+          "generated_at_utc": "2026-06-16T05:27:19+00:00"
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+          "generated_at_utc": "2026-06-16T05:27:20+00:00"
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+          "generated_at_utc": "2026-06-16T05:27:08+00:00"
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@@ -97,8 +97,8 @@
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-        "Qwen3-Omni": 143,
+        "20-task": 35,
+        "Qwen3-Omni": 146,
         "128-episode pilot": 1
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@@ -130,6 +130,8 @@
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         "data/task_suite_20.json": 42,
+        "data/unified_task_model_radar.json": 16,
+        "assets/charts/unified_task_model_radar.svg": 15,
         "data/tier2_task_suite.json": 11
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     },

data/publication_audit.json

@@ -1,6 +1,6 @@
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+  "generated_at_utc": "2026-06-16T05:27:13+00:00",
   "checks": [
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@@ -98,6 +98,7 @@
     "docs/data/website_integrity.json": true,
     "docs/data/summary_metrics.json": true,
     "docs/data/task_suite_20.json": true,
+    "docs/data/unified_task_model_radar.json": true,
     "docs/data/task_suite_enhancement_128.json": true,
     "docs/assets/modalities/video.jpg": true,
     "docs/assets/modalities/audio.png": true,
@@ -114,6 +115,7 @@
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     "docs/assets/brand/xperience10m-logo-social-card.png": true,
     "docs/assets/task_suite_infographic.png": true,
+    "docs/assets/charts/unified_task_model_radar.svg": true,
     "docs/assets/pipeline_diagram.png": true,
     "docs/assets/task_architectures.png": true,
     "results/episode_task_suite/summary_report.json": true,
@@ -127,6 +129,7 @@
     "scripts/build_brand_assets.py": true,
     "scripts/build_evaluation_protocol.py": true,
     "scripts/build_unified_task_suite.py": true,
+    "scripts/build_unified_task_model_radar.py": true,
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@@ -190,8 +193,8 @@
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-      "text_file_count": 793,
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@@ -201,8 +204,8 @@
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@@ -212,8 +215,8 @@
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@@ -223,8 +226,8 @@
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data/quality_gates.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:20+00:00",
+  "generated_at_utc": "2026-06-16T05:27:29+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
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data/reproducibility_matrix.json

@@ -52,8 +52,8 @@
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       "status": "reproducible",
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+      "command": "python scripts/tier2_task_suite.py && python scripts/build_unified_task_suite.py && python scripts/build_unified_task_model_radar.py",
+      "expected": "tasks 13-20 metrics, prediction/rank artifacts, TASK_SUITE_20.md, docs/data/task_suite_20.json, docs/data/tier2_task_suite.json, docs/assets/charts/tier2_task_suite.svg, docs/data/unified_task_model_radar.json, and docs/assets/charts/unified_task_model_radar.svg",
       "boundary": "requires local public-sample annotation.hdf5 plus HOMIE Toolkit or h5py for tasks 13-20; raw HDF5 and MP4 files are not redistributed"
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data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-16T04:56:59+00:00",
+  "generated_at_utc": "2026-06-16T05:27:20+00:00",
   "summary": {
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     "dataset_manifest_num_episodes": 119,

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:56+00:00",
+  "generated_at_utc": "2026-06-16T05:27:19+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:56+00:00",
+  "generated_at_utc": "2026-06-16T05:27:19+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

data/unified_task_model_radar.json

@@ -0,0 +1,802 @@
+{
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+    "raw_values": "raw metric values, metric keys, and sources are retained in this JSON; the SVG is an overview, not a replacement for the metric table",
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+}

data/website_integrity.json

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@@ -579,6 +584,13 @@
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 {
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@@ -907,8 +941,8 @@
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docs/data/figure_index.json

@@ -1,9 +1,9 @@
 {
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-  "generated_at_utc": "2026-06-16T04:56:21+00:00",
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@@ -351,6 +351,24 @@
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       "title": "Feature block chart",

docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
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@@ -138,45 +138,45 @@
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@@ -334,45 +334,45 @@
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@@ -432,45 +432,45 @@
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@@ -825,44 +825,44 @@
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@@ -873,45 +873,45 @@
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@@ -1896,6 +1896,55 @@
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-          "sha256": "3d0525785f9a1eaad399aeed249fc899d0b141efd778d0d2f3f229dd9b6594a3"
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-          "sha256": "3d0525785f9a1eaad399aeed249fc899d0b141efd778d0d2f3f229dd9b6594a3"
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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": "2026-06-16T04:56:20+00:00",
+  "generated_at_utc": "2026-06-16T05:27:29+00:00",
   "scope": "Repo README, GitHub Pages HTML, Hugging Face Space card, artifact dataset card, and model card.",
   "checks": [
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@@ -18,7 +18,7 @@
         "website_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:49:24+00:00"
+          "generated_at_utc": "2026-06-16T05:27:06+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:49:21+00:00"
+          "generated_at_utc": "2026-06-16T05:27:19+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:50:14+00:00"
+          "generated_at_utc": "2026-06-16T05:27:19+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:49:25+00:00"
+          "generated_at_utc": "2026-06-16T05:27:20+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:51:36+00:00"
+          "generated_at_utc": "2026-06-16T05:27:13+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T04:51:56+00:00"
+          "generated_at_utc": "2026-06-16T05:27:08+00:00"
         }
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       "failures": {}
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "20-task": 20,
-        "Qwen3-Omni": 143,
+        "20-task": 35,
+        "Qwen3-Omni": 146,
         "128-episode pilot": 1
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     },
@@ -130,6 +130,8 @@
         "data/research_roadmap.json": 15,
         "data/task_suite_enhancement_128.json": 28,
         "data/task_suite_20.json": 42,
+        "data/unified_task_model_radar.json": 16,
+        "assets/charts/unified_task_model_radar.svg": 15,
         "data/tier2_task_suite.json": 11
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     },

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T05:07:05+00:00",
+  "generated_at_utc": "2026-06-16T05:27:13+00:00",
   "checks": [
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       "name": "required_publication_assets_present",
@@ -98,6 +98,7 @@
     "docs/data/website_integrity.json": true,
     "docs/data/summary_metrics.json": true,
     "docs/data/task_suite_20.json": true,
+    "docs/data/unified_task_model_radar.json": true,
     "docs/data/task_suite_enhancement_128.json": true,
     "docs/assets/modalities/video.jpg": true,
     "docs/assets/modalities/audio.png": true,
@@ -114,6 +115,7 @@
     "docs/assets/brand/xperience10m-logo-mark-512.png": true,
     "docs/assets/brand/xperience10m-logo-social-card.png": true,
     "docs/assets/task_suite_infographic.png": true,
+    "docs/assets/charts/unified_task_model_radar.svg": true,
     "docs/assets/pipeline_diagram.png": true,
     "docs/assets/task_architectures.png": true,
     "results/episode_task_suite/summary_report.json": true,
@@ -127,6 +129,7 @@
     "scripts/build_brand_assets.py": true,
     "scripts/build_evaluation_protocol.py": true,
     "scripts/build_unified_task_suite.py": true,
+    "scripts/build_unified_task_model_radar.py": true,
     "scripts/build_figure_index.py": true,
     "scripts/build_quality_gates.py": true,
     "scripts/build_public_surface_qa.py": true,
@@ -190,8 +193,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 972,
-      "text_file_count": 793,
+      "file_count": 975,
+      "text_file_count": 796,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -201,8 +204,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 757,
-      "text_file_count": 617,
+      "file_count": 759,
+      "text_file_count": 619,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -212,8 +215,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 1876,
-      "text_file_count": 793,
+      "file_count": 1881,
+      "text_file_count": 798,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -223,8 +226,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 2299,
-      "text_file_count": 951,
+      "file_count": 2304,
+      "text_file_count": 956,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:20+00:00",
+  "generated_at_utc": "2026-06-16T05:27:29+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/reproducibility_matrix.json

@@ -52,8 +52,8 @@
     {
       "id": "tasks_13_to_20_and_unified_index",
       "status": "reproducible",
-      "command": "python scripts/tier2_task_suite.py && python scripts/build_unified_task_suite.py",
-      "expected": "tasks 13-20 metrics, prediction/rank artifacts, TASK_SUITE_20.md, docs/data/task_suite_20.json, docs/data/tier2_task_suite.json, and docs/assets/charts/tier2_task_suite.svg",
+      "command": "python scripts/tier2_task_suite.py && python scripts/build_unified_task_suite.py && python scripts/build_unified_task_model_radar.py",
+      "expected": "tasks 13-20 metrics, prediction/rank artifacts, TASK_SUITE_20.md, docs/data/task_suite_20.json, docs/data/tier2_task_suite.json, docs/assets/charts/tier2_task_suite.svg, docs/data/unified_task_model_radar.json, and docs/assets/charts/unified_task_model_radar.svg",
       "boundary": "requires local public-sample annotation.hdf5 plus HOMIE Toolkit or h5py for tasks 13-20; raw HDF5 and MP4 files are not redistributed"
     },
     {

docs/data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:59+00:00",
+  "generated_at_utc": "2026-06-16T05:27:20+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T04:56:56+00:00",
+  "generated_at_utc": "2026-06-16T05:27:19+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": "2026-06-16T04:56:56+00:00",
+  "generated_at_utc": "2026-06-16T05:27:19+00:00",
   "summary": {
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     "expected_task_count": 12,

docs/data/unified_task_model_radar.json

@@ -0,0 +1,802 @@
+{
+  "title": "Unified 20-Task Model Radar",
+  "status": "pass",
+  "generated_at_utc": "2026-06-16T05:26:39+00:00",
+  "task_count": 20,
+  "normalization_policy": {
+    "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
+    "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
+    "raw_values": "raw metric values, metric keys, and sources are retained in this JSON; the SVG is an overview, not a replacement for the metric table",
+    "foundation_model_overlay": "Qwen3/Cosmos points are plotted only on task-aligned axes. Missing axes mean the public result does not evaluate that task contract."
+  },
+  "series": [
+    {
+      "id": "minimal",
+      "label": "Minimal",
+      "short_label": "Min",
+      "color": "#ccffa0",
+      "kind": "full_20_task_baseline",
+      "scope": "1 public sample episode",
+      "stroke_dasharray": null,
+      "covered_task_count": 20,
+      "coverage_fraction": 1.0
+    },
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+      "label": "Neural MLP",
+      "short_label": "NN",
+      "color": "#67e8d1",
+      "kind": "full_20_task_baseline",
+      "scope": "1 public sample episode",
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+      "label": "Qwen3-Omni v6 LoRA",
+      "short_label": "Qwen3",
+      "color": "#9bb8ff",
+      "kind": "partial_128_episode_foundation_model_overlay",
+      "scope": "128 selected episodes, held-out test",
+      "stroke_dasharray": "7 7",
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+      "coverage_fraction": 0.3
+    },
+    {
+      "id": "cosmos3_super_reasoner",
+      "label": "Cosmos3-Super Reasoner",
+      "short_label": "C3-S",
+      "color": "#ff9c7a",
+      "kind": "partial_128_episode_foundation_model_overlay",
+      "scope": "128 selected episodes, held-out test",
+      "stroke_dasharray": "4 7",
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+      "coverage_fraction": 0.3
+    },
+    {
+      "id": "cosmos3_nano_future_window",
+      "label": "Cosmos3-Nano Future Window",
+      "short_label": "C3-N",
+      "color": "#d9c7ff",
+      "kind": "partial_128_episode_world_model_overlay",
+      "scope": "128 selected episodes, held-out test",
+      "stroke_dasharray": "2 7",
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+      "coverage_fraction": 0.25
+    }
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+      "task_id": "timeline_action",
+      "label": "Action Recognition",
+      "short_label": "Action",
+      "origin": "original_public_sample_tasks",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
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+          "metric_key": "macro_f1",
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+          "scope": "single_episode_public_sample",
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+          "raw_text": "0.0500"
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+          "raw_text": "0.0148"
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+        "qwen3_omni_v6_lora": {
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+          "metric_key": "action_macro_f1",
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+          "raw_text": "0.0029"
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+          "metric_key": "action_macro_f1",
+          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "normalized_score": 0.0008284021201089245,
+          "raw_text": "0.0008"
+        },
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+      "label": "Procedure Step Recognition",
+      "short_label": "Step",
+      "origin": "original_public_sample_tasks",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
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+          "scope": "multi_episode_128_partial_model_overlay",
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+          "raw_text": "0.0000"
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+      "metric_key": "macro_f1",
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+          "scope": "multi_episode_128_partial_model_overlay",
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+          "raw_text": "0.9898"
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+          "metric_key": "transition_accuracy",
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+          "scope": "multi_episode_128_partial_model_overlay",
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+          "raw_text": "0.3683"
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+      "task_id": "next_action",
+      "label": "Next-Action Prediction",
+      "short_label": "Next act",
+      "origin": "original_public_sample_tasks",
+      "metric_key": "macro_f1",
+      "metric_name": "macro-F1",
+      "metric_direction": "higher",
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+          "scope": "multi_episode_128_partial_model_overlay",
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+          "raw_text": "0.0134"
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+          "metric_key": "action_accuracy_from_retrieved_future",
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+  ]
+}

docs/data/website_integrity.json

@@ -1,14 +1,14 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T05:06:53+00:00",
+  "generated_at_utc": "2026-06-16T05:27:06+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 154,
+    "local_references": 156,
     "external_reference_count": 121,
-    "json_files": 41,
-    "image_assets_referenced": 22,
+    "json_files": 42,
+    "image_assets_referenced": 23,
     "failure_count": 0
   },
   "failures": {
@@ -180,7 +180,7 @@
       "status": "pass",
       "reason": "The Suite anchor should show the task-suite map before the modality atlas.",
       "first_marker_index": 471,
-      "second_marker_index": 763
+      "second_marker_index": 1676
     },
     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,8 +277,8 @@
     {
       "path": "index.html",
       "id_count": 90,
-      "reference_count": 130,
-      "image_count": 25
+      "reference_count": 132,
+      "image_count": 26
     },
     {
       "path": "research_roadmap.html",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 93924,
+      "bytes": 95405,
       "top_level_type": "dict"
     },
     {
@@ -326,7 +326,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 14939,
+      "bytes": 15699,
       "top_level_type": "dict"
     },
     {
@@ -336,7 +336,7 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 126966,
+      "bytes": 125973,
       "top_level_type": "dict"
     },
     {
@@ -381,7 +381,7 @@
     },
     {
       "path": "data/public_surface_qa.json",
-      "bytes": 5698,
+      "bytes": 5804,
       "top_level_type": "dict"
     },
     {
@@ -416,7 +416,7 @@
     },
     {
       "path": "data/reproducibility_matrix.json",
-      "bytes": 6673,
+      "bytes": 6815,
       "top_level_type": "dict"
     },
     {
@@ -489,6 +489,11 @@
       "bytes": 33402,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/unified_task_model_radar.json",
+      "bytes": 31157,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/website_integrity.json",
       "bytes": 17815,
@@ -579,6 +584,13 @@
       "format": "SVG",
       "has_viewbox": true
     },
+    {
+      "path": "assets/charts/unified_task_model_radar.svg",
+      "exists": true,
+      "bytes": 26681,
+      "format": "SVG",
+      "has_viewbox": true
+    },
     {
       "path": "assets/modalities/audio.png",
       "exists": true,

docs/index.html

@@ -2951,10 +2951,11 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Figures are indexed</h3>
-            <p>The visual set includes the logo, modality atlas, task-suite figure, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
+            <p>The visual set includes the logo, modality atlas, task-suite figure, unified 20-task model radar, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">figure guide</a>
               <a href="assets/task_suite_infographic.png">task-suite figure</a>
+              <a href="assets/charts/unified_task_model_radar.svg">20-task radar</a>
               <a href="assets/qwen3_omni_lora_pipeline.png">LoRA figure</a>
             </div>
           </article>
@@ -3187,6 +3188,17 @@
         <div class="figure-pan" id="task-suite-map">
           <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing Ropedia Xperience-10M task families with enlarged full-width modality cards">
         </div>
+        <div class="figure-brief">
+          <article class="figure-brief-card">
+            <h3>Unified 20-task polygon</h3>
+            <p>The radar uses all 20 tasks as axes. Minimal and neural MLP heads are filled polygons because both cover the full suite; Qwen3/Cosmos branches are colored overlays only on task-aligned public metrics.</p>
+          </article>
+          <article class="figure-brief-card">
+            <h3>Metric normalization</h3>
+            <p>Higher-is-better metrics are plotted directly on 0-1 axes. Lower-is-better metrics are converted to best/value within the task, while raw metric values remain in the JSON mirror.</p>
+          </article>
+        </div>
+        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v1" alt="Unified 20-task radar comparing minimal and neural MLP baselines with Qwen3 and Cosmos3 task-aligned overlays">
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

index.html

@@ -2951,10 +2951,11 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Figures are indexed</h3>
-            <p>The visual set includes the logo, modality atlas, task-suite figure, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
+            <p>The visual set includes the logo, modality atlas, task-suite figure, unified 20-task model radar, model-architecture figure, tasks 13-20 chart, and Qwen3-Omni LoRA training-flow figure.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/FIGURE_INDEX.md">figure guide</a>
               <a href="assets/task_suite_infographic.png">task-suite figure</a>
+              <a href="assets/charts/unified_task_model_radar.svg">20-task radar</a>
               <a href="assets/qwen3_omni_lora_pipeline.png">LoRA figure</a>
             </div>
           </article>
@@ -3187,6 +3188,17 @@
         <div class="figure-pan" id="task-suite-map">
           <img class="task-suite-image" src="assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl" alt="Infographic showing Ropedia Xperience-10M task families with enlarged full-width modality cards">
         </div>
+        <div class="figure-brief">
+          <article class="figure-brief-card">
+            <h3>Unified 20-task polygon</h3>
+            <p>The radar uses all 20 tasks as axes. Minimal and neural MLP heads are filled polygons because both cover the full suite; Qwen3/Cosmos branches are colored overlays only on task-aligned public metrics.</p>
+          </article>
+          <article class="figure-brief-card">
+            <h3>Metric normalization</h3>
+            <p>Higher-is-better metrics are plotted directly on 0-1 axes. Lower-is-better metrics are converted to best/value within the task, while raw metric values remain in the JSON mirror.</p>
+          </article>
+        </div>
+        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v1" alt="Unified 20-task radar comparing minimal and neural MLP baselines with Qwen3 and Cosmos3 task-aligned overlays">
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

scripts/build_artifact_index.py

@@ -409,6 +409,30 @@ ARTIFACTS = [
         "surface": "repo_hf",
         "shows": "Regenerates the unified 20-task JSON and Markdown from the original 12-task metrics plus the tasks 13-20 result bundle.",
     },
+    {
+        "id": "unified_task_model_radar_json",
+        "title": "Unified 20-task model radar JSON",
+        "path": "docs/data/unified_task_model_radar.json",
+        "kind": "website_data",
+        "surface": "website_hf",
+        "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, and branch-card caveats.",
+    },
+    {
+        "id": "unified_task_model_radar_chart",
+        "title": "Unified 20-task model radar",
+        "path": "docs/assets/charts/unified_task_model_radar.svg",
+        "kind": "generated_figure",
+        "surface": "website_hf",
+        "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
+    },
+    {
+        "id": "unified_task_model_radar_builder",
+        "title": "Unified 20-task model radar builder",
+        "path": "scripts/build_unified_task_model_radar.py",
+        "kind": "visualization_builder",
+        "surface": "repo_hf",
+        "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
+    },
     {
         "id": "research_takeaways",
         "title": "Research takeaways",

scripts/build_figure_index.py

@@ -178,6 +178,14 @@ FIGURES = [
         "source_script": "scripts/tier2_task_suite.py",
         "surface": "website unified task section, README, HF mirrors",
     },
+    {
+        "id": "unified_task_model_radar",
+        "title": "Unified 20-task model radar",
+        "path": "docs/assets/charts/unified_task_model_radar.svg",
+        "role": "Twenty-axis direction-aware comparison of minimal and neural MLP baselines, with Qwen3/Cosmos task-aligned overlay points and branch notes.",
+        "source_script": "scripts/build_unified_task_model_radar.py",
+        "surface": "website unified task section, README, HF mirrors",
+    },
     {
         "id": "feature_blocks_chart",
         "title": "Feature block chart",

scripts/build_public_surface_qa.py

@@ -175,6 +175,8 @@ def build_report() -> dict:
         "data/research_roadmap.json",
         "data/task_suite_enhancement_128.json",
         "data/task_suite_20.json",
+        "data/unified_task_model_radar.json",
+        "assets/charts/unified_task_model_radar.svg",
         "data/tier2_task_suite.json",
     ]
 

scripts/build_unified_task_model_radar.py

@@ -0,0 +1,447 @@
+#!/usr/bin/env python3
+"""Build a unified 20-task radar chart for baseline and model-branch metrics."""
+
+from __future__ import annotations
+
+import html
+import json
+import math
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parents[1]
+TASK_SUITE_PATH = ROOT / "docs/data/task_suite_20.json"
+QWEN_V6_METRICS_PATH = (
+    ROOT
+    / "results/omni_finetune/verified_public"
+    / "xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full"
+    / "eval/metrics.json"
+)
+COSMOS_SUPER_REASONER_METRICS_PATH = (
+    ROOT
+    / "results/omni_finetune/verified_public"
+    / "xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607"
+    / "eval/metrics.json"
+)
+COSMOS_NANO_METRICS_PATH = (
+    ROOT
+    / "results/omni_finetune/verified_public"
+    / "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full"
+    / "eval/metrics.json"
+)
+COSMOS_SUPER_FD_METRICS_PATH = (
+    ROOT
+    / "results/omni_finetune/verified_public"
+    / "xperience10m_cosmos3_super_forward_dynamics_lora_128ep_train1epoch_256_attn_full8gpu_20260608_eval_test_full_fsdp"
+    / "eval/metrics.json"
+)
+OUTPUT_JSON = ROOT / "docs/data/unified_task_model_radar.json"
+OUTPUT_SVG = ROOT / "docs/assets/charts/unified_task_model_radar.svg"
+
+
+SERIES = {
+    "minimal": {
+        "label": "Minimal",
+        "short_label": "Min",
+        "color": "#ccffa0",
+        "kind": "full_20_task_baseline",
+        "scope": "1 public sample episode",
+        "stroke_dasharray": None,
+    },
+    "neural_mlp": {
+        "label": "Neural MLP",
+        "short_label": "NN",
+        "color": "#67e8d1",
+        "kind": "full_20_task_baseline",
+        "scope": "1 public sample episode",
+        "stroke_dasharray": None,
+    },
+    "qwen3_omni_v6_lora": {
+        "label": "Qwen3-Omni v6 LoRA",
+        "short_label": "Qwen3",
+        "color": "#9bb8ff",
+        "kind": "partial_128_episode_foundation_model_overlay",
+        "scope": "128 selected episodes, held-out test",
+        "stroke_dasharray": "7 7",
+    },
+    "cosmos3_super_reasoner": {
+        "label": "Cosmos3-Super Reasoner",
+        "short_label": "C3-S",
+        "color": "#ff9c7a",
+        "kind": "partial_128_episode_foundation_model_overlay",
+        "scope": "128 selected episodes, held-out test",
+        "stroke_dasharray": "4 7",
+    },
+    "cosmos3_nano_future_window": {
+        "label": "Cosmos3-Nano Future Window",
+        "short_label": "C3-N",
+        "color": "#d9c7ff",
+        "kind": "partial_128_episode_world_model_overlay",
+        "scope": "128 selected episodes, held-out test",
+        "stroke_dasharray": "2 7",
+    },
+}
+
+FOUNDATION_TASK_METRICS = {
+    "timeline_action": {
+        "qwen3_omni_v6_lora": "action_macro_f1",
+        "cosmos3_super_reasoner": "action_macro_f1",
+        "cosmos3_nano_future_window": "action_accuracy_from_retrieved_future",
+    },
+    "timeline_subtask": {
+        "qwen3_omni_v6_lora": "subtask_accuracy",
+        "cosmos3_super_reasoner": "subtask_accuracy",
+    },
+    "transition_detection": {
+        "qwen3_omni_v6_lora": "transition_accuracy",
+        "cosmos3_super_reasoner": "transition_accuracy",
+        "cosmos3_nano_future_window": "transition_accuracy",
+    },
+    "next_action": {
+        "qwen3_omni_v6_lora": "next_action_accuracy",
+        "cosmos3_super_reasoner": "next_action_accuracy",
+        "cosmos3_nano_future_window": "action_accuracy_from_retrieved_future",
+    },
+    "contact_prediction": {
+        "qwen3_omni_v6_lora": "contact_accuracy",
+        "cosmos3_super_reasoner": "contact_accuracy",
+        "cosmos3_nano_future_window": "contact_accuracy",
+    },
+    "object_relevance": {
+        "qwen3_omni_v6_lora": "object_micro_f1",
+        "cosmos3_super_reasoner": "object_micro_f1",
+    },
+    "cross_modal_retrieval": {
+        "cosmos3_nano_future_window": "future_retrieval_mrr",
+    },
+}
+
+SHORT_TASK_LABELS = {
+    "timeline_action": "Action",
+    "timeline_subtask": "Step",
+    "transition_detection": "Boundary",
+    "next_action": "Next act",
+    "hand_trajectory_forecast": "Hand traj",
+    "contact_prediction": "Contact",
+    "object_relevance": "Objects",
+    "caption_grounding": "Language",
+    "cross_modal_retrieval": "X-modal",
+    "modality_reconstruction": "Recon",
+    "temporal_order": "Order",
+    "misalignment_detection": "Sync",
+    "long_horizon_next_action": "Long act",
+    "next_subtask_forecast": "Long step",
+    "interaction_text_prediction": "Interact txt",
+    "action_object_relation": "Act+obj",
+    "object_set_forecast": "Future obj",
+    "imu_to_hand_pose": "IMU->hand",
+    "camera_view_sync_retrieval": "Cam sync",
+    "time_to_transition": "Time2bdry",
+}
+
+
+def read_json(path: Path) -> dict[str, Any]:
+    return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}
+
+
+def clamp01(value: float) -> float:
+    return max(0.0, min(1.0, value))
+
+
+def score_from_raw(value: float | None, direction: str, best_lower: float | None = None) -> float | None:
+    if value is None:
+        return None
+    if direction == "lower":
+        if value <= 0:
+            return 1.0
+        if best_lower is None or best_lower <= 0:
+            return None
+        return clamp01(best_lower / value)
+    return clamp01(value)
+
+
+def format_metric(value: float | None) -> str:
+    if value is None:
+        return "n/a"
+    if abs(value) >= 10:
+        return f"{value:.2f}"
+    if abs(value) >= 1:
+        return f"{value:.3f}"
+    return f"{value:.4f}"
+
+
+def point(cx: float, cy: float, radius: float, angle: float) -> tuple[float, float]:
+    return cx + math.cos(angle) * radius, cy + math.sin(angle) * radius
+
+
+def svg_text(
+    x: float,
+    y: float,
+    text: str,
+    *,
+    size: int = 16,
+    fill: str = "#f4f8ef",
+    anchor: str = "start",
+    weight: int | str = 600,
+    opacity: float = 1.0,
+) -> str:
+    return (
+        f'<text x="{x:.1f}" y="{y:.1f}" text-anchor="{anchor}" '
+        f'font-family="Space Grotesk, Arial, sans-serif" font-size="{size}" '
+        f'font-weight="{weight}" fill="{fill}" opacity="{opacity:.3f}">{html.escape(text)}</text>'
+    )
+
+
+def polyline(points: list[tuple[float, float]], *, fill: str, stroke: str, opacity: float, stroke_width: float, dash: str | None = None) -> str:
+    coords = " ".join(f"{x:.1f},{y:.1f}" for x, y in points)
+    dash_attr = f' stroke-dasharray="{dash}"' if dash else ""
+    return (
+        f'<polygon points="{coords}" fill="{fill}" fill-opacity="{opacity:.3f}" '
+        f'stroke="{stroke}" stroke-opacity="0.92" stroke-width="{stroke_width}"{dash_attr}/>'
+    )
+
+
+def build_payload() -> dict[str, Any]:
+    suite = read_json(TASK_SUITE_PATH)
+    qwen = read_json(QWEN_V6_METRICS_PATH)
+    cosmos_super = read_json(COSMOS_SUPER_REASONER_METRICS_PATH)
+    cosmos_nano = read_json(COSMOS_NANO_METRICS_PATH)
+    cosmos_fd = read_json(COSMOS_SUPER_FD_METRICS_PATH)
+    foundation_metrics = {
+        "qwen3_omni_v6_lora": qwen,
+        "cosmos3_super_reasoner": cosmos_super,
+        "cosmos3_nano_future_window": cosmos_nano,
+    }
+
+    tasks: list[dict[str, Any]] = []
+    for row in suite.get("tasks", []):
+        values: dict[str, dict[str, Any]] = {
+            "minimal": {
+                "raw": row.get("minimal_primary_metric"),
+                "metric_key": row.get("metric_key"),
+                "source": row.get("artifact_sources", {}).get("minimal_metrics"),
+                "scope": "single_episode_public_sample",
+            },
+            "neural_mlp": {
+                "raw": row.get("neural_primary_metric"),
+                "metric_key": row.get("metric_key"),
+                "source": row.get("artifact_sources", {}).get("neural_metrics"),
+                "scope": "single_episode_public_sample",
+            },
+        }
+        for series_id, metric_key in FOUNDATION_TASK_METRICS.get(row["task_id"], {}).items():
+            raw = foundation_metrics.get(series_id, {}).get(metric_key)
+            values[series_id] = {
+                "raw": raw,
+                "metric_key": metric_key,
+                "source": str(
+                    {
+                        "qwen3_omni_v6_lora": QWEN_V6_METRICS_PATH,
+                        "cosmos3_super_reasoner": COSMOS_SUPER_REASONER_METRICS_PATH,
+                        "cosmos3_nano_future_window": COSMOS_NANO_METRICS_PATH,
+                    }[series_id].relative_to(ROOT)
+                ),
+                "scope": "multi_episode_128_partial_model_overlay",
+            }
+
+        lower_values = [
+            item["raw"]
+            for item in values.values()
+            if row.get("metric_direction") == "lower" and isinstance(item.get("raw"), (int, float)) and item["raw"] > 0
+        ]
+        best_lower = min(lower_values) if lower_values else None
+        for item in values.values():
+            item["normalized_score"] = score_from_raw(item.get("raw"), row.get("metric_direction", "higher"), best_lower)
+            item["raw_text"] = format_metric(item.get("raw"))
+
+        tasks.append(
+            {
+                "task_number": row["task_number"],
+                "task_id": row["task_id"],
+                "label": row.get("task_display_name", row["task_id"]),
+                "short_label": SHORT_TASK_LABELS.get(row["task_id"], row["task_id"].replace("_", " ").title()),
+                "origin": row.get("origin"),
+                "metric_key": row.get("metric_key"),
+                "metric_name": row.get("metric_name"),
+                "metric_direction": row.get("metric_direction"),
+                "values": values,
+            }
+        )
+
+    series_records = []
+    for series_id, spec in SERIES.items():
+        covered = sum(1 for task in tasks if task["values"].get(series_id, {}).get("normalized_score") is not None)
+        series_records.append(
+            {
+                "id": series_id,
+                **spec,
+                "covered_task_count": covered,
+                "coverage_fraction": covered / max(len(tasks), 1),
+            }
+        )
+
+    fd_loss = (cosmos_fd.get("loss_summary") or {}).get("mean")
+    return {
+        "title": "Unified 20-Task Model Radar",
+        "status": "pass",
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "task_count": len(tasks),
+        "normalization_policy": {
+            "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
+            "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
+            "raw_values": "raw metric values, metric keys, and sources are retained in this JSON; the SVG is an overview, not a replacement for the metric table",
+            "foundation_model_overlay": "Qwen3/Cosmos points are plotted only on task-aligned axes. Missing axes mean the public result does not evaluate that task contract.",
+        },
+        "series": series_records,
+        "tasks": tasks,
+        "model_branch_cards": [
+            {
+                "id": "qwen3_omni_v6_lora",
+                "title": "Qwen3-Omni v6 LoRA",
+                "status": "verified",
+                "task_aligned_axes": SERIES["qwen3_omni_v6_lora"]["short_label"],
+                "coverage": f"{next(item for item in series_records if item['id'] == 'qwen3_omni_v6_lora')['covered_task_count']}/20 task-aligned axes",
+                "headline": f"JSON validity {format_metric(qwen.get('json_validity_rate'))}; action macro-F1 {format_metric(qwen.get('action_macro_f1'))}",
+                "source": str(QWEN_V6_METRICS_PATH.relative_to(ROOT)),
+            },
+            {
+                "id": "cosmos3_super_reasoner",
+                "title": "Cosmos3-Super Reasoner",
+                "status": "verified_base_weight_eval",
+                "coverage": f"{next(item for item in series_records if item['id'] == 'cosmos3_super_reasoner')['covered_task_count']}/20 task-aligned axes",
+                "headline": f"JSON validity {format_metric(cosmos_super.get('json_validity_rate'))}; action macro-F1 {format_metric(cosmos_super.get('action_macro_f1'))}",
+                "source": str(COSMOS_SUPER_REASONER_METRICS_PATH.relative_to(ROOT)),
+            },
+            {
+                "id": "cosmos3_nano_future_window",
+                "title": "Cosmos3-Nano Future Window",
+                "status": "verified_compatibility_eval",
+                "coverage": f"{next(item for item in series_records if item['id'] == 'cosmos3_nano_future_window')['covered_task_count']}/20 task-aligned axes",
+                "headline": f"future retrieval MRR {format_metric(cosmos_nano.get('future_retrieval_mrr'))}; transition accuracy {format_metric(cosmos_nano.get('transition_accuracy'))}",
+                "source": str(COSMOS_NANO_METRICS_PATH.relative_to(ROOT)),
+            },
+            {
+                "id": "cosmos3_super_forward_dynamics_lora",
+                "title": "Cosmos3-Super Forward-Dynamics LoRA",
+                "status": "verified_finetuned_adapter",
+                "coverage": "separate camera-pose proxy target, not plotted on the 20 task axes",
+                "headline": f"test MSE {format_metric(fd_loss)} over 448 held-out rows",
+                "source": str(COSMOS_SUPER_FD_METRICS_PATH.relative_to(ROOT)),
+            },
+        ],
+    }
+
+
+def render_svg(payload: dict[str, Any]) -> str:
+    width, height = 1720, 1220
+    cx, cy, radius = 570, 585, 330
+    tasks = payload["tasks"]
+    n = len(tasks)
+    angles = [-math.pi / 2 + 2 * math.pi * i / n for i in range(n)]
+    parts = [
+        f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
+        "<defs>",
+        '<filter id="softGlow"><feGaussianBlur stdDeviation="5" result="blur"/><feMerge><feMergeNode in="blur"/><feMergeNode in="SourceGraphic"/></feMerge></filter>',
+        '<pattern id="dots" width="22" height="22" patternUnits="userSpaceOnUse"><circle cx="2" cy="2" r="1.15" fill="#ccffa0" opacity="0.16"/></pattern>',
+        "</defs>",
+        '<rect width="100%" height="100%" fill="#020502"/>',
+        '<rect width="100%" height="100%" fill="url(#dots)" opacity="0.45"/>',
+        '<rect x="28" y="28" width="1664" height="1164" rx="18" fill="#061006" fill-opacity="0.86" stroke="#ccffa0" stroke-opacity="0.22"/>',
+        svg_text(70, 86, "Unified 20-Task Model Radar", size=34, weight=800),
+        svg_text(70, 122, "Direction-aware normalized scores across the single-episode task suite, with Qwen3/Cosmos task-aligned overlays.", size=17, fill="#a5afa2", weight=560),
+        svg_text(70, 156, "Filled polygons: same 20 public-sample tasks. Points: 128-episode model branches only where the public metric maps to that task.", size=15, fill="#a5afa2", weight=560),
+    ]
+
+    for level in range(1, 6):
+        r = radius * level / 5
+        ring = [point(cx, cy, r, angle) for angle in angles]
+        parts.append(polyline(ring, fill="none", stroke="#ccffa0", opacity=0, stroke_width=1.1))
+        parts[-1] = parts[-1].replace('fill="none" fill-opacity="0.000"', 'fill="none"').replace('stroke-opacity="0.92"', 'stroke-opacity="0.15"')
+        parts.append(svg_text(cx + 8, cy - r + 4, f"{level / 5:.1f}", size=11, fill="#a5afa2", weight=600, opacity=0.75))
+
+    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="0.12" stroke-width="1"/>')
+        lx, ly = point(cx, cy, radius + 58, angle)
+        anchor = "middle"
+        if math.cos(angle) > 0.25:
+            anchor = "start"
+        elif math.cos(angle) < -0.25:
+            anchor = "end"
+        parts.append(svg_text(lx, ly - 7, f"{task['task_number']:02d}", size=11, fill="#ccffa0", anchor=anchor, weight=800, opacity=0.9))
+        parts.append(svg_text(lx, ly + 13, task["short_label"], size=12, fill="#dce8d7", anchor=anchor, weight=650))
+
+    for series_id in ("minimal", "neural_mlp"):
+        spec = SERIES[series_id]
+        points = []
+        for task, angle in zip(tasks, angles):
+            score = task["values"].get(series_id, {}).get("normalized_score")
+            points.append(point(cx, cy, radius * float(score or 0.0), angle))
+        parts.append(polyline(points, fill=spec["color"], stroke=spec["color"], opacity=0.18 if series_id == "minimal" else 0.16, stroke_width=4.2))
+        for x, y in points:
+            parts.append(f'<circle cx="{x:.1f}" cy="{y:.1f}" r="4.0" fill="{spec["color"]}" stroke="#020502" stroke-width="1.1"/>')
+
+    for series_id in ("qwen3_omni_v6_lora", "cosmos3_super_reasoner", "cosmos3_nano_future_window"):
+        spec = SERIES[series_id]
+        for task, angle in zip(tasks, angles):
+            score = task["values"].get(series_id, {}).get("normalized_score")
+            if score is None:
+                continue
+            x, y = point(cx, cy, radius * float(score), angle)
+            parts.append(
+                f'<circle cx="{x:.1f}" cy="{y:.1f}" r="8.0" fill="{spec["color"]}" fill-opacity="0.92" '
+                f'stroke="#020502" stroke-width="2.0"/>'
+            )
+
+    legend_x, legend_y = 1105, 210
+    parts.append(f'<rect x="{legend_x - 34}" y="{legend_y - 44}" width="520" height="860" rx="12" fill="#020502" fill-opacity="0.58" stroke="#ccffa0" stroke-opacity="0.20"/>')
+    parts.append(svg_text(legend_x, legend_y, "How to read it", size=24, weight=800))
+    parts.append(svg_text(legend_x, legend_y + 30, "Score radius is normalized by metric direction.", size=14, fill="#a5afa2", weight=560))
+    parts.append(svg_text(legend_x, legend_y + 52, "Raw values stay in unified_task_model_radar.json.", size=14, fill="#a5afa2", weight=560))
+
+    cursor = legend_y + 100
+    for record in payload["series"]:
+        color = record["color"]
+        parts.append(f'<line x1="{legend_x}" y1="{cursor - 4}" x2="{legend_x + 48}" y2="{cursor - 4}" stroke="{color}" stroke-width="7" stroke-linecap="round"/>')
+        if record["kind"].startswith("partial"):
+            parts.append(f'<circle cx="{legend_x + 24}" cy="{cursor - 4}" r="7" fill="{color}" stroke="#020502" stroke-width="2"/>')
+        parts.append(svg_text(legend_x + 64, cursor, record["label"], size=16, weight=800))
+        parts.append(svg_text(legend_x + 64, cursor + 22, f"{record['covered_task_count']}/20 axes · {record['scope']}", size=12, fill="#a5afa2", weight=560))
+        cursor += 62
+
+    cursor += 16
+    parts.append(svg_text(legend_x, cursor, "Model branch notes", size=20, weight=800))
+    cursor += 32
+    for card in payload["model_branch_cards"]:
+        parts.append(f'<rect x="{legend_x}" y="{cursor - 18}" width="445" height="78" rx="8" fill="#081408" stroke="#ccffa0" stroke-opacity="0.15"/>')
+        parts.append(svg_text(legend_x + 16, cursor + 3, card["title"], size=14, weight=800))
+        parts.append(svg_text(legend_x + 16, cursor + 24, card["coverage"], size=11, fill="#a5afa2", weight=600))
+        parts.append(svg_text(legend_x + 16, cursor + 45, card["headline"], size=11, fill="#dce8d7", weight=600))
+        cursor += 92
+
+    table_y = 1090
+    parts.append(f'<rect x="70" y="{table_y - 35}" width="1540" height="86" rx="10" fill="#020502" fill-opacity="0.54" stroke="#ccffa0" stroke-opacity="0.16"/>')
+    parts.append(svg_text(96, table_y - 8, "Caveat", size=15, fill="#ccffa0", weight=800))
+    parts.append(svg_text(170, table_y - 8, "This chart compares normalized metric direction, not identical raw units.", size=14, fill="#dce8d7", weight=650))
+    parts.append(svg_text(170, table_y + 18, "Qwen3/Cosmos overlays use 128-episode held-out branches and are plotted only on semantically aligned task axes.", size=14, fill="#a5afa2", weight=560))
+    parts.append(svg_text(170, table_y + 44, "Cosmos3-Super forward-dynamics LoRA is kept as a branch card because its camera-pose proxy MSE is not one of the 20 task metrics.", size=14, fill="#a5afa2", weight=560))
+
+    parts.append("</svg>")
+    return "\n".join(parts) + "\n"
+
+
+def main() -> int:
+    payload = build_payload()
+    OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_SVG.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_JSON.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+    OUTPUT_SVG.write_text(render_svg(payload), encoding="utf-8")
+    print(f"PASS: wrote {OUTPUT_JSON}")
+    print(f"PASS: wrote {OUTPUT_SVG}")
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/sync_hf_publish_mirrors.py

@@ -46,7 +46,9 @@ The public-sample task surface is now one unified 20-task suite in
 original sample tasks; Tasks 13-20 reuse the same 20-frame windows, 5-frame
 stride, feature manifest, chronological split, and minimal/neural head pattern.
 The historical `tier2_task_suite` path is retained only for stable artifact
-links to tasks 13-20.
+links to tasks 13-20. The unified radar chart is published as
+`docs/assets/charts/unified_task_model_radar.svg` with values in
+`docs/data/unified_task_model_radar.json`.
 """
 QWEN_COMPARISON_MARKER = "docs/data/qwen3_v5_v6_comparison.json"
 QWEN_COMPARISON_ROW = (
@@ -144,6 +146,13 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
             "original sample tasks; tasks 13-20 reuse",
             "original sample tasks; Tasks 13-20 reuse",
         )
+        if "docs/data/unified_task_model_radar.json" not in text:
+            text = text.replace(
+                "links to tasks 13-20.\n",
+                "links to tasks 13-20. The unified radar chart is published as\n"
+                "`docs/assets/charts/unified_task_model_radar.svg` with values in\n"
+                "`docs/data/unified_task_model_radar.json`.\n",
+            )
         if TIER2_MARKER in text:
             if not dry_run:
                 path.write_text(text, encoding="utf-8")

scripts/validate_mirror_parity.py

@@ -58,6 +58,7 @@ DATA_FILES = [
     "task_surface_integrity.json",
     "task_walkthroughs.json",
     "tier2_task_suite.json",
+    "unified_task_model_radar.json",
     "website_integrity.json",
     "xperience10m_dataset_card_alignment.json",
 ]
@@ -65,6 +66,7 @@ DATA_FILES = [
 ASSET_FILES = [
     "charts/audio_ablation_delta.svg",
     "charts/tier2_task_suite.svg",
+    "charts/unified_task_model_radar.svg",
     "brand/xperience10m-logo-apple-touch.png",
     "brand/xperience10m-logo-favicon-32.png",
     "brand/xperience10m-logo-favicon-64.png",
@@ -121,6 +123,7 @@ SCRIPT_FILES = [
     "build_single_episode_explorer.py",
     "build_research_takeaways.py",
     "build_unified_task_suite.py",
+    "build_unified_task_model_radar.py",
     "single_episode_diagnostics.py",
     "verify_live_publication.py",
     "validate_mirror_parity.py",

scripts/validate_publication_package.py

@@ -309,6 +309,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/data/website_integrity.json",
         "docs/data/summary_metrics.json",
         "docs/data/task_suite_20.json",
+        "docs/data/unified_task_model_radar.json",
         "docs/data/task_suite_enhancement_128.json",
         "docs/assets/modalities/video.jpg",
         "docs/assets/modalities/audio.png",
@@ -325,6 +326,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/assets/brand/xperience10m-logo-mark-512.png",
         "docs/assets/brand/xperience10m-logo-social-card.png",
         "docs/assets/task_suite_infographic.png",
+        "docs/assets/charts/unified_task_model_radar.svg",
         "docs/assets/pipeline_diagram.png",
         "docs/assets/task_architectures.png",
         "results/episode_task_suite/summary_report.json",
@@ -338,6 +340,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "scripts/build_brand_assets.py",
         "scripts/build_evaluation_protocol.py",
         "scripts/build_unified_task_suite.py",
+        "scripts/build_unified_task_model_radar.py",
         "scripts/build_figure_index.py",
         "scripts/build_quality_gates.py",
         "scripts/build_public_surface_qa.py",

scripts/verify_live_publication.py

@@ -75,6 +75,28 @@ HASH_GROUPS = [
             "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/task_suite_20.json",
         },
     },
+    {
+        "id": "unified_task_model_radar_json",
+        "title": "Unified 20-task model radar JSON",
+        "local_path": "docs/data/unified_task_model_radar.json",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/unified_task_model_radar.json",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/raw/main/data/unified_task_model_radar.json",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/data/unified_task_model_radar.json",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/unified_task_model_radar.json",
+        },
+    },
+    {
+        "id": "unified_task_model_radar_svg",
+        "title": "Unified 20-task model radar SVG",
+        "local_path": "docs/assets/charts/unified_task_model_radar.svg",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/charts/unified_task_model_radar.svg",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/resolve/main/assets/charts/unified_task_model_radar.svg",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/assets/charts/unified_task_model_radar.svg",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/assets/charts/unified_task_model_radar.svg",
+        },
+    },
     {
         "id": "tier2_task_suite_json",
         "title": "Tasks 13-20 result JSON",
@@ -438,6 +460,8 @@ MARKER_CHECKS = [
             "Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch",
             "task_suite_20.json",
             "Unified 20-Task Suite",
+            "unified_task_model_radar.svg",
+            "Unified 20-task polygon",
             "tier2_task_suite.json",
             "Tasks 13-20",
             "Long-Horizon Next-Action Forecasting",
@@ -477,6 +501,8 @@ MARKER_CHECKS = [
             "Cosmos3-Super has a verified base-weight JSON-task evaluation plus a fine-tuned forward-dynamics LoRA branch",
             "task_suite_20.json",
             "Unified 20-Task Suite",
+            "unified_task_model_radar.svg",
+            "Unified 20-task polygon",
             "tier2_task_suite.json",
             "Tasks 13-20",
             "Long-Horizon Next-Action Forecasting",
@@ -502,6 +528,8 @@ MARKER_CHECKS = [
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
             "docs/data/task_suite_20.json",
             "Unified 20-Task Suite",
+            "docs/data/unified_task_model_radar.json",
+            "docs/assets/charts/unified_task_model_radar.svg",
             "docs/data/tier2_task_suite.json",
             "Tasks 13-20",
         ],
@@ -558,6 +586,8 @@ MARKER_CHECKS = [
             "ropedia-cosmos3-super-forward-dynamics-lora-128ep",
             "docs/data/task_suite_20.json",
             "Unified 20-Task Suite",
+            "docs/data/unified_task_model_radar.json",
+            "docs/assets/charts/unified_task_model_radar.svg",
             "docs/data/tier2_task_suite.json",
             "Tasks 13-20",
         ],