Polish homepage radar comparison

FIGURE_INDEX.md

@@ -31,7 +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 1500 | `scripts/build_unified_task_model_radar.py` | Twenty-axis direction-aware comparison of minimal and neural MLP baselines, with 128-episode metadata, Qwen3, and Cosmos task-aligned overlay points and branch notes. |
+| Unified 20-task model radar | `docs/assets/charts/unified_task_model_radar.svg` | 1920 x 1640 | `scripts/build_unified_task_model_radar.py` | Twenty-axis direction-aware comparison of minimal and neural MLP baselines, with 128-episode metadata, Qwen3, and 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. |

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-16T08:49:31+00:00",
+  "generated_at_utc": "2026-06-16T09:44:42+00:00",
   "status": "pass",
   "artifact_count": 177,
   "missing": [],
@@ -465,7 +465,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "412c42101e98f05afb9f4e79c3884930b07c9c5ff8dcb0b108fecfc1c0af5e24"
+      "sha256": "356ddd96f691b7194737e75be8bd33884c61dc83e3cb4dc16c7a842d168f6276"
     },
     {
       "id": "source_alignment_validator",
@@ -585,8 +585,8 @@
       "surface": "website_hf",
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, and branch-card caveats.",
       "exists": true,
-      "bytes": 54075,
-      "sha256": "86d82d6cb9dde65aded4fbfe4d621b511f4a7e7630aef357ce270e1bc37ab50a"
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     },
     {
       "id": "unified_task_model_radar_chart",
@@ -596,8 +596,8 @@
       "surface": "website_hf",
       "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
       "exists": true,
-      "bytes": 37507,
-      "sha256": "4bb600ac9ce7901a3c94ca5851f6975751b0a7b6e1d72f397565ecfb8dfba0df"
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+      "sha256": "c10c80db297ff1b955bae7826673755d626ddf278aad7f5975d99ba3a5379bbf"
     },
     {
       "id": "unified_task_model_radar_builder",
@@ -607,8 +607,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
       "exists": true,
-      "bytes": 27347,
-      "sha256": "8b5eb0a12fb320024f942bc650fb0fe779a0a59e835f26e71ccb55885695adda"
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     },
     {
       "id": "a100_128_metadata_task_baselines",
@@ -740,7 +740,7 @@
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
       "bytes": 15726,
-      "sha256": "587fc0e7e9e93f06341f520acf89b7ef81eeb98caaa90135881bc0d681ac2679"
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     },
     {
       "id": "figure_index_builder",
@@ -817,7 +817,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8100,
-      "sha256": "52c8d858159ef519fae7382a3cac2ae7edd755f22e8f3ac3b8a63a7a90dba96a"
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     },
     {
       "id": "public_surface_qa",
@@ -920,7 +920,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 131868,
+      "bytes": 131877,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1000,7 +1000,7 @@
       "volatile": true,
       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
       "exists": true,
-      "bytes": 832111,
+      "bytes": 838389,
       "hash_policy": "existence_and_size_only"
     },
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data/figure_index.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:49:33+00:00",
+  "generated_at_utc": "2026-06-16T09:44:44+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
   "figure_count": 24,
   "figures": [
@@ -359,13 +359,13 @@
       "source_script": "scripts/build_unified_task_model_radar.py",
       "surface": "website unified task section, README, HF mirrors",
       "exists": true,
-      "bytes": 37974,
-      "sha256": "75f037b5e2517a64e6bafb1f6bf92cf67d5144299c94ff5a7f3a900ca2cbf267",
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       "dimensions": {
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-        "width": 1720,
-        "height": 1500,
-        "view_box": "0 0 1720 1500"
+        "width": 1920,
+        "height": 1640,
+        "view_box": "0 0 1920 1640"
       },
       "source_script_exists": true
     },

data/mirror_parity.json

@@ -1,23 +1,16 @@
 {
-  "status": "fail",
-  "generated_at_utc": "2026-06-16T08:51:01+00:00",
+  "status": "pass",
+  "generated_at_utc": "2026-06-16T08:51:51+00:00",
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-      "hf_artifacts": 4,
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-      "hf_model_docs_data": 4,
-      "hf_model": 4
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+    "failures_by_surface": {}
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-      "status": "fail"
+      "status": "pass"
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@@ -832,44 +825,44 @@
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         "bytes": 7883,
-        "sha256": "b03bbd311826f039108bc8bebb2cd03ce8864b7aec335c12b5b5c804e9f15575"
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@@ -1464,7 +1457,7 @@
     },
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-      "status": "fail",
+      "status": "pass",
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@@ -1476,83 +1469,40 @@
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+      "failures": []
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@@ -1605,7 +1555,7 @@
     },
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-      "status": "fail",
+      "status": "pass",
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@@ -1617,83 +1567,40 @@
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@@ -1844,7 +1751,7 @@
     },
     {
       "name": "data/task_surface_integrity.json",
-      "status": "fail",
+      "status": "pass",
       "local": {
         "path": "repo:docs/data/task_surface_integrity.json",
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@@ -1856,83 +1763,40 @@
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-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model",
-      "kind": "hash_mismatch",
-      "path": "hf_model:metrics/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    }
-  ]
+  "failures": []
 }

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:02+00:00",
+  "generated_at_utc": "2026-06-16T09:45:50+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": "2026-06-16T07:52:05+00:00"
+          "generated_at_utc": "2026-06-16T08:50:28+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:01+00:00"
+          "generated_at_utc": "2026-06-16T08:50:24+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:02+00:00"
+          "generated_at_utc": "2026-06-16T08:50:24+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:11+00:00"
+          "generated_at_utc": "2026-06-16T08:50:31+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:48+00:00"
+          "generated_at_utc": "2026-06-16T08:51:12+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T08:01:18+00:00"
+          "generated_at_utc": "2026-06-16T08:51:51+00:00"
         }
       },
       "failures": {}
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "20-task": 35,
-        "Qwen3-Omni": 146,
+        "20-task": 39,
+        "Qwen3-Omni": 148,
         "128-episode pilot": 1
       }
     },
@@ -130,8 +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/unified_task_model_radar.json": 17,
+        "assets/charts/unified_task_model_radar.svg": 17,
         "data/tier2_task_suite.json": 11
       }
     },

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:51:12+00:00",
+  "generated_at_utc": "2026-06-16T09:46:35+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:01+00:00",
+  "generated_at_utc": "2026-06-16T09:45:50+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": [
     {

data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:31+00:00",
+  "generated_at_utc": "2026-06-16T09:46:26+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,
@@ -9,7 +9,7 @@
     "eval_num_samples": 4032,
     "eval_json_validity_rate": 0.9990079365079365,
     "quality_target_met": true,
-    "historical_identifier_count": 1800,
+    "historical_identifier_count": 1802,
     "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
@@ -84,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=1800",
+      "detail": "historical identifiers found in result provenance files=1802",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -115,6 +115,24 @@
       ],
       "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
     },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary.json",
+      "line": 2,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "\"dataset_jsonl\": \"/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset.jsonl\","
+    },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary.json",
+      "line": 3,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "\"feature_manifest_json\": \"/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset_dense_20f_stride10/dataset_manifest.json\","
+    },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
@@ -400,30 +418,8 @@
         "ropedia-episode-task-suite"
       ],
       "example": "{\"id\": \"xperience-10m-sample:qa:51\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1020, \"end_frame\": 1039, \"num_frames\": 20}, \"media\": {\"video_path"
-    },
-    {
-      "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/dataset.jsonl",
-      "line": 27,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:52\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1040, \"end_frame\": 1059, \"num_frames\": 20}, \"media\": {\"video_path"
-    },
-    {
-      "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/dataset.jsonl",
-      "line": 28,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:53\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1060, \"end_frame\": 1079, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 1800,
+  "historical_identifier_total_count": 1802,
   "failures": []
 }

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:24+00:00",
+  "generated_at_utc": "2026-06-16T09:46:19+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": "2026-06-16T08:50:24+00:00",
+  "generated_at_utc": "2026-06-16T09:46:19+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

data/unified_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:49:30+00:00",
+  "generated_at_utc": "2026-06-16T09:43:00+00:00",
   "task_count": 20,
   "normalization_policy": {
     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
@@ -20,6 +20,8 @@
       "kind": "full_20_task_baseline",
       "scope": "1 public sample episode",
       "stroke_dasharray": null,
+      "method_detail": "Single-episode simple heads over the public sample split.",
+      "plotted_as": "filled polygon",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -31,6 +33,8 @@
       "kind": "full_20_task_baseline",
       "scope": "1 public sample episode",
       "stroke_dasharray": null,
+      "method_detail": "Single-episode compact PyTorch MLP heads on the same 20 task contracts.",
+      "plotted_as": "filled polygon",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -42,6 +46,8 @@
       "kind": "partial_128_episode_metadata_baseline",
       "scope": "128 selected episodes, JSONL metadata/text only",
       "stroke_dasharray": "9 6",
+      "method_detail": "128-episode JSONL metadata/text simple baselines.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 8,
       "coverage_fraction": 0.4
     },
@@ -53,6 +59,8 @@
       "kind": "partial_128_episode_metadata_baseline",
       "scope": "128 selected episodes, JSONL metadata/text only",
       "stroke_dasharray": "3 6",
+      "method_detail": "128-episode JSONL metadata/text MLP baselines.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -64,6 +72,8 @@
       "kind": "complete_128_episode_raw_feature_baseline",
       "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
       "stroke_dasharray": "8 4",
+      "method_detail": "128-episode 4430-dim sensor NPZ simple heads; tasks 15/19 use compact proxies.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -75,6 +85,8 @@
       "kind": "complete_128_episode_raw_feature_baseline",
       "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
       "stroke_dasharray": "2 5",
+      "method_detail": "128-episode 4430-dim sensor NPZ MLP heads; tasks 15/19 use compact proxies.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -86,6 +98,8 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "7 7",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics on task-aligned JSON outputs.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -97,6 +111,8 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "4 7",
+      "method_detail": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -108,6 +124,8 @@
       "kind": "partial_128_episode_world_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "2 7",
+      "method_detail": "Verified Cosmos3-Nano future-window compatibility metrics.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 5,
       "coverage_fraction": 0.25
     }
@@ -117,11 +135,13 @@
       "task_number": 1,
       "task_id": "timeline_action",
       "label": "Action Recognition",
+      "axis_label": "01 Action Recognition",
       "short_label": "Action",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05,
@@ -201,11 +221,13 @@
       "task_number": 2,
       "task_id": "timeline_subtask",
       "label": "Procedure Step Recognition",
+      "axis_label": "02 Procedure Step Recognition",
       "short_label": "Step",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05056355513846935,
@@ -277,11 +299,13 @@
       "task_number": 3,
       "task_id": "transition_detection",
       "label": "Action Boundary Detection",
+      "axis_label": "03 Action Boundary Detection",
       "short_label": "Boundary",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.6118237590630229,
@@ -361,11 +385,13 @@
       "task_number": 4,
       "task_id": "next_action",
       "label": "Next-Action Prediction",
+      "axis_label": "04 Next-Action Prediction",
       "short_label": "Next act",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05925925925925927,
@@ -445,11 +471,13 @@
       "task_number": 5,
       "task_id": "hand_trajectory_forecast",
       "label": "Hand Trajectory Forecasting",
+      "axis_label": "05 Hand Trajectory Forecasting",
       "short_label": "Hand traj",
       "origin": "original_public_sample_tasks",
       "metric_key": "mpjpe",
       "metric_name": "MPJPE",
       "metric_direction": "lower",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.8646570444107056,
@@ -489,11 +517,13 @@
       "task_number": 6,
       "task_id": "contact_prediction",
       "label": "Contact State Prediction",
+      "axis_label": "06 Contact State Prediction",
       "short_label": "Contact",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 1.0,
@@ -573,11 +603,13 @@
       "task_number": 7,
       "task_id": "object_relevance",
       "label": "Object Relevance Prediction",
+      "axis_label": "07 Object Relevance Prediction",
       "short_label": "Objects",
       "origin": "original_public_sample_tasks",
       "metric_key": "micro_f1",
       "metric_name": "micro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.18034382095361662,
@@ -649,11 +681,13 @@
       "task_number": 8,
       "task_id": "caption_grounding",
       "label": "Language Grounding",
+      "axis_label": "08 Language Grounding",
       "short_label": "Language",
       "origin": "original_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.016023479050338015,
@@ -701,11 +735,13 @@
       "task_number": 9,
       "task_id": "cross_modal_retrieval",
       "label": "Cross-Modal Retrieval",
+      "axis_label": "09 Cross-Modal Retrieval",
       "short_label": "X-modal",
       "origin": "original_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.26925966892956127,
@@ -753,11 +789,13 @@
       "task_number": 10,
       "task_id": "modality_reconstruction",
       "label": "Cross-Modal Reconstruction",
+      "axis_label": "10 Cross-Modal Reconstruction",
       "short_label": "Recon",
       "origin": "original_public_sample_tasks",
       "metric_key": "r2",
       "metric_name": "R2",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": -0.015271898913936655,
@@ -797,11 +835,13 @@
       "task_number": 11,
       "task_id": "temporal_order",
       "label": "Temporal Order Verification",
+      "axis_label": "11 Temporal Order Verification",
       "short_label": "Order",
       "origin": "original_public_sample_tasks",
       "metric_key": "f1",
       "metric_name": "F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.5399515738498789,
@@ -849,11 +889,13 @@
       "task_number": 12,
       "task_id": "misalignment_detection",
       "label": "Multimodal Synchronization Detection",
+      "axis_label": "12 Multimodal Synchronization Detection",
       "short_label": "Sync",
       "origin": "original_public_sample_tasks",
       "metric_key": "f1",
       "metric_name": "F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.5051698670605613,
@@ -893,11 +935,13 @@
       "task_number": 13,
       "task_id": "long_horizon_next_action",
       "label": "Long-Horizon Next-Action Forecasting",
+      "axis_label": "13 Long-Horizon Next-Action Forecasting",
       "short_label": "Long act",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.07499999999999998,
@@ -937,11 +981,13 @@
       "task_number": 14,
       "task_id": "next_subtask_forecast",
       "label": "Long-Horizon Next-Subtask Forecasting",
+      "axis_label": "14 Long-Horizon Next-Subtask Forecasting",
       "short_label": "Long step",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.04545454545454545,
@@ -981,11 +1027,13 @@
       "task_number": 15,
       "task_id": "interaction_text_prediction",
       "label": "Interaction Text Prediction",
+      "axis_label": "15 Interaction Text Prediction",
       "short_label": "Interact txt",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": true,
       "values": {
         "minimal": {
           "raw": 0.04444444444444444,
@@ -1025,11 +1073,13 @@
       "task_number": 16,
       "task_id": "action_object_relation",
       "label": "Action-Object Relation Prediction",
+      "axis_label": "16 Action-Object Relation Prediction",
       "short_label": "Act+obj",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.0,
@@ -1069,11 +1119,13 @@
       "task_number": 17,
       "task_id": "object_set_forecast",
       "label": "Future Object-Set Forecasting",
+      "axis_label": "17 Future Object-Set Forecasting",
       "short_label": "Future obj",
       "origin": "additional_public_sample_tasks",
       "metric_key": "micro_f1",
       "metric_name": "micro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.16939890710382516,
@@ -1113,11 +1165,13 @@
       "task_number": 18,
       "task_id": "imu_to_hand_pose",
       "label": "IMU-to-Hand Pose Reconstruction",
+      "axis_label": "18 IMU-to-Hand Pose Reconstruction",
       "short_label": "IMU->hand",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mae",
       "metric_name": "MAE",
       "metric_direction": "lower",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.042049407958984375,
@@ -1157,11 +1211,13 @@
       "task_number": 19,
       "task_id": "camera_view_sync_retrieval",
       "label": "Camera-View Synchronization Retrieval",
+      "axis_label": "19 Camera-View Synchronization Retrieval",
       "short_label": "Cam sync",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
+      "raw128_proxy_axis": true,
       "values": {
         "minimal": {
           "raw": 0.4943004846572876,
@@ -1201,11 +1257,13 @@
       "task_number": 20,
       "task_id": "time_to_transition",
       "label": "Time-to-Next-Transition Regression",
+      "axis_label": "20 Time-to-Next-Transition Regression",
       "short_label": "Time2bdry",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mae",
       "metric_name": "MAE frames",
       "metric_direction": "lower",
+      "raw128_proxy_axis": false,
       "values": {
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data/website_integrity.json

@@ -1,11 +1,11 @@
 {
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-  "generated_at_utc": "2026-06-16T08:50:28+00:00",
+  "generated_at_utc": "2026-06-16T09:46:21+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
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+    "local_references": 161,
     "external_reference_count": 121,
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@@ -80,8 +80,8 @@
       "name": "project_overview_precedes_progress_ledger",
       "status": "pass",
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+      "overview_index": 85350,
+      "evidence_index": 111180
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@@ -159,9 +159,9 @@
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       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
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@@ -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": 1726
+      "second_marker_index": 1880
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       "name": "suite_modality_atlas_contains_seven_cards",
@@ -264,7 +264,7 @@
       "name": "task_cards_use_human_research_names",
       "status": "pass",
       "reason": "The public task surface should use readable research task names.",
-      "marker_count": 3
+      "marker_count": 4
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@@ -277,8 +277,8 @@
     {
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-      "reference_count": 132,
-      "image_count": 26
+      "reference_count": 137,
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@@ -336,12 +336,12 @@
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@@ -491,7 +491,7 @@
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docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-16T08:49:31+00:00",
+  "generated_at_utc": "2026-06-16T09:44:42+00:00",
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@@ -465,7 +465,7 @@
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@@ -585,8 +585,8 @@
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@@ -596,8 +596,8 @@
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@@ -607,8 +607,8 @@
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@@ -740,7 +740,7 @@
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       "bytes": 15726,
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@@ -920,7 +920,7 @@
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@@ -1000,7 +1000,7 @@
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docs/data/figure_index.json

@@ -1,7 +1,7 @@
 {
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   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:49:33+00:00",
+  "generated_at_utc": "2026-06-16T09:44:44+00:00",
   "scope": "Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience-10M videos, annotations, RRD files, and Qwen weights are excluded.",
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@@ -359,13 +359,13 @@
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docs/data/mirror_parity.json

@@ -1,23 +1,16 @@
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@@ -832,44 +825,44 @@
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@@ -1464,7 +1457,7 @@
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@@ -1476,83 +1469,40 @@
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@@ -1605,7 +1555,7 @@
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@@ -1617,83 +1567,40 @@
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-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_artifacts_data",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:data/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_artifacts",
-      "kind": "hash_mismatch",
-      "path": "hf_artifacts:docs/data/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:data/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model_docs_data",
-      "kind": "hash_mismatch",
-      "path": "hf_model:docs/data/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    },
-    {
-      "group": "data/website_integrity.json",
-      "surface": "hf_model",
-      "kind": "hash_mismatch",
-      "path": "hf_model:metrics/website_integrity.json",
-      "expected_sha256": "af57a790c6ab36c3e3b53200a9f4f6bf0212ef5acad31c86205db9f895b61327",
-      "actual_sha256": "4abc99da0f4262b9a3c8bb3eca32658317a862ec3c80a3c87e68e091a39d869b"
-    }
-  ]
+  "failures": []
 }

docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:02+00:00",
+  "generated_at_utc": "2026-06-16T09:45:50+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": "2026-06-16T07:52:05+00:00"
+          "generated_at_utc": "2026-06-16T08:50:28+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:01+00:00"
+          "generated_at_utc": "2026-06-16T08:50:24+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:02+00:00"
+          "generated_at_utc": "2026-06-16T08:50:24+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:11+00:00"
+          "generated_at_utc": "2026-06-16T08:50:31+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T07:54:48+00:00"
+          "generated_at_utc": "2026-06-16T08:51:12+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T08:01:18+00:00"
+          "generated_at_utc": "2026-06-16T08:51:51+00:00"
         }
       },
       "failures": {}
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "20-task": 35,
-        "Qwen3-Omni": 146,
+        "20-task": 39,
+        "Qwen3-Omni": 148,
         "128-episode pilot": 1
       }
     },
@@ -130,8 +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/unified_task_model_radar.json": 17,
+        "assets/charts/unified_task_model_radar.svg": 17,
         "data/tier2_task_suite.json": 11
       }
     },

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:51:12+00:00",
+  "generated_at_utc": "2026-06-16T09:46:35+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:01+00:00",
+  "generated_at_utc": "2026-06-16T09:45:50+00:00",
   "rule": "A release is current when the automated reports pass and the live GitHub/Hugging Face mirrors are verified after publishing.",
   "automated_gates": [
     {

docs/data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:31+00:00",
+  "generated_at_utc": "2026-06-16T09:46:26+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,
@@ -9,7 +9,7 @@
     "eval_num_samples": 4032,
     "eval_json_validity_rate": 0.9990079365079365,
     "quality_target_met": true,
-    "historical_identifier_count": 1800,
+    "historical_identifier_count": 1802,
     "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
@@ -84,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=1800",
+      "detail": "historical identifiers found in result provenance files=1802",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -115,6 +115,24 @@
       ],
       "example": "export TRAINING_REPO=/path/to/ropedia-episode-task-suite"
     },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary.json",
+      "line": 2,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "\"dataset_jsonl\": \"/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset.jsonl\","
+    },
+    {
+      "classification": "historical_identifier_in_readiness_artifact",
+      "path": "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z/run_summary.json",
+      "line": 3,
+      "patterns": [
+        "ropedia-episode-task-suite"
+      ],
+      "example": "\"feature_manifest_json\": \"/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset_dense_20f_stride10/dataset_manifest.json\","
+    },
     {
       "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
@@ -400,30 +418,8 @@
         "ropedia-episode-task-suite"
       ],
       "example": "{\"id\": \"xperience-10m-sample:qa:51\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1020, \"end_frame\": 1039, \"num_frames\": 20}, \"media\": {\"video_path"
-    },
-    {
-      "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/dataset.jsonl",
-      "line": 27,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:52\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1040, \"end_frame\": 1059, \"num_frames\": 20}, \"media\": {\"video_path"
-    },
-    {
-      "classification": "historical_identifier_in_readiness_artifact",
-      "path": "results/omni_finetune/dataset.jsonl",
-      "line": 28,
-      "patterns": [
-        "qwen3_omni_32ep",
-        "xperience10m_qwen3_omni_32ep",
-        "ropedia-episode-task-suite"
-      ],
-      "example": "{\"id\": \"xperience-10m-sample:qa:53\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1060, \"end_frame\": 1079, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 1800,
+  "historical_identifier_total_count": 1802,
   "failures": []
 }

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:24+00:00",
+  "generated_at_utc": "2026-06-16T09:46:19+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": "2026-06-16T08:50:24+00:00",
+  "generated_at_utc": "2026-06-16T09:46:19+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

docs/data/unified_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:49:30+00:00",
+  "generated_at_utc": "2026-06-16T09:43:00+00:00",
   "task_count": 20,
   "normalization_policy": {
     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
@@ -20,6 +20,8 @@
       "kind": "full_20_task_baseline",
       "scope": "1 public sample episode",
       "stroke_dasharray": null,
+      "method_detail": "Single-episode simple heads over the public sample split.",
+      "plotted_as": "filled polygon",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -31,6 +33,8 @@
       "kind": "full_20_task_baseline",
       "scope": "1 public sample episode",
       "stroke_dasharray": null,
+      "method_detail": "Single-episode compact PyTorch MLP heads on the same 20 task contracts.",
+      "plotted_as": "filled polygon",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -42,6 +46,8 @@
       "kind": "partial_128_episode_metadata_baseline",
       "scope": "128 selected episodes, JSONL metadata/text only",
       "stroke_dasharray": "9 6",
+      "method_detail": "128-episode JSONL metadata/text simple baselines.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 8,
       "coverage_fraction": 0.4
     },
@@ -53,6 +59,8 @@
       "kind": "partial_128_episode_metadata_baseline",
       "scope": "128 selected episodes, JSONL metadata/text only",
       "stroke_dasharray": "3 6",
+      "method_detail": "128-episode JSONL metadata/text MLP baselines.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -64,6 +72,8 @@
       "kind": "complete_128_episode_raw_feature_baseline",
       "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
       "stroke_dasharray": "8 4",
+      "method_detail": "128-episode 4430-dim sensor NPZ simple heads; tasks 15/19 use compact proxies.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -75,6 +85,8 @@
       "kind": "complete_128_episode_raw_feature_baseline",
       "scope": "128 selected episodes, staged 4430-dim sensor NPZ features; 2 compact proxy axes",
       "stroke_dasharray": "2 5",
+      "method_detail": "128-episode 4430-dim sensor NPZ MLP heads; tasks 15/19 use compact proxies.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 20,
       "coverage_fraction": 1.0
     },
@@ -86,6 +98,8 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "7 7",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics on task-aligned JSON outputs.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -97,6 +111,8 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "4 7",
+      "method_detail": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 6,
       "coverage_fraction": 0.3
     },
@@ -108,6 +124,8 @@
       "kind": "partial_128_episode_world_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "2 7",
+      "method_detail": "Verified Cosmos3-Nano future-window compatibility metrics.",
+      "plotted_as": "colored point overlay",
       "covered_task_count": 5,
       "coverage_fraction": 0.25
     }
@@ -117,11 +135,13 @@
       "task_number": 1,
       "task_id": "timeline_action",
       "label": "Action Recognition",
+      "axis_label": "01 Action Recognition",
       "short_label": "Action",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05,
@@ -201,11 +221,13 @@
       "task_number": 2,
       "task_id": "timeline_subtask",
       "label": "Procedure Step Recognition",
+      "axis_label": "02 Procedure Step Recognition",
       "short_label": "Step",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05056355513846935,
@@ -277,11 +299,13 @@
       "task_number": 3,
       "task_id": "transition_detection",
       "label": "Action Boundary Detection",
+      "axis_label": "03 Action Boundary Detection",
       "short_label": "Boundary",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.6118237590630229,
@@ -361,11 +385,13 @@
       "task_number": 4,
       "task_id": "next_action",
       "label": "Next-Action Prediction",
+      "axis_label": "04 Next-Action Prediction",
       "short_label": "Next act",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.05925925925925927,
@@ -445,11 +471,13 @@
       "task_number": 5,
       "task_id": "hand_trajectory_forecast",
       "label": "Hand Trajectory Forecasting",
+      "axis_label": "05 Hand Trajectory Forecasting",
       "short_label": "Hand traj",
       "origin": "original_public_sample_tasks",
       "metric_key": "mpjpe",
       "metric_name": "MPJPE",
       "metric_direction": "lower",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.8646570444107056,
@@ -489,11 +517,13 @@
       "task_number": 6,
       "task_id": "contact_prediction",
       "label": "Contact State Prediction",
+      "axis_label": "06 Contact State Prediction",
       "short_label": "Contact",
       "origin": "original_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 1.0,
@@ -573,11 +603,13 @@
       "task_number": 7,
       "task_id": "object_relevance",
       "label": "Object Relevance Prediction",
+      "axis_label": "07 Object Relevance Prediction",
       "short_label": "Objects",
       "origin": "original_public_sample_tasks",
       "metric_key": "micro_f1",
       "metric_name": "micro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.18034382095361662,
@@ -649,11 +681,13 @@
       "task_number": 8,
       "task_id": "caption_grounding",
       "label": "Language Grounding",
+      "axis_label": "08 Language Grounding",
       "short_label": "Language",
       "origin": "original_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.016023479050338015,
@@ -701,11 +735,13 @@
       "task_number": 9,
       "task_id": "cross_modal_retrieval",
       "label": "Cross-Modal Retrieval",
+      "axis_label": "09 Cross-Modal Retrieval",
       "short_label": "X-modal",
       "origin": "original_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.26925966892956127,
@@ -753,11 +789,13 @@
       "task_number": 10,
       "task_id": "modality_reconstruction",
       "label": "Cross-Modal Reconstruction",
+      "axis_label": "10 Cross-Modal Reconstruction",
       "short_label": "Recon",
       "origin": "original_public_sample_tasks",
       "metric_key": "r2",
       "metric_name": "R2",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": -0.015271898913936655,
@@ -797,11 +835,13 @@
       "task_number": 11,
       "task_id": "temporal_order",
       "label": "Temporal Order Verification",
+      "axis_label": "11 Temporal Order Verification",
       "short_label": "Order",
       "origin": "original_public_sample_tasks",
       "metric_key": "f1",
       "metric_name": "F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.5399515738498789,
@@ -849,11 +889,13 @@
       "task_number": 12,
       "task_id": "misalignment_detection",
       "label": "Multimodal Synchronization Detection",
+      "axis_label": "12 Multimodal Synchronization Detection",
       "short_label": "Sync",
       "origin": "original_public_sample_tasks",
       "metric_key": "f1",
       "metric_name": "F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.5051698670605613,
@@ -893,11 +935,13 @@
       "task_number": 13,
       "task_id": "long_horizon_next_action",
       "label": "Long-Horizon Next-Action Forecasting",
+      "axis_label": "13 Long-Horizon Next-Action Forecasting",
       "short_label": "Long act",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.07499999999999998,
@@ -937,11 +981,13 @@
       "task_number": 14,
       "task_id": "next_subtask_forecast",
       "label": "Long-Horizon Next-Subtask Forecasting",
+      "axis_label": "14 Long-Horizon Next-Subtask Forecasting",
       "short_label": "Long step",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
+      "raw128_proxy_axis": false,
       "values": {
         "minimal": {
           "raw": 0.04545454545454545,
@@ -981,11 +1027,13 @@
       "task_number": 15,
       "task_id": "interaction_text_prediction",
       "label": "Interaction Text Prediction",
+      "axis_label": "15 Interaction Text Prediction",
       "short_label": "Interact txt",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
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@@ -1025,11 +1073,13 @@
       "task_number": 16,
       "task_id": "action_object_relation",
       "label": "Action-Object Relation Prediction",
+      "axis_label": "16 Action-Object Relation Prediction",
       "short_label": "Act+obj",
       "origin": "additional_public_sample_tasks",
       "metric_key": "macro_f1",
       "metric_name": "macro-F1",
       "metric_direction": "higher",
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@@ -1069,11 +1119,13 @@
       "task_number": 17,
       "task_id": "object_set_forecast",
       "label": "Future Object-Set Forecasting",
+      "axis_label": "17 Future Object-Set Forecasting",
       "short_label": "Future obj",
       "origin": "additional_public_sample_tasks",
       "metric_key": "micro_f1",
       "metric_name": "micro-F1",
       "metric_direction": "higher",
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@@ -1113,11 +1165,13 @@
       "task_number": 18,
       "task_id": "imu_to_hand_pose",
       "label": "IMU-to-Hand Pose Reconstruction",
+      "axis_label": "18 IMU-to-Hand Pose Reconstruction",
       "short_label": "IMU->hand",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mae",
       "metric_name": "MAE",
       "metric_direction": "lower",
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@@ -1157,11 +1211,13 @@
       "task_number": 19,
       "task_id": "camera_view_sync_retrieval",
       "label": "Camera-View Synchronization Retrieval",
+      "axis_label": "19 Camera-View Synchronization Retrieval",
       "short_label": "Cam sync",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mrr",
       "metric_name": "MRR",
       "metric_direction": "higher",
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@@ -1201,11 +1257,13 @@
       "task_number": 20,
       "task_id": "time_to_transition",
       "label": "Time-to-Next-Transition Regression",
+      "axis_label": "20 Time-to-Next-Transition Regression",
       "short_label": "Time2bdry",
       "origin": "additional_public_sample_tasks",
       "metric_key": "mae",
       "metric_name": "MAE frames",
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@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T08:50:28+00:00",
+  "generated_at_utc": "2026-06-16T09:46:21+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
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@@ -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": 76641,
-      "evidence_index": 102471
+      "overview_index": 85350,
+      "evidence_index": 111180
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@@ -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": 76641,
-      "protocol_index": 98652,
-      "evidence_index": 102471
+      "overview_index": 85350,
+      "protocol_index": 107361,
+      "evidence_index": 111180
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     {
       "name": "evaluation_protocol_links_json",
@@ -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": 1726
+      "second_marker_index": 1880
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     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -264,7 +264,7 @@
       "name": "task_cards_use_human_research_names",
       "status": "pass",
       "reason": "The public task surface should use readable research task names.",
-      "marker_count": 3
+      "marker_count": 4
     }
   ],
   "html_pages": [
@@ -277,8 +277,8 @@
     {
       "path": "index.html",
       "id_count": 90,
-      "reference_count": 132,
-      "image_count": 26
+      "reference_count": 137,
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     {
       "path": "research_roadmap.html",
@@ -336,12 +336,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 131868,
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@@ -491,7 +491,7 @@
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docs/index.html

@@ -2312,12 +2312,275 @@
       width: 100%;
       height: 6px;
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+      max-width: 1180px;
+      margin-top: 34px;
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+      border: 1px solid rgba(204, 255, 160, 0.16);
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+      background: #020502;
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+      height: min(430px, 44vh);
+      min-height: 300px;
+      object-fit: contain;
+      background: #020502;
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+      flex-direction: column;
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+      height: 10px;
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+      gap: 6px;
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+        grid-template-columns: minmax(0, 0.92fr) minmax(470px, 0.78fr);
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+        min-height: min(880px, calc(100vh - 24px));
+        padding: 92px 0 58px;
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+      .hero-inner > div:first-child {
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+        max-width: 660px;
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+        max-width: 520px;
+        margin-top: 28px;
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+      .hero-task-strip span {
+        font-size: 9px;
+        padding: 5px 6px;
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+      .hero-method {
+        padding: 8px;
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+      .hero-method span {
+        font-size: 10.5px;
+        line-height: 1.25;
+      }
+    }
     @media (max-width: 960px) {
       .hero-inner, .two-col { grid-template-columns: 1fr; }
       .hero,
       .hero-inner { min-height: 0; }
       .hero-inner { padding: 76px 0 58px; }
       .hero-panel { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+      .hero-radar-layout { grid-template-columns: 1fr; }
+      .hero-radar-frame img { height: min(430px, 56vw); }
       .signal:nth-child(2n + 1) { border-right: 0; }
       .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); }
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@@ -2398,6 +2661,12 @@
           url("assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl") center / cover no-repeat;
       }
       .hero-panel { grid-template-columns: 1fr; }
+      .hero-radar-panel { padding: 12px; }
+      .hero-radar-top { display: block; }
+      .hero-radar-top span { display: block; margin-top: 5px; }
+      .hero-radar-frame img { height: 320px; min-height: 260px; }
+      .hero-radar-stats,
+      .hero-task-strip { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .signal { border-right: 0; border-bottom: 1px solid rgba(245, 247, 240, 0.10); }
       .signal:last-child { border-bottom: 0; }
       .brief-panel { padding: 18px; }
@@ -2497,18 +2766,46 @@
           <div class="stat"><strong>20</strong><span>unified task contracts</span></div>
         </div>
       </div>
-      <div class="hero-panel" aria-label="Feature allocation summary">
-        <div class="panel-top">
-          <span>current feature allocation</span>
-          <span>aligned window</span>
+      <div class="hero-radar-panel" aria-label="Unified 20-task radar comparison">
+        <div class="hero-radar-top">
+          <strong>home radar comparison</strong>
+          <span>20 named tasks / 9 method series / raw128 complete with 2 documented proxy axes</span>
+        </div>
+        <div class="hero-radar-layout">
+          <a class="hero-radar-frame" href="#suite" aria-label="Open full unified 20-task model radar">
+            <img src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v4" alt="Unified 20-task model radar with full task-name key, method legend, coverage counts, and raw128 proxy notes">
+          </a>
+          <div class="hero-radar-copy">
+            <h2>Model comparison uses explicit task names and method contracts.</h2>
+            <p>The full SVG names every axis and keeps method coverage, raw metric sources, and proxy notes attached to the same comparison view.</p>
+            <div class="hero-radar-stats" aria-label="Radar coverage summary">
+              <div class="hero-radar-stat"><strong>20/20</strong><span>task axes named</span></div>
+              <div class="hero-radar-stat"><strong>9</strong><span>method series</span></div>
+              <div class="hero-radar-stat"><strong>40/40</strong><span>raw128 pass</span></div>
+              <div class="hero-radar-stat"><strong>34,269</strong><span>128ep windows</span></div>
+            </div>
+            <div class="hero-method-list" aria-label="Method families shown in the radar">
+              <div class="hero-method" style="--method-color:#67e8d1"><strong>Minimal + Neural MLP</strong><span>Single public-sample episode, full 20-task filled polygons.</span></div>
+              <div class="hero-method" style="--method-color:#f59e0b"><strong>128ep Metadata + Raw Baselines</strong><span>Simple and MLP heads; raw NPZ features cover all 20 axes with tasks 15 and 19 marked as compact proxies.</span></div>
+              <div class="hero-method" style="--method-color:#9bb8ff"><strong>Qwen3-Omni + Cosmos</strong><span>Verified held-out model branches plotted only on task-aligned public metrics.</span></div>
+            </div>
+            <div class="hero-task-strip" aria-label="Radar task axis examples">
+              <span>01 Action Recognition</span>
+              <span>05 Hand Trajectory Forecasting</span>
+              <span>08 Language Grounding</span>
+              <span>12 Multimodal Sync Detection</span>
+              <span>15 Interaction Text Prediction</span>
+              <span>18 IMU-to-Hand Pose Reconstruction</span>
+              <span>19 Camera-View Sync Retrieval</span>
+              <span>20 Time-to-Next-Transition Regression</span>
+            </div>
+            <div class="hero-radar-links">
+              <a href="#suite">Open full radar</a>
+              <a href="assets/charts/unified_task_model_radar.svg">Open SVG</a>
+              <a href="data/unified_task_model_radar.json">Open radar JSON</a>
+            </div>
+          </div>
         </div>
-        <div class="signal"><code>mocap</code><div class="track"><span style="--w:24.8%;--c:#ccffa0"></span></div><strong>2,121</strong></div>
-        <div class="signal"><code>camera+imu</code><div class="track"><span style="--w:1.5%;--c:#7ae5c3"></span></div><strong>126</strong></div>
-        <div class="signal"><code>depth</code><div class="track"><span style="--w:11.5%;--c:#d8f4a5"></span></div><strong>980</strong></div>
-        <div class="signal"><code>video</code><div class="track"><span style="--w:48.2%;--c:#9bdfff"></span></div><strong>4,116</strong></div>
-        <div class="signal"><code>audio</code><div class="track"><span style="--w:2.0%;--c:#f0a45e"></span></div><strong>168</strong></div>
-        <div class="signal"><code>language</code><div class="track"><span style="--w:10.5%;--c:#f4f8ef"></span></div><strong>896</strong></div>
-        <div class="signal"><code>static</code><div class="track"><span style="--w:1.6%;--c:#a5afa2"></span></div><strong>139</strong></div>
       </div>
     </div>
   </header>
@@ -3191,14 +3488,14 @@
         <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; 128-episode metadata, Qwen3, and Cosmos branches are colored overlays only on task-aligned public metrics.</p>
+            <p>The radar uses all 20 tasks as axes and lists each full task name in the chart key. Minimal and neural MLP heads are filled single-episode polygons; 128-episode metadata/raw baselines, Qwen3, and Cosmos branches are colored method overlays with explicit coverage counts.</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>
+            <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, method details, sources, and the two raw128 compact proxy notes remain in the JSON mirror.</p>
           </article>
         </div>
-        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v2" alt="Unified 20-task radar comparing minimal and neural MLP baselines with 128-episode metadata, Qwen3, and Cosmos3 task-aligned overlays">
+        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v4" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, coverage counts, and proxy notes">
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

index.html

@@ -2312,12 +2312,275 @@
       width: 100%;
       height: 6px;
     }
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+      box-shadow: none;
+      backdrop-filter: blur(12px);
+      padding: 16px;
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+      gap: 14px;
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+      border-bottom: 1px solid rgba(245, 247, 240, 0.12);
+      color: rgba(245, 247, 240, 0.68);
+      font-family: var(--font-mono);
+      font-size: 12px;
+      text-transform: uppercase;
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+      display: block;
+      width: 100%;
+      height: min(430px, 44vh);
+      min-height: 300px;
+      object-fit: contain;
+      background: #020502;
+    }
+    .hero-radar-copy {
+      min-width: 0;
+      display: flex;
+      flex-direction: column;
+      gap: 13px;
+    }
+    .hero-radar-copy h2 {
+      margin: 0;
+      color: #f7fff0;
+      font-family: var(--font-ui);
+      font-size: clamp(22px, 2.1vw, 32px);
+      line-height: 1.04;
+      letter-spacing: 0;
+      overflow-wrap: anywhere;
+    }
+    .hero-radar-copy p {
+      margin: 0;
+      color: rgba(245, 247, 240, 0.72);
+      font-size: 14px;
+      line-height: 1.52;
+    }
+    .hero-radar-stats,
+    .hero-method-list {
+      display: grid;
+      gap: 8px;
+    }
+    .hero-radar-stats {
+      grid-template-columns: repeat(4, minmax(0, 1fr));
+    }
+    .hero-radar-stat,
+    .hero-method {
+      border: 1px solid rgba(204, 255, 160, 0.14);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.54);
+      min-width: 0;
+    }
+    .hero-radar-stat {
+      padding: 10px;
+    }
+    .hero-radar-stat strong {
+      display: block;
+      color: var(--green);
+      font-family: var(--font-mono);
+      font-size: 15px;
+      line-height: 1;
+      font-variant-numeric: tabular-nums;
+    }
+    .hero-radar-stat span {
+      display: block;
+      margin-top: 6px;
+      color: rgba(245, 247, 240, 0.66);
+      font-size: 11px;
+      line-height: 1.2;
+    }
+    .hero-method {
+      display: grid;
+      grid-template-columns: 10px minmax(0, 1fr);
+      gap: 10px;
+      padding: 10px;
+      align-items: start;
+    }
+    .hero-method::before {
+      content: "";
+      grid-column: 1;
+      grid-row: 1 / span 2;
+      width: 10px;
+      height: 10px;
+      margin-top: 4px;
+      border-radius: 999px;
+      background: var(--method-color);
+      box-shadow: 0 0 18px color-mix(in srgb, var(--method-color), transparent 48%);
+    }
+    .hero-method strong,
+    .hero-method span {
+      grid-column: 2;
+    }
+    .hero-method strong {
+      display: block;
+      color: #f7fff0;
+      font-family: var(--font-ui);
+      font-size: 13px;
+      line-height: 1.18;
+    }
+    .hero-method span {
+      display: block;
+      margin-top: 3px;
+      color: rgba(245, 247, 240, 0.66);
+      font-size: 11px;
+      line-height: 1.32;
+    }
+    .hero-task-strip {
+      display: grid;
+      grid-template-columns: repeat(4, minmax(0, 1fr));
+      gap: 6px;
+      margin-top: 2px;
+    }
+    .hero-task-strip span {
+      min-width: 0;
+      border: 1px solid rgba(245, 247, 240, 0.10);
+      border-radius: 5px;
+      background: rgba(255, 255, 255, 0.04);
+      padding: 6px 7px;
+      color: rgba(245, 247, 240, 0.80);
+      font-size: 10px;
+      line-height: 1.15;
+      overflow-wrap: anywhere;
+    }
+    .hero-radar-links {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+      margin-top: auto;
+    }
+    .hero-radar-links a {
+      min-height: 34px;
+      display: inline-flex;
+      align-items: center;
+      border: 1px solid rgba(204, 255, 160, 0.18);
+      border-radius: 6px;
+      background: rgba(2, 5, 2, 0.42);
+      color: var(--cyan);
+      padding: 7px 10px;
+      font-family: var(--font-btn);
+      font-size: 12px;
+      font-weight: 700;
+      text-decoration: none;
+    }
+    .hero-radar-links a:hover {
+      border-color: var(--green);
+      color: var(--ink);
+    }
+    @media (min-width: 1121px) {
+      .hero-inner {
+        grid-template-columns: minmax(0, 0.92fr) minmax(470px, 0.78fr);
+        gap: 34px;
+        align-items: center;
+        min-height: min(880px, calc(100vh - 24px));
+        padding: 92px 0 58px;
+      }
+      .hero-inner > div:first-child {
+        max-width: 720px;
+      }
+      .hero h1 {
+        font-size: clamp(46px, 5.4vw, 78px);
+      }
+      .hero-copy {
+        max-width: 660px;
+        font-size: 17px;
+        line-height: 1.58;
+      }
+      .hero-actions {
+        margin-top: 28px;
+      }
+      .hero-stats {
+        grid-template-columns: repeat(2, minmax(0, 1fr));
+        max-width: 520px;
+        margin-top: 28px;
+      }
+      .hero-radar-panel {
+        max-width: none;
+        margin-top: 0;
+      }
+      .hero-radar-top {
+        align-items: start;
+      }
+      .hero-radar-top span {
+        max-width: 300px;
+        text-align: right;
+        line-height: 1.35;
+      }
+      .hero-radar-layout {
+        grid-template-columns: 1fr;
+        gap: 12px;
+      }
+      .hero-radar-frame img {
+        height: 250px;
+        min-height: 230px;
+      }
+      .hero-radar-copy {
+        gap: 8px;
+      }
+      .hero-radar-copy h2 {
+        font-size: 22px;
+        line-height: 1.06;
+      }
+      .hero-radar-copy p {
+        font-size: 12px;
+        line-height: 1.40;
+      }
+      .hero-radar-stats {
+        grid-template-columns: repeat(4, minmax(0, 1fr));
+      }
+      .hero-task-strip {
+        grid-template-columns: repeat(4, minmax(0, 1fr));
+      }
+      .hero-task-strip span {
+        font-size: 9px;
+        padding: 5px 6px;
+      }
+      .hero-method {
+        padding: 8px;
+      }
+      .hero-method span {
+        font-size: 10.5px;
+        line-height: 1.25;
+      }
+    }
     @media (max-width: 960px) {
       .hero-inner, .two-col { grid-template-columns: 1fr; }
       .hero,
       .hero-inner { min-height: 0; }
       .hero-inner { padding: 76px 0 58px; }
       .hero-panel { grid-template-columns: repeat(2, minmax(0, 1fr)); }
+      .hero-radar-layout { grid-template-columns: 1fr; }
+      .hero-radar-frame img { height: min(430px, 56vw); }
       .signal:nth-child(2n + 1) { border-right: 0; }
       .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); }
       .section-tabs { padding-top: 10px; }
@@ -2398,6 +2661,12 @@
           url("assets/task_suite_infographic.png?v=xperience10m-taskfirst-v13-modality-xl") center / cover no-repeat;
       }
       .hero-panel { grid-template-columns: 1fr; }
+      .hero-radar-panel { padding: 12px; }
+      .hero-radar-top { display: block; }
+      .hero-radar-top span { display: block; margin-top: 5px; }
+      .hero-radar-frame img { height: 320px; min-height: 260px; }
+      .hero-radar-stats,
+      .hero-task-strip { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .signal { border-right: 0; border-bottom: 1px solid rgba(245, 247, 240, 0.10); }
       .signal:last-child { border-bottom: 0; }
       .brief-panel { padding: 18px; }
@@ -2497,18 +2766,46 @@
           <div class="stat"><strong>20</strong><span>unified task contracts</span></div>
         </div>
       </div>
-      <div class="hero-panel" aria-label="Feature allocation summary">
-        <div class="panel-top">
-          <span>current feature allocation</span>
-          <span>aligned window</span>
+      <div class="hero-radar-panel" aria-label="Unified 20-task radar comparison">
+        <div class="hero-radar-top">
+          <strong>home radar comparison</strong>
+          <span>20 named tasks / 9 method series / raw128 complete with 2 documented proxy axes</span>
+        </div>
+        <div class="hero-radar-layout">
+          <a class="hero-radar-frame" href="#suite" aria-label="Open full unified 20-task model radar">
+            <img src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v4" alt="Unified 20-task model radar with full task-name key, method legend, coverage counts, and raw128 proxy notes">
+          </a>
+          <div class="hero-radar-copy">
+            <h2>Model comparison uses explicit task names and method contracts.</h2>
+            <p>The full SVG names every axis and keeps method coverage, raw metric sources, and proxy notes attached to the same comparison view.</p>
+            <div class="hero-radar-stats" aria-label="Radar coverage summary">
+              <div class="hero-radar-stat"><strong>20/20</strong><span>task axes named</span></div>
+              <div class="hero-radar-stat"><strong>9</strong><span>method series</span></div>
+              <div class="hero-radar-stat"><strong>40/40</strong><span>raw128 pass</span></div>
+              <div class="hero-radar-stat"><strong>34,269</strong><span>128ep windows</span></div>
+            </div>
+            <div class="hero-method-list" aria-label="Method families shown in the radar">
+              <div class="hero-method" style="--method-color:#67e8d1"><strong>Minimal + Neural MLP</strong><span>Single public-sample episode, full 20-task filled polygons.</span></div>
+              <div class="hero-method" style="--method-color:#f59e0b"><strong>128ep Metadata + Raw Baselines</strong><span>Simple and MLP heads; raw NPZ features cover all 20 axes with tasks 15 and 19 marked as compact proxies.</span></div>
+              <div class="hero-method" style="--method-color:#9bb8ff"><strong>Qwen3-Omni + Cosmos</strong><span>Verified held-out model branches plotted only on task-aligned public metrics.</span></div>
+            </div>
+            <div class="hero-task-strip" aria-label="Radar task axis examples">
+              <span>01 Action Recognition</span>
+              <span>05 Hand Trajectory Forecasting</span>
+              <span>08 Language Grounding</span>
+              <span>12 Multimodal Sync Detection</span>
+              <span>15 Interaction Text Prediction</span>
+              <span>18 IMU-to-Hand Pose Reconstruction</span>
+              <span>19 Camera-View Sync Retrieval</span>
+              <span>20 Time-to-Next-Transition Regression</span>
+            </div>
+            <div class="hero-radar-links">
+              <a href="#suite">Open full radar</a>
+              <a href="assets/charts/unified_task_model_radar.svg">Open SVG</a>
+              <a href="data/unified_task_model_radar.json">Open radar JSON</a>
+            </div>
+          </div>
         </div>
-        <div class="signal"><code>mocap</code><div class="track"><span style="--w:24.8%;--c:#ccffa0"></span></div><strong>2,121</strong></div>
-        <div class="signal"><code>camera+imu</code><div class="track"><span style="--w:1.5%;--c:#7ae5c3"></span></div><strong>126</strong></div>
-        <div class="signal"><code>depth</code><div class="track"><span style="--w:11.5%;--c:#d8f4a5"></span></div><strong>980</strong></div>
-        <div class="signal"><code>video</code><div class="track"><span style="--w:48.2%;--c:#9bdfff"></span></div><strong>4,116</strong></div>
-        <div class="signal"><code>audio</code><div class="track"><span style="--w:2.0%;--c:#f0a45e"></span></div><strong>168</strong></div>
-        <div class="signal"><code>language</code><div class="track"><span style="--w:10.5%;--c:#f4f8ef"></span></div><strong>896</strong></div>
-        <div class="signal"><code>static</code><div class="track"><span style="--w:1.6%;--c:#a5afa2"></span></div><strong>139</strong></div>
       </div>
     </div>
   </header>
@@ -3191,14 +3488,14 @@
         <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; 128-episode metadata, Qwen3, and Cosmos branches are colored overlays only on task-aligned public metrics.</p>
+            <p>The radar uses all 20 tasks as axes and lists each full task name in the chart key. Minimal and neural MLP heads are filled single-episode polygons; 128-episode metadata/raw baselines, Qwen3, and Cosmos branches are colored method overlays with explicit coverage counts.</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>
+            <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, method details, sources, and the two raw128 compact proxy notes remain in the JSON mirror.</p>
           </article>
         </div>
-        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v2" alt="Unified 20-task radar comparing minimal and neural MLP baselines with 128-episode metadata, Qwen3, and Cosmos3 task-aligned overlays">
+        <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v4" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, coverage counts, and proxy notes">
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

scripts/build_unified_task_model_radar.py

@@ -186,6 +186,20 @@ SHORT_TASK_LABELS = {
     "time_to_transition": "Time2bdry",
 }
 
+METHOD_DETAILS = {
+    "minimal": "Single-episode simple heads over the public sample split.",
+    "neural_mlp": "Single-episode compact PyTorch MLP heads on the same 20 task contracts.",
+    "metadata128_simple": "128-episode JSONL metadata/text simple baselines.",
+    "metadata128_neural_mlp": "128-episode JSONL metadata/text MLP baselines.",
+    "raw128_simple": "128-episode 4430-dim sensor NPZ simple heads; tasks 15/19 use compact proxies.",
+    "raw128_neural_mlp": "128-episode 4430-dim sensor NPZ MLP heads; tasks 15/19 use compact proxies.",
+    "qwen3_omni_v6_lora": "Verified held-out Qwen3-Omni v6 LoRA metrics on task-aligned JSON outputs.",
+    "cosmos3_super_reasoner": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation.",
+    "cosmos3_nano_future_window": "Verified Cosmos3-Nano future-window compatibility metrics.",
+}
+
+PROXY_TASK_IDS = {"interaction_text_prediction", "camera_view_sync_retrieval"}
+
 
 def read_json(path: Path) -> dict[str, Any]:
     return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}
@@ -287,6 +301,40 @@ def svg_text(
     )
 
 
+def split_text(text: str, max_chars: int) -> list[str]:
+    words = text.split()
+    if not words:
+        return [""]
+    lines: list[str] = []
+    current = words[0]
+    for word in words[1:]:
+        if len(current) + 1 + len(word) <= max_chars:
+            current += " " + word
+        else:
+            lines.append(current)
+            current = word
+    lines.append(current)
+    return lines
+
+
+def svg_text_lines(
+    x: float,
+    y: float,
+    lines: list[str],
+    *,
+    size: int = 14,
+    fill: str = "#f4f8ef",
+    anchor: str = "start",
+    weight: int | str = 600,
+    line_height: float = 18,
+    opacity: float = 1.0,
+) -> list[str]:
+    return [
+        svg_text(x, y + idx * line_height, line, size=size, fill=fill, anchor=anchor, weight=weight, opacity=opacity)
+        for idx, line in enumerate(lines)
+    ]
+
+
 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 ""
@@ -366,11 +414,13 @@ def build_payload() -> dict[str, Any]:
                 "task_number": row["task_number"],
                 "task_id": row["task_id"],
                 "label": row.get("task_display_name", row["task_id"]),
+                "axis_label": f"{row['task_number']:02d} {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"),
+                "raw128_proxy_axis": row["task_id"] in PROXY_TASK_IDS,
                 "values": values,
             }
         )
@@ -382,6 +432,8 @@ def build_payload() -> dict[str, Any]:
             {
                 "id": series_id,
                 **spec,
+                "method_detail": METHOD_DETAILS.get(series_id, spec["scope"]),
+                "plotted_as": "filled polygon" if spec["kind"].startswith("full_20_task_baseline") else "colored point overlay",
                 "covered_task_count": covered,
                 "coverage_fraction": covered / max(len(tasks), 1),
             }
@@ -474,8 +526,8 @@ def build_payload() -> dict[str, Any]:
 
 
 def render_svg(payload: dict[str, Any]) -> str:
-    width, height = 1720, 1500
-    cx, cy, radius = 570, 585, 330
+    width, height = 1920, 1640
+    cx, cy, radius = 550, 760, 370
     tasks = payload["tasks"]
     n = len(tasks)
     angles = [-math.pi / 2 + 2 * math.pi * i / n for i in range(n)]
@@ -487,11 +539,28 @@ def render_svg(payload: dict[str, Any]) -> str:
         "</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="1444" 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 128ep metadata/raw and Qwen3/Cosmos overlays.", size=17, fill="#a5afa2", weight=560),
-        svg_text(70, 156, "Filled polygons: same 20 public-sample tasks. Points: 128-episode branches only where their public metrics map to that task.", size=15, fill="#a5afa2", weight=560),
+        '<rect x="28" y="28" width="1864" height="1584" rx="18" fill="#061006" fill-opacity="0.88" stroke="#ccffa0" stroke-opacity="0.22"/>',
+        svg_text(70, 86, "Unified 20-Task Model Radar", size=36, weight=800),
+        svg_text(70, 122, "Task names, methods, coverage, and metric normalization in one comparison view.", size=18, fill="#dce8d7", weight=650),
+        svg_text(70, 150, "Filled areas show single-episode baselines; colored points show 128-episode and foundation-model branches on task-aligned axes.", size=15, fill="#a5afa2", weight=560),
+    ]
+
+    chip_specs = [
+        ("20 task axes", "#ccffa0"),
+        ("2 baseline polygons", "#67e8d1"),
+        ("40/40 raw128 pass", "#f59e0b"),
+        ("2 compact proxy axes", "#f472b6"),
     ]
+    chip_x = 70
+    for label, color in chip_specs:
+        chip_w = 168 if len(label) < 15 else 206
+        parts.append(f'<rect x="{chip_x}" y="174" width="{chip_w}" 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=760))
+        chip_x += chip_w + 12
+
+    parts.append('<rect x="54" y="235" width="920" height="980" rx="14" fill="#020502" fill-opacity="0.42" stroke="#ccffa0" stroke-opacity="0.14"/>')
+    parts.append(svg_text(84, 276, "Normalized task scores", size=23, weight=800))
+    parts.append(svg_text(84, 302, "Each axis is one task. Longer radius means better after metric-direction normalization.", size=13, fill="#a5afa2", weight=560))
 
     for level in range(1, 6):
         r = radius * level / 5
@@ -503,14 +572,14 @@ def render_svg(payload: dict[str, Any]) -> str:
     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)
+        lx, ly = point(cx, cy, radius + 70, 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))
+        parts.append(svg_text(lx, ly - 7, f"{task['task_number']:02d}", size=12, fill="#ccffa0", anchor=anchor, weight=800, opacity=0.95))
+        parts.append(svg_text(lx, ly + 14, task["short_label"], size=12, fill="#dce8d7", anchor=anchor, weight=700))
 
     for series_id in ("minimal", "neural_mlp"):
         spec = SERIES[series_id]
@@ -543,38 +612,49 @@ def render_svg(payload: dict[str, Any]) -> str:
                 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="1030" 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))
+    legend_x, legend_y = 1030, 178
+    parts.append(f'<rect x="{legend_x - 30}" y="{legend_y - 38}" width="820" height="560" rx="14" fill="#020502" fill-opacity="0.58" stroke="#ccffa0" stroke-opacity="0.20"/>')
+    parts.append(svg_text(legend_x, legend_y, "Methods compared", size=25, weight=800))
+    parts.append(svg_text(legend_x, legend_y + 30, "Coverage is shown per method; raw metric values and sources stay in the JSON mirror.", size=13, fill="#a5afa2", weight=560))
 
-    cursor = legend_y + 100
+    cursor = legend_y + 74
     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"/>')
+        parts.append(f'<line x1="{legend_x}" y1="{cursor - 7}" x2="{legend_x + 50}" y2="{cursor - 7}" stroke="{color}" stroke-width="7" stroke-linecap="round"/>')
         if not record["kind"].startswith("full_20_task_baseline"):
-            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))
+            parts.append(f'<circle cx="{legend_x + 25}" cy="{cursor - 7}" r="7" fill="{color}" stroke="#020502" stroke-width="2"/>')
+        parts.append(svg_text(legend_x + 66, cursor - 12, record["label"], size=15, weight=800))
+        parts.append(svg_text(legend_x + 330, cursor - 12, f"{record['covered_task_count']}/20 axes", size=13, fill=color, weight=800))
+        detail_lines = split_text(METHOD_DETAILS.get(record["id"], record["scope"]), 64)[:2]
+        parts.extend(svg_text_lines(legend_x + 66, cursor + 8, detail_lines, size=11, fill="#a5afa2", weight=560, line_height=15))
         cursor += 50
 
-    cursor += 10
-    parts.append(svg_text(legend_x, cursor, "Model branch notes", size=20, weight=800))
-    cursor += 28
-    for card in payload["model_branch_cards"]:
-        parts.append(f'<rect x="{legend_x}" y="{cursor - 18}" width="445" height="64" 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 += 74
-
-    table_y = 1370
-    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, "128-episode metadata/raw, Qwen3, and Cosmos overlays are plotted only on semantically aligned task axes.", size=14, fill="#a5afa2", weight=560))
-    parts.append(svg_text(170, table_y + 44, "Raw128 tasks 15 and 19 are documented compact proxies because raw interaction strings and paired video-view embeddings are absent.", size=14, fill="#a5afa2", weight=560))
+    key_x, key_y = 1030, 780
+    parts.append(f'<rect x="{key_x - 30}" y="{key_y - 44}" width="820" height="610" rx="14" fill="#020502" fill-opacity="0.58" stroke="#ccffa0" stroke-opacity="0.20"/>')
+    parts.append(svg_text(key_x, key_y, "Task axis key", size=25, weight=800))
+    parts.append(svg_text(key_x, key_y + 30, "Full task names are listed here so the polygon remains readable at homepage scale.", size=13, fill="#a5afa2", weight=560))
+    for idx, task in enumerate(tasks):
+        col = 0 if idx < 10 else 1
+        row = idx if idx < 10 else idx - 10
+        x0 = key_x + col * 405
+        y0 = key_y + 74 + row * 48
+        proxy = task["task_id"] in PROXY_TASK_IDS
+        badge_fill = "#f472b6" if proxy else "#ccffa0"
+        parts.append(f'<rect x="{x0}" y="{y0 - 16}" width="36" height="26" rx="6" fill="{badge_fill}" fill-opacity="0.14" stroke="{badge_fill}" stroke-opacity="0.40"/>')
+        parts.append(svg_text(x0 + 18, y0 + 2, f"{task['task_number']:02d}", size=11, fill=badge_fill, anchor="middle", weight=800))
+        name_lines = split_text(str(task["label"]), 32)[:2]
+        parts.extend(svg_text_lines(x0 + 48, y0 - 3, name_lines, size=12, fill="#f4f8ef", weight=760, line_height=14))
+        metric_label = f"{task.get('metric_name') or task.get('metric_key')} / {'lower better' if task.get('metric_direction') == 'lower' else 'higher better'}"
+        if proxy:
+            metric_label += " / raw128 proxy"
+        parts.append(svg_text(x0 + 48, y0 + 29, metric_label, size=10, fill="#a5afa2", weight=560))
+
+    table_y = 1468
+    parts.append(f'<rect x="70" y="{table_y - 38}" width="1780" height="120" rx="12" fill="#020502" fill-opacity="0.58" stroke="#ccffa0" stroke-opacity="0.16"/>')
+    parts.append(svg_text(100, table_y - 10, "Reading rules", size=16, fill="#ccffa0", weight=800))
+    parts.append(svg_text(220, table_y - 10, "Radius is direction-normalized, so compare shape first and raw values second.", size=14, fill="#dce8d7", weight=650))
+    parts.append(svg_text(220, table_y + 18, "Raw128 completion: 18 direct task targets plus 2 compact proxies. Task 15 predicts the dominant caption/object/interaction hash bin; task 19 retrieves depth/audio sync from camera pose.", size=13, fill="#a5afa2", weight=560))
+    parts.append(svg_text(220, table_y + 44, "Single-episode task-head scores, 128-episode baselines, Qwen3, and Cosmos branches use different data/model contracts; sources and raw metrics are in docs/data/unified_task_model_radar.json.", size=13, fill="#a5afa2", weight=560))
 
     parts.append("</svg>")
     return "\n".join(parts) + "\n"