Add files using upload-large-folder tool

FIGURE_INDEX.md

@@ -32,6 +32,8 @@ Public figures, diagrams, charts, and derived modality thumbnails. Raw Xperience
 | 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` | 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. |
+| Single-episode 20-task model radar | `docs/assets/charts/single_episode_task_model_radar.svg` | 1920 x 1640 | `scripts/build_unified_task_model_radar.py` | Twenty-axis split radar for the one public-sample episode, comparing Minimal and Neural MLP as two complete 20/20 scored polygons. |
+| 128-episode 20-task model radar | `docs/assets/charts/episode128_task_model_radar.svg` | 1920 x 1640 | `scripts/build_unified_task_model_radar.py` | Twenty-axis split radar for selected 128-episode methods: raw-feature simple/NN as complete scored polygons and metadata/Qwen/Cosmos as task-aligned overlays. |
 | Feature block chart | `docs/assets/charts/feature_blocks.svg` | 1100 x 760 | `scripts/generate_visualizations.py` | Feature allocation by modality block. |
 | Minimal task score chart | `docs/assets/charts/episode_task_scores.svg` | 1100 x 556 | `scripts/generate_visualizations.py` | Minimal baseline metric snapshot across the task suite. |
 | Cross-modal retrieval chart | `docs/assets/charts/cross_modal_retrieval.svg` | 1100 x 284 | `scripts/generate_visualizations.py` | Retrieval behavior chart for the cross-modal task. |

PROJECT_README.md

@@ -332,6 +332,18 @@ and
 the reader-facing matrix is
 [`TASK_METHOD_20_RESULT_MATRIX.md`](TASK_METHOD_20_RESULT_MATRIX.md).
 
+For easier reading, the same source data is also split into two focused radars:
+
+![Single-episode 20-task model radar](docs/assets/charts/single_episode_task_model_radar.svg)
+
+![128-episode 20-task model radar](docs/assets/charts/episode128_task_model_radar.svg)
+
+The single-episode radar isolates Minimal vs Neural MLP, both with 20/20 scored
+public-sample axes. The 128-episode radar isolates metadata/raw baselines and
+Qwen3/Cosmos branches: raw-feature simple/NN baselines are the current complete
+20/20 scored multi-episode results, while metadata and foundation-model rows
+retain explicit scoreless records where no public target was evaluated.
+
 The website also includes a responsive native modality atlas backed by
 [`docs/data/modality_atlas.json`](docs/data/modality_atlas.json) and
 [`docs/assets/modalities/`](docs/assets/modalities/). Those assets are small
@@ -407,6 +419,8 @@ docs/
   data/summary_metrics.json         # website-readable metrics bundle
   data/task_suite_20.json           # unified 20-task suite bundle
   data/unified_task_model_radar.json # 20-task radar values and model-branch overlays
+  data/single_episode_task_model_radar.json # 1-episode split radar values
+  data/episode128_task_model_radar.json # 128-episode split radar values
   data/task_method_20_result_matrix.json # 9-method x 20-task result matrix
   data/evidence_contract.json       # machine-readable project scope
   data/artifact_index.json          # compact project-artifact catalog
@@ -429,6 +443,8 @@ docs/
   assets/qwen3_omni_lora_pipeline.png # Qwen3-Omni LoRA training-flow figure
   assets/task_architectures.png     # verified task-head architecture map
   assets/charts/unified_task_model_radar.svg # 20-task minimal/NN/Qwen/Cosmos radar
+  assets/charts/single_episode_task_model_radar.svg # 1-episode split radar
+  assets/charts/episode128_task_model_radar.svg # 128-episode split radar
   assets/charts/*.svg               # regenerated visualizations
 
 notes/
@@ -988,14 +1004,23 @@ stable artifact links. They should be read as the result bundle for tasks
 - [`TASK_SUITE_20.md`](TASK_SUITE_20.md)
 - [`docs/data/task_suite_20.json`](docs/data/task_suite_20.json)
 - [`docs/data/unified_task_model_radar.json`](docs/data/unified_task_model_radar.json)
+- [`docs/data/single_episode_task_model_radar.json`](docs/data/single_episode_task_model_radar.json)
+- [`docs/data/episode128_task_model_radar.json`](docs/data/episode128_task_model_radar.json)
+- [`docs/data/task_method_20_result_matrix.json`](docs/data/task_method_20_result_matrix.json)
 - [`TIER2_TASK_BASELINES.md`](results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md)
 - [`tier2_task_suite_results.json`](results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json)
 - [`docs/data/tier2_task_suite.json`](docs/data/tier2_task_suite.json)
 - [`unified_task_model_radar.svg`](docs/assets/charts/unified_task_model_radar.svg)
+- [`single_episode_task_model_radar.svg`](docs/assets/charts/single_episode_task_model_radar.svg)
+- [`episode128_task_model_radar.svg`](docs/assets/charts/episode128_task_model_radar.svg)
 - [`tier2_task_suite.svg`](docs/assets/charts/tier2_task_suite.svg)
 
 ![Unified 20-task model radar](docs/assets/charts/unified_task_model_radar.svg)
 
+![Single-episode 20-task model radar](docs/assets/charts/single_episode_task_model_radar.svg)
+
+![128-episode 20-task model radar](docs/assets/charts/episode128_task_model_radar.svg)
+
 ![Tasks 13-20 baseline chart](docs/assets/charts/tier2_task_suite.svg)
 
 | # | Task | Input | Output | Minimal | Neural MLP | Meaning |

README.md

@@ -63,7 +63,9 @@ The historical `tier2_task_suite` path is retained only for stable artifact
 links to tasks 13-20. The unified radar chart is published as
 `docs/assets/charts/unified_task_model_radar.svg` with values in
 `docs/data/unified_task_model_radar.json`; the 9-method by 20-task
-completion matrix is in `docs/data/task_method_20_result_matrix.json`.
+completion matrix is in `docs/data/task_method_20_result_matrix.json`. Split radars are in
+`docs/assets/charts/single_episode_task_model_radar.svg` and
+`docs/assets/charts/episode128_task_model_radar.svg`.
 
 ## Dataset Boundary
 

data/artifact_index.json

@@ -1,8 +1,8 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T10:59:46+00:00",
   "status": "pass",
-  "artifact_count": 179,
+  "artifact_count": 183,
   "missing": [],
   "by_kind": {
     "project_path": 14,
@@ -13,8 +13,8 @@
     "project_scope": 1,
     "source_alignment": 5,
     "evaluation_protocol": 7,
-    "website_data": 7,
-    "generated_figure": 5,
+    "website_data": 9,
+    "generated_figure": 7,
     "visualization_builder": 1,
     "model_result": 2,
     "result_interpretation": 5,
@@ -465,7 +465,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "c9c3258a8ba9680b75dffbee1bdac194312d491664412de1b25a3e8543e0da60"
+      "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
     },
     {
       "id": "source_alignment_validator",
@@ -586,7 +586,29 @@
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, and explicit scoreless status records.",
       "exists": true,
       "bytes": 231290,
-      "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+      "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
+    },
+    {
+      "id": "single_episode_task_model_radar_json",
+      "title": "Single-episode 20-task model radar JSON",
+      "path": "docs/data/single_episode_task_model_radar.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
+      "exists": true,
+      "bytes": 50973,
+      "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+    },
+    {
+      "id": "episode128_task_model_radar_json",
+      "title": "128-episode 20-task model radar JSON",
+      "path": "docs/data/episode128_task_model_radar.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, preserving explicit scoreless cells.",
+      "exists": true,
+      "bytes": 187439,
+      "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
     },
     {
       "id": "task_method_20_result_matrix_json",
@@ -597,7 +619,7 @@
       "shows": "Machine-readable 9-method by 20-task matrix where every method has 20 records and scoreless cells carry unsupported/not-evaluated reasons.",
       "exists": true,
       "bytes": 129711,
-      "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+      "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
     },
     {
       "id": "task_method_20_result_matrix",
@@ -618,8 +640,30 @@
       "surface": "website_hf",
       "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
       "exists": true,
-      "bytes": 51944,
-      "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+      "bytes": 51920,
+      "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
+    },
+    {
+      "id": "single_episode_task_model_radar_chart",
+      "title": "Single-episode 20-task model radar",
+      "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+      "kind": "generated_figure",
+      "surface": "website_hf",
+      "shows": "Separates the one-episode Minimal and Neural MLP 20/20 scored baselines into a clean two-polygon radar.",
+      "exists": true,
+      "bytes": 37447,
+      "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+    },
+    {
+      "id": "episode128_task_model_radar_chart",
+      "title": "128-episode 20-task model radar",
+      "path": "docs/assets/charts/episode128_task_model_radar.svg",
+      "kind": "generated_figure",
+      "surface": "website_hf",
+      "shows": "Separates the selected 128-episode methods: raw-feature simple/NN as complete 20/20 scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+      "exists": true,
+      "bytes": 45734,
+      "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
     },
     {
       "id": "unified_task_model_radar_builder",
@@ -629,8 +673,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
       "exists": true,
-      "bytes": 40863,
-      "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+      "bytes": 47659,
+      "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
     },
     {
       "id": "a100_128_metadata_task_baselines",
@@ -762,7 +806,7 @@
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
       "bytes": 15726,
-      "sha256": "6fba864ee3ff5238622fa83e5cbc37cd80fe9e264fcc07ec2f1734e801d69ef8"
+      "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
     },
     {
       "id": "figure_index_builder",
@@ -772,8 +816,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 14431,
-      "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+      "bytes": 15412,
+      "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
     },
     {
       "id": "brand_assets_json",
@@ -839,7 +883,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8100,
-      "sha256": "389978ed6602ba98f2400af159179184c53a53bdcc831f4d2de3b7cb15d5267b"
+      "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
     },
     {
       "id": "public_surface_qa",
@@ -861,7 +905,7 @@
       "volatile": true,
       "shows": "Machine-readable report for SEO/social metadata, accessible tab semantics, public links, project links, and clear project presentation.",
       "exists": true,
-      "bytes": 5806,
+      "bytes": 6092,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -872,8 +916,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the public presentation report before release.",
       "exists": true,
-      "bytes": 12335,
-      "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+      "bytes": 12605,
+      "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
     },
     {
       "id": "task_surface_integrity",
@@ -942,7 +986,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 131884,
+      "bytes": 134796,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -953,8 +997,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 51736,
-      "sha256": "9e5a9507bf6a8b1de06b2b1555c96411a6f420df3152c73ae65ce4e4f982aa59"
+      "bytes": 55921,
+      "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
     },
     {
       "id": "reproducibility_contract",
@@ -986,8 +1030,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 49735,
-      "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+      "bytes": 51367,
+      "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
     },
     {
       "id": "publication_audit",
@@ -1022,7 +1066,7 @@
       "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": 838389,
+      "bytes": 841326,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1034,7 +1078,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 18101,
+      "bytes": 18225,
       "hash_policy": "existence_and_size_only"
     },
     {

data/figure_index.json

@@ -1,9 +1,9 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T10:59:49+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,
+  "figure_count": 26,
   "figures": [
     {
       "id": "brand_logo_mark",
@@ -359,8 +359,44 @@
       "source_script": "scripts/build_unified_task_model_radar.py",
       "surface": "website unified task section, README, HF mirrors",
       "exists": true,
-      "bytes": 51944,
-      "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72",
+      "bytes": 51920,
+      "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a",
+      "dimensions": {
+        "format": "SVG",
+        "width": 1920,
+        "height": 1640,
+        "view_box": "0 0 1920 1640"
+      },
+      "source_script_exists": true
+    },
+    {
+      "id": "single_episode_task_model_radar",
+      "title": "Single-episode 20-task model radar",
+      "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+      "role": "Twenty-axis split radar for the one public-sample episode, comparing Minimal and Neural MLP as two complete 20/20 scored polygons.",
+      "source_script": "scripts/build_unified_task_model_radar.py",
+      "surface": "website unified task section, README, HF mirrors",
+      "exists": true,
+      "bytes": 37447,
+      "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795",
+      "dimensions": {
+        "format": "SVG",
+        "width": 1920,
+        "height": 1640,
+        "view_box": "0 0 1920 1640"
+      },
+      "source_script_exists": true
+    },
+    {
+      "id": "episode128_task_model_radar",
+      "title": "128-episode 20-task model radar",
+      "path": "docs/assets/charts/episode128_task_model_radar.svg",
+      "role": "Twenty-axis split radar for selected 128-episode methods: raw-feature simple/NN as complete scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+      "source_script": "scripts/build_unified_task_model_radar.py",
+      "surface": "website unified task section, README, HF mirrors",
+      "exists": true,
+      "bytes": 45734,
+      "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761",
       "dimensions": {
         "format": "SVG",
         "width": 1920,

data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:47:17+00:00",
+  "generated_at_utc": "2026-06-16T11:02:20+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 567,
+    "group_count": 571,
     "failure_count": 0,
     "failures_by_surface": {}
   },
@@ -138,45 +138,45 @@
       "local": {
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
-        "bytes": 97873,
-        "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+        "bytes": 99966,
+        "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         }
       },
       "failures": []
@@ -334,45 +334,45 @@
       "local": {
         "path": "repo:docs/data/figure_index.json",
         "exists": true,
-        "bytes": 15726,
-        "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+        "bytes": 17287,
+        "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model_data": {
           "path": "hf_model:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model": {
           "path": "hf_model:metrics/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         }
       },
       "failures": []
@@ -824,45 +824,45 @@
       "local": {
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
-        "bytes": 7883,
-        "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+        "bytes": 8188,
+        "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model_data": {
           "path": "hf_model:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model": {
           "path": "hf_model:metrics/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         }
       },
       "failures": []
@@ -873,45 +873,45 @@
       "local": {
         "path": "repo:docs/data/public_surface_qa.json",
         "exists": true,
-        "bytes": 5806,
-        "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+        "bytes": 6092,
+        "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model_data": {
           "path": "hf_model:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model": {
           "path": "hf_model:metrics/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         }
       },
       "failures": []
@@ -1021,44 +1021,44 @@
         "path": "repo:docs/data/quality_gates.json",
         "exists": true,
         "bytes": 8100,
-        "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+        "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model_data": {
           "path": "hf_model:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model": {
           "path": "hf_model:metrics/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         }
       },
       "failures": []
@@ -1462,44 +1462,44 @@
         "path": "repo:docs/data/scope_claims_audit.json",
         "exists": true,
         "bytes": 21630,
-        "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+        "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model_data": {
           "path": "hf_model:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model": {
           "path": "hf_model:metrics/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         }
       },
       "failures": []
@@ -1560,44 +1560,44 @@
         "path": "repo:docs/data/source_alignment_audit.json",
         "exists": true,
         "bytes": 4432,
-        "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+        "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model_data": {
           "path": "hf_model:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model": {
           "path": "hf_model:metrics/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         }
       },
       "failures": []
@@ -1651,6 +1651,104 @@
       },
       "failures": []
     },
+    {
+      "name": "data/single_episode_task_model_radar.json",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/data/single_episode_task_model_radar.json",
+        "exists": true,
+        "bytes": 50973,
+        "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_artifacts_data": {
+          "path": "hf_artifacts:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:docs/data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model_data": {
+          "path": "hf_model:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model_docs_data": {
+          "path": "hf_model:docs/data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model": {
+          "path": "hf_model:metrics/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        }
+      },
+      "failures": []
+    },
+    {
+      "name": "data/episode128_task_model_radar.json",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/data/episode128_task_model_radar.json",
+        "exists": true,
+        "bytes": 187439,
+        "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_artifacts_data": {
+          "path": "hf_artifacts:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:docs/data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model_data": {
+          "path": "hf_model:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model_docs_data": {
+          "path": "hf_model:docs/data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model": {
+          "path": "hf_model:metrics/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "data/task_suite_20.json",
       "status": "pass",
@@ -1756,44 +1854,44 @@
         "path": "repo:docs/data/task_surface_integrity.json",
         "exists": true,
         "bytes": 45779,
-        "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+        "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         }
       },
       "failures": []
@@ -1854,44 +1952,44 @@
         "path": "repo:docs/data/task_method_20_result_matrix.json",
         "exists": true,
         "bytes": 129711,
-        "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+        "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         }
       },
       "failures": []
@@ -1952,44 +2050,44 @@
         "path": "repo:docs/data/unified_task_model_radar.json",
         "exists": true,
         "bytes": 231290,
-        "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+        "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model_data": {
           "path": "hf_model:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model": {
           "path": "hf_model:metrics/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         }
       },
       "failures": []
@@ -2000,45 +2098,45 @@
       "local": {
         "path": "repo:docs/data/website_integrity.json",
         "exists": true,
-        "bytes": 18225,
-        "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+        "bytes": 18814,
+        "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model_data": {
           "path": "hf_model:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model": {
           "path": "hf_model:metrics/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         }
       },
       "failures": []
@@ -2129,6 +2227,80 @@
       },
       "failures": []
     },
+    {
+      "name": "assets/charts/single_episode_task_model_radar.svg",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/assets/charts/single_episode_task_model_radar.svg",
+        "exists": true,
+        "bytes": 37447,
+        "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_artifacts_docs": {
+          "path": "hf_artifacts:docs/assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_artifacts_card": {
+          "path": "hf_artifacts:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_model": {
+          "path": "hf_model:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        }
+      },
+      "failures": []
+    },
+    {
+      "name": "assets/charts/episode128_task_model_radar.svg",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/assets/charts/episode128_task_model_radar.svg",
+        "exists": true,
+        "bytes": 45734,
+        "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_artifacts_docs": {
+          "path": "hf_artifacts:docs/assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_artifacts_card": {
+          "path": "hf_artifacts:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_model": {
+          "path": "hf_model:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "assets/charts/tier2_task_suite.svg",
       "status": "pass",
@@ -2172,33 +2344,33 @@
       "local": {
         "path": "repo:docs/assets/charts/unified_task_model_radar.svg",
         "exists": true,
-        "bytes": 51944,
-        "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+        "bytes": 51920,
+        "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_artifacts_card": {
           "path": "hf_artifacts:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_model": {
           "path": "hf_model:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         }
       },
       "failures": []
@@ -3535,21 +3707,21 @@
       "local": {
         "path": "repo:scripts/build_artifact_index.py",
         "exists": true,
-        "bytes": 49735,
-        "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+        "bytes": 51367,
+        "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_artifact_index.py",
           "exists": true,
-          "bytes": 49735,
-          "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+          "bytes": 51367,
+          "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_artifact_index.py",
           "exists": true,
-          "bytes": 49735,
-          "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+          "bytes": 51367,
+          "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
         }
       },
       "failures": []
@@ -3610,21 +3782,21 @@
       "local": {
         "path": "repo:scripts/build_figure_index.py",
         "exists": true,
-        "bytes": 14431,
-        "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+        "bytes": 15412,
+        "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_figure_index.py",
           "exists": true,
-          "bytes": 14431,
-          "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+          "bytes": 15412,
+          "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_figure_index.py",
           "exists": true,
-          "bytes": 14431,
-          "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+          "bytes": 15412,
+          "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
         }
       },
       "failures": []
@@ -3660,21 +3832,21 @@
       "local": {
         "path": "repo:scripts/build_public_surface_qa.py",
         "exists": true,
-        "bytes": 12335,
-        "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+        "bytes": 12605,
+        "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_public_surface_qa.py",
           "exists": true,
-          "bytes": 12335,
-          "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+          "bytes": 12605,
+          "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_public_surface_qa.py",
           "exists": true,
-          "bytes": 12335,
-          "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+          "bytes": 12605,
+          "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
         }
       },
       "failures": []
@@ -3810,21 +3982,21 @@
       "local": {
         "path": "repo:scripts/build_unified_task_model_radar.py",
         "exists": true,
-        "bytes": 40863,
-        "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+        "bytes": 47659,
+        "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_unified_task_model_radar.py",
           "exists": true,
-          "bytes": 40863,
-          "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+          "bytes": 47659,
+          "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_unified_task_model_radar.py",
           "exists": true,
-          "bytes": 40863,
-          "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+          "bytes": 47659,
+          "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
         }
       },
       "failures": []
@@ -3860,21 +4032,21 @@
       "local": {
         "path": "repo:scripts/verify_live_publication.py",
         "exists": true,
-        "bytes": 52549,
-        "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+        "bytes": 55921,
+        "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/verify_live_publication.py",
           "exists": true,
-          "bytes": 52549,
-          "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+          "bytes": 55921,
+          "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
         },
         "hf_model": {
           "path": "hf_model:scripts/verify_live_publication.py",
           "exists": true,
-          "bytes": 52549,
-          "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+          "bytes": 55921,
+          "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
         }
       },
       "failures": []
@@ -3885,21 +4057,21 @@
       "local": {
         "path": "repo:scripts/validate_mirror_parity.py",
         "exists": true,
-        "bytes": 23698,
-        "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+        "bytes": 23878,
+        "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/validate_mirror_parity.py",
           "exists": true,
-          "bytes": 23698,
-          "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+          "bytes": 23878,
+          "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
         },
         "hf_model": {
           "path": "hf_model:scripts/validate_mirror_parity.py",
           "exists": true,
-          "bytes": 23698,
-          "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+          "bytes": 23878,
+          "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
         }
       },
       "failures": []
@@ -3910,21 +4082,21 @@
       "local": {
         "path": "repo:scripts/validate_publication_package.py",
         "exists": true,
-        "bytes": 18279,
-        "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+        "bytes": 18574,
+        "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/validate_publication_package.py",
           "exists": true,
-          "bytes": 18279,
-          "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+          "bytes": 18574,
+          "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
         },
         "hf_model": {
           "path": "hf_model:scripts/validate_publication_package.py",
           "exists": true,
-          "bytes": 18279,
-          "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+          "bytes": 18574,
+          "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
         }
       },
       "failures": []
@@ -4035,21 +4207,21 @@
       "local": {
         "path": "repo:scripts/sync_hf_publish_mirrors.py",
         "exists": true,
-        "bytes": 16249,
-        "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+        "bytes": 17161,
+        "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/sync_hf_publish_mirrors.py",
           "exists": true,
-          "bytes": 16249,
-          "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+          "bytes": 17161,
+          "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
         },
         "hf_model": {
           "path": "hf_model:scripts/sync_hf_publish_mirrors.py",
           "exists": true,
-          "bytes": 16249,
-          "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+          "bytes": 17161,
+          "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
         }
       },
       "failures": []
@@ -4239,39 +4411,39 @@
       "local": {
         "path": "repo:docs/index.html",
         "exists": true,
-        "bytes": 227441,
-        "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+        "bytes": 230528,
+        "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_artifacts_root": {
           "path": "hf_artifacts:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_model": {
           "path": "hf_model:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_model_docs": {
           "path": "hf_model:docs/index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         }
       },
       "failures": []
@@ -17896,27 +18068,27 @@
       "local": {
         "path": "repo:FIGURE_INDEX.md",
         "exists": true,
-        "bytes": 5614,
-        "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+        "bytes": 6217,
+        "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         },
         "hf_model": {
           "path": "hf_model:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         }
       },
       "failures": []

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:46:48+00:00",
+  "generated_at_utc": "2026-06-16T11:08:23+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": "2026-06-16T10:35:38+00:00"
+          "generated_at_utc": "2026-06-16T11:00:25+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:37+00:00"
+          "generated_at_utc": "2026-06-16T11:00:22+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:37+00:00"
+          "generated_at_utc": "2026-06-16T11:00:22+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:40+00:00"
+          "generated_at_utc": "2026-06-16T11:00:26+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:47+00:00"
+          "generated_at_utc": "2026-06-16T11:00:39+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:36:24+00:00"
+          "generated_at_utc": "2026-06-16T11:02:20+00:00"
         }
       },
       "failures": {}
@@ -81,7 +81,7 @@
         "aria-controls": 11,
         "moveProjectTabFocus": 2,
         "ArrowRight": 6,
-        "Home": 6,
+        "Home": 7,
         "End": 6
       }
     },
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "20-task": 43,
-        "Qwen3-Omni": 148,
+        "20-task": 60,
+        "Qwen3-Omni": 151,
         "128-episode pilot": 1
       }
     },
@@ -131,7 +131,12 @@
         "data/task_suite_enhancement_128.json": 28,
         "data/task_suite_20.json": 42,
         "data/unified_task_model_radar.json": 17,
+        "data/single_episode_task_model_radar.json": 11,
+        "data/episode128_task_model_radar.json": 11,
+        "data/task_method_20_result_matrix.json": 17,
         "assets/charts/unified_task_model_radar.svg": 17,
+        "assets/charts/single_episode_task_model_radar.svg": 18,
+        "assets/charts/episode128_task_model_radar.svg": 18,
         "data/tier2_task_suite.json": 11
       }
     },

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:47+00:00",
+  "generated_at_utc": "2026-06-16T11:08:38+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -99,6 +99,9 @@
     "docs/data/summary_metrics.json": true,
     "docs/data/task_suite_20.json": true,
     "docs/data/unified_task_model_radar.json": true,
+    "docs/data/single_episode_task_model_radar.json": true,
+    "docs/data/episode128_task_model_radar.json": true,
+    "docs/data/task_method_20_result_matrix.json": true,
     "docs/data/task_suite_enhancement_128.json": true,
     "docs/assets/modalities/video.jpg": true,
     "docs/assets/modalities/audio.png": true,
@@ -116,6 +119,8 @@
     "docs/assets/brand/xperience10m-logo-social-card.png": true,
     "docs/assets/task_suite_infographic.png": true,
     "docs/assets/charts/unified_task_model_radar.svg": true,
+    "docs/assets/charts/single_episode_task_model_radar.svg": true,
+    "docs/assets/charts/episode128_task_model_radar.svg": true,
     "docs/assets/pipeline_diagram.png": true,
     "docs/assets/task_architectures.png": true,
     "results/episode_task_suite/summary_report.json": true,
@@ -193,8 +198,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1185,
-      "text_file_count": 990,
+      "file_count": 1189,
+      "text_file_count": 994,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -204,8 +209,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 968,
-      "text_file_count": 812,
+      "file_count": 972,
+      "text_file_count": 816,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -215,8 +220,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2155,
-      "text_file_count": 992,
+      "file_count": 2298,
+      "text_file_count": 1000,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -226,8 +231,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 2585,
-      "text_file_count": 1151,
+      "file_count": 2729,
+      "text_file_count": 1159,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+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": "2026-06-16T10:35:40+00:00",
+  "generated_at_utc": "2026-06-16T11:08:50+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

data/single_episode_task_model_radar.json

@@ -0,0 +1,1473 @@
+{
+  "title": "Single-Episode 20-Task Radar",
+  "status": "pass",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
+  "description": "Minimal and Neural MLP baselines on the one public sample episode, both scored on all 20 task contracts.",
+  "task_count": 20,
+  "method_count": 2,
+  "method_task_record_count": 40,
+  "scored_method_task_count": 40,
+  "normalization_policy": {
+    "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
+    "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
+    "raw_values": "raw metric values, metric keys, and sources are retained in this JSON; the SVG is an overview, not a replacement for the metric table",
+    "result_record_policy": "every method has 20 task records; records without a numeric score carry explicit unsupported/not-evaluated status and reason fields",
+    "foundation_model_overlay": "Qwen3/Cosmos points are plotted only on task-aligned axes. Scoreless records mean the public result does not evaluate that task contract.",
+    "metadata_128_overlay": "128-episode metadata baselines have 20 records, but numeric scores only where the public JSONL contains enough task labels without raw feature blocks.",
+    "raw_128_overlay": "128-episode raw-feature baselines use staged sensor NPZ features. Eighteen axes use direct task targets; interaction text and camera-view sync are completed with documented compact proxies because raw interaction strings and paired video-view embeddings are absent from the 128 export."
+  },
+  "source_unified_radar": "docs/data/unified_task_model_radar.json",
+  "source_result_matrix": "docs/data/task_method_20_result_matrix.json",
+  "series": [
+    {
+      "id": "minimal",
+      "label": "Minimal",
+      "short_label": "Min",
+      "color": "#ccffa0",
+      "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",
+      "result_record_count": 20,
+      "scored_task_count": 20,
+      "covered_task_count": 20,
+      "proxy_scored_task_count": 0,
+      "scoreless_task_count": 0,
+      "unsupported_task_count": 0,
+      "not_evaluated_task_count": 0,
+      "status_counts": {
+        "scored": 20
+      },
+      "coverage_fraction": 1.0,
+      "result_record_fraction": 1.0
+    },
+    {
+      "id": "neural_mlp",
+      "label": "Neural MLP",
+      "short_label": "NN",
+      "color": "#67e8d1",
+      "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",
+      "result_record_count": 20,
+      "scored_task_count": 20,
+      "covered_task_count": 20,
+      "proxy_scored_task_count": 0,
+      "scoreless_task_count": 0,
+      "unsupported_task_count": 0,
+      "not_evaluated_task_count": 0,
+      "status_counts": {
+        "scored": 20
+      },
+      "coverage_fraction": 1.0,
+      "result_record_fraction": 1.0
+    }
+  ],
+  "tasks": [
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/timeline_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05,
+          "raw_text": "0.0500",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.014814814814814814,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.014814814814814814,
+          "raw_text": "0.0148",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/timeline_subtask/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05056355513846935,
+          "raw_text": "0.0506",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.02810810810810811,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.02810810810810811,
+          "raw_text": "0.0281",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/transition_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.6118237590630229,
+          "raw_text": "0.6118",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.5862068965517241,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5862068965517241,
+          "raw_text": "0.5862",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05925925925925927,
+          "raw_text": "0.0593",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.04186046511627907,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04186046511627907,
+          "raw_text": "0.0419",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mpjpe",
+          "source": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.12473175026322614,
+          "raw_text": "0.8647",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.10785018652677536,
+          "metric_key": "mpjpe",
+          "source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "0.1079",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/contact_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "1.000",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 1.0,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "1.000",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/object_relevance/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.18034382095361662,
+          "raw_text": "0.1803",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.1679279279279279,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.1679279279279279,
+          "raw_text": "0.1679",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/caption_grounding/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.016023479050338015,
+          "raw_text": "0.0160",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.01684125567132316,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.01684125567132316,
+          "raw_text": "0.0168",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.26925966892956127,
+          "raw_text": "0.2693",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.1299971898648288,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.1299971898648288,
+          "raw_text": "0.1300",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "r2",
+          "source": "results/episode_task_suite/modality_reconstruction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "-0.0153",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": -0.010171410134180991,
+          "metric_key": "r2",
+          "source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "-0.0102",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/temporal_order/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5399515738498789,
+          "raw_text": "0.5400",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.8520179372197308,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.8520179372197308,
+          "raw_text": "0.8520",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/misalignment_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5051698670605613,
+          "raw_text": "0.5052",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.7152682255845944,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.7152682255845944,
+          "raw_text": "0.7153",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.07499999999999998,
+          "raw_text": "0.0750",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.06545454545454546,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.06545454545454546,
+          "raw_text": "0.0655",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04545454545454545,
+          "raw_text": "0.0455",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.050724637681159424,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.050724637681159424,
+          "raw_text": "0.0507",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04444444444444444,
+          "raw_text": "0.0444",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.0380952380952381,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0380952380952381,
+          "raw_text": "0.0381",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "0.0000",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.0,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "0.0000",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.16939890710382516,
+          "raw_text": "0.1694",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.19718309859154928,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.19718309859154928,
+          "raw_text": "0.1972",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "0.0420",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.042562149465084076,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.9879531106266066,
+          "raw_text": "0.0426",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.4943004846572876,
+          "raw_text": "0.4943",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.24086658656597137,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.24086658656597137,
+          "raw_text": "0.2409",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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": {
+        "minimal": {
+          "raw": 10.53735637664795,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "10.54",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 10.55449390411377,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.9983762814568361,
+          "raw_text": "10.55",
+          "status_label": "scored"
+        }
+      }
+    }
+  ],
+  "task_method_result_matrix": [
+    {
+      "task_number": 1,
+      "task_id": "timeline_action",
+      "task_label": "Action Recognition",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05,
+      "raw_text": "0.0500",
+      "normalized_score": 0.05,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/timeline_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 1,
+      "task_id": "timeline_action",
+      "task_label": "Action Recognition",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.014814814814814814,
+      "raw_text": "0.0148",
+      "normalized_score": 0.014814814814814814,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 2,
+      "task_id": "timeline_subtask",
+      "task_label": "Procedure Step Recognition",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05056355513846935,
+      "raw_text": "0.0506",
+      "normalized_score": 0.05056355513846935,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/timeline_subtask/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 2,
+      "task_id": "timeline_subtask",
+      "task_label": "Procedure Step Recognition",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.02810810810810811,
+      "raw_text": "0.0281",
+      "normalized_score": 0.02810810810810811,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 3,
+      "task_id": "transition_detection",
+      "task_label": "Action Boundary Detection",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.6118237590630229,
+      "raw_text": "0.6118",
+      "normalized_score": 0.6118237590630229,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/transition_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 3,
+      "task_id": "transition_detection",
+      "task_label": "Action Boundary Detection",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5862068965517241,
+      "raw_text": "0.5862",
+      "normalized_score": 0.5862068965517241,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 4,
+      "task_id": "next_action",
+      "task_label": "Next-Action Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05925925925925927,
+      "raw_text": "0.0593",
+      "normalized_score": 0.05925925925925927,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 4,
+      "task_id": "next_action",
+      "task_label": "Next-Action Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04186046511627907,
+      "raw_text": "0.0419",
+      "normalized_score": 0.04186046511627907,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 5,
+      "task_id": "hand_trajectory_forecast",
+      "task_label": "Hand Trajectory Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.8646570444107056,
+      "raw_text": "0.8647",
+      "normalized_score": 0.12473175026322614,
+      "metric_key": "mpjpe",
+      "source": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 5,
+      "task_id": "hand_trajectory_forecast",
+      "task_label": "Hand Trajectory Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.10785018652677536,
+      "raw_text": "0.1079",
+      "normalized_score": 1.0,
+      "metric_key": "mpjpe",
+      "source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 6,
+      "task_id": "contact_prediction",
+      "task_label": "Contact State Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 1.0,
+      "raw_text": "1.000",
+      "normalized_score": 1.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/contact_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 6,
+      "task_id": "contact_prediction",
+      "task_label": "Contact State Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 1.0,
+      "raw_text": "1.000",
+      "normalized_score": 1.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 7,
+      "task_id": "object_relevance",
+      "task_label": "Object Relevance Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.18034382095361662,
+      "raw_text": "0.1803",
+      "normalized_score": 0.18034382095361662,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/object_relevance/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 7,
+      "task_id": "object_relevance",
+      "task_label": "Object Relevance Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.1679279279279279,
+      "raw_text": "0.1679",
+      "normalized_score": 0.1679279279279279,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 8,
+      "task_id": "caption_grounding",
+      "task_label": "Language Grounding",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.016023479050338015,
+      "raw_text": "0.0160",
+      "normalized_score": 0.016023479050338015,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/caption_grounding/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 8,
+      "task_id": "caption_grounding",
+      "task_label": "Language Grounding",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.01684125567132316,
+      "raw_text": "0.0168",
+      "normalized_score": 0.01684125567132316,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 9,
+      "task_id": "cross_modal_retrieval",
+      "task_label": "Cross-Modal Retrieval",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.26925966892956127,
+      "raw_text": "0.2693",
+      "normalized_score": 0.26925966892956127,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 9,
+      "task_id": "cross_modal_retrieval",
+      "task_label": "Cross-Modal Retrieval",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.1299971898648288,
+      "raw_text": "0.1300",
+      "normalized_score": 0.1299971898648288,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 10,
+      "task_id": "modality_reconstruction",
+      "task_label": "Cross-Modal Reconstruction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": -0.015271898913936655,
+      "raw_text": "-0.0153",
+      "normalized_score": 0.0,
+      "metric_key": "r2",
+      "source": "results/episode_task_suite/modality_reconstruction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 10,
+      "task_id": "modality_reconstruction",
+      "task_label": "Cross-Modal Reconstruction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": -0.010171410134180991,
+      "raw_text": "-0.0102",
+      "normalized_score": 0.0,
+      "metric_key": "r2",
+      "source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 11,
+      "task_id": "temporal_order",
+      "task_label": "Temporal Order Verification",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5399515738498789,
+      "raw_text": "0.5400",
+      "normalized_score": 0.5399515738498789,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/temporal_order/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 11,
+      "task_id": "temporal_order",
+      "task_label": "Temporal Order Verification",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.8520179372197308,
+      "raw_text": "0.8520",
+      "normalized_score": 0.8520179372197308,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 12,
+      "task_id": "misalignment_detection",
+      "task_label": "Multimodal Synchronization Detection",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5051698670605613,
+      "raw_text": "0.5052",
+      "normalized_score": 0.5051698670605613,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/misalignment_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 12,
+      "task_id": "misalignment_detection",
+      "task_label": "Multimodal Synchronization Detection",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.7152682255845944,
+      "raw_text": "0.7153",
+      "normalized_score": 0.7152682255845944,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 13,
+      "task_id": "long_horizon_next_action",
+      "task_label": "Long-Horizon Next-Action Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.07499999999999998,
+      "raw_text": "0.0750",
+      "normalized_score": 0.07499999999999998,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 13,
+      "task_id": "long_horizon_next_action",
+      "task_label": "Long-Horizon Next-Action Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.06545454545454546,
+      "raw_text": "0.0655",
+      "normalized_score": 0.06545454545454546,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 14,
+      "task_id": "next_subtask_forecast",
+      "task_label": "Long-Horizon Next-Subtask Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04545454545454545,
+      "raw_text": "0.0455",
+      "normalized_score": 0.04545454545454545,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 14,
+      "task_id": "next_subtask_forecast",
+      "task_label": "Long-Horizon Next-Subtask Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.050724637681159424,
+      "raw_text": "0.0507",
+      "normalized_score": 0.050724637681159424,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 15,
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04444444444444444,
+      "raw_text": "0.0444",
+      "normalized_score": 0.04444444444444444,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 15,
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0380952380952381,
+      "raw_text": "0.0381",
+      "normalized_score": 0.0380952380952381,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 16,
+      "task_id": "action_object_relation",
+      "task_label": "Action-Object Relation Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0,
+      "raw_text": "0.0000",
+      "normalized_score": 0.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 16,
+      "task_id": "action_object_relation",
+      "task_label": "Action-Object Relation Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0,
+      "raw_text": "0.0000",
+      "normalized_score": 0.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 17,
+      "task_id": "object_set_forecast",
+      "task_label": "Future Object-Set Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.16939890710382516,
+      "raw_text": "0.1694",
+      "normalized_score": 0.16939890710382516,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 17,
+      "task_id": "object_set_forecast",
+      "task_label": "Future Object-Set Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.19718309859154928,
+      "raw_text": "0.1972",
+      "normalized_score": 0.19718309859154928,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 18,
+      "task_id": "imu_to_hand_pose",
+      "task_label": "IMU-to-Hand Pose Reconstruction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.042049407958984375,
+      "raw_text": "0.0420",
+      "normalized_score": 1.0,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 18,
+      "task_id": "imu_to_hand_pose",
+      "task_label": "IMU-to-Hand Pose Reconstruction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.042562149465084076,
+      "raw_text": "0.0426",
+      "normalized_score": 0.9879531106266066,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 19,
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.4943004846572876,
+      "raw_text": "0.4943",
+      "normalized_score": 0.4943004846572876,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 19,
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.24086658656597137,
+      "raw_text": "0.2409",
+      "normalized_score": 0.24086658656597137,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 20,
+      "task_id": "time_to_transition",
+      "task_label": "Time-to-Next-Transition Regression",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 10.53735637664795,
+      "raw_text": "10.54",
+      "normalized_score": 1.0,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 20,
+      "task_id": "time_to_transition",
+      "task_label": "Time-to-Next-Transition Regression",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 10.55449390411377,
+      "raw_text": "10.55",
+      "normalized_score": 0.9983762814568361,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    }
+  ]
+}

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:37+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_method_20_result_matrix.json

@@ -1,7 +1,7 @@
 {
   "title": "Task Method 20-Result Matrix",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:34:53+00:00",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:37+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+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": "2026-06-16T10:34:53+00:00",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,

data/website_integrity.json

@@ -1,14 +1,14 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:38+00:00",
+  "generated_at_utc": "2026-06-16T11:08:31+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 162,
+    "local_references": 176,
     "external_reference_count": 121,
-    "json_files": 43,
-    "image_assets_referenced": 23,
+    "json_files": 45,
+    "image_assets_referenced": 25,
     "failure_count": 0
   },
   "failures": {
@@ -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": 85521,
-      "evidence_index": 111351
+      "overview_index": 87077,
+      "evidence_index": 114423
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 85521,
-      "protocol_index": 107532,
-      "evidence_index": 111351
+      "overview_index": 87077,
+      "protocol_index": 110604,
+      "evidence_index": 114423
     },
     {
       "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": 1831
+      "second_marker_index": 3362
     },
     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,8 +277,8 @@
     {
       "path": "index.html",
       "id_count": 90,
-      "reference_count": 138,
-      "image_count": 27
+      "reference_count": 152,
+      "image_count": 31
     },
     {
       "path": "research_roadmap.html",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 97873,
+      "bytes": 99966,
       "top_level_type": "dict"
     },
     {
@@ -314,6 +314,11 @@
       "bytes": 3967,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/episode128_task_model_radar.json",
+      "bytes": 187439,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/evaluation_protocol.json",
       "bytes": 24020,
@@ -326,7 +331,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 15726,
+      "bytes": 17287,
       "top_level_type": "dict"
     },
     {
@@ -336,12 +341,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 131884,
+      "bytes": 134796,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 838389,
+      "bytes": 847909,
       "top_level_type": "dict"
     },
     {
@@ -381,12 +386,12 @@
     },
     {
       "path": "data/public_surface_qa.json",
-      "bytes": 5806,
+      "bytes": 6096,
       "top_level_type": "dict"
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 7883,
+      "bytes": 8188,
       "top_level_type": "dict"
     },
     {
@@ -454,6 +459,11 @@
       "bytes": 4305626,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/single_episode_task_model_radar.json",
+      "bytes": 50973,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/source_alignment_audit.json",
       "bytes": 4432,
@@ -501,7 +511,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 18101,
+      "bytes": 18814,
       "top_level_type": "dict"
     },
     {
@@ -533,6 +543,13 @@
       "format": "SVG",
       "has_viewbox": true
     },
+    {
+      "path": "assets/charts/episode128_task_model_radar.svg",
+      "exists": true,
+      "bytes": 45734,
+      "format": "SVG",
+      "has_viewbox": true
+    },
     {
       "path": "assets/charts/episode_task_scores.svg",
       "exists": true,
@@ -582,6 +599,13 @@
       "format": "SVG",
       "has_viewbox": true
     },
+    {
+      "path": "assets/charts/single_episode_task_model_radar.svg",
+      "exists": true,
+      "bytes": 37447,
+      "format": "SVG",
+      "has_viewbox": true
+    },
     {
       "path": "assets/charts/tier2_task_suite.svg",
       "exists": true,
@@ -592,7 +616,7 @@
     {
       "path": "assets/charts/unified_task_model_radar.svg",
       "exists": true,
-      "bytes": 51944,
+      "bytes": 51920,
       "format": "SVG",
       "has_viewbox": true
     },

docs/data/artifact_index.json

@@ -1,8 +1,8 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T10:59:46+00:00",
   "status": "pass",
-  "artifact_count": 179,
+  "artifact_count": 183,
   "missing": [],
   "by_kind": {
     "project_path": 14,
@@ -13,8 +13,8 @@
     "project_scope": 1,
     "source_alignment": 5,
     "evaluation_protocol": 7,
-    "website_data": 7,
-    "generated_figure": 5,
+    "website_data": 9,
+    "generated_figure": 7,
     "visualization_builder": 1,
     "model_result": 2,
     "result_interpretation": 5,
@@ -465,7 +465,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "c9c3258a8ba9680b75dffbee1bdac194312d491664412de1b25a3e8543e0da60"
+      "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
     },
     {
       "id": "source_alignment_validator",
@@ -586,7 +586,29 @@
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, and explicit scoreless status records.",
       "exists": true,
       "bytes": 231290,
-      "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+      "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
+    },
+    {
+      "id": "single_episode_task_model_radar_json",
+      "title": "Single-episode 20-task model radar JSON",
+      "path": "docs/data/single_episode_task_model_radar.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
+      "exists": true,
+      "bytes": 50973,
+      "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+    },
+    {
+      "id": "episode128_task_model_radar_json",
+      "title": "128-episode 20-task model radar JSON",
+      "path": "docs/data/episode128_task_model_radar.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, preserving explicit scoreless cells.",
+      "exists": true,
+      "bytes": 187439,
+      "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
     },
     {
       "id": "task_method_20_result_matrix_json",
@@ -597,7 +619,7 @@
       "shows": "Machine-readable 9-method by 20-task matrix where every method has 20 records and scoreless cells carry unsupported/not-evaluated reasons.",
       "exists": true,
       "bytes": 129711,
-      "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+      "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
     },
     {
       "id": "task_method_20_result_matrix",
@@ -618,8 +640,30 @@
       "surface": "website_hf",
       "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
       "exists": true,
-      "bytes": 51944,
-      "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+      "bytes": 51920,
+      "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
+    },
+    {
+      "id": "single_episode_task_model_radar_chart",
+      "title": "Single-episode 20-task model radar",
+      "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+      "kind": "generated_figure",
+      "surface": "website_hf",
+      "shows": "Separates the one-episode Minimal and Neural MLP 20/20 scored baselines into a clean two-polygon radar.",
+      "exists": true,
+      "bytes": 37447,
+      "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+    },
+    {
+      "id": "episode128_task_model_radar_chart",
+      "title": "128-episode 20-task model radar",
+      "path": "docs/assets/charts/episode128_task_model_radar.svg",
+      "kind": "generated_figure",
+      "surface": "website_hf",
+      "shows": "Separates the selected 128-episode methods: raw-feature simple/NN as complete 20/20 scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+      "exists": true,
+      "bytes": 45734,
+      "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
     },
     {
       "id": "unified_task_model_radar_builder",
@@ -629,8 +673,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
       "exists": true,
-      "bytes": 40863,
-      "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+      "bytes": 47659,
+      "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
     },
     {
       "id": "a100_128_metadata_task_baselines",
@@ -762,7 +806,7 @@
       "shows": "Machine-readable visual asset index for website and Hugging Face mirrors.",
       "exists": true,
       "bytes": 15726,
-      "sha256": "6fba864ee3ff5238622fa83e5cbc37cd80fe9e264fcc07ec2f1734e801d69ef8"
+      "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
     },
     {
       "id": "figure_index_builder",
@@ -772,8 +816,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates visual-asset hashes, dimensions, and source-script provenance.",
       "exists": true,
-      "bytes": 14431,
-      "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+      "bytes": 15412,
+      "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
     },
     {
       "id": "brand_assets_json",
@@ -839,7 +883,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8100,
-      "sha256": "389978ed6602ba98f2400af159179184c53a53bdcc831f4d2de3b7cb15d5267b"
+      "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
     },
     {
       "id": "public_surface_qa",
@@ -861,7 +905,7 @@
       "volatile": true,
       "shows": "Machine-readable report for SEO/social metadata, accessible tab semantics, public links, project links, and clear project presentation.",
       "exists": true,
-      "bytes": 5806,
+      "bytes": 6092,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -872,8 +916,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the public presentation report before release.",
       "exists": true,
-      "bytes": 12335,
-      "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+      "bytes": 12605,
+      "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
     },
     {
       "id": "task_surface_integrity",
@@ -942,7 +986,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 131884,
+      "bytes": 134796,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -953,8 +997,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 51736,
-      "sha256": "9e5a9507bf6a8b1de06b2b1555c96411a6f420df3152c73ae65ce4e4f982aa59"
+      "bytes": 55921,
+      "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
     },
     {
       "id": "reproducibility_contract",
@@ -986,8 +1030,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 49735,
-      "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+      "bytes": 51367,
+      "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
     },
     {
       "id": "publication_audit",
@@ -1022,7 +1066,7 @@
       "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": 838389,
+      "bytes": 841326,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1034,7 +1078,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 18101,
+      "bytes": 18225,
       "hash_policy": "existence_and_size_only"
     },
     {

docs/data/figure_index.json

@@ -1,9 +1,9 @@
 {
   "title": "Ropedia Xperience-10M Figure Index",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T10:59:49+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,
+  "figure_count": 26,
   "figures": [
     {
       "id": "brand_logo_mark",
@@ -359,8 +359,44 @@
       "source_script": "scripts/build_unified_task_model_radar.py",
       "surface": "website unified task section, README, HF mirrors",
       "exists": true,
-      "bytes": 51944,
-      "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72",
+      "bytes": 51920,
+      "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a",
+      "dimensions": {
+        "format": "SVG",
+        "width": 1920,
+        "height": 1640,
+        "view_box": "0 0 1920 1640"
+      },
+      "source_script_exists": true
+    },
+    {
+      "id": "single_episode_task_model_radar",
+      "title": "Single-episode 20-task model radar",
+      "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+      "role": "Twenty-axis split radar for the one public-sample episode, comparing Minimal and Neural MLP as two complete 20/20 scored polygons.",
+      "source_script": "scripts/build_unified_task_model_radar.py",
+      "surface": "website unified task section, README, HF mirrors",
+      "exists": true,
+      "bytes": 37447,
+      "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795",
+      "dimensions": {
+        "format": "SVG",
+        "width": 1920,
+        "height": 1640,
+        "view_box": "0 0 1920 1640"
+      },
+      "source_script_exists": true
+    },
+    {
+      "id": "episode128_task_model_radar",
+      "title": "128-episode 20-task model radar",
+      "path": "docs/assets/charts/episode128_task_model_radar.svg",
+      "role": "Twenty-axis split radar for selected 128-episode methods: raw-feature simple/NN as complete scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+      "source_script": "scripts/build_unified_task_model_radar.py",
+      "surface": "website unified task section, README, HF mirrors",
+      "exists": true,
+      "bytes": 45734,
+      "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761",
       "dimensions": {
         "format": "SVG",
         "width": 1920,

docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:47:17+00:00",
+  "generated_at_utc": "2026-06-16T11:02:20+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 567,
+    "group_count": 571,
     "failure_count": 0,
     "failures_by_surface": {}
   },
@@ -138,45 +138,45 @@
       "local": {
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
-        "bytes": 97873,
-        "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+        "bytes": 99966,
+        "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model_data": {
           "path": "hf_model:data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         },
         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
-          "bytes": 97873,
-          "sha256": "df3145031ba238a3028f0915d133e6d7cdfd7e3ae85df7f35f73b3e42fa5644f"
+          "bytes": 99966,
+          "sha256": "ecc5804741d820fcecbce5b39e66aa1f78df1c28a8a572268e21a4da7e1c84ee"
         }
       },
       "failures": []
@@ -334,45 +334,45 @@
       "local": {
         "path": "repo:docs/data/figure_index.json",
         "exists": true,
-        "bytes": 15726,
-        "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+        "bytes": 17287,
+        "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model_data": {
           "path": "hf_model:data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         },
         "hf_model": {
           "path": "hf_model:metrics/figure_index.json",
           "exists": true,
-          "bytes": 15726,
-          "sha256": "2264cbe15c2fa6e8081a49595ec3bda3ecdeb68452fc76f80c539fab61fb0c0b"
+          "bytes": 17287,
+          "sha256": "3a29f7fb5ea8f03231583df87e678384978911b52486e43f0d805d6d9f13f3d5"
         }
       },
       "failures": []
@@ -824,45 +824,45 @@
       "local": {
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
-        "bytes": 7883,
-        "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+        "bytes": 8188,
+        "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model_data": {
           "path": "hf_model:data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         },
         "hf_model": {
           "path": "hf_model:metrics/publication_audit.json",
           "exists": true,
-          "bytes": 7883,
-          "sha256": "feb0868eded3c8f03ddf04e0c0fa43d192ae2e25d7110bbd86610c3aeaae1e5f"
+          "bytes": 8188,
+          "sha256": "f214291f310cd44e7d0dcaa0a7a7b7472ee094e1c62e78b8331cce846cc5b748"
         }
       },
       "failures": []
@@ -873,45 +873,45 @@
       "local": {
         "path": "repo:docs/data/public_surface_qa.json",
         "exists": true,
-        "bytes": 5806,
-        "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+        "bytes": 6092,
+        "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model_data": {
           "path": "hf_model:data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         },
         "hf_model": {
           "path": "hf_model:metrics/public_surface_qa.json",
           "exists": true,
-          "bytes": 5806,
-          "sha256": "831b0506d5699ec79d2a0303d653784b4dae224a31d329958fb44495353de336"
+          "bytes": 6092,
+          "sha256": "1e4d6ffe014d27e8e050d17019e1f07ba1ed69a80483589ea59ae2a76b2359cf"
         }
       },
       "failures": []
@@ -1021,44 +1021,44 @@
         "path": "repo:docs/data/quality_gates.json",
         "exists": true,
         "bytes": 8100,
-        "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+        "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model_data": {
           "path": "hf_model:data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         },
         "hf_model": {
           "path": "hf_model:metrics/quality_gates.json",
           "exists": true,
           "bytes": 8100,
-          "sha256": "5d3b15059c47b03b9a0470ea583fc56bf558826163da056d89fe2c8a979f0ded"
+          "sha256": "4c9cc7df18cb579500ca99b0a4851c33759a8e63f64d25904566ae09b49c9162"
         }
       },
       "failures": []
@@ -1462,44 +1462,44 @@
         "path": "repo:docs/data/scope_claims_audit.json",
         "exists": true,
         "bytes": 21630,
-        "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+        "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model_data": {
           "path": "hf_model:data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         },
         "hf_model": {
           "path": "hf_model:metrics/scope_claims_audit.json",
           "exists": true,
           "bytes": 21630,
-          "sha256": "b2617b3502852834d97ed3e62c3f46836f398f813fcb8a15dd6e9d8bcc6e82c7"
+          "sha256": "791daa59609c9749ad4261b6bfb33c40366f70698c58540e84c366f546f67c64"
         }
       },
       "failures": []
@@ -1560,44 +1560,44 @@
         "path": "repo:docs/data/source_alignment_audit.json",
         "exists": true,
         "bytes": 4432,
-        "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+        "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model_data": {
           "path": "hf_model:data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         },
         "hf_model": {
           "path": "hf_model:metrics/source_alignment_audit.json",
           "exists": true,
           "bytes": 4432,
-          "sha256": "780dca9aeba3490d9ed15a5c06b145ecf6228723d80a844dc0a506220424d9eb"
+          "sha256": "27c22202ee4da69403d883345d2d078e668d6e8e430e98f90fd1a09256f93cd2"
         }
       },
       "failures": []
@@ -1651,6 +1651,104 @@
       },
       "failures": []
     },
+    {
+      "name": "data/single_episode_task_model_radar.json",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/data/single_episode_task_model_radar.json",
+        "exists": true,
+        "bytes": 50973,
+        "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_artifacts_data": {
+          "path": "hf_artifacts:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:docs/data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model_data": {
+          "path": "hf_model:data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model_docs_data": {
+          "path": "hf_model:docs/data/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        },
+        "hf_model": {
+          "path": "hf_model:metrics/single_episode_task_model_radar.json",
+          "exists": true,
+          "bytes": 50973,
+          "sha256": "176b7ea925bfcfcff410da85bfd67d247a16b0293a69328acda27c0092207728"
+        }
+      },
+      "failures": []
+    },
+    {
+      "name": "data/episode128_task_model_radar.json",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/data/episode128_task_model_radar.json",
+        "exists": true,
+        "bytes": 187439,
+        "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_artifacts_data": {
+          "path": "hf_artifacts:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_artifacts": {
+          "path": "hf_artifacts:docs/data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model_data": {
+          "path": "hf_model:data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model_docs_data": {
+          "path": "hf_model:docs/data/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        },
+        "hf_model": {
+          "path": "hf_model:metrics/episode128_task_model_radar.json",
+          "exists": true,
+          "bytes": 187439,
+          "sha256": "78d612f3a19f24ec974afa8413b030cf1a4768ac8f122e5207fdb4262a9219f3"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "data/task_suite_20.json",
       "status": "pass",
@@ -1756,44 +1854,44 @@
         "path": "repo:docs/data/task_surface_integrity.json",
         "exists": true,
         "bytes": 45779,
-        "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+        "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_surface_integrity.json",
           "exists": true,
           "bytes": 45779,
-          "sha256": "1d5d8ee9dee62a08a56466354c8d5f07a372c50982f149ee36dcf33821ed2325"
+          "sha256": "76beadf89d1f73a2806fd0a71f821bb57551deb9f675b22f5033b7e8423e8df1"
         }
       },
       "failures": []
@@ -1854,44 +1952,44 @@
         "path": "repo:docs/data/task_method_20_result_matrix.json",
         "exists": true,
         "bytes": 129711,
-        "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+        "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model_data": {
           "path": "hf_model:data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         },
         "hf_model": {
           "path": "hf_model:metrics/task_method_20_result_matrix.json",
           "exists": true,
           "bytes": 129711,
-          "sha256": "a8f7363399ddb3c57f9cf66a329d9c22e652ee0063de49e91a8adb8325a36927"
+          "sha256": "3e51941540266ca9e20d674d9eb8b80b01f28d82d5179d31ff0ea5bb9b3aa847"
         }
       },
       "failures": []
@@ -1952,44 +2050,44 @@
         "path": "repo:docs/data/unified_task_model_radar.json",
         "exists": true,
         "bytes": 231290,
-        "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+        "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model_data": {
           "path": "hf_model:data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         },
         "hf_model": {
           "path": "hf_model:metrics/unified_task_model_radar.json",
           "exists": true,
           "bytes": 231290,
-          "sha256": "1238f6cf12f3196f483f42a9da8e405bfdef264645c2fcbb24842abcb81031e1"
+          "sha256": "80122b2530677a10eff841d29eb116934f0603233a7c763c0c5dedc8154bb467"
         }
       },
       "failures": []
@@ -2000,45 +2098,45 @@
       "local": {
         "path": "repo:docs/data/website_integrity.json",
         "exists": true,
-        "bytes": 18225,
-        "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+        "bytes": 18814,
+        "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_artifacts_data": {
           "path": "hf_artifacts:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:docs/data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model_data": {
           "path": "hf_model:data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model_docs_data": {
           "path": "hf_model:docs/data/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         },
         "hf_model": {
           "path": "hf_model:metrics/website_integrity.json",
           "exists": true,
-          "bytes": 18225,
-          "sha256": "4c0dd7325beb469f5d690282ca49b483ac11e0c82e636ac2d4de5f66e8afc065"
+          "bytes": 18814,
+          "sha256": "059ff4a4479408b9ac68880f2425b4f5bf67fab7830d269dbf38991cda362e2e"
         }
       },
       "failures": []
@@ -2129,6 +2227,80 @@
       },
       "failures": []
     },
+    {
+      "name": "assets/charts/single_episode_task_model_radar.svg",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/assets/charts/single_episode_task_model_radar.svg",
+        "exists": true,
+        "bytes": 37447,
+        "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_artifacts_docs": {
+          "path": "hf_artifacts:docs/assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_artifacts_card": {
+          "path": "hf_artifacts:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        },
+        "hf_model": {
+          "path": "hf_model:assets/charts/single_episode_task_model_radar.svg",
+          "exists": true,
+          "bytes": 37447,
+          "sha256": "4fe5979adc1f5b27edcb2f3527407a2cba1ccda94b05fc0a0dfa7b2767fc6795"
+        }
+      },
+      "failures": []
+    },
+    {
+      "name": "assets/charts/episode128_task_model_radar.svg",
+      "status": "pass",
+      "local": {
+        "path": "repo:docs/assets/charts/episode128_task_model_radar.svg",
+        "exists": true,
+        "bytes": 45734,
+        "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+      },
+      "mirrors": {
+        "hf_space": {
+          "path": "hf_space:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_artifacts_docs": {
+          "path": "hf_artifacts:docs/assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_artifacts_card": {
+          "path": "hf_artifacts:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        },
+        "hf_model": {
+          "path": "hf_model:assets/charts/episode128_task_model_radar.svg",
+          "exists": true,
+          "bytes": 45734,
+          "sha256": "412169278a61e2dc27cf84528cd3c80dc9f9db2d711e624e5436cf4fd4299761"
+        }
+      },
+      "failures": []
+    },
     {
       "name": "assets/charts/tier2_task_suite.svg",
       "status": "pass",
@@ -2172,33 +2344,33 @@
       "local": {
         "path": "repo:docs/assets/charts/unified_task_model_radar.svg",
         "exists": true,
-        "bytes": 51944,
-        "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+        "bytes": 51920,
+        "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_artifacts_card": {
           "path": "hf_artifacts:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         },
         "hf_model": {
           "path": "hf_model:assets/charts/unified_task_model_radar.svg",
           "exists": true,
-          "bytes": 51944,
-          "sha256": "c7b460e28e6639958817b41e0f35290c32a87018714b13e4531cf5e5a44b8f72"
+          "bytes": 51920,
+          "sha256": "2350a795fed9630f88112a18af54f99bfbf27a57f24f9a2b5ed3398a2a2c053a"
         }
       },
       "failures": []
@@ -3535,21 +3707,21 @@
       "local": {
         "path": "repo:scripts/build_artifact_index.py",
         "exists": true,
-        "bytes": 49735,
-        "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+        "bytes": 51367,
+        "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_artifact_index.py",
           "exists": true,
-          "bytes": 49735,
-          "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+          "bytes": 51367,
+          "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_artifact_index.py",
           "exists": true,
-          "bytes": 49735,
-          "sha256": "62ff86d0086204dd938a43587c4bb1030932d6d5116b2bd05566d1f0f1c618e1"
+          "bytes": 51367,
+          "sha256": "808ea80ed1c1a047121d86dd976822b96bf6da8176204a6121b8890955d3ab67"
         }
       },
       "failures": []
@@ -3610,21 +3782,21 @@
       "local": {
         "path": "repo:scripts/build_figure_index.py",
         "exists": true,
-        "bytes": 14431,
-        "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+        "bytes": 15412,
+        "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_figure_index.py",
           "exists": true,
-          "bytes": 14431,
-          "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+          "bytes": 15412,
+          "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_figure_index.py",
           "exists": true,
-          "bytes": 14431,
-          "sha256": "655bb0a46b785f51bd2aa3522be358ea1d52d891bf78f9d13c32ceb04ee80f52"
+          "bytes": 15412,
+          "sha256": "12eb9fa001f911f90d228362bb67f0fbc4a99be46180147d85a65742190dfd13"
         }
       },
       "failures": []
@@ -3660,21 +3832,21 @@
       "local": {
         "path": "repo:scripts/build_public_surface_qa.py",
         "exists": true,
-        "bytes": 12335,
-        "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+        "bytes": 12605,
+        "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_public_surface_qa.py",
           "exists": true,
-          "bytes": 12335,
-          "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+          "bytes": 12605,
+          "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_public_surface_qa.py",
           "exists": true,
-          "bytes": 12335,
-          "sha256": "7db82698275cd3a9ffc1ae71e5dbfae3e0d2c3694fbe296dfafb1e01714e47d4"
+          "bytes": 12605,
+          "sha256": "3ad531eaea623a977b7f6831b9e1e49a60d61604f7639d5e7e47a1741cc05bab"
         }
       },
       "failures": []
@@ -3810,21 +3982,21 @@
       "local": {
         "path": "repo:scripts/build_unified_task_model_radar.py",
         "exists": true,
-        "bytes": 40863,
-        "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+        "bytes": 47659,
+        "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/build_unified_task_model_radar.py",
           "exists": true,
-          "bytes": 40863,
-          "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+          "bytes": 47659,
+          "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
         },
         "hf_model": {
           "path": "hf_model:scripts/build_unified_task_model_radar.py",
           "exists": true,
-          "bytes": 40863,
-          "sha256": "aa9a09bbc87955f773a1734fb58a69c4a27112d6fc933188036f771ae152cec6"
+          "bytes": 47659,
+          "sha256": "62a499f89e57ab174e75ef965080b168c1a851a9830ac950f93ed9ee7c767df0"
         }
       },
       "failures": []
@@ -3860,21 +4032,21 @@
       "local": {
         "path": "repo:scripts/verify_live_publication.py",
         "exists": true,
-        "bytes": 52549,
-        "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+        "bytes": 55921,
+        "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/verify_live_publication.py",
           "exists": true,
-          "bytes": 52549,
-          "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+          "bytes": 55921,
+          "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
         },
         "hf_model": {
           "path": "hf_model:scripts/verify_live_publication.py",
           "exists": true,
-          "bytes": 52549,
-          "sha256": "83f646690a17f3df195f69c3a21fba944ed2fab9cc176465ff8262e8395903b1"
+          "bytes": 55921,
+          "sha256": "feacfcf3b228c4270645a07effadf28372fcc0c86d753bb85e1bb23535acacb4"
         }
       },
       "failures": []
@@ -3885,21 +4057,21 @@
       "local": {
         "path": "repo:scripts/validate_mirror_parity.py",
         "exists": true,
-        "bytes": 23698,
-        "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+        "bytes": 23878,
+        "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/validate_mirror_parity.py",
           "exists": true,
-          "bytes": 23698,
-          "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+          "bytes": 23878,
+          "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
         },
         "hf_model": {
           "path": "hf_model:scripts/validate_mirror_parity.py",
           "exists": true,
-          "bytes": 23698,
-          "sha256": "b2f362dcc5b8357c00f73773d2e3808347bcc5505336b6b2629f23f2a473edf1"
+          "bytes": 23878,
+          "sha256": "b4ce0df1795e387fea1efeda00e263e2581675525ea22b1c0bcd376ea2807312"
         }
       },
       "failures": []
@@ -3910,21 +4082,21 @@
       "local": {
         "path": "repo:scripts/validate_publication_package.py",
         "exists": true,
-        "bytes": 18279,
-        "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+        "bytes": 18574,
+        "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/validate_publication_package.py",
           "exists": true,
-          "bytes": 18279,
-          "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+          "bytes": 18574,
+          "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
         },
         "hf_model": {
           "path": "hf_model:scripts/validate_publication_package.py",
           "exists": true,
-          "bytes": 18279,
-          "sha256": "f05b1a098c504a3ba22075cf10c1658c0a5daeaad2c64c4199f20a06071a4dc2"
+          "bytes": 18574,
+          "sha256": "8d30905b146b06a3cd9ec53c0e52e41a790dcd71cd6c2036511d5be73bdf21eb"
         }
       },
       "failures": []
@@ -4035,21 +4207,21 @@
       "local": {
         "path": "repo:scripts/sync_hf_publish_mirrors.py",
         "exists": true,
-        "bytes": 16249,
-        "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+        "bytes": 17161,
+        "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
       },
       "mirrors": {
         "hf_artifacts": {
           "path": "hf_artifacts:scripts/sync_hf_publish_mirrors.py",
           "exists": true,
-          "bytes": 16249,
-          "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+          "bytes": 17161,
+          "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
         },
         "hf_model": {
           "path": "hf_model:scripts/sync_hf_publish_mirrors.py",
           "exists": true,
-          "bytes": 16249,
-          "sha256": "692efd01ee8b0ca2bdab69cc5e0f0081d3e60856bafa5be78e7bb954bf47fcda"
+          "bytes": 17161,
+          "sha256": "0898c8014180cab8f52e523547fd89232f72daced718b7729ca3086a07a12681"
         }
       },
       "failures": []
@@ -4239,39 +4411,39 @@
       "local": {
         "path": "repo:docs/index.html",
         "exists": true,
-        "bytes": 227441,
-        "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+        "bytes": 230528,
+        "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_artifacts_root": {
           "path": "hf_artifacts:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_artifacts_docs": {
           "path": "hf_artifacts:docs/index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_model": {
           "path": "hf_model:index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         },
         "hf_model_docs": {
           "path": "hf_model:docs/index.html",
           "exists": true,
-          "bytes": 227441,
-          "sha256": "acb9de8c04909e01909b6c52781ba4e616cf09b108497a8d37a9bc567f339756"
+          "bytes": 230528,
+          "sha256": "0885f52fd117b0bfd63466e6024951736fed21f228cc5b96a9357c999cd79e58"
         }
       },
       "failures": []
@@ -17896,27 +18068,27 @@
       "local": {
         "path": "repo:FIGURE_INDEX.md",
         "exists": true,
-        "bytes": 5614,
-        "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+        "bytes": 6217,
+        "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
       },
       "mirrors": {
         "hf_space": {
           "path": "hf_space:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         },
         "hf_artifacts": {
           "path": "hf_artifacts:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         },
         "hf_model": {
           "path": "hf_model:FIGURE_INDEX.md",
           "exists": true,
-          "bytes": 5614,
-          "sha256": "cd2a72c7a9872ca653a2fba4a28df8fbca7c16f52f6d7d84e26f6ae74f13e503"
+          "bytes": 6217,
+          "sha256": "d16e8f64afc588d6a69498244a9db6304350b14feee571b8c5e9c41f08541a00"
         }
       },
       "failures": []

docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:46:48+00:00",
+  "generated_at_utc": "2026-06-16T11:08:23+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": "2026-06-16T10:35:38+00:00"
+          "generated_at_utc": "2026-06-16T11:00:25+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:37+00:00"
+          "generated_at_utc": "2026-06-16T11:00:22+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:37+00:00"
+          "generated_at_utc": "2026-06-16T11:00:22+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:40+00:00"
+          "generated_at_utc": "2026-06-16T11:00:26+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:35:47+00:00"
+          "generated_at_utc": "2026-06-16T11:00:39+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-16T10:36:24+00:00"
+          "generated_at_utc": "2026-06-16T11:02:20+00:00"
         }
       },
       "failures": {}
@@ -81,7 +81,7 @@
         "aria-controls": 11,
         "moveProjectTabFocus": 2,
         "ArrowRight": 6,
-        "Home": 6,
+        "Home": 7,
         "End": 6
       }
     },
@@ -97,8 +97,8 @@
       "marker_counts": {
         "Ropedia Xperience-10M Task Suite": 16,
         "Xperience-10M": 149,
-        "20-task": 43,
-        "Qwen3-Omni": 148,
+        "20-task": 60,
+        "Qwen3-Omni": 151,
         "128-episode pilot": 1
       }
     },
@@ -131,7 +131,12 @@
         "data/task_suite_enhancement_128.json": 28,
         "data/task_suite_20.json": 42,
         "data/unified_task_model_radar.json": 17,
+        "data/single_episode_task_model_radar.json": 11,
+        "data/episode128_task_model_radar.json": 11,
+        "data/task_method_20_result_matrix.json": 17,
         "assets/charts/unified_task_model_radar.svg": 17,
+        "assets/charts/single_episode_task_model_radar.svg": 18,
+        "assets/charts/episode128_task_model_radar.svg": 18,
         "data/tier2_task_suite.json": 11
       }
     },

docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:14+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+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/single_episode_task_model_radar.json

@@ -0,0 +1,1473 @@
+{
+  "title": "Single-Episode 20-Task Radar",
+  "status": "pass",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
+  "description": "Minimal and Neural MLP baselines on the one public sample episode, both scored on all 20 task contracts.",
+  "task_count": 20,
+  "method_count": 2,
+  "method_task_record_count": 40,
+  "scored_method_task_count": 40,
+  "normalization_policy": {
+    "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
+    "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
+    "raw_values": "raw metric values, metric keys, and sources are retained in this JSON; the SVG is an overview, not a replacement for the metric table",
+    "result_record_policy": "every method has 20 task records; records without a numeric score carry explicit unsupported/not-evaluated status and reason fields",
+    "foundation_model_overlay": "Qwen3/Cosmos points are plotted only on task-aligned axes. Scoreless records mean the public result does not evaluate that task contract.",
+    "metadata_128_overlay": "128-episode metadata baselines have 20 records, but numeric scores only where the public JSONL contains enough task labels without raw feature blocks.",
+    "raw_128_overlay": "128-episode raw-feature baselines use staged sensor NPZ features. Eighteen axes use direct task targets; interaction text and camera-view sync are completed with documented compact proxies because raw interaction strings and paired video-view embeddings are absent from the 128 export."
+  },
+  "source_unified_radar": "docs/data/unified_task_model_radar.json",
+  "source_result_matrix": "docs/data/task_method_20_result_matrix.json",
+  "series": [
+    {
+      "id": "minimal",
+      "label": "Minimal",
+      "short_label": "Min",
+      "color": "#ccffa0",
+      "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",
+      "result_record_count": 20,
+      "scored_task_count": 20,
+      "covered_task_count": 20,
+      "proxy_scored_task_count": 0,
+      "scoreless_task_count": 0,
+      "unsupported_task_count": 0,
+      "not_evaluated_task_count": 0,
+      "status_counts": {
+        "scored": 20
+      },
+      "coverage_fraction": 1.0,
+      "result_record_fraction": 1.0
+    },
+    {
+      "id": "neural_mlp",
+      "label": "Neural MLP",
+      "short_label": "NN",
+      "color": "#67e8d1",
+      "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",
+      "result_record_count": 20,
+      "scored_task_count": 20,
+      "covered_task_count": 20,
+      "proxy_scored_task_count": 0,
+      "scoreless_task_count": 0,
+      "unsupported_task_count": 0,
+      "not_evaluated_task_count": 0,
+      "status_counts": {
+        "scored": 20
+      },
+      "coverage_fraction": 1.0,
+      "result_record_fraction": 1.0
+    }
+  ],
+  "tasks": [
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/timeline_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05,
+          "raw_text": "0.0500",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.014814814814814814,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.014814814814814814,
+          "raw_text": "0.0148",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/timeline_subtask/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05056355513846935,
+          "raw_text": "0.0506",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.02810810810810811,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.02810810810810811,
+          "raw_text": "0.0281",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/transition_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.6118237590630229,
+          "raw_text": "0.6118",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.5862068965517241,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5862068965517241,
+          "raw_text": "0.5862",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.05925925925925927,
+          "raw_text": "0.0593",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.04186046511627907,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04186046511627907,
+          "raw_text": "0.0419",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mpjpe",
+          "source": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.12473175026322614,
+          "raw_text": "0.8647",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.10785018652677536,
+          "metric_key": "mpjpe",
+          "source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "0.1079",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/contact_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "1.000",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 1.0,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "1.000",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/object_relevance/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.18034382095361662,
+          "raw_text": "0.1803",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.1679279279279279,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.1679279279279279,
+          "raw_text": "0.1679",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/caption_grounding/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.016023479050338015,
+          "raw_text": "0.0160",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.01684125567132316,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.01684125567132316,
+          "raw_text": "0.0168",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.26925966892956127,
+          "raw_text": "0.2693",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.1299971898648288,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.1299971898648288,
+          "raw_text": "0.1300",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "r2",
+          "source": "results/episode_task_suite/modality_reconstruction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "-0.0153",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": -0.010171410134180991,
+          "metric_key": "r2",
+          "source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "-0.0102",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/temporal_order/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5399515738498789,
+          "raw_text": "0.5400",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.8520179372197308,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.8520179372197308,
+          "raw_text": "0.8520",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/misalignment_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.5051698670605613,
+          "raw_text": "0.5052",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.7152682255845944,
+          "metric_key": "f1",
+          "source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.7152682255845944,
+          "raw_text": "0.7153",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.07499999999999998,
+          "raw_text": "0.0750",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.06545454545454546,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.06545454545454546,
+          "raw_text": "0.0655",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04545454545454545,
+          "raw_text": "0.0455",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.050724637681159424,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.050724637681159424,
+          "raw_text": "0.0507",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.04444444444444444,
+          "raw_text": "0.0444",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.0380952380952381,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0380952380952381,
+          "raw_text": "0.0381",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "0.0000",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.0,
+          "metric_key": "macro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.0,
+          "raw_text": "0.0000",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.16939890710382516,
+          "raw_text": "0.1694",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.19718309859154928,
+          "metric_key": "micro_f1",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.19718309859154928,
+          "raw_text": "0.1972",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "0.0420",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.042562149465084076,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.9879531106266066,
+          "raw_text": "0.0426",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.4943004846572876,
+          "raw_text": "0.4943",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 0.24086658656597137,
+          "metric_key": "mrr",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.24086658656597137,
+          "raw_text": "0.2409",
+          "status_label": "scored"
+        }
+      }
+    },
+    {
+      "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": {
+        "minimal": {
+          "raw": 10.53735637664795,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 1.0,
+          "raw_text": "10.54",
+          "status_label": "scored"
+        },
+        "neural_mlp": {
+          "raw": 10.55449390411377,
+          "metric_key": "mae",
+          "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json",
+          "scope": "single_episode_public_sample",
+          "status": "scored",
+          "normalized_score": 0.9983762814568361,
+          "raw_text": "10.55",
+          "status_label": "scored"
+        }
+      }
+    }
+  ],
+  "task_method_result_matrix": [
+    {
+      "task_number": 1,
+      "task_id": "timeline_action",
+      "task_label": "Action Recognition",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05,
+      "raw_text": "0.0500",
+      "normalized_score": 0.05,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/timeline_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 1,
+      "task_id": "timeline_action",
+      "task_label": "Action Recognition",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.014814814814814814,
+      "raw_text": "0.0148",
+      "normalized_score": 0.014814814814814814,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 2,
+      "task_id": "timeline_subtask",
+      "task_label": "Procedure Step Recognition",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05056355513846935,
+      "raw_text": "0.0506",
+      "normalized_score": 0.05056355513846935,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/timeline_subtask/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 2,
+      "task_id": "timeline_subtask",
+      "task_label": "Procedure Step Recognition",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.02810810810810811,
+      "raw_text": "0.0281",
+      "normalized_score": 0.02810810810810811,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 3,
+      "task_id": "transition_detection",
+      "task_label": "Action Boundary Detection",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.6118237590630229,
+      "raw_text": "0.6118",
+      "normalized_score": 0.6118237590630229,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/transition_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 3,
+      "task_id": "transition_detection",
+      "task_label": "Action Boundary Detection",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5862068965517241,
+      "raw_text": "0.5862",
+      "normalized_score": 0.5862068965517241,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/transition_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 4,
+      "task_id": "next_action",
+      "task_label": "Next-Action Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.05925925925925927,
+      "raw_text": "0.0593",
+      "normalized_score": 0.05925925925925927,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 4,
+      "task_id": "next_action",
+      "task_label": "Next-Action Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04186046511627907,
+      "raw_text": "0.0419",
+      "normalized_score": 0.04186046511627907,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 5,
+      "task_id": "hand_trajectory_forecast",
+      "task_label": "Hand Trajectory Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.8646570444107056,
+      "raw_text": "0.8647",
+      "normalized_score": 0.12473175026322614,
+      "metric_key": "mpjpe",
+      "source": "results/episode_task_suite/hand_trajectory_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 5,
+      "task_id": "hand_trajectory_forecast",
+      "task_label": "Hand Trajectory Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.10785018652677536,
+      "raw_text": "0.1079",
+      "normalized_score": 1.0,
+      "metric_key": "mpjpe",
+      "source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 6,
+      "task_id": "contact_prediction",
+      "task_label": "Contact State Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 1.0,
+      "raw_text": "1.000",
+      "normalized_score": 1.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/contact_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 6,
+      "task_id": "contact_prediction",
+      "task_label": "Contact State Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 1.0,
+      "raw_text": "1.000",
+      "normalized_score": 1.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 7,
+      "task_id": "object_relevance",
+      "task_label": "Object Relevance Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.18034382095361662,
+      "raw_text": "0.1803",
+      "normalized_score": 0.18034382095361662,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/object_relevance/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 7,
+      "task_id": "object_relevance",
+      "task_label": "Object Relevance Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.1679279279279279,
+      "raw_text": "0.1679",
+      "normalized_score": 0.1679279279279279,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 8,
+      "task_id": "caption_grounding",
+      "task_label": "Language Grounding",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.016023479050338015,
+      "raw_text": "0.0160",
+      "normalized_score": 0.016023479050338015,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/caption_grounding/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 8,
+      "task_id": "caption_grounding",
+      "task_label": "Language Grounding",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.01684125567132316,
+      "raw_text": "0.0168",
+      "normalized_score": 0.01684125567132316,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 9,
+      "task_id": "cross_modal_retrieval",
+      "task_label": "Cross-Modal Retrieval",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.26925966892956127,
+      "raw_text": "0.2693",
+      "normalized_score": 0.26925966892956127,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/cross_modal_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 9,
+      "task_id": "cross_modal_retrieval",
+      "task_label": "Cross-Modal Retrieval",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.1299971898648288,
+      "raw_text": "0.1300",
+      "normalized_score": 0.1299971898648288,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 10,
+      "task_id": "modality_reconstruction",
+      "task_label": "Cross-Modal Reconstruction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": -0.015271898913936655,
+      "raw_text": "-0.0153",
+      "normalized_score": 0.0,
+      "metric_key": "r2",
+      "source": "results/episode_task_suite/modality_reconstruction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 10,
+      "task_id": "modality_reconstruction",
+      "task_label": "Cross-Modal Reconstruction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": -0.010171410134180991,
+      "raw_text": "-0.0102",
+      "normalized_score": 0.0,
+      "metric_key": "r2",
+      "source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 11,
+      "task_id": "temporal_order",
+      "task_label": "Temporal Order Verification",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5399515738498789,
+      "raw_text": "0.5400",
+      "normalized_score": 0.5399515738498789,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/temporal_order/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 11,
+      "task_id": "temporal_order",
+      "task_label": "Temporal Order Verification",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.8520179372197308,
+      "raw_text": "0.8520",
+      "normalized_score": 0.8520179372197308,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 12,
+      "task_id": "misalignment_detection",
+      "task_label": "Multimodal Synchronization Detection",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.5051698670605613,
+      "raw_text": "0.5052",
+      "normalized_score": 0.5051698670605613,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/misalignment_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 12,
+      "task_id": "misalignment_detection",
+      "task_label": "Multimodal Synchronization Detection",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.7152682255845944,
+      "raw_text": "0.7153",
+      "normalized_score": 0.7152682255845944,
+      "metric_key": "f1",
+      "source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 13,
+      "task_id": "long_horizon_next_action",
+      "task_label": "Long-Horizon Next-Action Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.07499999999999998,
+      "raw_text": "0.0750",
+      "normalized_score": 0.07499999999999998,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 13,
+      "task_id": "long_horizon_next_action",
+      "task_label": "Long-Horizon Next-Action Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.06545454545454546,
+      "raw_text": "0.0655",
+      "normalized_score": 0.06545454545454546,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 14,
+      "task_id": "next_subtask_forecast",
+      "task_label": "Long-Horizon Next-Subtask Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04545454545454545,
+      "raw_text": "0.0455",
+      "normalized_score": 0.04545454545454545,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 14,
+      "task_id": "next_subtask_forecast",
+      "task_label": "Long-Horizon Next-Subtask Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.050724637681159424,
+      "raw_text": "0.0507",
+      "normalized_score": 0.050724637681159424,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 15,
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.04444444444444444,
+      "raw_text": "0.0444",
+      "normalized_score": 0.04444444444444444,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 15,
+      "task_id": "interaction_text_prediction",
+      "task_label": "Interaction Text Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0380952380952381,
+      "raw_text": "0.0381",
+      "normalized_score": 0.0380952380952381,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 16,
+      "task_id": "action_object_relation",
+      "task_label": "Action-Object Relation Prediction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0,
+      "raw_text": "0.0000",
+      "normalized_score": 0.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 16,
+      "task_id": "action_object_relation",
+      "task_label": "Action-Object Relation Prediction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.0,
+      "raw_text": "0.0000",
+      "normalized_score": 0.0,
+      "metric_key": "macro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 17,
+      "task_id": "object_set_forecast",
+      "task_label": "Future Object-Set Forecasting",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.16939890710382516,
+      "raw_text": "0.1694",
+      "normalized_score": 0.16939890710382516,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 17,
+      "task_id": "object_set_forecast",
+      "task_label": "Future Object-Set Forecasting",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.19718309859154928,
+      "raw_text": "0.1972",
+      "normalized_score": 0.19718309859154928,
+      "metric_key": "micro_f1",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 18,
+      "task_id": "imu_to_hand_pose",
+      "task_label": "IMU-to-Hand Pose Reconstruction",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.042049407958984375,
+      "raw_text": "0.0420",
+      "normalized_score": 1.0,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 18,
+      "task_id": "imu_to_hand_pose",
+      "task_label": "IMU-to-Hand Pose Reconstruction",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.042562149465084076,
+      "raw_text": "0.0426",
+      "normalized_score": 0.9879531106266066,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 19,
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.4943004846572876,
+      "raw_text": "0.4943",
+      "normalized_score": 0.4943004846572876,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 19,
+      "task_id": "camera_view_sync_retrieval",
+      "task_label": "Camera-View Synchronization Retrieval",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 0.24086658656597137,
+      "raw_text": "0.2409",
+      "normalized_score": 0.24086658656597137,
+      "metric_key": "mrr",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 20,
+      "task_id": "time_to_transition",
+      "task_label": "Time-to-Next-Transition Regression",
+      "series_id": "minimal",
+      "method": "Minimal",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 10.53735637664795,
+      "raw_text": "10.54",
+      "normalized_score": 1.0,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    },
+    {
+      "task_number": 20,
+      "task_id": "time_to_transition",
+      "task_label": "Time-to-Next-Transition Regression",
+      "series_id": "neural_mlp",
+      "method": "Neural MLP",
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
+      "proxy_scored": false,
+      "raw": 10.55449390411377,
+      "raw_text": "10.55",
+      "normalized_score": 0.9983762814568361,
+      "metric_key": "mae",
+      "source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json",
+      "scope": "single_episode_public_sample",
+      "reason": null
+    }
+  ]
+}

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:37+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/task_method_20_result_matrix.json

@@ -1,7 +1,7 @@
 {
   "title": "Task Method 20-Result Matrix",
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:34:53+00:00",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:37+00:00",
+  "generated_at_utc": "2026-06-16T11:08:48+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": "2026-06-16T10:34:53+00:00",
+  "generated_at_utc": "2026-06-16T10:59:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,

docs/data/website_integrity.json

@@ -1,14 +1,14 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T10:35:38+00:00",
+  "generated_at_utc": "2026-06-16T11:08:31+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 162,
+    "local_references": 176,
     "external_reference_count": 121,
-    "json_files": 43,
-    "image_assets_referenced": 23,
+    "json_files": 45,
+    "image_assets_referenced": 25,
     "failure_count": 0
   },
   "failures": {
@@ -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": 85521,
-      "evidence_index": 111351
+      "overview_index": 87077,
+      "evidence_index": 114423
     },
     {
       "name": "project_status_links_json",
@@ -159,9 +159,9 @@
       "name": "evaluation_protocol_between_overview_and_progress",
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
-      "overview_index": 85521,
-      "protocol_index": 107532,
-      "evidence_index": 111351
+      "overview_index": 87077,
+      "protocol_index": 110604,
+      "evidence_index": 114423
     },
     {
       "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": 1831
+      "second_marker_index": 3362
     },
     {
       "name": "suite_modality_atlas_contains_seven_cards",
@@ -277,8 +277,8 @@
     {
       "path": "index.html",
       "id_count": 90,
-      "reference_count": 138,
-      "image_count": 27
+      "reference_count": 152,
+      "image_count": 31
     },
     {
       "path": "research_roadmap.html",
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 97873,
+      "bytes": 99966,
       "top_level_type": "dict"
     },
     {
@@ -314,6 +314,11 @@
       "bytes": 3967,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/episode128_task_model_radar.json",
+      "bytes": 187439,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/evaluation_protocol.json",
       "bytes": 24020,
@@ -326,7 +331,7 @@
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 15726,
+      "bytes": 17287,
       "top_level_type": "dict"
     },
     {
@@ -336,12 +341,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 131884,
+      "bytes": 134796,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 838389,
+      "bytes": 847909,
       "top_level_type": "dict"
     },
     {
@@ -381,12 +386,12 @@
     },
     {
       "path": "data/public_surface_qa.json",
-      "bytes": 5806,
+      "bytes": 6096,
       "top_level_type": "dict"
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 7883,
+      "bytes": 8188,
       "top_level_type": "dict"
     },
     {
@@ -454,6 +459,11 @@
       "bytes": 4305626,
       "top_level_type": "dict"
     },
+    {
+      "path": "data/single_episode_task_model_radar.json",
+      "bytes": 50973,
+      "top_level_type": "dict"
+    },
     {
       "path": "data/source_alignment_audit.json",
       "bytes": 4432,
@@ -501,7 +511,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 18101,
+      "bytes": 18814,
       "top_level_type": "dict"
     },
     {
@@ -533,6 +543,13 @@
       "format": "SVG",
       "has_viewbox": true
     },
+    {
+      "path": "assets/charts/episode128_task_model_radar.svg",
+      "exists": true,
+      "bytes": 45734,
+      "format": "SVG",
+      "has_viewbox": true
+    },
     {
       "path": "assets/charts/episode_task_scores.svg",
       "exists": true,
@@ -582,6 +599,13 @@
       "format": "SVG",
       "has_viewbox": true
     },
+    {
+      "path": "assets/charts/single_episode_task_model_radar.svg",
+      "exists": true,
+      "bytes": 37447,
+      "format": "SVG",
+      "has_viewbox": true
+    },
     {
       "path": "assets/charts/tier2_task_suite.svg",
       "exists": true,
@@ -592,7 +616,7 @@
     {
       "path": "assets/charts/unified_task_model_radar.svg",
       "exists": true,
-      "bytes": 51944,
+      "bytes": 51920,
       "format": "SVG",
       "has_viewbox": true
     },

docs/index.html

@@ -583,6 +583,58 @@
       line-height: 1.6;
       font-size: 14px;
     }
+    .split-radar-grid {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 18px;
+      margin: 22px 0 28px;
+    }
+    .split-radar-card {
+      min-width: 0;
+      border: 1px solid var(--line);
+      border-radius: var(--radius);
+      background:
+        linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(6, 14, 7, 0.82)),
+        var(--surface);
+      padding: 16px;
+    }
+    .split-radar-card h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: 18px;
+      line-height: 1.18;
+      letter-spacing: 0;
+    }
+    .split-radar-card p {
+      margin: 8px 0 14px;
+      color: var(--muted);
+      font-size: 13px;
+      line-height: 1.5;
+    }
+    .split-radar-card img {
+      display: block;
+      width: 100%;
+      border: 1px solid rgba(204, 255, 160, 0.14);
+      border-radius: 6px;
+      background: #020502;
+    }
+    .split-radar-links {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+      margin-top: 12px;
+    }
+    .split-radar-links a {
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      color: var(--cyan);
+      font-size: 12px;
+      font-weight: 700;
+      padding: 7px 8px;
+      text-decoration: none;
+      background: rgba(2, 5, 2, 0.42);
+    }
+    .split-radar-links a:hover { border-color: var(--green); color: var(--ink); }
     .task-suite-image {
       display: block;
       margin-top: 30px;
@@ -2584,7 +2636,8 @@
       .signal:nth-child(2n + 1) { border-right: 0; }
       .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); }
       .section-tabs { padding-top: 10px; }
-      .figure-brief { grid-template-columns: 1fr; }
+      .figure-brief,
+      .split-radar-grid { grid-template-columns: 1fr; }
       .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .brief-panel-head { grid-template-columns: 1fr; align-items: start; }
       .task-player { grid-template-columns: 1fr; }
@@ -2802,6 +2855,8 @@
             <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="assets/charts/single_episode_task_model_radar.svg">1-episode radar</a>
+              <a href="assets/charts/episode128_task_model_radar.svg">128ep radar</a>
               <a href="data/unified_task_model_radar.json">Open radar JSON</a>
               <a href="data/task_method_20_result_matrix.json">Open 20-result matrix</a>
             </div>
@@ -2902,6 +2957,26 @@
             <a href="#takeaways">Current takeaways</a>
           </div>
         </div>
+        <div class="split-radar-grid" aria-label="Homepage split 20-task radar comparisons">
+          <article class="split-radar-card">
+            <h3>1-Episode 20-Task Radar</h3>
+            <p>Minimal and Neural MLP baselines over the original public-sample episode, with 40/40 scored method-task records.</p>
+            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+            <div class="split-radar-links">
+              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+              <a href="data/single_episode_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+          <article class="split-radar-card">
+            <h3>128-Episode 20-Task Radar</h3>
+            <p>Metadata, raw-feature, Qwen3-Omni, and Cosmos3 branches on the aligned 128-episode surface, with scoreless axes kept explicit.</p>
+            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="128-episode 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 branches with explicit scored-axis counts">
+            <div class="split-radar-links">
+              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+              <a href="data/episode128_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+        </div>
         <div class="snapshot-grid">
           <article class="snapshot-card">
             <span class="status-pill">featured</span>
@@ -3488,8 +3563,8 @@
         </div>
         <div class="figure-brief">
           <article class="figure-brief-card">
-            <h3>Unified 20-task polygon</h3>
-            <p>The radar uses all 20 tasks as axes and lists each full task name in the chart key. All 9 method series now expose 20 result records; colored overlays appear only where those records contain numeric scores.</p>
+            <h3>Unified plus split radars</h3>
+            <p>The unified radar keeps all 9 methods in one view. The two split radars separate the clean 1-episode Minimal/NN baseline comparison from the 128-episode metadata/raw/Qwen/Cosmos comparison.</p>
           </article>
           <article class="figure-brief-card">
             <h3>Metric normalization</h3>
@@ -3497,6 +3572,26 @@
           </article>
         </div>
         <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v5" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, scored-axis counts, and proxy notes">
+        <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
+          <article class="split-radar-card">
+            <h3>1-Episode 20-Task Radar</h3>
+            <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts, shown as two filled polygons without 128-episode overlays.</p>
+            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+            <div class="split-radar-links">
+              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+              <a href="data/single_episode_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+          <article class="split-radar-card">
+            <h3>128-Episode 20-Task Radar</h3>
+            <p>Raw128 Simple and Raw128 NN score all 20 axes; metadata, Qwen3, and Cosmos branches keep 20 records but only plot evaluated numeric targets.</p>
+            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="128-episode 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 branches with explicit scored-axis counts">
+            <div class="split-radar-links">
+              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+              <a href="data/episode128_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+        </div>
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

index.html

@@ -583,6 +583,58 @@
       line-height: 1.6;
       font-size: 14px;
     }
+    .split-radar-grid {
+      display: grid;
+      grid-template-columns: repeat(2, minmax(0, 1fr));
+      gap: 18px;
+      margin: 22px 0 28px;
+    }
+    .split-radar-card {
+      min-width: 0;
+      border: 1px solid var(--line);
+      border-radius: var(--radius);
+      background:
+        linear-gradient(180deg, rgba(204, 255, 160, 0.06), rgba(6, 14, 7, 0.82)),
+        var(--surface);
+      padding: 16px;
+    }
+    .split-radar-card h3 {
+      margin: 0;
+      font-family: var(--font-ui);
+      font-size: 18px;
+      line-height: 1.18;
+      letter-spacing: 0;
+    }
+    .split-radar-card p {
+      margin: 8px 0 14px;
+      color: var(--muted);
+      font-size: 13px;
+      line-height: 1.5;
+    }
+    .split-radar-card img {
+      display: block;
+      width: 100%;
+      border: 1px solid rgba(204, 255, 160, 0.14);
+      border-radius: 6px;
+      background: #020502;
+    }
+    .split-radar-links {
+      display: flex;
+      flex-wrap: wrap;
+      gap: 8px;
+      margin-top: 12px;
+    }
+    .split-radar-links a {
+      border: 1px solid var(--soft-line);
+      border-radius: 6px;
+      color: var(--cyan);
+      font-size: 12px;
+      font-weight: 700;
+      padding: 7px 8px;
+      text-decoration: none;
+      background: rgba(2, 5, 2, 0.42);
+    }
+    .split-radar-links a:hover { border-color: var(--green); color: var(--ink); }
     .task-suite-image {
       display: block;
       margin-top: 30px;
@@ -2584,7 +2636,8 @@
       .signal:nth-child(2n + 1) { border-right: 0; }
       .project-tabs { grid-template-columns: repeat(3, minmax(0, 1fr)); }
       .section-tabs { padding-top: 10px; }
-      .figure-brief { grid-template-columns: 1fr; }
+      .figure-brief,
+      .split-radar-grid { grid-template-columns: 1fr; }
       .hero-stats, .models, .task-grid, .artifact-grid, .evidence-grid, .reading-grid, .snapshot-grid, .roadmap-grid, .brief-grid, .boundary-strip, .callout-row, .direction-grid, .baseline-strip, .extension-grid { grid-template-columns: repeat(2, minmax(0, 1fr)); }
       .brief-panel-head { grid-template-columns: 1fr; align-items: start; }
       .task-player { grid-template-columns: 1fr; }
@@ -2802,6 +2855,8 @@
             <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="assets/charts/single_episode_task_model_radar.svg">1-episode radar</a>
+              <a href="assets/charts/episode128_task_model_radar.svg">128ep radar</a>
               <a href="data/unified_task_model_radar.json">Open radar JSON</a>
               <a href="data/task_method_20_result_matrix.json">Open 20-result matrix</a>
             </div>
@@ -2902,6 +2957,26 @@
             <a href="#takeaways">Current takeaways</a>
           </div>
         </div>
+        <div class="split-radar-grid" aria-label="Homepage split 20-task radar comparisons">
+          <article class="split-radar-card">
+            <h3>1-Episode 20-Task Radar</h3>
+            <p>Minimal and Neural MLP baselines over the original public-sample episode, with 40/40 scored method-task records.</p>
+            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+            <div class="split-radar-links">
+              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+              <a href="data/single_episode_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+          <article class="split-radar-card">
+            <h3>128-Episode 20-Task Radar</h3>
+            <p>Metadata, raw-feature, Qwen3-Omni, and Cosmos3 branches on the aligned 128-episode surface, with scoreless axes kept explicit.</p>
+            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="128-episode 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 branches with explicit scored-axis counts">
+            <div class="split-radar-links">
+              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+              <a href="data/episode128_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+        </div>
         <div class="snapshot-grid">
           <article class="snapshot-card">
             <span class="status-pill">featured</span>
@@ -3488,8 +3563,8 @@
         </div>
         <div class="figure-brief">
           <article class="figure-brief-card">
-            <h3>Unified 20-task polygon</h3>
-            <p>The radar uses all 20 tasks as axes and lists each full task name in the chart key. All 9 method series now expose 20 result records; colored overlays appear only where those records contain numeric scores.</p>
+            <h3>Unified plus split radars</h3>
+            <p>The unified radar keeps all 9 methods in one view. The two split radars separate the clean 1-episode Minimal/NN baseline comparison from the 128-episode metadata/raw/Qwen/Cosmos comparison.</p>
           </article>
           <article class="figure-brief-card">
             <h3>Metric normalization</h3>
@@ -3497,6 +3572,26 @@
           </article>
         </div>
         <img class="chart" src="assets/charts/unified_task_model_radar.svg?v=xperience10m-20task-radar-v5" alt="Unified 20-task radar comparing Minimal, Neural MLP, 128-episode metadata/raw baselines, Qwen3-Omni, and Cosmos3 with task names, method details, 20-record counts, scored-axis counts, and proxy notes">
+        <div class="split-radar-grid" aria-label="Split 20-task radar comparisons">
+          <article class="split-radar-card">
+            <h3>1-Episode 20-Task Radar</h3>
+            <p>Minimal and Neural MLP are both scored on all 20 public-sample task contracts, shown as two filled polygons without 128-episode overlays.</p>
+            <img src="assets/charts/single_episode_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="Single-episode 20-task radar comparing Minimal and Neural MLP across all 20 scored task axes">
+            <div class="split-radar-links">
+              <a href="assets/charts/single_episode_task_model_radar.svg">Open SVG</a>
+              <a href="data/single_episode_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+          <article class="split-radar-card">
+            <h3>128-Episode 20-Task Radar</h3>
+            <p>Raw128 Simple and Raw128 NN score all 20 axes; metadata, Qwen3, and Cosmos branches keep 20 records but only plot evaluated numeric targets.</p>
+            <img src="assets/charts/episode128_task_model_radar.svg?v=xperience10m-split-radar-v1" alt="128-episode 20-task radar comparing raw-feature baselines, metadata baselines, Qwen3-Omni, and Cosmos3 branches with explicit scored-axis counts">
+            <div class="split-radar-links">
+              <a href="assets/charts/episode128_task_model_radar.svg">Open SVG</a>
+              <a href="data/episode128_task_model_radar.json">Open JSON</a>
+            </div>
+          </article>
+        </div>
         <div class="modality-atlas-panel" id="modality-atlas" aria-labelledby="modality-atlas-title">
           <div class="atlas-head">
             <div>

scripts/build_artifact_index.py

@@ -417,6 +417,22 @@ ARTIFACTS = [
         "surface": "website_hf",
         "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, and explicit scoreless status records.",
     },
+    {
+        "id": "single_episode_task_model_radar_json",
+        "title": "Single-episode 20-task model radar JSON",
+        "path": "docs/data/single_episode_task_model_radar.json",
+        "kind": "website_data",
+        "surface": "website_hf",
+        "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
+    },
+    {
+        "id": "episode128_task_model_radar_json",
+        "title": "128-episode 20-task model radar JSON",
+        "path": "docs/data/episode128_task_model_radar.json",
+        "kind": "website_data",
+        "surface": "website_hf",
+        "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, preserving explicit scoreless cells.",
+    },
     {
         "id": "task_method_20_result_matrix_json",
         "title": "Task-method 20-result matrix JSON",
@@ -441,6 +457,22 @@ ARTIFACTS = [
         "surface": "website_hf",
         "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
     },
+    {
+        "id": "single_episode_task_model_radar_chart",
+        "title": "Single-episode 20-task model radar",
+        "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+        "kind": "generated_figure",
+        "surface": "website_hf",
+        "shows": "Separates the one-episode Minimal and Neural MLP 20/20 scored baselines into a clean two-polygon radar.",
+    },
+    {
+        "id": "episode128_task_model_radar_chart",
+        "title": "128-episode 20-task model radar",
+        "path": "docs/assets/charts/episode128_task_model_radar.svg",
+        "kind": "generated_figure",
+        "surface": "website_hf",
+        "shows": "Separates the selected 128-episode methods: raw-feature simple/NN as complete 20/20 scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+    },
     {
         "id": "unified_task_model_radar_builder",
         "title": "Unified 20-task model radar builder",

scripts/build_figure_index.py

@@ -186,6 +186,22 @@ FIGURES = [
         "source_script": "scripts/build_unified_task_model_radar.py",
         "surface": "website unified task section, README, HF mirrors",
     },
+    {
+        "id": "single_episode_task_model_radar",
+        "title": "Single-episode 20-task model radar",
+        "path": "docs/assets/charts/single_episode_task_model_radar.svg",
+        "role": "Twenty-axis split radar for the one public-sample episode, comparing Minimal and Neural MLP as two complete 20/20 scored polygons.",
+        "source_script": "scripts/build_unified_task_model_radar.py",
+        "surface": "website unified task section, README, HF mirrors",
+    },
+    {
+        "id": "episode128_task_model_radar",
+        "title": "128-episode 20-task model radar",
+        "path": "docs/assets/charts/episode128_task_model_radar.svg",
+        "role": "Twenty-axis split radar for selected 128-episode methods: raw-feature simple/NN as complete scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
+        "source_script": "scripts/build_unified_task_model_radar.py",
+        "surface": "website unified task section, README, HF mirrors",
+    },
     {
         "id": "feature_blocks_chart",
         "title": "Feature block chart",

scripts/build_public_surface_qa.py

@@ -176,7 +176,12 @@ def build_report() -> dict:
         "data/task_suite_enhancement_128.json",
         "data/task_suite_20.json",
         "data/unified_task_model_radar.json",
+        "data/single_episode_task_model_radar.json",
+        "data/episode128_task_model_radar.json",
+        "data/task_method_20_result_matrix.json",
         "assets/charts/unified_task_model_radar.svg",
+        "assets/charts/single_episode_task_model_radar.svg",
+        "assets/charts/episode128_task_model_radar.svg",
         "data/tier2_task_suite.json",
     ]
 

scripts/build_unified_task_model_radar.py

@@ -40,9 +40,13 @@ COSMOS_SUPER_FD_METRICS_PATH = (
 METADATA128_BASELINE_DIR = ROOT / "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2"
 RAW128_BASELINE_DIR = ROOT / "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z"
 OUTPUT_JSON = ROOT / "docs/data/unified_task_model_radar.json"
+OUTPUT_SINGLE_JSON = ROOT / "docs/data/single_episode_task_model_radar.json"
+OUTPUT_128_JSON = ROOT / "docs/data/episode128_task_model_radar.json"
 OUTPUT_MATRIX_JSON = ROOT / "docs/data/task_method_20_result_matrix.json"
 OUTPUT_MATRIX_MD = ROOT / "TASK_METHOD_20_RESULT_MATRIX.md"
 OUTPUT_SVG = ROOT / "docs/assets/charts/unified_task_model_radar.svg"
+OUTPUT_SINGLE_SVG = ROOT / "docs/assets/charts/single_episode_task_model_radar.svg"
+OUTPUT_128_SVG = ROOT / "docs/assets/charts/episode128_task_model_radar.svg"
 
 
 SERIES = {
@@ -190,6 +194,16 @@ METHOD_DETAILS = {
 }
 
 PROXY_TASK_IDS = {"interaction_text_prediction", "camera_view_sync_retrieval"}
+SINGLE_EPISODE_SERIES = ("minimal", "neural_mlp")
+EPISODE128_SERIES = (
+    "metadata128_simple",
+    "metadata128_neural_mlp",
+    "raw128_simple",
+    "raw128_neural_mlp",
+    "qwen3_omni_v6_lora",
+    "cosmos3_super_reasoner",
+    "cosmos3_nano_future_window",
+)
 
 STATUS_LABELS = {
     "scored": "scored",
@@ -405,6 +419,47 @@ def render_matrix_markdown(payload: dict[str, Any]) -> str:
     return "\n".join(lines)
 
 
+def filtered_radar_payload(
+    payload: dict[str, Any],
+    series_ids: tuple[str, ...],
+    *,
+    title: str,
+    description: str,
+) -> dict[str, Any]:
+    selected = set(series_ids)
+    series = [json.loads(json.dumps(record)) for record in payload["series"] if record["id"] in selected]
+    tasks = []
+    for task in payload["tasks"]:
+        task_copy = {key: json.loads(json.dumps(value)) for key, value in task.items() if key != "values"}
+        task_copy["values"] = {
+            series_id: json.loads(json.dumps(task["values"][series_id]))
+            for series_id in series_ids
+            if series_id in task["values"]
+        }
+        tasks.append(task_copy)
+    rows = [
+        json.loads(json.dumps(row))
+        for row in payload["task_method_result_matrix"]
+        if row.get("series_id") in selected
+    ]
+    return {
+        "title": title,
+        "status": payload["status"],
+        "generated_at_utc": payload["generated_at_utc"],
+        "description": description,
+        "task_count": payload["task_count"],
+        "method_count": len(series),
+        "method_task_record_count": sum(record.get("result_record_count", 0) for record in series),
+        "scored_method_task_count": sum(record.get("scored_task_count", 0) for record in series),
+        "normalization_policy": payload["normalization_policy"],
+        "source_unified_radar": "docs/data/unified_task_model_radar.json",
+        "source_result_matrix": "docs/data/task_method_20_result_matrix.json",
+        "series": series,
+        "tasks": tasks,
+        "task_method_result_matrix": rows,
+    }
+
+
 def point(cx: float, cy: float, radius: float, angle: float) -> tuple[float, float]:
     return cx + math.cos(angle) * radius, cy + math.sin(angle) * radius
 
@@ -682,12 +737,26 @@ def build_payload() -> dict[str, Any]:
     return payload
 
 
-def render_svg(payload: dict[str, Any]) -> str:
+def render_svg(
+    payload: dict[str, Any],
+    *,
+    series_ids: tuple[str, ...] | None = None,
+    polygon_series_ids: tuple[str, ...] = ("minimal", "neural_mlp"),
+    title: str | None = None,
+    subtitle: str | None = None,
+    context_line: str | None = None,
+    chip_specs: list[tuple[str, str]] | None = None,
+    reading_rules: tuple[str, str, str] | None = None,
+) -> str:
     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)]
+    if series_ids is None:
+        series_ids = tuple(record["id"] for record in payload["series"])
+    polygon_series_set = set(polygon_series_ids)
+    series_records = [record for record in payload["series"] if record["id"] in set(series_ids)]
     parts = [
         f'<svg xmlns="http://www.w3.org/2000/svg" width="{width}" height="{height}" viewBox="0 0 {width} {height}">',
         "<defs>",
@@ -697,18 +766,34 @@ def render_svg(payload: dict[str, Any]) -> str:
         '<rect width="100%" height="100%" fill="#020502"/>',
         '<rect width="100%" height="100%" fill="url(#dots)" opacity="0.45"/>',
         '<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),
+        svg_text(70, 86, title or payload.get("title", "20-Task Model Radar"), size=36, weight=800),
+        svg_text(
+            70,
+            122,
+            subtitle or "Task names, methods, coverage, and metric normalization in one comparison view.",
+            size=18,
+            fill="#dce8d7",
+            weight=650,
+        ),
+        svg_text(
+            70,
+            150,
+            context_line
+            or "Filled areas show complete scored baselines; colored points show partial branches on task-aligned axes.",
+            size=15,
+            fill="#a5afa2",
+            weight=560,
+        ),
     ]
 
-    chip_specs = [
-        ("20 task axes", "#ccffa0"),
-        (f"{payload['method_task_record_count']} method-task records", "#67e8d1"),
-        (f"{payload['scored_method_task_count']} scored axes", "#22d3ee"),
-        ("40/40 raw128 pass", "#f59e0b"),
-        ("2 compact proxy axes", "#f472b6"),
-    ]
+    if chip_specs is None:
+        chip_specs = [
+            ("20 task axes", "#ccffa0"),
+            (f"{payload['method_task_record_count']} method-task records", "#67e8d1"),
+            (f"{payload['scored_method_task_count']} scored axes", "#22d3ee"),
+            ("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 250
@@ -739,25 +824,21 @@ def render_svg(payload: dict[str, Any]) -> str:
         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"):
+    for series_id in series_ids:
+        if series_id not in polygon_series_set:
+            continue
         spec = SERIES[series_id]
         points = []
         for task, angle in zip(tasks, angles):
             score = task["values"].get(series_id, {}).get("normalized_score")
             points.append(point(cx, cy, radius * float(score or 0.0), angle))
-        parts.append(polyline(points, fill=spec["color"], stroke=spec["color"], opacity=0.18 if series_id == "minimal" else 0.16, stroke_width=4.2))
+        parts.append(polyline(points, fill=spec["color"], stroke=spec["color"], opacity=0.18 if series_id in {"minimal", "raw128_simple"} else 0.16, stroke_width=4.2, dash=spec.get("stroke_dasharray")))
         for x, y in points:
             parts.append(f'<circle cx="{x:.1f}" cy="{y:.1f}" r="4.0" fill="{spec["color"]}" stroke="#020502" stroke-width="1.1"/>')
 
-    for series_id in (
-        "metadata128_simple",
-        "metadata128_neural_mlp",
-        "raw128_simple",
-        "raw128_neural_mlp",
-        "qwen3_omni_v6_lora",
-        "cosmos3_super_reasoner",
-        "cosmos3_nano_future_window",
-    ):
+    for series_id in series_ids:
+        if series_id in polygon_series_set:
+            continue
         spec = SERIES[series_id]
         for task, angle in zip(tasks, angles):
             score = task["values"].get(series_id, {}).get("normalized_score")
@@ -776,10 +857,10 @@ def render_svg(payload: dict[str, Any]) -> str:
     parts.append(svg_text(legend_x, legend_y + 30, "Each method has 20 records; scored axes and scoreless statuses stay in the JSON matrix.", size=13, fill="#a5afa2", weight=560))
 
     cursor = legend_y + 74
-    for record in payload["series"]:
+    for record in series_records:
         color = record["color"]
         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"):
+        if record["id"] not in polygon_series_set:
             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"20 records / {record['scored_task_count']} scored", size=13, fill=color, weight=800))
@@ -808,11 +889,17 @@ def render_svg(payload: dict[str, Any]) -> str:
         parts.append(svg_text(x0 + 48, y0 + 29, metric_label, size=10, fill="#a5afa2", weight=560))
 
     table_y = 1468
+    if reading_rules is None:
+        reading_rules = (
+            "Every method has 20 task records; radius appears only where a numeric task score exists.",
+            "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.",
+            "Scoreless metadata/Qwen/Cosmos records are explicit unsupported or not-evaluated cells in docs/data/task_method_20_result_matrix.json.",
+        )
     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, "Every method has 20 task records; radius appears only where a numeric task score exists.", 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, "Scoreless metadata/Qwen/Cosmos records are explicit unsupported or not-evaluated cells in docs/data/task_method_20_result_matrix.json.", size=13, fill="#a5afa2", weight=560))
+    parts.append(svg_text(220, table_y - 10, reading_rules[0], size=14, fill="#dce8d7", weight=650))
+    parts.append(svg_text(220, table_y + 18, reading_rules[1], size=13, fill="#a5afa2", weight=560))
+    parts.append(svg_text(220, table_y + 44, reading_rules[2], size=13, fill="#a5afa2", weight=560))
 
     parts.append("</svg>")
     return "\n".join(parts) + "\n"
@@ -820,10 +907,28 @@ def render_svg(payload: dict[str, Any]) -> str:
 
 def main() -> int:
     payload = build_payload()
+    single_payload = filtered_radar_payload(
+        payload,
+        SINGLE_EPISODE_SERIES,
+        title="Single-Episode 20-Task Radar",
+        description="Minimal and Neural MLP baselines on the one public sample episode, both scored on all 20 task contracts.",
+    )
+    episode128_payload = filtered_radar_payload(
+        payload,
+        EPISODE128_SERIES,
+        title="128-Episode 20-Task Radar",
+        description="Selected 128-episode metadata/raw baselines plus verified Qwen3/Cosmos branches. Every method has 20 records; numeric scores appear only where the public artifact produced that task target.",
+    )
     OUTPUT_JSON.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_SINGLE_JSON.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_128_JSON.parent.mkdir(parents=True, exist_ok=True)
     OUTPUT_MATRIX_JSON.parent.mkdir(parents=True, exist_ok=True)
     OUTPUT_SVG.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_SINGLE_SVG.parent.mkdir(parents=True, exist_ok=True)
+    OUTPUT_128_SVG.parent.mkdir(parents=True, exist_ok=True)
     OUTPUT_JSON.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8")
+    OUTPUT_SINGLE_JSON.write_text(json.dumps(single_payload, indent=2) + "\n", encoding="utf-8")
+    OUTPUT_128_JSON.write_text(json.dumps(episode128_payload, indent=2) + "\n", encoding="utf-8")
     matrix_payload = {
         "title": "Task Method 20-Result Matrix",
         "status": "pass",
@@ -838,10 +943,59 @@ def main() -> int:
     OUTPUT_MATRIX_JSON.write_text(json.dumps(matrix_payload, indent=2) + "\n", encoding="utf-8")
     OUTPUT_MATRIX_MD.write_text(render_matrix_markdown(payload), encoding="utf-8")
     OUTPUT_SVG.write_text(render_svg(payload), encoding="utf-8")
+    OUTPUT_SINGLE_SVG.write_text(
+        render_svg(
+            single_payload,
+            series_ids=SINGLE_EPISODE_SERIES,
+            polygon_series_ids=SINGLE_EPISODE_SERIES,
+            title="Single-Episode 20-Task Radar",
+            subtitle="One public sample episode; both baseline heads score every task axis.",
+            context_line="This view isolates the 1-episode task-head setup from the multi-episode model branches.",
+            chip_specs=[
+                ("20 task axes", "#ccffa0"),
+                ("40 method-task records", "#67e8d1"),
+                ("40 scored axes", "#22d3ee"),
+                ("2 filled baseline polygons", "#f472b6"),
+            ],
+            reading_rules=(
+                "Both single-episode methods have numeric scores on every one of the 20 task contracts.",
+                "This radar is the cleanest view of public-sample Minimal vs Neural MLP behavior before any 128-episode scale-up.",
+                "Raw metric values and sources remain in docs/data/single_episode_task_model_radar.json and docs/data/task_method_20_result_matrix.json.",
+            ),
+        ),
+        encoding="utf-8",
+    )
+    OUTPUT_128_SVG.write_text(
+        render_svg(
+            episode128_payload,
+            series_ids=EPISODE128_SERIES,
+            polygon_series_ids=("raw128_simple", "raw128_neural_mlp"),
+            title="128-Episode 20-Task Radar",
+            subtitle="Selected 96/16/16 episode split; raw-feature heads score all 20 axes.",
+            context_line="Raw128 baselines are filled polygons; metadata, Qwen3, and Cosmos branches plot only evaluated task targets.",
+            chip_specs=[
+                ("20 task axes", "#ccffa0"),
+                ("140 method-task records", "#67e8d1"),
+                ("71 scored axes", "#22d3ee"),
+                ("40/40 raw128 pass", "#f59e0b"),
+                ("69 explicit scoreless", "#f472b6"),
+            ],
+            reading_rules=(
+                "Every 128-episode method has 20 result records; radius appears only where a numeric score exists.",
+                "Raw128 Simple and Raw128 NN are the current complete 20/20 scored multi-episode baselines; tasks 15/19 are documented compact proxies.",
+                "Metadata-only and Qwen/Cosmos scoreless cells are explicit not-supported or not-evaluated records, not hidden failures.",
+            ),
+        ),
+        encoding="utf-8",
+    )
     print(f"PASS: wrote {OUTPUT_JSON}")
+    print(f"PASS: wrote {OUTPUT_SINGLE_JSON}")
+    print(f"PASS: wrote {OUTPUT_128_JSON}")
     print(f"PASS: wrote {OUTPUT_MATRIX_JSON}")
     print(f"PASS: wrote {OUTPUT_MATRIX_MD}")
     print(f"PASS: wrote {OUTPUT_SVG}")
+    print(f"PASS: wrote {OUTPUT_SINGLE_SVG}")
+    print(f"PASS: wrote {OUTPUT_128_SVG}")
     return 0
 
 

scripts/sync_hf_publish_mirrors.py

@@ -49,7 +49,10 @@ The historical `tier2_task_suite` path is retained only for stable artifact
 links to tasks 13-20. The unified radar chart is published as
 `docs/assets/charts/unified_task_model_radar.svg` with values in
 `docs/data/unified_task_model_radar.json`; the 9-method by 20-task completion
-matrix is in `docs/data/task_method_20_result_matrix.json`.
+matrix is in `docs/data/task_method_20_result_matrix.json`. Split radars for
+the one-episode baselines and selected 128-episode methods are published as
+`docs/assets/charts/single_episode_task_model_radar.svg` and
+`docs/assets/charts/episode128_task_model_radar.svg`.
 """
 QWEN_COMPARISON_MARKER = "docs/data/qwen3_v5_v6_comparison.json"
 QWEN_COMPARISON_ROW = (
@@ -154,7 +157,9 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "`docs/assets/charts/unified_task_model_radar.svg` with values in\n"
                 "`docs/data/unified_task_model_radar.json`; the 9-method by\n"
                 "20-task completion matrix is in\n"
-                "`docs/data/task_method_20_result_matrix.json`.\n",
+                "`docs/data/task_method_20_result_matrix.json`. Split radars are in\n"
+                "`docs/assets/charts/single_episode_task_model_radar.svg` and\n"
+                "`docs/assets/charts/episode128_task_model_radar.svg`.\n",
             )
         if (
             "docs/data/unified_task_model_radar.json" in text
@@ -165,6 +170,16 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "`docs/data/unified_task_model_radar.json`; the 9-method by 20-task\n"
                 "completion matrix is in `docs/data/task_method_20_result_matrix.json`.",
             )
+        if (
+            "docs/data/task_method_20_result_matrix.json" in text
+            and "docs/assets/charts/single_episode_task_model_radar.svg" not in text
+        ):
+            text = text.replace(
+                "`docs/data/task_method_20_result_matrix.json`.",
+                "`docs/data/task_method_20_result_matrix.json`. Split radars are in\n"
+                "`docs/assets/charts/single_episode_task_model_radar.svg` and\n"
+                "`docs/assets/charts/episode128_task_model_radar.svg`.",
+            )
         if TIER2_MARKER in text:
             if not dry_run:
                 path.write_text(text, encoding="utf-8")

scripts/validate_mirror_parity.py

@@ -53,6 +53,8 @@ DATA_FILES = [
     "single_episode_explorer.json",
     "source_alignment_audit.json",
     "summary_metrics.json",
+    "single_episode_task_model_radar.json",
+    "episode128_task_model_radar.json",
     "task_suite_20.json",
     "task_suite_enhancement_128.json",
     "task_surface_integrity.json",
@@ -66,6 +68,8 @@ DATA_FILES = [
 
 ASSET_FILES = [
     "charts/audio_ablation_delta.svg",
+    "charts/single_episode_task_model_radar.svg",
+    "charts/episode128_task_model_radar.svg",
     "charts/tier2_task_suite.svg",
     "charts/unified_task_model_radar.svg",
     "brand/xperience10m-logo-apple-touch.png",

scripts/validate_publication_package.py

@@ -310,6 +310,9 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/data/summary_metrics.json",
         "docs/data/task_suite_20.json",
         "docs/data/unified_task_model_radar.json",
+        "docs/data/single_episode_task_model_radar.json",
+        "docs/data/episode128_task_model_radar.json",
+        "docs/data/task_method_20_result_matrix.json",
         "docs/data/task_suite_enhancement_128.json",
         "docs/assets/modalities/video.jpg",
         "docs/assets/modalities/audio.png",
@@ -327,6 +330,8 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/assets/brand/xperience10m-logo-social-card.png",
         "docs/assets/task_suite_infographic.png",
         "docs/assets/charts/unified_task_model_radar.svg",
+        "docs/assets/charts/single_episode_task_model_radar.svg",
+        "docs/assets/charts/episode128_task_model_radar.svg",
         "docs/assets/pipeline_diagram.png",
         "docs/assets/task_architectures.png",
         "results/episode_task_suite/summary_report.json",

scripts/verify_live_publication.py

@@ -97,6 +97,28 @@ HASH_GROUPS = [
             "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/task_method_20_result_matrix.json",
         },
     },
+    {
+        "id": "single_episode_task_model_radar_json",
+        "title": "Single-episode 20-task model radar JSON",
+        "local_path": "docs/data/single_episode_task_model_radar.json",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/single_episode_task_model_radar.json",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/raw/main/data/single_episode_task_model_radar.json",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/data/single_episode_task_model_radar.json",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/single_episode_task_model_radar.json",
+        },
+    },
+    {
+        "id": "episode128_task_model_radar_json",
+        "title": "128-episode 20-task model radar JSON",
+        "local_path": "docs/data/episode128_task_model_radar.json",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/episode128_task_model_radar.json",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/raw/main/data/episode128_task_model_radar.json",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/data/episode128_task_model_radar.json",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/episode128_task_model_radar.json",
+        },
+    },
     {
         "id": "unified_task_model_radar_svg",
         "title": "Unified 20-task model radar SVG",
@@ -108,6 +130,28 @@ HASH_GROUPS = [
             "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/assets/charts/unified_task_model_radar.svg",
         },
     },
+    {
+        "id": "single_episode_task_model_radar_svg",
+        "title": "Single-episode 20-task model radar SVG",
+        "local_path": "docs/assets/charts/single_episode_task_model_radar.svg",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/charts/single_episode_task_model_radar.svg",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/resolve/main/assets/charts/single_episode_task_model_radar.svg",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/assets/charts/single_episode_task_model_radar.svg",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/assets/charts/single_episode_task_model_radar.svg",
+        },
+    },
+    {
+        "id": "episode128_task_model_radar_svg",
+        "title": "128-episode 20-task model radar SVG",
+        "local_path": "docs/assets/charts/episode128_task_model_radar.svg",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/assets/charts/episode128_task_model_radar.svg",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/resolve/main/assets/charts/episode128_task_model_radar.svg",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/assets/charts/episode128_task_model_radar.svg",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/assets/charts/episode128_task_model_radar.svg",
+        },
+    },
     {
         "id": "tier2_task_suite_json",
         "title": "Tasks 13-20 result JSON",