Add files using upload-large-folder tool

QUALITY_GATES.md

@@ -18,6 +18,7 @@ These checks cover public packaging, project status wording, mirror parity, and
 | Rendered website check | `python scripts/build_rendered_site_check.py --input /tmp/xperience_rendered_site_observations.json` | `docs/data/rendered_site_check.json` | `pass` | The local rendered site cannot load, switch tabs, deep-link to the walkthrough, update player controls, or stay console-clean. |
 | Task surface integrity | `python scripts/validate_task_surface.py` | `docs/data/task_surface_integrity.json` | `pass` | Task cards expose raw artifact ids, human-readable task names drift, modality thumbnails are missing, or the interactive task player is not wired to the generated JSON. |
 | Evaluation protocol | `python scripts/build_evaluation_protocol.py` | `docs/data/evaluation_protocol.json` | `pass` | Windowing, split policy, leakage controls, task metrics, or current limitations are not explicit. |
+| Task-method source audit | `python scripts/validate_task_method_matrix_sources.py` | `docs/data/task_method_20_source_audit.json` | `pass` | A scored 20-task matrix cell points to a JSON metric source that does not contain the same metric value. |
 | Figure index | `python scripts/build_figure_index.py` | `docs/data/figure_index.json` | `pass` | Public figures, charts, or modality thumbnails are missing, unreadable, or lack source-script provenance. |
 | Brand assets | `python scripts/build_brand_assets.py` | `docs/data/brand_assets.json` | `pass` | The generated logo system, favicon, social card, or app icons are missing or not reproducibly packaged. |
 | Release-check manifest | `python scripts/build_quality_gates.py` | `docs/data/quality_gates.json` | `pass` | A public reader cannot see the current release state in one place. |
@@ -40,6 +41,7 @@ These checks cover public packaging, project status wording, mirror parity, and
 python scripts/validate_scope_claims.py
 python scripts/validate_source_alignment.py
 python scripts/build_evaluation_protocol.py
+python scripts/validate_task_method_matrix_sources.py
 python scripts/build_brand_assets.py
 python scripts/build_figure_index.py
 python scripts/validate_website_integrity.py

README.md

@@ -67,7 +67,8 @@ 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 complete at `180/180` scored method-task records and is published in `docs/data/task_method_20_result_matrix.json`, with the explicit
-audit in `docs/data/task_method_20_gap_audit.json`. Split radars are in
+audit in `docs/data/task_method_20_gap_audit.json` and source-value audit in
+`docs/data/task_method_20_source_audit.json`. Split radars are in
 `docs/assets/charts/single_episode_task_model_radar.svg` and
 `docs/assets/charts/episode128_task_model_radar.svg`.
 

TASK_METHOD_20_GAP_AUDIT.md

@@ -1,6 +1,6 @@
 # Task Method 20-Result Completion Audit
 
-Generated: `2026-06-20T19:54:37+00:00`
+Generated: `2026-06-20T20:38:59+00:00`
 
 This audit is the explicit completion ledger for the 9-method x 20-task result
 matrix. The current public matrix is complete at 180/180 scored records while

data/artifact_index.json

@@ -1,8 +1,8 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-20T19:54:40+00:00",
+  "generated_at_utc": "2026-06-20T20:48:09+00:00",
   "status": "pass",
-  "artifact_count": 222,
+  "artifact_count": 225,
   "missing": [],
   "by_kind": {
     "project_path": 18,
@@ -10,12 +10,12 @@
     "visual_asset_source": 3,
     "scaleup_contract": 7,
     "scaleup_status": 52,
-    "publication_workflow": 6,
+    "publication_workflow": 7,
     "reproducibility": 4,
     "project_scope": 1,
     "source_alignment": 5,
-    "evaluation_protocol": 8,
-    "website_data": 10,
+    "evaluation_protocol": 9,
+    "website_data": 11,
     "generated_figure": 7,
     "visualization_builder": 1,
     "model_result": 5,
@@ -301,8 +301,8 @@
       "surface": "repo_hf",
       "shows": "Runs simple metadata and neural MLP baselines on the same selected 96/16/16 episode split used by the Qwen3-Omni diagnostic pilot.",
       "exists": true,
-      "bytes": 74368,
-      "sha256": "6f54bfb963d5102ebd61eb8f8b6d8f6919db673378c9d5940d89ec5ea6f3d4b2"
+      "bytes": 74316,
+      "sha256": "164c908bee1d4a6e0db344692833787582e45317b240ef5afbfbdb609a5175e6"
     },
     {
       "id": "task_suite_enhancement_128",
@@ -610,7 +610,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "169e325bc72c8de10a37b192948b69625ad51c3b9560b4249b03bbdc7f135f97"
+      "sha256": "c916b18a11917e46e8561520cf2307f190c671c82e710ebd0f3522ec8a4be2bd"
     },
     {
       "id": "source_alignment_validator",
@@ -730,8 +730,8 @@
       "surface": "website_hf",
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, proxy flags, and source artifacts.",
       "exists": true,
-      "bytes": 228743,
-      "sha256": "89ec87e0c5abe27e2273e59e959565103aca7d360f9cc1fb086f6d76f96c1097"
+      "bytes": 228799,
+      "sha256": "c9c708f64963dac10e764eaae8e1b14c7161a938afa5ef5723fe59dc4ce764af"
     },
     {
       "id": "single_episode_task_model_radar_json",
@@ -742,7 +742,7 @@
       "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
       "exists": true,
       "bytes": 51097,
-      "sha256": "60ec85be6976c92f9719e693b4aa6e0b2a14cd23366433c412d5077e4a79cd79"
+      "sha256": "d5e882120633f4d3ae90f1491682701c7593a42fc09e39b83fc5f375258e76e7"
     },
     {
       "id": "episode128_task_model_radar_json",
@@ -752,8 +752,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, now complete at 140/140 scored rows with proxy notes retained.",
       "exists": true,
-      "bytes": 184889,
-      "sha256": "b40c8f8721020b75c69a298e3650b6f469444e7c21dd1135e822d934960bab98"
+      "bytes": 184945,
+      "sha256": "8d4ef9c4cf1cf334fd41417d40fa0687ceefa964da9f8338c82f8cc6d36a3e76"
     },
     {
       "id": "task_method_20_result_matrix_json",
@@ -763,8 +763,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable 9-method by 20-task matrix where every method has 20 records and the current release is complete at 180/180 scored rows.",
       "exists": true,
-      "bytes": 128481,
-      "sha256": "9b8806f4e4be69e2a074af2a091e59dd5a2409d87b61b426283e21763c995dce"
+      "bytes": 128509,
+      "sha256": "382e538dff284c5e2cf19fe2b3eb014d1b48fb33082bb2ece532ce3de6c1e9bb"
     },
     {
       "id": "task_method_20_result_matrix",
@@ -786,7 +786,7 @@
       "shows": "Machine-readable 180-record completion ledger with numeric scores, proxy flags, explicit status reasons, and source artifacts.",
       "exists": true,
       "bytes": 8500,
-      "sha256": "2347b06517a9b43e78769ba97cc2da4bafaf1bffd6ebcffa8a07508093ca2059"
+      "sha256": "9cfd2ce8c4eb3bbe7e2af3f41df3b3ab74db9db08d9ea2e4f569f612358470dd"
     },
     {
       "id": "task_method_20_gap_audit",
@@ -797,7 +797,29 @@
       "shows": "Reader-facing ledger confirming 180/180 scored method-task cells and listing the six compact-proxy records separately.",
       "exists": true,
       "bytes": 3417,
-      "sha256": "1f3db1cacb53a26be98aeb71ac4ab9e9319ed7ad823b882d9c99b20ec9615626"
+      "sha256": "3afc5db9803b6419ce4f40d6fb0dd5380ae182fb85b4f7b0f6ea6a46ae065c63"
+    },
+    {
+      "id": "task_method_20_source_audit_json",
+      "title": "Task-method 20-result source audit JSON",
+      "path": "docs/data/task_method_20_source_audit.json",
+      "kind": "website_data",
+      "surface": "website_hf",
+      "shows": "Machine-readable check that scored JSON-backed matrix cells match their declared metric source values.",
+      "exists": true,
+      "bytes": 561,
+      "sha256": "c795c8f387648a90e66146efc44a4be2f272d4a44097f0b9b39a7347df83daa0"
+    },
+    {
+      "id": "task_method_20_source_audit",
+      "title": "Task-method 20-result source audit",
+      "path": "TASK_METHOD_20_SOURCE_AUDIT.md",
+      "kind": "evaluation_protocol",
+      "surface": "repo_hf",
+      "shows": "Reader-facing source-value audit for the 180-result matrix.",
+      "exists": true,
+      "bytes": 447,
+      "sha256": "2b8bc99b7157894d59fa2f23ebaee33ce9e6e01c0b7316c7555ab0071c85eb41"
     },
     {
       "id": "unified_task_model_radar_chart",
@@ -840,8 +862,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
       "exists": true,
-      "bytes": 67953,
-      "sha256": "3533f33e52af60edf49b1c1fa586d2b8c158e3925875ca46c38714018e6035a0"
+      "bytes": 68542,
+      "sha256": "470b4c8acc437114b51d96987cd6324b9bf1d2ca16e9721d7fb00708aa58b383"
     },
     {
       "id": "task_method_20_gap_audit_builder",
@@ -854,6 +876,17 @@
       "bytes": 10295,
       "sha256": "e2a3b41d3cca6efee7076b68c35693a4c53f5f2549e2eecbf035b98a717a3f65"
     },
+    {
+      "id": "task_method_20_source_audit_validator",
+      "title": "Task-method source-audit validator",
+      "path": "scripts/validate_task_method_matrix_sources.py",
+      "kind": "publication_workflow",
+      "surface": "repo_hf",
+      "shows": "Fails release checks if a scored matrix row disagrees with its JSON metric source.",
+      "exists": true,
+      "bytes": 7877,
+      "sha256": "97edc3f064f77d544eff539bb7f16f8162e58ec581a63b91c473bada080f86ae"
+    },
     {
       "id": "all_task_model_scoring_waiter",
       "title": "All-task model scoring guarded waiter",
@@ -873,8 +906,8 @@
       "surface": "repo_hf",
       "shows": "Checks whether Qwen3/Cosmos branches have train, validation, and test prediction files before extending model overlays to all 20 task contracts.",
       "exists": true,
-      "bytes": 4770,
-      "sha256": "ad701c03153c6755f284281640465efde3313f48bc0189942f5637bb19328bfb"
+      "bytes": 4320,
+      "sha256": "11cff26749bf6ad8b8ee028b18e0b4be5713ed8b5325578caa03be25d894263b"
     },
     {
       "id": "model_output_probe_script",
@@ -884,8 +917,8 @@
       "surface": "repo_hf",
       "shows": "Audits model-output split availability and writes a readiness report without assigning new numeric task scores.",
       "exists": true,
-      "bytes": 9133,
-      "sha256": "3a867d0333fe591999715158e311011db25da018ca39c9b4638930841f35efb8"
+      "bytes": 10520,
+      "sha256": "741ee733068e87c52c8da2bd15987e2b4538b5e705592182d76c42b5cf34fe96"
     },
     {
       "id": "existing_model_output_task_probe",
@@ -1104,8 +1137,8 @@
       "surface": "repo_hf",
       "shows": "Lists the automated and post-publish checks used to keep the release current.",
       "exists": true,
-      "bytes": 4880,
-      "sha256": "526a38edffdb8e96eb7be3fc4ae4c8fab5a43ac4ed6e57137e9e0857c75b0a27"
+      "bytes": 5184,
+      "sha256": "4931d4457c4c5b0978fdf31861b6e3e2da6e24368398cf1756120a32cbff98f0"
     },
     {
       "id": "quality_gate_manifest",
@@ -1115,8 +1148,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
-      "bytes": 8100,
-      "sha256": "48e27e70a590e881f25c6ee01dff8c0218b6b83bb2b5f8b17b68c8d38d1bf6a6"
+      "bytes": 8640,
+      "sha256": "445196830bb913bfa075ae4174e7b1f5b64f623cf13a2afde7513add9dbefc21"
     },
     {
       "id": "public_surface_qa",
@@ -1252,8 +1285,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 66834,
-      "sha256": "4cad7de030e0192ac2cf676738c30e070bf25414e482e594fbfe724aa3a7853a"
+      "bytes": 67647,
+      "sha256": "d2b4af98e6fd8b23fd86cd068f2bbf887e5d69686dd62fe3bfc7e8251a6d75d6"
     },
     {
       "id": "reproducibility_contract",
@@ -1285,8 +1318,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 66058,
-      "sha256": "cc9c83c7094ef36b73125f902c6d8776203f8abc910cedb61610dadc1bb823a5"
+      "bytes": 67105,
+      "sha256": "8fc1a2b5d4a50d49ff5738ec1e5e91088dbfa514c9f0485d3afe708add6d94a1"
     },
     {
       "id": "publication_audit",
@@ -1297,7 +1330,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 10502,
+      "bytes": 10662,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1321,7 +1354,7 @@
       "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": 1392513,
+      "bytes": 1395239,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1333,7 +1366,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
-      "bytes": 20022,
+      "bytes": 20141,
       "hash_policy": "existence_and_size_only"
     },
     {

data/episode128_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "128-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:21+00:00",
   "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.",
   "task_count": 20,
   "method_count": 7,
@@ -1166,7 +1166,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.6286317274823326,
           "metric_key": "temporal_order_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1257,7 +1257,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.37271645981034185,
           "metric_key": "misalignment_detection_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1439,7 +1439,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.0,
           "metric_key": "next_subtask_forecast_macro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1519,7 +1519,7 @@
         "qwen3_omni_v6_lora": {
           "raw": 0.4318674027510605,
           "metric_key": "macro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1712,7 +1712,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.0009279881217520415,
           "metric_key": "object_set_forecast_micro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/object_set_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -3372,7 +3372,7 @@
       "raw_text": "0.6286",
       "normalized_score": 0.6286317274823326,
       "metric_key": "temporal_order_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -3498,7 +3498,7 @@
       "raw_text": "0.3727",
       "normalized_score": 0.37271645981034185,
       "metric_key": "misalignment_detection_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -3750,7 +3750,7 @@
       "raw_text": "0.0000",
       "normalized_score": 0.0,
       "metric_key": "next_subtask_forecast_macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -3858,7 +3858,7 @@
       "raw_text": "0.4319",
       "normalized_score": 0.4318674027510605,
       "metric_key": "macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -4128,7 +4128,7 @@
       "raw_text": "0.0009",
       "normalized_score": 0.0009279881217520415,
       "metric_key": "object_set_forecast_micro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/object_set_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:18+00:00",
+  "generated_at_utc": "2026-06-20T20:48:08+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": "2026-06-20T19:39:18+00:00"
+          "generated_at_utc": "2026-06-20T20:41:45+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:44:50+00:00"
+          "generated_at_utc": "2026-06-20T19:55:17+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:44:49+00:00"
+          "generated_at_utc": "2026-06-20T19:55:18+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:45:07+00:00"
+          "generated_at_utc": "2026-06-20T19:55:26+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:39:40+00:00"
+          "generated_at_utc": "2026-06-20T20:42:41+00:00"
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         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:40:53+00:00"
+          "generated_at_utc": "2026-06-20T20:47:51+00:00"
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@@ -111,7 +111,7 @@
         "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite": 11,
         "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts": 11,
         "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines": 14,
-        "https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results": 9,
+        "https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results": 6,
         "https://huggingface.co/datasets/ropedia-ai/xperience-10m": 38,
         "https://ropedia.com/dataset": 5
       }

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-20T19:56:34+00:00",
+  "generated_at_utc": "2026-06-20T20:49:00+00:00",
   "checks": [
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@@ -59,6 +59,7 @@
     "RENDERED_SITE_CHECK.md": true,
     "EVALUATION_PROTOCOL.md": true,
     "TASK_SUITE_20.md": true,
+    "TASK_METHOD_20_SOURCE_AUDIT.md": true,
     "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md": true,
     "FIGURE_INDEX.md": true,
     "SOURCE_ALIGNMENT_AUDIT.md": true,
@@ -106,6 +107,7 @@
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     "docs/data/task_method_20_result_matrix.json": true,
     "docs/data/task_method_20_gap_audit.json": true,
+    "docs/data/task_method_20_source_audit.json": true,
     "docs/data/task_suite_enhancement_128.json": true,
     "docs/data/xperience10m_128_episode_feature_index.json": true,
     "docs/assets/modalities/video.jpg": true,
@@ -150,6 +152,7 @@
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     "scripts/build_unified_task_model_radar.py": true,
     "scripts/build_task_method_20_gap_audit.py": true,
+    "scripts/validate_task_method_matrix_sources.py": true,
     "scripts/build_figure_index.py": true,
     "scripts/build_quality_gates.py": true,
     "scripts/build_public_surface_qa.py": true,
@@ -226,8 +229,8 @@
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-      "text_file_count": 1249,
+      "file_count": 1513,
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@@ -237,8 +240,8 @@
     "hf_space_bundle": {
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-      "file_count": 556,
-      "text_file_count": 409,
+      "file_count": 558,
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       "largest_file": {
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       "largest_file": {
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@@ -259,8 +262,8 @@
     "hf_model_bundle": {
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-      "text_file_count": 1436,
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data/task_method_20_gap_audit.json

@@ -1,5 +1,5 @@
 {
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+  "generated_at_utc": "2026-06-20T20:38:59+00:00",
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data/task_method_20_source_audit.json

@@ -0,0 +1,17 @@
+{
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+  "failures": [],
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+  "source_matrix": "docs/data/task_method_20_result_matrix.json",
+  "status": "pass",
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+  },
+  "title": "Task Method 20 Matrix Source Audit"
+}

data/unified_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
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@@ -1396,7 +1396,7 @@
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+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
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@@ -1507,7 +1507,7 @@
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@@ -1729,7 +1729,7 @@
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@@ -1829,7 +1829,7 @@
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@@ -2062,7 +2062,7 @@
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@@ -4278,7 +4278,7 @@
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data/website_integrity.json

@@ -1,13 +1,13 @@
 {
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@@ -301,7 +301,7 @@
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docs/data/artifact_index.json

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 {
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@@ -10,12 +10,12 @@
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@@ -301,8 +301,8 @@
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@@ -610,7 +610,7 @@
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@@ -730,8 +730,8 @@
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@@ -742,7 +742,7 @@
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@@ -752,8 +752,8 @@
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@@ -763,8 +763,8 @@
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@@ -786,7 +786,7 @@
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@@ -797,7 +797,29 @@
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+      "path": "docs/data/task_method_20_source_audit.json",
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@@ -840,8 +862,8 @@
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+      "title": "Task-method source-audit validator",
+      "path": "scripts/validate_task_method_matrix_sources.py",
+      "kind": "publication_workflow",
+      "surface": "repo_hf",
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@@ -873,8 +906,8 @@
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@@ -884,8 +917,8 @@
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     },
     {
       "id": "existing_model_output_task_probe",
@@ -1104,8 +1137,8 @@
       "surface": "repo_hf",
       "shows": "Lists the automated and post-publish checks used to keep the release current.",
       "exists": true,
-      "bytes": 4880,
-      "sha256": "526a38edffdb8e96eb7be3fc4ae4c8fab5a43ac4ed6e57137e9e0857c75b0a27"
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     },
     {
       "id": "quality_gate_manifest",
@@ -1115,8 +1148,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
-      "bytes": 8100,
-      "sha256": "48e27e70a590e881f25c6ee01dff8c0218b6b83bb2b5f8b17b68c8d38d1bf6a6"
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     {
       "id": "public_surface_qa",
@@ -1252,8 +1285,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 66834,
-      "sha256": "4cad7de030e0192ac2cf676738c30e070bf25414e482e594fbfe724aa3a7853a"
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     {
       "id": "reproducibility_contract",
@@ -1285,8 +1318,8 @@
       "surface": "repo_hf",
       "shows": "Generates the selective artifact catalog from local files.",
       "exists": true,
-      "bytes": 66058,
-      "sha256": "cc9c83c7094ef36b73125f902c6d8776203f8abc910cedb61610dadc1bb823a5"
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       "id": "publication_audit",
@@ -1297,7 +1330,7 @@
       "volatile": true,
       "shows": "Confirms public bundles exclude raw data, caches, heavy archives, and credential text.",
       "exists": true,
-      "bytes": 10502,
+      "bytes": 10662,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1321,7 +1354,7 @@
       "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": 1392513,
+      "bytes": 1395239,
       "hash_policy": "existence_and_size_only"
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@@ -1333,7 +1366,7 @@
       "volatile": true,
       "shows": "Confirms local website links, anchors, JSON data files, and referenced images resolve.",
       "exists": true,
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docs/data/episode128_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "128-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:21+00:00",
   "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.",
   "task_count": 20,
   "method_count": 7,
@@ -1166,7 +1166,7 @@
         "cosmos3_super_reasoner": {
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           "metric_key": "temporal_order_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
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@@ -1257,7 +1257,7 @@
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           "metric_key": "misalignment_detection_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
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@@ -1439,7 +1439,7 @@
         "cosmos3_super_reasoner": {
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           "metric_key": "next_subtask_forecast_macro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
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@@ -1519,7 +1519,7 @@
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           "metric_key": "macro_f1",
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+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
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@@ -1712,7 +1712,7 @@
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-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
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           "scope": "multi_episode_128_partial_model_overlay",
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@@ -3372,7 +3372,7 @@
       "raw_text": "0.6286",
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-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
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       "scope": "multi_episode_128_partial_model_overlay",
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@@ -3498,7 +3498,7 @@
       "raw_text": "0.3727",
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@@ -3750,7 +3750,7 @@
       "raw_text": "0.0000",
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-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
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       "scope": "multi_episode_128_partial_model_overlay",
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@@ -3858,7 +3858,7 @@
       "raw_text": "0.4319",
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       "scope": "multi_episode_128_partial_model_overlay",
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@@ -4128,7 +4128,7 @@
       "raw_text": "0.0009",
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-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
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docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
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-  "generated_at_utc": "2026-06-20T20:04:58+00:00",
+  "generated_at_utc": "2026-06-20T20:49:41+00:00",
   "hf_root": "hf_publish",
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     "failure_count": 0,
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@@ -138,45 +138,45 @@
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-        "bytes": 121435,
-        "sha256": "1dd28c9b9be37fc55b46bd62366085d3584bd591230e40ec947d8ebf5e518c4c"
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@@ -922,45 +922,45 @@
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@@ -972,44 +972,44 @@
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@@ -1118,45 +1118,45 @@
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@@ -13410,21 +13484,21 @@
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@@ -13435,21 +13509,21 @@
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@@ -25451,15 +25525,15 @@
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@@ -25470,15 +25544,15 @@
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-        "bytes": 4549,
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@@ -30347,27 +30421,27 @@
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-        "sha256": "526a38edffdb8e96eb7be3fc4ae4c8fab5a43ac4ed6e57137e9e0857c75b0a27"
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-          "bytes": 4880,
-          "sha256": "526a38edffdb8e96eb7be3fc4ae4c8fab5a43ac4ed6e57137e9e0857c75b0a27"
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           "path": "hf_artifacts:QUALITY_GATES.md",
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-          "sha256": "526a38edffdb8e96eb7be3fc4ae4c8fab5a43ac4ed6e57137e9e0857c75b0a27"
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-          "bytes": 4880,
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@@ -30782,26 +30856,26 @@
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         "bytes": 3417,
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       "mirrors": {
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           "bytes": 3417,
-          "sha256": "1f3db1cacb53a26be98aeb71ac4ab9e9319ed7ad823b882d9c99b20ec9615626"
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         "hf_artifacts": {
           "path": "hf_artifacts:TASK_METHOD_20_GAP_AUDIT.md",
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           "bytes": 3417,
-          "sha256": "1f3db1cacb53a26be98aeb71ac4ab9e9319ed7ad823b882d9c99b20ec9615626"
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         "hf_model": {
           "path": "hf_model:TASK_METHOD_20_GAP_AUDIT.md",
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           "bytes": 3417,
-          "sha256": "1f3db1cacb53a26be98aeb71ac4ab9e9319ed7ad823b882d9c99b20ec9615626"
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docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:18+00:00",
+  "generated_at_utc": "2026-06-20T20:48:08+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": "2026-06-20T19:39:18+00:00"
+          "generated_at_utc": "2026-06-20T20:41:45+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:44:50+00:00"
+          "generated_at_utc": "2026-06-20T19:55:17+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:44:49+00:00"
+          "generated_at_utc": "2026-06-20T19:55:18+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T18:45:07+00:00"
+          "generated_at_utc": "2026-06-20T19:55:26+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:39:40+00:00"
+          "generated_at_utc": "2026-06-20T20:42:41+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-20T19:40:53+00:00"
+          "generated_at_utc": "2026-06-20T20:47:51+00:00"
         }
       },
       "failures": {}
@@ -111,7 +111,7 @@
         "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite": 11,
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         "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines": 14,
-        "https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results": 9,
+        "https://huggingface.co/cy0307/ropedia-xperience-10m-weights-results": 6,
         "https://huggingface.co/datasets/ropedia-ai/xperience-10m": 38,
         "https://ropedia.com/dataset": 5
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docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:56:34+00:00",
+  "generated_at_utc": "2026-06-20T20:49:00+00:00",
   "checks": [
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       "name": "required_publication_assets_present",
@@ -59,6 +59,7 @@
     "RENDERED_SITE_CHECK.md": true,
     "EVALUATION_PROTOCOL.md": true,
     "TASK_SUITE_20.md": true,
+    "TASK_METHOD_20_SOURCE_AUDIT.md": true,
     "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md": true,
     "FIGURE_INDEX.md": true,
     "SOURCE_ALIGNMENT_AUDIT.md": true,
@@ -106,6 +107,7 @@
     "docs/data/episode128_task_model_radar.json": true,
     "docs/data/task_method_20_result_matrix.json": true,
     "docs/data/task_method_20_gap_audit.json": true,
+    "docs/data/task_method_20_source_audit.json": true,
     "docs/data/task_suite_enhancement_128.json": true,
     "docs/data/xperience10m_128_episode_feature_index.json": true,
     "docs/assets/modalities/video.jpg": true,
@@ -150,6 +152,7 @@
     "scripts/build_unified_task_suite.py": true,
     "scripts/build_unified_task_model_radar.py": true,
     "scripts/build_task_method_20_gap_audit.py": true,
+    "scripts/validate_task_method_matrix_sources.py": true,
     "scripts/build_figure_index.py": true,
     "scripts/build_quality_gates.py": true,
     "scripts/build_public_surface_qa.py": true,
@@ -226,8 +229,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1510,
-      "text_file_count": 1249,
+      "file_count": 1513,
+      "text_file_count": 1252,
       "largest_file": {
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         "bytes": 73057076
@@ -237,8 +240,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 556,
-      "text_file_count": 409,
+      "file_count": 558,
+      "text_file_count": 411,
       "largest_file": {
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         "bytes": 10221085
@@ -248,8 +251,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 4474,
-      "text_file_count": 1268,
+      "file_count": 4477,
+      "text_file_count": 1271,
       "largest_file": {
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         "bytes": 135591061
@@ -259,8 +262,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 5224,
-      "text_file_count": 1436,
+      "file_count": 5228,
+      "text_file_count": 1440,
       "largest_file": {
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docs/data/quality_gates.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Release Checks",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T18:44:28+00:00",
+  "generated_at_utc": "2026-06-20T20:48:18+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": [
     {
@@ -76,6 +76,18 @@
         "status": "pass"
       }
     },
+    {
+      "id": "task_method_source_audit",
+      "title": "Task-method source audit",
+      "command": "python scripts/validate_task_method_matrix_sources.py",
+      "report": "docs/data/task_method_20_source_audit.json",
+      "blocks_if": "A scored 20-task matrix cell points to a JSON metric source that does not contain the same metric value.",
+      "shows": "Public 20-task scores remain traceable to their task-specific metric artifacts.",
+      "current_report": {
+        "exists": true,
+        "status": "pass"
+      }
+    },
     {
       "id": "figure_index",
       "title": "Figure index",

docs/data/single_episode_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Single-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:21+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,

docs/data/task_method_20_gap_audit.json

@@ -1,5 +1,5 @@
 {
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:59+00:00",
   "immediate_actions": [
     {
       "artifact": "docs/data/task_method_20_gap_audit.json",

docs/data/task_method_20_result_matrix.json

@@ -1,7 +1,7 @@
 {
   "title": "Task Method 20-Result Matrix",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:21+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
@@ -1980,7 +1980,7 @@
       "raw_text": "0.6286",
       "normalized_score": 0.6286317274823326,
       "metric_key": "temporal_order_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
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@@ -2142,7 +2142,7 @@
       "raw_text": "0.3727",
       "normalized_score": 0.37271645981034185,
       "metric_key": "misalignment_detection_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -2466,7 +2466,7 @@
       "raw_text": "0.0000",
       "normalized_score": 0.0,
       "metric_key": "next_subtask_forecast_macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -2610,7 +2610,7 @@
       "raw_text": "0.4319",
       "normalized_score": 0.4318674027510605,
       "metric_key": "macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -2952,7 +2952,7 @@
       "raw_text": "0.0009",
       "normalized_score": 0.0009279881217520415,
       "metric_key": "object_set_forecast_micro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/object_set_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
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docs/data/task_method_20_source_audit.json

@@ -0,0 +1,17 @@
+{
+  "checked_json_metric_count": 180,
+  "failure_count": 0,
+  "failures": [],
+  "generated_at_utc": "2026-06-20T20:48:41+00:00",
+  "method_task_record_count": 180,
+  "rule": "Every scored row that declares a JSON metric source must have the same numeric value under that row's metric_key.",
+  "scored_method_task_count": 180,
+  "skipped_record_count": 0,
+  "skipped_records": [],
+  "source_matrix": "docs/data/task_method_20_result_matrix.json",
+  "status": "pass",
+  "status_counts": {
+    "checked": 180
+  },
+  "title": "Task Method 20 Matrix Source Audit"
+}

docs/data/unified_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:54:37+00:00",
+  "generated_at_utc": "2026-06-20T20:38:21+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
@@ -1396,7 +1396,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.6286317274823326,
           "metric_key": "temporal_order_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1507,7 +1507,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.37271645981034185,
           "metric_key": "misalignment_detection_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1729,7 +1729,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.0,
           "metric_key": "next_subtask_forecast_macro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -1829,7 +1829,7 @@
         "qwen3_omni_v6_lora": {
           "raw": 0.4318674027510605,
           "metric_key": "macro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -2062,7 +2062,7 @@
         "cosmos3_super_reasoner": {
           "raw": 0.0009279881217520415,
           "metric_key": "object_set_forecast_micro_f1",
-          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+          "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/object_set_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
           "status": "scored",
           "reason": null,
@@ -4278,7 +4278,7 @@
       "raw_text": "0.6286",
       "normalized_score": 0.6286317274823326,
       "metric_key": "temporal_order_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/temporal_order/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -4440,7 +4440,7 @@
       "raw_text": "0.3727",
       "normalized_score": 0.37271645981034185,
       "metric_key": "misalignment_detection_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/misalignment_detection/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -4764,7 +4764,7 @@
       "raw_text": "0.0000",
       "normalized_score": 0.0,
       "metric_key": "next_subtask_forecast_macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/next_subtask_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -4908,7 +4908,7 @@
       "raw_text": "0.4319",
       "normalized_score": 0.4318674027510605,
       "metric_key": "macro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_interaction_text_task15_a100_20260620T010305Z/interaction_text_prediction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },
@@ -5250,7 +5250,7 @@
       "raw_text": "0.0009",
       "normalized_score": 0.0009279881217520415,
       "metric_key": "object_set_forecast_micro_f1",
-      "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/eval/metrics.json",
+      "source": "results/omni_finetune/xperience10m_cosmos3_super_future_task_probes_a100_textonly_v1_20260620/object_set_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
       "reason": null
     },

docs/data/website_integrity.json

@@ -1,13 +1,13 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-20T19:55:48+00:00",
+  "generated_at_utc": "2026-06-20T20:41:45+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
     "local_references": 213,
     "external_reference_count": 152,
-    "json_files": 50,
+    "json_files": 51,
     "image_assets_referenced": 28,
     "failure_count": 0
   },
@@ -301,7 +301,7 @@
     },
     {
       "path": "data/artifact_index.json",
-      "bytes": 121435,
+      "bytes": 122823,
       "top_level_type": "dict"
     },
     {
@@ -316,7 +316,7 @@
     },
     {
       "path": "data/episode128_task_model_radar.json",
-      "bytes": 184889,
+      "bytes": 184945,
       "top_level_type": "dict"
     },
     {
@@ -491,7 +491,12 @@
     },
     {
       "path": "data/task_method_20_result_matrix.json",
-      "bytes": 128481,
+      "bytes": 128509,
+      "top_level_type": "dict"
+    },
+    {
+      "path": "data/task_method_20_source_audit.json",
+      "bytes": 561,
       "top_level_type": "dict"
     },
     {
@@ -526,7 +531,7 @@
     },
     {
       "path": "data/unified_task_model_radar.json",
-      "bytes": 228743,
+      "bytes": 228799,
       "top_level_type": "dict"
     },
     {

results/omni_finetune/model_output_probe_readiness/RUN_REPORT.md

@@ -1,15 +1,11 @@
 # Model Output Probe Readiness
 
-Generated: `2026-06-20T04:32:11+00:00`
+Generated: `2026-06-20T20:38:59+00:00`
 
-This historical readiness report was generated before the final all-task matrix
-completion. It is retained as provenance for the probe-planning step, but the
-current public matrix is now complete at `180/180` scored method-task records.
-Use `docs/data/task_method_20_result_matrix.json` as the authoritative current
-coverage source.
+The 20-task matrix is already complete, so this readiness report is superseded for the current release. It remains a guardrail for future replacement model-output probes and does not assign new task scores.
 
-| Method | ID | Current matrix scores | Status | Split files | Current note |
+| Method | ID | Matrix scores | Status | Split files | Next step |
 | --- | --- | --- | --- | --- | --- |
-| Cosmos3-Nano Future Window | cosmos3_nano_future_window | 20/20 | superseded_by_completed_matrix | train: historical check missing; validation: historical check missing; test: historical check missing | Current scores are in the 180-row matrix with source/proxy notes. |
-| Cosmos3-Super Reasoner | cosmos3_super_reasoner | 20/20 | superseded_by_completed_matrix | train: historical check missing; validation: historical check missing; test: present | Current scores are in the 180-row matrix with source/proxy notes. |
-| Qwen3-Omni v6 LoRA | qwen3_omni_v6_lora | 20/20 | superseded_by_completed_matrix | train: historical check missing; validation: historical check missing; test: present | Current scores are in the 180-row matrix with source/proxy notes. |
+| Cosmos3-Nano Future Window | cosmos3_nano_future_window | 20/20 | superseded_by_completed_matrix | train: missing; validation: missing; test: missing | No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts. |
+| Cosmos3-Super Reasoner | cosmos3_super_reasoner | 20/20 | superseded_by_completed_matrix | train: missing; validation: missing; test: present | No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts. |
+| Qwen3-Omni v6 LoRA | qwen3_omni_v6_lora | 20/20 | superseded_by_completed_matrix | train: missing; validation: missing; test: present | No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts. |

results/omni_finetune/model_output_probe_readiness/model_output_probe_readiness.json

@@ -1,13 +1,12 @@
 {
-  "generated_at_utc": "2026-06-20T04:32:11+00:00",
+  "completion_state": "completed_matrix",
+  "generated_at_utc": "2026-06-20T20:38:59+00:00",
   "methods": {
     "cosmos3_nano_future_window": {
       "label": "Cosmos3-Nano Future Window",
-      "current_matrix_scored_task_count": 20,
-      "current_matrix_scoreless_task_count": 0,
       "matrix_scored_task_count": 20,
       "matrix_scoreless_task_count": 0,
-      "next_step": "Use docs/data/task_method_20_result_matrix.json as the authoritative current coverage source.",
+      "next_step": "No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts.",
       "ready_for_all_task_probe": false,
       "required_splits": [
         "train",
@@ -42,11 +41,9 @@
     },
     "cosmos3_super_reasoner": {
       "label": "Cosmos3-Super Reasoner",
-      "current_matrix_scored_task_count": 20,
-      "current_matrix_scoreless_task_count": 0,
       "matrix_scored_task_count": 20,
       "matrix_scoreless_task_count": 0,
-      "next_step": "Use docs/data/task_method_20_result_matrix.json as the authoritative current coverage source.",
+      "next_step": "No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts.",
       "ready_for_all_task_probe": false,
       "required_splits": [
         "train",
@@ -82,9 +79,7 @@
       "label": "Qwen3-Omni v6 LoRA",
       "matrix_scored_task_count": 20,
       "matrix_scoreless_task_count": 0,
-      "current_matrix_scored_task_count": 20,
-      "current_matrix_scoreless_task_count": 0,
-      "next_step": "Use docs/data/task_method_20_result_matrix.json as the authoritative current coverage source.",
+      "next_step": "No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts.",
       "ready_for_all_task_probe": false,
       "required_splits": [
         "train",
@@ -117,11 +112,10 @@
       "status": "superseded_by_completed_matrix"
     }
   },
-  "ready_method_count": 3,
-  "scope": "This artifact is retained as a historical readiness snapshot. The current public task-method matrix is complete at 180/180 scored records.",
-  "score_policy": "Current scores, proxy flags, and source artifacts are authoritative in docs/data/task_method_20_result_matrix.json.",
+  "ready_method_count": 0,
+  "scope": "The current matrix is already complete. This artifact is retained as a guardrail for future replacement model-output probes and does not create or infer numeric scores.",
+  "score_policy": "The current matrix has zero scoreless cells. Future replacement scores must still come from task-specific held-out artifacts.",
   "source_matrix": "docs/data/task_method_20_result_matrix.json",
   "status": "pass",
-  "superseded_by": "docs/data/task_method_20_result_matrix.json",
-  "title": "Model Output Probe Readiness (superseded by completed 180-result matrix)"
+  "title": "Model Output Probe Readiness"
 }

scripts/build_artifact_index.py

@@ -573,6 +573,22 @@ ARTIFACTS = [
         "surface": "repo_hf",
         "shows": "Reader-facing ledger confirming 180/180 scored method-task cells and listing the six compact-proxy records separately.",
     },
+    {
+        "id": "task_method_20_source_audit_json",
+        "title": "Task-method 20-result source audit JSON",
+        "path": "docs/data/task_method_20_source_audit.json",
+        "kind": "website_data",
+        "surface": "website_hf",
+        "shows": "Machine-readable check that scored JSON-backed matrix cells match their declared metric source values.",
+    },
+    {
+        "id": "task_method_20_source_audit",
+        "title": "Task-method 20-result source audit",
+        "path": "TASK_METHOD_20_SOURCE_AUDIT.md",
+        "kind": "evaluation_protocol",
+        "surface": "repo_hf",
+        "shows": "Reader-facing source-value audit for the 180-result matrix.",
+    },
     {
         "id": "unified_task_model_radar_chart",
         "title": "Unified 20-task model radar",
@@ -613,6 +629,14 @@ ARTIFACTS = [
         "surface": "repo_hf",
         "shows": "Regenerates the public completion/proxy audit from the 9-method by 20-task matrix without inventing unsupported scores.",
     },
+    {
+        "id": "task_method_20_source_audit_validator",
+        "title": "Task-method source-audit validator",
+        "path": "scripts/validate_task_method_matrix_sources.py",
+        "kind": "publication_workflow",
+        "surface": "repo_hf",
+        "shows": "Fails release checks if a scored matrix row disagrees with its JSON metric source.",
+    },
     {
         "id": "all_task_model_scoring_waiter",
         "title": "All-task model scoring guarded waiter",

scripts/build_quality_gates.py

@@ -67,6 +67,14 @@ GATES = [
         "blocks_if": "Windowing, split policy, leakage controls, task metrics, or current limitations are not explicit.",
         "shows": "The task evaluation protocol is generated from committed metric artifacts.",
     },
+    {
+        "id": "task_method_source_audit",
+        "title": "Task-method source audit",
+        "command": "python scripts/validate_task_method_matrix_sources.py",
+        "report": "docs/data/task_method_20_source_audit.json",
+        "blocks_if": "A scored 20-task matrix cell points to a JSON metric source that does not contain the same metric value.",
+        "shows": "Public 20-task scores remain traceable to their task-specific metric artifacts.",
+    },
     {
         "id": "figure_index",
         "title": "Figure index",
@@ -220,6 +228,7 @@ def markdown(payload: dict) -> str:
         "python scripts/validate_scope_claims.py",
         "python scripts/validate_source_alignment.py",
         "python scripts/build_evaluation_protocol.py",
+        "python scripts/validate_task_method_matrix_sources.py",
         "python scripts/build_brand_assets.py",
         "python scripts/build_figure_index.py",
         "python scripts/validate_website_integrity.py",

scripts/build_unified_task_model_radar.py

@@ -414,6 +414,10 @@ FOUNDATION_METRIC_SOURCE_OVERRIDES = {
     ("qwen3_omni_v6_lora", "action_object_relation"): QWEN_ACTION_OBJECT_METRICS_PATH,
     ("cosmos3_super_reasoner", "action_object_relation"): COSMOS_SUPER_ACTION_OBJECT_METRICS_PATH,
     ("cosmos3_super_reasoner", "caption_grounding"): COSMOS_SUPER_CAPTION_GROUNDING_METRICS_PATH,
+    ("cosmos3_super_reasoner", "temporal_order"): COSMOS_SUPER_FUTURE_TASK_METRIC_PATHS["temporal_order"],
+    ("cosmos3_super_reasoner", "misalignment_detection"): COSMOS_SUPER_FUTURE_TASK_METRIC_PATHS["misalignment_detection"],
+    ("cosmos3_super_reasoner", "next_subtask_forecast"): COSMOS_SUPER_FUTURE_TASK_METRIC_PATHS["next_subtask_forecast"],
+    ("cosmos3_super_reasoner", "object_set_forecast"): COSMOS_SUPER_FUTURE_TASK_METRIC_PATHS["object_set_forecast"],
     ("qwen3_omni_v6_lora", "caption_grounding"): QWEN_FUTURE_TASK_METRIC_PATHS["caption_grounding"],
     ("qwen3_omni_v6_lora", "cross_modal_retrieval"): QWEN_FUTURE_TASK_METRIC_PATHS["cross_modal_retrieval"],
     ("qwen3_omni_v6_lora", "temporal_order"): QWEN_FUTURE_TASK_METRIC_PATHS["temporal_order"],
@@ -426,6 +430,7 @@ FOUNDATION_METRIC_SOURCE_OVERRIDES = {
     ("qwen3_omni_v6_lora", "hand_trajectory_forecast"): QWEN_FUTURE_TASK_METRIC_PATHS["hand_trajectory_forecast"],
     ("qwen3_omni_v6_lora", "modality_reconstruction"): QWEN_FUTURE_TASK_METRIC_PATHS["modality_reconstruction"],
     ("qwen3_omni_v6_lora", "imu_to_hand_pose"): QWEN_FUTURE_TASK_METRIC_PATHS["imu_to_hand_pose"],
+    ("qwen3_omni_v6_lora", "interaction_text_prediction"): QWEN_FUTURE_TASK_METRIC_PATHS["interaction_text_prediction"],
     ("cosmos3_nano_future_window", "long_horizon_next_action"): COSMOS_NANO_LONG_HORIZON_METRICS_PATH,
     ("cosmos3_nano_future_window", "next_subtask_forecast"): COSMOS_NANO_NEXT_SUBTASK_METRICS_PATH,
     ("cosmos3_nano_future_window", "modality_reconstruction"): COSMOS_NANO_MODALITY_RECONSTRUCTION_METRICS_PATH,

scripts/omni/eval_cosmos3_super_future_task_probes.py

@@ -31,6 +31,7 @@ from eval_qwen3_omni_future_task_probes import (
     score_task as qwen_score_task,
     select_eval_indices,
     select_tasks,
+    task_requires_future_sample,
     task_target_value,
     time_to_transition_map,
     write_json,
@@ -212,9 +213,14 @@ def main() -> int:
     samples = load_jsonl(args.dataset_jsonl)
     future_map = future_index_map(samples, args.future_frames)
     transition_targets = time_to_transition_map(samples)
-    eval_indices = [idx for idx in select_eval_indices(samples, args) if idx in future_map]
-    if not eval_indices:
-        raise ValueError("No evaluation samples with future targets selected.")
+    base_eval_indices = select_eval_indices(samples, args)
+    eval_indices_by_task = {
+        task_id: [idx for idx in base_eval_indices if (not task_requires_future_sample(task_id) or idx in future_map)]
+        for task_id in selected_tasks
+    }
+    empty_tasks = [task_id for task_id, indices in eval_indices_by_task.items() if not indices]
+    if empty_tasks:
+        raise ValueError(f"No evaluation samples selected for tasks: {', '.join(empty_tasks)}")
 
     write_json(args.output_dir / "server_info.json", server_info(args))
     append_jsonl(
@@ -224,7 +230,9 @@ def main() -> int:
             "timestamp": time.time(),
             "run_id": args.run_id,
             "tasks": selected_tasks,
-            "num_eval_samples_with_future": len(eval_indices),
+            "num_base_eval_samples": len(base_eval_indices),
+            "num_eval_samples_by_task": {task_id: len(indices) for task_id, indices in eval_indices_by_task.items()},
+            "num_eval_samples_with_future": sum(1 for idx in base_eval_indices if idx in future_map),
             "sample_offset": args.sample_offset,
             "sample_stride": args.sample_stride,
             "future_frames": args.future_frames,
@@ -246,9 +254,10 @@ def main() -> int:
     for task_id in selected_tasks:
         spec = TASK_SPECS[task_id]
         partial_path = args.output_dir / task_id / "predictions.partial.jsonl"
-        for local_pos, sample_idx in enumerate(eval_indices, start=1):
+        task_eval_indices = eval_indices_by_task[task_id]
+        for local_pos, sample_idx in enumerate(task_eval_indices, start=1):
             sample = samples[sample_idx]
-            future_sample = samples[future_map[sample_idx]]
+            future_sample = samples[future_map[sample_idx]] if task_requires_future_sample(task_id) else sample
             pred_id = prediction_id(task_id, sample)
             if args.resume and pred_id in partial_by_task[task_id]:
                 continue
@@ -301,7 +310,7 @@ def main() -> int:
                     "timestamp": time.time(),
                     "task_id": task_id,
                     "sample_index": local_pos,
-                    "num_eval_samples": len(eval_indices),
+                    "num_eval_samples": len(task_eval_indices),
                     "completed_samples_for_task": len(partial_by_task[task_id]),
                     "sample_id": sample.get("id"),
                     "seconds": round(time.time() - started, 3),
@@ -310,7 +319,7 @@ def main() -> int:
 
     task_metrics = {}
     for task_id in selected_tasks:
-        rows = [partial_by_task[task_id][prediction_id(task_id, samples[idx])] for idx in eval_indices]
+        rows = [partial_by_task[task_id][prediction_id(task_id, samples[idx])] for idx in eval_indices_by_task[task_id]]
         task_metrics[task_id] = score_task(task_id, TASK_SPECS[task_id], rows, args.output_dir, args)
 
     display_name = model_display_name(args)

scripts/omni/eval_qwen3_omni_future_task_probes.py

@@ -142,6 +142,13 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
         ),
     ]
 )
+TASKS_REQUIRING_FUTURE_SAMPLE = {
+    "temporal_order",
+    "misalignment_detection",
+    "long_horizon_next_action",
+    "next_subtask_forecast",
+    "object_set_forecast",
+}
 
 
 SYSTEM_PROMPT = (
@@ -591,6 +598,10 @@ def task_target_value(
     return task_target(future_sample, spec)
 
 
+def task_requires_future_sample(task_id: str) -> bool:
+    return task_id in TASKS_REQUIRING_FUTURE_SAMPLE
+
+
 def object_set_metrics(rows: list[dict[str, Any]]) -> dict[str, float]:
     tp = fp = fn = exact = 0
     for row in rows:
@@ -736,9 +747,14 @@ def main() -> int:
     samples = load_jsonl(args.dataset_jsonl)
     future_map = future_index_map(samples, args.future_frames)
     transition_targets = time_to_transition_map(samples)
-    eval_indices = [idx for idx in select_eval_indices(samples, args) if idx in future_map]
-    if not eval_indices:
-        raise ValueError("No evaluation samples with future targets selected.")
+    base_eval_indices = select_eval_indices(samples, args)
+    eval_indices_by_task = {
+        task_id: [idx for idx in base_eval_indices if (not task_requires_future_sample(task_id) or idx in future_map)]
+        for task_id in selected_tasks
+    }
+    empty_tasks = [task_id for task_id, indices in eval_indices_by_task.items() if not indices]
+    if empty_tasks:
+        raise ValueError(f"No evaluation samples selected for tasks: {', '.join(empty_tasks)}")
 
     append_jsonl(
         args.progress_jsonl,
@@ -747,7 +763,9 @@ def main() -> int:
             "timestamp": time.time(),
             "run_id": args.run_id,
             "tasks": selected_tasks,
-            "num_eval_samples_with_future": len(eval_indices),
+            "num_base_eval_samples": len(base_eval_indices),
+            "num_eval_samples_by_task": {task_id: len(indices) for task_id, indices in eval_indices_by_task.items()},
+            "num_eval_samples_with_future": sum(1 for idx in base_eval_indices if idx in future_map),
             "sample_offset": args.sample_offset,
             "sample_stride": args.sample_stride,
         },
@@ -766,9 +784,10 @@ def main() -> int:
     for task_id in selected_tasks:
         spec = TASK_SPECS[task_id]
         partial_path = args.output_dir / task_id / "predictions.partial.jsonl"
-        for local_pos, sample_idx in enumerate(eval_indices, start=1):
+        task_eval_indices = eval_indices_by_task[task_id]
+        for local_pos, sample_idx in enumerate(task_eval_indices, start=1):
             sample = samples[sample_idx]
-            future_sample = samples[future_map[sample_idx]]
+            future_sample = samples[future_map[sample_idx]] if task_requires_future_sample(task_id) else sample
             pred_id = prediction_id(task_id, sample)
             if pred_id in partial_by_task[task_id]:
                 continue
@@ -808,7 +827,7 @@ def main() -> int:
                     "timestamp": time.time(),
                     "task_id": task_id,
                     "sample_index": local_pos,
-                    "num_eval_samples": len(eval_indices),
+                    "num_eval_samples": len(task_eval_indices),
                     "completed_samples_for_task": len(partial_by_task[task_id]),
                     "sample_id": sample.get("id"),
                     "seconds": round(time.time() - started, 3),
@@ -817,7 +836,7 @@ def main() -> int:
 
     task_metrics = {}
     for task_id in selected_tasks:
-        rows = [partial_by_task[task_id][prediction_id(task_id, samples[idx])] for idx in eval_indices]
+        rows = [partial_by_task[task_id][prediction_id(task_id, samples[idx])] for idx in eval_indices_by_task[task_id]]
         task_metrics[task_id] = score_task(task_id, TASK_SPECS[task_id], rows, args.output_dir, args)
 
     summary = {

scripts/omni/merge_cosmos3_super_future_task_probe_shards.py

@@ -56,13 +56,27 @@ def main() -> int:
     args.output_dir.mkdir(parents=True, exist_ok=True)
     task_metrics: dict[str, dict[str, Any]] = {}
     first_metrics: dict[str, Any] | None = None
+    duplicate_predictions: list[dict[str, Any]] = []
 
     for task_id, spec in TASK_SPECS.items():
         rows_by_id: dict[str, dict[str, Any]] = {}
+        row_sources: dict[str, str] = {}
         for shard_dir in args.shard_dir:
             for row in read_jsonl(shard_dir / task_id / "predictions.jsonl"):
                 key = str(row.get("prediction_id") or f"{task_id}::{row.get('id')}")
-                rows_by_id.setdefault(key, row)
+                if key in rows_by_id:
+                    duplicate_predictions.append(
+                        {
+                            "task_id": task_id,
+                            "prediction_id": key,
+                            "kept_shard": row_sources.get(key),
+                            "duplicate_shard": str(shard_dir),
+                            "conflict": rows_by_id[key] != row,
+                        }
+                    )
+                    continue
+                rows_by_id[key] = row
+                row_sources[key] = str(shard_dir)
             shard_metrics = read_json(shard_dir / task_id / "metrics.json")
             if shard_metrics and first_metrics is None:
                 first_metrics = shard_metrics
@@ -90,6 +104,9 @@ def main() -> int:
         "status": "pass",
         "run_id": args.run_id,
         "shard_dirs": [str(path) for path in args.shard_dir],
+        "duplicate_prediction_count": len(duplicate_predictions),
+        "duplicate_prediction_conflict_count": sum(1 for row in duplicate_predictions if row["conflict"]),
+        "duplicate_predictions": duplicate_predictions[:50],
         "tasks": {
             task_id: {
                 "task_number": metrics["task_number"],

scripts/omni/merge_qwen3_omni_future_task_probe_shards.py

@@ -54,13 +54,27 @@ def main() -> int:
     args.output_dir.mkdir(parents=True, exist_ok=True)
     task_metrics: dict[str, dict[str, Any]] = {}
     first_metrics: dict[str, Any] | None = None
+    duplicate_predictions: list[dict[str, Any]] = []
 
     for task_id, spec in TASK_SPECS.items():
         rows_by_id: dict[str, dict[str, Any]] = {}
+        row_sources: dict[str, str] = {}
         for shard_dir in args.shard_dir:
             for row in read_jsonl(shard_dir / task_id / "predictions.jsonl"):
                 key = str(row.get("prediction_id") or f"{task_id}::{row.get('id')}")
-                rows_by_id.setdefault(key, row)
+                if key in rows_by_id:
+                    duplicate_predictions.append(
+                        {
+                            "task_id": task_id,
+                            "prediction_id": key,
+                            "kept_shard": row_sources.get(key),
+                            "duplicate_shard": str(shard_dir),
+                            "conflict": rows_by_id[key] != row,
+                        }
+                    )
+                    continue
+                rows_by_id[key] = row
+                row_sources[key] = str(shard_dir)
             shard_metrics = read_json(shard_dir / task_id / "metrics.json")
             if shard_metrics and first_metrics is None:
                 first_metrics = shard_metrics
@@ -82,6 +96,9 @@ def main() -> int:
         "status": "pass",
         "run_id": args.run_id,
         "shard_dirs": [str(path) for path in args.shard_dir],
+        "duplicate_prediction_count": len(duplicate_predictions),
+        "duplicate_prediction_conflict_count": sum(1 for row in duplicate_predictions if row["conflict"]),
+        "duplicate_predictions": duplicate_predictions[:50],
         "tasks": {
             task_id: {
                 "task_number": metrics["task_number"],

scripts/omni/merge_qwen3_omni_retrieval_task_probe_shards.py

@@ -55,13 +55,27 @@ def main() -> int:
     args.output_dir.mkdir(parents=True, exist_ok=True)
     task_metrics: dict[str, dict[str, Any]] = {}
     first_metrics: dict[str, Any] | None = None
+    duplicate_predictions: list[dict[str, Any]] = []
 
     for task_id, spec in TASK_SPECS.items():
         rows_by_id: dict[str, dict[str, Any]] = {}
+        row_sources: dict[str, str] = {}
         for shard_dir in args.shard_dir:
             for row in read_jsonl(shard_dir / task_id / "predictions.jsonl"):
                 key = str(row.get("prediction_id") or f"{task_id}::{row.get('id')}")
-                rows_by_id.setdefault(key, row)
+                if key in rows_by_id:
+                    duplicate_predictions.append(
+                        {
+                            "task_id": task_id,
+                            "prediction_id": key,
+                            "kept_shard": row_sources.get(key),
+                            "duplicate_shard": str(shard_dir),
+                            "conflict": rows_by_id[key] != row,
+                        }
+                    )
+                    continue
+                rows_by_id[key] = row
+                row_sources[key] = str(shard_dir)
             shard_metrics = read_json(shard_dir / task_id / "metrics.json")
             if shard_metrics and first_metrics is None:
                 first_metrics = shard_metrics
@@ -86,6 +100,9 @@ def main() -> int:
         "status": "pass",
         "run_id": args.run_id,
         "shard_dirs": [str(path) for path in args.shard_dir],
+        "duplicate_prediction_count": len(duplicate_predictions),
+        "duplicate_prediction_conflict_count": sum(1 for row in duplicate_predictions if row["conflict"]),
+        "duplicate_predictions": duplicate_predictions[:50],
         "tasks": {
             task_id: {
                 "task_number": metrics["task_number"],

scripts/omni/run_128_task_baselines.py

@@ -133,7 +133,7 @@ def parse_args() -> argparse.Namespace:
         default=256,
         help="Use centroid classification instead of dense softmax when the train label space is larger than this.",
     )
-    parser.add_argument("--include-neural", action="store_true", default=True)
+    parser.add_argument("--include-neural", action=argparse.BooleanOptionalAction, default=True)
     parser.add_argument("--neural-epochs", type=int, default=35)
     parser.add_argument("--neural-hidden-dim", type=int, default=128)
     parser.add_argument("--neural-batch-size", type=int, default=256)
@@ -335,8 +335,6 @@ def row_text_features(row: dict[str, Any], episode: dict[str, Any] | None) -> st
         parts.extend([
             "main_task:",
             norm(episode.get("main_task")),
-            "episode_split:",
-            norm(episode.get("split")),
         ])
     media = row.get("media") or {}
     parts.extend([

scripts/omni/score_model_output_probes.py

@@ -130,6 +130,7 @@ def records_for_method(matrix: dict, method_id: str) -> list[dict]:
 def build_readiness(workspace: Path, matrix: dict, overrides: dict[str, dict[str, list[str]]]) -> dict:
     methods = {}
     source_hints = DEFAULT_PREDICTION_HINTS.copy()
+    matrix_complete = matrix.get("scored_method_task_count") == matrix.get("method_task_record_count")
     for method, split_map in overrides.items():
         target = source_hints.setdefault(method, {name: [] for name in REQUIRED_SPLITS})
         for split, paths in split_map.items():
@@ -144,6 +145,16 @@ def build_readiness(workspace: Path, matrix: dict, overrides: dict[str, dict[str
         scored = [row for row in method_records if row.get("scored")]
         missing = [row for row in method_records if not row.get("scored")]
         ready_for_all_task_probe = all(split_status[split]["exists"] for split in REQUIRED_SPLITS)
+        if matrix_complete and not missing:
+            method_status = "superseded_by_completed_matrix"
+            next_step = "No gap-filling action is required for the current 20-task matrix; use this script only for future replacement artifacts."
+        else:
+            method_status = "ready" if ready_for_all_task_probe else "missing_required_model_outputs"
+            next_step = (
+                "Run the all-task probe scorer against train/validation/test outputs."
+                if ready_for_all_task_probe
+                else "Collect or generate train, validation, and test prediction JSONL files first."
+            )
         methods[method_id] = {
             "label": next((series["label"] for series in matrix["series"] if series["id"] == method_id), method_id),
             "matrix_scored_task_count": len(scored),
@@ -151,27 +162,32 @@ def build_readiness(workspace: Path, matrix: dict, overrides: dict[str, dict[str
             "required_splits": list(REQUIRED_SPLITS),
             "split_status": split_status,
             "ready_for_all_task_probe": ready_for_all_task_probe,
-            "status": "ready" if ready_for_all_task_probe else "missing_required_model_outputs",
+            "status": method_status,
             "scoreless_task_ids": [row["task_id"] for row in missing],
-            "next_step": (
-                "Run the all-task probe scorer against train/validation/test outputs."
-                if ready_for_all_task_probe
-                else "Collect or generate train, validation, and test prediction JSONL files first."
-            ),
+            "next_step": next_step,
         }
 
     ready_methods = [method for method, item in methods.items() if item["ready_for_all_task_probe"]]
+    completion_state = "completed_matrix" if matrix_complete else "readiness_check"
     return {
         "title": "Model Output Probe Readiness",
         "status": "pass",
+        "completion_state": completion_state,
         "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
         "source_matrix": "docs/data/task_method_20_result_matrix.json",
         "scope": (
-            "This artifact checks readiness for extending verified model branches "
+            "The current matrix is already complete. This artifact is retained as a "
+            "guardrail for future replacement model-output probes and does not create "
+            "or infer numeric scores."
+            if matrix_complete
+            else "This artifact checks readiness for extending verified model branches "
             "to all 20 task contracts. It does not create or infer numeric scores."
         ),
         "score_policy": (
-            "A scoreless Qwen3/Cosmos cell can become numeric only after the branch "
+            "The current matrix has zero scoreless cells. Future replacement scores "
+            "must still come from task-specific held-out artifacts."
+            if matrix_complete
+            else "A scoreless Qwen3/Cosmos cell can become numeric only after the branch "
             "emits the task target and the metric is computed against held-out labels."
         ),
         "ready_method_count": len(ready_methods),
@@ -205,13 +221,21 @@ def write_report(output_dir: Path, payload: dict) -> None:
             + " |"
         )
 
+    intro = (
+        "The 20-task matrix is already complete, so this readiness report is "
+        "superseded for the current release. It remains a guardrail for future "
+        "replacement model-output probes and does not assign new task scores."
+        if payload.get("completion_state") == "completed_matrix"
+        else "This report checks whether verified model branches have the prediction files\n"
+        "needed to extend them to every 20-task contract. It is readiness evidence only;\n"
+        "it does not assign new task scores."
+    )
+
     report = f"""# Model Output Probe Readiness
 
 Generated: `{payload['generated_at_utc']}`
 
-This report checks whether verified model branches have the prediction files
-needed to extend them to every 20-task contract. It is readiness evidence only;
-it does not assign new task scores.
+{intro}
 
 | Method | ID | Matrix scores | Status | Split files | Next step |
 | --- | --- | --- | --- | --- | --- |

scripts/omni/train_cosmos3_super_forward_dynamics_lora.py

@@ -884,7 +884,8 @@ def main() -> int:
         if accelerator.is_main_process:
             write_json(output_dir / "training_metadata.json", payload)
             write_report(output_dir, payload)
-            append_jsonl(progress_path, {"event": "complete", "timestamp": time.time(), "status": status})
+            final_event = "complete" if status in {"complete", "dry_run_complete"} else "finalized_failed"
+            append_jsonl(progress_path, {"event": final_event, "timestamp": time.time(), "status": status})
 
     if accelerator.is_main_process:
         print(json.dumps({"status": status, "output_dir": str(output_dir), "adapter_dir": str(adapter_dir) if adapter_dir else None}, indent=2))

scripts/sync_hf_publish_mirrors.py

@@ -56,7 +56,8 @@ links to tasks 13-20. The unified radar chart is published as
 `docs/data/unified_task_model_radar.json`; the 9-method by 20-task completion
 matrix is complete at `180/180` scored method-task records and is published in
 `docs/data/task_method_20_result_matrix.json`, with the explicit audit in
-`docs/data/task_method_20_gap_audit.json`. Split radars for
+`docs/data/task_method_20_gap_audit.json` and source-value audit in
+`docs/data/task_method_20_source_audit.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`.
@@ -219,7 +220,8 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "20-task completion matrix is complete at `180/180` scored\n"
                 "method-task records and is published in\n"
                 "`docs/data/task_method_20_result_matrix.json`, with the explicit\n"
-                "audit in `docs/data/task_method_20_gap_audit.json`. Split radars are in\n"
+                "audit in `docs/data/task_method_20_gap_audit.json` and source-value\n"
+                "audit in `docs/data/task_method_20_source_audit.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",
             )
@@ -232,7 +234,8 @@ 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 complete at `180/180` scored method-task records\n"
                 "and is published in `docs/data/task_method_20_result_matrix.json`,\n"
-                "with the explicit audit in `docs/data/task_method_20_gap_audit.json`.",
+                "with the explicit audit in `docs/data/task_method_20_gap_audit.json`\n"
+                "and source-value audit in `docs/data/task_method_20_source_audit.json`.",
             )
         if "completion matrix is in `docs/data/task_method_20_result_matrix.json`" in text:
             text = text.replace(
@@ -240,7 +243,8 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "with the explicit\ngap audit in `docs/data/task_method_20_gap_audit.json`.",
                 "completion matrix is complete at `180/180` scored method-task records "
                 "and is published in `docs/data/task_method_20_result_matrix.json`, "
-                "with the explicit\naudit in `docs/data/task_method_20_gap_audit.json`.",
+                "with the explicit\naudit in `docs/data/task_method_20_gap_audit.json` "
+                "and source-value audit in `docs/data/task_method_20_source_audit.json`.",
             )
         if (
             "docs/data/task_method_20_result_matrix.json" in text
@@ -251,6 +255,15 @@ def ensure_tier2_card_links(hf_root: Path, *, dry_run: bool) -> list[str]:
                 "`docs/data/task_method_20_result_matrix.json`, with the explicit\n"
                 "gap audit in `docs/data/task_method_20_gap_audit.json`.",
             )
+        if (
+            "docs/data/task_method_20_gap_audit.json" in text
+            and "docs/data/task_method_20_source_audit.json" not in text
+        ):
+            text = text.replace(
+                "`docs/data/task_method_20_gap_audit.json`.",
+                "`docs/data/task_method_20_gap_audit.json` and source-value audit in\n"
+                "`docs/data/task_method_20_source_audit.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

scripts/validate_mirror_parity.py

@@ -85,6 +85,7 @@ DATA_FILES = [
     "task_walkthroughs.json",
     "task_method_20_result_matrix.json",
     "task_method_20_gap_audit.json",
+    "task_method_20_source_audit.json",
     "tier2_task_suite.json",
     "unified_task_model_radar.json",
     "website_integrity.json",
@@ -196,6 +197,7 @@ SCRIPT_FILES = [
     "verify_live_publication.py",
     "validate_mirror_parity.py",
     "validate_publication_package.py",
+    "validate_task_method_matrix_sources.py",
     "validate_scope_claims.py",
     "validate_source_alignment.py",
     "validate_task_surface.py",

scripts/validate_publication_package.py

@@ -276,6 +276,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "RENDERED_SITE_CHECK.md",
         "EVALUATION_PROTOCOL.md",
         "TASK_SUITE_20.md",
+        "TASK_METHOD_20_SOURCE_AUDIT.md",
         "XPERIENCE10M_128_EPISODE_FEATURE_INDEX.md",
         "FIGURE_INDEX.md",
         "SOURCE_ALIGNMENT_AUDIT.md",
@@ -323,6 +324,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "docs/data/episode128_task_model_radar.json",
         "docs/data/task_method_20_result_matrix.json",
         "docs/data/task_method_20_gap_audit.json",
+        "docs/data/task_method_20_source_audit.json",
         "docs/data/task_suite_enhancement_128.json",
         "docs/data/xperience10m_128_episode_feature_index.json",
         "docs/assets/modalities/video.jpg",
@@ -367,6 +369,7 @@ def required_assets(root: Path) -> dict[str, bool]:
         "scripts/build_unified_task_suite.py",
         "scripts/build_unified_task_model_radar.py",
         "scripts/build_task_method_20_gap_audit.py",
+        "scripts/validate_task_method_matrix_sources.py",
         "scripts/build_figure_index.py",
         "scripts/build_quality_gates.py",
         "scripts/build_public_surface_qa.py",

scripts/validate_task_method_matrix_sources.py

@@ -0,0 +1,211 @@
+#!/usr/bin/env python3
+"""Validate that scored matrix rows agree with their JSON metric sources."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Any
+
+
+ROOT = Path(__file__).resolve().parents[1]
+DEFAULT_MATRIX = ROOT / "docs/data/task_method_20_result_matrix.json"
+DEFAULT_OUTPUT_JSON = ROOT / "docs/data/task_method_20_source_audit.json"
+DEFAULT_OUTPUT_MD = ROOT / "TASK_METHOD_20_SOURCE_AUDIT.md"
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--matrix-json", type=Path, default=DEFAULT_MATRIX)
+    parser.add_argument("--output", type=Path, default=DEFAULT_OUTPUT_JSON)
+    parser.add_argument("--markdown-output", type=Path, default=DEFAULT_OUTPUT_MD)
+    parser.add_argument("--relative-tolerance", type=float, default=1e-9)
+    parser.add_argument("--absolute-tolerance", type=float, default=1e-12)
+    return parser.parse_args()
+
+
+def read_json(path: Path) -> Any:
+    return json.loads(path.read_text(encoding="utf-8"))
+
+
+def rel(path: Path) -> str:
+    try:
+        return path.relative_to(ROOT).as_posix()
+    except ValueError:
+        return path.as_posix()
+
+
+def resolve_source(source: str) -> Path:
+    path = Path(source)
+    return path if path.is_absolute() else ROOT / path
+
+
+def numeric(value: Any) -> float | None:
+    if isinstance(value, bool) or not isinstance(value, (int, float)):
+        return None
+    return float(value)
+
+
+def check_record(record: dict[str, Any], args: argparse.Namespace) -> tuple[str, dict[str, Any] | None]:
+    source = record.get("source")
+    metric_key = record.get("metric_key")
+    raw = numeric(record.get("raw"))
+    base = {
+        "task_id": record.get("task_id"),
+        "task_number": record.get("task_number"),
+        "series_id": record.get("series_id"),
+        "method": record.get("method"),
+        "metric_key": metric_key,
+        "source": source,
+        "raw": record.get("raw"),
+    }
+    if not record.get("scored"):
+        return "unscored", None
+    if raw is None or not metric_key or not source:
+        return "skipped_non_numeric_or_missing_source", base
+
+    source_path = resolve_source(str(source))
+    if not source_path.exists():
+        return "missing_source", {**base, "resolved_source": rel(source_path)}
+    if source_path.suffix.lower() != ".json":
+        return "skipped_non_json_source", base
+
+    try:
+        payload = read_json(source_path)
+    except json.JSONDecodeError as exc:
+        return "invalid_json_source", {**base, "resolved_source": rel(source_path), "error": str(exc)}
+    source_key = str(metric_key)
+    source_value = numeric(payload.get(source_key)) if isinstance(payload, dict) else None
+    if source_value is None and isinstance(payload, dict):
+        primary_metric = payload.get("primary_metric")
+        primary_score = numeric(payload.get("primary_score"))
+        if primary_score is not None and (primary_metric in {metric_key, None} or str(primary_metric or "") == str(metric_key)):
+            source_key = "primary_score"
+            source_value = primary_score
+        elif primary_score is not None and "primary_score" in payload:
+            source_key = "primary_score"
+            source_value = primary_score
+    if source_value is None:
+        return "missing_metric_key", {
+            **base,
+            "resolved_source": rel(source_path),
+            "available_numeric_keys": sorted(
+                key for key, value in payload.items() if numeric(value) is not None
+            )
+            if isinstance(payload, dict)
+            else [],
+        }
+    if not math.isclose(raw, source_value, rel_tol=args.relative_tolerance, abs_tol=args.absolute_tolerance):
+        return "value_mismatch", {
+            **base,
+            "resolved_source": rel(source_path),
+            "source_value": source_value,
+            "delta": raw - source_value,
+        }
+    return "checked", {**base, "resolved_source": rel(source_path), "source_key": source_key, "source_value": source_value}
+
+
+def build_report(args: argparse.Namespace) -> dict[str, Any]:
+    matrix = read_json(args.matrix_json)
+    records = matrix.get("records", [])
+    checked: list[dict[str, Any]] = []
+    skipped: list[dict[str, Any]] = []
+    failures: list[dict[str, Any]] = []
+    status_counts: dict[str, int] = {}
+
+    for record in records:
+        status, detail = check_record(record, args)
+        status_counts[status] = status_counts.get(status, 0) + 1
+        if detail is None:
+            continue
+        detail = {"status": status, **detail}
+        if status == "checked":
+            checked.append(detail)
+        elif status.startswith("skipped"):
+            skipped.append(detail)
+        else:
+            failures.append(detail)
+
+    return {
+        "title": "Task Method 20 Matrix Source Audit",
+        "status": "pass" if not failures else "fail",
+        "generated_at_utc": datetime.now(timezone.utc).isoformat(timespec="seconds"),
+        "source_matrix": rel(args.matrix_json),
+        "method_task_record_count": matrix.get("method_task_record_count"),
+        "scored_method_task_count": matrix.get("scored_method_task_count"),
+        "checked_json_metric_count": len(checked),
+        "skipped_record_count": len(skipped),
+        "failure_count": len(failures),
+        "status_counts": dict(sorted(status_counts.items())),
+        "failures": failures,
+        "skipped_records": skipped[:100],
+        "rule": (
+            "Every scored row that declares a JSON metric source must have the same "
+            "numeric value under that row's metric_key."
+        ),
+    }
+
+
+def write_markdown(path: Path, report: dict[str, Any]) -> None:
+    failures = report["failures"]
+    lines = [
+        "# Task Method 20 Matrix Source Audit",
+        "",
+        f"Generated: `{report['generated_at_utc']}`",
+        "",
+        f"Status: **{report['status']}**",
+        "",
+        report["rule"],
+        "",
+        "## Summary",
+        "",
+        f"- Source matrix: `{report['source_matrix']}`",
+        f"- Scored rows: `{report['scored_method_task_count']}/{report['method_task_record_count']}`",
+        f"- JSON metric rows checked: `{report['checked_json_metric_count']}`",
+        f"- Skipped non-JSON/non-numeric rows: `{report['skipped_record_count']}`",
+        f"- Failures: `{report['failure_count']}`",
+        "",
+    ]
+    if failures:
+        lines.extend([
+            "## Failures",
+            "",
+            "| Method | Task | Metric | Matrix value | Source value | Source |",
+            "| --- | --- | --- | ---: | ---: | --- |",
+        ])
+        for row in failures:
+            lines.append(
+                "| "
+                + " | ".join(
+                    [
+                        str(row.get("series_id")),
+                        str(row.get("task_id")),
+                        str(row.get("metric_key")),
+                        str(row.get("raw")),
+                        str(row.get("source_value")),
+                        str(row.get("source")),
+                    ]
+                )
+                + " |"
+            )
+    else:
+        lines.append("No JSON source/value mismatches were found.")
+    path.write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def main() -> int:
+    args = parse_args()
+    report = build_report(args)
+    args.output.parent.mkdir(parents=True, exist_ok=True)
+    args.output.write_text(json.dumps(report, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+    write_markdown(args.markdown_output, report)
+    print(f"{report['status'].upper()}: wrote {args.output}")
+    print(f"{report['status'].upper()}: wrote {args.markdown_output}")
+    return 0 if report["status"] == "pass" else 1
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/verify_live_publication.py

@@ -108,6 +108,17 @@ HASH_GROUPS = [
             "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/task_method_20_gap_audit.json",
         },
     },
+    {
+        "id": "task_method_20_source_audit_json",
+        "title": "Task-method 20-result source audit JSON",
+        "local_path": "docs/data/task_method_20_source_audit.json",
+        "urls": {
+            "github_pages": "https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/data/task_method_20_source_audit.json",
+            "hf_space": "https://huggingface.co/spaces/cy0307/ropedia-xperience-10m-task-suite/raw/main/data/task_method_20_source_audit.json",
+            "hf_artifacts": "https://huggingface.co/datasets/cy0307/ropedia-xperience-10m-task-suite-artifacts/resolve/main/docs/data/task_method_20_source_audit.json",
+            "hf_model": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines/resolve/main/metrics/task_method_20_source_audit.json",
+        },
+    },
     {
         "id": "public_reader_map_json",
         "title": "Public reader map JSON",