Add files using upload-large-folder tool

TASK_METHOD_20_GAP_AUDIT.md

@@ -1,6 +1,6 @@
 # Task Method 20-Result Gap Audit
 
-Generated: `2026-06-16T13:35:38+00:00`
+Generated: `2026-06-16T15:06:55+00:00`
 
 This audit is the explicit gap ledger for the 9-method x 20-task result matrix.
 It keeps missing cells visible while preserving the rule that a numeric score

data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-16T13:35:38+00:00",
+  "generated_at_utc": "2026-06-16T15:06:56+00:00",
   "status": "pass",
   "artifact_count": 191,
   "missing": [],
@@ -465,7 +465,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
       "exists": true,
       "bytes": 4432,
-      "sha256": "4c6e5505884c13ad9d60195e977d1554adafe2a83d07307644532d77f36850bb"
+      "sha256": "9848a62718df2058294e77349fb0f9f5af4f778a929ff50bad7c761419634015"
     },
     {
       "id": "source_alignment_validator",
@@ -585,8 +585,8 @@
       "surface": "website_hf",
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, and explicit scoreless status records.",
       "exists": true,
-      "bytes": 231251,
-      "sha256": "5cbcdf8e3a659ef98bbc623ee3e434055ad6479599c95178587b3dab84a6072d"
+      "bytes": 231282,
+      "sha256": "f07f229a075ea63156b3ad87588ea5f495fbb6db6d33bd6055fc4c9343447564"
     },
     {
       "id": "single_episode_task_model_radar_json",
@@ -597,7 +597,7 @@
       "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
       "exists": true,
       "bytes": 50973,
-      "sha256": "10f9ca6afc9f9d69d2380faab2e2f1ebd0114bf7ab778ea30188ba849e9a44c1"
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     },
     {
       "id": "episode128_task_model_radar_json",
@@ -607,8 +607,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, preserving explicit scoreless cells.",
       "exists": true,
-      "bytes": 187400,
-      "sha256": "fab3dc021cfeca3547b72fd2e1321a1258c0c48a8eaa5ea5cc28600badcd2c68"
+      "bytes": 187431,
+      "sha256": "cf65729b44a6a678d14e9970ac809eb4fef688505dd5578c80c194650d6c1322"
     },
     {
       "id": "task_method_20_result_matrix_json",
@@ -618,8 +618,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable 9-method by 20-task matrix where every method has 20 records and scoreless cells carry unsupported/not-evaluated reasons.",
       "exists": true,
-      "bytes": 129740,
-      "sha256": "e7e398b98e4e188ee883a126bd77ad966ba563f2c43da56e13e871b6dc90f1ea"
+      "bytes": 129771,
+      "sha256": "fa81542abb5fe7f0b8fd0cbeb29f8a8c8ce4f19a6e6ac3647c1078a554ee04be"
     },
     {
       "id": "task_method_20_result_matrix",
@@ -641,7 +641,7 @@
       "shows": "Machine-readable 180-record gap ledger with numeric scores, scoreless cells, explicit status reasons, and next evidence needed before new scores can be published.",
       "exists": true,
       "bytes": 57943,
-      "sha256": "a1246c07c569238e8589fab0fb90e86733b7799816e8ef6690036a0fac289ecb"
+      "sha256": "d9ce17695e9186e6ccb784d3c9af0d4e09addca71559526517bcc5f79f8bf89e"
     },
     {
       "id": "task_method_20_gap_audit",
@@ -652,7 +652,7 @@
       "shows": "Reader-facing ledger that lists every scoreless method-task cell and the concrete target or model-output evidence required before it can become numeric.",
       "exists": true,
       "bytes": 16234,
-      "sha256": "351e55637374770221115486fd6ef46550ae923c4fae5c01d3bb1e777b5a44fb"
+      "sha256": "01ae782b632d9f5c23cce5ebd7b99963cca9c4419193865bff434d9ec0b51dce"
     },
     {
       "id": "unified_task_model_radar_chart",
@@ -662,8 +662,8 @@
       "surface": "website_hf",
       "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
       "exists": true,
-      "bytes": 49695,
-      "sha256": "2ad63af1cd1e3e6f9c354b20792c20683e8877e2f92d9da5b5284dce56e668b7"
+      "bytes": 49726,
+      "sha256": "1eb08975b1ee030bb2ca229eb68a68a6ffd8e7324d6870c7804997f676bc334a"
     },
     {
       "id": "single_episode_task_model_radar_chart",
@@ -684,8 +684,8 @@
       "surface": "website_hf",
       "shows": "Separates the selected 128-episode methods: raw-feature simple/NN as complete 20/20 scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
       "exists": true,
-      "bytes": 43679,
-      "sha256": "51991fd33719be32da422f559b7d27895fc96b1332b0952f0c19b573d58c1325"
+      "bytes": 43710,
+      "sha256": "b8086051008c30ff5c224099023eab58883cb1ca025b2612231f4578ceec29a0"
     },
     {
       "id": "unified_task_model_radar_builder",
@@ -695,8 +695,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
       "exists": true,
-      "bytes": 48861,
-      "sha256": "621ff19f057e6ed279388c8e944f2e4a2cfc87b9be1bd9f1feee6d329f0ec431"
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+      "sha256": "c9c88870b9b6e72f290ea2f8ad5378dfb90f4aef5278f597f9a8d69c748ab352"
     },
     {
       "id": "task_method_20_gap_audit_builder",
@@ -971,7 +971,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
       "exists": true,
       "bytes": 8100,
-      "sha256": "b9f9236ae07336c8984a65bcda558d49453c2e57f6c903a44235bec4fd0d8df0"
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     },
     {
       "id": "public_surface_qa",
@@ -1074,7 +1074,7 @@
       "volatile": true,
       "shows": "Records the last live GitHub/HF URL verification after upload.",
       "exists": true,
-      "bytes": 134796,
+      "bytes": 149715,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -1085,8 +1085,8 @@
       "surface": "repo",
       "shows": "Fetches the published GitHub/HF URLs and compares live hashes and public-card markers against the release assets.",
       "exists": true,
-      "bytes": 56713,
-      "sha256": "d716fcaf5fe20a38fd4aad7a2abdb51585bb58ffdcff81141da649e342d19c6d"
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+      "sha256": "15d7f587cfdef1a4169f1a376f2c9f189422bf00de38dc94decacad6f331b3a6"
     },
     {
       "id": "reproducibility_contract",
@@ -1154,7 +1154,7 @@
       "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": 868302,
+      "bytes": 871973,
       "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": "2026-06-16T13:35:38+00:00",
+  "generated_at_utc": "2026-06-16T15:06:55+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,
@@ -124,7 +124,7 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "7 7",
-      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 scored from existing verified action/object JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future-task probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
       "scored_task_count": 7,

data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-16T13:50:22+00:00",
+  "generated_at_utc": "2026-06-16T15:07:43+00:00",
   "hf_root": "hf_publish",
   "summary": {
-    "group_count": 587,
+    "group_count": 591,
     "failure_count": 0,
     "failures_by_surface": {}
   },
@@ -139,44 +139,44 @@
         "path": "repo:docs/data/artifact_index.json",
         "exists": true,
         "bytes": 104199,
-        "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
+        "sha256": "6cc6eb2bc1b59a2eb5ec0e7bd7eae86689441be2f9290d9856058a63eceb1851"
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           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
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           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
+          "sha256": "6cc6eb2bc1b59a2eb5ec0e7bd7eae86689441be2f9290d9856058a63eceb1851"
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           "path": "hf_artifacts:docs/data/artifact_index.json",
           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
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           "path": "hf_model:data/artifact_index.json",
           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
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           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
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         "hf_model": {
           "path": "hf_model:metrics/artifact_index.json",
           "exists": true,
           "bytes": 104199,
-          "sha256": "785793bcd8ec76149d03ca6fe7f9539ce1bb0fbdf62091fc57de87c123bc49c6"
+          "sha256": "6cc6eb2bc1b59a2eb5ec0e7bd7eae86689441be2f9290d9856058a63eceb1851"
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       "failures": []
@@ -825,44 +825,44 @@
         "path": "repo:docs/data/publication_audit.json",
         "exists": true,
         "bytes": 8298,
-        "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
+        "sha256": "d6e6eb5072ab9ca66a55457f4805b01060f549634827df26d649cbdbd8256222"
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           "exists": true,
           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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           "path": "hf_artifacts:docs/data/publication_audit.json",
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           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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           "path": "hf_model:docs/data/publication_audit.json",
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           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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           "path": "hf_model:metrics/publication_audit.json",
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           "bytes": 8298,
-          "sha256": "ce6d6f889ae6adc7e3c06dea764cf4dca828840e00583230431a962f5283000c"
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@@ -874,44 +874,44 @@
         "path": "repo:docs/data/public_surface_qa.json",
         "exists": true,
         "bytes": 6146,
-        "sha256": "99757ce49f9ce9de6edaabafb33d4027e4c4febffd575a8b8bf63432216b54a4"
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           "exists": true,
           "bytes": 6146,
-          "sha256": "99757ce49f9ce9de6edaabafb33d4027e4c4febffd575a8b8bf63432216b54a4"
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           "bytes": 6146,
-          "sha256": "99757ce49f9ce9de6edaabafb33d4027e4c4febffd575a8b8bf63432216b54a4"
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           "bytes": 6146,
-          "sha256": "99757ce49f9ce9de6edaabafb33d4027e4c4febffd575a8b8bf63432216b54a4"
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           "bytes": 6146,
-          "sha256": "99757ce49f9ce9de6edaabafb33d4027e4c4febffd575a8b8bf63432216b54a4"
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           "bytes": 6146,
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@@ -1021,44 +1021,44 @@
         "path": "repo:docs/data/quality_gates.json",
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         "bytes": 8100,
-        "sha256": "5b2b61d9856e2d1de4386882bd4d619f13071515015aa080f3dc8fd2b3da7952"
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-          "sha256": "5b2b61d9856e2d1de4386882bd4d619f13071515015aa080f3dc8fd2b3da7952"
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@@ -1560,44 +1560,44 @@
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         "bytes": 4432,
-        "sha256": "3e2021bb443cb8f8f844aa1491e8874ea6f3d7c500c40a616213e6812236ffd6"
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@@ -511,7 +511,7 @@
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@@ -551,7 +551,7 @@
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@@ -621,7 +621,7 @@
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docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
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-  "generated_at_utc": "2026-06-16T13:35:38+00:00",
+  "generated_at_utc": "2026-06-16T15:06:56+00:00",
   "status": "pass",
   "artifact_count": 191,
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@@ -465,7 +465,7 @@
       "shows": "Machine-readable source-alignment pass/fail check for repo, website, and HF surfaces.",
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       "bytes": 4432,
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@@ -585,8 +585,8 @@
       "surface": "website_hf",
       "shows": "Stores normalized 20-axis radar values, raw task metrics, Qwen3/Cosmos overlay mappings, branch-card caveats, and explicit scoreless status records.",
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@@ -597,7 +597,7 @@
       "shows": "Machine-readable split radar for the one-episode Minimal and Neural MLP baselines, both scored on all 20 task contracts.",
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@@ -607,8 +607,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable split radar for selected 128-episode metadata/raw baselines and verified Qwen3/Cosmos branches, preserving explicit scoreless cells.",
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@@ -618,8 +618,8 @@
       "surface": "website_hf",
       "shows": "Machine-readable 9-method by 20-task matrix where every method has 20 records and scoreless cells carry unsupported/not-evaluated reasons.",
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@@ -641,7 +641,7 @@
       "shows": "Machine-readable 180-record gap ledger with numeric scores, scoreless cells, explicit status reasons, and next evidence needed before new scores can be published.",
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       "bytes": 57943,
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@@ -652,7 +652,7 @@
       "shows": "Reader-facing ledger that lists every scoreless method-task cell and the concrete target or model-output evidence required before it can become numeric.",
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       "bytes": 16234,
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@@ -662,8 +662,8 @@
       "surface": "website_hf",
       "shows": "Compares minimal and neural MLP baselines across all 20 tasks, with Qwen3/Cosmos task-aligned model overlays.",
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-      "bytes": 49695,
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@@ -684,8 +684,8 @@
       "surface": "website_hf",
       "shows": "Separates the selected 128-episode methods: raw-feature simple/NN as complete 20/20 scored polygons and metadata/Qwen/Cosmos as task-aligned overlays.",
       "exists": true,
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@@ -695,8 +695,8 @@
       "surface": "repo_hf",
       "shows": "Regenerates the direction-aware radar chart and machine-readable metric overlay JSON.",
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@@ -971,7 +971,7 @@
       "shows": "Machine-readable release-check summary for validators, mirrors, and public project surfaces.",
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       "bytes": 8100,
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@@ -1074,7 +1074,7 @@
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       "shows": "Records the last live GitHub/HF URL verification after upload.",
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@@ -1085,8 +1085,8 @@
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@@ -1154,7 +1154,7 @@
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       "shows": "Confirms prepared GitHub/HF Space/artifact/model mirrors share the same critical data, figure, website HTML, and validator files.",
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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": "2026-06-16T13:35:38+00:00",
+  "generated_at_utc": "2026-06-16T15:06:55+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,
@@ -124,7 +124,7 @@
       "kind": "partial_128_episode_foundation_model_overlay",
       "scope": "128 selected episodes, held-out test",
       "stroke_dasharray": "7 7",
-      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 scored from existing verified action/object JSON.",
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       "plotted_as": "colored point overlay",
       "result_record_count": 20,
       "scored_task_count": 7,

docs/data/mirror_parity.json

@@ -1,9 +1,9 @@
 {
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   "hf_root": "hf_publish",
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@@ -139,44 +139,44 @@
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@@ -825,44 +825,44 @@
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@@ -874,44 +874,44 @@
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@@ -1021,44 +1021,44 @@
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@@ -1560,44 +1560,44 @@
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       "path": "data/live_publication_status.json",
-      "bytes": 134796,
+      "bytes": 149715,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 868302,
+      "bytes": 871973,
       "top_level_type": "dict"
     },
     {
@@ -481,7 +481,7 @@
     },
     {
       "path": "data/task_method_20_result_matrix.json",
-      "bytes": 129740,
+      "bytes": 129771,
       "top_level_type": "dict"
     },
     {
@@ -511,7 +511,7 @@
     },
     {
       "path": "data/unified_task_model_radar.json",
-      "bytes": 231251,
+      "bytes": 231282,
       "top_level_type": "dict"
     },
     {
@@ -551,7 +551,7 @@
     {
       "path": "assets/charts/episode128_task_model_radar.svg",
       "exists": true,
-      "bytes": 43679,
+      "bytes": 43710,
       "format": "SVG",
       "has_viewbox": true
     },
@@ -621,7 +621,7 @@
     {
       "path": "assets/charts/unified_task_model_radar.svg",
       "exists": true,
-      "bytes": 49695,
+      "bytes": 49726,
       "format": "SVG",
       "has_viewbox": true
     },

scripts/build_unified_task_model_radar.py

@@ -40,12 +40,27 @@ COSMOS_SUPER_FD_METRICS_PATH = (
 METADATA128_BASELINE_DIR = ROOT / "results/omni_finetune/a100_128_metadata_task_baselines_20260616_v2"
 RAW128_BASELINE_DIR = ROOT / "results/omni_finetune/a100_128_raw20_task_baselines_complete20_proxy_20260616T091500Z"
 MODEL_OUTPUT_TASK_PROBE_DIR = ROOT / "results/omni_finetune/model_output_task_probes_20260616"
+QWEN_FUTURE_TASK_PROBE_DIR = (
+    ROOT
+    / "results/omni_finetune"
+    / "xperience10m_qwen3_omni_v6_future_task_probes_a100_20260616T143608Z"
+)
 QWEN_ACTION_OBJECT_METRICS_PATH = (
     MODEL_OUTPUT_TASK_PROBE_DIR / "action_object_relation/qwen3_omni_v6_lora/metrics.json"
 )
 COSMOS_SUPER_ACTION_OBJECT_METRICS_PATH = (
     MODEL_OUTPUT_TASK_PROBE_DIR / "action_object_relation/cosmos3_super_reasoner/metrics.json"
 )
+QWEN_FUTURE_TASK_METRIC_PATHS = {
+    "long_horizon_next_action": QWEN_FUTURE_TASK_PROBE_DIR / "long_horizon_next_action/metrics.json",
+    "next_subtask_forecast": QWEN_FUTURE_TASK_PROBE_DIR / "next_subtask_forecast/metrics.json",
+    "object_set_forecast": QWEN_FUTURE_TASK_PROBE_DIR / "object_set_forecast/metrics.json",
+}
+QWEN_FUTURE_TASK_METRIC_KEYS = {
+    "long_horizon_next_action": "long_horizon_next_action_macro_f1",
+    "next_subtask_forecast": "next_subtask_forecast_macro_f1",
+    "object_set_forecast": "object_set_forecast_micro_f1",
+}
 OUTPUT_JSON = ROOT / "docs/data/unified_task_model_radar.json"
 OUTPUT_SINGLE_JSON = ROOT / "docs/data/single_episode_task_model_radar.json"
 OUTPUT_128_JSON = ROOT / "docs/data/episode128_task_model_radar.json"
@@ -178,6 +193,9 @@ FOUNDATION_METRIC_PATHS = {
 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,
+    ("qwen3_omni_v6_lora", "long_horizon_next_action"): QWEN_FUTURE_TASK_METRIC_PATHS["long_horizon_next_action"],
+    ("qwen3_omni_v6_lora", "next_subtask_forecast"): QWEN_FUTURE_TASK_METRIC_PATHS["next_subtask_forecast"],
+    ("qwen3_omni_v6_lora", "object_set_forecast"): QWEN_FUTURE_TASK_METRIC_PATHS["object_set_forecast"],
 }
 
 SHORT_TASK_LABELS = {
@@ -210,7 +228,7 @@ METHOD_DETAILS = {
     "metadata128_neural_mlp": "128-episode JSONL metadata/text MLP baselines.",
     "raw128_simple": "128-episode 4430-dim sensor NPZ simple heads; tasks 15/19 use compact proxies.",
     "raw128_neural_mlp": "128-episode 4430-dim sensor NPZ MLP heads; tasks 15/19 use compact proxies.",
-    "qwen3_omni_v6_lora": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 scored from existing verified action/object JSON.",
+    "qwen3_omni_v6_lora": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future-task probes scored from task-specific JSON.",
     "cosmos3_super_reasoner": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation, plus task 16 scored from existing verified action/object JSON.",
     "cosmos3_nano_future_window": "Verified Cosmos3-Nano future-window compatibility metrics.",
 }
@@ -250,6 +268,19 @@ def read_json(path: Path) -> dict[str, Any]:
     return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}
 
 
+def foundation_task_metric_mapping(qwen_metrics: dict[str, Any]) -> dict[str, dict[str, str]]:
+    mapping = {task_id: dict(series_metrics) for task_id, series_metrics in FOUNDATION_TASK_METRICS.items()}
+    for task_id, path in QWEN_FUTURE_TASK_METRIC_PATHS.items():
+        payload = read_json(path)
+        metric_key = QWEN_FUTURE_TASK_METRIC_KEYS[task_id]
+        metric_value = payload.get(metric_key)
+        if payload.get("status") != "pass" or not isinstance(metric_value, (int, float)):
+            continue
+        qwen_metrics[metric_key] = metric_value
+        mapping.setdefault(task_id, {})["qwen3_omni_v6_lora"] = metric_key
+    return mapping
+
+
 def read_a100_metadata_record(task_id: str, *, neural: bool = False) -> dict[str, Any] | None:
     path = METADATA128_BASELINE_DIR / ("neural_mlp" if neural else "") / task_id / "metrics.json"
     if not path.exists():
@@ -555,6 +586,7 @@ def build_payload() -> dict[str, Any]:
     cosmos_fd = read_json(COSMOS_SUPER_FD_METRICS_PATH)
     qwen.update(read_json(QWEN_ACTION_OBJECT_METRICS_PATH))
     cosmos_super.update(read_json(COSMOS_SUPER_ACTION_OBJECT_METRICS_PATH))
+    foundation_task_metrics = foundation_task_metric_mapping(qwen)
     foundation_metrics = {
         "qwen3_omni_v6_lora": qwen,
         "cosmos3_super_reasoner": cosmos_super,
@@ -579,7 +611,7 @@ def build_payload() -> dict[str, Any]:
                 "status": "scored",
             },
         }
-        for series_id, metric_key in FOUNDATION_TASK_METRICS.get(row["task_id"], {}).items():
+        for series_id, metric_key in foundation_task_metrics.get(row["task_id"], {}).items():
             raw = foundation_metrics.get(series_id, {}).get(metric_key)
             values[series_id] = {
                 "raw": raw,

scripts/omni/collect_qwen3_future_task_probe_results.sh

@@ -0,0 +1,91 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
+
+GPU_HOST_SUFFIX="${GPU_HOST_SUFFIX:-$(printf 'A%s-80Gx4' 100)}"
+REMOTE_HOST="${REMOTE_HOST:-ANGEL-${GPU_HOST_SUFFIX}}"
+REMOTE_ROOT="${REMOTE_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite}"
+RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_future_task_probes_a100_20260616T143608Z}"
+RESULT_ROOT="${RESULT_ROOT:-results/omni_finetune}"
+
+REMOTE_RUN_DIR="${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}"
+LOCAL_RUN_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/${RUN_ID}"
+LOCAL_LAUNCHER_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/deferred_launchers"
+REMOTE_LAUNCHER_LOG="${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launcher.log"
+
+TASKS=(
+  long_horizon_next_action
+  next_subtask_forecast
+  object_set_forecast
+)
+
+echo "checking remote run ${REMOTE_HOST}:${REMOTE_RUN_DIR}"
+ssh "$REMOTE_HOST" "cd '$REMOTE_ROOT' && test -s '${RESULT_ROOT}/${RUN_ID}/summary.json'"
+for task_id in "${TASKS[@]}"; do
+  ssh "$REMOTE_HOST" "cd '$REMOTE_ROOT' && test -s '${RESULT_ROOT}/${RUN_ID}/${task_id}/metrics.json'"
+done
+
+mkdir -p "$LOCAL_RUN_DIR" "$LOCAL_LAUNCHER_DIR"
+rsync -av "${REMOTE_HOST}:${REMOTE_RUN_DIR}/" "$LOCAL_RUN_DIR/"
+ssh "$REMOTE_HOST" "test -s '$REMOTE_LAUNCHER_LOG'" >/dev/null 2>&1 \
+  && rsync -av "${REMOTE_HOST}:${REMOTE_LAUNCHER_LOG}" "$LOCAL_LAUNCHER_DIR/" \
+  || true
+
+python3 - "$PROJECT_ROOT" "$RUN_ID" <<'PY'
+import json
+import sys
+from pathlib import Path
+
+root = Path(sys.argv[1])
+run_id = sys.argv[2]
+run_dir = root / "results/omni_finetune" / run_id
+expected = {
+    "long_horizon_next_action": "long_horizon_next_action_macro_f1",
+    "next_subtask_forecast": "next_subtask_forecast_macro_f1",
+    "object_set_forecast": "object_set_forecast_micro_f1",
+}
+
+summary_path = run_dir / "summary.json"
+if not summary_path.exists():
+    raise SystemExit(f"missing summary: {summary_path}")
+summary = json.loads(summary_path.read_text(encoding="utf-8"))
+if summary.get("status") != "pass":
+    raise SystemExit(f"run summary is not pass: {summary.get('status')}")
+
+records = []
+for task_id, metric_key in expected.items():
+    metrics_path = run_dir / task_id / "metrics.json"
+    if not metrics_path.exists():
+        raise SystemExit(f"missing metrics: {metrics_path}")
+    metrics = json.loads(metrics_path.read_text(encoding="utf-8"))
+    score = metrics.get(metric_key)
+    if metrics.get("status") != "pass" or not isinstance(score, (int, float)):
+        raise SystemExit(f"invalid {task_id} metric {metric_key}: {score!r}")
+    records.append(
+        {
+            "task_id": task_id,
+            "metric_key": metric_key,
+            "primary_score": score,
+            "num_samples": metrics.get("num_samples"),
+            "source": str(metrics_path.relative_to(root)),
+        }
+    )
+
+validation = {
+    "title": "Qwen3 Future Task Probe Collection Validation",
+    "status": "pass",
+    "run_id": run_id,
+    "summary": str(summary_path.relative_to(root)),
+    "validated_task_count": len(records),
+    "records": records,
+}
+(run_dir / "collection_validation.json").write_text(
+    json.dumps(validation, indent=2, sort_keys=True) + "\n",
+    encoding="utf-8",
+)
+print(json.dumps(validation, indent=2, sort_keys=True))
+PY
+
+echo "collected and validated ${LOCAL_RUN_DIR}"

scripts/omni/eval_qwen3_omni_future_task_probes.py

@@ -0,0 +1,643 @@
+#!/usr/bin/env python3
+"""Evaluate Qwen3-Omni on future-target task probes from the 128-episode JSON.
+
+This runner scores only task targets that can be derived from the current
+multi-episode JSON export:
+
+- Task 13: long-horizon next action, +100 frames.
+- Task 14: long-horizon next subtask, +100 frames.
+- Task 17: future object set, +100 frames.
+
+It does not fabricate scores for regression, retrieval, raw-caption, or
+missing-modality targets.
+"""
+
+from __future__ import annotations
+
+import argparse
+import csv
+import json
+import time
+from collections import OrderedDict
+from pathlib import Path
+from typing import Any
+
+import numpy as np
+import torch
+
+from eval_qwen3_omni_lora import dtype_arg, load_model_processor, move_inputs
+from qwen3_omni_dataset_utils import (
+    class_metrics,
+    has_empty_audio_items,
+    is_empty_audio_exception,
+    load_jsonl,
+    match_label,
+)
+
+
+TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
+    [
+        (
+            "long_horizon_next_action",
+            {
+                "task_number": 13,
+                "label": "Long-Horizon Next-Action Forecasting",
+                "family": "classification",
+                "metric_key": "macro_f1",
+                "prediction_key": "long_horizon_next_action",
+                "target_field": "action",
+                "option_field": "action_options",
+            },
+        ),
+        (
+            "next_subtask_forecast",
+            {
+                "task_number": 14,
+                "label": "Long-Horizon Next-Subtask Forecasting",
+                "family": "classification",
+                "metric_key": "macro_f1",
+                "prediction_key": "next_subtask_forecast",
+                "target_field": "subtask",
+                "option_field": "subtask_options",
+            },
+        ),
+        (
+            "object_set_forecast",
+            {
+                "task_number": 17,
+                "label": "Future Object-Set Forecasting",
+                "family": "multi_label",
+                "metric_key": "micro_f1",
+                "prediction_key": "object_set_forecast",
+                "target_field": "objects",
+                "option_field": None,
+            },
+        ),
+    ]
+)
+
+
+SYSTEM_PROMPT = (
+    "You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
+    "Return exactly one compact valid JSON object and no markdown, prose, code "
+    "fences, explanations, or repeated text. If the target cannot be inferred "
+    "from the visible/audio evidence, return unknown for the requested field."
+)
+
+
+def parse_args() -> argparse.Namespace:
+    root = Path(__file__).resolve().parents[2]
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--dataset-jsonl", type=Path, required=True)
+    parser.add_argument("--run-id", default="qwen3_future_task_probes")
+    parser.add_argument("--output-dir", type=Path)
+    parser.add_argument("--model-id", required=True)
+    parser.add_argument("--adapter-dir", type=Path, required=True)
+    parser.add_argument("--eval-split", default="test")
+    parser.add_argument("--train-split", default="train")
+    parser.add_argument("--tasks", default="long_horizon_next_action,next_subtask_forecast,object_set_forecast")
+    parser.add_argument("--future-frames", type=int, default=100)
+    parser.add_argument("--sample-limit", type=int, default=0)
+    parser.add_argument("--sample-offset", type=int, default=0)
+    parser.add_argument("--sample-stride", type=int, default=1)
+    parser.add_argument("--max-new-tokens", type=int, default=48)
+    parser.add_argument("--device-map", default="auto")
+    parser.add_argument("--dtype", default="bfloat16", choices=["auto", "bfloat16", "float16", "float32"])
+    parser.add_argument("--local-files-only", action="store_true")
+    parser.add_argument("--trust-remote-code", action="store_true")
+    parser.add_argument("--use-audio-in-video", action=argparse.BooleanOptionalAction, default=False)
+    parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--progress-jsonl", type=Path)
+    return parser.parse_args()
+
+
+def write_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, indent=2, sort_keys=True) + "\n", encoding="utf-8")
+
+
+def write_jsonl(path: Path, rows: list[dict[str, Any]]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", encoding="utf-8") as handle:
+        for row in rows:
+            handle.write(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n")
+
+
+def append_jsonl(path: Path, row: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("a", encoding="utf-8") as handle:
+        handle.write(json.dumps(row, ensure_ascii=False, sort_keys=True) + "\n")
+
+
+def write_csv(path: Path, rows: list[dict[str, Any]], fieldnames: list[str]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("w", newline="", encoding="utf-8") as handle:
+        writer = csv.DictWriter(handle, fieldnames=fieldnames, extrasaction="ignore", lineterminator="\n")
+        writer.writeheader()
+        writer.writerows(rows)
+
+
+def read_jsonl_if_exists(path: Path) -> list[dict[str, Any]]:
+    if not path.exists():
+        return []
+    rows: list[dict[str, Any]] = []
+    with path.open("r", encoding="utf-8") as handle:
+        for line in handle:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                rows.append(json.loads(line))
+            except json.JSONDecodeError:
+                continue
+    return rows
+
+
+def normalize_text(value: Any) -> str:
+    return " ".join(str(value or "").strip().strip("`'\". ").split())
+
+
+def normalize_objects(value: Any) -> list[str]:
+    if not isinstance(value, list):
+        return []
+    seen: set[str] = set()
+    out: list[str] = []
+    for item in value:
+        text = normalize_text(item).casefold()
+        if not text or text in seen:
+            continue
+        seen.add(text)
+        out.append(text)
+    return sorted(out)
+
+
+def answer(sample: dict[str, Any]) -> dict[str, Any]:
+    payload = sample.get("answer_json")
+    return payload if isinstance(payload, dict) else {}
+
+
+def row_start(sample: dict[str, Any]) -> int:
+    window = sample.get("center_window") if isinstance(sample.get("center_window"), dict) else {}
+    return int(window.get("start_frame", 0) or 0)
+
+
+def row_end(sample: dict[str, Any]) -> int:
+    window = sample.get("center_window") if isinstance(sample.get("center_window"), dict) else {}
+    return int(window.get("end_frame", row_start(sample)) or row_start(sample))
+
+
+def by_episode_sorted(samples: list[dict[str, Any]]) -> dict[str, list[int]]:
+    grouped: dict[str, list[int]] = {}
+    for idx, sample in enumerate(samples):
+        grouped.setdefault(str(sample.get("episode_id")), []).append(idx)
+    for indices in grouped.values():
+        indices.sort(key=lambda i: row_start(samples[i]))
+    return grouped
+
+
+def future_index_map(samples: list[dict[str, Any]], frame_offset: int) -> dict[int, int]:
+    mapping: dict[int, int] = {}
+    for indices in by_episode_sorted(samples).values():
+        starts = np.asarray([row_start(samples[i]) for i in indices], dtype=np.int64)
+        for idx in indices:
+            target_start = row_start(samples[idx]) + frame_offset
+            future_pos = int(np.searchsorted(starts, target_start, side="left"))
+            if future_pos < len(indices):
+                mapping[idx] = indices[future_pos]
+    return mapping
+
+
+def parse_json_object(text: str) -> dict[str, Any]:
+    raw = str(text or "").strip()
+    if raw.startswith("```"):
+        raw = raw.strip("`").strip()
+        if raw.lower().startswith("json"):
+            raw = raw[4:].strip()
+    try:
+        payload = json.loads(raw)
+    except json.JSONDecodeError:
+        start = raw.find("{")
+        end = raw.rfind("}")
+        if start < 0 or end <= start:
+            return {}
+        try:
+            payload = json.loads(raw[start : end + 1])
+        except json.JSONDecodeError:
+            return {}
+    return payload if isinstance(payload, dict) else {}
+
+
+def task_options(sample: dict[str, Any], spec: dict[str, Any]) -> list[str]:
+    option_field = spec.get("option_field")
+    options = sample.get(option_field) if option_field else None
+    if isinstance(options, list) and options:
+        return [str(item) for item in options]
+    if spec["target_field"] == "action":
+        options = sample.get("label_options")
+        return [str(item) for item in options] if isinstance(options, list) else []
+    return []
+
+
+def build_task_prompt(sample: dict[str, Any], future_sample: dict[str, Any], task_id: str, spec: dict[str, Any], future_frames: int) -> str:
+    start = row_start(sample)
+    end = row_end(sample)
+    future_start = row_start(future_sample)
+    prediction_key = spec["prediction_key"]
+    lines = [
+        f"Task {spec['task_number']}: {spec['label']}",
+        f"Episode: {sample.get('episode_id')}",
+        f"Current visible/audio context frames: {start}-{end}",
+        f"Predict the target at the future window starting near frame {start + future_frames} (resolved target start frame {future_start}).",
+    ]
+    options = task_options(sample, spec)
+    if task_id == "long_horizon_next_action":
+        lines.extend(
+            [
+                "Return JSON only with this schema:",
+                f'{{"{prediction_key}":"<exact action option or unknown>"}}',
+                "Copy exactly one action label from this list:",
+                "\n".join(f"- {option}" for option in options),
+            ]
+        )
+    elif task_id == "next_subtask_forecast":
+        lines.extend(
+            [
+                "Return JSON only with this schema:",
+                f'{{"{prediction_key}":"<exact subtask option or unknown>"}}',
+                "Copy exactly one subtask label from this list:",
+                "\n".join(f"- {option}" for option in options),
+            ]
+        )
+    elif task_id == "object_set_forecast":
+        lines.extend(
+            [
+                "Return JSON only with this schema:",
+                f'{{"{prediction_key}":["<0 to 8 short object names>"]}}',
+                "List the objects likely to be active or manipulated in that future window. Use short object names.",
+            ]
+        )
+    else:
+        raise ValueError(f"unknown task: {task_id}")
+    return "\n".join(lines)
+
+
+def build_messages(
+    sample: dict[str, Any],
+    future_sample: dict[str, Any],
+    task_id: str,
+    spec: dict[str, Any],
+    future_frames: int,
+    *,
+    include_audio: bool = True,
+) -> list[dict[str, Any]]:
+    media = sample.get("media") if isinstance(sample.get("media"), dict) else {}
+    video_path = media.get("mosaic_video_path") or sample.get("primary_video_path")
+    audio_path = media.get("audio_path")
+    content: list[dict[str, Any]] = []
+    if video_path:
+        content.append({"type": "video", "video": video_path})
+    if include_audio and audio_path:
+        content.append({"type": "audio", "audio": audio_path})
+    content.append({"type": "text", "text": build_task_prompt(sample, future_sample, task_id, spec, future_frames)})
+    return [
+        {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
+        {"role": "user", "content": content},
+    ]
+
+
+def generate_messages(
+    model,
+    processor,
+    sample: dict[str, Any],
+    future_sample: dict[str, Any],
+    task_id: str,
+    spec: dict[str, Any],
+    args: argparse.Namespace,
+) -> str:
+    from qwen_omni_utils import process_mm_info
+
+    for include_audio in (True, False):
+        messages = build_messages(sample, future_sample, task_id, spec, args.future_frames, include_audio=include_audio)
+        text = processor.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
+        audios, images, videos = process_mm_info(messages, use_audio_in_video=args.use_audio_in_video)
+        if include_audio and has_empty_audio_items(audios):
+            continue
+        try:
+            inputs = processor(
+                text=text,
+                audio=audios,
+                images=images,
+                videos=videos,
+                return_tensors="pt",
+                padding=True,
+                use_audio_in_video=args.use_audio_in_video,
+            )
+            break
+        except RuntimeError as exc:
+            if include_audio and is_empty_audio_exception(exc):
+                continue
+            raise
+    else:
+        raise RuntimeError("Unable to prepare multimodal task prompt after dropping empty audio.")
+    inputs = move_inputs(inputs, model)
+    with torch.no_grad():
+        generated = model.generate(
+            **inputs,
+            thinker_return_dict_in_generate=True,
+            use_audio_in_video=args.use_audio_in_video,
+            return_audio=False,
+            max_new_tokens=args.max_new_tokens,
+        )
+    text_ids = generated[0] if isinstance(generated, tuple) else generated
+    sequences = text_ids.sequences if hasattr(text_ids, "sequences") else text_ids
+    output_ids = sequences[:, inputs["input_ids"].shape[1] :]
+    decoded = processor.batch_decode(output_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)
+    return decoded[0] if decoded else ""
+
+
+def select_tasks(spec: str) -> list[str]:
+    if spec.strip().lower() == "all":
+        return list(TASK_SPECS)
+    tasks = [item.strip() for item in spec.split(",") if item.strip()]
+    unknown = [task for task in tasks if task not in TASK_SPECS]
+    if unknown:
+        raise ValueError(f"unknown tasks: {unknown}")
+    return tasks
+
+
+def select_eval_indices(samples: list[dict[str, Any]], args: argparse.Namespace) -> list[int]:
+    indices = [idx for idx, sample in enumerate(samples) if sample.get("split") == args.eval_split]
+    if args.sample_stride < 1:
+        raise ValueError("--sample-stride must be >= 1")
+    if args.sample_offset < 0 or args.sample_offset >= args.sample_stride:
+        raise ValueError("--sample-offset must satisfy 0 <= offset < stride")
+    if args.sample_stride > 1:
+        indices = [idx for local_idx, idx in enumerate(indices) if local_idx % args.sample_stride == args.sample_offset]
+    if args.sample_limit > 0:
+        indices = indices[: args.sample_limit]
+    return indices
+
+
+def prediction_id(task_id: str, sample: dict[str, Any]) -> str:
+    return f"{task_id}::{sample.get('id')}"
+
+
+def task_target(future_sample: dict[str, Any], spec: dict[str, Any]) -> Any:
+    value = answer(future_sample).get(spec["target_field"])
+    if spec["family"] == "multi_label":
+        return normalize_objects(value)
+    return normalize_text(value)
+
+
+def extract_prediction(raw: str, sample: dict[str, Any], spec: dict[str, Any]) -> Any:
+    payload = parse_json_object(raw)
+    value = payload.get(spec["prediction_key"])
+    if spec["family"] == "multi_label":
+        return normalize_objects(value)
+    options = task_options(sample, spec)
+    return match_label(str(value or raw), options) if options else normalize_text(value)
+
+
+def object_set_metrics(rows: list[dict[str, Any]]) -> dict[str, float]:
+    tp = fp = fn = exact = 0
+    for row in rows:
+        true_set = set(row.get("true_value") or [])
+        pred_set = set(row.get("predicted_value") or [])
+        tp += len(true_set & pred_set)
+        fp += len(pred_set - true_set)
+        fn += len(true_set - pred_set)
+        exact += int(true_set == pred_set)
+    precision = tp / (tp + fp) if tp + fp else 0.0
+    recall = tp / (tp + fn) if tp + fn else 0.0
+    micro_f1 = 2.0 * precision * recall / (precision + recall) if precision + recall else 0.0
+    return {
+        "num_samples": len(rows),
+        "micro_f1": micro_f1,
+        "precision": precision,
+        "recall": recall,
+        "exact_match": exact / len(rows) if rows else 0.0,
+    }
+
+
+def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], output_dir: Path, args: argparse.Namespace) -> dict[str, Any]:
+    task_dir = output_dir / task_id
+    task_dir.mkdir(parents=True, exist_ok=True)
+    write_jsonl(task_dir / "predictions.jsonl", rows)
+    csv_rows = []
+    for row in rows:
+        csv_rows.append(
+            {
+                "id": row["id"],
+                "episode_id": row["episode_id"],
+                "split": row["split"],
+                "start_frame": row["start_frame"],
+                "end_frame": row["end_frame"],
+                "future_start_frame": row["future_start_frame"],
+                "true_value": json.dumps(row["true_value"], ensure_ascii=False)
+                if isinstance(row["true_value"], list)
+                else row["true_value"],
+                "predicted_value": json.dumps(row["predicted_value"], ensure_ascii=False)
+                if isinstance(row["predicted_value"], list)
+                else row["predicted_value"],
+                "raw_prediction": row["raw_prediction"],
+                "correct": row.get("correct"),
+            }
+        )
+    write_csv(
+        task_dir / "predictions.csv",
+        csv_rows,
+        [
+            "id",
+            "episode_id",
+            "split",
+            "start_frame",
+            "end_frame",
+            "future_start_frame",
+            "true_value",
+            "predicted_value",
+            "raw_prediction",
+            "correct",
+        ],
+    )
+
+    if spec["family"] == "classification":
+        options = sorted({str(row["true_value"]) for row in rows if row.get("true_value")})
+        metrics, per_class, _ = class_metrics(
+            [str(row["true_value"]) for row in rows],
+            [str(row["predicted_value"]) for row in rows],
+            options,
+        )
+        metrics[f"{task_id}_macro_f1"] = metrics["macro_f1"]
+        metrics[f"{task_id}_accuracy"] = metrics["accuracy"]
+        write_csv(task_dir / "per_class_metrics.csv", per_class, ["class_name", "support", "predicted", "precision", "recall", "f1"])
+        primary_score = metrics["macro_f1"]
+    else:
+        metrics = object_set_metrics(rows)
+        metrics[f"{task_id}_micro_f1"] = metrics["micro_f1"]
+        metrics[f"{task_id}_exact_match"] = metrics["exact_match"]
+        primary_score = metrics["micro_f1"]
+
+    metrics.update(
+        {
+            "title": f"Qwen3-Omni v6 {spec['label']}",
+            "status": "pass",
+            "run_id": args.run_id,
+            "task_id": task_id,
+            "task_number": spec["task_number"],
+            "task_label": spec["label"],
+            "metric_key": spec["metric_key"],
+            "primary_metric": spec["metric_key"],
+            "primary_score": primary_score,
+            "model_id": args.model_id,
+            "adapter_dir": str(args.adapter_dir),
+            "dataset_jsonl": str(args.dataset_jsonl),
+            "eval_split": args.eval_split,
+            "future_frames": args.future_frames,
+            "sample_offset": args.sample_offset,
+            "sample_stride": args.sample_stride,
+            "scope": "held_out_test_qwen3_future_task_probe",
+            "score_policy": (
+                "GPU-backed Qwen3-Omni v6 generation for targets derivable from the current "
+                "128-episode JSON export. This package does not score tasks whose target fields "
+                "are absent from the export."
+            ),
+        }
+    )
+    write_json(task_dir / "metrics.json", metrics)
+    return metrics
+
+
+def main() -> int:
+    args = parse_args()
+    if args.output_dir is None:
+        args.output_dir = Path(__file__).resolve().parents[2] / "results/omni_finetune" / args.run_id
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    args.progress_jsonl = args.progress_jsonl or args.output_dir / "progress.jsonl"
+    selected_tasks = select_tasks(args.tasks)
+    samples = load_jsonl(args.dataset_jsonl)
+    future_map = future_index_map(samples, args.future_frames)
+    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.")
+
+    append_jsonl(
+        args.progress_jsonl,
+        {
+            "event": "eval_start",
+            "timestamp": time.time(),
+            "run_id": args.run_id,
+            "tasks": selected_tasks,
+            "num_eval_samples_with_future": len(eval_indices),
+            "sample_offset": args.sample_offset,
+            "sample_stride": args.sample_stride,
+        },
+    )
+
+    model, processor = load_model_processor(args)
+    partial_by_task = {
+        task_id: {
+            row.get("prediction_id"): row
+            for row in read_jsonl_if_exists(args.output_dir / task_id / "predictions.partial.jsonl")
+            if row.get("prediction_id")
+        }
+        for task_id in selected_tasks
+    }
+
+    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):
+            sample = samples[sample_idx]
+            future_sample = samples[future_map[sample_idx]]
+            pred_id = prediction_id(task_id, sample)
+            if pred_id in partial_by_task[task_id]:
+                continue
+            started = time.time()
+            raw = generate_messages(model, processor, sample, future_sample, task_id, spec, args)
+            true_value = task_target(future_sample, spec)
+            predicted_value = extract_prediction(raw, sample, spec)
+            row = {
+                "prediction_id": pred_id,
+                "id": sample.get("id"),
+                "target_future_id": future_sample.get("id"),
+                "task_id": task_id,
+                "task_label": spec["label"],
+                "split": sample.get("split"),
+                "episode_id": sample.get("episode_id"),
+                "start_frame": row_start(sample),
+                "end_frame": row_end(sample),
+                "future_start_frame": row_start(future_sample),
+                "future_end_frame": row_end(future_sample),
+                "true_value": true_value,
+                "predicted_value": predicted_value,
+                "raw_prediction": raw,
+                "correct": int(true_value == predicted_value) if spec["family"] == "classification" else int(set(true_value) == set(predicted_value)),
+            }
+            partial_by_task[task_id][pred_id] = row
+            append_jsonl(partial_path, row)
+            append_jsonl(
+                args.progress_jsonl,
+                {
+                    "event": "sample_done",
+                    "timestamp": time.time(),
+                    "task_id": task_id,
+                    "sample_index": local_pos,
+                    "num_eval_samples": len(eval_indices),
+                    "completed_samples_for_task": len(partial_by_task[task_id]),
+                    "sample_id": sample.get("id"),
+                    "seconds": round(time.time() - started, 3),
+                },
+            )
+
+    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]
+        task_metrics[task_id] = score_task(task_id, TASK_SPECS[task_id], rows, args.output_dir, args)
+
+    summary = {
+        "title": "Qwen3-Omni v6 Future Task Probes",
+        "status": "pass",
+        "run_id": args.run_id,
+        "model_id": args.model_id,
+        "adapter_dir": str(args.adapter_dir),
+        "dataset_jsonl": str(args.dataset_jsonl),
+        "eval_split": args.eval_split,
+        "future_frames": args.future_frames,
+        "sample_offset": args.sample_offset,
+        "sample_stride": args.sample_stride,
+        "tasks": {
+            task_id: {
+                "task_number": metrics["task_number"],
+                "task_label": metrics["task_label"],
+                "metric_key": metrics["metric_key"],
+                "primary_score": metrics["primary_score"],
+                "num_samples": metrics["num_samples"],
+                "metrics_json": str(args.output_dir / task_id / "metrics.json"),
+            }
+            for task_id, metrics in task_metrics.items()
+        },
+    }
+    write_json(args.output_dir / "summary.json", summary)
+    report_lines = [
+        "# Qwen3-Omni v6 Future Task Probes",
+        "",
+        f"- Run ID: `{args.run_id}`",
+        f"- Dataset: `{args.dataset_jsonl}`",
+        f"- Future offset: `{args.future_frames}` frames",
+        f"- Shard: offset `{args.sample_offset}` / stride `{args.sample_stride}`",
+        "",
+        "| Task | Metric | Score | Samples |",
+        "| --- | --- | ---: | ---: |",
+    ]
+    for task_id, metrics in task_metrics.items():
+        report_lines.append(
+            f"| {metrics['task_label']} | {metrics['metric_key']} | {metrics['primary_score']:.6f} | {metrics['num_samples']} |"
+        )
+    (args.output_dir / "RUN_REPORT.md").write_text("\n".join(report_lines) + "\n", encoding="utf-8")
+    append_jsonl(args.progress_jsonl, {"event": "eval_complete", "timestamp": time.time(), "run_id": args.run_id})
+    print(json.dumps(summary, indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/omni/merge_qwen3_omni_future_task_probe_shards.py

@@ -0,0 +1,117 @@
+#!/usr/bin/env python3
+"""Merge Qwen3 future-task probe shards into one result package."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import shutil
+from pathlib import Path
+from typing import Any
+
+from eval_qwen3_omni_future_task_probes import TASK_SPECS, score_task, write_json, write_jsonl
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--run-id", required=True)
+    parser.add_argument("--output-dir", type=Path, required=True)
+    parser.add_argument("--shard-dir", type=Path, nargs="+", required=True)
+    return parser.parse_args()
+
+
+def read_jsonl(path: Path) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    if not path.exists():
+        return rows
+    with path.open("r", encoding="utf-8") as handle:
+        for line in handle:
+            line = line.strip()
+            if line:
+                rows.append(json.loads(line))
+    return rows
+
+
+def read_json(path: Path) -> dict[str, Any]:
+    return json.loads(path.read_text(encoding="utf-8")) if path.exists() else {}
+
+
+def fake_args(run_id: str, first_metrics: dict[str, Any]) -> argparse.Namespace:
+    return argparse.Namespace(
+        run_id=run_id,
+        model_id=first_metrics.get("model_id"),
+        adapter_dir=Path(first_metrics.get("adapter_dir", "")),
+        dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
+        eval_split=first_metrics.get("eval_split", "test"),
+        future_frames=int(first_metrics.get("future_frames", 100) or 100),
+        sample_offset=0,
+        sample_stride=1,
+    )
+
+
+def main() -> int:
+    args = parse_args()
+    args.output_dir.mkdir(parents=True, exist_ok=True)
+    task_metrics: dict[str, dict[str, Any]] = {}
+    first_metrics: dict[str, Any] | None = None
+
+    for task_id, spec in TASK_SPECS.items():
+        rows_by_id: dict[str, dict[str, Any]] = {}
+        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)
+            shard_metrics = read_json(shard_dir / task_id / "metrics.json")
+            if shard_metrics and first_metrics is None:
+                first_metrics = shard_metrics
+        if not rows_by_id:
+            continue
+        ordered_rows = sorted(rows_by_id.values(), key=lambda row: (str(row.get("episode_id")), int(row.get("start_frame", 0)), str(row.get("id"))))
+        task_dir = args.output_dir / task_id
+        task_dir.mkdir(parents=True, exist_ok=True)
+        write_jsonl(task_dir / "predictions.jsonl", ordered_rows)
+        metrics = score_task(task_id, spec, ordered_rows, args.output_dir, fake_args(args.run_id, first_metrics or {}))
+        task_metrics[task_id] = metrics
+
+    for shard_dir in args.shard_dir:
+        if (shard_dir / "progress.jsonl").exists():
+            shutil.copy2(shard_dir / "progress.jsonl", args.output_dir / f"{shard_dir.name}.progress.jsonl")
+
+    summary = {
+        "title": "Qwen3-Omni v6 Future Task Probes",
+        "status": "pass",
+        "run_id": args.run_id,
+        "shard_dirs": [str(path) for path in args.shard_dir],
+        "tasks": {
+            task_id: {
+                "task_number": metrics["task_number"],
+                "task_label": metrics["task_label"],
+                "metric_key": metrics["metric_key"],
+                "primary_score": metrics["primary_score"],
+                "num_samples": metrics["num_samples"],
+                "metrics_json": str(args.output_dir / task_id / "metrics.json"),
+            }
+            for task_id, metrics in task_metrics.items()
+        },
+    }
+    write_json(args.output_dir / "summary.json", summary)
+    report = [
+        "# Qwen3-Omni v6 Future Task Probes",
+        "",
+        f"- Run ID: `{args.run_id}`",
+        f"- Shards: `{len(args.shard_dir)}`",
+        "",
+        "| Task | Metric | Score | Samples |",
+        "| --- | --- | ---: | ---: |",
+    ]
+    for metrics in task_metrics.values():
+        report.append(
+            f"| {metrics['task_label']} | {metrics['metric_key']} | {metrics['primary_score']:.6f} | {metrics['num_samples']} |"
+        )
+    (args.output_dir / "RUN_REPORT.md").write_text("\n".join(report) + "\n", encoding="utf-8")
+    print(json.dumps(summary, indent=2, sort_keys=True))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/omni/run_qwen3_omni_future_task_probes_sharded.sh

@@ -0,0 +1,115 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+ROOT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)"
+cd "$ROOT_DIR"
+
+VENV_PY="${VENV_PY:-$ROOT_DIR/.venv/bin/python}"
+STAGING_ROOT="${STAGING_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side}"
+DATASET_JSONL="${DATASET_JSONL:-$ROOT_DIR/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl}"
+MODEL_DIR="${MODEL_DIR:-$STAGING_ROOT/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct}"
+ADAPTER_DIR="${ADAPTER_DIR:-$ROOT_DIR/checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora}"
+RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_future_task_probes_$(date -u +%Y%m%dT%H%M%SZ)}"
+TASKS="${TASKS:-long_horizon_next_action,next_subtask_forecast,object_set_forecast}"
+EVAL_SPLIT="${EVAL_SPLIT:-test}"
+FUTURE_FRAMES="${FUTURE_FRAMES:-100}"
+MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-48}"
+SAMPLE_LIMIT="${SAMPLE_LIMIT:-0}"
+DEVICE_MAP="${DEVICE_MAP:-auto}"
+DTYPE="${DTYPE:-bfloat16}"
+LOCAL_FILES_ONLY="${LOCAL_FILES_ONLY:-1}"
+CUDA_DEVICE_GROUPS="${CUDA_DEVICE_GROUPS:-0,1 2,3}"
+
+for path in "$VENV_PY" "$DATASET_JSONL" "$MODEL_DIR" "$ADAPTER_DIR"; do
+  if [[ ! -e "$path" ]]; then
+    echo "Missing required path: $path" >&2
+    exit 2
+  fi
+done
+
+"$VENV_PY" scripts/omni/patch_qwen3_omni_video_features.py --apply --strict-hash
+
+read -r -a GPU_GROUP_ARRAY <<< "$CUDA_DEVICE_GROUPS"
+SHARDS="${SHARDS:-${#GPU_GROUP_ARRAY[@]}}"
+if [[ "$SHARDS" -lt 1 ]]; then
+  echo "SHARDS must be >= 1" >&2
+  exit 2
+fi
+if [[ "${#GPU_GROUP_ARRAY[@]}" -lt "$SHARDS" ]]; then
+  echo "CUDA_DEVICE_GROUPS must provide at least SHARDS groups." >&2
+  exit 2
+fi
+
+OUT_DIR="results/omni_finetune/$RUN_ID"
+mkdir -p "$OUT_DIR"
+{
+  echo "run_id=$RUN_ID"
+  echo "dataset_jsonl=$DATASET_JSONL"
+  echo "model_dir=$MODEL_DIR"
+  echo "adapter_dir=$ADAPTER_DIR"
+  echo "tasks=$TASKS"
+  echo "future_frames=$FUTURE_FRAMES"
+  echo "cuda_device_groups=$CUDA_DEVICE_GROUPS"
+  echo "shards=$SHARDS"
+  echo "started_at=$(date -Is)"
+} >"$OUT_DIR/launch_env.txt"
+
+COMMON_ARGS=(
+  --dataset-jsonl "$DATASET_JSONL"
+  --model-id "$MODEL_DIR"
+  --adapter-dir "$ADAPTER_DIR"
+  --tasks "$TASKS"
+  --eval-split "$EVAL_SPLIT"
+  --future-frames "$FUTURE_FRAMES"
+  --sample-limit "$SAMPLE_LIMIT"
+  --max-new-tokens "$MAX_NEW_TOKENS"
+  --device-map "$DEVICE_MAP"
+  --dtype "$DTYPE"
+)
+if [[ "$LOCAL_FILES_ONLY" == "1" ]]; then
+  COMMON_ARGS+=(--local-files-only)
+fi
+
+declare -a PIDS=()
+declare -a SHARD_DIRS=()
+
+for ((offset = 0; offset < SHARDS; offset++)); do
+  shard_run_id="${RUN_ID}_shard${offset}"
+  shard_dir="results/omni_finetune/${shard_run_id}"
+  mkdir -p "$shard_dir"
+  SHARD_DIRS+=("$shard_dir")
+  (
+    export CUDA_VISIBLE_DEVICES="${GPU_GROUP_ARRAY[$offset]}"
+    "$VENV_PY" scripts/omni/eval_qwen3_omni_future_task_probes.py \
+      "${COMMON_ARGS[@]}" \
+      --run-id "$shard_run_id" \
+      --output-dir "$shard_dir" \
+      --sample-offset "$offset" \
+      --sample-stride "$SHARDS"
+  ) >"$shard_dir/eval.log" 2>&1 &
+  pid="$!"
+  PIDS+=("$pid")
+  echo "$pid" >"$shard_dir/eval.pid"
+  echo "launched shard $offset/$SHARDS on CUDA_VISIBLE_DEVICES=${GPU_GROUP_ARRAY[$offset]} pid=$pid"
+done
+
+failed=0
+for pid in "${PIDS[@]}"; do
+  if ! wait "$pid"; then
+    failed=1
+  fi
+done
+if [[ "$failed" != "0" ]]; then
+  echo "At least one future-task probe shard failed; inspect ${SHARD_DIRS[*]}" >&2
+  echo "exit_code=1" >>"$OUT_DIR/launch_env.txt"
+  exit 1
+fi
+
+"$VENV_PY" scripts/omni/merge_qwen3_omni_future_task_probe_shards.py \
+  --run-id "$RUN_ID" \
+  --output-dir "$OUT_DIR" \
+  --shard-dir "${SHARD_DIRS[@]}"
+
+echo "finished_at=$(date -Is)" >>"$OUT_DIR/launch_env.txt"
+echo "exit_code=0" >>"$OUT_DIR/launch_env.txt"
+echo "Qwen3 future task probes complete: $OUT_DIR"

scripts/validate_mirror_parity.py

@@ -103,17 +103,21 @@ SCRIPT_FILES = [
     "omni/audit_cosmos3_super_training_contract.py",
     "omni/build_omni_model_comparison.py",
     "omni/build_qwen3_full_parameter_gate_summary.py",
+    "omni/collect_qwen3_future_task_probe_results.sh",
     "omni/collect_qwen3_v4_release_artifacts.py",
     "omni/defer_cosmos3_super_after_qwen_v4.sh",
     "omni/defer_qwen3_fullparam_after_verified_qwen.sh",
+    "omni/eval_qwen3_omni_future_task_probes.py",
     "omni/export_cosmos3_camera_pose_targets.py",
     "omni/launch_all_task_model_scoring_when_free.sh",
+    "omni/merge_qwen3_omni_future_task_probe_shards.py",
     "omni/pack_cosmos3_super_action_batch.py",
     "omni/prepare_cosmos3_super_lora_hf_package.py",
     "omni/prepare_qwen3_lora_hf_package.py",
     "omni/patch_qwen3_omni_video_features.py",
     "omni/probe_cosmos3_super_training_readiness.py",
     "omni/run_private_gpu_qwen3_v6_repro_smoke.sh",
+    "omni/run_qwen3_omni_future_task_probes_sharded.sh",
     "omni/score_existing_model_output_task_probes.py",
     "omni/score_model_output_probes.py",
     "omni/run_128_task_baselines.py",