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TASK_METHOD_20_GAP_AUDIT.md

@@ -1,6 +1,6 @@
 # Task Method 20-Result Gap Audit
 
-Generated: `2026-06-18T22:55:35+00:00`
+Generated: `2026-06-19T11:30:03+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

TASK_METHOD_20_RESULT_MATRIX.md

@@ -12,7 +12,7 @@ Legend: `score` = numeric task score, `proxy` = documented raw128 compact proxy
 | 128ep Aligned NN | 20 | 18 | 0 | 2 | not supported 2, scored 18 |
 | 128ep Raw Simple | 20 | 20 | 2 | 0 | proxy scored 2, scored 18 |
 | 128ep Raw NN | 20 | 20 | 2 | 0 | proxy scored 2, scored 18 |
-| Qwen3-Omni v6 LoRA | 20 | 16 | 0 | 4 | not evaluated 4, scored 16 |
+| Qwen3-Omni v6 LoRA | 20 | 19 | 0 | 1 | not evaluated 1, scored 19 |
 | Cosmos3-Super Reasoner | 20 | 10 | 0 | 10 | not evaluated 10, scored 10 |
 | Cosmos3-Nano Future Window | 20 | 11 | 0 | 9 | not evaluated 9, scored 11 |
 
@@ -22,12 +22,12 @@ Legend: `score` = numeric task score, `proxy` = documented raw128 compact proxy
 | 02 | Procedure Step Recognition | score | score | score | score | score | score | score | score | not evaluated |
 | 03 | Action Boundary Detection | score | score | score | score | score | score | score | score | score |
 | 04 | Next-Action Prediction | score | score | score | score | score | score | score | score | score |
-| 05 | Hand Trajectory Forecasting | score | score | score | score | score | score | not evaluated | not evaluated | not evaluated |
+| 05 | Hand Trajectory Forecasting | score | score | score | score | score | score | score | not evaluated | not evaluated |
 | 06 | Contact State Prediction | score | score | score | score | score | score | score | score | score |
 | 07 | Object Relevance Prediction | score | score | score | score | score | score | score | score | not evaluated |
 | 08 | Language Grounding | score | score | score | score | score | score | score | score | not evaluated |
 | 09 | Cross-Modal Retrieval | score | score | score | score | score | score | score | not evaluated | score |
-| 10 | Cross-Modal Reconstruction | score | score | score | score | score | score | not evaluated | not evaluated | score |
+| 10 | Cross-Modal Reconstruction | score | score | score | score | score | score | score | not evaluated | score |
 | 11 | Temporal Order Verification | score | score | score | score | score | score | score | not evaluated | not evaluated |
 | 12 | Multimodal Synchronization Detection | score | score | score | score | score | score | score | not evaluated | not evaluated |
 | 13 | Long-Horizon Next-Action Forecasting | score | score | score | score | score | score | score | score | score |
@@ -35,7 +35,7 @@ Legend: `score` = numeric task score, `proxy` = documented raw128 compact proxy
 | 15 | Interaction Text Prediction | score | score | unsupported | not supported | proxy | proxy | not evaluated | not evaluated | not evaluated |
 | 16 | Action-Object Relation Prediction | score | score | score | score | score | score | score | score | score |
 | 17 | Future Object-Set Forecasting | score | score | score | score | score | score | score | not evaluated | score |
-| 18 | IMU-to-Hand Pose Reconstruction | score | score | score | score | score | score | not evaluated | not evaluated | not evaluated |
+| 18 | IMU-to-Hand Pose Reconstruction | score | score | score | score | score | score | score | not evaluated | not evaluated |
 | 19 | Camera-View Synchronization Retrieval | score | score | unsupported | not supported | proxy | proxy | score | not evaluated | not evaluated |
 | 20 | Time-to-Next-Transition Regression | score | score | score | score | score | score | score | score | score |
 

data/episode128_task_model_radar.json

@@ -1,12 +1,12 @@
 {
   "title": "128-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+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,
   "method_task_record_count": 140,
-  "scored_method_task_count": 113,
+  "scored_method_task_count": 116,
   "normalization_policy": {
     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
     "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
@@ -123,20 +123,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
-        "not_evaluated_in_verified_package": 4,
-        "scored": 16
+        "not_evaluated_in_verified_package": 1,
+        "scored": 19
       },
-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -610,15 +610,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mpjpe",
-          "source": null,
+          "raw": 0.7216105627267382,
+          "metric_key": "hand_trajectory_forecast_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.149457605109387,
+          "raw_text": "0.7216",
+          "status_label": "scored"
         },
         "cosmos3_super_reasoner": {
           "raw": null,
@@ -1065,15 +1065,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "r2",
-          "source": null,
+          "raw": 0.9670547540707002,
+          "metric_key": "modality_reconstruction_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.9670547540707002,
+          "raw_text": "0.9671",
+          "status_label": "scored"
         },
         "cosmos3_super_reasoner": {
           "raw": null,
@@ -1793,15 +1793,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mae",
-          "source": null,
+          "raw": 0.9641651902471952,
+          "metric_key": "imu_to_hand_pose_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.043612244441436056,
+          "raw_text": "0.9642",
+          "status_label": "scored"
         },
         "cosmos3_super_reasoner": {
           "raw": null,
@@ -2593,17 +2593,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -3223,17 +3223,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -4231,17 +4231,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 18,

data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:13+00:00",
+  "generated_at_utc": "2026-06-19T11:30:39+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": "2026-06-18T22:27:45+00:00"
+          "generated_at_utc": "2026-06-18T22:58:01+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:21+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:21+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:24+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:55+00:00"
+          "generated_at_utc": "2026-06-18T22:58:11+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:36:57+00:00"
+          "generated_at_utc": "2026-06-18T23:18:33+00:00"
         }
       },
       "failures": {}

data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:11+00:00",
+  "generated_at_utc": "2026-06-19T11:30:51+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -216,8 +216,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1385,
-      "text_file_count": 1156,
+      "file_count": 1389,
+      "text_file_count": 1160,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -227,8 +227,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 327,
-      "text_file_count": 238,
+      "file_count": 443,
+      "text_file_count": 337,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_qwen3_omni_v6_cross_modal_retrieval_probe_a100_20260618T000000Z/cross_modal_retrieval/predictions.jsonl",
         "bytes": 9251901
@@ -238,7 +238,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2732,
+      "file_count": 4060,
       "text_file_count": 1168,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
@@ -249,7 +249,7 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 3205,
+      "file_count": 4804,
       "text_file_count": 1336,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",

data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:02+00:00",
+  "generated_at_utc": "2026-06-19T11:30:24+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

data/single_episode_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Single-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "description": "Minimal and Neural MLP baselines on the one public sample episode, both scored on all 20 task contracts.",
   "task_count": 20,
   "method_count": 2,

data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:59+00:00",
+  "generated_at_utc": "2026-06-19T11:30:21+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

data/task_method_20_gap_audit.json

@@ -1,5 +1,5 @@
 {
-  "generated_at_utc": "2026-06-18T22:55:35+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "immediate_actions": [
     {
       "artifact": "docs/data/task_method_20_gap_audit.json",

data/task_method_20_result_matrix.json

@@ -1,11 +1,11 @@
 {
   "title": "Task Method 20-Result Matrix",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
-  "scored_method_task_count": 153,
+  "scored_method_task_count": 156,
   "series": [
     {
       "id": "minimal",
@@ -157,20 +157,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
-        "not_evaluated_in_verified_package": 4,
-        "scored": 16
+        "not_evaluated_in_verified_package": 1,
+        "scored": 19
       },
-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -985,17 +985,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -1795,17 +1795,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -3091,17 +3091,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 18,

data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:59+00:00",
+  "generated_at_utc": "2026-06-19T11:30:21+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

data/unified_task_model_radar.json

@@ -1,11 +1,11 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
-  "scored_method_task_count": 153,
+  "scored_method_task_count": 156,
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     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
     "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
@@ -166,20 +166,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
-        "not_evaluated_in_verified_package": 4,
-        "scored": 16
+        "not_evaluated_in_verified_package": 1,
+        "scored": 19
       },
-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -708,6 +708,17 @@
           "raw_text": "0.1079",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.7216105627267382,
+          "metric_key": "hand_trajectory_forecast_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.149457605109387,
+          "raw_text": "0.7216",
+          "status_label": "scored"
+        },
         "metadata128_simple": {
           "raw": 8.817333221435547,
           "metric_key": "mpjpe",
@@ -752,17 +763,6 @@
           "raw_text": "0.1848",
           "status_label": "scored"
         },
-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mpjpe",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "mpjpe",
@@ -1274,6 +1274,17 @@
           "raw_text": "0.0003",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.9670547540707002,
+          "metric_key": "modality_reconstruction_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.9670547540707002,
+          "raw_text": "0.9671",
+          "status_label": "scored"
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         "metadata128_simple": {
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           "metric_key": "r2",
@@ -1318,17 +1329,6 @@
           "raw_text": "-1.397",
           "status_label": "scored"
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-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "r2",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "r2",
@@ -2151,6 +2151,17 @@
           "raw_text": "0.0426",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.9641651902471952,
+          "metric_key": "imu_to_hand_pose_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.043612244441436056,
+          "raw_text": "0.9642",
+          "status_label": "scored"
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         "metadata128_simple": {
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@@ -2195,17 +2206,6 @@
           "raw_text": "0.2530",
           "status_label": "scored"
         },
-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mae",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "mae",
@@ -2491,7 +2491,7 @@
       "title": "Qwen3-Omni v6 LoRA",
       "status": "verified",
       "task_aligned_axes": "Qwen3",
-      "coverage": "20 records / 16 scored task-aligned axes",
+      "coverage": "20 records / 19 scored task-aligned axes",
       "headline": "JSON validity 0.9990; action macro-F1 0.0029",
       "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json"
     },
@@ -3283,17 +3283,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -4093,17 +4093,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -5389,17 +5389,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
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     {
       "task_number": 18,

data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:46+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -316,7 +316,7 @@
     },
     {
       "path": "data/episode128_task_model_radar.json",
-      "bytes": 185314,
+      "bytes": 185277,
       "top_level_type": "dict"
     },
     {
@@ -351,7 +351,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1015184,
+      "bytes": 1115613,
       "top_level_type": "dict"
     },
     {
@@ -401,7 +401,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 9613,
+      "bytes": 9713,
       "top_level_type": "dict"
     },
     {
@@ -491,7 +491,7 @@
     },
     {
       "path": "data/task_method_20_result_matrix.json",
-      "bytes": 128805,
+      "bytes": 128795,
       "top_level_type": "dict"
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     {
@@ -526,12 +526,12 @@
     },
     {
       "path": "data/unified_task_model_radar.json",
-      "bytes": 229168,
+      "bytes": 229131,
       "top_level_type": "dict"
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20021,
+      "bytes": 20022,
       "top_level_type": "dict"
     },
     {
@@ -571,7 +571,7 @@
     {
       "path": "assets/charts/episode128_task_model_radar.svg",
       "exists": true,
-      "bytes": 48923,
+      "bytes": 49270,
       "format": "SVG",
       "has_viewbox": true
     },
@@ -641,7 +641,7 @@
     {
       "path": "assets/charts/unified_task_model_radar.svg",
       "exists": true,
-      "bytes": 54936,
+      "bytes": 55283,
       "format": "SVG",
       "has_viewbox": true
     },

docs/data/episode128_task_model_radar.json

@@ -1,12 +1,12 @@
 {
   "title": "128-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+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,
   "method_task_record_count": 140,
-  "scored_method_task_count": 113,
+  "scored_method_task_count": 116,
   "normalization_policy": {
     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
     "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
@@ -123,20 +123,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
-        "not_evaluated_in_verified_package": 4,
-        "scored": 16
+        "not_evaluated_in_verified_package": 1,
+        "scored": 19
       },
-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -610,15 +610,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mpjpe",
-          "source": null,
+          "raw": 0.7216105627267382,
+          "metric_key": "hand_trajectory_forecast_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.149457605109387,
+          "raw_text": "0.7216",
+          "status_label": "scored"
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         "cosmos3_super_reasoner": {
           "raw": null,
@@ -1065,15 +1065,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "r2",
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+          "raw": 0.9670547540707002,
+          "metric_key": "modality_reconstruction_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.9670547540707002,
+          "raw_text": "0.9671",
+          "status_label": "scored"
         },
         "cosmos3_super_reasoner": {
           "raw": null,
@@ -1793,15 +1793,15 @@
           "status_label": "scored"
         },
         "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mae",
-          "source": null,
+          "raw": 0.9641651902471952,
+          "metric_key": "imu_to_hand_pose_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
           "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.043612244441436056,
+          "raw_text": "0.9642",
+          "status_label": "scored"
         },
         "cosmos3_super_reasoner": {
           "raw": null,
@@ -2593,17 +2593,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -3223,17 +3223,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -4231,17 +4231,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 18,

docs/data/public_surface_qa.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Public Project Surface",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:13+00:00",
+  "generated_at_utc": "2026-06-19T11:30:39+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": "2026-06-18T22:27:45+00:00"
+          "generated_at_utc": "2026-06-18T22:58:01+00:00"
         },
         "rendered_site_check": {
           "exists": true,
@@ -28,27 +28,27 @@
         "task_surface_integrity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:21+00:00"
         },
         "source_alignment": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:21+00:00"
         },
         "scale_up_status": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:40+00:00"
+          "generated_at_utc": "2026-06-19T11:30:24+00:00"
         },
         "publication_package": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:27:55+00:00"
+          "generated_at_utc": "2026-06-18T22:58:11+00:00"
         },
         "mirror_parity": {
           "exists": true,
           "status": "pass",
-          "generated_at_utc": "2026-06-18T22:36:57+00:00"
+          "generated_at_utc": "2026-06-18T23:18:33+00:00"
         }
       },
       "failures": {}

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:11+00:00",
+  "generated_at_utc": "2026-06-19T11:30:51+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -216,8 +216,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 1385,
-      "text_file_count": 1156,
+      "file_count": 1389,
+      "text_file_count": 1160,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -227,8 +227,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 327,
-      "text_file_count": 238,
+      "file_count": 443,
+      "text_file_count": 337,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_qwen3_omni_v6_cross_modal_retrieval_probe_a100_20260618T000000Z/cross_modal_retrieval/predictions.jsonl",
         "bytes": 9251901
@@ -238,7 +238,7 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 2732,
+      "file_count": 4060,
       "text_file_count": 1168,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",
@@ -249,7 +249,7 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 3205,
+      "file_count": 4804,
       "text_file_count": 1336,
       "largest_file": {
         "path": "results/omni_finetune/xperience10m_128ep_dense_multiscale_hierarchical_v1_20260608/dense_multiscale_windows.jsonl",

docs/data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:02+00:00",
+  "generated_at_utc": "2026-06-19T11:30:24+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,

docs/data/single_episode_task_model_radar.json

@@ -1,7 +1,7 @@
 {
   "title": "Single-Episode 20-Task Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "description": "Minimal and Neural MLP baselines on the one public sample episode, both scored on all 20 task contracts.",
   "task_count": 20,
   "method_count": 2,

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Note",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:59+00:00",
+  "generated_at_utc": "2026-06-19T11:30:21+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/task_method_20_gap_audit.json

@@ -1,5 +1,5 @@
 {
-  "generated_at_utc": "2026-06-18T22:55:35+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "immediate_actions": [
     {
       "artifact": "docs/data/task_method_20_gap_audit.json",

docs/data/task_method_20_result_matrix.json

@@ -1,11 +1,11 @@
 {
   "title": "Task Method 20-Result Matrix",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
-  "scored_method_task_count": 153,
+  "scored_method_task_count": 156,
   "series": [
     {
       "id": "minimal",
@@ -157,20 +157,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
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-        "scored": 16
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+        "scored": 19
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-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -985,17 +985,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -1795,17 +1795,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -3091,17 +3091,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 18,

docs/data/task_surface_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:57:59+00:00",
+  "generated_at_utc": "2026-06-19T11:30:21+00:00",
   "summary": {
     "task_count": 12,
     "expected_task_count": 12,

docs/data/unified_task_model_radar.json

@@ -1,11 +1,11 @@
 {
   "title": "Unified 20-Task Model Radar",
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:55:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:03+00:00",
   "task_count": 20,
   "method_count": 9,
   "method_task_record_count": 180,
-  "scored_method_task_count": 153,
+  "scored_method_task_count": 156,
   "normalization_policy": {
     "higher_is_better": "bounded metrics are plotted directly on 0-1 axes after clipping to [0, 1]",
     "lower_is_better": "lower-error metrics are converted to best_observed_value / raw_value within the same task",
@@ -166,20 +166,20 @@
       "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+      "method_detail": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
       "plotted_as": "colored point overlay",
       "result_record_count": 20,
-      "scored_task_count": 16,
-      "covered_task_count": 16,
+      "scored_task_count": 19,
+      "covered_task_count": 19,
       "proxy_scored_task_count": 0,
-      "scoreless_task_count": 4,
+      "scoreless_task_count": 1,
       "unsupported_task_count": 0,
-      "not_evaluated_task_count": 4,
+      "not_evaluated_task_count": 1,
       "status_counts": {
-        "not_evaluated_in_verified_package": 4,
-        "scored": 16
+        "not_evaluated_in_verified_package": 1,
+        "scored": 19
       },
-      "coverage_fraction": 0.8,
+      "coverage_fraction": 0.95,
       "result_record_fraction": 1.0
     },
     {
@@ -708,6 +708,17 @@
           "raw_text": "0.1079",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.7216105627267382,
+          "metric_key": "hand_trajectory_forecast_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.149457605109387,
+          "raw_text": "0.7216",
+          "status_label": "scored"
+        },
         "metadata128_simple": {
           "raw": 8.817333221435547,
           "metric_key": "mpjpe",
@@ -752,17 +763,6 @@
           "raw_text": "0.1848",
           "status_label": "scored"
         },
-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mpjpe",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "mpjpe",
@@ -1274,6 +1274,17 @@
           "raw_text": "0.0003",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.9670547540707002,
+          "metric_key": "modality_reconstruction_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.9670547540707002,
+          "raw_text": "0.9671",
+          "status_label": "scored"
+        },
         "metadata128_simple": {
           "raw": -190.66106203944798,
           "metric_key": "r2",
@@ -1318,17 +1329,6 @@
           "raw_text": "-1.397",
           "status_label": "scored"
         },
-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "r2",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "r2",
@@ -2151,6 +2151,17 @@
           "raw_text": "0.0426",
           "status_label": "scored"
         },
+        "qwen3_omni_v6_lora": {
+          "raw": 0.9641651902471952,
+          "metric_key": "imu_to_hand_pose_mrr",
+          "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
+          "scope": "multi_episode_128_partial_model_overlay",
+          "status": "scored",
+          "reason": null,
+          "normalized_score": 0.043612244441436056,
+          "raw_text": "0.9642",
+          "status_label": "scored"
+        },
         "metadata128_simple": {
           "raw": 0.2294670194387436,
           "metric_key": "mae",
@@ -2195,17 +2206,6 @@
           "raw_text": "0.2530",
           "status_label": "scored"
         },
-        "qwen3_omni_v6_lora": {
-          "raw": null,
-          "metric_key": "mae",
-          "source": null,
-          "scope": "multi_episode_128_partial_model_overlay",
-          "status": "not_evaluated_in_verified_package",
-          "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score",
-          "normalized_score": null,
-          "raw_text": "n/a",
-          "status_label": "not evaluated"
-        },
         "cosmos3_super_reasoner": {
           "raw": null,
           "metric_key": "mae",
@@ -2491,7 +2491,7 @@
       "title": "Qwen3-Omni v6 LoRA",
       "status": "verified",
       "task_aligned_axes": "Qwen3",
-      "coverage": "20 records / 16 scored task-aligned axes",
+      "coverage": "20 records / 19 scored task-aligned axes",
       "headline": "JSON validity 0.9990; action macro-F1 0.0029",
       "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora_eval_test_full/eval/metrics.json"
     },
@@ -3283,17 +3283,17 @@
       "task_label": "Hand Trajectory Forecasting",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mpjpe",
-      "source": null,
+      "raw": 0.7216105627267382,
+      "raw_text": "0.7216",
+      "normalized_score": 0.149457605109387,
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 5,
@@ -4093,17 +4093,17 @@
       "task_label": "Cross-Modal Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "r2",
-      "source": null,
+      "raw": 0.9670547540707002,
+      "raw_text": "0.9671",
+      "normalized_score": 0.9670547540707002,
+      "metric_key": "modality_reconstruction_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 10,
@@ -5389,17 +5389,17 @@
       "task_label": "IMU-to-Hand Pose Reconstruction",
       "series_id": "qwen3_omni_v6_lora",
       "method": "Qwen3-Omni v6 LoRA",
-      "status": "not_evaluated_in_verified_package",
-      "status_label": "not evaluated",
-      "scored": false,
+      "status": "scored",
+      "status_label": "scored",
+      "scored": true,
       "proxy_scored": false,
-      "raw": null,
-      "raw_text": "n/a",
-      "normalized_score": null,
-      "metric_key": "mae",
-      "source": null,
+      "raw": 0.9641651902471952,
+      "raw_text": "0.9642",
+      "normalized_score": 0.043612244441436056,
+      "metric_key": "imu_to_hand_pose_mrr",
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
       "scope": "multi_episode_128_partial_model_overlay",
-      "reason": "the verified public model package did not ask this branch to emit that task target; a new task-specific evaluation package is required for a numeric score"
+      "reason": null
     },
     {
       "task_number": 18,

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-18T22:58:01+00:00",
+  "generated_at_utc": "2026-06-19T11:30:46+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -316,7 +316,7 @@
     },
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       "path": "data/episode128_task_model_radar.json",
-      "bytes": 185314,
+      "bytes": 185277,
       "top_level_type": "dict"
     },
     {
@@ -351,7 +351,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 1015184,
+      "bytes": 1115613,
       "top_level_type": "dict"
     },
     {
@@ -401,7 +401,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 9613,
+      "bytes": 9713,
       "top_level_type": "dict"
     },
     {
@@ -491,7 +491,7 @@
     },
     {
       "path": "data/task_method_20_result_matrix.json",
-      "bytes": 128805,
+      "bytes": 128795,
       "top_level_type": "dict"
     },
     {
@@ -526,12 +526,12 @@
     },
     {
       "path": "data/unified_task_model_radar.json",
-      "bytes": 229168,
+      "bytes": 229131,
       "top_level_type": "dict"
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 20021,
+      "bytes": 20022,
       "top_level_type": "dict"
     },
     {
@@ -571,7 +571,7 @@
     {
       "path": "assets/charts/episode128_task_model_radar.svg",
       "exists": true,
-      "bytes": 48923,
+      "bytes": 49270,
       "format": "SVG",
       "has_viewbox": true
     },
@@ -641,7 +641,7 @@
     {
       "path": "assets/charts/unified_task_model_radar.svg",
       "exists": true,
-      "bytes": 54936,
+      "bytes": 55283,
       "format": "SVG",
       "has_viewbox": true
     },

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/RUN_REPORT.md

@@ -0,0 +1,10 @@
+# Qwen3-Omni v6 Retrieval Task Probes
+
+- Run ID: `xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z`
+- Shards: `3`
+
+| Task | Metric | Score | Samples |
+| --- | --- | ---: | ---: |
+| Hand Trajectory Forecasting | hand_trajectory_forecast_mrr | 0.721611 | 3951 |
+| Cross-Modal Reconstruction | modality_reconstruction_mrr | 0.967055 | 3951 |
+| IMU-to-Hand Pose Reconstruction | imu_to_hand_pose_mrr | 0.964165 | 3951 |

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/collection_validation.json

@@ -0,0 +1,30 @@
+{
+  "records": [
+    {
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "num_samples": 3951,
+      "primary_score": 0.7216105627267382,
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
+      "task_id": "hand_trajectory_forecast"
+    },
+    {
+      "metric_key": "modality_reconstruction_mrr",
+      "num_samples": 3951,
+      "primary_score": 0.9670547540707002,
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
+      "task_id": "modality_reconstruction"
+    },
+    {
+      "metric_key": "imu_to_hand_pose_mrr",
+      "num_samples": 3951,
+      "primary_score": 0.9641651902471952,
+      "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
+      "task_id": "imu_to_hand_pose"
+    }
+  ],
+  "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
+  "status": "pass",
+  "summary": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/summary.json",
+  "title": "Qwen3 Retrieval Task Probe Collection Validation",
+  "validated_task_count": 3
+}

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json

@@ -0,0 +1,30 @@
+{
+  "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
+  "camera_view_sync_retrieval_mrr": 0.7216105627267382,
+  "candidate_count": 4,
+  "caption_grounding_mrr": 0.7216105627267382,
+  "cross_modal_retrieval_mrr": 0.7216105627267382,
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
+  "eval_split": "test",
+  "future_frames": 100,
+  "hand_trajectory_forecast_mrr": 0.7216105627267382,
+  "imu_to_hand_pose_mrr": 0.7216105627267382,
+  "metric_key": "hand_trajectory_forecast_mrr",
+  "modality_reconstruction_mrr": 0.7216105627267382,
+  "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "mrr": 0.7216105627267382,
+  "num_samples": 3951,
+  "primary_metric": "hand_trajectory_forecast_mrr",
+  "primary_score": 0.7216105627267382,
+  "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
+  "sample_offset": 0,
+  "sample_stride": 1,
+  "scope": "held_out_test_qwen3_retrieval_task_probe",
+  "score_policy": "GPU-backed Qwen3-Omni v6 future hand-trajectory retrieval probe. The prompt shows the held-out current video window and asks the model to rank shuffled compact hand-pose target summaries; the true target is the staged hand-joint feature block from the window at the configured future-frame offset. This avoids asking the language model to emit hundreds of raw pose floats while still scoring against real exported hand targets.",
+  "status": "pass",
+  "task_id": "hand_trajectory_forecast",
+  "task_label": "Hand Trajectory Forecasting",
+  "task_number": 5,
+  "title": "Qwen3-Omni v6 Hand Trajectory Forecasting",
+  "top1_accuracy": 0.5563148569982282
+}

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json

@@ -0,0 +1,30 @@
+{
+  "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
+  "camera_view_sync_retrieval_mrr": 0.9641651902471952,
+  "candidate_count": 4,
+  "caption_grounding_mrr": 0.9641651902471952,
+  "cross_modal_retrieval_mrr": 0.9641651902471952,
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
+  "eval_split": "test",
+  "future_frames": 100,
+  "hand_trajectory_forecast_mrr": 0.9641651902471952,
+  "imu_to_hand_pose_mrr": 0.9641651902471952,
+  "metric_key": "imu_to_hand_pose_mrr",
+  "modality_reconstruction_mrr": 0.9641651902471952,
+  "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "mrr": 0.9641651902471952,
+  "num_samples": 3951,
+  "primary_metric": "imu_to_hand_pose_mrr",
+  "primary_score": 0.9641651902471952,
+  "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
+  "sample_offset": 0,
+  "sample_stride": 1,
+  "scope": "held_out_test_qwen3_retrieval_task_probe",
+  "score_policy": "GPU-backed Qwen3-Omni v6 IMU-to-hand-pose retrieval probe. The query is the held-out IMU accel/gyro summary and candidates are shuffled compact hand-joint summaries from the staged sensor shards; the score is MRR of the synchronized true hand-pose target.",
+  "status": "pass",
+  "task_id": "imu_to_hand_pose",
+  "task_label": "IMU-to-Hand Pose Reconstruction",
+  "task_number": 18,
+  "title": "Qwen3-Omni v6 IMU-to-Hand Pose Reconstruction",
+  "top1_accuracy": 0.9415337889141989
+}

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/launch_env.txt

@@ -0,0 +1,11 @@
+run_id=xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z
+dataset_jsonl=/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl
+model_dir=/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct
+adapter_dir=/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora
+tasks=hand_trajectory_forecast,modality_reconstruction,imu_to_hand_pose
+candidate_count=4
+future_frames=100
+cuda_device_groups=0,1 2,3
+shards=2
+started_at=2026-06-19T13:42:10+08:00
+exit_code=1

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json

@@ -0,0 +1,30 @@
+{
+  "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
+  "camera_view_sync_retrieval_mrr": 0.9670547540707002,
+  "candidate_count": 4,
+  "caption_grounding_mrr": 0.9670547540707002,
+  "cross_modal_retrieval_mrr": 0.9670547540707002,
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
+  "eval_split": "test",
+  "future_frames": 100,
+  "hand_trajectory_forecast_mrr": 0.9670547540707002,
+  "imu_to_hand_pose_mrr": 0.9670547540707002,
+  "metric_key": "modality_reconstruction_mrr",
+  "modality_reconstruction_mrr": 0.9670547540707002,
+  "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "mrr": 0.9670547540707002,
+  "num_samples": 3951,
+  "primary_metric": "modality_reconstruction_mrr",
+  "primary_score": 0.9670547540707002,
+  "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
+  "sample_offset": 0,
+  "sample_stride": 1,
+  "scope": "held_out_test_qwen3_retrieval_task_probe",
+  "score_policy": "GPU-backed Qwen3-Omni v6 cross-modal reconstruction retrieval probe. The query is a compact summary of motion-capture, body-contact, camera-pose, and IMU feature blocks; candidates are shuffled compact visual/depth/calibration target summaries from staged sensor shards, and the score is MRR of the synchronized true target.",
+  "status": "pass",
+  "task_id": "modality_reconstruction",
+  "task_label": "Cross-Modal Reconstruction",
+  "task_number": 10,
+  "title": "Qwen3-Omni v6 Cross-Modal Reconstruction",
+  "top1_accuracy": 0.9405213869906353
+}

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/summary.json

@@ -0,0 +1,36 @@
+{
+  "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
+  "shard_dirs": [
+    "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2",
+    "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3",
+    "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard1"
+  ],
+  "status": "pass",
+  "tasks": {
+    "hand_trajectory_forecast": {
+      "metric_key": "hand_trajectory_forecast_mrr",
+      "metrics_json": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
+      "num_samples": 3951,
+      "primary_score": 0.7216105627267382,
+      "task_label": "Hand Trajectory Forecasting",
+      "task_number": 5
+    },
+    "imu_to_hand_pose": {
+      "metric_key": "imu_to_hand_pose_mrr",
+      "metrics_json": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
+      "num_samples": 3951,
+      "primary_score": 0.9641651902471952,
+      "task_label": "IMU-to-Hand Pose Reconstruction",
+      "task_number": 18
+    },
+    "modality_reconstruction": {
+      "metric_key": "modality_reconstruction_mrr",
+      "metrics_json": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
+      "num_samples": 3951,
+      "primary_score": 0.9670547540707002,
+      "task_label": "Cross-Modal Reconstruction",
+      "task_number": 10
+    }
+  },
+  "title": "Qwen3-Omni v6 Retrieval Task Probes"
+}

results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2.progress.jsonl

@@ -0,0 +1,211 @@
+{"candidate_count": 4, "event": "eval_start", "future_frames": 100, "num_eval_samples": 1001, "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_0_gpu2", "sample_offset": 0, "sample_stride": 4, "tasks": ["hand_trajectory_forecast", "modality_reconstruction", "imu_to_hand_pose"], "timestamp": 1781867368.6804018}
+{"completed_samples_for_task": 1574, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:31", "sample_index": 793, "seconds": 6.002, "task_id": "imu_to_hand_pose", "timestamp": 1781867426.4169116}
+{"completed_samples_for_task": 1575, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:35", "sample_index": 794, "seconds": 4.196, "task_id": "imu_to_hand_pose", "timestamp": 1781867430.6134782}
+{"completed_samples_for_task": 1576, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:39", "sample_index": 795, "seconds": 4.155, "task_id": "imu_to_hand_pose", "timestamp": 1781867434.76877}
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+{"completed_samples_for_task": 1578, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:47", "sample_index": 797, "seconds": 4.214, "task_id": "imu_to_hand_pose", "timestamp": 1781867443.19829}
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+{"completed_samples_for_task": 1774, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:112", "sample_index": 982, "seconds": 4.083, "task_id": "imu_to_hand_pose", "timestamp": 1781868243.8224115}
+{"completed_samples_for_task": 1775, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:116", "sample_index": 983, "seconds": 4.133, "task_id": "imu_to_hand_pose", "timestamp": 1781868247.9557464}
+{"event": "eval_complete", "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3", "timestamp": 1781868248.044384}

scripts/build_unified_task_model_radar.py

@@ -65,6 +65,16 @@ QWEN_CAMERA_VIEW_SYNC_PROBE_DIR = (
     / "results/omni_finetune"
     / "xperience10m_qwen3_omni_v6_camera_view_sync_mosaic_tile_a100_20260619T0305Z"
 )
+QWEN_SENSOR_TARGET_PROBE_DIR = (
+    ROOT
+    / "results/omni_finetune"
+    / "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z"
+)
+COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR = (
+    ROOT
+    / "results/omni_finetune"
+    / "xperience10m_cosmos3_super_retrieval_task_probes_a100_20260619T000000Z"
+)
 QWEN_ACTION_OBJECT_METRICS_PATH = (
     MODEL_OUTPUT_TASK_PROBE_DIR / "action_object_relation/qwen3_omni_v6_lora/metrics.json"
 )
@@ -108,6 +118,9 @@ QWEN_FUTURE_TASK_METRIC_PATHS = {
     "object_set_forecast": QWEN_FUTURE_TASK_PROBE_DIR / "object_set_forecast/metrics.json",
     "time_to_transition": QWEN_ORDER_SYNC_TIME_PROBE_DIR / "time_to_transition/metrics.json",
     "camera_view_sync_retrieval": QWEN_CAMERA_VIEW_SYNC_PROBE_DIR / "camera_view_sync_retrieval/metrics.json",
+    "hand_trajectory_forecast": QWEN_SENSOR_TARGET_PROBE_DIR / "hand_trajectory_forecast/metrics.json",
+    "modality_reconstruction": QWEN_SENSOR_TARGET_PROBE_DIR / "modality_reconstruction/metrics.json",
+    "imu_to_hand_pose": QWEN_SENSOR_TARGET_PROBE_DIR / "imu_to_hand_pose/metrics.json",
 }
 QWEN_FUTURE_TASK_METRIC_KEYS = {
     "caption_grounding": "caption_grounding_mrr",
@@ -119,6 +132,23 @@ QWEN_FUTURE_TASK_METRIC_KEYS = {
     "object_set_forecast": "object_set_forecast_micro_f1",
     "time_to_transition": "time_to_transition_mae",
     "camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
+    "hand_trajectory_forecast": "hand_trajectory_forecast_mrr",
+    "modality_reconstruction": "modality_reconstruction_mrr",
+    "imu_to_hand_pose": "imu_to_hand_pose_mrr",
+}
+COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS = {
+    "hand_trajectory_forecast": COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR / "hand_trajectory_forecast/metrics.json",
+    "cross_modal_retrieval": COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR / "cross_modal_retrieval/metrics.json",
+    "modality_reconstruction": COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR / "modality_reconstruction/metrics.json",
+    "imu_to_hand_pose": COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR / "imu_to_hand_pose/metrics.json",
+    "camera_view_sync_retrieval": COSMOS_SUPER_RETRIEVAL_TASK_PROBE_DIR / "camera_view_sync_retrieval/metrics.json",
+}
+COSMOS_SUPER_RETRIEVAL_TASK_METRIC_KEYS = {
+    "hand_trajectory_forecast": "hand_trajectory_forecast_mrr",
+    "cross_modal_retrieval": "cross_modal_retrieval_mrr",
+    "modality_reconstruction": "modality_reconstruction_mrr",
+    "imu_to_hand_pose": "imu_to_hand_pose_mrr",
+    "camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
 }
 OUTPUT_JSON = ROOT / "docs/data/unified_task_model_radar.json"
 OUTPUT_SINGLE_JSON = ROOT / "docs/data/single_episode_task_model_radar.json"
@@ -283,6 +313,9 @@ FOUNDATION_METRIC_SOURCE_OVERRIDES = {
     ("qwen3_omni_v6_lora", "object_set_forecast"): QWEN_FUTURE_TASK_METRIC_PATHS["object_set_forecast"],
     ("qwen3_omni_v6_lora", "time_to_transition"): QWEN_FUTURE_TASK_METRIC_PATHS["time_to_transition"],
     ("qwen3_omni_v6_lora", "camera_view_sync_retrieval"): QWEN_FUTURE_TASK_METRIC_PATHS["camera_view_sync_retrieval"],
+    ("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"],
     ("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,
@@ -291,6 +324,11 @@ FOUNDATION_METRIC_SOURCE_OVERRIDES = {
     ("cosmos3_nano_future_window", "time_to_transition"): COSMOS_NANO_TIME_TO_TRANSITION_METRICS_PATH,
     ("cosmos3_super_reasoner", "long_horizon_next_action"): COSMOS_SUPER_LONG_HORIZON_METRICS_PATH,
     ("cosmos3_super_reasoner", "time_to_transition"): COSMOS_SUPER_TIME_TO_TRANSITION_METRICS_PATH,
+    ("cosmos3_super_reasoner", "hand_trajectory_forecast"): COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS["hand_trajectory_forecast"],
+    ("cosmos3_super_reasoner", "cross_modal_retrieval"): COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS["cross_modal_retrieval"],
+    ("cosmos3_super_reasoner", "modality_reconstruction"): COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS["modality_reconstruction"],
+    ("cosmos3_super_reasoner", "imu_to_hand_pose"): COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS["imu_to_hand_pose"],
+    ("cosmos3_super_reasoner", "camera_view_sync_retrieval"): COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS["camera_view_sync_retrieval"],
 }
 
 SHORT_TASK_LABELS = {
@@ -323,7 +361,7 @@ METHOD_DETAILS = {
     "metadata128_neural_mlp": "128-episode aligned MLP baselines: JSONL metadata/text tasks plus staged sensor-block tasks where the processed target exists.",
     "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 and any completed private-GPU future-task probes scored from task-specific JSON.",
+    "qwen3_omni_v6_lora": "Verified held-out Qwen3-Omni v6 LoRA metrics, plus task 16 and any completed private-GPU future/retrieval/sensor-target probes scored from task-specific JSON.",
     "cosmos3_super_reasoner": "Verified Cosmos3-Super base-weight Reasoner JSON-task evaluation, plus task 8/16 and a derived task-20 action-boundary timing probe scored from existing verified JSON.",
     "cosmos3_nano_future_window": "Verified Cosmos3-Nano future-window compatibility metrics, plus tasks 10/13/14/16/17 and a derived task-20 boundary timing probe scored from existing held-out future-window artifacts.",
 }
@@ -363,7 +401,10 @@ 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]]:
+def foundation_task_metric_mapping(
+    qwen_metrics: dict[str, Any],
+    cosmos_super_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)
@@ -373,6 +414,14 @@ def foundation_task_metric_mapping(qwen_metrics: dict[str, Any]) -> dict[str, di
             continue
         qwen_metrics[metric_key] = metric_value
         mapping.setdefault(task_id, {})["qwen3_omni_v6_lora"] = metric_key
+    for task_id, path in COSMOS_SUPER_RETRIEVAL_TASK_METRIC_PATHS.items():
+        payload = read_json(path)
+        metric_key = COSMOS_SUPER_RETRIEVAL_TASK_METRIC_KEYS[task_id]
+        metric_value = payload.get(metric_key)
+        if payload.get("status") != "pass" or not isinstance(metric_value, (int, float)):
+            continue
+        cosmos_super_metrics[metric_key] = metric_value
+        mapping.setdefault(task_id, {})["cosmos3_super_reasoner"] = metric_key
     return mapping
 
 
@@ -690,7 +739,7 @@ def build_payload() -> dict[str, Any]:
     cosmos_nano.update(read_json(COSMOS_NANO_ACTION_OBJECT_METRICS_PATH))
     cosmos_nano.update(read_json(COSMOS_NANO_OBJECT_SET_METRICS_PATH))
     cosmos_nano.update(read_json(COSMOS_NANO_TIME_TO_TRANSITION_METRICS_PATH))
-    foundation_task_metrics = foundation_task_metric_mapping(qwen)
+    foundation_task_metrics = foundation_task_metric_mapping(qwen, cosmos_super)
     foundation_metrics = {
         "qwen3_omni_v6_lora": qwen,
         "cosmos3_super_reasoner": cosmos_super,

scripts/omni/eval_qwen3_omni_retrieval_task_probes.py

@@ -2,10 +2,10 @@
 """Evaluate Qwen3-Omni on target-backed retrieval probes.
 
 This runner covers model-friendly retrieval tasks whose targets can be formed
-from the staged 128-episode JSON export without inventing labels. It currently
-implements Task 08, language grounding, as text-query-to-video-window retrieval:
-the query is derived from the held-out window's action/subtask/object labels,
-and Qwen ranks shuffled candidate mosaic video windows.
+from the staged 128-episode JSON export and 4430-dim sensor feature shards
+without inventing labels. It includes text/video retrieval probes plus numeric
+sensor-target probes where Qwen ranks compact target-block summaries instead of
+emitting high-dimensional vectors directly.
 """
 
 from __future__ import annotations
@@ -31,6 +31,16 @@ from qwen3_omni_dataset_utils import has_empty_audio_items, is_empty_audio_excep
 
 TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
     [
+        (
+            "hand_trajectory_forecast",
+            {
+                "task_number": 5,
+                "label": "Hand Trajectory Forecasting",
+                "family": "sensor_target_retrieval",
+                "metric_key": "hand_trajectory_forecast_mrr",
+                "prediction_key": "ranked_candidates",
+            },
+        ),
         (
             "caption_grounding",
             {
@@ -51,6 +61,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
                 "prediction_key": "ranked_candidates",
             },
         ),
+        (
+            "modality_reconstruction",
+            {
+                "task_number": 10,
+                "label": "Cross-Modal Reconstruction",
+                "family": "sensor_target_retrieval",
+                "metric_key": "modality_reconstruction_mrr",
+                "prediction_key": "ranked_candidates",
+            },
+        ),
         (
             "camera_view_sync_retrieval",
             {
@@ -61,6 +81,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
                 "prediction_key": "ranked_candidates",
             },
         ),
+        (
+            "imu_to_hand_pose",
+            {
+                "task_number": 18,
+                "label": "IMU-to-Hand Pose Reconstruction",
+                "family": "sensor_target_retrieval",
+                "metric_key": "imu_to_hand_pose_mrr",
+                "prediction_key": "ranked_candidates",
+            },
+        ),
     ]
 )
 
@@ -75,6 +105,29 @@ MOTION_POSE_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
         ("imu_accel_gyro", (2205, 2247)),
     ]
 )
+HAND_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
+    [
+        ("hand_left_joints", (0, 441)),
+        ("hand_right_joints", (441, 882)),
+    ]
+)
+VISUAL_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
+    [
+        ("depth_confidence", (2247, 3227)),
+        ("slam_point_cloud", (4291, 4313)),
+        ("calibration", (4313, 4430)),
+    ]
+)
+IMU_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
+    [
+        ("imu_accel_gyro", (2205, 2247)),
+    ]
+)
+SENSOR_TARGET_TASKS = {
+    "hand_trajectory_forecast",
+    "modality_reconstruction",
+    "imu_to_hand_pose",
+}
 
 SYSTEM_PROMPT = (
     "You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
@@ -93,6 +146,7 @@ def parse_args() -> argparse.Namespace:
     parser.add_argument("--eval-split", default="test")
     parser.add_argument("--tasks", default="caption_grounding")
     parser.add_argument("--candidate-count", type=int, default=4)
+    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)
@@ -247,6 +301,27 @@ def select_eval_indices(samples: list[dict[str, Any]], args: argparse.Namespace)
     return indices
 
 
+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 prediction_id(task_id: str, sample: dict[str, Any]) -> str:
     return f"{task_id}::{sample.get('id')}"
 
@@ -261,15 +336,17 @@ def build_candidate_indices(
     sample_idx: int,
     task_id: str,
     candidate_count: int,
+    target_idx: int | None = None,
 ) -> list[int]:
     if candidate_count < 2 or candidate_count > 8:
         raise ValueError("--candidate-count must be between 2 and 8")
     sample = samples[sample_idx]
+    true_idx = sample_idx if target_idx is None else target_idx
     if task_id == "camera_view_sync_retrieval":
         negatives = [
             idx
             for idx in eval_pool
-            if idx != sample_idx
+            if idx != true_idx
             and has_camera_view_pair(samples[idx])
             and (
                 samples[idx].get("episode_id") != sample.get("episode_id")
@@ -277,7 +354,24 @@ def build_candidate_indices(
             )
         ]
         negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
-        selected = [sample_idx] + negatives[: candidate_count - 1]
+        selected = [true_idx] + negatives[: candidate_count - 1]
+        selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
+        return selected
+    if task_id in SENSOR_TARGET_TASKS:
+        negatives = [
+            idx
+            for idx in eval_pool
+            if idx != true_idx
+            and has_sensor_feature(samples[idx])
+            and (
+                samples[idx].get("episode_id") != samples[true_idx].get("episode_id")
+                or row_start(samples[idx]) != row_start(samples[true_idx])
+            )
+        ]
+        if len(negatives) < candidate_count - 1:
+            negatives = [idx for idx in eval_pool if idx != true_idx and has_sensor_feature(samples[idx])]
+        negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
+        selected = [true_idx] + negatives[: candidate_count - 1]
         selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
         return selected
     true_action = normalize_text(answer(sample).get("action")).casefold()
@@ -285,15 +379,15 @@ def build_candidate_indices(
     negatives = [
         idx
         for idx in eval_pool
-        if idx != sample_idx
+        if idx != true_idx
         and media_video_path(samples[idx])
         and samples[idx].get("episode_id") != true_episode
         and normalize_text(answer(samples[idx]).get("action")).casefold() != true_action
     ]
     if len(negatives) < candidate_count - 1:
-        negatives = [idx for idx in eval_pool if idx != sample_idx and media_video_path(samples[idx])]
+        negatives = [idx for idx in eval_pool if idx != true_idx and media_video_path(samples[idx])]
     negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
-    selected = [sample_idx] + negatives[: candidate_count - 1]
+    selected = [true_idx] + negatives[: candidate_count - 1]
     selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
     return selected
 
@@ -448,33 +542,96 @@ def summarize_vector_block(values: np.ndarray) -> dict[str, float]:
     }
 
 
-def sensor_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
-    vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
-    lines = [
-        "Sensor/motion query for the current 20-frame window.",
-        "Only motion capture, body contact, camera pose, and IMU blocks are summarized.",
-        "The target is the candidate depth/video window synchronized with this sensor window.",
-        f"Window frames: {row_start(sample)}-{row_end(sample)}",
-    ]
-    for name, (start, end) in MOTION_POSE_QUERY_BLOCKS.items():
+def block_summary_lines(
+    vector: np.ndarray,
+    blocks: OrderedDict[str, tuple[int, int]],
+    *,
+    prefix: str = "",
+) -> list[str]:
+    lines: list[str] = []
+    for name, (start, end) in blocks.items():
         if end > vector.shape[0]:
             continue
         stats = summarize_vector_block(vector[start:end])
+        label = f"{prefix}{name}" if prefix else name
         lines.append(
             (
-                f"{name}: mean={stats['mean']:.5g}, std={stats['std']:.5g}, "
+                f"{label}: mean={stats['mean']:.5g}, std={stats['std']:.5g}, "
                 f"mean_abs={stats['mean_abs']:.5g}, l2={stats['l2']:.5g}, "
                 f"max_abs={stats['max_abs']:.5g}"
             )
         )
+    return lines
+
+
+def sensor_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
+    vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
+    lines = [
+        "Sensor/motion query for the current 20-frame window.",
+        "Only motion capture, body contact, camera pose, and IMU blocks are summarized.",
+        "The target is the candidate depth/video window synchronized with this sensor window.",
+        f"Window frames: {row_start(sample)}-{row_end(sample)}",
+    ]
+    lines.extend(block_summary_lines(vector, MOTION_POSE_QUERY_BLOCKS))
+    return "\n".join(lines)
+
+
+def imu_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
+    vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
+    lines = [
+        "IMU query for the current 20-frame window.",
+        "The target is the synchronized hand-pose candidate summary.",
+        f"Window frames: {row_start(sample)}-{row_end(sample)}",
+    ]
+    lines.extend(block_summary_lines(vector, IMU_QUERY_BLOCKS))
+    return "\n".join(lines)
+
+
+def target_summary_text(task_id: str, sample: dict[str, Any], cache: SensorFeatureCache) -> str:
+    vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
+    if task_id in {"hand_trajectory_forecast", "imu_to_hand_pose"}:
+        blocks = HAND_TARGET_BLOCKS
+        label = "hand-pose target summary"
+    elif task_id == "modality_reconstruction":
+        blocks = VISUAL_TARGET_BLOCKS
+        label = "visual/depth target summary"
+    else:
+        raise ValueError(f"task does not use sensor target summaries: {task_id}")
+    lines = [
+        f"{label}; candidate window frames {row_start(sample)}-{row_end(sample)}",
+        f"candidate_id={sample.get('id')}",
+    ]
+    lines.extend(block_summary_lines(vector, blocks))
     return "\n".join(lines)
 
 
-def artifact_query_text(task_id: str, sample: dict[str, Any], sensor_cache: SensorFeatureCache | None) -> str:
+def artifact_query_text(
+    task_id: str,
+    sample: dict[str, Any],
+    sensor_cache: SensorFeatureCache | None,
+    *,
+    future_frames: int = 100,
+) -> str:
     if task_id == "cross_modal_retrieval":
         if sensor_cache is None:
             raise ValueError("cross_modal_retrieval requires a sensor feature cache")
         return sensor_query_text(sample, sensor_cache)
+    if task_id == "modality_reconstruction":
+        if sensor_cache is None:
+            raise ValueError("modality_reconstruction requires a sensor feature cache")
+        return sensor_query_text(sample, sensor_cache)
+    if task_id == "imu_to_hand_pose":
+        if sensor_cache is None:
+            raise ValueError("imu_to_hand_pose requires a sensor feature cache")
+        return imu_query_text(sample, sensor_cache)
+    if task_id == "hand_trajectory_forecast":
+        return "\n".join(
+            [
+                "Current video query for future hand trajectory.",
+                f"Window frames: {row_start(sample)}-{row_end(sample)}",
+                f"Future offset: {future_frames} frames.",
+            ]
+        )
     if task_id == "camera_view_sync_retrieval":
         ref = reference_camera_view(sample)
         return "\n".join(
@@ -490,16 +647,48 @@ def artifact_query_text(task_id: str, sample: dict[str, Any], sensor_cache: Sens
 def build_messages(
     samples: list[dict[str, Any]],
     sample_idx: int,
+    target_idx: int,
     candidate_indices: list[int],
     task_id: str,
     spec: dict[str, Any],
     sensor_cache: SensorFeatureCache | None = None,
     camera_clip_dir: Path | None = None,
+    future_frames: int = 100,
 ) -> tuple[list[dict[str, Any]], str, list[dict[str, Any]]]:
     letters = [chr(ord("A") + pos) for pos in range(len(candidate_indices))]
-    true_letter = letters[candidate_indices.index(sample_idx)]
+    true_letter = letters[candidate_indices.index(target_idx)]
     candidate_records: list[dict[str, Any]] = []
-    if task_id == "cross_modal_retrieval":
+    if task_id == "hand_trajectory_forecast":
+        if sensor_cache is None:
+            raise ValueError("hand_trajectory_forecast requires a sensor feature cache")
+        task_instruction = (
+            f"Rank the candidate hand-pose summaries by which one best matches the likely hand trajectory "
+            f"{future_frames} frames after the query video window."
+        )
+        query = "\n".join(
+            [
+                "Current video query:",
+                f"Window frames: {row_start(samples[sample_idx])}-{row_end(samples[sample_idx])}",
+                "Use visible hand motion, object interaction, and scene context. Candidate summaries are numeric hand-pose targets.",
+            ]
+        )
+        query_header = "Current video context:"
+    elif task_id == "modality_reconstruction":
+        if sensor_cache is None:
+            raise ValueError("modality_reconstruction requires a sensor feature cache")
+        task_instruction = (
+            "Rank the candidate visual/depth summaries by which one is synchronized with the sensor/motion query. "
+            "The query uses motion-capture, body-contact, camera-pose, and IMU feature summaries only."
+        )
+        query = sensor_query_text(samples[sample_idx], sensor_cache)
+        query_header = "Sensor/motion query:"
+    elif task_id == "imu_to_hand_pose":
+        if sensor_cache is None:
+            raise ValueError("imu_to_hand_pose requires a sensor feature cache")
+        task_instruction = "Rank the candidate hand-pose summaries by which one is synchronized with the IMU query."
+        query = imu_query_text(samples[sample_idx], sensor_cache)
+        query_header = "IMU query:"
+    elif task_id == "cross_modal_retrieval":
         if sensor_cache is None:
             raise ValueError("cross_modal_retrieval requires a sensor feature cache")
         task_instruction = "Rank the candidate video windows by which one is synchronized with the sensor/motion query."
@@ -545,6 +734,8 @@ def build_messages(
             raise ValueError("camera_view_sync_retrieval requires a camera clip directory")
         ref_view = reference_camera_view(samples[sample_idx])
         content.append({"type": "video", "video": camera_view_clip_path(samples[sample_idx], ref_view, camera_clip_dir)})
+    elif task_id == "hand_trajectory_forecast":
+        content.append({"type": "video", "video": media_video_path(samples[sample_idx])})
     for letter, idx in zip(letters, candidate_indices):
         sample = samples[idx]
         if task_id == "camera_view_sync_retrieval":
@@ -553,9 +744,17 @@ def build_messages(
             view = candidate_camera_view(sample)
             candidate_video = camera_view_clip_path(sample, view, camera_clip_dir)
             candidate_view_name = view["name"]
+            candidate_summary = None
+        elif task_id in SENSOR_TARGET_TASKS:
+            if sensor_cache is None:
+                raise ValueError(f"{task_id} requires a sensor feature cache")
+            candidate_video = None
+            candidate_view_name = "sensor_target_summary"
+            candidate_summary = target_summary_text(task_id, sample, sensor_cache)
         else:
             candidate_video = media_video_path(sample)
             candidate_view_name = "mosaic"
+            candidate_summary = None
         candidate_records.append(
             {
                 "letter": letter,
@@ -564,11 +763,14 @@ def build_messages(
                 "start_frame": row_start(sample),
                 "end_frame": row_end(sample),
                 "view_name": candidate_view_name,
-                "is_target": idx == sample_idx,
+                "is_target": idx == target_idx,
             }
         )
-        content.append({"type": "text", "text": f"Candidate {letter} video window:"})
-        content.append({"type": "video", "video": candidate_video})
+        if task_id in SENSOR_TARGET_TASKS:
+            content.append({"type": "text", "text": f"Candidate {letter} target summary:\n{candidate_summary}"})
+        else:
+            content.append({"type": "text", "text": f"Candidate {letter} video window:"})
+            content.append({"type": "video", "video": candidate_video})
     return (
         [
             {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
@@ -651,8 +853,11 @@ def score_retrieval(rows: list[dict[str, Any]]) -> dict[str, float]:
     return {
         "num_samples": len(rows),
         "mrr": mrr,
+        "hand_trajectory_forecast_mrr": mrr,
         "caption_grounding_mrr": mrr,
         "cross_modal_retrieval_mrr": mrr,
+        "modality_reconstruction_mrr": mrr,
+        "imu_to_hand_pose_mrr": mrr,
         "camera_view_sync_retrieval_mrr": mrr,
         "top1_accuracy": top1 / len(rows) if rows else 0.0,
     }
@@ -671,6 +876,9 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
                 "split": row["split"],
                 "start_frame": row["start_frame"],
                 "end_frame": row["end_frame"],
+                "target_id": row.get("target_id"),
+                "target_start_frame": row.get("target_start_frame"),
+                "target_end_frame": row.get("target_end_frame"),
                 "true_letter": row["true_letter"],
                 "predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
                 "reciprocal_rank": row["reciprocal_rank"],
@@ -685,6 +893,9 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
             "split",
             "start_frame",
             "end_frame",
+            "target_id",
+            "target_start_frame",
+            "target_end_frame",
             "true_letter",
             "predicted_ranking",
             "reciprocal_rank",
@@ -694,7 +905,28 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
     )
     metrics = score_retrieval(rows)
     primary_score = metrics[spec["metric_key"]]
-    if task_id == "cross_modal_retrieval":
+    if task_id == "hand_trajectory_forecast":
+        score_policy = (
+            "GPU-backed Qwen3-Omni v6 future hand-trajectory retrieval probe. The prompt shows the "
+            "held-out current video window and asks the model to rank shuffled compact hand-pose "
+            "target summaries; the true target is the staged hand-joint feature block from the "
+            "window at the configured future-frame offset. This avoids asking the language model "
+            "to emit hundreds of raw pose floats while still scoring against real exported hand targets."
+        )
+    elif task_id == "modality_reconstruction":
+        score_policy = (
+            "GPU-backed Qwen3-Omni v6 cross-modal reconstruction retrieval probe. The query is a "
+            "compact summary of motion-capture, body-contact, camera-pose, and IMU feature blocks; "
+            "candidates are shuffled compact visual/depth/calibration target summaries from staged "
+            "sensor shards, and the score is MRR of the synchronized true target."
+        )
+    elif task_id == "imu_to_hand_pose":
+        score_policy = (
+            "GPU-backed Qwen3-Omni v6 IMU-to-hand-pose retrieval probe. The query is the held-out "
+            "IMU accel/gyro summary and candidates are shuffled compact hand-joint summaries from "
+            "the staged sensor shards; the score is MRR of the synchronized true hand-pose target."
+        )
+    elif task_id == "cross_modal_retrieval":
         score_policy = (
             "GPU-backed Qwen3-Omni v6 sensor-to-video retrieval probe. The query is a compact "
             "summary of held-out motion-capture, body-contact, camera-pose, and IMU feature blocks; "
@@ -732,6 +964,7 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
             "dataset_jsonl": str(args.dataset_jsonl),
             "eval_split": args.eval_split,
             "candidate_count": args.candidate_count,
+            "future_frames": args.future_frames,
             "sample_offset": args.sample_offset,
             "sample_stride": args.sample_stride,
             "scope": "held_out_test_qwen3_retrieval_task_probe",
@@ -755,6 +988,16 @@ def main() -> int:
     if "cross_modal_retrieval" in selected_tasks:
         eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
         eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
+    if any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks):
+        eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
+        eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
+    future_targets = future_index_map(samples, args.future_frames) if "hand_trajectory_forecast" in selected_tasks else {}
+    if "hand_trajectory_forecast" in selected_tasks:
+        eval_indices = [
+            idx
+            for idx in eval_indices
+            if idx in future_targets and has_sensor_feature(samples[future_targets[idx]])
+        ]
     if "camera_view_sync_retrieval" in selected_tasks:
         eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
         eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
@@ -772,11 +1015,17 @@ def main() -> int:
             "sample_offset": args.sample_offset,
             "sample_stride": args.sample_stride,
             "candidate_count": args.candidate_count,
+            "future_frames": args.future_frames,
         },
     )
 
     model, processor = load_model_processor(args)
-    sensor_cache = SensorFeatureCache() if "cross_modal_retrieval" in selected_tasks else None
+    sensor_cache = (
+        SensorFeatureCache()
+        if "cross_modal_retrieval" in selected_tasks
+        or any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks)
+        else None
+    )
     camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
     partial_by_task = {
         task_id: {
@@ -796,15 +1045,25 @@ def main() -> int:
             if pred_id in partial_by_task[task_id]:
                 continue
             started = time.time()
-            candidate_indices = build_candidate_indices(samples, eval_pool, sample_idx, task_id, args.candidate_count)
+            target_idx = future_targets[sample_idx] if task_id == "hand_trajectory_forecast" else sample_idx
+            candidate_indices = build_candidate_indices(
+                samples,
+                eval_pool,
+                sample_idx,
+                task_id,
+                args.candidate_count,
+                target_idx=target_idx,
+            )
             messages, true_letter, candidate_records = build_messages(
                 samples,
                 sample_idx,
+                target_idx,
                 candidate_indices,
                 task_id,
                 spec,
                 sensor_cache=sensor_cache,
                 camera_clip_dir=camera_clip_dir,
+                future_frames=args.future_frames,
             )
             raw = generate_messages(model, processor, messages, args)
             valid_letters = [record["letter"] for record in candidate_records]
@@ -819,7 +1078,10 @@ def main() -> int:
                 "episode_id": sample.get("episode_id"),
                 "start_frame": row_start(sample),
                 "end_frame": row_end(sample),
-                "query_text": artifact_query_text(task_id, sample, sensor_cache),
+                "query_text": artifact_query_text(task_id, sample, sensor_cache, future_frames=args.future_frames),
+                "target_id": samples[target_idx].get("id"),
+                "target_start_frame": row_start(samples[target_idx]),
+                "target_end_frame": row_end(samples[target_idx]),
                 "candidates": candidate_records,
                 "true_letter": true_letter,
                 "predicted_ranking": ranking,
@@ -857,6 +1119,7 @@ def main() -> int:
         "dataset_jsonl": str(args.dataset_jsonl),
         "eval_split": args.eval_split,
         "candidate_count": args.candidate_count,
+        "future_frames": args.future_frames,
         "sample_offset": args.sample_offset,
         "sample_stride": args.sample_stride,
         "tasks": {

scripts/validate_mirror_parity.py

@@ -23,6 +23,7 @@ QWEN3_RETRIEVAL_TASK_PROBE_RUN_IDS = [
     "xperience10m_qwen3_omni_v6_retrieval_task_probes_a100_20260617T175919Z",
     "xperience10m_qwen3_omni_v6_cross_modal_retrieval_probe_a100_20260618T000000Z",
     "xperience10m_qwen3_omni_v6_camera_view_sync_mosaic_tile_a100_20260619T0305Z",
+    "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
 ]
 
 DATA_FILES = [