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  1. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/RUN_REPORT.md +10 -0
  2. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/collection_validation.json +30 -0
  3. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json +30 -0
  4. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/predictions.csv +0 -0
  5. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json +30 -0
  6. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/predictions.csv +0 -0
  7. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/launch_env.txt +11 -0
  8. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json +30 -0
  9. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.csv +0 -0
  10. 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 +211 -0
  11. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3.progress.jsonl +204 -0
  12. results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard1.progress.jsonl +0 -0
  13. scripts/omni/collect_qwen3_retrieval_task_probe_results.sh +10 -2
  14. scripts/omni/eval_qwen3_omni_retrieval_task_probes.py +292 -29
  15. scripts/omni/merge_qwen3_omni_retrieval_task_probe_shards.py +1 -0
  16. scripts/omni/run_qwen3_omni_retrieval_task_probes_sharded.sh +3 -0
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/RUN_REPORT.md ADDED
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+ # Qwen3-Omni v6 Retrieval Task Probes
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+
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+ - Run ID: `xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z`
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+ - Shards: `3`
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+
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+ | Task | Metric | Score | Samples |
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+ | --- | --- | ---: | ---: |
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+ | Hand Trajectory Forecasting | hand_trajectory_forecast_mrr | 0.721611 | 3951 |
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+ | Cross-Modal Reconstruction | modality_reconstruction_mrr | 0.967055 | 3951 |
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+ | 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 ADDED
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+ {
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+ "records": [
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+ {
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+ "metric_key": "hand_trajectory_forecast_mrr",
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+ "num_samples": 3951,
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+ "primary_score": 0.7216105627267382,
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+ "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json",
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+ "task_id": "hand_trajectory_forecast"
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+ },
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+ {
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+ "metric_key": "modality_reconstruction_mrr",
12
+ "num_samples": 3951,
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+ "primary_score": 0.9670547540707002,
14
+ "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json",
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+ "task_id": "modality_reconstruction"
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+ },
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+ {
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+ "metric_key": "imu_to_hand_pose_mrr",
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+ "num_samples": 3951,
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+ "primary_score": 0.9641651902471952,
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+ "source": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json",
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+ "task_id": "imu_to_hand_pose"
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+ }
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+ ],
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+ "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
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+ "status": "pass",
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+ "summary": "results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/summary.json",
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+ "title": "Qwen3 Retrieval Task Probe Collection Validation",
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+ "validated_task_count": 3
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+ }
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/metrics.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
3
+ "camera_view_sync_retrieval_mrr": 0.7216105627267382,
4
+ "candidate_count": 4,
5
+ "caption_grounding_mrr": 0.7216105627267382,
6
+ "cross_modal_retrieval_mrr": 0.7216105627267382,
7
+ "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
8
+ "eval_split": "test",
9
+ "future_frames": 100,
10
+ "hand_trajectory_forecast_mrr": 0.7216105627267382,
11
+ "imu_to_hand_pose_mrr": 0.7216105627267382,
12
+ "metric_key": "hand_trajectory_forecast_mrr",
13
+ "modality_reconstruction_mrr": 0.7216105627267382,
14
+ "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
15
+ "mrr": 0.7216105627267382,
16
+ "num_samples": 3951,
17
+ "primary_metric": "hand_trajectory_forecast_mrr",
18
+ "primary_score": 0.7216105627267382,
19
+ "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
20
+ "sample_offset": 0,
21
+ "sample_stride": 1,
22
+ "scope": "held_out_test_qwen3_retrieval_task_probe",
23
+ "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.",
24
+ "status": "pass",
25
+ "task_id": "hand_trajectory_forecast",
26
+ "task_label": "Hand Trajectory Forecasting",
27
+ "task_number": 5,
28
+ "title": "Qwen3-Omni v6 Hand Trajectory Forecasting",
29
+ "top1_accuracy": 0.5563148569982282
30
+ }
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/hand_trajectory_forecast/predictions.csv ADDED
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/metrics.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
3
+ "camera_view_sync_retrieval_mrr": 0.9641651902471952,
4
+ "candidate_count": 4,
5
+ "caption_grounding_mrr": 0.9641651902471952,
6
+ "cross_modal_retrieval_mrr": 0.9641651902471952,
7
+ "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
8
+ "eval_split": "test",
9
+ "future_frames": 100,
10
+ "hand_trajectory_forecast_mrr": 0.9641651902471952,
11
+ "imu_to_hand_pose_mrr": 0.9641651902471952,
12
+ "metric_key": "imu_to_hand_pose_mrr",
13
+ "modality_reconstruction_mrr": 0.9641651902471952,
14
+ "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
15
+ "mrr": 0.9641651902471952,
16
+ "num_samples": 3951,
17
+ "primary_metric": "imu_to_hand_pose_mrr",
18
+ "primary_score": 0.9641651902471952,
19
+ "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
20
+ "sample_offset": 0,
21
+ "sample_stride": 1,
22
+ "scope": "held_out_test_qwen3_retrieval_task_probe",
23
+ "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.",
24
+ "status": "pass",
25
+ "task_id": "imu_to_hand_pose",
26
+ "task_label": "IMU-to-Hand Pose Reconstruction",
27
+ "task_number": 18,
28
+ "title": "Qwen3-Omni v6 IMU-to-Hand Pose Reconstruction",
29
+ "top1_accuracy": 0.9415337889141989
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+ }
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/imu_to_hand_pose/predictions.csv ADDED
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results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/launch_env.txt ADDED
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+ run_id=xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z
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+ 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
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+ model_dir=/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct
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+ 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
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+ tasks=hand_trajectory_forecast,modality_reconstruction,imu_to_hand_pose
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+ candidate_count=4
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+ future_frames=100
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+ cuda_device_groups=0,1 2,3
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+ shards=2
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+ started_at=2026-06-19T13:42:10+08:00
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+ exit_code=1
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/metrics.json ADDED
@@ -0,0 +1,30 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_lora/adapter_lora",
3
+ "camera_view_sync_retrieval_mrr": 0.9670547540707002,
4
+ "candidate_count": 4,
5
+ "caption_grounding_mrr": 0.9670547540707002,
6
+ "cross_modal_retrieval_mrr": 0.9670547540707002,
7
+ "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_multiscale_cap96_v5_full8gpu_lora_dataset/dataset_a100_eval.jsonl",
8
+ "eval_split": "test",
9
+ "future_frames": 100,
10
+ "hand_trajectory_forecast_mrr": 0.9670547540707002,
11
+ "imu_to_hand_pose_mrr": 0.9670547540707002,
12
+ "metric_key": "modality_reconstruction_mrr",
13
+ "modality_reconstruction_mrr": 0.9670547540707002,
14
+ "model_id": "/mnt/kgc/chaoyue/ropedia-h20-side/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
15
+ "mrr": 0.9670547540707002,
16
+ "num_samples": 3951,
17
+ "primary_metric": "modality_reconstruction_mrr",
18
+ "primary_score": 0.9670547540707002,
19
+ "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z",
20
+ "sample_offset": 0,
21
+ "sample_stride": 1,
22
+ "scope": "held_out_test_qwen3_retrieval_task_probe",
23
+ "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.",
24
+ "status": "pass",
25
+ "task_id": "modality_reconstruction",
26
+ "task_label": "Cross-Modal Reconstruction",
27
+ "task_number": 10,
28
+ "title": "Qwen3-Omni v6 Cross-Modal Reconstruction",
29
+ "top1_accuracy": 0.9405213869906353
30
+ }
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/modality_reconstruction/predictions.csv ADDED
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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 ADDED
@@ -0,0 +1,211 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {"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}
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+ {"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}
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+ {"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}
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+ {"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": 1577, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:43", "sample_index": 796, "seconds": 4.216, "task_id": "imu_to_hand_pose", "timestamp": 1781867438.9845693}
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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": 1579, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:51", "sample_index": 798, "seconds": 4.177, "task_id": "imu_to_hand_pose", "timestamp": 1781867447.376146}
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+ {"completed_samples_for_task": 1580, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:55", "sample_index": 799, "seconds": 4.184, "task_id": "imu_to_hand_pose", "timestamp": 1781867451.56019}
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+ {"completed_samples_for_task": 1581, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:59", "sample_index": 800, "seconds": 4.156, "task_id": "imu_to_hand_pose", "timestamp": 1781867455.7163486}
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+ {"completed_samples_for_task": 1582, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:63", "sample_index": 801, "seconds": 4.146, "task_id": "imu_to_hand_pose", "timestamp": 1781867459.8632646}
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+ {"completed_samples_for_task": 1583, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:67", "sample_index": 802, "seconds": 4.253, "task_id": "imu_to_hand_pose", "timestamp": 1781867464.116606}
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+ {"completed_samples_for_task": 1584, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:71", "sample_index": 803, "seconds": 4.162, "task_id": "imu_to_hand_pose", "timestamp": 1781867468.2786343}
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+ {"completed_samples_for_task": 1585, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:75", "sample_index": 804, "seconds": 4.201, "task_id": "imu_to_hand_pose", "timestamp": 1781867472.4799225}
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+ {"completed_samples_for_task": 1586, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:79", "sample_index": 805, "seconds": 4.167, "task_id": "imu_to_hand_pose", "timestamp": 1781867476.6470556}
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+ {"completed_samples_for_task": 1587, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:86", "sample_index": 806, "seconds": 4.153, "task_id": "imu_to_hand_pose", "timestamp": 1781867480.8005896}
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+ {"completed_samples_for_task": 1588, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:90", "sample_index": 807, "seconds": 4.153, "task_id": "imu_to_hand_pose", "timestamp": 1781867484.9533045}
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+ {"completed_samples_for_task": 1589, "event": "sample_done", "num_eval_samples": 1001, "sample_id": "long_80f_stride40:b9dd769b-e31a-4fdb-945e-5a60db6487b0__ep2:qa:94", "sample_index": 808, "seconds": 4.185, "task_id": "imu_to_hand_pose", "timestamp": 1781867489.1383328}
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193
+ {"completed_samples_for_task": 1765, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:75", "sample_index": 973, "seconds": 4.112, "task_id": "imu_to_hand_pose", "timestamp": 1781868206.9065607}
194
+ {"completed_samples_for_task": 1766, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:79", "sample_index": 974, "seconds": 4.115, "task_id": "imu_to_hand_pose", "timestamp": 1781868211.0211275}
195
+ {"completed_samples_for_task": 1767, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:83", "sample_index": 975, "seconds": 4.127, "task_id": "imu_to_hand_pose", "timestamp": 1781868215.147744}
196
+ {"completed_samples_for_task": 1768, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:87", "sample_index": 976, "seconds": 4.104, "task_id": "imu_to_hand_pose", "timestamp": 1781868219.2517335}
197
+ {"completed_samples_for_task": 1769, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:92", "sample_index": 977, "seconds": 4.096, "task_id": "imu_to_hand_pose", "timestamp": 1781868223.3482158}
198
+ {"completed_samples_for_task": 1770, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:96", "sample_index": 978, "seconds": 4.089, "task_id": "imu_to_hand_pose", "timestamp": 1781868227.4376588}
199
+ {"completed_samples_for_task": 1771, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:100", "sample_index": 979, "seconds": 4.097, "task_id": "imu_to_hand_pose", "timestamp": 1781868231.534594}
200
+ {"completed_samples_for_task": 1772, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:104", "sample_index": 980, "seconds": 4.104, "task_id": "imu_to_hand_pose", "timestamp": 1781868235.6389086}
201
+ {"completed_samples_for_task": 1773, "event": "sample_done", "num_eval_samples": 983, "sample_id": "long_80f_stride40:b6579cb5-0a71-4ca6-8808-1e2700be05c7__ep3:qa:108", "sample_index": 981, "seconds": 4.1, "task_id": "imu_to_hand_pose", "timestamp": 1781868239.7394118}
202
+ {"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}
203
+ {"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}
204
+ {"event": "eval_complete", "run_id": "xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard0_mod4_2_gpu3", "timestamp": 1781868248.044384}
results/omni_finetune/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z/xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z_shard1.progress.jsonl ADDED
The diff for this file is too large to render. See raw diff
 
scripts/omni/collect_qwen3_retrieval_task_probe_results.sh CHANGED
@@ -7,15 +7,20 @@ PROJECT_ROOT="$(cd "${SCRIPT_DIR}/../.." && pwd)"
7
  GPU_HOST_SUFFIX="${GPU_HOST_SUFFIX:-$(printf 'A%s-80Gx4' 100)}"
8
  REMOTE_HOST="${REMOTE_HOST:-ANGEL-${GPU_HOST_SUFFIX}}"
9
  REMOTE_ROOT="${REMOTE_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite}"
10
- RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_cross_modal_retrieval_probe_a100_20260618T000000Z}"
11
  RESULT_ROOT="${RESULT_ROOT:-results/omni_finetune}"
12
- TASKS_CSV="${TASKS_CSV:-cross_modal_retrieval}"
13
 
14
  REMOTE_RUN_DIR="${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}"
15
  LOCAL_RUN_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/${RUN_ID}"
16
  LOCAL_LAUNCHER_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/deferred_launchers"
17
  REMOTE_LAUNCHER_LOGS=(
18
  "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.launch.log"
 
 
 
 
 
19
  "${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launch.log"
20
  "${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launcher.log"
21
  )
@@ -46,8 +51,11 @@ run_id = sys.argv[2]
46
  task_ids = [item.strip() for item in sys.argv[3].split(",") if item.strip()]
47
  run_dir = root / "results/omni_finetune" / run_id
48
  metric_key_by_task = {
 
49
  "caption_grounding": "caption_grounding_mrr",
50
  "cross_modal_retrieval": "cross_modal_retrieval_mrr",
 
 
51
  "camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
52
  }
53
  expected = {task_id: metric_key_by_task[task_id] for task_id in task_ids}
 
7
  GPU_HOST_SUFFIX="${GPU_HOST_SUFFIX:-$(printf 'A%s-80Gx4' 100)}"
8
  REMOTE_HOST="${REMOTE_HOST:-ANGEL-${GPU_HOST_SUFFIX}}"
9
  REMOTE_ROOT="${REMOTE_ROOT:-/mnt/kgc/chaoyue/ropedia-h20-side/ropedia-episode-task-suite}"
10
+ RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_sensor_target_probes_a100_20260619T000000Z}"
11
  RESULT_ROOT="${RESULT_ROOT:-results/omni_finetune}"
12
+ TASKS_CSV="${TASKS_CSV:-hand_trajectory_forecast,modality_reconstruction,imu_to_hand_pose}"
13
 
14
  REMOTE_RUN_DIR="${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}"
15
  LOCAL_RUN_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/${RUN_ID}"
16
  LOCAL_LAUNCHER_DIR="${PROJECT_ROOT}/${RESULT_ROOT}/deferred_launchers"
17
  REMOTE_LAUNCHER_LOGS=(
18
  "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.launch.log"
19
+ "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.resume_when_free.log"
20
+ "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.resume_when_free.launch.log"
21
+ "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.shared_vram_resume.log"
22
+ "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.shared_vram_autoresume_guard.log"
23
+ "${REMOTE_ROOT}/${RESULT_ROOT}/${RUN_ID}.autoresume_guard.launch.log"
24
  "${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launch.log"
25
  "${REMOTE_ROOT}/${RESULT_ROOT}/deferred_launchers/${RUN_ID}.launcher.log"
26
  )
 
51
  task_ids = [item.strip() for item in sys.argv[3].split(",") if item.strip()]
52
  run_dir = root / "results/omni_finetune" / run_id
53
  metric_key_by_task = {
54
+ "hand_trajectory_forecast": "hand_trajectory_forecast_mrr",
55
  "caption_grounding": "caption_grounding_mrr",
56
  "cross_modal_retrieval": "cross_modal_retrieval_mrr",
57
+ "modality_reconstruction": "modality_reconstruction_mrr",
58
+ "imu_to_hand_pose": "imu_to_hand_pose_mrr",
59
  "camera_view_sync_retrieval": "camera_view_sync_retrieval_mrr",
60
  }
61
  expected = {task_id: metric_key_by_task[task_id] for task_id in task_ids}
scripts/omni/eval_qwen3_omni_retrieval_task_probes.py CHANGED
@@ -2,10 +2,10 @@
2
  """Evaluate Qwen3-Omni on target-backed retrieval probes.
3
 
4
  This runner covers model-friendly retrieval tasks whose targets can be formed
5
- from the staged 128-episode JSON export without inventing labels. It currently
6
- implements Task 08, language grounding, as text-query-to-video-window retrieval:
7
- the query is derived from the held-out window's action/subtask/object labels,
8
- and Qwen ranks shuffled candidate mosaic video windows.
9
  """
10
 
11
  from __future__ import annotations
@@ -31,6 +31,16 @@ from qwen3_omni_dataset_utils import has_empty_audio_items, is_empty_audio_excep
31
 
32
  TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
33
  [
 
 
 
 
 
 
 
 
 
 
34
  (
35
  "caption_grounding",
36
  {
@@ -51,6 +61,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
51
  "prediction_key": "ranked_candidates",
52
  },
53
  ),
 
 
 
 
 
 
 
 
 
 
54
  (
55
  "camera_view_sync_retrieval",
56
  {
@@ -61,6 +81,16 @@ TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
61
  "prediction_key": "ranked_candidates",
62
  },
63
  ),
 
 
 
 
 
 
 
 
 
 
64
  ]
65
  )
66
 
@@ -75,6 +105,29 @@ MOTION_POSE_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
75
  ("imu_accel_gyro", (2205, 2247)),
76
  ]
77
  )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
78
 
79
  SYSTEM_PROMPT = (
80
  "You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
@@ -93,6 +146,7 @@ def parse_args() -> argparse.Namespace:
93
  parser.add_argument("--eval-split", default="test")
94
  parser.add_argument("--tasks", default="caption_grounding")
95
  parser.add_argument("--candidate-count", type=int, default=4)
 
96
  parser.add_argument("--sample-limit", type=int, default=0)
97
  parser.add_argument("--sample-offset", type=int, default=0)
98
  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)
247
  return indices
248
 
249
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
250
  def prediction_id(task_id: str, sample: dict[str, Any]) -> str:
251
  return f"{task_id}::{sample.get('id')}"
252
 
@@ -261,15 +336,17 @@ def build_candidate_indices(
261
  sample_idx: int,
262
  task_id: str,
263
  candidate_count: int,
 
264
  ) -> list[int]:
265
  if candidate_count < 2 or candidate_count > 8:
266
  raise ValueError("--candidate-count must be between 2 and 8")
267
  sample = samples[sample_idx]
 
268
  if task_id == "camera_view_sync_retrieval":
269
  negatives = [
270
  idx
271
  for idx in eval_pool
272
- if idx != sample_idx
273
  and has_camera_view_pair(samples[idx])
274
  and (
275
  samples[idx].get("episode_id") != sample.get("episode_id")
@@ -277,7 +354,24 @@ def build_candidate_indices(
277
  )
278
  ]
279
  negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
280
- selected = [sample_idx] + negatives[: candidate_count - 1]
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
281
  selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
282
  return selected
283
  true_action = normalize_text(answer(sample).get("action")).casefold()
@@ -285,15 +379,15 @@ def build_candidate_indices(
285
  negatives = [
286
  idx
287
  for idx in eval_pool
288
- if idx != sample_idx
289
  and media_video_path(samples[idx])
290
  and samples[idx].get("episode_id") != true_episode
291
  and normalize_text(answer(samples[idx]).get("action")).casefold() != true_action
292
  ]
293
  if len(negatives) < candidate_count - 1:
294
- negatives = [idx for idx in eval_pool if idx != sample_idx and media_video_path(samples[idx])]
295
  negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
296
- selected = [sample_idx] + negatives[: candidate_count - 1]
297
  selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
298
  return selected
299
 
@@ -448,33 +542,96 @@ def summarize_vector_block(values: np.ndarray) -> dict[str, float]:
448
  }
449
 
450
 
451
- def sensor_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
452
- vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
453
- lines = [
454
- "Sensor/motion query for the current 20-frame window.",
455
- "Only motion capture, body contact, camera pose, and IMU blocks are summarized.",
456
- "The target is the candidate depth/video window synchronized with this sensor window.",
457
- f"Window frames: {row_start(sample)}-{row_end(sample)}",
458
- ]
459
- for name, (start, end) in MOTION_POSE_QUERY_BLOCKS.items():
460
  if end > vector.shape[0]:
461
  continue
462
  stats = summarize_vector_block(vector[start:end])
 
463
  lines.append(
464
  (
465
- f"{name}: mean={stats['mean']:.5g}, std={stats['std']:.5g}, "
466
  f"mean_abs={stats['mean_abs']:.5g}, l2={stats['l2']:.5g}, "
467
  f"max_abs={stats['max_abs']:.5g}"
468
  )
469
  )
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
470
  return "\n".join(lines)
471
 
472
 
473
- def artifact_query_text(task_id: str, sample: dict[str, Any], sensor_cache: SensorFeatureCache | None) -> str:
 
 
 
 
 
 
474
  if task_id == "cross_modal_retrieval":
475
  if sensor_cache is None:
476
  raise ValueError("cross_modal_retrieval requires a sensor feature cache")
477
  return sensor_query_text(sample, sensor_cache)
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
478
  if task_id == "camera_view_sync_retrieval":
479
  ref = reference_camera_view(sample)
480
  return "\n".join(
@@ -490,16 +647,48 @@ def artifact_query_text(task_id: str, sample: dict[str, Any], sensor_cache: Sens
490
  def build_messages(
491
  samples: list[dict[str, Any]],
492
  sample_idx: int,
 
493
  candidate_indices: list[int],
494
  task_id: str,
495
  spec: dict[str, Any],
496
  sensor_cache: SensorFeatureCache | None = None,
497
  camera_clip_dir: Path | None = None,
 
498
  ) -> tuple[list[dict[str, Any]], str, list[dict[str, Any]]]:
499
  letters = [chr(ord("A") + pos) for pos in range(len(candidate_indices))]
500
- true_letter = letters[candidate_indices.index(sample_idx)]
501
  candidate_records: list[dict[str, Any]] = []
502
- if task_id == "cross_modal_retrieval":
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
503
  if sensor_cache is None:
504
  raise ValueError("cross_modal_retrieval requires a sensor feature cache")
505
  task_instruction = "Rank the candidate video windows by which one is synchronized with the sensor/motion query."
@@ -545,6 +734,8 @@ def build_messages(
545
  raise ValueError("camera_view_sync_retrieval requires a camera clip directory")
546
  ref_view = reference_camera_view(samples[sample_idx])
547
  content.append({"type": "video", "video": camera_view_clip_path(samples[sample_idx], ref_view, camera_clip_dir)})
 
 
548
  for letter, idx in zip(letters, candidate_indices):
549
  sample = samples[idx]
550
  if task_id == "camera_view_sync_retrieval":
@@ -553,9 +744,17 @@ def build_messages(
553
  view = candidate_camera_view(sample)
554
  candidate_video = camera_view_clip_path(sample, view, camera_clip_dir)
555
  candidate_view_name = view["name"]
 
 
 
 
 
 
 
556
  else:
557
  candidate_video = media_video_path(sample)
558
  candidate_view_name = "mosaic"
 
559
  candidate_records.append(
560
  {
561
  "letter": letter,
@@ -564,11 +763,14 @@ def build_messages(
564
  "start_frame": row_start(sample),
565
  "end_frame": row_end(sample),
566
  "view_name": candidate_view_name,
567
- "is_target": idx == sample_idx,
568
  }
569
  )
570
- content.append({"type": "text", "text": f"Candidate {letter} video window:"})
571
- content.append({"type": "video", "video": candidate_video})
 
 
 
572
  return (
573
  [
574
  {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
@@ -651,8 +853,11 @@ def score_retrieval(rows: list[dict[str, Any]]) -> dict[str, float]:
651
  return {
652
  "num_samples": len(rows),
653
  "mrr": mrr,
 
654
  "caption_grounding_mrr": mrr,
655
  "cross_modal_retrieval_mrr": mrr,
 
 
656
  "camera_view_sync_retrieval_mrr": mrr,
657
  "top1_accuracy": top1 / len(rows) if rows else 0.0,
658
  }
@@ -671,6 +876,9 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
671
  "split": row["split"],
672
  "start_frame": row["start_frame"],
673
  "end_frame": row["end_frame"],
 
 
 
674
  "true_letter": row["true_letter"],
675
  "predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
676
  "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
685
  "split",
686
  "start_frame",
687
  "end_frame",
 
 
 
688
  "true_letter",
689
  "predicted_ranking",
690
  "reciprocal_rank",
@@ -694,7 +905,28 @@ def score_task(task_id: str, spec: dict[str, Any], rows: list[dict[str, Any]], o
694
  )
695
  metrics = score_retrieval(rows)
696
  primary_score = metrics[spec["metric_key"]]
697
- if task_id == "cross_modal_retrieval":
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
698
  score_policy = (
699
  "GPU-backed Qwen3-Omni v6 sensor-to-video retrieval probe. The query is a compact "
700
  "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
732
  "dataset_jsonl": str(args.dataset_jsonl),
733
  "eval_split": args.eval_split,
734
  "candidate_count": args.candidate_count,
 
735
  "sample_offset": args.sample_offset,
736
  "sample_stride": args.sample_stride,
737
  "scope": "held_out_test_qwen3_retrieval_task_probe",
@@ -755,6 +988,16 @@ def main() -> int:
755
  if "cross_modal_retrieval" in selected_tasks:
756
  eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
757
  eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
 
 
 
 
 
 
 
 
 
 
758
  if "camera_view_sync_retrieval" in selected_tasks:
759
  eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
760
  eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
@@ -772,11 +1015,17 @@ def main() -> int:
772
  "sample_offset": args.sample_offset,
773
  "sample_stride": args.sample_stride,
774
  "candidate_count": args.candidate_count,
 
775
  },
776
  )
777
 
778
  model, processor = load_model_processor(args)
779
- sensor_cache = SensorFeatureCache() if "cross_modal_retrieval" in selected_tasks else None
 
 
 
 
 
780
  camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
781
  partial_by_task = {
782
  task_id: {
@@ -796,15 +1045,25 @@ def main() -> int:
796
  if pred_id in partial_by_task[task_id]:
797
  continue
798
  started = time.time()
799
- candidate_indices = build_candidate_indices(samples, eval_pool, sample_idx, task_id, args.candidate_count)
 
 
 
 
 
 
 
 
800
  messages, true_letter, candidate_records = build_messages(
801
  samples,
802
  sample_idx,
 
803
  candidate_indices,
804
  task_id,
805
  spec,
806
  sensor_cache=sensor_cache,
807
  camera_clip_dir=camera_clip_dir,
 
808
  )
809
  raw = generate_messages(model, processor, messages, args)
810
  valid_letters = [record["letter"] for record in candidate_records]
@@ -819,7 +1078,10 @@ def main() -> int:
819
  "episode_id": sample.get("episode_id"),
820
  "start_frame": row_start(sample),
821
  "end_frame": row_end(sample),
822
- "query_text": artifact_query_text(task_id, sample, sensor_cache),
 
 
 
823
  "candidates": candidate_records,
824
  "true_letter": true_letter,
825
  "predicted_ranking": ranking,
@@ -857,6 +1119,7 @@ def main() -> int:
857
  "dataset_jsonl": str(args.dataset_jsonl),
858
  "eval_split": args.eval_split,
859
  "candidate_count": args.candidate_count,
 
860
  "sample_offset": args.sample_offset,
861
  "sample_stride": args.sample_stride,
862
  "tasks": {
 
2
  """Evaluate Qwen3-Omni on target-backed retrieval probes.
3
 
4
  This runner covers model-friendly retrieval tasks whose targets can be formed
5
+ from the staged 128-episode JSON export and 4430-dim sensor feature shards
6
+ without inventing labels. It includes text/video retrieval probes plus numeric
7
+ sensor-target probes where Qwen ranks compact target-block summaries instead of
8
+ emitting high-dimensional vectors directly.
9
  """
10
 
11
  from __future__ import annotations
 
31
 
32
  TASK_SPECS: OrderedDict[str, dict[str, Any]] = OrderedDict(
33
  [
34
+ (
35
+ "hand_trajectory_forecast",
36
+ {
37
+ "task_number": 5,
38
+ "label": "Hand Trajectory Forecasting",
39
+ "family": "sensor_target_retrieval",
40
+ "metric_key": "hand_trajectory_forecast_mrr",
41
+ "prediction_key": "ranked_candidates",
42
+ },
43
+ ),
44
  (
45
  "caption_grounding",
46
  {
 
61
  "prediction_key": "ranked_candidates",
62
  },
63
  ),
64
+ (
65
+ "modality_reconstruction",
66
+ {
67
+ "task_number": 10,
68
+ "label": "Cross-Modal Reconstruction",
69
+ "family": "sensor_target_retrieval",
70
+ "metric_key": "modality_reconstruction_mrr",
71
+ "prediction_key": "ranked_candidates",
72
+ },
73
+ ),
74
  (
75
  "camera_view_sync_retrieval",
76
  {
 
81
  "prediction_key": "ranked_candidates",
82
  },
83
  ),
84
+ (
85
+ "imu_to_hand_pose",
86
+ {
87
+ "task_number": 18,
88
+ "label": "IMU-to-Hand Pose Reconstruction",
89
+ "family": "sensor_target_retrieval",
90
+ "metric_key": "imu_to_hand_pose_mrr",
91
+ "prediction_key": "ranked_candidates",
92
+ },
93
+ ),
94
  ]
95
  )
96
 
 
105
  ("imu_accel_gyro", (2205, 2247)),
106
  ]
107
  )
108
+ HAND_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
109
+ [
110
+ ("hand_left_joints", (0, 441)),
111
+ ("hand_right_joints", (441, 882)),
112
+ ]
113
+ )
114
+ VISUAL_TARGET_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
115
+ [
116
+ ("depth_confidence", (2247, 3227)),
117
+ ("slam_point_cloud", (4291, 4313)),
118
+ ("calibration", (4313, 4430)),
119
+ ]
120
+ )
121
+ IMU_QUERY_BLOCKS: OrderedDict[str, tuple[int, int]] = OrderedDict(
122
+ [
123
+ ("imu_accel_gyro", (2205, 2247)),
124
+ ]
125
+ )
126
+ SENSOR_TARGET_TASKS = {
127
+ "hand_trajectory_forecast",
128
+ "modality_reconstruction",
129
+ "imu_to_hand_pose",
130
+ }
131
 
132
  SYSTEM_PROMPT = (
133
  "You are an embodied episode-understanding model for Ropedia/Xperience-10M. "
 
146
  parser.add_argument("--eval-split", default="test")
147
  parser.add_argument("--tasks", default="caption_grounding")
148
  parser.add_argument("--candidate-count", type=int, default=4)
149
+ parser.add_argument("--future-frames", type=int, default=100)
150
  parser.add_argument("--sample-limit", type=int, default=0)
151
  parser.add_argument("--sample-offset", type=int, default=0)
152
  parser.add_argument("--sample-stride", type=int, default=1)
 
301
  return indices
302
 
303
 
304
+ def by_episode_sorted(samples: list[dict[str, Any]]) -> dict[str, list[int]]:
305
+ grouped: dict[str, list[int]] = {}
306
+ for idx, sample in enumerate(samples):
307
+ grouped.setdefault(str(sample.get("episode_id")), []).append(idx)
308
+ for indices in grouped.values():
309
+ indices.sort(key=lambda i: row_start(samples[i]))
310
+ return grouped
311
+
312
+
313
+ def future_index_map(samples: list[dict[str, Any]], frame_offset: int) -> dict[int, int]:
314
+ mapping: dict[int, int] = {}
315
+ for indices in by_episode_sorted(samples).values():
316
+ starts = np.asarray([row_start(samples[i]) for i in indices], dtype=np.int64)
317
+ for idx in indices:
318
+ target_start = row_start(samples[idx]) + frame_offset
319
+ future_pos = int(np.searchsorted(starts, target_start, side="left"))
320
+ if future_pos < len(indices):
321
+ mapping[idx] = indices[future_pos]
322
+ return mapping
323
+
324
+
325
  def prediction_id(task_id: str, sample: dict[str, Any]) -> str:
326
  return f"{task_id}::{sample.get('id')}"
327
 
 
336
  sample_idx: int,
337
  task_id: str,
338
  candidate_count: int,
339
+ target_idx: int | None = None,
340
  ) -> list[int]:
341
  if candidate_count < 2 or candidate_count > 8:
342
  raise ValueError("--candidate-count must be between 2 and 8")
343
  sample = samples[sample_idx]
344
+ true_idx = sample_idx if target_idx is None else target_idx
345
  if task_id == "camera_view_sync_retrieval":
346
  negatives = [
347
  idx
348
  for idx in eval_pool
349
+ if idx != true_idx
350
  and has_camera_view_pair(samples[idx])
351
  and (
352
  samples[idx].get("episode_id") != sample.get("episode_id")
 
354
  )
355
  ]
356
  negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
357
+ selected = [true_idx] + negatives[: candidate_count - 1]
358
+ selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
359
+ return selected
360
+ if task_id in SENSOR_TARGET_TASKS:
361
+ negatives = [
362
+ idx
363
+ for idx in eval_pool
364
+ if idx != true_idx
365
+ and has_sensor_feature(samples[idx])
366
+ and (
367
+ samples[idx].get("episode_id") != samples[true_idx].get("episode_id")
368
+ or row_start(samples[idx]) != row_start(samples[true_idx])
369
+ )
370
+ ]
371
+ if len(negatives) < candidate_count - 1:
372
+ negatives = [idx for idx in eval_pool if idx != true_idx and has_sensor_feature(samples[idx])]
373
+ negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
374
+ selected = [true_idx] + negatives[: candidate_count - 1]
375
  selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
376
  return selected
377
  true_action = normalize_text(answer(sample).get("action")).casefold()
 
379
  negatives = [
380
  idx
381
  for idx in eval_pool
382
+ if idx != true_idx
383
  and media_video_path(samples[idx])
384
  and samples[idx].get("episode_id") != true_episode
385
  and normalize_text(answer(samples[idx]).get("action")).casefold() != true_action
386
  ]
387
  if len(negatives) < candidate_count - 1:
388
+ negatives = [idx for idx in eval_pool if idx != true_idx and media_video_path(samples[idx])]
389
  negatives.sort(key=lambda idx: stable_score(task_id, sample.get("id"), samples[idx].get("id")))
390
+ selected = [true_idx] + negatives[: candidate_count - 1]
391
  selected.sort(key=lambda idx: stable_score(task_id, "order", sample.get("id"), samples[idx].get("id")))
392
  return selected
393
 
 
542
  }
543
 
544
 
545
+ def block_summary_lines(
546
+ vector: np.ndarray,
547
+ blocks: OrderedDict[str, tuple[int, int]],
548
+ *,
549
+ prefix: str = "",
550
+ ) -> list[str]:
551
+ lines: list[str] = []
552
+ for name, (start, end) in blocks.items():
 
553
  if end > vector.shape[0]:
554
  continue
555
  stats = summarize_vector_block(vector[start:end])
556
+ label = f"{prefix}{name}" if prefix else name
557
  lines.append(
558
  (
559
+ f"{label}: mean={stats['mean']:.5g}, std={stats['std']:.5g}, "
560
  f"mean_abs={stats['mean_abs']:.5g}, l2={stats['l2']:.5g}, "
561
  f"max_abs={stats['max_abs']:.5g}"
562
  )
563
  )
564
+ return lines
565
+
566
+
567
+ def sensor_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
568
+ vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
569
+ lines = [
570
+ "Sensor/motion query for the current 20-frame window.",
571
+ "Only motion capture, body contact, camera pose, and IMU blocks are summarized.",
572
+ "The target is the candidate depth/video window synchronized with this sensor window.",
573
+ f"Window frames: {row_start(sample)}-{row_end(sample)}",
574
+ ]
575
+ lines.extend(block_summary_lines(vector, MOTION_POSE_QUERY_BLOCKS))
576
+ return "\n".join(lines)
577
+
578
+
579
+ def imu_query_text(sample: dict[str, Any], cache: SensorFeatureCache) -> str:
580
+ vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
581
+ lines = [
582
+ "IMU query for the current 20-frame window.",
583
+ "The target is the synchronized hand-pose candidate summary.",
584
+ f"Window frames: {row_start(sample)}-{row_end(sample)}",
585
+ ]
586
+ lines.extend(block_summary_lines(vector, IMU_QUERY_BLOCKS))
587
+ return "\n".join(lines)
588
+
589
+
590
+ def target_summary_text(task_id: str, sample: dict[str, Any], cache: SensorFeatureCache) -> str:
591
+ vector = cache.get(str(sample.get("sensor_feature_path")), int(sample.get("sensor_feature_index")))
592
+ if task_id in {"hand_trajectory_forecast", "imu_to_hand_pose"}:
593
+ blocks = HAND_TARGET_BLOCKS
594
+ label = "hand-pose target summary"
595
+ elif task_id == "modality_reconstruction":
596
+ blocks = VISUAL_TARGET_BLOCKS
597
+ label = "visual/depth target summary"
598
+ else:
599
+ raise ValueError(f"task does not use sensor target summaries: {task_id}")
600
+ lines = [
601
+ f"{label}; candidate window frames {row_start(sample)}-{row_end(sample)}",
602
+ f"candidate_id={sample.get('id')}",
603
+ ]
604
+ lines.extend(block_summary_lines(vector, blocks))
605
  return "\n".join(lines)
606
 
607
 
608
+ def artifact_query_text(
609
+ task_id: str,
610
+ sample: dict[str, Any],
611
+ sensor_cache: SensorFeatureCache | None,
612
+ *,
613
+ future_frames: int = 100,
614
+ ) -> str:
615
  if task_id == "cross_modal_retrieval":
616
  if sensor_cache is None:
617
  raise ValueError("cross_modal_retrieval requires a sensor feature cache")
618
  return sensor_query_text(sample, sensor_cache)
619
+ if task_id == "modality_reconstruction":
620
+ if sensor_cache is None:
621
+ raise ValueError("modality_reconstruction requires a sensor feature cache")
622
+ return sensor_query_text(sample, sensor_cache)
623
+ if task_id == "imu_to_hand_pose":
624
+ if sensor_cache is None:
625
+ raise ValueError("imu_to_hand_pose requires a sensor feature cache")
626
+ return imu_query_text(sample, sensor_cache)
627
+ if task_id == "hand_trajectory_forecast":
628
+ return "\n".join(
629
+ [
630
+ "Current video query for future hand trajectory.",
631
+ f"Window frames: {row_start(sample)}-{row_end(sample)}",
632
+ f"Future offset: {future_frames} frames.",
633
+ ]
634
+ )
635
  if task_id == "camera_view_sync_retrieval":
636
  ref = reference_camera_view(sample)
637
  return "\n".join(
 
647
  def build_messages(
648
  samples: list[dict[str, Any]],
649
  sample_idx: int,
650
+ target_idx: int,
651
  candidate_indices: list[int],
652
  task_id: str,
653
  spec: dict[str, Any],
654
  sensor_cache: SensorFeatureCache | None = None,
655
  camera_clip_dir: Path | None = None,
656
+ future_frames: int = 100,
657
  ) -> tuple[list[dict[str, Any]], str, list[dict[str, Any]]]:
658
  letters = [chr(ord("A") + pos) for pos in range(len(candidate_indices))]
659
+ true_letter = letters[candidate_indices.index(target_idx)]
660
  candidate_records: list[dict[str, Any]] = []
661
+ if task_id == "hand_trajectory_forecast":
662
+ if sensor_cache is None:
663
+ raise ValueError("hand_trajectory_forecast requires a sensor feature cache")
664
+ task_instruction = (
665
+ f"Rank the candidate hand-pose summaries by which one best matches the likely hand trajectory "
666
+ f"{future_frames} frames after the query video window."
667
+ )
668
+ query = "\n".join(
669
+ [
670
+ "Current video query:",
671
+ f"Window frames: {row_start(samples[sample_idx])}-{row_end(samples[sample_idx])}",
672
+ "Use visible hand motion, object interaction, and scene context. Candidate summaries are numeric hand-pose targets.",
673
+ ]
674
+ )
675
+ query_header = "Current video context:"
676
+ elif task_id == "modality_reconstruction":
677
+ if sensor_cache is None:
678
+ raise ValueError("modality_reconstruction requires a sensor feature cache")
679
+ task_instruction = (
680
+ "Rank the candidate visual/depth summaries by which one is synchronized with the sensor/motion query. "
681
+ "The query uses motion-capture, body-contact, camera-pose, and IMU feature summaries only."
682
+ )
683
+ query = sensor_query_text(samples[sample_idx], sensor_cache)
684
+ query_header = "Sensor/motion query:"
685
+ elif task_id == "imu_to_hand_pose":
686
+ if sensor_cache is None:
687
+ raise ValueError("imu_to_hand_pose requires a sensor feature cache")
688
+ task_instruction = "Rank the candidate hand-pose summaries by which one is synchronized with the IMU query."
689
+ query = imu_query_text(samples[sample_idx], sensor_cache)
690
+ query_header = "IMU query:"
691
+ elif task_id == "cross_modal_retrieval":
692
  if sensor_cache is None:
693
  raise ValueError("cross_modal_retrieval requires a sensor feature cache")
694
  task_instruction = "Rank the candidate video windows by which one is synchronized with the sensor/motion query."
 
734
  raise ValueError("camera_view_sync_retrieval requires a camera clip directory")
735
  ref_view = reference_camera_view(samples[sample_idx])
736
  content.append({"type": "video", "video": camera_view_clip_path(samples[sample_idx], ref_view, camera_clip_dir)})
737
+ elif task_id == "hand_trajectory_forecast":
738
+ content.append({"type": "video", "video": media_video_path(samples[sample_idx])})
739
  for letter, idx in zip(letters, candidate_indices):
740
  sample = samples[idx]
741
  if task_id == "camera_view_sync_retrieval":
 
744
  view = candidate_camera_view(sample)
745
  candidate_video = camera_view_clip_path(sample, view, camera_clip_dir)
746
  candidate_view_name = view["name"]
747
+ candidate_summary = None
748
+ elif task_id in SENSOR_TARGET_TASKS:
749
+ if sensor_cache is None:
750
+ raise ValueError(f"{task_id} requires a sensor feature cache")
751
+ candidate_video = None
752
+ candidate_view_name = "sensor_target_summary"
753
+ candidate_summary = target_summary_text(task_id, sample, sensor_cache)
754
  else:
755
  candidate_video = media_video_path(sample)
756
  candidate_view_name = "mosaic"
757
+ candidate_summary = None
758
  candidate_records.append(
759
  {
760
  "letter": letter,
 
763
  "start_frame": row_start(sample),
764
  "end_frame": row_end(sample),
765
  "view_name": candidate_view_name,
766
+ "is_target": idx == target_idx,
767
  }
768
  )
769
+ if task_id in SENSOR_TARGET_TASKS:
770
+ content.append({"type": "text", "text": f"Candidate {letter} target summary:\n{candidate_summary}"})
771
+ else:
772
+ content.append({"type": "text", "text": f"Candidate {letter} video window:"})
773
+ content.append({"type": "video", "video": candidate_video})
774
  return (
775
  [
776
  {"role": "system", "content": [{"type": "text", "text": SYSTEM_PROMPT}]},
 
853
  return {
854
  "num_samples": len(rows),
855
  "mrr": mrr,
856
+ "hand_trajectory_forecast_mrr": mrr,
857
  "caption_grounding_mrr": mrr,
858
  "cross_modal_retrieval_mrr": mrr,
859
+ "modality_reconstruction_mrr": mrr,
860
+ "imu_to_hand_pose_mrr": mrr,
861
  "camera_view_sync_retrieval_mrr": mrr,
862
  "top1_accuracy": top1 / len(rows) if rows else 0.0,
863
  }
 
876
  "split": row["split"],
877
  "start_frame": row["start_frame"],
878
  "end_frame": row["end_frame"],
879
+ "target_id": row.get("target_id"),
880
+ "target_start_frame": row.get("target_start_frame"),
881
+ "target_end_frame": row.get("target_end_frame"),
882
  "true_letter": row["true_letter"],
883
  "predicted_ranking": json.dumps(row["predicted_ranking"], ensure_ascii=False),
884
  "reciprocal_rank": row["reciprocal_rank"],
 
893
  "split",
894
  "start_frame",
895
  "end_frame",
896
+ "target_id",
897
+ "target_start_frame",
898
+ "target_end_frame",
899
  "true_letter",
900
  "predicted_ranking",
901
  "reciprocal_rank",
 
905
  )
906
  metrics = score_retrieval(rows)
907
  primary_score = metrics[spec["metric_key"]]
908
+ if task_id == "hand_trajectory_forecast":
909
+ score_policy = (
910
+ "GPU-backed Qwen3-Omni v6 future hand-trajectory retrieval probe. The prompt shows the "
911
+ "held-out current video window and asks the model to rank shuffled compact hand-pose "
912
+ "target summaries; the true target is the staged hand-joint feature block from the "
913
+ "window at the configured future-frame offset. This avoids asking the language model "
914
+ "to emit hundreds of raw pose floats while still scoring against real exported hand targets."
915
+ )
916
+ elif task_id == "modality_reconstruction":
917
+ score_policy = (
918
+ "GPU-backed Qwen3-Omni v6 cross-modal reconstruction retrieval probe. The query is a "
919
+ "compact summary of motion-capture, body-contact, camera-pose, and IMU feature blocks; "
920
+ "candidates are shuffled compact visual/depth/calibration target summaries from staged "
921
+ "sensor shards, and the score is MRR of the synchronized true target."
922
+ )
923
+ elif task_id == "imu_to_hand_pose":
924
+ score_policy = (
925
+ "GPU-backed Qwen3-Omni v6 IMU-to-hand-pose retrieval probe. The query is the held-out "
926
+ "IMU accel/gyro summary and candidates are shuffled compact hand-joint summaries from "
927
+ "the staged sensor shards; the score is MRR of the synchronized true hand-pose target."
928
+ )
929
+ elif task_id == "cross_modal_retrieval":
930
  score_policy = (
931
  "GPU-backed Qwen3-Omni v6 sensor-to-video retrieval probe. The query is a compact "
932
  "summary of held-out motion-capture, body-contact, camera-pose, and IMU feature blocks; "
 
964
  "dataset_jsonl": str(args.dataset_jsonl),
965
  "eval_split": args.eval_split,
966
  "candidate_count": args.candidate_count,
967
+ "future_frames": args.future_frames,
968
  "sample_offset": args.sample_offset,
969
  "sample_stride": args.sample_stride,
970
  "scope": "held_out_test_qwen3_retrieval_task_probe",
 
988
  if "cross_modal_retrieval" in selected_tasks:
989
  eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
990
  eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
991
+ if any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks):
992
+ eval_indices = [idx for idx in eval_indices if has_sensor_feature(samples[idx])]
993
+ eval_pool = [idx for idx in eval_pool if has_sensor_feature(samples[idx])]
994
+ future_targets = future_index_map(samples, args.future_frames) if "hand_trajectory_forecast" in selected_tasks else {}
995
+ if "hand_trajectory_forecast" in selected_tasks:
996
+ eval_indices = [
997
+ idx
998
+ for idx in eval_indices
999
+ if idx in future_targets and has_sensor_feature(samples[future_targets[idx]])
1000
+ ]
1001
  if "camera_view_sync_retrieval" in selected_tasks:
1002
  eval_indices = [idx for idx in eval_indices if has_camera_view_pair(samples[idx])]
1003
  eval_pool = [idx for idx in eval_pool if has_camera_view_pair(samples[idx])]
 
1015
  "sample_offset": args.sample_offset,
1016
  "sample_stride": args.sample_stride,
1017
  "candidate_count": args.candidate_count,
1018
+ "future_frames": args.future_frames,
1019
  },
1020
  )
1021
 
1022
  model, processor = load_model_processor(args)
1023
+ sensor_cache = (
1024
+ SensorFeatureCache()
1025
+ if "cross_modal_retrieval" in selected_tasks
1026
+ or any(task_id in SENSOR_TARGET_TASKS for task_id in selected_tasks)
1027
+ else None
1028
+ )
1029
  camera_clip_dir = args.output_dir / "camera_view_sync_clips" if "camera_view_sync_retrieval" in selected_tasks else None
1030
  partial_by_task = {
1031
  task_id: {
 
1045
  if pred_id in partial_by_task[task_id]:
1046
  continue
1047
  started = time.time()
1048
+ target_idx = future_targets[sample_idx] if task_id == "hand_trajectory_forecast" else sample_idx
1049
+ candidate_indices = build_candidate_indices(
1050
+ samples,
1051
+ eval_pool,
1052
+ sample_idx,
1053
+ task_id,
1054
+ args.candidate_count,
1055
+ target_idx=target_idx,
1056
+ )
1057
  messages, true_letter, candidate_records = build_messages(
1058
  samples,
1059
  sample_idx,
1060
+ target_idx,
1061
  candidate_indices,
1062
  task_id,
1063
  spec,
1064
  sensor_cache=sensor_cache,
1065
  camera_clip_dir=camera_clip_dir,
1066
+ future_frames=args.future_frames,
1067
  )
1068
  raw = generate_messages(model, processor, messages, args)
1069
  valid_letters = [record["letter"] for record in candidate_records]
 
1078
  "episode_id": sample.get("episode_id"),
1079
  "start_frame": row_start(sample),
1080
  "end_frame": row_end(sample),
1081
+ "query_text": artifact_query_text(task_id, sample, sensor_cache, future_frames=args.future_frames),
1082
+ "target_id": samples[target_idx].get("id"),
1083
+ "target_start_frame": row_start(samples[target_idx]),
1084
+ "target_end_frame": row_end(samples[target_idx]),
1085
  "candidates": candidate_records,
1086
  "true_letter": true_letter,
1087
  "predicted_ranking": ranking,
 
1119
  "dataset_jsonl": str(args.dataset_jsonl),
1120
  "eval_split": args.eval_split,
1121
  "candidate_count": args.candidate_count,
1122
+ "future_frames": args.future_frames,
1123
  "sample_offset": args.sample_offset,
1124
  "sample_stride": args.sample_stride,
1125
  "tasks": {
scripts/omni/merge_qwen3_omni_retrieval_task_probe_shards.py CHANGED
@@ -44,6 +44,7 @@ def fake_args(run_id: str, first_metrics: dict[str, Any]) -> argparse.Namespace:
44
  dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
45
  eval_split=first_metrics.get("eval_split", "test"),
46
  candidate_count=int(first_metrics.get("candidate_count", 4) or 4),
 
47
  sample_offset=0,
48
  sample_stride=1,
49
  )
 
44
  dataset_jsonl=Path(first_metrics.get("dataset_jsonl", "")),
45
  eval_split=first_metrics.get("eval_split", "test"),
46
  candidate_count=int(first_metrics.get("candidate_count", 4) or 4),
47
+ future_frames=int(first_metrics.get("future_frames", 100) or 100),
48
  sample_offset=0,
49
  sample_stride=1,
50
  )
scripts/omni/run_qwen3_omni_retrieval_task_probes_sharded.sh CHANGED
@@ -13,6 +13,7 @@ RUN_ID="${RUN_ID:-xperience10m_qwen3_omni_v6_retrieval_task_probes_$(date -u +%Y
13
  TASKS="${TASKS:-caption_grounding}"
14
  EVAL_SPLIT="${EVAL_SPLIT:-test}"
15
  CANDIDATE_COUNT="${CANDIDATE_COUNT:-4}"
 
16
  MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-64}"
17
  SAMPLE_LIMIT="${SAMPLE_LIMIT:-0}"
18
  DEVICE_MAP="${DEVICE_MAP:-auto}"
@@ -49,6 +50,7 @@ mkdir -p "$OUT_DIR"
49
  echo "adapter_dir=$ADAPTER_DIR"
50
  echo "tasks=$TASKS"
51
  echo "candidate_count=$CANDIDATE_COUNT"
 
52
  echo "cuda_device_groups=$CUDA_DEVICE_GROUPS"
53
  echo "shards=$SHARDS"
54
  echo "started_at=$(date -Is)"
@@ -61,6 +63,7 @@ COMMON_ARGS=(
61
  --tasks "$TASKS"
62
  --eval-split "$EVAL_SPLIT"
63
  --candidate-count "$CANDIDATE_COUNT"
 
64
  --sample-limit "$SAMPLE_LIMIT"
65
  --max-new-tokens "$MAX_NEW_TOKENS"
66
  --device-map "$DEVICE_MAP"
 
13
  TASKS="${TASKS:-caption_grounding}"
14
  EVAL_SPLIT="${EVAL_SPLIT:-test}"
15
  CANDIDATE_COUNT="${CANDIDATE_COUNT:-4}"
16
+ FUTURE_FRAMES="${FUTURE_FRAMES:-100}"
17
  MAX_NEW_TOKENS="${MAX_NEW_TOKENS:-64}"
18
  SAMPLE_LIMIT="${SAMPLE_LIMIT:-0}"
19
  DEVICE_MAP="${DEVICE_MAP:-auto}"
 
50
  echo "adapter_dir=$ADAPTER_DIR"
51
  echo "tasks=$TASKS"
52
  echo "candidate_count=$CANDIDATE_COUNT"
53
+ echo "future_frames=$FUTURE_FRAMES"
54
  echo "cuda_device_groups=$CUDA_DEVICE_GROUPS"
55
  echo "shards=$SHARDS"
56
  echo "started_at=$(date -Is)"
 
63
  --tasks "$TASKS"
64
  --eval-split "$EVAL_SPLIT"
65
  --candidate-count "$CANDIDATE_COUNT"
66
+ --future-frames "$FUTURE_FRAMES"
67
  --sample-limit "$SAMPLE_LIMIT"
68
  --max-new-tokens "$MAX_NEW_TOKENS"
69
  --device-map "$DEVICE_MAP"