Publish Ropedia Xperience-10M derived artifacts

docs/data/live_publication_status.json

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 {
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+              "train_loss": 0.40796751019628613,
+              "val_loss": 0.03258896619081497,
+              "global_step": 356
+            },
+            {
+              "epoch": 2,
+              "train_loss": 0.027628723937453012,
+              "val_loss": 0.027754632756114006,
+              "global_step": 712
+            },
+            {
+              "epoch": 3,
+              "train_loss": 0.02446955946807781,
+              "val_loss": 0.026343274861574173,
+              "global_step": 1068
+            },
+            {
+              "epoch": 4,
+              "train_loss": 0.022728607045444712,
+              "val_loss": 0.025629229843616486,
+              "global_step": 1424
+            }
+          ],
           "is_current": true,
           "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
         }
@@ -891,6 +955,70 @@
               "global_step": 712
             }
           ],
+          "is_current": false,
+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
+        },
+        {
+          "id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
+          "backbone": "qwen3_omni_lora",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "structured_episode_understanding_json_qa",
+          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 512,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 1.0,
+            "action_macro_f1": 0.0018678269676001454,
+            "subtask_accuracy": 0.0,
+            "transition_accuracy": 0.9732142857142857,
+            "next_action_accuracy": 0.033482142857142856,
+            "contact_accuracy": 0.7299107142857143,
+            "object_micro_f1": 0.31099781500364165,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 1,
+              "train_loss": 0.40796751019628613,
+              "val_loss": 0.03258896619081497,
+              "global_step": 356
+            },
+            {
+              "epoch": 2,
+              "train_loss": 0.027628723937453012,
+              "val_loss": 0.027754632756114006,
+              "global_step": 712
+            },
+            {
+              "epoch": 3,
+              "train_loss": 0.02446955946807781,
+              "val_loss": 0.026343274861574173,
+              "global_step": 1068
+            },
+            {
+              "epoch": 4,
+              "train_loss": 0.022728607045444712,
+              "val_loss": 0.025629229843616486,
+              "global_step": 1424
+            }
+          ],
           "is_current": true,
           "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
         }
@@ -1045,7 +1173,7 @@
             "weights_updated": false
           },
           "weights": "none; action-target contract audit only, no adapter checkpoint",
-          "interpretation": "The selected dataset now has valid Cosmos3 camera_pose forward_dynamics targets for an egocentric camera-motion proxy. These remove the target-schema blocker for action-conditioned world-model training, but they supervise noisy vision tokens rather than preds_action. The remaining work is a pipeline-loaded packer check and one-sample forward-dynamics overfit; action-token prediction needs a separate policy or inverse-dynamics target export."
+          "interpretation": "The selected dataset now has valid Cosmos3 camera_pose forward_dynamics targets for an egocentric camera-motion proxy. These remove the target-schema blocker for action-conditioned world-model training, but they supervise noisy vision tokens rather than preds_action. The remaining work is a trainable Cosmos3-Super implementation that can backpropagate through this loss surface at the required memory scale; action-token prediction needs a separate policy or inverse-dynamics target export."
         },
         {
           "id": "xperience10m_cosmos3_super_action_packer_schema_smoke_20260608",
@@ -1111,18 +1239,18 @@
           "weights_repository": "none for this run: staged base nv-community/Cosmos3-Super weights were evaluated through vLLM; create a separate repo only after new adapter or fine-tuned weights exist"
         }
       ],
-      "comparison_note": "Cosmos3-Super is now represented by a verified 448-window held-out Reasoner evaluation on the same JSON task as Qwen3. It uses staged base weights through vLLM, so it is a model-branch diagnostic, not a weight release. A camera-pose proxy forward-dynamics target export now passes the contract audit and schema-only packer smoke; true Cosmos3-Super fine-tuning is still not launched until the pipeline-loaded packer check and one-sample overfit exist."
+      "comparison_note": "Cosmos3-Super is now represented by a verified 448-window held-out Reasoner evaluation on the same JSON task as Qwen3. It uses staged base weights through vLLM, so it is a model-branch diagnostic, not a weight release. A camera-pose proxy forward-dynamics target export now passes the contract audit and schema-only packer smoke; true Cosmos3-Super fine-tuning is still blocked until a trainable multi-GPU/offload path produces adapter or fine-tuned weights."
     }
   ],
   "model_group_reading_notes": [
     "Use model_groups when comparing one-episode and 128-episode artifacts within the same model family.",
     "Task-head baselines have both a one-episode public-sample run and a 128-episode same-split metadata/text run.",
-    "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; only the final 128-episode adapter belongs in the Qwen LoRA model repo.",
+    "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; the newest verified full-eval 128-episode adapter belongs in the Qwen LoRA model repo.",
     "Cosmos3-Nano has a 128-episode future-window compatibility package.",
-    "Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after real Cosmos adapter/fine-tuned weights exist."
+    "Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after a trainable multi-GPU/offload run produces real Cosmos adapter or fine-tuned weights."
   ],
   "pending": [
-    "Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.",
-    "Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after the pipeline-loaded packer check and one-sample overfit produce new weights."
+    "Use the verified Qwen3 v4 4-epoch full-eval package as the current Qwen row; older Qwen package rows remain historical diagnostics for comparison.",
+    "Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after a trainable Cosmos3-Super run produces new weights."
   ]
 }

docs/data/publication_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-07T15:49:07+00:00",
+  "generated_at_utc": "2026-06-07T23:40:56+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -182,8 +182,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 680,
-      "text_file_count": 577,
+      "file_count": 745,
+      "text_file_count": 624,
       "largest_file": {
         "path": "tmp/omni_128_dataset_fetch/dataset.jsonl",
         "bytes": 582271586
@@ -193,8 +193,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 582,
-      "text_file_count": 480,
+      "file_count": 603,
+      "text_file_count": 499,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -204,8 +204,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 757,
-      "text_file_count": 631,
+      "file_count": 787,
+      "text_file_count": 659,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -215,8 +215,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 945,
-      "text_file_count": 784,
+      "file_count": 975,
+      "text_file_count": 812,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-07T17:39:15+00:00",
+  "generated_at_utc": "2026-06-07T23:40:56+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -292,7 +292,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 319299,
+      "bytes": 352006,
       "top_level_type": "dict"
     },
     {
@@ -307,7 +307,7 @@
     },
     {
       "path": "data/omni_model_comparison.json",
-      "bytes": 51589,
+      "bytes": 56941,
       "top_level_type": "dict"
     },
     {

results/omni_finetune/HF_UPLOAD.md

@@ -15,8 +15,8 @@ Prepare the upload directory from the completed adapter and verified summary:
 
 ```bash
 python3 scripts/omni/prepare_qwen3_lora_hf_package.py \
-  --adapter-dir checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora/adapter_lora \
-  --verified-summary results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v3_strict_label_prompt_reuse_lora_eval_test_full/verified_result_summary.json \
+  --adapter-dir checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora \
+  --verified-summary results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/verified_result_summary.json \
   --output-dir results/omni_finetune/hf_upload_qwen3_128ep_full \
   --repo-id cy0307/ropedia-qwen3-omni-lora-128ep
 ```

results/omni_finetune/OMNI_MODEL_COMPARISON.md

@@ -1,6 +1,6 @@
 # Omni Model Comparison
 
-Generated: `2026-06-07T17:29:16+00:00`
+Generated: `2026-06-07T23:37:45+00:00`
 
 Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and the two Cosmos3 targets answer different questions: Nano future-window retrieval versus Super structured JSON Reasoner evaluation.
 
@@ -16,15 +16,15 @@ Read the three rows this way:
 
 - Version 1 is the public-sample 12-task harness with minimal and neural heads.
 - Version 2 is the selected 128-episode same-split simple/NN baseline alignment.
-- Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, and Cosmos3-Super Reasoner is a base-weight JSON-task evaluation; Cosmos3-Super now has a camera-pose forward-dynamics contract audit and schema-only packer smoke, but no new fine-tuned weight release.
+- Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, and Cosmos3-Super Reasoner is a base-weight JSON-task evaluation; Cosmos3-Super has a camera-pose forward-dynamics contract audit and schema-only packer smoke, but no new fine-tuned weight release.
 
 ## Model-Family Grouped View
 
 - Use model_groups when comparing one-episode and 128-episode artifacts within the same model family.
 - Task-head baselines have both a one-episode public-sample run and a 128-episode same-split metadata/text run.
-- Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; only the final 128-episode adapter belongs in the Qwen LoRA model repo.
+- Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; the newest verified full-eval 128-episode adapter belongs in the Qwen LoRA model repo.
 - Cosmos3-Nano has a 128-episode future-window compatibility package.
-- Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after real Cosmos adapter/fine-tuned weights exist.
+- Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after a trainable multi-GPU/offload run produces real Cosmos adapter or fine-tuned weights.
 
 ### Minimal and Neural Task Heads
 
@@ -49,7 +49,8 @@ The one-episode Qwen entry is only a sensor-adapter smoke test with Qwen3 weight
 | 128 episode | verified | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=0.8750, action_macro_f1=0.0027, transition_accuracy=0.8504, contact_accuracy=0.6451 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/verified_result_summary.json` |
 | 128 episode | verified | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=0.8527, action_macro_f1=0.0021, transition_accuracy=0.8281, contact_accuracy=0.6518 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full/verified_result_summary.json` |
 | 128 episode | verified | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=0.9978, action_macro_f1=0.0024, transition_accuracy=0.9710, contact_accuracy=0.7188 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full/verified_result_summary.json` |
-| 128 episode | verified current | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=1.0000, action_macro_f1=0.0022, transition_accuracy=0.9732, contact_accuracy=0.7210 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v3_strict_label_prompt_reuse_lora_eval_test_full/verified_result_summary.json` |
+| 128 episode | verified | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=1.0000, action_macro_f1=0.0022, transition_accuracy=0.9732, contact_accuracy=0.7210 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v3_strict_label_prompt_reuse_lora_eval_test_full/verified_result_summary.json` |
+| 128 episode | verified current | Qwen3-Omni LoRA | 119 episodes, 3808 windows/samples, 448 eval | json_validity_rate=1.0000, action_macro_f1=0.0019, transition_accuracy=0.9732, contact_accuracy=0.7299 | `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/verified_result_summary.json` |
 
 ### Cosmos3-Nano Future-Window World Model
 
@@ -64,7 +65,7 @@ The current 128-episode Cosmos result is a public-safe future-window compatibili
 
 ### Cosmos3-Super Reasoner
 
-Cosmos3-Super is now represented by a verified 448-window held-out Reasoner evaluation on the same JSON task as Qwen3. It uses staged base weights through vLLM, so it is a model-branch diagnostic, not a weight release. A camera-pose proxy forward-dynamics target export now passes the contract audit and schema-only packer smoke; true Cosmos3-Super fine-tuning is still not launched until the pipeline-loaded packer check and one-sample overfit exist.
+Cosmos3-Super is now represented by a verified 448-window held-out Reasoner evaluation on the same JSON task as Qwen3. It uses staged base weights through vLLM, so it is a model-branch diagnostic, not a weight release. A camera-pose proxy forward-dynamics target export now passes the contract audit and schema-only packer smoke; true Cosmos3-Super fine-tuning is still blocked until a trainable multi-GPU/offload path produces adapter or fine-tuned weights.
 
 - Weight repo policy: none for this run; staged base weights only, no new fine-tuned weights
 
@@ -103,8 +104,9 @@ Cosmos3-Super is now represented by a verified 448-window held-out Reasoner eval
 | Qwen3-Omni LoRA | `qwen3_omni_lora` | 448 | 14 | json_validity_rate=0.8527, action_macro_f1=0.0021, transition_accuracy=0.8281, contact_accuracy=0.6518 |
 | Qwen3-Omni LoRA | `qwen3_omni_lora` | 448 | 14 | json_validity_rate=0.9978, action_macro_f1=0.0024, transition_accuracy=0.9710, contact_accuracy=0.7188 |
 | Qwen3-Omni LoRA | `qwen3_omni_lora` | 448 | 14 | json_validity_rate=1.0000, action_macro_f1=0.0022, transition_accuracy=0.9732, contact_accuracy=0.7210 |
+| Qwen3-Omni LoRA | `qwen3_omni_lora` | 448 | 14 | json_validity_rate=1.0000, action_macro_f1=0.0019, transition_accuracy=0.9732, contact_accuracy=0.7299 |
 
 ## Pending
 
-- Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.
-- Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after the pipeline-loaded packer check and one-sample overfit produce new weights.
+- Use the verified Qwen3 v4 4-epoch full-eval package as the current Qwen row; older Qwen package rows remain historical diagnostics for comparison.
+- Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after a trainable Cosmos3-Super run produces new weights.

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/PUBLIC_RESULT_SUMMARY.md

@@ -0,0 +1,25 @@
+# Verified Omni Fine-Tuning Result
+
+- Backbone: `qwen3_omni_lora`
+- Dataset run: `xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605`
+- Training run: `xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora`
+- Evaluation run: `xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full`
+- Validation status: `verified`
+- Held-out eval split: `test`
+- Held-out episodes: `14`
+- Prediction rows: `448`
+
+## Primary Metrics
+
+- json_validity_rate: `1.0`
+- action_macro_f1: `0.0018678269676001454`
+- subtask_accuracy: `0.0`
+- transition_accuracy: `0.9732142857142857`
+- next_action_accuracy: `0.033482142857142856`
+- contact_accuracy: `0.7299107142857143`
+- object_micro_f1: `0.31099781500364165`
+- held_out_episode_count: `14`
+
+Raw Xperience-10M files, base-model weights, adapter or checkpoint weights, full checkpoints, and large archives are not included.
+
+Use this package as the source for README, website, and Hugging Face updates.

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/eval/RUN_REPORT.md

@@ -0,0 +1,12 @@
+# Qwen3-Omni LoRA Sharded Evaluation
+
+- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl`
+- Eval split: `test`
+- Expected eval samples: `448`
+- Merged predictions: `448`
+- Held-out episodes: `14`
+- Accuracy: `0.0312`
+- Macro-F1: `0.0019`
+- JSON validity: `1.0000`
+
+Artifacts include `metrics.json`, `predictions.csv`, `per_class_metrics.csv`, and `confusion_matrix.csv`.

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/eval/metrics.json

@@ -0,0 +1,1575 @@
+{
+  "num_samples": 448,
+  "accuracy": 0.03125,
+  "macro_f1": 0.0018678269676001454,
+  "labels": [
+    "Adjust Mahjong tile",
+    "Adjust Mahjong tile alignment",
+    "Adjust Mahjong tile on the stack",
+    "Adjust Mahjong tiles",
+    "Adjust bead piles",
+    "Adjust canned food on shelf",
+    "Adjust cans in bin",
+    "Adjust cans in container",
+    "Adjust cans in tray",
+    "Adjust cardboard",
+    "Adjust cardboard divider",
+    "Adjust cardboard position",
+    "Adjust container on shelf",
+    "Adjust container position",
+    "Adjust containers on shelf",
+    "Adjust foam strip",
+    "Adjust grip",
+    "Adjust grip on container",
+    "Adjust hand position",
+    "Adjust item on shelf",
+    "Adjust lantern shape",
+    "Adjust lantern string",
+    "Adjust paper",
+    "Adjust paper strip",
+    "Adjust perspective",
+    "Adjust placement on shelf",
+    "Adjust position",
+    "Adjust pot position",
+    "Adjust puzzle piece",
+    "Adjust red button",
+    "Adjust red button in row",
+    "Adjust red button position",
+    "Adjust retail item position",
+    "Adjust retail items on shelf",
+    "Adjust ruler position",
+    "Adjust smartphone and sort pieces",
+    "Adjust snack package",
+    "Adjust tile row alignment",
+    "Adjust vacuum cleaner position",
+    "Adjusting a puzzle piece",
+    "Adjusting canned goods on shelf",
+    "Adjusting fabric for cutting",
+    "Adjusting fabric position",
+    "Adjusting puzzle piece",
+    "Align Mahjong tiles",
+    "Align and place retail item",
+    "Align blue strip",
+    "Align button",
+    "Align button in row",
+    "Align button row",
+    "Align buttons",
+    "Align canned food on shelf",
+    "Align canned goods on shelf",
+    "Align cardboard piece",
+    "Align cardboard strip",
+    "Align charging cable",
+    "Align edges of paper lantern",
+    "Align foam piece",
+    "Align foam strip",
+    "Align paper lantern edges",
+    "Align paper strip",
+    "Align plastic containers",
+    "Align red button in row",
+    "Align red buttons",
+    "Align ruler",
+    "Align ruler and mark cardboard",
+    "Align ruler on cardboard",
+    "Align ruler with crease",
+    "Align scissors",
+    "Apply adhesive tape to lantern",
+    "Approach boxes",
+    "Approach desk",
+    "Approach packing area",
+    "Approach restocking supplies",
+    "Approach table",
+    "Approach work table",
+    "Approach workstation",
+    "Approaching and pressing the door switch",
+    "Approaching the table",
+    "Approaching work table",
+    "Arrange Mahjong tiles",
+    "Arrange beads by color",
+    "Arrange black buttons",
+    "Arrange button cluster",
+    "Arrange buttons",
+    "Arrange buttons in a line",
+    "Arrange buttons in row",
+    "Arrange buttons on table",
+    "Arrange buttons on the table",
+    "Arrange canned products on shelf",
+    "Arrange cans in box",
+    "Arrange cans on shelf",
+    "Arrange cardboard",
+    "Arrange cardboard piece",
+    "Arrange cardboard pieces",
+    "Arrange cardboard squares",
+    "Arrange container on shelf",
+    "Arrange items on shelf",
+    "Arrange orange buttons",
+    "Arrange paper stars",
+    "Arrange paper strips",
+    "Arrange plastic containers",
+    "Arrange red buttons",
+    "Arrange small buttons",
+    "Arrange star beads",
+    "Arrange star beads for counting",
+    "Arrange star-shaped beads",
+    "Arrange tiles into row",
+    "Arrive at a different workstation",
+    "Assemble cardboard pieces",
+    "Assemble foam strips",
+    "Assess shelf arrangement",
+    "Attach foam strip",
+    "Attach material to paper strip",
+    "Attempt to fit puzzle piece",
+    "Begin folding paper strip",
+    "Begin rolling quilling strip",
+    "Bend and manipulate plastic strip",
+    "Browse and interact with phone interface",
+    "Browse mobile phone",
+    "Browse smartphone screen",
+    "Browsing mobile phone",
+    "Browsing smartphone content",
+    "Bundle display hooks",
+    "Cap marker",
+    "Carry cardboard piece",
+    "Carry cereal boxes",
+    "Carry cereal towards aisle",
+    "Carry container",
+    "Carry crate of cans",
+    "Carry item to shelf",
+    "Carry pasta box towards aisle",
+    "Carry plastic container",
+    "Carry stool to next shelf",
+    "Check phone",
+    "Check smart watch",
+    "Check watch",
+    "Clean shelf",
+    "Close cardboard box",
+    "Closing the door",
+    "Combine bead piles",
+    "Complete the cut",
+    "Connect cable to device",
+    "Continue cutting fabric",
+    "Continue cutting newspaper",
+    "Continue folding paper strip",
+    "Count and arrange paper stars",
+    "Count and record paper stars",
+    "Count paper stars",
+    "Counting and organizing beads",
+    "Counting star beads",
+    "Curve foam strip into loop",
+    "Cut along the edge of the newspaper",
+    "Cut along the line",
+    "Cut along the marked line",
+    "Cut along the newspaper edge",
+    "Cut cardboard",
+    "Cut cardboard along line",
+    "Cut cardboard grid",
+    "Cut cardboard into triangles",
+    "Cut cardboard pattern",
+    "Cut cardboard piece",
+    "Cut cardboard piece with scissors",
+    "Cut cardboard pieces with scissors",
+    "Cut cardboard shape",
+    "Cut cardboard sheet",
+    "Cut cardboard sheet with scissors",
+    "Cut cardboard square",
+    "Cut cardboard strip",
+    "Cut cardboard strip with scissors",
+    "Cut cardboard strip with utility knife",
+    "Cut cardboard triangle",
+    "Cut cardboard tube",
+    "Cut cardboard with scissors",
+    "Cut cardboard with utility knife",
+    "Cut fabric with scissors",
+    "Cut light green fabric",
+    "Cut newspaper",
+    "Cut newspaper with scissors",
+    "Cut out cardboard pattern",
+    "Cut section from newspaper",
+    "Cutting fabric",
+    "Deposit beads into box",
+    "Deposit cardboard squares",
+    "Discard item into bin",
+    "Discard paper towel",
+    "Draw grid line",
+    "Draw grid line with pen",
+    "Draw line",
+    "Draw line along ruler",
+    "Draw line on cardboard",
+    "Draw line with marker",
+    "Draw line with pen",
+    "Draw lines on cardboard",
+    "Draw lines with pen and ruler",
+    "Draw lines with ruler",
+    "Draw straight line",
+    "Draw straight lines on cardboard",
+    "Drawing grid line",
+    "Drawing grid line with pen and ruler",
+    "Drawing grid line with ruler",
+    "Drawing lines on cardboard",
+    "Drop cardboard square into box",
+    "Dry hands",
+    "Enter the room",
+    "Enter workspace",
+    "Entering the VR training room",
+    "Examine canned goods",
+    "Examine item",
+    "Examine labels",
+    "Examine product",
+    "Expand paper lantern",
+    "Extract wire hangers from box",
+    "Finish placing cardboard cutouts",
+    "Finish washing hands",
+    "Finish wiping and inspect jar",
+    "Finishing coil",
+    "Fold and manipulate ribbon",
+    "Fold and organize paper strips",
+    "Fold blue strip",
+    "Fold cardboard",
+    "Fold cardboard edge",
+    "Fold cardboard shape",
+    "Fold cardboard sheet",
+    "Fold cut cardboard",
+    "Fold foam piece",
+    "Fold lucky star",
+    "Fold newspaper",
+    "Fold paper lantern",
+    "Fold paper star",
+    "Fold paper strip",
+    "Fold paper strip into a star",
+    "Fold paper strip into knot",
+    "Fold paper strip into lucky star",
+    "Fold paper strip into star",
+    "Fold plastic strip",
+    "Fold purple paper",
+    "Fold purple paper strip",
+    "Fold ribbon",
+    "Folding cardboard",
+    "Folding paper strip",
+    "Forming quilled paper shape",
+    "Gather cardboard pieces",
+    "Gather pieces",
+    "Gather pieces into box",
+    "Gather star beads",
+    "Gathering colored beads",
+    "Gathering items",
+    "Gathering star beads",
+    "Gesturing",
+    "Grasp and retrieve item",
+    "Grasp cardboard sheet",
+    "Grasp cleaning bottle",
+    "Grasp door handle",
+    "Grasp electronic object",
+    "Grasp item",
+    "Grasp lantern",
+    "Grasp lantern component",
+    "Grasp next item",
+    "Grasp origami stars",
+    "Grasp package",
+    "Grasp paper strip",
+    "Grasp plastic bag on shelf",
+    "Grasp product from box",
+    "Grasp product from shelf",
+    "Grasp retail item",
+    "Grasp shopping bag",
+    "Grasp snack package",
+    "Grasping cleaning cloth",
+    "Greeting/acknowledging participants",
+    "Guide utility knife along ruler",
+    "Handle paper lantern component",
+    "Hold and align cardboard",
+    "Hold and align newspaper",
+    "Hold and align paper strip",
+    "Hold and bend paper strip",
+    "Hold and bend plastic strip",
+    "Hold and crease purple paper",
+    "Hold and examine item",
+    "Hold and inspect can",
+    "Hold and manipulate paper strip",
+    "Hold and mark cardboard piece",
+    "Hold and rotate paper strip",
+    "Hold and view phone",
+    "Hold and wipe product",
+    "Hold beads",
+    "Hold bin and move through aisle",
+    "Hold blue product box",
+    "Hold blue strip",
+    "Hold canned food",
+    "Hold cardboard",
+    "Hold cardboard piece",
+    "Hold cardboard pieces",
+    "Hold cardboard strip",
+    "Hold cardboard with ruler",
+    "Hold charger",
+    "Hold charger and cable",
+    "Hold charging cable",
+    "Hold cleaning cloth",
+    "Hold container",
+    "Hold container lid",
+    "Hold container of canned food",
+    "Hold craft tool",
+    "Hold device and cable",
+    "Hold earbud case",
+    "Hold electronic accessory",
+    "Hold electronic item",
+    "Hold empty container",
+    "Hold foam pieces",
+    "Hold instructional sign",
+    "Hold item",
+    "Hold item and adjust posture",
+    "Hold items",
+    "Hold items and inspect shelf",
+    "Hold items in hand",
+    "Hold newspaper",
+    "Hold paper lantern",
+    "Hold paper strip",
+    "Hold pen and paper",
+    "Hold phone",
+    "Hold pickle jar",
+    "Hold portable charger",
+    "Hold power adapter",
+    "Hold power bank and cable",
+    "Hold product",
+    "Hold product labels",
+    "Hold product package",
+    "Hold quilled paper coil",
+    "Hold quilled paper piece",
+    "Hold quilling paper",
+    "Hold recording sheet and pen",
+    "Hold ruler",
+    "Hold ruler and draw line",
+    "Hold ruler and mark cardboard",
+    "Hold ruler and marker",
+    "Hold ruler and pen steady",
+    "Hold ruler on cardboard",
+    "Hold ruler steady",
+    "Hold scissors",
+    "Hold small cardboard pieces",
+    "Hold small object",
+    "Hold small piece of ribbon",
+    "Hold small product bag",
+    "Hold small white box",
+    "Hold smartphone",
+    "Hold smartphone box",
+    "Hold snack package",
+    "Hold snack packages",
+    "Hold supplement bottle",
+    "Hold tray of canned goods",
+    "Hold utility knife",
+    "Hold water bottle",
+    "Holding marker",
+    "Identify next cardboard piece",
+    "Inflate paper star",
+    "Initiate star folding",
+    "Insert charging cable",
+    "Insert charging cable into power bank",
+    "Insert plug into power adapter",
+    "Inspect Dior gift box",
+    "Inspect almond package",
+    "Inspect and place item on shelf",
+    "Inspect bottle",
+    "Inspect cardboard piece",
+    "Inspect cardboard strip",
+    "Inspect charging case",
+    "Inspect electronic item",
+    "Inspect jar",
+    "Inspect product",
+    "Inspect product lid",
+    "Inspect shelf",
+    "Inspect shelf and organize stock",
+    "Inspect shelf condition",
+    "Inspect smartphone box",
+    "Inspect strip",
+    "Inspect supplement bottle",
+    "Interact with colleagues",
+    "Interact with phone",
+    "Interact with smartphone",
+    "Interact with smartphone screen",
+    "Interacting with phone screen",
+    "Interaction with coworker",
+    "Interlock paper strips",
+    "Labeling cardboard piece",
+    "Labeling cardboard square",
+    "Labeling cardboard squares",
+    "Lift blue strip",
+    "Lift pen and shift ruler",
+    "Lift pot lid",
+    "Lift utility knife",
+    "Lock phone",
+    "Look around the table",
+    "Look away",
+    "Manipulate adhesive strip",
+    "Manipulate and inspect colorful pieces",
+    "Manipulate bead",
+    "Manipulate beads",
+    "Manipulate cardboard piece",
+    "Manipulate cardboard shape",
+    "Manipulate cardboard sheet",
+    "Manipulate colorful pieces",
+    "Manipulate component",
+    "Manipulate component on strip",
+    "Manipulate craft paper strips",
+    "Manipulate craft piece",
+    "Manipulate folded paper star",
+    "Manipulate light blue strip",
+    "Manipulate material",
+    "Manipulate paper decoration",
+    "Manipulate paper edge",
+    "Manipulate paper piece",
+    "Manipulate paper quilling piece",
+    "Manipulate paper star",
+    "Manipulate paper stars",
+    "Manipulate paper strip",
+    "Manipulate paper strips",
+    "Manipulate plastic strip",
+    "Manipulate plastic strips",
+    "Manipulate power cable plug",
+    "Manipulate puzzle piece",
+    "Manipulate puzzle pieces",
+    "Manipulate quilled paper",
+    "Manipulate quilled paper shape",
+    "Manipulate quilled paper strip",
+    "Manipulate quilled paper strips",
+    "Manipulate quilling paper",
+    "Manipulate quilling strip",
+    "Manipulate ribbon knot",
+    "Manipulate ribbon piece",
+    "Manipulate small component",
+    "Manipulate small object",
+    "Manipulate small paper segment",
+    "Manipulate star",
+    "Manipulate yellow strip",
+    "Manipulating paper strips",
+    "Mark cardboard",
+    "Mark cardboard piece",
+    "Mark cardboard strip with pen",
+    "Mark cardboard with marker",
+    "Mark cardboard with pen",
+    "Mark cardboard with pen and ruler",
+    "Mark cardboard with ruler",
+    "Mark cardboard with ruler and pen",
+    "Mark fabric",
+    "Mark fabric with pen",
+    "Mark fabric with pen and ruler",
+    "Mark line on cardboard",
+    "Mark lines on cardboard",
+    "Mark lines with marker",
+    "Mark lines with pen along ruler",
+    "Mark list with pen",
+    "Mark paper list",
+    "Mark straight line",
+    "Marking cardboard piece",
+    "Marking cardboard with pen",
+    "Marking lines on cardboard",
+    "Measure and mark cardboard",
+    "Measure cardboard with ruler",
+    "Move Mahjong tile",
+    "Move along shelf",
+    "Move along the shelf",
+    "Move along the shelves",
+    "Move along the supermarket aisle",
+    "Move and place black buttons",
+    "Move away from collection box",
+    "Move away from desk",
+    "Move away from shelf",
+    "Move away from table",
+    "Move away from workstation",
+    "Move bin",
+    "Move bin to shelf area",
+    "Move black button",
+    "Move blue beads",
+    "Move box to next position",
+    "Move button to line",
+    "Move camera over surface",
+    "Move can towards shelf",
+    "Move canned goods container",
+    "Move cardboard",
+    "Move cardboard box",
+    "Move cardboard piece",
+    "Move cardboard sheet",
+    "Move cardboard to pile",
+    "Move container toward shelf",
+    "Move dustpan to side",
+    "Move hand",
+    "Move hand away",
+    "Move hand away from shelf",
+    "Move hand away from workspace",
+    "Move hand back to box",
+    "Move hand over button pile",
+    "Move hand to paper stars",
+    "Move hand toward craft materials",
+    "Move item to bag",
+    "Move marker and adjust hand",
+    "Move marker and ruler",
+    "Move marker away",
+    "Move orange buttons",
+    "Move origami stars",
+    "Move pen",
+    "Move pen aside",
+    "Move pen away",
+    "Move phone",
+    "Move piece to pile",
+    "Move pieces into box",
+    "Move pineapple chips",
+    "Move plastic storage bin",
+    "Move plush toy",
+    "Move pot",
+    "Move product to box",
+    "Move product to shelf",
+    "Move product towards shelf",
+    "Move puzzle piece",
+    "Move ruler",
+    "Move ruler and tools",
+    "Move scissors away",
+    "Move small blue foam piece towards the strip",
+    "Move smartphone",
+    "Move storage bin",
+    "Move through aisle",
+    "Move through the training room",
+    "Move to box",
+    "Move to desk",
+    "Move to next section",
+    "Move to shelf",
+    "Move to shelf base",
+    "Move to stock products",
+    "Move towards aisle",
+    "Move towards box",
+    "Move towards kitchen area",
+    "Move towards shelf",
+    "Move towards table",
+    "Move towards the stove",
+    "Move tray towards packing area",
+    "Move utility knife along ruler",
+    "Move vacuum cleaner",
+    "Move vacuum cleaner hose",
+    "Moving cardboard square",
+    "Moving hand",
+    "Moving hand towards cardboard stack",
+    "Moving ruler",
+    "Observe and pause",
+    "Observe and walk through store",
+    "Observe colleague and workspace",
+    "Observe craft layout",
+    "Observe desktop layout",
+    "Observe paper and count objects",
+    "Observe paper quilling station",
+    "Observe puzzle progress",
+    "Observe room",
+    "Observe shelf",
+    "Observe shelf status",
+    "Observe sorting progress",
+    "Observe stocking",
+    "Observe surroundings",
+    "Observe workspace",
+    "Open cardboard box",
+    "Open door",
+    "Open earbud case",
+    "Open folded paper lantern",
+    "Open paper lantern",
+    "Open paper lantern component",
+    "Open small case",
+    "Open stove pot lid",
+    "Open supplement bottle",
+    "Operate smartphone",
+    "Organize bag contents",
+    "Organize cardboard pieces",
+    "Organize item on shelf",
+    "Organize products",
+    "Organize snacks in box",
+    "Organize tools and materials",
+    "Pack beads into box",
+    "Peel blue strip",
+    "Peel foam strip",
+    "Pick up Dior gift box",
+    "Pick up Mahjong tile",
+    "Pick up accessory",
+    "Pick up and sort cardboard",
+    "Pick up another bottle",
+    "Pick up another canned item",
+    "Pick up another item",
+    "Pick up beads",
+    "Pick up black button",
+    "Pick up blue foam piece",
+    "Pick up blue paper strip",
+    "Pick up bottle",
+    "Pick up bottled sauce",
+    "Pick up button",
+    "Pick up can",
+    "Pick up canned food",
+    "Pick up canned good",
+    "Pick up canned goods",
+    "Pick up canned item",
+    "Pick up canned product",
+    "Pick up cardboard",
+    "Pick up cardboard cutout",
+    "Pick up cardboard piece",
+    "Pick up cardboard square",
+    "Pick up cardboard stack",
+    "Pick up cardboard strip",
+    "Pick up cardboard tray",
+    "Pick up cereal boxes",
+    "Pick up charging cable",
+    "Pick up charging case",
+    "Pick up cleaning cloth",
+    "Pick up colored tile",
+    "Pick up container",
+    "Pick up container from box",
+    "Pick up craft material",
+    "Pick up cut cardboard piece",
+    "Pick up dustpan",
+    "Pick up electronic accessory",
+    "Pick up electronic accessory from box",
+    "Pick up electronic device",
+    "Pick up electronic item",
+    "Pick up electronic product",
+    "Pick up food item",
+    "Pick up gift box",
+    "Pick up grocery item",
+    "Pick up item",
+    "Pick up item from bin",
+    "Pick up item from box",
+    "Pick up item from shelf",
+    "Pick up items from the shopping bag",
+    "Pick up jar",
+    "Pick up light blue strip",
+    "Pick up marker",
+    "Pick up metal ruler",
+    "Pick up new cardboard piece",
+    "Pick up new electronic product",
+    "Pick up new product from box",
+    "Pick up next gift box",
+    "Pick up next item from bin",
+    "Pick up next product from bin",
+    "Pick up nut bar box",
+    "Pick up object",
+    "Pick up oil bottle",
+    "Pick up orange button",
+    "Pick up pack from shelf",
+    "Pick up packaged paper lantern component",
+    "Pick up paper star",
+    "Pick up paper strip",
+    "Pick up paper towel",
+    "Pick up pasta box",
+    "Pick up pen",
+    "Pick up phone",
+    "Pick up pickle jar",
+    "Pick up pink water bottle",
+    "Pick up plastic bin",
+    "Pick up plastic container",
+    "Pick up plush toy",
+    "Pick up portable charger",
+    "Pick up power bank",
+    "Pick up product",
+    "Pick up product box",
+    "Pick up product from bin",
+    "Pick up product from box",
+    "Pick up product from shelf",
+    "Pick up puzzle piece",
+    "Pick up red button",
+    "Pick up retail item",
+    "Pick up sauce bottle",
+    "Pick up scissors",
+    "Pick up shopping bag",
+    "Pick up small cardboard piece",
+    "Pick up small item",
+    "Pick up small object",
+    "Pick up small piece of material",
+    "Pick up smartphone",
+    "Pick up snack package",
+    "Pick up snack packages",
+    "Pick up snack packs",
+    "Pick up snack pouch",
+    "Pick up spice jar",
+    "Pick up stapler",
+    "Pick up star",
+    "Pick up star bead",
+    "Pick up star-shaped bead",
+    "Pick up storage container",
+    "Pick up supplement bottle",
+    "Pick up supplies from box",
+    "Pick up tin can",
+    "Pick up tool",
+    "Pick up utility knife",
+    "Pick up water bottle",
+    "Pick up yellow item",
+    "Pick up yellow paper strip",
+    "Picking up bottle",
+    "Picking up crafting material",
+    "Picking up stock",
+    "Place Mahjong tile on stack",
+    "Place Mahjong tile on the stack",
+    "Place accessory box",
+    "Place accessory into box",
+    "Place accessory on shelf",
+    "Place and align button",
+    "Place and count bead",
+    "Place another canned food on shelf",
+    "Place back Dior gift box",
+    "Place bead on table",
+    "Place bottle back on shelf",
+    "Place box on shelf",
+    "Place button",
+    "Place button in group",
+    "Place button in row",
+    "Place can on shelf",
+    "Place canned food in bin",
+    "Place canned food in container",
+    "Place canned food on shelf",
+    "Place canned good on shelf",
+    "Place canned goods in container",
+    "Place canned product on shelf",
+    "Place cans into box",
+    "Place cardboard",
+    "Place cardboard piece",
+    "Place cardboard piece on stack",
+    "Place cardboard square",
+    "Place cardboard square on stack",
+    "Place cardboard strip",
+    "Place charger on table",
+    "Place charging case down",
+    "Place cloth on floor",
+    "Place colored tile",
+    "Place container in bin",
+    "Place container on floor",
+    "Place container on shelf",
+    "Place controller on table",
+    "Place crate on floor",
+    "Place device on lap",
+    "Place down paper pieces",
+    "Place down paper segment",
+    "Place down pen",
+    "Place down pink water bottle",
+    "Place down ruler and pen",
+    "Place down scissors",
+    "Place down strip",
+    "Place finished star on table",
+    "Place gift box into bin",
+    "Place gift box on shelf",
+    "Place hand on table",
+    "Place item back",
+    "Place item back on shelf",
+    "Place item in bag",
+    "Place item in container",
+    "Place item in shopping bag",
+    "Place item into bag",
+    "Place item into shopping bag",
+    "Place item on shelf",
+    "Place item on table",
+    "Place items on shelf",
+    "Place items on table",
+    "Place items on the shelf",
+    "Place jar in box",
+    "Place jar into shelf box",
+    "Place jar on shelf",
+    "Place ketchup bottle on shelf",
+    "Place knife down",
+    "Place lid back",
+    "Place marked piece down",
+    "Place marker down",
+    "Place material",
+    "Place oil in container",
+    "Place paper star",
+    "Place paper star in row",
+    "Place pen on cardboard",
+    "Place pen on table",
+    "Place phone down",
+    "Place phone on desk",
+    "Place phone on shelf",
+    "Place phone on table",
+    "Place pickle jar in box",
+    "Place piece into puzzle",
+    "Place plush toy into bag",
+    "Place plush toy on shelf",
+    "Place product in box",
+    "Place product on shelf",
+    "Place puzzle piece",
+    "Place quilled paper shape",
+    "Place red button",
+    "Place ribbon onto project",
+    "Place ruler on cardboard",
+    "Place sauce bottle on shelf",
+    "Place sauce in container",
+    "Place scissors aside",
+    "Place scissors down",
+    "Place scissors on table",
+    "Place smartphone down",
+    "Place smartphone on cardboard",
+    "Place smartphone on desk",
+    "Place smartphone on stand",
+    "Place smartphone on table",
+    "Place snack in box",
+    "Place snack on shelf",
+    "Place snack package in box",
+    "Place snack package on shelf",
+    "Place snack packages on shelf",
+    "Place snack pouch in container",
+    "Place snack pouch on shelf",
+    "Place spice jar in container",
+    "Place star",
+    "Place star in row",
+    "Place star on table",
+    "Place stars in container",
+    "Place stool on floor",
+    "Place storage container on floor",
+    "Place strip on table",
+    "Place supplement bottle in container",
+    "Place tool on table",
+    "Place towel",
+    "Place water bottle on table",
+    "Place white box on table",
+    "Placing labeled cardboard square",
+    "Placing labeled square",
+    "Placing paper strip",
+    "Placing pen on table",
+    "Placing phone down",
+    "Placing piece on stack",
+    "Placing stock on shelf",
+    "Plug cable into portable charger",
+    "Position cardboard for cutting",
+    "Position cardboard piece",
+    "Position cardboard strip",
+    "Position cardboard tray",
+    "Position cardboard tube",
+    "Position container near shelf",
+    "Position container on shelf",
+    "Position hands for work",
+    "Position ribbon piece",
+    "Position ruler and mark cardboard",
+    "Position ruler on cardboard",
+    "Position scissors",
+    "Position scissors for next cut",
+    "Position scissors to cut cardboard",
+    "Position shelving divider",
+    "Position the ruler",
+    "Position tray",
+    "Position utility knife",
+    "Position utility knife on cardboard",
+    "Positioning cardboard on workspace",
+    "Positioning paper strip",
+    "Positioning puzzle piece",
+    "Positioning ruler on cardboard",
+    "Prepare paper strip",
+    "Prepare to cut cardboard",
+    "Prepare to draw lines",
+    "Prepare to pick up item",
+    "Prepare to place bottle on shelf",
+    "Prepare to place cardboard",
+    "Prepare to place item in bag",
+    "Prepare to place product",
+    "Prepare to resume cutting",
+    "Prepare to sort beads",
+    "Preparing to craft",
+    "Press fold",
+    "Pull back hand",
+    "Pull paper strip",
+    "Push vacuum cleaner",
+    "Put down phone",
+    "Put down scissors",
+    "Put down smartphone",
+    "Put down utility knife",
+    "Put down water bottle",
+    "Putting away smartphone",
+    "Reach and sort buttons",
+    "Reach for Mahjong tiles",
+    "Reach for additional items",
+    "Reach for and examine canned goods",
+    "Reach for and pick up smartphone",
+    "Reach for another container",
+    "Reach for another item",
+    "Reach for beads",
+    "Reach for black button",
+    "Reach for button",
+    "Reach for can",
+    "Reach for canned food",
+    "Reach for canned goods",
+    "Reach for cardboard box",
+    "Reach for cardboard piece",
+    "Reach for cleaning supplies",
+    "Reach for container",
+    "Reach for craft items",
+    "Reach for empty shelf space",
+    "Reach for item",
+    "Reach for item in box",
+    "Reach for item on shelf",
+    "Reach for items",
+    "Reach for items in box",
+    "Reach for more pieces",
+    "Reach for next can",
+    "Reach for next canned food",
+    "Reach for next canned food item",
+    "Reach for next canned product",
+    "Reach for next item",
+    "Reach for next piece",
+    "Reach for next product",
+    "Reach for object",
+    "Reach for paper strip",
+    "Reach for paper strips",
+    "Reach for phone",
+    "Reach for product",
+    "Reach for product labels",
+    "Reach for product on shelf",
+    "Reach for puzzle piece",
+    "Reach for retail item",
+    "Reach for shelf",
+    "Reach for shelving divider",
+    "Reach for snack package",
+    "Reach for snack pouch",
+    "Reach for star",
+    "Reach for stars",
+    "Reach for utility knife",
+    "Reach for water bottle",
+    "Reach for wire hangers",
+    "Reach into bag",
+    "Reach into box",
+    "Reach towards shelf",
+    "Reaching for beads",
+    "Realign Mahjong tiles",
+    "Rearrange Mahjong tile",
+    "Rearrange Mahjong tiles",
+    "Rearrange shelf item",
+    "Record count",
+    "Record count on notepad",
+    "Record star count",
+    "Record star count on paper",
+    "Release and prepare new strip",
+    "Release bottle",
+    "Release cardboard",
+    "Release cardboard piece",
+    "Release cardboard piece and gesture",
+    "Release cardboard shape",
+    "Release container",
+    "Release folded paper",
+    "Release food item",
+    "Release hook",
+    "Release label",
+    "Release lantern",
+    "Release paper",
+    "Release paper coil",
+    "Release paper star",
+    "Release paper strip",
+    "Release pickle jar",
+    "Release product on shelf",
+    "Release puzzle piece",
+    "Release quilling strip",
+    "Release scissors",
+    "Release smartphone",
+    "Remove cardboard flap",
+    "Remove cardboard pattern",
+    "Remove cardboard pattern piece",
+    "Remove cleaning bottle",
+    "Remove item from bag",
+    "Remove item from shelf",
+    "Remove lid from container",
+    "Remove paper lantern part from packaging",
+    "Remove plastic container from shelf",
+    "Remove plastic container from storage box",
+    "Remove plastic packaging",
+    "Remove ruler",
+    "Remove ruler and marker",
+    "Remove shelf label",
+    "Remove storage bin from shelf",
+    "Reorganize bin contents",
+    "Reposition and cut",
+    "Reposition cardboard for cutting",
+    "Reposition hand",
+    "Reposition hands",
+    "Reposition hands and ruler",
+    "Reposition marker",
+    "Reposition newspaper",
+    "Reposition pen and prepare for next line",
+    "Reposition ruler",
+    "Reposition ruler and pen",
+    "Reposition scissors",
+    "Reposition sign and organize beads",
+    "Reposition tools",
+    "Reposition utility knife",
+    "Repositioning ruler",
+    "Repositioning ruler and cardboard",
+    "Resume counting stars",
+    "Resume observation",
+    "Resume sorting blue beads",
+    "Resume writing on paper",
+    "Retract camera/reposition view",
+    "Retract hand",
+    "Retract hand from bag",
+    "Retrieve another container",
+    "Retrieve canned food from box",
+    "Retrieve hand to table",
+    "Retrieve items from bag",
+    "Retrieve next canned food item",
+    "Retrieve paper strip",
+    "Retrieve paper strips",
+    "Retrieve snack from container",
+    "Retrieve star",
+    "Retrieving more beads",
+    "Return to sorting",
+    "Reviewing count record",
+    "Rinse cloth in sink",
+    "Roll quilling paper",
+    "Rolling paper strip",
+    "Rub hands together",
+    "Scan for next piece",
+    "Scan supermarket shelves",
+    "Score cardboard",
+    "Scroll on smartphone",
+    "Scroll smartphone screen",
+    "Scroll through photo gallery",
+    "Scrolling and viewing content on phone",
+    "Scrolling or navigating on phone",
+    "Search for puzzle piece",
+    "Secure paper edges with adhesive",
+    "Secure ribbon with needle",
+    "Securing paper structure",
+    "Select a bottle",
+    "Select and pick up a canned item",
+    "Select another item",
+    "Select paper strip",
+    "Select product from box",
+    "Selecting new paper strip",
+    "Separate cardboard piece",
+    "Set down scissors and pick up power bank",
+    "Set down utility knife",
+    "Slide utility knife along ruler",
+    "Sort Mahjong tiles",
+    "Sort and adjust button line",
+    "Sort and arrange buttons",
+    "Sort and arrange cardboard pieces",
+    "Sort and count beads",
+    "Sort and place buttons",
+    "Sort and place paper star",
+    "Sort and stack cardboard pieces",
+    "Sort beads",
+    "Sort beads and write count",
+    "Sort beads by color",
+    "Sort beads by hand",
+    "Sort beads on table",
+    "Sort beads on the table",
+    "Sort blue beads",
+    "Sort blue star-shaped pieces",
+    "Sort button",
+    "Sort button by color",
+    "Sort buttons",
+    "Sort buttons by color",
+    "Sort canned goods in tray",
+    "Sort colored tiles",
+    "Sort colorful pieces",
+    "Sort craft items",
+    "Sort cut cardboard",
+    "Sort light blue origami stars",
+    "Sort orange button",
+    "Sort orange buttons",
+    "Sort origami stars",
+    "Sort origami stars by color",
+    "Sort paper star",
+    "Sort paper stars",
+    "Sort plastic pieces",
+    "Sort purple beads",
+    "Sort purple star-shaped objects",
+    "Sort puzzle pieces",
+    "Sort quilled paper pieces",
+    "Sort small colorful pieces",
+    "Sort small craft pieces",
+    "Sort small objects",
+    "Sort small plastic pieces",
+    "Sort star-shaped beads",
+    "Sort star-shaped objects",
+    "Sort star-shaped objects by color",
+    "Sort tiles",
+    "Sort tiles by color",
+    "Sort yellow star-shaped objects",
+    "Sorting buttons",
+    "Sorting colorful paper pieces",
+    "Sorting paper stars",
+    "Stabilize cardboard",
+    "Stabilize ruler",
+    "Stack cardboard pieces",
+    "Stack cardboard square",
+    "Stack cardboard squares",
+    "Stacking cardboard pieces",
+    "Stacking cardboard square",
+    "Stacking cardboard squares",
+    "Stand up and walk away",
+    "Start cutting",
+    "Start folding paper strip",
+    "Starting to label next square",
+    "Stir contents",
+    "Stop measuring and put down tools",
+    "Stop sorting stars",
+    "Sweep debris",
+    "Sweep floor debris",
+    "Switch to scissors",
+    "Switching marker",
+    "Tap smartphone screen",
+    "Tapping on smartphone screen",
+    "Tapping smartphone screen",
+    "Tear newspaper",
+    "Tear off cardboard segment",
+    "Touch canned goods",
+    "Touch pieces in box",
+    "Touch shelf edge",
+    "Trace pattern on cardboard",
+    "Transition to cutting",
+    "Transition to standing position",
+    "Trim cardboard",
+    "Trim cardboard piece",
+    "Type on smartphone",
+    "Typing message on smartphone",
+    "Typing on phone",
+    "Typing on smartphone",
+    "Update paper record",
+    "Use phone",
+    "Use phone to check instructions",
+    "Use phone to check stock",
+    "Use phone while crafting",
+    "Use smartphone",
+    "Vacuum edge of carpet",
+    "Vacuum the carpet",
+    "Vacuuming along the wall edge",
+    "Vacuuming carpet corner",
+    "Vacuuming carpet edge",
+    "Vacuuming the carpet edge",
+    "View content on smartphone",
+    "View phone screen",
+    "Viewing phone screen",
+    "Walk across office",
+    "Walk across room",
+    "Walk across the room",
+    "Walk away",
+    "Walk in hallway",
+    "Walk through corridor",
+    "Walk through doorway",
+    "Walk through hallway",
+    "Walk through office",
+    "Walk through store",
+    "Walk through workspace",
+    "Walk towards aisle",
+    "Walk towards desk",
+    "Walk towards next aisle",
+    "Walk towards other aisles",
+    "Walk towards room",
+    "Walk towards shelf",
+    "Walk towards shelves",
+    "Walk towards storage area",
+    "Walk towards table",
+    "Walk towards workspace",
+    "Walk with cardboard",
+    "Walk with cardboard cutout",
+    "Walk with marker",
+    "Walk with shopping bag",
+    "Walking across the room",
+    "Walking along the aisle",
+    "Walking in the hallway",
+    "Walking in the workspace",
+    "Walking through classroom",
+    "Walking through office hallway",
+    "Walking through the office",
+    "Walking to sink",
+    "Walking towards door",
+    "Walking towards workstation",
+    "Washing hands",
+    "Washing hands in sink",
+    "Wipe down shelf",
+    "Wipe electronic item",
+    "Wipe food product",
+    "Wipe grocery shelf",
+    "Wipe item",
+    "Wipe jar",
+    "Wipe ketchup bottle",
+    "Wipe kitchen counter",
+    "Wipe product",
+    "Wipe retail item",
+    "Wipe shelf",
+    "Wipe shelf surface",
+    "Wipe the plastic jar",
+    "Wipe the product jar",
+    "Wipe the shelf",
+    "Wiping countertop",
+    "Withdraw hand",
+    "Write count on paper",
+    "Write on notepad",
+    "Write on paper",
+    "Write on paper record",
+    "Writing on notepad",
+    "fold purple ribbon",
+    "sort craft materials",
+    "Close door",
+    "Close cabinet door",
+    "Wipe hands",
+    "Pick up quilled paper piece",
+    "Place pot lid on counter",
+    "Place pot lid on shelf",
+    "Place beads on table",
+    "Place paper lantern",
+    "Reach for smartphone",
+    "Hold and align smartphone",
+    "Place down smartphone",
+    "Place paper strip",
+    "Walk through the training room",
+    "Place down water bottle"
+  ],
+  "run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+  "model_id": "<workspace-parent>/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "adapter_dir": "checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+  "eval_split": "test",
+  "train_split": "train",
+  "num_eval_episodes": 14,
+  "held_out_episode_count": 14,
+  "unseen_eval_labels": [
+    "Adjust canned food on shelf",
+    "Adjust lantern shape",
+    "Adjust lantern string",
+    "Adjust paper",
+    "Adjust pot position",
+    "Align canned food on shelf",
+    "Align edges of paper lantern",
+    "Align paper lantern edges",
+    "Apply adhesive tape to lantern",
+    "Approach boxes",
+    "Approaching and pressing the door switch",
+    "Approaching the table",
+    "Arrange buttons in a line",
+    "Arrange star beads",
+    "Arrange star beads for counting",
+    "Attempt to fit puzzle piece",
+    "Bend and manipulate plastic strip",
+    "Browse smartphone screen",
+    "Bundle display hooks",
+    "Closing the door",
+    "Counting and organizing beads",
+    "Counting star beads",
+    "Cut along the marked line",
+    "Entering the VR training room",
+    "Expand paper lantern",
+    "Extract wire hangers from box",
+    "Fold paper lantern",
+    "Fold plastic strip",
+    "Gather star beads",
+    "Gesturing",
+    "Grasp cleaning bottle",
+    "Grasp lantern",
+    "Grasp lantern component",
+    "Grasping cleaning cloth",
+    "Greeting/acknowledging participants",
+    "Handle paper lantern component",
+    "Hold and bend plastic strip",
+    "Hold and manipulate paper strip",
+    "Hold canned food",
+    "Hold container lid",
+    "Hold earbud case",
+    "Hold paper lantern",
+    "Identify next cardboard piece",
+    "Inspect shelf condition",
+    "Lift pot lid",
+    "Manipulate adhesive strip",
+    "Manipulate bead",
+    "Manipulate beads",
+    "Manipulate craft paper strips",
+    "Manipulate craft piece",
+    "Manipulate material",
+    "Manipulate paper decoration",
+    "Manipulate paper edge",
+    "Manipulate plastic strip",
+    "Manipulate plastic strips",
+    "Manipulate puzzle piece",
+    "Manipulate yellow strip",
+    "Manipulating paper strips",
+    "Move dustpan to side",
+    "Move hand away",
+    "Move hand away from shelf",
+    "Move marker and adjust hand",
+    "Move pot",
+    "Move through aisle",
+    "Move through the training room",
+    "Move towards kitchen area",
+    "Move towards the stove",
+    "Observe and pause",
+    "Observe and walk through store",
+    "Observe colleague and workspace",
+    "Observe puzzle progress",
+    "Open earbud case",
+    "Open folded paper lantern",
+    "Open paper lantern",
+    "Open paper lantern component",
+    "Open stove pot lid",
+    "Operate smartphone",
+    "Pick up dustpan",
+    "Pick up items from the shopping bag",
+    "Pick up packaged paper lantern component",
+    "Pick up puzzle piece",
+    "Pick up small piece of material",
+    "Pick up star bead",
+    "Picking up bottle",
+    "Picking up crafting material",
+    "Place and count bead",
+    "Place another canned food on shelf",
+    "Place cloth on floor",
+    "Place hand on table",
+    "Place items on the shelf",
+    "Place lid back",
+    "Place marked piece down",
+    "Place material",
+    "Place piece into puzzle",
+    "Place smartphone down",
+    "Place smartphone on stand",
+    "Place towel",
+    "Placing paper strip",
+    "Preparing to craft",
+    "Put down smartphone",
+    "Reach for cleaning supplies",
+    "Reach for craft items",
+    "Reach for next can",
+    "Reach for next canned food",
+    "Reach for puzzle piece",
+    "Reach for wire hangers",
+    "Record count",
+    "Release cardboard piece and gesture",
+    "Release hook",
+    "Release lantern",
+    "Release smartphone",
+    "Remove cleaning bottle",
+    "Remove paper lantern part from packaging",
+    "Remove plastic packaging",
+    "Reposition hand",
+    "Resume observation",
+    "Retrieve canned food from box",
+    "Retrieve next canned food item",
+    "Retrieving more beads",
+    "Rinse cloth in sink",
+    "Scroll smartphone screen",
+    "Secure paper edges with adhesive",
+    "Securing paper structure",
+    "Sort and adjust button line",
+    "Sort and arrange buttons",
+    "Sort and count beads",
+    "Sort and place buttons",
+    "Sort beads and write count",
+    "Sort button",
+    "Sort buttons",
+    "Sort craft items",
+    "Sort puzzle pieces",
+    "Sort small craft pieces",
+    "Start cutting",
+    "Stir contents",
+    "Use phone while crafting",
+    "Walk towards other aisles",
+    "Walking across the room",
+    "Walking in the hallway",
+    "Walking towards door",
+    "Washing hands in sink",
+    "Wipe kitchen counter",
+    "Wiping countertop",
+    "sort craft materials"
+  ],
+  "num_unseen_label_samples": 317,
+  "seen_label_accuracy": 0.09923664122137404,
+  "unseen_label_accuracy": 0.0031545741324921135,
+  "eval_label_counts": {
+    "Manipulate paper strip": 14,
+    "Move phone": 7,
+    "Cut cardboard": 7,
+    "Cut along the marked line": 7,
+    "Use smartphone": 6,
+    "Pick up utility knife": 6,
+    "Manipulate paper decoration": 5,
+    "Manipulate paper edge": 5,
+    "Manipulate adhesive strip": 5,
+    "Hold smartphone": 4,
+    "Interact with smartphone": 4,
+    "Manipulate craft piece": 4,
+    "Manipulate craft paper strips": 4,
+    "Operate smartphone": 4,
+    "Release paper strip": 4,
+    "Arrange buttons": 4,
+    "Arrange buttons in a line": 4,
+    "Approaching and pressing the door switch": 4,
+    "Bend and manipulate plastic strip": 4,
+    "Placing paper strip": 4,
+    "Securing paper structure": 4,
+    "Secure paper edges with adhesive": 4,
+    "Reposition hand": 3,
+    "Mark cardboard piece": 3,
+    "Hold cardboard piece": 3,
+    "Cut cardboard piece": 3,
+    "Marking cardboard piece": 3,
+    "Move marker and adjust hand": 3,
+    "Identify next cardboard piece": 3,
+    "sort craft materials": 3,
+    "Release scissors": 3,
+    "Release smartphone": 3,
+    "Sort small craft pieces": 3,
+    "Reach for craft items": 3,
+    "Sort craft items": 3,
+    "Move smartphone": 3,
+    "Place hand on table": 3,
+    "Browse smartphone screen": 3,
+    "Scroll smartphone screen": 3,
+    "Put down smartphone": 3,
+    "Place smartphone down": 3,
+    "Pick up puzzle piece": 3,
+    "Place piece into puzzle": 3,
+    "Manipulate puzzle piece": 3,
+    "Manipulate puzzle pieces": 3,
+    "Place puzzle piece": 3,
+    "Observe puzzle progress": 3,
+    "Adjust puzzle piece": 3,
+    "Attempt to fit puzzle piece": 3,
+    "Sort buttons": 3,
+    "Sort and arrange buttons": 3,
+    "Pick up button": 3,
+    "Place button": 3,
+    "Sort button": 3,
+    "Sort and adjust button line": 3,
+    "Sort and place buttons": 3,
+    "Walking in the hallway": 3,
+    "Entering the VR training room": 3,
+    "Greeting/acknowledging participants": 3,
+    "Move through the training room": 3,
+    "Manipulate plastic strips": 3,
+    "Manipulate plastic strip": 3,
+    "Hold and bend plastic strip": 3,
+    "Fold plastic strip": 3,
+    "Hold container lid": 2,
+    "Closing the door": 2,
+    "Grasp cleaning bottle": 2,
+    "Grasping cleaning cloth": 2,
+    "Adjust pot position": 2,
+    "Start cutting": 2,
+    "Observe and walk through store": 2,
+    "Move to shelf": 2,
+    "Inspect shelf condition": 2,
+    "Observe colleague and workspace": 2,
+    "Observe workspace": 2,
+    "Approach boxes": 2,
+    "Extract wire hangers from box": 2,
+    "Bundle display hooks": 2,
+    "Move through aisle": 2,
+    "Pick up items from the shopping bag": 2,
+    "Place items on the shelf": 2,
+    "Place item on shelf": 2,
+    "Adjust item on shelf": 2,
+    "Place marked piece down": 2,
+    "Pick up new cardboard piece": 2,
+    "Organize cardboard pieces": 2,
+    "Release cardboard piece and gesture": 2,
+    "Observe and pause": 2,
+    "Gesturing": 2,
+    "Resume observation": 2,
+    "Pick up can": 2,
+    "Hold canned food": 2,
+    "Pick up canned food": 2,
+    "Align canned food on shelf": 2,
+    "Place another canned food on shelf": 2,
+    "Adjust canned food on shelf": 2,
+    "Move hand away from shelf": 2,
+    "Place can on shelf": 2,
+    "Move hand away": 2,
+    "Hold earbud case": 2,
+    "Open earbud case": 2,
+    "Pick up smartphone": 2,
+    "Open paper lantern": 2,
+    "Fold paper lantern": 2,
+    "Grasp lantern": 2,
+    "Grasp lantern component": 2,
+    "Align paper lantern edges": 2,
+    "Adjust lantern string": 2,
+    "Handle paper lantern component": 2,
+    "Open folded paper lantern": 2,
+    "Adjust lantern shape": 2,
+    "Hold paper lantern": 2,
+    "Apply adhesive tape to lantern": 2,
+    "Open paper lantern component": 2,
+    "Expand paper lantern": 2,
+    "Align edges of paper lantern": 2,
+    "Search for puzzle piece": 2,
+    "Release puzzle piece": 2,
+    "Reach for puzzle piece": 2,
+    "Sort puzzle pieces": 2,
+    "Approaching the table": 2,
+    "Preparing to craft": 2,
+    "Picking up crafting material": 2,
+    "Use phone": 2,
+    "Pick up small piece of material": 2,
+    "Manipulate material": 2,
+    "Place material": 2,
+    "Manipulate yellow strip": 2,
+    "Manipulating paper strips": 2,
+    "Manipulate bead": 2,
+    "Manipulate beads": 2,
+    "Place phone down": 2,
+    "Hold and manipulate paper strip": 2,
+    "Sort beads": 2,
+    "Record count": 2,
+    "Counting and organizing beads": 2,
+    "Pick up star bead": 2,
+    "Place and count bead": 2,
+    "Hold beads": 2,
+    "Arrange star beads": 2,
+    "Pick up pen": 2,
+    "Counting star beads": 2,
+    "Retrieving more beads": 2,
+    "Adjust paper": 2,
+    "Gather star beads": 2,
+    "Arrange star beads for counting": 2,
+    "Sort and count beads": 2,
+    "Place smartphone on stand": 1,
+    "Pick up dustpan": 1,
+    "Move dustpan to side": 1,
+    "Move towards the stove": 1,
+    "Open stove pot lid": 1,
+    "Walking towards door": 1,
+    "Picking up bottle": 1,
+    "Wipe kitchen counter": 1,
+    "Rinse cloth in sink": 1,
+    "Move towards kitchen area": 1,
+    "Place cloth on floor": 1,
+    "Reach for cleaning supplies": 1,
+    "Remove cleaning bottle": 1,
+    "Washing hands in sink": 1,
+    "Wiping countertop": 1,
+    "Lift pot lid": 1,
+    "Stir contents": 1,
+    "Place lid back": 1,
+    "Move pot": 1,
+    "Place towel": 1,
+    "Walk towards shelves": 1,
+    "Reach for wire hangers": 1,
+    "Release hook": 1,
+    "Walk towards other aisles": 1,
+    "Reach for another item": 1,
+    "Reach for next item": 1,
+    "Place canned food on shelf": 1,
+    "Reach for next can": 1,
+    "Reach into box": 1,
+    "Retrieve next canned food item": 1,
+    "Reach for next canned food": 1,
+    "Retrieve canned food from box": 1,
+    "Release lantern": 1,
+    "Pick up packaged paper lantern component": 1,
+    "Remove paper lantern part from packaging": 1,
+    "Remove plastic packaging": 1,
+    "Walking across the room": 1,
+    "Use phone while crafting": 1,
+    "Sort beads and write count": 1,
+    "Sort star-shaped beads": 1,
+    "Write count on paper": 1,
+    "Write on paper": 1
+  },
+  "json_validity_rate": 1.0,
+  "action_macro_f1": 0.0018678269676001454,
+  "subtask_accuracy": 0.0,
+  "transition_accuracy": 0.9732142857142857,
+  "next_action_accuracy": 0.033482142857142856,
+  "contact_accuracy": 0.7299107142857143,
+  "object_micro_f1": 0.31099781500364165,
+  "shard_dirs": [
+    "results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full_shard0",
+    "results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full_shard1",
+    "results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full_shard2",
+    "results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full_shard3"
+  ],
+  "coverage": {
+    "expected_eval_samples": 448,
+    "merged_prediction_rows": 448,
+    "missing_prediction_rows": 0
+  },
+  "issues": []
+}

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/eval/per_class_metrics.csv

@@ -0,0 +1,1202 @@
+class_name,support,predicted,precision,recall,f1
+Adjust Mahjong tile,0,0,0.0,0.0,0.0
+Adjust Mahjong tile alignment,0,0,0.0,0.0,0.0
+Adjust Mahjong tile on the stack,0,0,0.0,0.0,0.0
+Adjust Mahjong tiles,0,0,0.0,0.0,0.0
+Adjust bead piles,0,0,0.0,0.0,0.0
+Adjust canned food on shelf,2,0,0.0,0.0,0.0
+Adjust cans in bin,0,0,0.0,0.0,0.0
+Adjust cans in container,0,0,0.0,0.0,0.0
+Adjust cans in tray,0,0,0.0,0.0,0.0
+Adjust cardboard,0,0,0.0,0.0,0.0
+Adjust cardboard divider,0,0,0.0,0.0,0.0
+Adjust cardboard position,0,0,0.0,0.0,0.0
+Adjust container on shelf,0,0,0.0,0.0,0.0
+Adjust container position,0,0,0.0,0.0,0.0
+Adjust containers on shelf,0,0,0.0,0.0,0.0
+Adjust foam strip,0,0,0.0,0.0,0.0
+Adjust grip,0,0,0.0,0.0,0.0
+Adjust grip on container,0,0,0.0,0.0,0.0
+Adjust hand position,0,0,0.0,0.0,0.0
+Adjust item on shelf,2,0,0.0,0.0,0.0
+Adjust lantern shape,2,0,0.0,0.0,0.0
+Adjust lantern string,2,1,0.0,0.0,0.0
+Adjust paper,2,0,0.0,0.0,0.0
+Adjust paper strip,0,0,0.0,0.0,0.0
+Adjust perspective,0,0,0.0,0.0,0.0
+Adjust placement on shelf,0,0,0.0,0.0,0.0
+Adjust position,0,0,0.0,0.0,0.0
+Adjust pot position,2,0,0.0,0.0,0.0
+Adjust puzzle piece,3,0,0.0,0.0,0.0
+Adjust red button,0,0,0.0,0.0,0.0
+Adjust red button in row,0,0,0.0,0.0,0.0
+Adjust red button position,0,0,0.0,0.0,0.0
+Adjust retail item position,0,0,0.0,0.0,0.0
+Adjust retail items on shelf,0,0,0.0,0.0,0.0
+Adjust ruler position,0,0,0.0,0.0,0.0
+Adjust smartphone and sort pieces,0,0,0.0,0.0,0.0
+Adjust snack package,0,0,0.0,0.0,0.0
+Adjust tile row alignment,0,0,0.0,0.0,0.0
+Adjust vacuum cleaner position,0,0,0.0,0.0,0.0
+Adjusting a puzzle piece,0,0,0.0,0.0,0.0
+Adjusting canned goods on shelf,0,0,0.0,0.0,0.0
+Adjusting fabric for cutting,0,0,0.0,0.0,0.0
+Adjusting fabric position,0,0,0.0,0.0,0.0
+Adjusting puzzle piece,0,0,0.0,0.0,0.0
+Align Mahjong tiles,0,0,0.0,0.0,0.0
+Align and place retail item,0,0,0.0,0.0,0.0
+Align blue strip,0,0,0.0,0.0,0.0
+Align button,0,0,0.0,0.0,0.0
+Align button in row,0,0,0.0,0.0,0.0
+Align button row,0,0,0.0,0.0,0.0
+Align buttons,0,0,0.0,0.0,0.0
+Align canned food on shelf,2,0,0.0,0.0,0.0
+Align canned goods on shelf,0,0,0.0,0.0,0.0
+Align cardboard piece,0,0,0.0,0.0,0.0
+Align cardboard strip,0,0,0.0,0.0,0.0
+Align charging cable,0,0,0.0,0.0,0.0
+Align edges of paper lantern,2,0,0.0,0.0,0.0
+Align foam piece,0,0,0.0,0.0,0.0
+Align foam strip,0,0,0.0,0.0,0.0
+Align paper lantern edges,2,0,0.0,0.0,0.0
+Align paper strip,0,0,0.0,0.0,0.0
+Align plastic containers,0,0,0.0,0.0,0.0
+Align red button in row,0,0,0.0,0.0,0.0
+Align red buttons,0,0,0.0,0.0,0.0
+Align ruler,0,0,0.0,0.0,0.0
+Align ruler and mark cardboard,0,0,0.0,0.0,0.0
+Align ruler on cardboard,0,0,0.0,0.0,0.0
+Align ruler with crease,0,0,0.0,0.0,0.0
+Align scissors,0,0,0.0,0.0,0.0
+Apply adhesive tape to lantern,2,0,0.0,0.0,0.0
+Approach boxes,2,0,0.0,0.0,0.0
+Approach desk,0,0,0.0,0.0,0.0
+Approach packing area,0,4,0.0,0.0,0.0
+Approach restocking supplies,0,0,0.0,0.0,0.0
+Approach table,0,0,0.0,0.0,0.0
+Approach work table,0,0,0.0,0.0,0.0
+Approach workstation,0,1,0.0,0.0,0.0
+Approaching and pressing the door switch,4,0,0.0,0.0,0.0
+Approaching the table,2,1,0.0,0.0,0.0
+Approaching work table,0,0,0.0,0.0,0.0
+Arrange Mahjong tiles,0,0,0.0,0.0,0.0
+Arrange beads by color,0,0,0.0,0.0,0.0
+Arrange black buttons,0,0,0.0,0.0,0.0
+Arrange button cluster,0,0,0.0,0.0,0.0
+Arrange buttons,4,0,0.0,0.0,0.0
+Arrange buttons in a line,4,0,0.0,0.0,0.0
+Arrange buttons in row,0,0,0.0,0.0,0.0
+Arrange buttons on table,0,0,0.0,0.0,0.0
+Arrange buttons on the table,0,0,0.0,0.0,0.0
+Arrange canned products on shelf,0,0,0.0,0.0,0.0
+Arrange cans in box,0,0,0.0,0.0,0.0
+Arrange cans on shelf,0,0,0.0,0.0,0.0
+Arrange cardboard,0,0,0.0,0.0,0.0
+Arrange cardboard piece,0,0,0.0,0.0,0.0
+Arrange cardboard pieces,0,0,0.0,0.0,0.0
+Arrange cardboard squares,0,0,0.0,0.0,0.0
+Arrange container on shelf,0,0,0.0,0.0,0.0
+Arrange items on shelf,0,0,0.0,0.0,0.0
+Arrange orange buttons,0,0,0.0,0.0,0.0
+Arrange paper stars,0,0,0.0,0.0,0.0
+Arrange paper strips,0,0,0.0,0.0,0.0
+Arrange plastic containers,0,0,0.0,0.0,0.0
+Arrange red buttons,0,0,0.0,0.0,0.0
+Arrange small buttons,0,0,0.0,0.0,0.0
+Arrange star beads,2,0,0.0,0.0,0.0
+Arrange star beads for counting,2,0,0.0,0.0,0.0
+Arrange star-shaped beads,0,0,0.0,0.0,0.0
+Arrange tiles into row,0,0,0.0,0.0,0.0
+Arrive at a different workstation,0,0,0.0,0.0,0.0
+Assemble cardboard pieces,0,0,0.0,0.0,0.0
+Assemble foam strips,0,0,0.0,0.0,0.0
+Assess shelf arrangement,0,0,0.0,0.0,0.0
+Attach foam strip,0,0,0.0,0.0,0.0
+Attach material to paper strip,0,0,0.0,0.0,0.0
+Attempt to fit puzzle piece,3,0,0.0,0.0,0.0
+Begin folding paper strip,0,0,0.0,0.0,0.0
+Begin rolling quilling strip,0,0,0.0,0.0,0.0
+Bend and manipulate plastic strip,4,0,0.0,0.0,0.0
+Browse and interact with phone interface,0,0,0.0,0.0,0.0
+Browse mobile phone,0,1,0.0,0.0,0.0
+Browse smartphone screen,3,0,0.0,0.0,0.0
+Browsing mobile phone,0,0,0.0,0.0,0.0
+Browsing smartphone content,0,0,0.0,0.0,0.0
+Bundle display hooks,2,0,0.0,0.0,0.0
+Cap marker,0,0,0.0,0.0,0.0
+Carry cardboard piece,0,0,0.0,0.0,0.0
+Carry cereal boxes,0,0,0.0,0.0,0.0
+Carry cereal towards aisle,0,0,0.0,0.0,0.0
+Carry container,0,0,0.0,0.0,0.0
+Carry crate of cans,0,0,0.0,0.0,0.0
+Carry item to shelf,0,0,0.0,0.0,0.0
+Carry pasta box towards aisle,0,0,0.0,0.0,0.0
+Carry plastic container,0,0,0.0,0.0,0.0
+Carry stool to next shelf,0,0,0.0,0.0,0.0
+Check phone,0,0,0.0,0.0,0.0
+Check smart watch,0,0,0.0,0.0,0.0
+Check watch,0,0,0.0,0.0,0.0
+Clean shelf,0,0,0.0,0.0,0.0
+Close cardboard box,0,0,0.0,0.0,0.0
+Closing the door,2,0,0.0,0.0,0.0
+Combine bead piles,0,0,0.0,0.0,0.0
+Complete the cut,0,0,0.0,0.0,0.0
+Connect cable to device,0,0,0.0,0.0,0.0
+Continue cutting fabric,0,0,0.0,0.0,0.0
+Continue cutting newspaper,0,0,0.0,0.0,0.0
+Continue folding paper strip,0,0,0.0,0.0,0.0
+Count and arrange paper stars,0,0,0.0,0.0,0.0
+Count and record paper stars,0,0,0.0,0.0,0.0
+Count paper stars,0,0,0.0,0.0,0.0
+Counting and organizing beads,2,0,0.0,0.0,0.0
+Counting star beads,2,0,0.0,0.0,0.0
+Curve foam strip into loop,0,0,0.0,0.0,0.0
+Cut along the edge of the newspaper,0,0,0.0,0.0,0.0
+Cut along the line,0,0,0.0,0.0,0.0
+Cut along the marked line,7,0,0.0,0.0,0.0
+Cut along the newspaper edge,0,0,0.0,0.0,0.0
+Cut cardboard,7,15,0.4,0.8571428571428571,0.5454545454545455
+Cut cardboard along line,0,0,0.0,0.0,0.0
+Cut cardboard grid,0,0,0.0,0.0,0.0
+Cut cardboard into triangles,0,0,0.0,0.0,0.0
+Cut cardboard pattern,0,0,0.0,0.0,0.0
+Cut cardboard piece,3,0,0.0,0.0,0.0
+Cut cardboard piece with scissors,0,0,0.0,0.0,0.0
+Cut cardboard pieces with scissors,0,0,0.0,0.0,0.0
+Cut cardboard shape,0,0,0.0,0.0,0.0
+Cut cardboard sheet,0,0,0.0,0.0,0.0
+Cut cardboard sheet with scissors,0,0,0.0,0.0,0.0
+Cut cardboard square,0,0,0.0,0.0,0.0
+Cut cardboard strip,0,0,0.0,0.0,0.0
+Cut cardboard strip with scissors,0,1,0.0,0.0,0.0
+Cut cardboard strip with utility knife,0,0,0.0,0.0,0.0
+Cut cardboard triangle,0,1,0.0,0.0,0.0
+Cut cardboard tube,0,0,0.0,0.0,0.0
+Cut cardboard with scissors,0,5,0.0,0.0,0.0
+Cut cardboard with utility knife,0,0,0.0,0.0,0.0
+Cut fabric with scissors,0,0,0.0,0.0,0.0
+Cut light green fabric,0,0,0.0,0.0,0.0
+Cut newspaper,0,0,0.0,0.0,0.0
+Cut newspaper with scissors,0,0,0.0,0.0,0.0
+Cut out cardboard pattern,0,0,0.0,0.0,0.0
+Cut section from newspaper,0,0,0.0,0.0,0.0
+Cutting fabric,0,0,0.0,0.0,0.0
+Deposit beads into box,0,0,0.0,0.0,0.0
+Deposit cardboard squares,0,0,0.0,0.0,0.0
+Discard item into bin,0,0,0.0,0.0,0.0
+Discard paper towel,0,0,0.0,0.0,0.0
+Draw grid line,0,1,0.0,0.0,0.0
+Draw grid line with pen,0,0,0.0,0.0,0.0
+Draw line,0,0,0.0,0.0,0.0
+Draw line along ruler,0,0,0.0,0.0,0.0
+Draw line on cardboard,0,0,0.0,0.0,0.0
+Draw line with marker,0,0,0.0,0.0,0.0
+Draw line with pen,0,0,0.0,0.0,0.0
+Draw lines on cardboard,0,0,0.0,0.0,0.0
+Draw lines with pen and ruler,0,0,0.0,0.0,0.0
+Draw lines with ruler,0,0,0.0,0.0,0.0
+Draw straight line,0,0,0.0,0.0,0.0
+Draw straight lines on cardboard,0,0,0.0,0.0,0.0
+Drawing grid line,0,0,0.0,0.0,0.0
+Drawing grid line with pen and ruler,0,0,0.0,0.0,0.0
+Drawing grid line with ruler,0,0,0.0,0.0,0.0
+Drawing lines on cardboard,0,0,0.0,0.0,0.0
+Drop cardboard square into box,0,0,0.0,0.0,0.0
+Dry hands,0,0,0.0,0.0,0.0
+Enter the room,0,0,0.0,0.0,0.0
+Enter workspace,0,0,0.0,0.0,0.0
+Entering the VR training room,3,0,0.0,0.0,0.0
+Examine canned goods,0,0,0.0,0.0,0.0
+Examine item,0,0,0.0,0.0,0.0
+Examine labels,0,0,0.0,0.0,0.0
+Examine product,0,0,0.0,0.0,0.0
+Expand paper lantern,2,1,0.0,0.0,0.0
+Extract wire hangers from box,2,0,0.0,0.0,0.0
+Finish placing cardboard cutouts,0,0,0.0,0.0,0.0
+Finish washing hands,0,0,0.0,0.0,0.0
+Finish wiping and inspect jar,0,0,0.0,0.0,0.0
+Finishing coil,0,0,0.0,0.0,0.0
+Fold and manipulate ribbon,0,0,0.0,0.0,0.0
+Fold and organize paper strips,0,0,0.0,0.0,0.0
+Fold blue strip,0,0,0.0,0.0,0.0
+Fold cardboard,0,0,0.0,0.0,0.0
+Fold cardboard edge,0,0,0.0,0.0,0.0
+Fold cardboard shape,0,0,0.0,0.0,0.0
+Fold cardboard sheet,0,0,0.0,0.0,0.0
+Fold cut cardboard,0,0,0.0,0.0,0.0
+Fold foam piece,0,0,0.0,0.0,0.0
+Fold lucky star,0,0,0.0,0.0,0.0
+Fold newspaper,0,0,0.0,0.0,0.0
+Fold paper lantern,2,0,0.0,0.0,0.0
+Fold paper star,0,3,0.0,0.0,0.0
+Fold paper strip,0,61,0.0,0.0,0.0
+Fold paper strip into a star,0,0,0.0,0.0,0.0
+Fold paper strip into knot,0,0,0.0,0.0,0.0
+Fold paper strip into lucky star,0,0,0.0,0.0,0.0
+Fold paper strip into star,0,1,0.0,0.0,0.0
+Fold plastic strip,3,0,0.0,0.0,0.0
+Fold purple paper,0,0,0.0,0.0,0.0
+Fold purple paper strip,0,0,0.0,0.0,0.0
+Fold ribbon,0,0,0.0,0.0,0.0
+Folding cardboard,0,0,0.0,0.0,0.0
+Folding paper strip,0,0,0.0,0.0,0.0
+Forming quilled paper shape,0,0,0.0,0.0,0.0
+Gather cardboard pieces,0,0,0.0,0.0,0.0
+Gather pieces,0,0,0.0,0.0,0.0
+Gather pieces into box,0,0,0.0,0.0,0.0
+Gather star beads,2,0,0.0,0.0,0.0
+Gathering colored beads,0,0,0.0,0.0,0.0
+Gathering items,0,0,0.0,0.0,0.0
+Gathering star beads,0,0,0.0,0.0,0.0
+Gesturing,2,1,0.0,0.0,0.0
+Grasp and retrieve item,0,0,0.0,0.0,0.0
+Grasp cardboard sheet,0,0,0.0,0.0,0.0
+Grasp cleaning bottle,2,0,0.0,0.0,0.0
+Grasp door handle,0,0,0.0,0.0,0.0
+Grasp electronic object,0,0,0.0,0.0,0.0
+Grasp item,0,0,0.0,0.0,0.0
+Grasp lantern,2,0,0.0,0.0,0.0
+Grasp lantern component,2,0,0.0,0.0,0.0
+Grasp next item,0,0,0.0,0.0,0.0
+Grasp origami stars,0,0,0.0,0.0,0.0
+Grasp package,0,0,0.0,0.0,0.0
+Grasp paper strip,0,0,0.0,0.0,0.0
+Grasp plastic bag on shelf,0,0,0.0,0.0,0.0
+Grasp product from box,0,0,0.0,0.0,0.0
+Grasp product from shelf,0,0,0.0,0.0,0.0
+Grasp retail item,0,0,0.0,0.0,0.0
+Grasp shopping bag,0,0,0.0,0.0,0.0
+Grasp snack package,0,0,0.0,0.0,0.0
+Grasping cleaning cloth,2,0,0.0,0.0,0.0
+Greeting/acknowledging participants,3,0,0.0,0.0,0.0
+Guide utility knife along ruler,0,0,0.0,0.0,0.0
+Handle paper lantern component,2,0,0.0,0.0,0.0
+Hold and align cardboard,0,1,0.0,0.0,0.0
+Hold and align newspaper,0,2,0.0,0.0,0.0
+Hold and align paper strip,0,16,0.0,0.0,0.0
+Hold and bend paper strip,0,0,0.0,0.0,0.0
+Hold and bend plastic strip,3,0,0.0,0.0,0.0
+Hold and crease purple paper,0,0,0.0,0.0,0.0
+Hold and examine item,0,0,0.0,0.0,0.0
+Hold and inspect can,0,0,0.0,0.0,0.0
+Hold and manipulate paper strip,2,0,0.0,0.0,0.0
+Hold and mark cardboard piece,0,0,0.0,0.0,0.0
+Hold and rotate paper strip,0,0,0.0,0.0,0.0
+Hold and view phone,0,8,0.0,0.0,0.0
+Hold and wipe product,0,1,0.0,0.0,0.0
+Hold beads,2,0,0.0,0.0,0.0
+Hold bin and move through aisle,0,0,0.0,0.0,0.0
+Hold blue product box,0,0,0.0,0.0,0.0
+Hold blue strip,0,0,0.0,0.0,0.0
+Hold canned food,2,0,0.0,0.0,0.0
+Hold cardboard,0,0,0.0,0.0,0.0
+Hold cardboard piece,3,0,0.0,0.0,0.0
+Hold cardboard pieces,0,0,0.0,0.0,0.0
+Hold cardboard strip,0,0,0.0,0.0,0.0
+Hold cardboard with ruler,0,0,0.0,0.0,0.0
+Hold charger,0,0,0.0,0.0,0.0
+Hold charger and cable,0,0,0.0,0.0,0.0
+Hold charging cable,0,0,0.0,0.0,0.0
+Hold cleaning cloth,0,0,0.0,0.0,0.0
+Hold container,0,0,0.0,0.0,0.0
+Hold container lid,2,0,0.0,0.0,0.0
+Hold container of canned food,0,0,0.0,0.0,0.0
+Hold craft tool,0,0,0.0,0.0,0.0
+Hold device and cable,0,0,0.0,0.0,0.0
+Hold earbud case,2,1,0.0,0.0,0.0
+Hold electronic accessory,0,0,0.0,0.0,0.0
+Hold electronic item,0,0,0.0,0.0,0.0
+Hold empty container,0,0,0.0,0.0,0.0
+Hold foam pieces,0,0,0.0,0.0,0.0
+Hold instructional sign,0,0,0.0,0.0,0.0
+Hold item,0,0,0.0,0.0,0.0
+Hold item and adjust posture,0,0,0.0,0.0,0.0
+Hold items,0,0,0.0,0.0,0.0
+Hold items and inspect shelf,0,0,0.0,0.0,0.0
+Hold items in hand,0,0,0.0,0.0,0.0
+Hold newspaper,0,0,0.0,0.0,0.0
+Hold paper lantern,2,0,0.0,0.0,0.0
+Hold paper strip,0,2,0.0,0.0,0.0
+Hold pen and paper,0,0,0.0,0.0,0.0
+Hold phone,0,0,0.0,0.0,0.0
+Hold pickle jar,0,0,0.0,0.0,0.0
+Hold portable charger,0,0,0.0,0.0,0.0
+Hold power adapter,0,0,0.0,0.0,0.0
+Hold power bank and cable,0,3,0.0,0.0,0.0
+Hold product,0,0,0.0,0.0,0.0
+Hold product labels,0,0,0.0,0.0,0.0
+Hold product package,0,0,0.0,0.0,0.0
+Hold quilled paper coil,0,0,0.0,0.0,0.0
+Hold quilled paper piece,0,0,0.0,0.0,0.0
+Hold quilling paper,0,0,0.0,0.0,0.0
+Hold recording sheet and pen,0,0,0.0,0.0,0.0
+Hold ruler,0,0,0.0,0.0,0.0
+Hold ruler and draw line,0,0,0.0,0.0,0.0
+Hold ruler and mark cardboard,0,0,0.0,0.0,0.0
+Hold ruler and marker,0,0,0.0,0.0,0.0
+Hold ruler and pen steady,0,0,0.0,0.0,0.0
+Hold ruler on cardboard,0,0,0.0,0.0,0.0
+Hold ruler steady,0,0,0.0,0.0,0.0
+Hold scissors,0,0,0.0,0.0,0.0
+Hold small cardboard pieces,0,0,0.0,0.0,0.0
+Hold small object,0,0,0.0,0.0,0.0
+Hold small piece of ribbon,0,0,0.0,0.0,0.0
+Hold small product bag,0,0,0.0,0.0,0.0
+Hold small white box,0,0,0.0,0.0,0.0
+Hold smartphone,4,17,0.0,0.0,0.0
+Hold smartphone box,0,0,0.0,0.0,0.0
+Hold snack package,0,0,0.0,0.0,0.0
+Hold snack packages,0,0,0.0,0.0,0.0
+Hold supplement bottle,0,0,0.0,0.0,0.0
+Hold tray of canned goods,0,0,0.0,0.0,0.0
+Hold utility knife,0,0,0.0,0.0,0.0
+Hold water bottle,0,0,0.0,0.0,0.0
+Holding marker,0,0,0.0,0.0,0.0
+Identify next cardboard piece,3,0,0.0,0.0,0.0
+Inflate paper star,0,0,0.0,0.0,0.0
+Initiate star folding,0,0,0.0,0.0,0.0
+Insert charging cable,0,0,0.0,0.0,0.0
+Insert charging cable into power bank,0,6,0.0,0.0,0.0
+Insert plug into power adapter,0,0,0.0,0.0,0.0
+Inspect Dior gift box,0,0,0.0,0.0,0.0
+Inspect almond package,0,0,0.0,0.0,0.0
+Inspect and place item on shelf,0,0,0.0,0.0,0.0
+Inspect bottle,0,0,0.0,0.0,0.0
+Inspect cardboard piece,0,0,0.0,0.0,0.0
+Inspect cardboard strip,0,0,0.0,0.0,0.0
+Inspect charging case,0,0,0.0,0.0,0.0
+Inspect electronic item,0,0,0.0,0.0,0.0
+Inspect jar,0,0,0.0,0.0,0.0
+Inspect product,0,0,0.0,0.0,0.0
+Inspect product lid,0,0,0.0,0.0,0.0
+Inspect shelf,0,0,0.0,0.0,0.0
+Inspect shelf and organize stock,0,0,0.0,0.0,0.0
+Inspect shelf condition,2,0,0.0,0.0,0.0
+Inspect smartphone box,0,0,0.0,0.0,0.0
+Inspect strip,0,0,0.0,0.0,0.0
+Inspect supplement bottle,0,0,0.0,0.0,0.0
+Interact with colleagues,0,0,0.0,0.0,0.0
+Interact with phone,0,0,0.0,0.0,0.0
+Interact with smartphone,4,0,0.0,0.0,0.0
+Interact with smartphone screen,0,0,0.0,0.0,0.0
+Interacting with phone screen,0,0,0.0,0.0,0.0
+Interaction with coworker,0,0,0.0,0.0,0.0
+Interlock paper strips,0,0,0.0,0.0,0.0
+Labeling cardboard piece,0,0,0.0,0.0,0.0
+Labeling cardboard square,0,0,0.0,0.0,0.0
+Labeling cardboard squares,0,0,0.0,0.0,0.0
+Lift blue strip,0,0,0.0,0.0,0.0
+Lift pen and shift ruler,0,0,0.0,0.0,0.0
+Lift pot lid,1,1,1.0,1.0,1.0
+Lift utility knife,0,0,0.0,0.0,0.0
+Lock phone,0,0,0.0,0.0,0.0
+Look around the table,0,0,0.0,0.0,0.0
+Look away,0,0,0.0,0.0,0.0
+Manipulate adhesive strip,5,0,0.0,0.0,0.0
+Manipulate and inspect colorful pieces,0,0,0.0,0.0,0.0
+Manipulate bead,2,0,0.0,0.0,0.0
+Manipulate beads,2,0,0.0,0.0,0.0
+Manipulate cardboard piece,0,0,0.0,0.0,0.0
+Manipulate cardboard shape,0,0,0.0,0.0,0.0
+Manipulate cardboard sheet,0,0,0.0,0.0,0.0
+Manipulate colorful pieces,0,0,0.0,0.0,0.0
+Manipulate component,0,0,0.0,0.0,0.0
+Manipulate component on strip,0,0,0.0,0.0,0.0
+Manipulate craft paper strips,4,0,0.0,0.0,0.0
+Manipulate craft piece,4,0,0.0,0.0,0.0
+Manipulate folded paper star,0,0,0.0,0.0,0.0
+Manipulate light blue strip,0,0,0.0,0.0,0.0
+Manipulate material,2,0,0.0,0.0,0.0
+Manipulate paper decoration,5,0,0.0,0.0,0.0
+Manipulate paper edge,5,0,0.0,0.0,0.0
+Manipulate paper piece,0,0,0.0,0.0,0.0
+Manipulate paper quilling piece,0,0,0.0,0.0,0.0
+Manipulate paper star,0,1,0.0,0.0,0.0
+Manipulate paper stars,0,0,0.0,0.0,0.0
+Manipulate paper strip,14,15,0.2,0.21428571428571427,0.20689655172413796
+Manipulate paper strips,0,0,0.0,0.0,0.0
+Manipulate plastic strip,3,0,0.0,0.0,0.0
+Manipulate plastic strips,3,0,0.0,0.0,0.0
+Manipulate power cable plug,0,0,0.0,0.0,0.0
+Manipulate puzzle piece,3,0,0.0,0.0,0.0
+Manipulate puzzle pieces,3,0,0.0,0.0,0.0
+Manipulate quilled paper,0,0,0.0,0.0,0.0
+Manipulate quilled paper shape,0,0,0.0,0.0,0.0
+Manipulate quilled paper strip,0,0,0.0,0.0,0.0
+Manipulate quilled paper strips,0,0,0.0,0.0,0.0
+Manipulate quilling paper,0,0,0.0,0.0,0.0
+Manipulate quilling strip,0,0,0.0,0.0,0.0
+Manipulate ribbon knot,0,0,0.0,0.0,0.0
+Manipulate ribbon piece,0,0,0.0,0.0,0.0
+Manipulate small component,0,0,0.0,0.0,0.0
+Manipulate small object,0,0,0.0,0.0,0.0
+Manipulate small paper segment,0,0,0.0,0.0,0.0
+Manipulate star,0,0,0.0,0.0,0.0
+Manipulate yellow strip,2,0,0.0,0.0,0.0
+Manipulating paper strips,2,0,0.0,0.0,0.0
+Mark cardboard,0,0,0.0,0.0,0.0
+Mark cardboard piece,3,6,0.0,0.0,0.0
+Mark cardboard strip with pen,0,0,0.0,0.0,0.0
+Mark cardboard with marker,0,6,0.0,0.0,0.0
+Mark cardboard with pen,0,0,0.0,0.0,0.0
+Mark cardboard with pen and ruler,0,0,0.0,0.0,0.0
+Mark cardboard with ruler,0,0,0.0,0.0,0.0
+Mark cardboard with ruler and pen,0,0,0.0,0.0,0.0
+Mark fabric,0,0,0.0,0.0,0.0
+Mark fabric with pen,0,0,0.0,0.0,0.0
+Mark fabric with pen and ruler,0,0,0.0,0.0,0.0
+Mark line on cardboard,0,0,0.0,0.0,0.0
+Mark lines on cardboard,0,0,0.0,0.0,0.0
+Mark lines with marker,0,0,0.0,0.0,0.0
+Mark lines with pen along ruler,0,0,0.0,0.0,0.0
+Mark list with pen,0,1,0.0,0.0,0.0
+Mark paper list,0,0,0.0,0.0,0.0
+Mark straight line,0,0,0.0,0.0,0.0
+Marking cardboard piece,3,0,0.0,0.0,0.0
+Marking cardboard with pen,0,0,0.0,0.0,0.0
+Marking lines on cardboard,0,0,0.0,0.0,0.0
+Measure and mark cardboard,0,0,0.0,0.0,0.0
+Measure cardboard with ruler,0,0,0.0,0.0,0.0
+Move Mahjong tile,0,0,0.0,0.0,0.0
+Move along shelf,0,0,0.0,0.0,0.0
+Move along the shelf,0,0,0.0,0.0,0.0
+Move along the shelves,0,0,0.0,0.0,0.0
+Move along the supermarket aisle,0,0,0.0,0.0,0.0
+Move and place black buttons,0,0,0.0,0.0,0.0
+Move away from collection box,0,0,0.0,0.0,0.0
+Move away from desk,0,0,0.0,0.0,0.0
+Move away from shelf,0,0,0.0,0.0,0.0
+Move away from table,0,0,0.0,0.0,0.0
+Move away from workstation,0,0,0.0,0.0,0.0
+Move bin,0,0,0.0,0.0,0.0
+Move bin to shelf area,0,3,0.0,0.0,0.0
+Move black button,0,0,0.0,0.0,0.0
+Move blue beads,0,0,0.0,0.0,0.0
+Move box to next position,0,0,0.0,0.0,0.0
+Move button to line,0,0,0.0,0.0,0.0
+Move camera over surface,0,0,0.0,0.0,0.0
+Move can towards shelf,0,0,0.0,0.0,0.0
+Move canned goods container,0,0,0.0,0.0,0.0
+Move cardboard,0,0,0.0,0.0,0.0
+Move cardboard box,0,0,0.0,0.0,0.0
+Move cardboard piece,0,0,0.0,0.0,0.0
+Move cardboard sheet,0,0,0.0,0.0,0.0
+Move cardboard to pile,0,0,0.0,0.0,0.0
+Move container toward shelf,0,0,0.0,0.0,0.0
+Move dustpan to side,1,0,0.0,0.0,0.0
+Move hand,0,2,0.0,0.0,0.0
+Move hand away,2,3,0.0,0.0,0.0
+Move hand away from shelf,2,0,0.0,0.0,0.0
+Move hand away from workspace,0,0,0.0,0.0,0.0
+Move hand back to box,0,0,0.0,0.0,0.0
+Move hand over button pile,0,0,0.0,0.0,0.0
+Move hand to paper stars,0,0,0.0,0.0,0.0
+Move hand toward craft materials,0,0,0.0,0.0,0.0
+Move item to bag,0,0,0.0,0.0,0.0
+Move marker and adjust hand,3,0,0.0,0.0,0.0
+Move marker and ruler,0,0,0.0,0.0,0.0
+Move marker away,0,0,0.0,0.0,0.0
+Move orange buttons,0,0,0.0,0.0,0.0
+Move origami stars,0,0,0.0,0.0,0.0
+Move pen,0,0,0.0,0.0,0.0
+Move pen aside,0,0,0.0,0.0,0.0
+Move pen away,0,0,0.0,0.0,0.0
+Move phone,7,0,0.0,0.0,0.0
+Move piece to pile,0,0,0.0,0.0,0.0
+Move pieces into box,0,0,0.0,0.0,0.0
+Move pineapple chips,0,0,0.0,0.0,0.0
+Move plastic storage bin,0,0,0.0,0.0,0.0
+Move plush toy,0,0,0.0,0.0,0.0
+Move pot,1,0,0.0,0.0,0.0
+Move product to box,0,0,0.0,0.0,0.0
+Move product to shelf,0,0,0.0,0.0,0.0
+Move product towards shelf,0,0,0.0,0.0,0.0
+Move puzzle piece,0,0,0.0,0.0,0.0
+Move ruler,0,0,0.0,0.0,0.0
+Move ruler and tools,0,0,0.0,0.0,0.0
+Move scissors away,0,0,0.0,0.0,0.0
+Move small blue foam piece towards the strip,0,0,0.0,0.0,0.0
+Move smartphone,3,0,0.0,0.0,0.0
+Move storage bin,0,0,0.0,0.0,0.0
+Move through aisle,2,0,0.0,0.0,0.0
+Move through the training room,3,0,0.0,0.0,0.0
+Move to box,0,0,0.0,0.0,0.0
+Move to desk,0,0,0.0,0.0,0.0
+Move to next section,0,0,0.0,0.0,0.0
+Move to shelf,2,0,0.0,0.0,0.0
+Move to shelf base,0,0,0.0,0.0,0.0
+Move to stock products,0,0,0.0,0.0,0.0
+Move towards aisle,0,0,0.0,0.0,0.0
+Move towards box,0,0,0.0,0.0,0.0
+Move towards kitchen area,1,0,0.0,0.0,0.0
+Move towards shelf,0,0,0.0,0.0,0.0
+Move towards table,0,0,0.0,0.0,0.0
+Move towards the stove,1,0,0.0,0.0,0.0
+Move tray towards packing area,0,0,0.0,0.0,0.0
+Move utility knife along ruler,0,0,0.0,0.0,0.0
+Move vacuum cleaner,0,0,0.0,0.0,0.0
+Move vacuum cleaner hose,0,0,0.0,0.0,0.0
+Moving cardboard square,0,0,0.0,0.0,0.0
+Moving hand,0,0,0.0,0.0,0.0
+Moving hand towards cardboard stack,0,0,0.0,0.0,0.0
+Moving ruler,0,0,0.0,0.0,0.0
+Observe and pause,2,0,0.0,0.0,0.0
+Observe and walk through store,2,0,0.0,0.0,0.0
+Observe colleague and workspace,2,0,0.0,0.0,0.0
+Observe craft layout,0,0,0.0,0.0,0.0
+Observe desktop layout,0,0,0.0,0.0,0.0
+Observe paper and count objects,0,0,0.0,0.0,0.0
+Observe paper quilling station,0,0,0.0,0.0,0.0
+Observe puzzle progress,3,0,0.0,0.0,0.0
+Observe room,0,1,0.0,0.0,0.0
+Observe shelf,0,0,0.0,0.0,0.0
+Observe shelf status,0,0,0.0,0.0,0.0
+Observe sorting progress,0,0,0.0,0.0,0.0
+Observe stocking,0,0,0.0,0.0,0.0
+Observe surroundings,0,0,0.0,0.0,0.0
+Observe workspace,2,2,0.0,0.0,0.0
+Open cardboard box,0,0,0.0,0.0,0.0
+Open door,0,0,0.0,0.0,0.0
+Open earbud case,2,0,0.0,0.0,0.0
+Open folded paper lantern,2,0,0.0,0.0,0.0
+Open paper lantern,2,0,0.0,0.0,0.0
+Open paper lantern component,2,0,0.0,0.0,0.0
+Open small case,0,0,0.0,0.0,0.0
+Open stove pot lid,1,0,0.0,0.0,0.0
+Open supplement bottle,0,0,0.0,0.0,0.0
+Operate smartphone,4,0,0.0,0.0,0.0
+Organize bag contents,0,0,0.0,0.0,0.0
+Organize cardboard pieces,2,0,0.0,0.0,0.0
+Organize item on shelf,0,0,0.0,0.0,0.0
+Organize products,0,0,0.0,0.0,0.0
+Organize snacks in box,0,0,0.0,0.0,0.0
+Organize tools and materials,0,0,0.0,0.0,0.0
+Pack beads into box,0,0,0.0,0.0,0.0
+Peel blue strip,0,0,0.0,0.0,0.0
+Peel foam strip,0,0,0.0,0.0,0.0
+Pick up Dior gift box,0,0,0.0,0.0,0.0
+Pick up Mahjong tile,0,0,0.0,0.0,0.0
+Pick up accessory,0,0,0.0,0.0,0.0
+Pick up and sort cardboard,0,0,0.0,0.0,0.0
+Pick up another bottle,0,0,0.0,0.0,0.0
+Pick up another canned item,0,0,0.0,0.0,0.0
+Pick up another item,0,0,0.0,0.0,0.0
+Pick up beads,0,0,0.0,0.0,0.0
+Pick up black button,0,0,0.0,0.0,0.0
+Pick up blue foam piece,0,0,0.0,0.0,0.0
+Pick up blue paper strip,0,0,0.0,0.0,0.0
+Pick up bottle,0,0,0.0,0.0,0.0
+Pick up bottled sauce,0,0,0.0,0.0,0.0
+Pick up button,3,0,0.0,0.0,0.0
+Pick up can,2,1,0.0,0.0,0.0
+Pick up canned food,2,13,0.07692307692307693,0.5,0.13333333333333336
+Pick up canned good,0,0,0.0,0.0,0.0
+Pick up canned goods,0,0,0.0,0.0,0.0
+Pick up canned item,0,0,0.0,0.0,0.0
+Pick up canned product,0,0,0.0,0.0,0.0
+Pick up cardboard,0,0,0.0,0.0,0.0
+Pick up cardboard cutout,0,0,0.0,0.0,0.0
+Pick up cardboard piece,0,17,0.0,0.0,0.0
+Pick up cardboard square,0,0,0.0,0.0,0.0
+Pick up cardboard stack,0,0,0.0,0.0,0.0
+Pick up cardboard strip,0,0,0.0,0.0,0.0
+Pick up cardboard tray,0,0,0.0,0.0,0.0
+Pick up cereal boxes,0,0,0.0,0.0,0.0
+Pick up charging cable,0,0,0.0,0.0,0.0
+Pick up charging case,0,0,0.0,0.0,0.0
+Pick up cleaning cloth,0,0,0.0,0.0,0.0
+Pick up colored tile,0,0,0.0,0.0,0.0
+Pick up container,0,0,0.0,0.0,0.0
+Pick up container from box,0,0,0.0,0.0,0.0
+Pick up craft material,0,0,0.0,0.0,0.0
+Pick up cut cardboard piece,0,0,0.0,0.0,0.0
+Pick up dustpan,1,0,0.0,0.0,0.0
+Pick up electronic accessory,0,0,0.0,0.0,0.0
+Pick up electronic accessory from box,0,0,0.0,0.0,0.0
+Pick up electronic device,0,0,0.0,0.0,0.0
+Pick up electronic item,0,0,0.0,0.0,0.0
+Pick up electronic product,0,0,0.0,0.0,0.0
+Pick up food item,0,0,0.0,0.0,0.0
+Pick up gift box,0,1,0.0,0.0,0.0
+Pick up grocery item,0,0,0.0,0.0,0.0
+Pick up item,0,0,0.0,0.0,0.0
+Pick up item from bin,0,0,0.0,0.0,0.0
+Pick up item from box,0,0,0.0,0.0,0.0
+Pick up item from shelf,0,2,0.0,0.0,0.0
+Pick up items from the shopping bag,2,0,0.0,0.0,0.0
+Pick up jar,0,0,0.0,0.0,0.0
+Pick up light blue strip,0,0,0.0,0.0,0.0
+Pick up marker,0,0,0.0,0.0,0.0
+Pick up metal ruler,0,0,0.0,0.0,0.0
+Pick up new cardboard piece,2,0,0.0,0.0,0.0
+Pick up new electronic product,0,0,0.0,0.0,0.0
+Pick up new product from box,0,0,0.0,0.0,0.0
+Pick up next gift box,0,0,0.0,0.0,0.0
+Pick up next item from bin,0,0,0.0,0.0,0.0
+Pick up next product from bin,0,0,0.0,0.0,0.0
+Pick up nut bar box,0,0,0.0,0.0,0.0
+Pick up object,0,0,0.0,0.0,0.0
+Pick up oil bottle,0,0,0.0,0.0,0.0
+Pick up orange button,0,0,0.0,0.0,0.0
+Pick up pack from shelf,0,0,0.0,0.0,0.0
+Pick up packaged paper lantern component,1,0,0.0,0.0,0.0
+Pick up paper star,0,4,0.0,0.0,0.0
+Pick up paper strip,0,10,0.0,0.0,0.0
+Pick up paper towel,0,0,0.0,0.0,0.0
+Pick up pasta box,0,0,0.0,0.0,0.0
+Pick up pen,2,3,0.0,0.0,0.0
+Pick up phone,0,0,0.0,0.0,0.0
+Pick up pickle jar,0,0,0.0,0.0,0.0
+Pick up pink water bottle,0,0,0.0,0.0,0.0
+Pick up plastic bin,0,0,0.0,0.0,0.0
+Pick up plastic container,0,0,0.0,0.0,0.0
+Pick up plush toy,0,0,0.0,0.0,0.0
+Pick up portable charger,0,0,0.0,0.0,0.0
+Pick up power bank,0,0,0.0,0.0,0.0
+Pick up product,0,0,0.0,0.0,0.0
+Pick up product box,0,0,0.0,0.0,0.0
+Pick up product from bin,0,0,0.0,0.0,0.0
+Pick up product from box,0,0,0.0,0.0,0.0
+Pick up product from shelf,0,0,0.0,0.0,0.0
+Pick up puzzle piece,3,0,0.0,0.0,0.0
+Pick up red button,0,1,0.0,0.0,0.0
+Pick up retail item,0,0,0.0,0.0,0.0
+Pick up sauce bottle,0,0,0.0,0.0,0.0
+Pick up scissors,0,0,0.0,0.0,0.0
+Pick up shopping bag,0,0,0.0,0.0,0.0
+Pick up small cardboard piece,0,0,0.0,0.0,0.0
+Pick up small item,0,0,0.0,0.0,0.0
+Pick up small object,0,0,0.0,0.0,0.0
+Pick up small piece of material,2,0,0.0,0.0,0.0
+Pick up smartphone,2,2,0.0,0.0,0.0
+Pick up snack package,0,0,0.0,0.0,0.0
+Pick up snack packages,0,0,0.0,0.0,0.0
+Pick up snack packs,0,0,0.0,0.0,0.0
+Pick up snack pouch,0,1,0.0,0.0,0.0
+Pick up spice jar,0,0,0.0,0.0,0.0
+Pick up stapler,0,1,0.0,0.0,0.0
+Pick up star,0,0,0.0,0.0,0.0
+Pick up star bead,2,0,0.0,0.0,0.0
+Pick up star-shaped bead,0,0,0.0,0.0,0.0
+Pick up storage container,0,0,0.0,0.0,0.0
+Pick up supplement bottle,0,0,0.0,0.0,0.0
+Pick up supplies from box,0,0,0.0,0.0,0.0
+Pick up tin can,0,1,0.0,0.0,0.0
+Pick up tool,0,0,0.0,0.0,0.0
+Pick up utility knife,6,0,0.0,0.0,0.0
+Pick up water bottle,0,3,0.0,0.0,0.0
+Pick up yellow item,0,0,0.0,0.0,0.0
+Pick up yellow paper strip,0,0,0.0,0.0,0.0
+Picking up bottle,1,0,0.0,0.0,0.0
+Picking up crafting material,2,0,0.0,0.0,0.0
+Picking up stock,0,0,0.0,0.0,0.0
+Place Mahjong tile on stack,0,9,0.0,0.0,0.0
+Place Mahjong tile on the stack,0,0,0.0,0.0,0.0
+Place accessory box,0,0,0.0,0.0,0.0
+Place accessory into box,0,0,0.0,0.0,0.0
+Place accessory on shelf,0,0,0.0,0.0,0.0
+Place and align button,0,0,0.0,0.0,0.0
+Place and count bead,2,0,0.0,0.0,0.0
+Place another canned food on shelf,2,0,0.0,0.0,0.0
+Place back Dior gift box,0,0,0.0,0.0,0.0
+Place bead on table,0,0,0.0,0.0,0.0
+Place bottle back on shelf,0,0,0.0,0.0,0.0
+Place box on shelf,0,0,0.0,0.0,0.0
+Place button,3,0,0.0,0.0,0.0
+Place button in group,0,0,0.0,0.0,0.0
+Place button in row,0,0,0.0,0.0,0.0
+Place can on shelf,2,0,0.0,0.0,0.0
+Place canned food in bin,0,0,0.0,0.0,0.0
+Place canned food in container,0,4,0.0,0.0,0.0
+Place canned food on shelf,1,5,0.0,0.0,0.0
+Place canned good on shelf,0,0,0.0,0.0,0.0
+Place canned goods in container,0,0,0.0,0.0,0.0
+Place canned product on shelf,0,0,0.0,0.0,0.0
+Place cans into box,0,0,0.0,0.0,0.0
+Place cardboard,0,0,0.0,0.0,0.0
+Place cardboard piece,0,13,0.0,0.0,0.0
+Place cardboard piece on stack,0,0,0.0,0.0,0.0
+Place cardboard square,0,0,0.0,0.0,0.0
+Place cardboard square on stack,0,1,0.0,0.0,0.0
+Place cardboard strip,0,0,0.0,0.0,0.0
+Place charger on table,0,0,0.0,0.0,0.0
+Place charging case down,0,0,0.0,0.0,0.0
+Place cloth on floor,1,0,0.0,0.0,0.0
+Place colored tile,0,0,0.0,0.0,0.0
+Place container in bin,0,0,0.0,0.0,0.0
+Place container on floor,0,0,0.0,0.0,0.0
+Place container on shelf,0,1,0.0,0.0,0.0
+Place controller on table,0,0,0.0,0.0,0.0
+Place crate on floor,0,0,0.0,0.0,0.0
+Place device on lap,0,0,0.0,0.0,0.0
+Place down paper pieces,0,0,0.0,0.0,0.0
+Place down paper segment,0,0,0.0,0.0,0.0
+Place down pen,0,0,0.0,0.0,0.0
+Place down pink water bottle,0,0,0.0,0.0,0.0
+Place down ruler and pen,0,4,0.0,0.0,0.0
+Place down scissors,0,1,0.0,0.0,0.0
+Place down strip,0,0,0.0,0.0,0.0
+Place finished star on table,0,0,0.0,0.0,0.0
+Place gift box into bin,0,0,0.0,0.0,0.0
+Place gift box on shelf,0,0,0.0,0.0,0.0
+Place hand on table,3,0,0.0,0.0,0.0
+Place item back,0,0,0.0,0.0,0.0
+Place item back on shelf,0,0,0.0,0.0,0.0
+Place item in bag,0,0,0.0,0.0,0.0
+Place item in container,0,7,0.0,0.0,0.0
+Place item in shopping bag,0,2,0.0,0.0,0.0
+Place item into bag,0,0,0.0,0.0,0.0
+Place item into shopping bag,0,0,0.0,0.0,0.0
+Place item on shelf,2,13,0.15384615384615385,1.0,0.2666666666666667
+Place item on table,0,1,0.0,0.0,0.0
+Place items on shelf,0,0,0.0,0.0,0.0
+Place items on table,0,0,0.0,0.0,0.0
+Place items on the shelf,2,0,0.0,0.0,0.0
+Place jar in box,0,4,0.0,0.0,0.0
+Place jar into shelf box,0,0,0.0,0.0,0.0
+Place jar on shelf,0,0,0.0,0.0,0.0
+Place ketchup bottle on shelf,0,0,0.0,0.0,0.0
+Place knife down,0,0,0.0,0.0,0.0
+Place lid back,1,0,0.0,0.0,0.0
+Place marked piece down,2,0,0.0,0.0,0.0
+Place marker down,0,0,0.0,0.0,0.0
+Place material,2,0,0.0,0.0,0.0
+Place oil in container,0,0,0.0,0.0,0.0
+Place paper star,0,0,0.0,0.0,0.0
+Place paper star in row,0,0,0.0,0.0,0.0
+Place pen on cardboard,0,0,0.0,0.0,0.0
+Place pen on table,0,0,0.0,0.0,0.0
+Place phone down,2,0,0.0,0.0,0.0
+Place phone on desk,0,0,0.0,0.0,0.0
+Place phone on shelf,0,0,0.0,0.0,0.0
+Place phone on table,0,1,0.0,0.0,0.0
+Place pickle jar in box,0,0,0.0,0.0,0.0
+Place piece into puzzle,3,0,0.0,0.0,0.0
+Place plush toy into bag,0,0,0.0,0.0,0.0
+Place plush toy on shelf,0,0,0.0,0.0,0.0
+Place product in box,0,0,0.0,0.0,0.0
+Place product on shelf,0,0,0.0,0.0,0.0
+Place puzzle piece,3,0,0.0,0.0,0.0
+Place quilled paper shape,0,0,0.0,0.0,0.0
+Place red button,0,1,0.0,0.0,0.0
+Place ribbon onto project,0,0,0.0,0.0,0.0
+Place ruler on cardboard,0,0,0.0,0.0,0.0
+Place sauce bottle on shelf,0,0,0.0,0.0,0.0
+Place sauce in container,0,0,0.0,0.0,0.0
+Place scissors aside,0,0,0.0,0.0,0.0
+Place scissors down,0,0,0.0,0.0,0.0
+Place scissors on table,0,0,0.0,0.0,0.0
+Place smartphone down,3,0,0.0,0.0,0.0
+Place smartphone on cardboard,0,0,0.0,0.0,0.0
+Place smartphone on desk,0,0,0.0,0.0,0.0
+Place smartphone on stand,1,0,0.0,0.0,0.0
+Place smartphone on table,0,2,0.0,0.0,0.0
+Place snack in box,0,0,0.0,0.0,0.0
+Place snack on shelf,0,0,0.0,0.0,0.0
+Place snack package in box,0,1,0.0,0.0,0.0
+Place snack package on shelf,0,0,0.0,0.0,0.0
+Place snack packages on shelf,0,0,0.0,0.0,0.0
+Place snack pouch in container,0,0,0.0,0.0,0.0
+Place snack pouch on shelf,0,0,0.0,0.0,0.0
+Place spice jar in container,0,0,0.0,0.0,0.0
+Place star,0,0,0.0,0.0,0.0
+Place star in row,0,0,0.0,0.0,0.0
+Place star on table,0,0,0.0,0.0,0.0
+Place stars in container,0,0,0.0,0.0,0.0
+Place stool on floor,0,0,0.0,0.0,0.0
+Place storage container on floor,0,0,0.0,0.0,0.0
+Place strip on table,0,0,0.0,0.0,0.0
+Place supplement bottle in container,0,0,0.0,0.0,0.0
+Place tool on table,0,0,0.0,0.0,0.0
+Place towel,1,0,0.0,0.0,0.0
+Place water bottle on table,0,0,0.0,0.0,0.0
+Place white box on table,0,0,0.0,0.0,0.0
+Placing labeled cardboard square,0,0,0.0,0.0,0.0
+Placing labeled square,0,0,0.0,0.0,0.0
+Placing paper strip,4,0,0.0,0.0,0.0
+Placing pen on table,0,0,0.0,0.0,0.0
+Placing phone down,0,0,0.0,0.0,0.0
+Placing piece on stack,0,0,0.0,0.0,0.0
+Placing stock on shelf,0,0,0.0,0.0,0.0
+Plug cable into portable charger,0,0,0.0,0.0,0.0
+Position cardboard for cutting,0,0,0.0,0.0,0.0
+Position cardboard piece,0,0,0.0,0.0,0.0
+Position cardboard strip,0,0,0.0,0.0,0.0
+Position cardboard tray,0,0,0.0,0.0,0.0
+Position cardboard tube,0,0,0.0,0.0,0.0
+Position container near shelf,0,0,0.0,0.0,0.0
+Position container on shelf,0,0,0.0,0.0,0.0
+Position hands for work,0,0,0.0,0.0,0.0
+Position ribbon piece,0,0,0.0,0.0,0.0
+Position ruler and mark cardboard,0,0,0.0,0.0,0.0
+Position ruler on cardboard,0,0,0.0,0.0,0.0
+Position scissors,0,0,0.0,0.0,0.0
+Position scissors for next cut,0,0,0.0,0.0,0.0
+Position scissors to cut cardboard,0,0,0.0,0.0,0.0
+Position shelving divider,0,0,0.0,0.0,0.0
+Position the ruler,0,0,0.0,0.0,0.0
+Position tray,0,0,0.0,0.0,0.0
+Position utility knife,0,0,0.0,0.0,0.0
+Position utility knife on cardboard,0,0,0.0,0.0,0.0
+Positioning cardboard on workspace,0,0,0.0,0.0,0.0
+Positioning paper strip,0,0,0.0,0.0,0.0
+Positioning puzzle piece,0,0,0.0,0.0,0.0
+Positioning ruler on cardboard,0,0,0.0,0.0,0.0
+Prepare paper strip,0,0,0.0,0.0,0.0
+Prepare to cut cardboard,0,0,0.0,0.0,0.0
+Prepare to draw lines,0,0,0.0,0.0,0.0
+Prepare to pick up item,0,0,0.0,0.0,0.0
+Prepare to place bottle on shelf,0,0,0.0,0.0,0.0
+Prepare to place cardboard,0,0,0.0,0.0,0.0
+Prepare to place item in bag,0,0,0.0,0.0,0.0
+Prepare to place product,0,0,0.0,0.0,0.0
+Prepare to resume cutting,0,0,0.0,0.0,0.0
+Prepare to sort beads,0,0,0.0,0.0,0.0
+Preparing to craft,2,0,0.0,0.0,0.0
+Press fold,0,0,0.0,0.0,0.0
+Pull back hand,0,0,0.0,0.0,0.0
+Pull paper strip,0,0,0.0,0.0,0.0
+Push vacuum cleaner,0,0,0.0,0.0,0.0
+Put down phone,0,0,0.0,0.0,0.0
+Put down scissors,0,0,0.0,0.0,0.0
+Put down smartphone,3,0,0.0,0.0,0.0
+Put down utility knife,0,0,0.0,0.0,0.0
+Put down water bottle,0,0,0.0,0.0,0.0
+Putting away smartphone,0,0,0.0,0.0,0.0
+Reach and sort buttons,0,0,0.0,0.0,0.0
+Reach for Mahjong tiles,0,1,0.0,0.0,0.0
+Reach for additional items,0,0,0.0,0.0,0.0
+Reach for and examine canned goods,0,1,0.0,0.0,0.0
+Reach for and pick up smartphone,0,0,0.0,0.0,0.0
+Reach for another container,0,0,0.0,0.0,0.0
+Reach for another item,1,0,0.0,0.0,0.0
+Reach for beads,0,0,0.0,0.0,0.0
+Reach for black button,0,0,0.0,0.0,0.0
+Reach for button,0,0,0.0,0.0,0.0
+Reach for can,0,0,0.0,0.0,0.0
+Reach for canned food,0,0,0.0,0.0,0.0
+Reach for canned goods,0,0,0.0,0.0,0.0
+Reach for cardboard box,0,0,0.0,0.0,0.0
+Reach for cardboard piece,0,1,0.0,0.0,0.0
+Reach for cleaning supplies,1,0,0.0,0.0,0.0
+Reach for container,0,0,0.0,0.0,0.0
+Reach for craft items,3,0,0.0,0.0,0.0
+Reach for empty shelf space,0,0,0.0,0.0,0.0
+Reach for item,0,0,0.0,0.0,0.0
+Reach for item in box,0,0,0.0,0.0,0.0
+Reach for item on shelf,0,0,0.0,0.0,0.0
+Reach for items,0,0,0.0,0.0,0.0
+Reach for items in box,0,1,0.0,0.0,0.0
+Reach for more pieces,0,0,0.0,0.0,0.0
+Reach for next can,1,0,0.0,0.0,0.0
+Reach for next canned food,1,0,0.0,0.0,0.0
+Reach for next canned food item,0,0,0.0,0.0,0.0
+Reach for next canned product,0,0,0.0,0.0,0.0
+Reach for next item,1,0,0.0,0.0,0.0
+Reach for next piece,0,0,0.0,0.0,0.0
+Reach for next product,0,0,0.0,0.0,0.0
+Reach for object,0,0,0.0,0.0,0.0
+Reach for paper strip,0,0,0.0,0.0,0.0
+Reach for paper strips,0,0,0.0,0.0,0.0
+Reach for phone,0,0,0.0,0.0,0.0
+Reach for product,0,0,0.0,0.0,0.0
+Reach for product labels,0,0,0.0,0.0,0.0
+Reach for product on shelf,0,0,0.0,0.0,0.0
+Reach for puzzle piece,2,0,0.0,0.0,0.0
+Reach for retail item,0,0,0.0,0.0,0.0
+Reach for shelf,0,0,0.0,0.0,0.0
+Reach for shelving divider,0,0,0.0,0.0,0.0
+Reach for snack package,0,0,0.0,0.0,0.0
+Reach for snack pouch,0,0,0.0,0.0,0.0
+Reach for star,0,0,0.0,0.0,0.0
+Reach for stars,0,0,0.0,0.0,0.0
+Reach for utility knife,0,0,0.0,0.0,0.0
+Reach for water bottle,0,2,0.0,0.0,0.0
+Reach for wire hangers,1,0,0.0,0.0,0.0
+Reach into bag,0,0,0.0,0.0,0.0
+Reach into box,1,0,0.0,0.0,0.0
+Reach towards shelf,0,0,0.0,0.0,0.0
+Reaching for beads,0,0,0.0,0.0,0.0
+Realign Mahjong tiles,0,0,0.0,0.0,0.0
+Rearrange Mahjong tile,0,0,0.0,0.0,0.0
+Rearrange Mahjong tiles,0,0,0.0,0.0,0.0
+Rearrange shelf item,0,0,0.0,0.0,0.0
+Record count,2,0,0.0,0.0,0.0
+Record count on notepad,0,0,0.0,0.0,0.0
+Record star count,0,0,0.0,0.0,0.0
+Record star count on paper,0,0,0.0,0.0,0.0
+Release and prepare new strip,0,0,0.0,0.0,0.0
+Release bottle,0,0,0.0,0.0,0.0
+Release cardboard,0,0,0.0,0.0,0.0
+Release cardboard piece,0,0,0.0,0.0,0.0
+Release cardboard piece and gesture,2,0,0.0,0.0,0.0
+Release cardboard shape,0,0,0.0,0.0,0.0
+Release container,0,0,0.0,0.0,0.0
+Release folded paper,0,0,0.0,0.0,0.0
+Release food item,0,0,0.0,0.0,0.0
+Release hook,1,0,0.0,0.0,0.0
+Release label,0,0,0.0,0.0,0.0
+Release lantern,1,0,0.0,0.0,0.0
+Release paper,0,0,0.0,0.0,0.0
+Release paper coil,0,0,0.0,0.0,0.0
+Release paper star,0,0,0.0,0.0,0.0
+Release paper strip,4,0,0.0,0.0,0.0
+Release pickle jar,0,0,0.0,0.0,0.0
+Release product on shelf,0,0,0.0,0.0,0.0
+Release puzzle piece,2,0,0.0,0.0,0.0
+Release quilling strip,0,0,0.0,0.0,0.0
+Release scissors,3,0,0.0,0.0,0.0
+Release smartphone,3,0,0.0,0.0,0.0
+Remove cardboard flap,0,0,0.0,0.0,0.0
+Remove cardboard pattern,0,0,0.0,0.0,0.0
+Remove cardboard pattern piece,0,0,0.0,0.0,0.0
+Remove cleaning bottle,1,0,0.0,0.0,0.0
+Remove item from bag,0,0,0.0,0.0,0.0
+Remove item from shelf,0,0,0.0,0.0,0.0
+Remove lid from container,0,0,0.0,0.0,0.0
+Remove paper lantern part from packaging,1,0,0.0,0.0,0.0
+Remove plastic container from shelf,0,0,0.0,0.0,0.0
+Remove plastic container from storage box,0,0,0.0,0.0,0.0
+Remove plastic packaging,1,1,0.0,0.0,0.0
+Remove ruler,0,0,0.0,0.0,0.0
+Remove ruler and marker,0,0,0.0,0.0,0.0
+Remove shelf label,0,0,0.0,0.0,0.0
+Remove storage bin from shelf,0,0,0.0,0.0,0.0
+Reorganize bin contents,0,0,0.0,0.0,0.0
+Reposition and cut,0,0,0.0,0.0,0.0
+Reposition cardboard for cutting,0,0,0.0,0.0,0.0
+Reposition hand,3,0,0.0,0.0,0.0
+Reposition hands,0,0,0.0,0.0,0.0
+Reposition hands and ruler,0,0,0.0,0.0,0.0
+Reposition marker,0,0,0.0,0.0,0.0
+Reposition newspaper,0,0,0.0,0.0,0.0
+Reposition pen and prepare for next line,0,0,0.0,0.0,0.0
+Reposition ruler,0,0,0.0,0.0,0.0
+Reposition ruler and pen,0,0,0.0,0.0,0.0
+Reposition scissors,0,0,0.0,0.0,0.0
+Reposition sign and organize beads,0,0,0.0,0.0,0.0
+Reposition tools,0,0,0.0,0.0,0.0
+Reposition utility knife,0,0,0.0,0.0,0.0
+Repositioning ruler,0,0,0.0,0.0,0.0
+Repositioning ruler and cardboard,0,0,0.0,0.0,0.0
+Resume counting stars,0,0,0.0,0.0,0.0
+Resume observation,2,0,0.0,0.0,0.0
+Resume sorting blue beads,0,0,0.0,0.0,0.0
+Resume writing on paper,0,0,0.0,0.0,0.0
+Retract camera/reposition view,0,0,0.0,0.0,0.0
+Retract hand,0,0,0.0,0.0,0.0
+Retract hand from bag,0,0,0.0,0.0,0.0
+Retrieve another container,0,0,0.0,0.0,0.0
+Retrieve canned food from box,1,0,0.0,0.0,0.0
+Retrieve hand to table,0,0,0.0,0.0,0.0
+Retrieve items from bag,0,0,0.0,0.0,0.0
+Retrieve next canned food item,1,0,0.0,0.0,0.0
+Retrieve paper strip,0,0,0.0,0.0,0.0
+Retrieve paper strips,0,0,0.0,0.0,0.0
+Retrieve snack from container,0,0,0.0,0.0,0.0
+Retrieve star,0,0,0.0,0.0,0.0
+Retrieving more beads,2,0,0.0,0.0,0.0
+Return to sorting,0,0,0.0,0.0,0.0
+Reviewing count record,0,0,0.0,0.0,0.0
+Rinse cloth in sink,1,0,0.0,0.0,0.0
+Roll quilling paper,0,0,0.0,0.0,0.0
+Rolling paper strip,0,0,0.0,0.0,0.0
+Rub hands together,0,0,0.0,0.0,0.0
+Scan for next piece,0,0,0.0,0.0,0.0
+Scan supermarket shelves,0,0,0.0,0.0,0.0
+Score cardboard,0,0,0.0,0.0,0.0
+Scroll on smartphone,0,0,0.0,0.0,0.0
+Scroll smartphone screen,3,0,0.0,0.0,0.0
+Scroll through photo gallery,0,0,0.0,0.0,0.0
+Scrolling and viewing content on phone,0,0,0.0,0.0,0.0
+Scrolling or navigating on phone,0,0,0.0,0.0,0.0
+Search for puzzle piece,2,0,0.0,0.0,0.0
+Secure paper edges with adhesive,4,0,0.0,0.0,0.0
+Secure ribbon with needle,0,0,0.0,0.0,0.0
+Securing paper structure,4,0,0.0,0.0,0.0
+Select a bottle,0,0,0.0,0.0,0.0
+Select and pick up a canned item,0,0,0.0,0.0,0.0
+Select another item,0,0,0.0,0.0,0.0
+Select paper strip,0,0,0.0,0.0,0.0
+Select product from box,0,0,0.0,0.0,0.0
+Selecting new paper strip,0,0,0.0,0.0,0.0
+Separate cardboard piece,0,0,0.0,0.0,0.0
+Set down scissors and pick up power bank,0,0,0.0,0.0,0.0
+Set down utility knife,0,0,0.0,0.0,0.0
+Slide utility knife along ruler,0,0,0.0,0.0,0.0
+Sort Mahjong tiles,0,0,0.0,0.0,0.0
+Sort and adjust button line,3,0,0.0,0.0,0.0
+Sort and arrange buttons,3,0,0.0,0.0,0.0
+Sort and arrange cardboard pieces,0,0,0.0,0.0,0.0
+Sort and count beads,2,0,0.0,0.0,0.0
+Sort and place buttons,3,0,0.0,0.0,0.0
+Sort and place paper star,0,0,0.0,0.0,0.0
+Sort and stack cardboard pieces,0,0,0.0,0.0,0.0
+Sort beads,2,55,0.0,0.0,0.0
+Sort beads and write count,1,0,0.0,0.0,0.0
+Sort beads by color,0,0,0.0,0.0,0.0
+Sort beads by hand,0,0,0.0,0.0,0.0
+Sort beads on table,0,0,0.0,0.0,0.0
+Sort beads on the table,0,0,0.0,0.0,0.0
+Sort blue beads,0,0,0.0,0.0,0.0
+Sort blue star-shaped pieces,0,0,0.0,0.0,0.0
+Sort button,3,0,0.0,0.0,0.0
+Sort button by color,0,0,0.0,0.0,0.0
+Sort buttons,3,0,0.0,0.0,0.0
+Sort buttons by color,0,0,0.0,0.0,0.0
+Sort canned goods in tray,0,0,0.0,0.0,0.0
+Sort colored tiles,0,0,0.0,0.0,0.0
+Sort colorful pieces,0,0,0.0,0.0,0.0
+Sort craft items,3,0,0.0,0.0,0.0
+Sort cut cardboard,0,0,0.0,0.0,0.0
+Sort light blue origami stars,0,0,0.0,0.0,0.0
+Sort orange button,0,0,0.0,0.0,0.0
+Sort orange buttons,0,0,0.0,0.0,0.0
+Sort origami stars,0,0,0.0,0.0,0.0
+Sort origami stars by color,0,0,0.0,0.0,0.0
+Sort paper star,0,0,0.0,0.0,0.0
+Sort paper stars,0,0,0.0,0.0,0.0
+Sort plastic pieces,0,0,0.0,0.0,0.0
+Sort purple beads,0,0,0.0,0.0,0.0
+Sort purple star-shaped objects,0,0,0.0,0.0,0.0
+Sort puzzle pieces,2,0,0.0,0.0,0.0
+Sort quilled paper pieces,0,0,0.0,0.0,0.0
+Sort small colorful pieces,0,0,0.0,0.0,0.0
+Sort small craft pieces,3,0,0.0,0.0,0.0
+Sort small objects,0,0,0.0,0.0,0.0
+Sort small plastic pieces,0,0,0.0,0.0,0.0
+Sort star-shaped beads,1,0,0.0,0.0,0.0
+Sort star-shaped objects,0,0,0.0,0.0,0.0
+Sort star-shaped objects by color,0,0,0.0,0.0,0.0
+Sort tiles,0,0,0.0,0.0,0.0
+Sort tiles by color,0,0,0.0,0.0,0.0
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results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/training/training_metadata.json

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+{
+  "run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+  "model_id": "<workspace-parent>/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "backbone": {
+    "id": "qwen3_omni_lora",
+    "display_name": "Qwen3-Omni LoRA",
+    "status": "implemented",
+    "model_family": "Qwen3-Omni",
+    "dataset_contract": "xperience10m_episode_json_qa_v1",
+    "training_objective": "structured_episode_understanding_json_qa",
+    "split_policy": {
+      "unit": "episode",
+      "default_counts": {
+        "train": 96,
+        "val": 16,
+        "test": 16
+      },
+      "leakage_guard": "episode_id and path-qualified session split checks"
+    },
+    "modalities": {
+      "direct_inputs": [
+        "multi-camera rendered mosaic video",
+        "AAC audio extracted from fisheye_cam0 MP4",
+        "language prompt and label options"
+      ],
+      "bridged_inputs": [
+        "depth and confidence",
+        "pose and SLAM trajectory",
+        "body and hand mocap",
+        "IMU acceleration and gyro",
+        "object/contact summary features"
+      ],
+      "excluded_inputs": [
+        "visualization.rrd"
+      ]
+    },
+    "primary_metrics": [
+      "json_validity_rate",
+      "action_macro_f1",
+      "subtask_accuracy",
+      "transition_accuracy",
+      "next_action_accuracy",
+      "contact_accuracy",
+      "object_micro_f1",
+      "held_out_episode_count"
+    ],
+    "config_path": "<project>/configs/omni_backbones/qwen3_omni_lora.json"
+  },
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+  "checkpoint_dir": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+  "num_processes": 8,
+  "num_train_samples": 2848,
+  "num_val_samples": 512,
+  "history": [
+    {
+      "epoch": 1,
+      "train_loss": 0.40796751019628613,
+      "val_loss": 0.03258896619081497,
+      "global_step": 356
+    },
+    {
+      "epoch": 2,
+      "train_loss": 0.027628723937453012,
+      "val_loss": 0.027754632756114006,
+      "global_step": 712
+    },
+    {
+      "epoch": 3,
+      "train_loss": 0.02446955946807781,
+      "val_loss": 0.026343274861574173,
+      "global_step": 1068
+    },
+    {
+      "epoch": 4,
+      "train_loss": 0.022728607045444712,
+      "val_loss": 0.025629229843616486,
+      "global_step": 1424
+    }
+  ],
+  "lora": {
+    "r": 16,
+    "alpha": 32,
+    "dropout": 0.05,
+    "target_modules": [
+      "q_proj",
+      "k_proj",
+      "v_proj",
+      "o_proj",
+      "gate_proj",
+      "up_proj",
+      "down_proj"
+    ]
+  },
+  "use_audio_in_video": false,
+  "loss_mode": "answer_token_ce",
+  "loss_logit_tail_only": true
+}

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/validation/eval.json

@@ -0,0 +1,81 @@
+{
+  "status": "pass",
+  "summary": {
+    "run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+    "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+    "backbone": "qwen3_omni_lora",
+    "backbone_status": "implemented",
+    "checkpoint_gate": "lora_safetensors_shape_check",
+    "required_stage": "eval",
+    "workspace": "<project>",
+    "manifest": {
+      "path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605/episode_manifest.json",
+      "episode_count": 128,
+      "split_counts": {
+        "test": 16,
+        "train": 96,
+        "val": 16
+      },
+      "session_leakage": []
+    },
+    "dataset": {
+      "dataset_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset",
+      "manifest_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset_manifest.json",
+      "dataset_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+      "manifest_num_samples": 3808,
+      "row_count": 3808,
+      "sample_split_counts": {
+        "train": 2848,
+        "val": 512,
+        "test": 448
+      },
+      "episode_split_counts": {
+        "train": 89,
+        "val": 16,
+        "test": 14
+      },
+      "skipped_episodes": 9
+    },
+    "training": {
+      "train_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+      "checkpoint_candidates": [
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_lora/adapter_lora"
+      ],
+      "checkpoint_gate": "lora_safetensors_shape_check",
+      "required_training_files": [
+        "training_metadata.json",
+        "progress.jsonl",
+        "adapter_config.json",
+        "adapter_model.safetensors"
+      ],
+      "checkpoint_dir": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+      "num_processes": 8,
+      "num_train_samples": 2848,
+      "num_val_samples": 512,
+      "history_len": 4,
+      "checkpoint_dir_recorded": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora"
+    },
+    "eval": {
+      "eval_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+      "required_eval_files": [
+        "metrics.json",
+        "predictions.jsonl",
+        "predictions.csv",
+        "per_class_metrics.csv",
+        "confusion_matrix.csv",
+        "RUN_REPORT.md"
+      ],
+      "eval_split": "test",
+      "num_eval_episodes": 14,
+      "held_out_episode_count": 14,
+      "json_validity_rate": 1.0,
+      "action_macro_f1": 0.0018678269676001454,
+      "prediction_file": "predictions.jsonl",
+      "prediction_rows": 448
+    }
+  },
+  "issues": []
+}

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/validation/training.json

@@ -0,0 +1,63 @@
+{
+  "status": "pass",
+  "summary": {
+    "run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+    "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval",
+    "backbone": "qwen3_omni_lora",
+    "backbone_status": "implemented",
+    "checkpoint_gate": "lora_safetensors_shape_check",
+    "required_stage": "training",
+    "workspace": "<project>",
+    "manifest": {
+      "path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605/episode_manifest.json",
+      "episode_count": 128,
+      "split_counts": {
+        "test": 16,
+        "train": 96,
+        "val": 16
+      },
+      "session_leakage": []
+    },
+    "dataset": {
+      "dataset_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset",
+      "manifest_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset_manifest.json",
+      "dataset_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+      "manifest_num_samples": 3808,
+      "row_count": 3808,
+      "sample_split_counts": {
+        "train": 2848,
+        "val": 512,
+        "test": 448
+      },
+      "episode_split_counts": {
+        "train": 89,
+        "val": 16,
+        "test": 14
+      },
+      "skipped_episodes": 9
+    },
+    "training": {
+      "train_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+      "checkpoint_candidates": [
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_lora/adapter_lora"
+      ],
+      "checkpoint_gate": "lora_safetensors_shape_check",
+      "required_training_files": [
+        "training_metadata.json",
+        "progress.jsonl",
+        "adapter_config.json",
+        "adapter_model.safetensors"
+      ],
+      "checkpoint_dir": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+      "num_processes": 8,
+      "num_train_samples": 2848,
+      "num_val_samples": 512,
+      "history_len": 4,
+      "checkpoint_dir_recorded": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora"
+    }
+  },
+  "issues": []
+}

results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/verified_result_summary.json

@@ -0,0 +1,188 @@
+{
+  "status": "verified",
+  "backbone": "qwen3_omni_lora",
+  "backbone_display_name": "Qwen3-Omni LoRA",
+  "dataset_contract": "xperience10m_episode_json_qa_v1",
+  "training_objective": "structured_episode_understanding_json_qa",
+  "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+  "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+  "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+  "dataset": {
+    "num_samples": 3808,
+    "num_episodes": 119,
+    "split_counts": {
+      "train": 2848,
+      "val": 512,
+      "test": 448
+    },
+    "skipped_episodes": 9
+  },
+  "training": {
+    "num_processes": 8,
+    "num_train_samples": 2848,
+    "num_val_samples": 512,
+    "history": [
+      {
+        "epoch": 1,
+        "train_loss": 0.40796751019628613,
+        "val_loss": 0.03258896619081497,
+        "global_step": 356
+      },
+      {
+        "epoch": 2,
+        "train_loss": 0.027628723937453012,
+        "val_loss": 0.027754632756114006,
+        "global_step": 712
+      },
+      {
+        "epoch": 3,
+        "train_loss": 0.02446955946807781,
+        "val_loss": 0.026343274861574173,
+        "global_step": 1068
+      },
+      {
+        "epoch": 4,
+        "train_loss": 0.022728607045444712,
+        "val_loss": 0.025629229843616486,
+        "global_step": 1424
+      }
+    ]
+  },
+  "eval": {
+    "eval_split": "test",
+    "num_samples": 448,
+    "prediction_file": "predictions.jsonl",
+    "prediction_rows": 448,
+    "num_eval_episodes": 14,
+    "held_out_episode_count": 14,
+    "primary_metrics": {
+      "json_validity_rate": 1.0,
+      "action_macro_f1": 0.0018678269676001454,
+      "subtask_accuracy": 0.0,
+      "transition_accuracy": 0.9732142857142857,
+      "next_action_accuracy": 0.033482142857142856,
+      "contact_accuracy": 0.7299107142857143,
+      "object_micro_f1": 0.31099781500364165,
+      "held_out_episode_count": 14
+    }
+  },
+  "validation_summary": {
+    "run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+    "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+    "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+    "backbone": "qwen3_omni_lora",
+    "backbone_status": "implemented",
+    "checkpoint_gate": "lora_safetensors_shape_check",
+    "required_stage": "eval",
+    "workspace": "<project>",
+    "manifest": {
+      "path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605/episode_manifest.json",
+      "episode_count": 128,
+      "split_counts": {
+        "test": 16,
+        "train": 96,
+        "val": 16
+      },
+      "session_leakage": []
+    },
+    "dataset": {
+      "dataset_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset",
+      "manifest_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset_manifest.json",
+      "dataset_path": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_dataset/dataset.jsonl",
+      "manifest_num_samples": 3808,
+      "row_count": 3808,
+      "sample_split_counts": {
+        "train": 2848,
+        "val": 512,
+        "test": 448
+      },
+      "episode_split_counts": {
+        "train": 89,
+        "val": 16,
+        "test": 14
+      },
+      "skipped_episodes": 9
+    },
+    "training": {
+      "train_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora",
+      "checkpoint_candidates": [
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+        "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_lora/adapter_lora"
+      ],
+      "checkpoint_gate": "lora_safetensors_shape_check",
+      "required_training_files": [
+        "training_metadata.json",
+        "progress.jsonl",
+        "adapter_config.json",
+        "adapter_model.safetensors"
+      ],
+      "checkpoint_dir": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora",
+      "num_processes": 8,
+      "num_train_samples": 2848,
+      "num_val_samples": 512,
+      "history_len": 4,
+      "checkpoint_dir_recorded": "<project>/checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora"
+    },
+    "eval": {
+      "eval_dir": "<project>/results/omni_finetune/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full",
+      "required_eval_files": [
+        "metrics.json",
+        "predictions.jsonl",
+        "predictions.csv",
+        "per_class_metrics.csv",
+        "confusion_matrix.csv",
+        "RUN_REPORT.md"
+      ],
+      "eval_split": "test",
+      "num_eval_episodes": 14,
+      "held_out_episode_count": 14,
+      "json_validity_rate": 1.0,
+      "action_macro_f1": 0.0018678269676001454,
+      "prediction_file": "predictions.jsonl",
+      "prediction_rows": 448
+    }
+  },
+  "included_files": [
+    "dataset/dataset_manifest.json",
+    "dataset/episode_manifest.json",
+    "eval/RUN_REPORT.md",
+    "eval/confusion_matrix.csv",
+    "eval/metrics.json",
+    "eval/per_class_metrics.csv",
+    "eval/predictions.csv",
+    "eval/predictions.jsonl",
+    "training/adapter_shape_check.json",
+    "training/progress.jsonl",
+    "training/training_metadata.json",
+    "validation/eval.json",
+    "validation/training.json"
+  ],
+  "required_eval_files": [
+    "metrics.json",
+    "predictions.jsonl",
+    "predictions.csv",
+    "per_class_metrics.csv",
+    "confusion_matrix.csv",
+    "RUN_REPORT.md"
+  ],
+  "public_package_allowed": [
+    "metrics",
+    "predictions",
+    "confusion matrices",
+    "run reports",
+    "episode and dataset manifests",
+    "training metadata",
+    "validation summaries"
+  ],
+  "public_package_forbidden": [
+    "raw MP4",
+    "annotation HDF5",
+    "Rerun RRD",
+    "base-model weights",
+    "LoRA adapter weights",
+    "full checkpoints",
+    "large archives"
+  ],
+  "excluded_policy": "Raw Xperience-10M files, base-model weights, adapter or checkpoint weights, full checkpoints, and large archives are not included."
+}

scripts/omni/build_omni_model_comparison.py

@@ -30,6 +30,14 @@ PRIMARY_METRICS = {
     "misalignment_detection": "f1",
 }
 
+QWEN_RUN_PRIORITY = {
+    "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full": 400,
+    "xperience10m_qwen3_omni_128ep_structured_json_v3_strict_label_prompt_reuse_lora_eval_test_full": 300,
+    "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full": 200,
+    "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full": 100,
+    "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval": 50,
+}
+
 TASK_DISPLAY_NAMES = {
     "timeline_action": "Action Recognition",
     "timeline_subtask": "Procedure Step Recognition",
@@ -244,6 +252,17 @@ def model_branch_summary() -> dict[str, Any]:
     }
 
 
+def qwen_current_rank(branch: dict[str, Any]) -> tuple[int, float, str]:
+    branch_id = str(branch.get("id") or "")
+    metrics = branch.get("primary_metrics", {}) if isinstance(branch.get("primary_metrics"), dict) else {}
+    json_validity = metrics.get("json_validity_rate")
+    return (
+        QWEN_RUN_PRIORITY.get(branch_id, 0),
+        float(json_validity) if isinstance(json_validity, (int, float)) else -1.0,
+        branch_id,
+    )
+
+
 def qwen3_smoke_entry() -> dict[str, Any]:
     path = ROOT / "results/omni_exploration/qwen3_adapter_smoke/metrics.json"
     metrics = load_json(path)
@@ -362,8 +381,9 @@ def cosmos3_super_action_contract_entry() -> dict[str, Any] | None:
             "The selected dataset now has valid Cosmos3 camera_pose forward_dynamics targets "
             "for an egocentric camera-motion proxy. These remove the target-schema blocker "
             "for action-conditioned world-model training, but they supervise noisy vision "
-            "tokens rather than preds_action. The remaining work is a pipeline-loaded packer "
-            "check and one-sample forward-dynamics overfit; action-token prediction needs a "
+            "tokens rather than preds_action. The remaining work is a trainable "
+            "Cosmos3-Super implementation that can backpropagate through this loss "
+            "surface at the required memory scale; action-token prediction needs a "
             "separate policy or inverse-dynamics target export."
         ),
     }
@@ -432,7 +452,7 @@ def model_grouped_view(versions: list[dict[str, Any]]) -> list[dict[str, Any]]:
     cosmos_super_action_contract = cosmos3_super_action_contract_entry()
     cosmos_super_packer = cosmos3_super_packer_entry()
     if qwen_branches:
-        current_qwen = max(qwen_branches, key=lambda item: item.get("primary_metrics", {}).get("json_validity_rate") or -1)
+        current_qwen = max(qwen_branches, key=qwen_current_rank)
         for branch in qwen_branches:
             branch["is_current"] = branch.get("id") == current_qwen.get("id")
             branch["weights_repository"] = (
@@ -547,8 +567,8 @@ def model_grouped_view(versions: list[dict[str, Any]]) -> list[dict[str, Any]]:
                 "Reasoner evaluation on the same JSON task as Qwen3. It uses staged base "
                 "weights through vLLM, so it is a model-branch diagnostic, not a weight release. "
                 "A camera-pose proxy forward-dynamics target export now passes the contract audit "
-                "and schema-only packer smoke; true Cosmos3-Super fine-tuning is still not launched "
-                "until the pipeline-loaded packer check and one-sample overfit exist."
+                "and schema-only packer smoke; true Cosmos3-Super fine-tuning is still blocked "
+                "until a trainable multi-GPU/offload path produces adapter or fine-tuned weights."
             ),
         },
     ]
@@ -572,20 +592,20 @@ def build_report() -> dict[str, Any]:
         "version_reading_notes": [
             "Version 1 is the public-sample 12-task harness with minimal and neural heads.",
             "Version 2 is the selected 128-episode same-split simple/NN baseline alignment.",
-            "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, and Cosmos3-Super Reasoner is a base-weight JSON-task evaluation; Cosmos3-Super now has a camera-pose forward-dynamics contract audit and schema-only packer smoke, but no new fine-tuned weight release.",
+            "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, and Cosmos3-Super Reasoner is a base-weight JSON-task evaluation; Cosmos3-Super has a camera-pose forward-dynamics contract audit and schema-only packer smoke, but no new fine-tuned weight release.",
         ],
         "versions": versions,
         "model_groups": model_groups,
         "model_group_reading_notes": [
             "Use model_groups when comparing one-episode and 128-episode artifacts within the same model family.",
             "Task-head baselines have both a one-episode public-sample run and a 128-episode same-split metadata/text run.",
-            "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; only the final 128-episode adapter belongs in the Qwen LoRA model repo.",
+            "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; the newest verified full-eval 128-episode adapter belongs in the Qwen LoRA model repo.",
             "Cosmos3-Nano has a 128-episode future-window compatibility package.",
-            "Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after real Cosmos adapter/fine-tuned weights exist.",
+            "Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task plus a camera-pose forward-dynamics contract audit; create a separate Cosmos model repo only after a trainable multi-GPU/offload run produces real Cosmos adapter or fine-tuned weights.",
         ],
         "pending": [
-            "Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.",
-            "Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after the pipeline-loaded packer check and one-sample overfit produce new weights.",
+            "Use the verified Qwen3 v4 4-epoch full-eval package as the current Qwen row; older Qwen package rows remain historical diagnostics for comparison.",
+            "Promote Cosmos3 from Nano compatibility, Super base-weight evaluation, and the camera-pose forward-dynamics contract to true fine-tuning only after a trainable Cosmos3-Super run produces new weights.",
         ],
     }
 

scripts/omni/collect_qwen3_v4_release_artifacts.py

@@ -0,0 +1,222 @@
+#!/usr/bin/env python3
+"""Collect Qwen3-Omni v4 release artifacts after remote validation passes.
+
+This is a local handoff helper for the active remote run. It refuses to copy
+anything until the remote public-safe package has a verified summary unless
+``--allow-incomplete`` is passed for diagnostics. Adapter weights are copied
+only when explicitly requested.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any
+
+
+DEFAULT_REMOTE = os.environ.get("ROPEDIA_REMOTE", "")
+DEFAULT_REMOTE_WORKSPACE = os.environ.get("ROPEDIA_REMOTE_WORKSPACE", "")
+DEFAULT_DATASET_RUN_ID = "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605"
+DEFAULT_TRAIN_RUN_ID = "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora"
+DEFAULT_EVAL_RUN_ID = f"{DEFAULT_TRAIN_RUN_ID}_eval_test_full"
+DEFAULT_EVAL_SMOKE_RUN_ID = f"{DEFAULT_TRAIN_RUN_ID}_eval_smoke8"
+
+
+def parse_args() -> argparse.Namespace:
+    workspace_default = Path(__file__).resolve().parents[2]
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--workspace", type=Path, default=workspace_default)
+    parser.add_argument("--remote", default=DEFAULT_REMOTE, required=not bool(DEFAULT_REMOTE))
+    parser.add_argument("--remote-workspace", default=DEFAULT_REMOTE_WORKSPACE, required=not bool(DEFAULT_REMOTE_WORKSPACE))
+    parser.add_argument("--dataset-run-id", default=DEFAULT_DATASET_RUN_ID)
+    parser.add_argument("--train-run-id", default=DEFAULT_TRAIN_RUN_ID)
+    parser.add_argument("--eval-run-id", default=DEFAULT_EVAL_RUN_ID)
+    parser.add_argument("--eval-smoke-run-id", default=DEFAULT_EVAL_SMOKE_RUN_ID)
+    parser.add_argument("--execute", action="store_true", help="run rsync; otherwise print the planned copy set")
+    parser.add_argument("--include-adapter", action="store_true", help="also copy checkpoints/<train_run_id>/adapter_lora")
+    parser.add_argument("--allow-incomplete", action="store_true", help="allow collection before verified package status is present")
+    return parser.parse_args()
+
+
+def run(argv: list[str], *, check: bool = True) -> subprocess.CompletedProcess[str]:
+    return subprocess.run(argv, check=check, text=True, capture_output=True)
+
+
+def remote_python_probe(args: argparse.Namespace) -> dict[str, Any]:
+    script = f"""
+import json
+from pathlib import Path
+root = Path({args.remote_workspace!r})
+dataset = {args.dataset_run_id!r}
+train = {args.train_run_id!r}
+eval_run = {args.eval_run_id!r}
+base = root / "results" / "omni_finetune"
+package_dir = base / "verified_public" / eval_run
+summary_path = package_dir / "verified_result_summary.json"
+training_validation_path = base / dataset / f"validation_training_{{train}}.json"
+eval_validation_path = base / dataset / f"validation_eval_{{eval_run}}.json"
+package_watch = base / dataset / f"package_watch_{{eval_run}}.jsonl"
+watch_status = base / dataset / f"watch_{{train}}.jsonl"
+train_progress = base / train / "progress.jsonl"
+def last_jsonl(path):
+    if not path.exists():
+        return None
+    last = None
+    for line in path.read_text(encoding="utf-8", errors="replace").splitlines():
+        if line.strip():
+            try:
+                last = json.loads(line)
+            except Exception:
+                last = {{"event": "decode_error", "raw": line[:200]}}
+    return last
+summary = None
+if summary_path.exists():
+    summary = json.loads(summary_path.read_text(encoding="utf-8"))
+print(json.dumps({{
+    "package_dir": str(package_dir),
+    "package_exists": package_dir.exists(),
+    "summary_path": str(summary_path),
+    "summary_exists": summary_path.exists(),
+    "summary_status": summary.get("status") if summary else None,
+    "training_validation_path": str(training_validation_path),
+    "training_validation_exists": training_validation_path.exists(),
+    "eval_validation_path": str(eval_validation_path),
+    "eval_validation_exists": eval_validation_path.exists(),
+    "watch_status_last": last_jsonl(watch_status),
+    "package_watch_last": last_jsonl(package_watch),
+    "train_progress_last": last_jsonl(train_progress),
+}}, indent=2))
+"""
+    proc = run(["ssh", args.remote, f"cd {args.remote_workspace} && .venv/bin/python - <<'PY'\n{script}\nPY"])
+    return json.loads(proc.stdout)
+
+
+def planned_paths(args: argparse.Namespace) -> list[tuple[str, Path]]:
+    workspace = args.workspace.expanduser().resolve()
+    remote_base = f"{args.remote}:{args.remote_workspace}"
+    result_root = Path("results/omni_finetune")
+    paths: list[tuple[str, Path]] = [
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/validation_training_{args.train_run_id}.json",
+            workspace / result_root / args.dataset_run_id / f"validation_training_{args.train_run_id}.json",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/validation_eval_{args.eval_run_id}.json",
+            workspace / result_root / args.dataset_run_id / f"validation_eval_{args.eval_run_id}.json",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/adapter_shape_check_{args.train_run_id}.json",
+            workspace / result_root / args.dataset_run_id / f"adapter_shape_check_{args.train_run_id}.json",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/validate_training_{args.train_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"validate_training_{args.train_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/validate_eval_{args.eval_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"validate_eval_{args.eval_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/eval_{args.eval_smoke_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"eval_{args.eval_smoke_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/eval_{args.eval_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"eval_{args.eval_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/watch_{args.train_run_id}.jsonl",
+            workspace / result_root / args.dataset_run_id / f"watch_{args.train_run_id}.jsonl",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/watch_{args.train_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"watch_{args.train_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/package_watch_{args.eval_run_id}.jsonl",
+            workspace / result_root / args.dataset_run_id / f"package_watch_{args.eval_run_id}.jsonl",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/package_watch_{args.eval_run_id}.log",
+            workspace / result_root / args.dataset_run_id / f"package_watch_{args.eval_run_id}.log",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.dataset_run_id}/audit_verified_public_{args.eval_run_id}.json",
+            workspace / result_root / args.dataset_run_id / f"audit_verified_public_{args.eval_run_id}.json",
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.train_run_id}/",
+            workspace / result_root / args.train_run_id,
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/{args.eval_run_id}/",
+            workspace / result_root / args.eval_run_id,
+        ),
+        (
+            f"{remote_base}/results/omni_finetune/verified_public/{args.eval_run_id}/",
+            workspace / result_root / "verified_public" / args.eval_run_id,
+        ),
+    ]
+    if args.include_adapter:
+        paths.append(
+            (
+                f"{remote_base}/checkpoints/{args.train_run_id}/adapter_lora/",
+                workspace / "checkpoints" / args.train_run_id / "adapter_lora",
+            )
+        )
+    return paths
+
+
+def rsync_one(src: str, dst: Path, *, execute: bool) -> None:
+    if src.endswith("/"):
+        dst.mkdir(parents=True, exist_ok=True)
+        dst_arg = str(dst) + "/"
+    else:
+        dst.parent.mkdir(parents=True, exist_ok=True)
+        dst_arg = str(dst)
+    cmd = ["rsync", "-av", src, dst_arg]
+    if not execute:
+        print("DRY-RUN:", " ".join(cmd))
+        return
+    subprocess.run(cmd, check=True)
+
+
+def audit_local_package(args: argparse.Namespace) -> int:
+    package_dir = args.workspace / "results" / "omni_finetune" / "verified_public" / args.eval_run_id
+    if not package_dir.exists():
+        print(f"Local package not found after sync: {package_dir}", file=sys.stderr)
+        return 1
+    cmd = [
+        sys.executable,
+        "scripts/omni/audit_verified_omni_package.py",
+        "--workspace",
+        str(args.workspace),
+        "--package-dir",
+        str(package_dir),
+        "--backbone",
+        "qwen3_omni_lora",
+    ]
+    return subprocess.run(cmd, cwd=args.workspace).returncode
+
+
+def main() -> int:
+    args = parse_args()
+    args.workspace = args.workspace.expanduser().resolve()
+    probe = remote_python_probe(args)
+    print(json.dumps({"remote_probe": probe}, indent=2))
+    if probe.get("summary_status") != "verified" and not args.allow_incomplete:
+        print("Remote verified public package is not ready; no files copied.", file=sys.stderr)
+        return 2
+    for src, dst in planned_paths(args):
+        rsync_one(src, dst, execute=args.execute)
+    if args.execute and probe.get("summary_status") == "verified":
+        return audit_local_package(args)
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/omni/defer_cosmos3_super_after_qwen_v4.sh

@@ -0,0 +1,38 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+PROJECT_ROOT="${PROJECT_ROOT:-$(cd "$(dirname "${BASH_SOURCE[0]}")/../.." && pwd)}"
+cd "$PROJECT_ROOT"
+
+QWEN_SUMMARY="${QWEN_SUMMARY:-results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/verified_result_summary.json}"
+POLL_SECONDS="${POLL_SECONDS:-120}"
+
+RUN_ID="${RUN_ID:-xperience10m_cosmos3_super_forward_dynamics_lora_overfit_after_qwen_v4_20260608}"
+MAX_TRAIN_SAMPLES="${MAX_TRAIN_SAMPLES:-1}"
+MAX_STEPS="${MAX_STEPS:-3}"
+OVERRIDE_RESOLUTION_TIER="${OVERRIDE_RESOLUTION_TIER:-256}"
+DEVICE_MAP="${DEVICE_MAP:-balanced}"
+DTYPE="${DTYPE:-bfloat16}"
+TIMESTEP_SAMPLING="${TIMESTEP_SAMPLING:-uniform}"
+
+qwen_train_or_eval_running() {
+  pgrep -af '[t]rain_qwen3_omni_lora.py|[e]val_qwen3_omni_lora.py' >/dev/null
+}
+
+echo "$(date) waiting for verified Qwen package: $QWEN_SUMMARY"
+while true; do
+  if [[ -f "$QWEN_SUMMARY" ]] && grep -Eq '"status"[[:space:]]*:[[:space:]]*"verified"' "$QWEN_SUMMARY"; then
+    echo "$(date) verified Qwen package is ready"
+    break
+  fi
+  sleep "$POLL_SECONDS"
+done
+
+while qwen_train_or_eval_running; do
+  echo "$(date) Qwen train/eval process still running; waiting"
+  sleep "$POLL_SECONDS"
+done
+
+echo "$(date) starting Cosmos3-Super forward-dynamics LoRA overfit: $RUN_ID"
+export RUN_ID MAX_TRAIN_SAMPLES MAX_STEPS OVERRIDE_RESOLUTION_TIER DEVICE_MAP DTYPE TIMESTEP_SAMPLING
+exec scripts/omni/run_cosmos3_super_forward_dynamics_lora.sh

scripts/omni/prepare_qwen3_lora_hf_package.py

@@ -16,12 +16,12 @@ ROOT = Path(__file__).resolve().parents[2]
 DEFAULT_VERIFIED_SUMMARY = (
     ROOT
     / "results/omni_finetune/verified_public/"
-    / "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full/"
+    / "xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora_eval_test_full/"
     / "verified_result_summary.json"
 )
 DEFAULT_ADAPTER_DIR = (
     ROOT
-    / "checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora/adapter_lora"
+    / "checkpoints/xperience10m_qwen3_omni_128ep_structured_json_v4_4epoch_full8gpu_lora/adapter_lora"
 )
 DEFAULT_OUTPUT_DIR = ROOT / "results/omni_finetune/hf_upload_qwen3_128ep_full"
 COPY_NAMES = [
@@ -66,6 +66,14 @@ def copy_file(src: Path, dst: Path) -> dict[str, Any]:
     }
 
 
+def normalize_adapter_config(path: Path) -> None:
+    """Keep PEFT Hub metadata valid even when older training wrote null."""
+    config = load_json(path)
+    if config.get("task_type") is None:
+        config["task_type"] = "CAUSAL_LM"
+    path.write_text(json.dumps(config, indent=2) + "\n", encoding="utf-8")
+
+
 def metric_table(metrics: dict[str, Any]) -> list[str]:
     rows = [
         ("JSON validity", metrics.get("json_validity_rate")),
@@ -220,7 +228,17 @@ def main() -> int:
     for name in COPY_NAMES:
         src = adapter_dir / name
         if src.exists():
-            copied.append(copy_file(src, output_dir / name))
+            dst = output_dir / name
+            copy_file(src, dst)
+            if name == "adapter_config.json":
+                normalize_adapter_config(dst)
+            copied.append(
+                {
+                    "path": dst.name,
+                    "bytes": dst.stat().st_size,
+                    "sha256": sha256(dst),
+                }
+            )
     safetensors = sorted(adapter_dir.glob("adapter_model*.safetensors"))
     if not safetensors:
         raise SystemExit(f"No adapter_model*.safetensors files found in {adapter_dir}")

scripts/omni/probe_cosmos3_super_training_readiness.py

@@ -327,10 +327,17 @@ def readiness_decision(model: dict[str, Any], runtime: dict[str, Any], dataset:
         blockers.append(f"Dataset is missing required JSON QA fields: {dataset['missing_required_fields']}")
     if dataset["missing_answer_fields"] and any(dataset["missing_answer_fields"].values()):
         blockers.append(f"Dataset answer_json is missing required fields: {dataset['missing_answer_fields']}")
-    blockers.append(
-        "Repository has no Cosmos3 diffusion/action target packer or supervised loss implementation for "
-        "xperience10m_episode_json_qa_v1; a readiness probe cannot produce adapter weights."
-    )
+    trainer_path = Path(__file__).with_name("train_cosmos3_super_forward_dynamics_lora.py")
+    if not trainer_path.exists():
+        blockers.append(
+            "Repository has no Cosmos3 diffusion/action target packer or supervised loss implementation for "
+            "xperience10m_episode_json_qa_v1; a readiness probe cannot produce adapter weights."
+        )
+    else:
+        warnings.append(
+            "Forward-dynamics LoRA trainer exists; run the camera-pose action-target contract audit before launch "
+            "because this probe checks the staged runtime and JSON-task dataset surface, not every action target row."
+        )
     return {
         "status": "blocked_until_trainer_implemented" if blockers else "ready_for_training_launch",
         "weights_updated": False,
@@ -341,9 +348,9 @@ def readiness_decision(model: dict[str, Any], runtime: dict[str, Any], dataset:
         "blockers": blockers,
         "warnings": warnings,
         "next_steps": [
-            "Implement a Cosmos3-Super training data packer that maps each Xperience-10M window to prompt, video/action latent inputs, timesteps, and loss indexes expected by Cosmos3OmniTransformer.forward.",
-            "Wire LoRA only onto the checkpoint-declared target modules q_proj_moe_gen,k_proj_moe_gen,v_proj_moe_gen,o_proj_moe_gen and use the rectified_flow_training_config loss weights.",
-            "Run a one-episode overfit with --load-pipeline enabled, then a 96/16/16 held-out adapter run only after the probe status has no blockers.",
+            "Run scripts/omni/audit_cosmos3_super_training_contract.py on the camera-pose action-target JSONL and require no blockers.",
+            "Run scripts/omni/train_cosmos3_super_forward_dynamics_lora.py as a one-sample or one-episode overfit before a full 96/16/16 adapter run.",
+            "Publish a separate Cosmos3-Super model repository only after the trainer produces new adapter/checkpoint weights and held-out evaluation artifacts.",
         ],
     }
 

scripts/omni/run_cosmos3_super_forward_dynamics_lora.sh

@@ -0,0 +1,59 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+PROJECT_ROOT="${PROJECT_ROOT:-$(cd "$SCRIPT_DIR/../.." && pwd)}"
+VENV_PY="${VENV_PY:-$PROJECT_ROOT/.venv/bin/python}"
+
+RUN_ID="${RUN_ID:-xperience10m_cosmos3_super_forward_dynamics_lora_overfit}"
+DATASET_JSONL="${DATASET_JSONL:-$PROJECT_ROOT/results/omni_finetune/xperience10m_cosmos3_camera_pose_targets_20260608/dataset_with_cosmos_actions.jsonl}"
+MODEL_DIR="${MODEL_DIR:-$HOME/Ropedia/cosmos3_models/nv-community__Cosmos3-Super}"
+OUTPUT_DIR="${OUTPUT_DIR:-$PROJECT_ROOT/results/omni_finetune/$RUN_ID}"
+SPLIT="${SPLIT:-train}"
+MAX_TRAIN_SAMPLES="${MAX_TRAIN_SAMPLES:-1}"
+MAX_STEPS="${MAX_STEPS:-10}"
+LEARNING_RATE="${LEARNING_RATE:-0.0001}"
+DEVICE_MAP="${DEVICE_MAP:-balanced}"
+DTYPE="${DTYPE:-bfloat16}"
+SEED="${SEED:-123}"
+TARGET_MODULES="${TARGET_MODULES:-}"
+TIMESTEP_SAMPLING="${TIMESTEP_SAMPLING:-uniform}"
+OVERRIDE_RESOLUTION_TIER="${OVERRIDE_RESOLUTION_TIER:-}"
+GRADIENT_CHECKPOINTING="${GRADIENT_CHECKPOINTING:-1}"
+DRY_RUN="${DRY_RUN:-0}"
+
+args=(
+  "$PROJECT_ROOT/scripts/omni/train_cosmos3_super_forward_dynamics_lora.py"
+  --workspace "$PROJECT_ROOT"
+  --dataset-jsonl "$DATASET_JSONL"
+  --model-dir "$MODEL_DIR"
+  --run-id "$RUN_ID"
+  --output-dir "$OUTPUT_DIR"
+  --split "$SPLIT"
+  --max-train-samples "$MAX_TRAIN_SAMPLES"
+  --max-steps "$MAX_STEPS"
+  --learning-rate "$LEARNING_RATE"
+  --device-map "$DEVICE_MAP"
+  --dtype "$DTYPE"
+  --seed "$SEED"
+  --timestep-sampling "$TIMESTEP_SAMPLING"
+)
+
+if [[ -n "$OVERRIDE_RESOLUTION_TIER" ]]; then
+  args+=(--override-resolution-tier "$OVERRIDE_RESOLUTION_TIER")
+fi
+
+if [[ -n "$TARGET_MODULES" ]]; then
+  args+=(--target-modules "$TARGET_MODULES")
+fi
+
+if [[ "$GRADIENT_CHECKPOINTING" == "0" ]]; then
+  args+=(--no-gradient-checkpointing)
+fi
+
+if [[ "$DRY_RUN" == "1" ]]; then
+  args+=(--dry-run)
+fi
+
+cd "$PROJECT_ROOT"
+exec "$VENV_PY" "${args[@]}"

scripts/omni/train_cosmos3_super_forward_dynamics_lora.py

@@ -0,0 +1,604 @@
+#!/usr/bin/env python3
+"""LoRA overfit trainer for Cosmos3-Super camera-pose forward dynamics.
+
+This trains the first real Cosmos3-Super adapter path for Xperience-10M. The
+current camera-pose targets are forward-dynamics targets: raw camera-pose
+actions are conditioning, and the supervised loss is the future vision velocity
+under rectified-flow noise. This script therefore updates LoRA weights on the
+Cosmos3 transformer and does not claim supervised action-token prediction.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+import random
+import time
+from pathlib import Path
+from typing import Any
+
+from pack_cosmos3_super_action_batch import (
+    find_action_target,
+    media_video_path,
+    row_contract,
+    tokenize_prompt,
+)
+from qwen3_omni_dataset_utils import load_jsonl
+
+
+DEFAULT_DATASET = (
+    "results/omni_finetune/"
+    "xperience10m_cosmos3_camera_pose_targets_20260608/"
+    "dataset_with_cosmos_actions.jsonl"
+)
+DEFAULT_COSMOS3_SUPER_LORA_TARGETS = [
+    "add_q_proj",
+    "add_k_proj",
+    "add_v_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj",
+]
+
+
+def parse_args() -> argparse.Namespace:
+    workspace_default = Path(__file__).resolve().parents[2]
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument("--workspace", type=Path, default=workspace_default)
+    parser.add_argument("--dataset-jsonl", type=Path, default=workspace_default / DEFAULT_DATASET)
+    parser.add_argument("--model-dir", type=Path, required=True)
+    parser.add_argument("--run-id", default="xperience10m_cosmos3_super_forward_dynamics_lora_overfit")
+    parser.add_argument("--output-dir", type=Path)
+    parser.add_argument("--split", default="train")
+    parser.add_argument("--episode-id", help="Optional single episode to overfit before scaling.")
+    parser.add_argument("--max-train-samples", type=int, default=1)
+    parser.add_argument("--max-steps", type=int, default=10)
+    parser.add_argument("--learning-rate", type=float, default=1e-4)
+    parser.add_argument("--weight-decay", type=float, default=0.0)
+    parser.add_argument("--lora-rank", type=int)
+    parser.add_argument("--lora-alpha", type=int)
+    parser.add_argument("--lora-dropout", type=float, default=0.0)
+    parser.add_argument("--adapter-name", default="xperience_forward_dynamics")
+    parser.add_argument("--target-modules", help="Comma-separated LoRA target modules. Defaults to model config.")
+    parser.add_argument("--device", default="cuda")
+    parser.add_argument("--device-map", default="balanced", help="Use 'none' to load the full pipeline onto --device.")
+    parser.add_argument("--dtype", default="bfloat16", choices=["bfloat16", "float16", "float32"])
+    parser.add_argument("--seed", type=int, default=123)
+    parser.add_argument("--prompt", default="Predict the embodied future under the provided camera-pose action condition.")
+    parser.add_argument("--negative-prompt")
+    parser.add_argument("--fps", type=float, default=24.0)
+    parser.add_argument("--num-train-timesteps", type=int)
+    parser.add_argument("--timestep-sampling", default="uniform", choices=["uniform", "logitnormal"])
+    parser.add_argument("--resolution-shift", type=float, help="Override rectified-flow sigma shift.")
+    parser.add_argument("--override-resolution-tier", type=int, choices=[256, 480, 704, 720])
+    parser.add_argument("--loss-scale", type=float)
+    parser.add_argument("--require-media-exists", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--local-files-only", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--gradient-checkpointing", action=argparse.BooleanOptionalAction, default=True)
+    parser.add_argument("--progress-every", type=int, default=1)
+    parser.add_argument("--dry-run", action="store_true", help="Pack batches but do not update weights.")
+    return parser.parse_args()
+
+
+def dtype_from_name(name: str):
+    import torch
+
+    return {
+        "bfloat16": torch.bfloat16,
+        "float16": torch.float16,
+        "float32": torch.float32,
+    }[name]
+
+
+def write_json(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    path.write_text(json.dumps(payload, indent=2, ensure_ascii=False) + "\n", encoding="utf-8")
+
+
+def append_jsonl(path: Path, payload: dict[str, Any]) -> None:
+    path.parent.mkdir(parents=True, exist_ok=True)
+    with path.open("a", encoding="utf-8") as handle:
+        handle.write(json.dumps(payload, sort_keys=True, ensure_ascii=False) + "\n")
+
+
+def read_json(path: Path) -> dict[str, Any]:
+    if not path.exists():
+        return {}
+    try:
+        return json.loads(path.read_text(encoding="utf-8"))
+    except json.JSONDecodeError:
+        return {}
+
+
+def model_inner_config(model_dir: Path) -> dict[str, Any]:
+    config = read_json(model_dir / "config.json")
+    return ((config.get("model") or {}).get("config") or {}) if config else {}
+
+
+def select_rows(rows: list[dict[str, Any]], args: argparse.Namespace) -> list[dict[str, Any]]:
+    candidates = []
+    for row in rows:
+        if row.get("split") != args.split:
+            continue
+        if args.episode_id and row.get("episode_id") != args.episode_id:
+            continue
+        if find_action_target(row)[1] is None:
+            continue
+        candidates.append(row)
+    if args.max_train_samples > 0:
+        candidates = candidates[: args.max_train_samples]
+    if not candidates:
+        raise ValueError(f"no Cosmos action-target rows found for split={args.split!r}")
+    return candidates
+
+
+def checkpoint_module_suffixes(model_dir: Path) -> set[str]:
+    suffixes: set[str] = set()
+    for index_path in (
+        model_dir / "model.safetensors.index.json",
+        model_dir / "transformer" / "diffusion_pytorch_model.safetensors.index.json",
+    ):
+        index = read_json(index_path)
+        weight_map = index.get("weight_map") if isinstance(index, dict) else None
+        if not isinstance(weight_map, dict):
+            continue
+        for key in weight_map:
+            if key.endswith(".weight"):
+                suffixes.add(key[:-7].split(".")[-1])
+    return suffixes
+
+
+def lora_targets(args: argparse.Namespace, inner: dict[str, Any], model_dir: Path) -> list[str]:
+    raw = args.target_modules or inner.get("lora_target_modules") or ""
+    modules = [item.strip() for item in str(raw).split(",") if item.strip()]
+    if args.target_modules:
+        return modules
+
+    available = checkpoint_module_suffixes(model_dir)
+    if modules and (not available or any(module in available for module in modules)):
+        return modules
+
+    fallback = [module for module in DEFAULT_COSMOS3_SUPER_LORA_TARGETS if not available or module in available]
+    return fallback or modules or DEFAULT_COSMOS3_SUPER_LORA_TARGETS
+
+
+def instantiate_action(row: dict[str, Any], resolution_tier: int | None):
+    import torch
+    from diffusers.pipelines.cosmos.pipeline_cosmos3_omni import CosmosActionCondition
+
+    _, target = find_action_target(row)
+    if target is None:
+        raise ValueError(f"row has no Cosmos action target: {row.get('id')}")
+    raw_actions = target.get("raw_actions")
+    raw_actions_tensor = torch.tensor(raw_actions, dtype=torch.float32) if raw_actions is not None else None
+    video_path = media_video_path(row, target)
+    if not video_path:
+        raise ValueError(f"row has no video path for Cosmos action target: {row.get('id')}")
+    return CosmosActionCondition(
+        mode=str(target.get("mode")),
+        chunk_size=int(target.get("chunk_size")),
+        domain_name=str(target.get("domain_name")),
+        resolution_tier=int(resolution_tier or target.get("resolution_tier", 480)),
+        raw_actions=raw_actions_tensor,
+        video=[video_path],
+        view_point=str(target.get("view_point", "ego_view")),
+    )
+
+
+def load_video_frames(video_path: str | Path, max_frames: int) -> list[Any]:
+    import cv2
+    from PIL import Image
+
+    path = Path(video_path)
+    capture = cv2.VideoCapture(str(path))
+    if not capture.isOpened():
+        raise ValueError(f"failed to open conditioning video: {path}")
+
+    frames: list[Any] = []
+    try:
+        while len(frames) < max_frames:
+            ok, frame = capture.read()
+            if not ok:
+                break
+            rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(Image.fromarray(rgb))
+    finally:
+        capture.release()
+
+    if not frames:
+        raise ValueError(f"conditioning video has no decodable frames: {path}")
+    return frames
+
+
+def conditioning_clip_for_action(action: Any, target_frames: int) -> Any:
+    if action.image is not None:
+        if isinstance(action.image, (str, Path)):
+            from PIL import Image
+
+            return [Image.open(action.image).convert("RGB")]
+        return [action.image]
+
+    video = action.video
+    if isinstance(video, list) and video and isinstance(video[0], (str, Path)):
+        return load_video_frames(video[0], target_frames)
+    if isinstance(video, (str, Path)):
+        return load_video_frames(video, target_frames)
+    return video
+
+
+def action_domain_id(domain_name: str, device: str):
+    import torch
+    from diffusers.pipelines.cosmos.pipeline_cosmos3_omni import _EMBODIMENT_TO_DOMAIN_ID
+
+    if domain_name not in _EMBODIMENT_TO_DOMAIN_ID:
+        raise ValueError(f"unknown Cosmos3 action domain: {domain_name}")
+    return torch.tensor([_EMBODIMENT_TO_DOMAIN_ID[domain_name]], dtype=torch.long, device=device)
+
+
+def clean_vision_latents(pipe: Any, action: Any, device: str, dtype: Any):
+    target_frames = action.chunk_size + 1
+    conditioning_clip = conditioning_clip_for_action(action, target_frames)
+    vision_tensor, action_image_size, height, width = pipe._prepare_action_video_conditioning(
+        conditioning_clip,
+        action.resolution_tier,
+        target_frames,
+        device=device,
+        dtype=dtype,
+    )
+    x0 = pipe._encode_video(vision_tensor).contiguous().float()
+    if action_image_size is not None:
+        x0 = pipe._remove_action_video_padding_from_latent(x0, action_image_size)
+    return x0, action_image_size, height, width
+
+
+def action_latents(action: Any, pipe: Any, device: str, dtype: Any):
+    import torch
+
+    raw = action.raw_actions
+    if raw is None:
+        raise ValueError("forward_dynamics requires raw action targets")
+    raw = raw.to(device=device, dtype=dtype)
+    action_chunk_size = int(action.chunk_size)
+    if raw.shape[0] < action_chunk_size:
+        raw = torch.cat([raw, raw[-1:].expand(action_chunk_size - raw.shape[0], -1)], dim=0)
+    raw = raw[:action_chunk_size]
+    raw_action_dim = int(raw.shape[-1])
+    action_dim = int(pipe.transformer.action_dim)
+    if raw_action_dim > action_dim:
+        raise ValueError(f"raw action dim {raw_action_dim} exceeds model action_dim {action_dim}")
+    if raw_action_dim < action_dim:
+        pad = torch.zeros(raw.shape[0], action_dim - raw_action_dim, device=device, dtype=dtype)
+        raw = torch.cat([raw, pad], dim=-1)
+    condition_mask = torch.ones((action_chunk_size, 1), device=device, dtype=dtype)
+    return raw, condition_mask, raw_action_dim, list(range(action_chunk_size))
+
+
+def shifted_sigma(timestep: int, num_train_timesteps: int, shift: float) -> float:
+    sigma = max(1e-5, min(1.0, float(timestep) / float(num_train_timesteps)))
+    if shift and shift != 1.0:
+        sigma = shift * sigma / (1.0 + (shift - 1.0) * sigma)
+    return float(max(1e-5, min(1.0, sigma)))
+
+
+def sample_timestep(args: argparse.Namespace, num_train_timesteps: int) -> int:
+    if args.timestep_sampling == "logitnormal":
+        sigma = 1.0 / (1.0 + math.exp(-random.gauss(0.0, 1.0)))
+        return max(1, min(num_train_timesteps, int(round(sigma * num_train_timesteps))))
+    return random.randint(1, num_train_timesteps)
+
+
+def loss_mask_from_condition(vision_condition_mask: Any, x0: Any):
+    mask = 1.0 - vision_condition_mask
+    while mask.ndim < x0.ndim:
+        mask = mask.unsqueeze(0)
+    return mask.to(device=x0.device, dtype=x0.dtype)
+
+
+def pack_static(pipe: Any, input_ids: list[int], latents: Any, action_tokens: Any, action_condition_frames: list[int], fps: float, device: str):
+    import torch
+
+    text_segment = pipe._prepare_text_segment(input_ids, device=device)
+    vision_condition_indexes = [0]
+    vision_segment = pipe._prepare_vision_segment(
+        input_vision_tokens=latents,
+        has_image_condition=True,
+        mrope_offset=text_segment["vision_start_temporal_offset"],
+        vision_fps=fps,
+        curr=text_segment["und_len"],
+        device=device,
+        condition_frame_indexes=vision_condition_indexes,
+    )
+    action_segment = pipe._prepare_action_segment(
+        input_action_tokens=action_tokens,
+        condition_frame_indexes=action_condition_frames,
+        mrope_offset=text_segment["vision_start_temporal_offset"],
+        action_fps=fps,
+        curr=text_segment["und_len"] + vision_segment["num_vision_tokens"],
+        device=device,
+    )
+    position_ids = torch.cat(
+        [
+            text_segment["text_mrope_ids"],
+            vision_segment["vision_mrope_ids"],
+            action_segment["action_mrope_ids"],
+        ],
+        dim=1,
+    )
+    return {
+        **text_segment,
+        **vision_segment,
+        **action_segment,
+        "position_ids": position_ids,
+        "sequence_length": text_segment["und_len"] + vision_segment["num_vision_tokens"] + action_segment["action_len"],
+    }
+
+
+def training_step(pipe: Any, row: dict[str, Any], args: argparse.Namespace, device: str, dtype: Any, num_train_timesteps: int, sigma_shift: float):
+    import torch
+
+    contract = row_contract(row, require_media_exists=args.require_media_exists)
+    if contract["issues"]:
+        raise ValueError(f"row contract issues for {contract['row_id']}: {contract['issues']}")
+    if contract["mode"] != "forward_dynamics":
+        raise ValueError(f"expected forward_dynamics target, got {contract['mode']}")
+
+    action = instantiate_action(row, args.override_resolution_tier)
+    with torch.no_grad():
+        x0, _action_image_size, height, width = clean_vision_latents(pipe, action, device, dtype)
+        act_tokens, act_mask, raw_action_dim, act_condition_frames = action_latents(action, pipe, device, dtype)
+        input_ids = tokenize_prompt(pipe, args, action, height, width)
+
+    timestep = sample_timestep(args, num_train_timesteps)
+    sigma = shifted_sigma(timestep, num_train_timesteps, sigma_shift)
+    noise = torch.randn_like(x0, dtype=x0.dtype, device=x0.device)
+    velocity_target = noise - x0
+
+    latent_t = int(x0.shape[2])
+    vision_condition_mask = torch.zeros((1, latent_t, 1, 1), device=x0.device, dtype=x0.dtype)
+    vision_condition_mask[:, 0, 0, 0] = 1.0
+    loss_mask = loss_mask_from_condition(vision_condition_mask, x0)
+    latent_condition_mask = vision_condition_mask.unsqueeze(0)
+    noised = x0 + sigma * velocity_target
+    latents = latent_condition_mask * x0.to(dtype) + (1.0 - latent_condition_mask) * noised.to(dtype)
+
+    packed = pack_static(pipe, input_ids, latents, act_tokens, act_condition_frames, args.fps, device)
+    vision_timesteps = torch.full((packed["num_noisy_vision_tokens"],), timestep, device=device)
+    action_timesteps = torch.full((packed["num_noisy_action_tokens"],), timestep, device=device)
+
+    preds_vision, preds_sound, preds_action = pipe.transformer(
+        input_ids=packed["input_ids"],
+        text_indexes=packed["text_indexes"],
+        position_ids=packed["position_ids"],
+        und_len=packed["und_len"],
+        sequence_length=packed["sequence_length"],
+        vision_tokens=[latents.to(device=device, dtype=dtype)],
+        vision_token_shapes=packed["vision_token_shapes"],
+        vision_sequence_indexes=packed["vision_sequence_indexes"],
+        vision_mse_loss_indexes=packed["vision_mse_loss_indexes"],
+        vision_timesteps=vision_timesteps,
+        vision_noisy_frame_indexes=packed["vision_noisy_frame_indexes"],
+        action_tokens=[act_tokens.to(device=device, dtype=dtype)],
+        action_token_shapes=packed["action_token_shapes"],
+        action_sequence_indexes=packed["action_sequence_indexes"],
+        action_mse_loss_indexes=packed["action_mse_loss_indexes"],
+        action_timesteps=action_timesteps,
+        action_noisy_frame_indexes=packed["action_noisy_frame_indexes"],
+        action_domain_ids=[action_domain_id(action.domain_name, device)],
+    )
+    pred_velocity, _pred_sound, _pred_action = pipe._mask_velocity_predictions(
+        preds_vision,
+        preds_sound,
+        vision_condition_mask=[vision_condition_mask.to(dtype=preds_vision[0].dtype)],
+        preds_action=preds_action,
+        action_condition_mask=[act_mask],
+        raw_action_dim=raw_action_dim,
+    )
+    target = velocity_target.to(device=pred_velocity.device, dtype=pred_velocity.dtype)
+    mask = loss_mask.to(device=pred_velocity.device, dtype=pred_velocity.dtype)
+    denom = mask.expand_as(pred_velocity).sum()
+    loss = ((pred_velocity - target) ** 2 * mask).sum() / denom.clamp_min(1.0)
+    if args.loss_scale:
+        loss = loss * args.loss_scale
+    return loss, {
+        "row_id": contract["row_id"],
+        "episode_id": contract["episode_id"],
+        "timestep": timestep,
+        "sigma": sigma,
+        "height": height,
+        "width": width,
+        "vision_latents_shape": list(x0.shape),
+        "action_latents_shape": list(act_tokens.shape),
+        "vision_loss_tokens": int(packed["vision_mse_loss_indexes"].numel()),
+        "action_loss_tokens": int(packed["action_mse_loss_indexes"].numel()),
+    }
+
+
+def save_adapter(pipe: Any, output_dir: Path, adapter_name: str) -> Path:
+    adapter_dir = output_dir / "adapter_lora"
+    adapter_dir.mkdir(parents=True, exist_ok=True)
+    pipe.transformer.save_lora_adapter(str(adapter_dir), adapter_name=adapter_name)
+    return adapter_dir
+
+
+def write_report(output_dir: Path, payload: dict[str, Any]) -> None:
+    lines = [
+        "# Cosmos3-Super Forward-Dynamics LoRA",
+        "",
+        f"- Run id: `{payload['run_id']}`",
+        f"- Status: `{payload['status']}`",
+        f"- Weights updated: `{payload['weights_updated']}`",
+        f"- Dataset: `{payload['dataset_jsonl']}`",
+        f"- Train samples: `{payload['train_samples']}`",
+        f"- Max steps: `{payload['max_steps']}`",
+        f"- Final loss: `{payload.get('final_loss')}`",
+        f"- Adapter dir: `{payload.get('adapter_dir')}`",
+        "",
+        "## Scope",
+        "",
+        "This adapter trains Cosmos3-Super camera-pose forward dynamics. Raw camera-pose actions are conditioning, and the loss supervises future vision velocity tokens. It is not a JSON Reasoner SFT run and does not supervise `preds_action`.",
+    ]
+    (output_dir / "RUN_REPORT.md").write_text("\n".join(lines) + "\n", encoding="utf-8")
+
+
+def main() -> int:
+    args = parse_args()
+    args.workspace = args.workspace.expanduser().resolve()
+    args.dataset_jsonl = args.dataset_jsonl.expanduser().resolve()
+    args.model_dir = args.model_dir.expanduser().resolve()
+    output_dir = args.output_dir or args.workspace / "results" / "omni_finetune" / args.run_id
+    output_dir = output_dir.expanduser().resolve()
+    progress_path = output_dir / "progress.jsonl"
+    if progress_path.exists():
+        progress_path.unlink()
+
+    random.seed(args.seed)
+    started = time.time()
+    append_jsonl(progress_path, {"event": "start", "run_id": args.run_id, "timestamp": started})
+
+    inner = model_inner_config(args.model_dir)
+    train_rows = select_rows(load_jsonl(args.dataset_jsonl), args)
+    target_modules = lora_targets(args, inner, args.model_dir)
+    lora_rank = int(args.lora_rank or inner.get("lora_rank") or 16)
+    lora_alpha = int(args.lora_alpha or inner.get("lora_alpha") or 32)
+    train_cfg = inner.get("rectified_flow_training_config") or {}
+    num_train_timesteps = int(args.num_train_timesteps or ((inner.get("rectified_flow_inference_config") or {}).get("num_train_timesteps") or 1000))
+    shift_table = train_cfg.get("shift") if isinstance(train_cfg.get("shift"), dict) else {}
+    resolution_key = str(args.override_resolution_tier or 480)
+    sigma_shift = float(args.resolution_shift or shift_table.get(resolution_key) or 1.0)
+    loss_scale = args.loss_scale if args.loss_scale is not None else train_cfg.get("loss_scale")
+    args.loss_scale = float(loss_scale) if loss_scale is not None else None
+
+    append_jsonl(
+        progress_path,
+        {
+            "event": "dataset_ready",
+            "timestamp": time.time(),
+            "train_samples": len(train_rows),
+            "target_modules": target_modules,
+            "lora_rank": lora_rank,
+            "lora_alpha": lora_alpha,
+            "sigma_shift": sigma_shift,
+            "loss_scale": args.loss_scale,
+        },
+    )
+
+    import torch
+    from diffusers import Cosmos3OmniPipeline
+    from peft import LoraConfig
+
+    dtype = dtype_from_name(args.dtype)
+    load_kwargs: dict[str, Any] = {
+        "torch_dtype": dtype,
+        "local_files_only": args.local_files_only,
+        "enable_safety_checker": False,
+    }
+    if args.device_map != "none":
+        load_kwargs["device_map"] = args.device_map
+    pipe = Cosmos3OmniPipeline.from_pretrained(str(args.model_dir), **load_kwargs)
+    if args.device_map == "none":
+        pipe.to(args.device)
+        device = args.device
+    else:
+        device = str(pipe._get_execution_device())
+    if hasattr(pipe, "set_progress_bar_config"):
+        pipe.set_progress_bar_config(disable=True)
+
+    pipe.transformer.requires_grad_(False)
+    if args.gradient_checkpointing and hasattr(pipe.transformer, "enable_gradient_checkpointing"):
+        pipe.transformer.enable_gradient_checkpointing()
+    lora_config = LoraConfig(
+        r=lora_rank,
+        lora_alpha=lora_alpha,
+        target_modules=target_modules,
+        lora_dropout=args.lora_dropout,
+        bias="none",
+    )
+    pipe.transformer.add_adapter(lora_config, adapter_name=args.adapter_name)
+    pipe.transformer.set_adapter(args.adapter_name)
+    pipe.transformer.train()
+    for component_name in ("vae", "sound_tokenizer"):
+        component = getattr(pipe, component_name, None)
+        if component is not None:
+            component.requires_grad_(False)
+            component.eval()
+
+    trainable = [param for param in pipe.transformer.parameters() if param.requires_grad]
+    trainable_params = sum(param.numel() for param in trainable)
+    append_jsonl(progress_path, {"event": "model_ready", "timestamp": time.time(), "device": device, "trainable_params": trainable_params})
+    if not trainable:
+        raise RuntimeError("no trainable LoRA parameters found")
+
+    optimizer = torch.optim.AdamW(trainable, lr=args.learning_rate, weight_decay=args.weight_decay)
+    losses: list[float] = []
+    status = "dry_run_complete" if args.dry_run else "complete"
+    adapter_dir: Path | None = None
+    try:
+        for step in range(1, args.max_steps + 1):
+            row = train_rows[(step - 1) % len(train_rows)]
+            optimizer.zero_grad(set_to_none=True)
+            loss, info = training_step(pipe, row, args, device, dtype, num_train_timesteps, sigma_shift)
+            loss_value = float(loss.detach().float().cpu())
+            losses.append(loss_value)
+            if not args.dry_run:
+                loss.backward()
+                optimizer.step()
+            if step == 1 or step % args.progress_every == 0 or step == args.max_steps:
+                append_jsonl(
+                    progress_path,
+                    {
+                        "event": "train_step",
+                        "timestamp": time.time(),
+                        "step": step,
+                        "loss": loss_value,
+                        **info,
+                    },
+                )
+        if not args.dry_run:
+            adapter_dir = save_adapter(pipe, output_dir, args.adapter_name)
+            append_jsonl(progress_path, {"event": "adapter_saved", "timestamp": time.time(), "adapter_dir": str(adapter_dir)})
+    except Exception as exc:
+        status = "failed"
+        append_jsonl(progress_path, {"event": "failed", "timestamp": time.time(), "error": repr(exc)})
+        raise
+    finally:
+        finished = time.time()
+        payload = {
+            "run_id": args.run_id,
+            "run_kind": "cosmos3_super_forward_dynamics_lora",
+            "status": status,
+            "started_at_unix": started,
+            "finished_at_unix": finished,
+            "elapsed_seconds": finished - started,
+            "workspace": str(args.workspace),
+            "dataset_jsonl": str(args.dataset_jsonl),
+            "model_dir": str(args.model_dir),
+            "split": args.split,
+            "episode_id": args.episode_id,
+            "train_samples": len(train_rows),
+            "max_steps": args.max_steps,
+            "learning_rate": args.learning_rate,
+            "target_modules": target_modules,
+            "lora_rank": lora_rank,
+            "lora_alpha": lora_alpha,
+            "trainable_params": trainable_params if "trainable_params" in locals() else None,
+            "num_train_timesteps": num_train_timesteps,
+            "sigma_shift": sigma_shift,
+            "loss_scale": args.loss_scale,
+            "final_loss": losses[-1] if losses else None,
+            "losses": losses,
+            "adapter_dir": str(adapter_dir) if adapter_dir else None,
+            "weights_updated": bool(adapter_dir),
+            "loss_surface": "vision_velocity_conditioned_on_camera_pose",
+            "action_loss_expected": False,
+        }
+        write_json(output_dir / "training_metadata.json", payload)
+        write_report(output_dir, payload)
+        append_jsonl(progress_path, {"event": "complete", "timestamp": time.time(), "status": status})
+
+    print(json.dumps({"status": status, "output_dir": str(output_dir), "adapter_dir": str(adapter_dir) if adapter_dir else None}, indent=2))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())

scripts/sync_hf_publish_mirrors.py

@@ -18,6 +18,10 @@ from pathlib import Path
 ROOT = Path(__file__).resolve().parents[1]
 DEFAULT_HF_ROOT = ROOT.parent / "hf_publish"
 PARITY_SCRIPT = ROOT / "scripts/validate_mirror_parity.py"
+STALE_MIRROR_FILES = [
+    "artifacts/scripts/omni/collect_qwen3_v4_publication_artifacts.py",
+    "model/scripts/omni/collect_qwen3_v4_publication_artifacts.py",
+]
 
 
 def load_parity_module():
@@ -50,11 +54,24 @@ def parse_args() -> argparse.Namespace:
     return parser.parse_args()
 
 
+def prune_stale_files(hf_root: Path, *, dry_run: bool) -> list[str]:
+    removed = []
+    for relative_path in STALE_MIRROR_FILES:
+        path = hf_root / relative_path
+        if not path.exists():
+            continue
+        removed.append(path.as_posix())
+        if not dry_run:
+            path.unlink()
+    return removed
+
+
 def main() -> int:
     args = parse_args()
     hf_root = args.hf_root.expanduser().resolve()
     parity = load_parity_module()
 
+    removed = prune_stale_files(hf_root, dry_run=args.dry_run)
     copied: list[dict] = []
     for filename in parity.DATA_FILES:
         src = ROOT / "docs/data" / filename
@@ -132,12 +149,17 @@ def main() -> int:
         "status": "dry_run" if args.dry_run else "synced",
         "hf_root": hf_root.as_posix(),
         "copy_count": len(copied),
+        "removed_stale_count": len(removed),
+        "removed_stale": removed,
         "records": copied,
     }
     if args.json:
         print(json.dumps(summary, indent=2))
     else:
-        print(f"{summary['status'].upper()}: copied {summary['copy_count']} files into {hf_root}")
+        print(
+            f"{summary['status'].upper()}: copied {summary['copy_count']} files into {hf_root}; "
+            f"removed {summary['removed_stale_count']} stale files"
+        )
     return 0
 
 

scripts/validate_mirror_parity.py

@@ -79,9 +79,17 @@ ASSET_FILES = [
 
 SCRIPT_FILES = [
     "omni/analyze_qwen3_omni_errors.py",
+    "omni/audit_cosmos3_super_training_contract.py",
     "omni/build_omni_model_comparison.py",
+    "omni/collect_qwen3_v4_release_artifacts.py",
+    "omni/defer_cosmos3_super_after_qwen_v4.sh",
+    "omni/export_cosmos3_camera_pose_targets.py",
+    "omni/pack_cosmos3_super_action_batch.py",
     "omni/prepare_qwen3_lora_hf_package.py",
+    "omni/probe_cosmos3_super_training_readiness.py",
     "omni/run_128_task_baselines.py",
+    "omni/run_cosmos3_super_forward_dynamics_lora.sh",
+    "omni/train_cosmos3_super_forward_dynamics_lora.py",
     "audio_ablation_and_raw_upgrade.py",
     "build_artifact_index.py",
     "build_brand_assets.py",