{ "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol", "status": "pass", "version": "2026-06-01", "generated_at_utc": "2026-06-21T15:20:33+00:00", "source_files": [ "docs/data/summary_metrics.json", "results/episode_task_suite/summary_report.json", "results/episode_task_suite/windows.csv", "results/episode_task_suite/feature_manifest.json", "docs/data/task_suite_20.json", "docs/data/tier2_task_suite.json", "results/episode_task_suite/tier2_task_suite/tier2_task_suite_results.json" ], "scope": { "validated_episode_count": 1, "annotation": "data/sample/xperience-10m-sample/annotation.hdf5", "num_frames": 5821, "num_windows": 1161, "feature_dim": 8546, "window_frames": 20, "stride_frames": 5, "audio_featurized": true, "raw_data_redistributed": false }, "task_suite": { "status": "unified_public_sample_suite", "task_count": 20, "public_framing": "all 20 public-sample task contracts are presented as one suite", "legacy_provenance_rows": 8, "unified_results": "docs/data/task_suite_20.json", "legacy_additional_task_result_path": "docs/data/tier2_task_suite.json", "legacy_path_note": "The tier2_task_suite path is retained for stable links only; it is provenance inside the same 20-task suite." }, "split_policy": { "name": "single_episode_chronological", "train_fraction": 0.7, "test_fraction": 0.3, "why": "The split preserves time order so future episode segments are not mixed randomly into the train set.", "limitation": "It is still one episode; cross-episode generalization is evaluated in the multi-episode stage." }, "feature_policy": { "input_contract": "8,546-dimensional current feature vector", "source_manifest": "results/episode_task_suite/feature_manifest.json", "normalization": "Scalers are fit on train windows only for the baseline heads.", "audio_status": "Audio is represented in the current feature vector." }, "baselines": [ { "name": "minimal", "heads": [ "softmax", "binary logistic", "multi-label logistic", "ridge regression", "ridge projection plus cosine ranking" ], "purpose": "Keep each task contract interpretable and easy to inspect." }, { "name": "neural_mlp", "heads": [ "PyTorch MLP classifier", "PyTorch MLP regressor", "PyTorch MLP multi-label head" ], "purpose": "Check nonlinear gains before larger omni-model fine-tuning.", "config": { "name": "neural_mlp", "type": "lightweight PyTorch MLP over shared window features", "epochs": 80, "hidden_dim": 128, "batch_size": 128, "learning_rate": 0.001, "weight_decay": 0.0001, "dropout": 0.1, "device": "auto" } } ], "task_protocols": [ { "task": "timeline_action", "task_display_name": "Action Recognition", "provenance_source": "walkthrough_backed_task_contract", "family": "supervised classification", "unit": "single window", "input": "current 20-frame all-feature window", "target": "current action label", "primary_metric": "macro_f1", "higher_is_better": true, "leakage_rule": "No future labels enter the input. Chronological split exposes unseen later action labels.", "counts": { "num_windows": 1144, "num_train_windows": 801, "num_test_windows": 343 }, "minimal_primary_metric": 0.05, "neural_primary_metric": 0.014814814814814814, "minimal_metric_source": "results/episode_task_suite/timeline_action/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/timeline_action/metrics.json", "task_number": 1, "suite_label": "Task 01" }, { "task": "timeline_subtask", "task_display_name": "Procedure Step Recognition", "provenance_source": "walkthrough_backed_task_contract", "family": "supervised classification", "unit": "single window", "input": "current 20-frame all-feature window", "target": "current subtask label", "primary_metric": "macro_f1", "higher_is_better": true, "leakage_rule": "No future labels enter the input. 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"results/episode_task_suite/hand_trajectory_forecast/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json", "task_number": 5, "suite_label": "Task 05" }, { "task": "contact_prediction", "task_display_name": "Contact State Prediction", "provenance_source": "walkthrough_backed_task_contract", "family": "binary classification", "unit": "single window", "input": "non-contact and non-caption feature blocks", "target": "any body contact", "primary_metric": "macro_f1", "higher_is_better": true, "leakage_rule": "Contact-derived fields and caption labels are excluded from inputs.", "counts": { "num_windows": 1161, "num_train_windows": 813, "num_test_windows": 348 }, "minimal_primary_metric": 1.0, "neural_primary_metric": 1.0, "minimal_metric_source": "results/episode_task_suite/contact_prediction/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/contact_prediction/metrics.json", "task_number": 6, "suite_label": "Task 06" }, { "task": "object_relevance", "task_display_name": "Object Relevance Prediction", "provenance_source": "walkthrough_backed_task_contract", "family": "multi-label classification", "unit": "single window", "input": "non-caption feature blocks", "target": "current relevant object set", "primary_metric": "micro_f1", "higher_is_better": true, "leakage_rule": "Caption/object-label fields are excluded from inputs.", "counts": { "num_windows": 1161, "num_train_windows": 813, "num_test_windows": 348 }, "minimal_primary_metric": 0.18034382095361662, "neural_primary_metric": 0.1679279279279279, "minimal_metric_source": "results/episode_task_suite/object_relevance/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/object_relevance/metrics.json", "task_number": 7, "suite_label": "Task 07" }, { "task": "caption_grounding", "task_display_name": "Language Grounding", "provenance_source": "walkthrough_backed_task_contract", "family": "retrieval", "unit": "caption query", "input": "caption object/interaction query plus candidate sensor windows", "target": "matching time window", "primary_metric": "mrr", "higher_is_better": true, "leakage_rule": "Queries are ranked against held-out candidate windows; reported ranks are computed after model scoring.", "counts": { "num_queries": 348, "num_train_windows": 813, "num_test_windows": 348 }, "minimal_primary_metric": 0.016023479050338015, "neural_primary_metric": 0.01684125567132316, "minimal_metric_source": "results/episode_task_suite/caption_grounding/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/caption_grounding/metrics.json", "task_number": 8, "suite_label": "Task 08" }, { "task": "cross_modal_retrieval", "task_display_name": "Cross-Modal Retrieval", "provenance_source": "walkthrough_backed_task_contract", "family": "retrieval", "unit": "sensor query", "input": "motion, IMU, and camera query features", "target": "matching depth/video window", "primary_metric": "top5_accuracy", "higher_is_better": true, "leakage_rule": "Query-side and candidate-side feature blocks are split before projection/ranking.", "counts": { "num_queries": 348, "num_train_windows": 813, "num_test_windows": 348 }, "minimal_primary_metric": 0.367816091954023, "neural_primary_metric": 0.19827586206896552, "minimal_metric_source": "results/episode_task_suite/cross_modal_retrieval/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json", "task_number": 9, "suite_label": "Task 09" }, { "task": "modality_reconstruction", "task_display_name": "Cross-Modal Reconstruction", "provenance_source": "walkthrough_backed_task_contract", "family": "cross-modal regression", "unit": "single window", "input": "motion, IMU, and camera features", "target": "depth/video feature vector", "primary_metric": "r2", "higher_is_better": true, "leakage_rule": "Target feature blocks are excluded from the input side.", "counts": { "num_train_windows": 813, "num_test_windows": 348 }, "minimal_primary_metric": -0.015271898913936655, "neural_primary_metric": -0.010171410134180991, "minimal_metric_source": "results/episode_task_suite/modality_reconstruction/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json", "task_number": 10, "suite_label": "Task 10" }, { "task": "temporal_order", "task_display_name": "Temporal Order Verification", "provenance_source": "walkthrough_backed_task_contract", "family": "pairwise diagnostic", "unit": "adjacent window pair", "input": "two adjacent windows", "target": "correct versus reversed order", "primary_metric": "f1", "higher_is_better": true, "leakage_rule": "Pairs are built after windowing; labels are synthetic order labels, not input features.", "counts": { "num_samples": 2320, "num_train_samples": 1624, "num_test_samples": 696 }, "minimal_primary_metric": 0.5399515738498789, "neural_primary_metric": 0.8520179372197308, "minimal_metric_source": "results/episode_task_suite/temporal_order/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/temporal_order/metrics.json", "task_number": 11, "suite_label": "Task 11" }, { "task": "misalignment_detection", "task_display_name": "Multimodal Synchronization Detection", "provenance_source": "walkthrough_backed_task_contract", "family": "pairwise diagnostic", "unit": "paired modality window", "input": "motion side plus visual/depth side", "target": "aligned versus shifted by 8 windows", "primary_metric": "f1", "higher_is_better": true, "leakage_rule": "Shift labels are synthetic targets; shifted visual/depth blocks are generated after feature splitting.", "counts": { "num_samples": 2306, "num_train_samples": 1614, "num_test_samples": 692 }, "minimal_primary_metric": 0.5051698670605613, "neural_primary_metric": 0.7152682255845944, "minimal_metric_source": "results/episode_task_suite/misalignment_detection/metrics.json", "neural_metric_source": "results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json", "task_number": 12, "suite_label": "Task 12" }, { "task": "long_horizon_next_action", "task_display_name": "Long-Horizon Next-Action Forecasting", "provenance_source": "historical_result_bundle", "family": "classification", "unit": "single aligned window", "input": "Current 20-frame non-caption multimodal window.", "target": "Action label five seconds later.", "primary_metric": "macro_f1", "higher_is_better": true, "minimal_primary_metric": 0.07499999999999998, "neural_primary_metric": 0.06545454545454546, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/long_horizon_next_action/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/long_horizon_next_action/metrics.json", "meaning": "Tests whether the current state carries enough procedure context to forecast beyond the one-second core next-action task.", "task_number": 13, "suite_label": "Task 13" }, { "task": "next_subtask_forecast", "task_display_name": "Long-Horizon Next-Subtask Forecasting", "provenance_source": "historical_result_bundle", "family": "classification", "unit": "single aligned window", "input": "Current 20-frame non-caption multimodal window.", "target": "Procedure subtask label five seconds later.", "primary_metric": "macro_f1", "higher_is_better": true, "minimal_primary_metric": 0.04545454545454545, "neural_primary_metric": 0.050724637681159424, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/next_subtask_forecast/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/next_subtask_forecast/metrics.json", "meaning": "Moves from immediate action anticipation to higher-level procedure-state prediction.", "task_number": 14, "suite_label": "Task 14" }, { "task": "interaction_text_prediction", "task_display_name": "Interaction Text Prediction", "provenance_source": "historical_result_bundle", "family": "classification", "unit": "single aligned window", "input": "Current 20-frame sensor window with caption-text features removed.", "target": "Raw annotation interaction phrase for the same window.", "primary_metric": "macro_f1", "higher_is_better": true, "minimal_primary_metric": 0.04444444444444444, "neural_primary_metric": 0.0380952380952381, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/interaction_text_prediction/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/interaction_text_prediction/metrics.json", "meaning": "Uses the raw caption JSON interaction field as a language target instead of only the hashed text feature.", "task_number": 15, "suite_label": "Task 15" }, { "task": "action_object_relation", "task_display_name": "Action-Object Relation Prediction", "provenance_source": "historical_result_bundle", "family": "classification", "unit": "single aligned window", "input": "Current 20-frame sensor window with caption-text features removed.", "target": "Joint action plus active object-set relation.", "primary_metric": "macro_f1", "higher_is_better": true, "minimal_primary_metric": 0.0, "neural_primary_metric": 0.0, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/action_object_relation/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/action_object_relation/metrics.json", "meaning": "Evaluates whether a model can bind what action is happening to which objects are involved.", "task_number": 16, "suite_label": "Task 16" }, { "task": "object_set_forecast", "task_display_name": "Future Object-Set Forecasting", "provenance_source": "historical_result_bundle", "family": "multi_label", "unit": "single aligned window", "input": "Current 20-frame sensor window with caption-text features removed.", "target": "Object set active five seconds later.", "primary_metric": "micro_f1", "higher_is_better": true, "minimal_primary_metric": 0.16939890710382516, "neural_primary_metric": 0.19718309859154928, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/object_set_forecast/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/object_set_forecast/metrics.json", "meaning": "Predicts which objects will become relevant soon, not only which objects are relevant now.", "task_number": 17, "suite_label": "Task 17" }, { "task": "imu_to_hand_pose", "task_display_name": "IMU-to-Hand Pose Reconstruction", "provenance_source": "historical_result_bundle", "family": "regression", "unit": "single aligned window", "input": "Current IMU acceleration/gyroscope feature block only.", "target": "Current left/right hand joint feature blocks.", "primary_metric": "mae", "higher_is_better": false, "minimal_primary_metric": 0.042049407958984375, "neural_primary_metric": 0.042562149465084076, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/imu_to_hand_pose/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/imu_to_hand_pose/metrics.json", "meaning": "A sensor-bridge probe for how much hand configuration can be recovered from inertial motion alone.", "task_number": 18, "suite_label": "Task 18" }, { "task": "camera_view_sync_retrieval", "task_display_name": "Camera-View Synchronization Retrieval", "provenance_source": "historical_result_bundle", "family": "retrieval", "unit": "held-out query window", "input": "Fisheye camera-1 feature query projected into fisheye camera-3 feature space.", "target": "The synchronized held-out camera-3 window.", "primary_metric": "mrr", "higher_is_better": true, "minimal_primary_metric": 0.4943004846572876, "neural_primary_metric": 0.24086658656597137, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/camera_view_sync_retrieval/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/camera_view_sync_retrieval/metrics.json", "meaning": "Stress-tests multi-camera time alignment beyond the core cross-modal retrieval task.", "task_number": 19, "suite_label": "Task 19" }, { "task": "time_to_transition", "task_display_name": "Time-to-Next-Transition Regression", "provenance_source": "historical_result_bundle", "family": "regression", "unit": "single aligned window", "input": "Current 20-frame non-caption multimodal window.", "target": "Frames until the next action-label boundary, capped at 200 frames.", "primary_metric": "mae", "higher_is_better": false, "minimal_primary_metric": 10.53735637664795, "neural_primary_metric": 10.55449390411377, "minimal_metric_source": "results/episode_task_suite/tier2_task_suite/time_to_transition/metrics.json", "neural_metric_source": "results/episode_task_suite/tier2_task_suite/neural_mlp/time_to_transition/metrics.json", "meaning": "Turns boundary detection into a continuous timing estimate for procedural control.", "task_number": 20, "suite_label": "Task 20" } ], "global_leakage_controls": [ "Use chronological train/test splits instead of random window shuffling.", "Fit scalers and learned projections on train windows only.", "Keep future labels, future mocap, contact labels, object labels, and caption labels on the target side unless a task explicitly treats language as the query.", "For cross-modal tasks, split query-side and candidate-side feature blocks before training and ranking.", "Report unseen test classes when the chronological split exposes labels absent from the train segment." ], "current_limitations": [ "Cross-episode generalization for Qwen3-Omni has a first verified diagnostic pilot, but strong model quality is not yet shown.", "Feature-vector reconstruction is separate from pixel depth, mesh, NeRF, or Gaussian reconstruction.", "The final verified Qwen3-Omni diagnostic result meets the strict-JSON target, but action/subtask held-out quality remains weak and needs error analysis before larger model-quality claims.", "Full audio-visual representation learning still needs multi-episode training; the current report includes single-episode audio/no-audio ablations." ], "scale_up_gate": { "required_before_next_omni_quality_pilot": [ "selected prepared Xperience-10M episodes", "held-out episode split with no train/test episode leakage", "validation samples during training", "manifest, training metadata, progress logs, metrics, predictions, and run report", "held-out evaluation on test episodes rather than train windows" ], "current_status": "verified diagnostic result; strict-JSON quality target met, action/subtask quality still weak", "evidence": [ "docs/data/omni_finetune_verified_result.json", "results/omni_finetune/verified_public/" ] } }