{ "title": "Ropedia Xperience-10M Current Result Versions", "generated_at_utc": "2026-06-06T23:26:13+00:00", "status": "pass", "version_count": 3, "comparison_rule": "Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and Cosmos3 future-window metrics answer different questions.", "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, while Cosmos3-Nano is a future-window compatibility result rather than a full Cosmos diffusion fine-tune." ], "versions": [ { "id": "v1_single_episode_public_sample", "title": "Single-Episode Public-Sample Task Suite", "status": "verified", "scope": "one public Xperience-10M sample episode", "source": "results/episode_task_suite/summary_report.json", "split": "chronological 70/30 within one episode", "counts": { "episodes": 1, "windows": 1161, "frames": 5821, "feature_dim": 8546, "task_count": 12, "neural_task_count": 12 }, "models": [ "minimal task heads", "compact neural MLP task heads" ], "task_metrics": [ { "task": "caption_grounding", "task_display_name": "Language Grounding", "simple_status": "pass", "simple_primary_metric": "mrr", "simple_primary_score": 0.016023479050338015, "neural_status": "pass", "neural_primary_metric": "mrr", "neural_primary_score": 0.01684125567132316 }, { "task": "contact_prediction", "task_display_name": "Contact State Prediction", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 1.0, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 1.0 }, { "task": "cross_modal_retrieval", "task_display_name": "Cross-Modal Retrieval", "simple_status": "pass", "simple_primary_metric": "mrr", "simple_primary_score": 0.26925966892956127, "neural_status": "pass", "neural_primary_metric": "mrr", "neural_primary_score": 0.1299971898648288 }, { "task": "hand_trajectory_forecast", "task_display_name": "Hand Trajectory Forecasting", "simple_status": "pass", "simple_primary_metric": "mpjpe", "simple_primary_score": 0.8646570444107056, "neural_status": "pass", "neural_primary_metric": "mpjpe", "neural_primary_score": 0.10785018652677536 }, { "task": "misalignment_detection", "task_display_name": "Multimodal Synchronization Detection", "simple_status": "pass", "simple_primary_metric": "f1", "simple_primary_score": 0.5051698670605613, "neural_status": "pass", "neural_primary_metric": "f1", "neural_primary_score": 0.7152682255845944 }, { "task": "modality_reconstruction", "task_display_name": "Cross-Modal Reconstruction", "simple_status": "pass", "simple_primary_metric": "r2", "simple_primary_score": -0.015271898913936655, "neural_status": "pass", "neural_primary_metric": "r2", "neural_primary_score": -0.010171410134180991 }, { "task": "next_action", "task_display_name": "Next-Action Prediction", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.05925925925925927, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.04186046511627907 }, { "task": "object_relevance", "task_display_name": "Object Relevance Prediction", "simple_status": "pass", "simple_primary_metric": "micro_f1", "simple_primary_score": 0.18034382095361662, "neural_status": "pass", "neural_primary_metric": "micro_f1", "neural_primary_score": 0.1679279279279279 }, { "task": "temporal_order", "task_display_name": "Temporal Order Verification", "simple_status": "pass", "simple_primary_metric": "accuracy", "simple_primary_score": 0.4540229885057471, "neural_status": "pass", "neural_primary_metric": "accuracy", "neural_primary_score": 0.8577586206896551 }, { "task": "timeline_action", "task_display_name": "Action Recognition", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.05, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.014814814814814814 }, { "task": "timeline_subtask", "task_display_name": "Procedure Step Recognition", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.05056355513846935, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.02810810810810811 }, { "task": "transition_detection", "task_display_name": "Action Boundary Detection", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.6118237590630229, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.5862068965517241 } ], "interpretation": "This layer verifies the 12 task contracts and raw multimodal feature pipeline on the public sample. It is not a cross-episode benchmark." }, { "id": "v2_multi_episode_128_aligned_metadata_baselines", "title": "128-Episode Aligned Simple/NN Baselines", "status": "pass", "scope": "selected 128-episode 96/16/16 split", "source": "results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md", "split": "train/val/test by selected episode/session", "counts": { "rows": 3808, "split_counts": { "train": 2848, "val": 512, "test": 448 }, "episode_counts": { "test": 16, "train": 96, "val": 16 }, "task_count": 12, "simple_supported_task_count": 8, "neural_supported_task_count": 6 }, "models": [ "metadata/text simple baselines", "metadata/text neural MLP baselines" ], "task_metrics": [ { "task": "timeline_action", "task_display_name": "Action Recognition", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.00017511601435951318, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.0 }, { "task": "timeline_subtask", "task_display_name": "Procedure Step Recognition", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.0, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.0 }, { "task": "transition_detection", "task_display_name": "Action Boundary Detection", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.5219803670507895, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.45822172492907925 }, { "task": "next_action", "task_display_name": "Next-Action Prediction", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.00019966057701906761, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.0 }, { "task": "hand_trajectory_forecast", "task_display_name": "Hand Trajectory Forecasting", "simple_status": "unsupported_without_raw_128_feature_blocks", "simple_primary_metric": "mpjpe", "simple_primary_score": null, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null }, { "task": "contact_prediction", "task_display_name": "Contact State Prediction", "simple_status": "pass", "simple_primary_metric": "macro_f1", "simple_primary_score": 0.5167950693374422, "neural_status": "pass", "neural_primary_metric": "macro_f1", "neural_primary_score": 0.21951219512195122 }, { "task": "object_relevance", "task_display_name": "Object Relevance Prediction", "simple_status": "pass", "simple_primary_metric": "micro_f1", "simple_primary_score": 0.18221614227086183, "neural_status": "pass", "neural_primary_metric": "micro_f1", "neural_primary_score": 0.1053878034339846 }, { "task": "caption_grounding", "task_display_name": "Language Grounding", "simple_status": "pass", "simple_primary_metric": "mrr", "simple_primary_score": 0.012785504572093487, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null }, { "task": "cross_modal_retrieval", "task_display_name": "Cross-Modal Retrieval", "simple_status": "unsupported_without_raw_128_feature_blocks", "simple_primary_metric": "mrr", "simple_primary_score": null, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null }, { "task": "modality_reconstruction", "task_display_name": "Cross-Modal Reconstruction", "simple_status": "unsupported_without_raw_128_feature_blocks", "simple_primary_metric": "r2", "simple_primary_score": null, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null }, { "task": "temporal_order", "task_display_name": "Temporal Order Verification", "simple_status": "pass", "simple_primary_metric": "f1", "simple_primary_score": 0.32713178294573647, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null }, { "task": "misalignment_detection", "task_display_name": "Multimodal Synchronization Detection", "simple_status": "unsupported_without_raw_128_feature_blocks", "simple_primary_metric": "f1", "simple_primary_score": null, "neural_status": "not_run", "neural_primary_metric": "", "neural_primary_score": null } ], "interpretation": "This layer aligns the previous simple and neural baseline framing to the same selected 96/16/16 split used by the model branches. It uses public-safe JSONL metadata/text features, so raw-feature-only tasks remain explicitly unsupported until 128-run sensor feature blocks exist." }, { "id": "v3_multi_episode_foundation_model_branches", "title": "128-Episode Foundation-Model Branches", "status": "partial_verified", "scope": "selected 128-episode split and compatible derived windows", "source": "results/omni_finetune/verified_public/", "split": "episode/session held-out split; exact task target depends on backbone contract", "counts": { "verified_branch_count": 4, "qwen3_verified_package_count": 3, "cosmos3_verified_package_count": 1 }, "models": [ "Qwen3-Omni LoRA", "Cosmos3-Nano future-window compatibility branch" ], "branches": [ { "id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full", "title": "Cosmos3-Nano Future-Window World Model", "status": "verified", "backbone": "cosmos_world_model", "dataset_contract": "xperience10m_future_window_world_model_v0", "training_objective": "future_window_and_action_conditioned_world_modeling", "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full/verified_result_summary.json", "dataset_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat", "train_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter", "eval_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full", "counts": { "dataset_samples": 3213, "dataset_episodes": 119, "split_counts": { "train": 2403, "test": 378, "val": 432 }, "train_samples": 2403, "val_samples": 432, "eval_samples": 378, "held_out_episode_count": 14, "num_processes": 1 }, "primary_metrics": { "future_retrieval_mrr": 0.022138720585222767, "future_retrieval_recall_at_5": 0.015873015873015872, "temporal_consistency": 0.09523809523809523, "feature_reconstruction_error": 3479.218317102503, "transition_accuracy": 0.9682539682539683, "contact_accuracy": 0.7433862433862434, "held_out_episode_count": 14 }, "history": [ { "epoch": 0, "train_loss": null, "val_loss": null, "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run" } ] }, { "id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval", "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_96train_16val_16test_valmon_20260605_eval/verified_result_summary.json", "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605", "train_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_lora", "eval_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval", "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": 0.875, "action_macro_f1": 0.0026621494447581404, "subtask_accuracy": 0.006696428571428571, "transition_accuracy": 0.8504464285714286, "next_action_accuracy": 0.024553571428571428, "contact_accuracy": 0.6450892857142857, "object_micro_f1": 0.22299431459254582, "held_out_episode_count": 14 }, "history": [ { "epoch": 1, "train_loss": 0.41304643672440994, "val_loss": 0.0330660454928875, "global_step": 356 } ] }, { "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_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_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full/verified_result_summary.json", "dataset_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu", "train_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6", "eval_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full", "counts": { "dataset_samples": 3808, "dataset_episodes": 119, "split_counts": { "train": 2848, "val": 512, "test": 448 }, "train_samples": 2848, "val_samples": 0, "eval_samples": 448, "held_out_episode_count": 14, "num_processes": 8 }, "primary_metrics": { "json_validity_rate": 0.8526785714285714, "action_macro_f1": 0.00213753459655099, "subtask_accuracy": 0.004464285714285714, "transition_accuracy": 0.828125, "next_action_accuracy": 0.022321428571428572, "contact_accuracy": 0.6517857142857143, "object_micro_f1": 0.23062730627306272, "held_out_episode_count": 14 }, "history": [ { "epoch": 1, "train_loss": 0.4121775626560694, "val_loss": null, "global_step": 356 } ] }, { "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_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_v2_reuse_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_v2_reuse_full8gpu_lora", "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_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": 0.9977678571428571, "action_macro_f1": 0.0024331644885523347, "subtask_accuracy": 0.002232142857142857, "transition_accuracy": 0.9709821428571429, "next_action_accuracy": 0.029017857142857144, "contact_accuracy": 0.71875, "object_micro_f1": 0.30160427807486634, "held_out_episode_count": 14 }, "history": [ { "epoch": 1, "train_loss": 0.41282760031950355, "val_loss": 0.03288277983665466, "global_step": 356 }, { "epoch": 2, "train_loss": 0.027745448225544075, "val_loss": 0.027823254466056824, "global_step": 712 } ] } ], "interpretation": "This layer contains the held-out foundation-model packages. Qwen3-Omni packages evaluate structured JSON task prediction; Cosmos3-Nano currently evaluates a future-window world-model compatibility adapter, not a full diffusion-weight fine-tune." } ], "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 compatibility adapter to full Cosmos3 fine-tuning only after a separate environment with matching Diffusers/Cosmos dependencies is prepared." ] }