PROJECT_STATUS.md

Project Status

This is the fastest way to understand the current research project state. It summarizes what has already been implemented from the public Xperience-10M sample, what the first multi-episode Qwen3-Omni diagnostic pilot shows, and which artifacts support the next development step.

Research Positioning

The project is a research-engineering study of Xperience-10M rather than a single demo result. It makes the public sample episode inspectable, defines embodied-AI tasks over synchronized modalities, records baseline behavior, and keeps the next multi-episode modeling stage explicit. The current evidence is useful for judging data understanding, task design, evaluation discipline, and scale-up readiness; it is not presented as final full-dataset model quality.

Area	Current state	Evidence	Research readout
Public-sample pipeline	Verified	results/episode_task_suite/summary_report.json, results/episode_task_suite/windows.csv, results/episode_task_suite/feature_manifest.json	One public Xperience-10M sample episode is converted into 5,821 frames, 1,161 aligned 20-frame windows, and an 8,546-dimensional current feature contract.
Task suite	Verified	scripts/episode_task_suite.py, results/episode_task_suite/, docs/data/summary_metrics.json	All 12 task contracts have committed metrics, predictions, and minimal baseline outputs.
Neural heads	Verified	scripts/neural_task_models.py, results/episode_task_suite/neural_mlp/	Each task also has a compact PyTorch MLP run over the same feature tensor and chronological split.
Audio contribution study	Verified	scripts/audio_ablation_and_raw_upgrade.py, results/audio_ablation/, docs/data/audio_ablation_summary.json	Audio variants are compared across all 12 task contracts; audio improves the primary metric on 6 of 12 tasks, and a 588-d audio-window representation improves over the baseline audio variant on 6 of 12 tasks.
Research takeaways	Verified	RESEARCH_TAKEAWAYS.md, docs/data/research_takeaways.json, scripts/build_research_takeaways.py	The main result interpretation is generated from committed metrics: chronological class shift, neural gains on dynamics/order/alignment, open retrieval/reconstruction problems, and the need for held-out episodes.
Research roadmap	Current	RESEARCH_ROADMAP.md, docs/data/research_roadmap.json	The roadmap connects public-sample task development to the final verified Qwen3-Omni diagnostic result, same-split baseline alignment, action/subtask error analysis, robustness runs, world/policy branches, and the future Xperience-native pretraining goal.
Foundation-model plan	Current	FOUNDATION_MODEL_PLAN.md, docs/data/foundation_model_plan.json	Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is added as the first world-model/action-generation branch; Cosmos3-Super now has camera-pose proxy action targets that pass the contract audit and a schema-only batch-packer smoke. The current target mode is forward-dynamics, so it supports vision-velocity training under action conditioning, not supervised action-token prediction. OpenVLA/openpi/GR00T are policy candidates after robot-compatible action targets are explicit.
Cosmos3-Super action-target contract	Ready for forward-dynamics trainer implementation	scripts/omni/export_cosmos3_camera_pose_targets.py, scripts/omni/pack_cosmos3_super_action_batch.py, results/omni_finetune/xperience10m_cosmos3_camera_pose_targets_20260608/target_manifest.json, results/omni_finetune/xperience10m_cosmos3_super_training_contract_audit_camera_pose_20260608/training_contract_audit.json, results/omni_finetune/xperience10m_cosmos3_super_action_packer_schema_smoke_20260608/packer_summary.json	The selected 128-episode JSONL is augmented with 3,808/3,808 valid camera_pose proxy cosmos_action_target records from SLAM pose deltas. The schema-only packer smoke confirms the current forward_dynamics target should supervise noisy vision tokens under camera-pose conditioning; it does not supervise preds_action. Remaining work is a pipeline-loaded packer check, one-sample forward-dynamics overfit, and a separate policy/inverse target export before claiming action-token prediction.
Omni model extension contract	Current	OMNI_MODEL_EXTENSION_CONTRACT.md, configs/omni_backbones/, scripts/omni/backbone_registry.py, scripts/omni/smoke_test_backbone_packaging.py	Future model branches must keep the same episode split discipline, held-out metrics, validation gate, public-safe package contract, and explicit forbidden-artifact policy before reporting results.
Xperience Embodied Foundation Model	Future goal	XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md	A future full-corpus pretraining plan describes target modules, objectives, staged scale-up, hardware ranges, and evaluation for a domain-specific embodied foundation model.
Evaluation protocol	Verified	EVALUATION_PROTOCOL.md, docs/data/evaluation_protocol.json, scripts/build_evaluation_protocol.py	Windowing, chronological split, per-task metrics, leakage controls, and current limitations are generated from committed metric artifacts.
Dataset context	Verified	XPERIENCE10M_DATASET_CARD_ALIGNMENT.md, official Xperience-10M and sample cards	The README and dashboard distinguish the public sample used here from the gated full dataset used for the selected multi-episode pilot.
Public dashboard and Hub pages	Verified	GitHub Pages, HF Space, artifact dataset, baseline model repo, Qwen3-Omni LoRA repo	Readers can move between the website, code, derived artifacts, baseline weights, and Qwen3-Omni pilot status without needing local infrastructure details.
Public package policy	Verified	DATA_NOTICE.md, REPRODUCIBILITY.md	Raw Xperience-10M data, private gated files, large archives, credentials, and full Qwen weights are not redistributed.
Reproducibility	Verified for the public sample	REPRODUCIBILITY.md, docs/data/reproducibility_matrix.json, notes/reproducibility_audit.md	The public sample workflow has explicit commands, expected outputs, and exact-match reproduction evidence.
128-episode aligned baselines	Verified companion result	results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md, results/omni_finetune/multi_episode_128_task_baselines/summary_report.json, scripts/omni/run_128_task_baselines.py	The earlier simple and neural baseline framing is aligned to the same selected 96/16/16 episode split used by the Qwen3-Omni pilot. JSON-supported tasks have metadata/text simple and neural MLP metrics; raw-feature-only tasks are explicitly marked unsupported until 128-run sensor feature blocks are available.
Qwen3-Omni fine-tuning	Final verified diagnostic held-out result; JSON target met	docs/data/omni_finetune_verified_result.json, results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v3_strict_label_prompt_reuse_lora_eval_test_full/, scripts/omni/package_verified_omni_result.py, scripts/omni/audit_verified_omni_package.py, scripts/omni/analyze_qwen3_omni_errors.py	The selected 96/16/16 episode split produced a current public-safe strict-label v3 held-out package with 3,808 exported windows, 512 validation windows, 448 test predictions, two training epochs, validation/audit summaries, and the reused public LoRA adapter. JSON validity is 100.00%, meeting the 98% target; transition accuracy is 97.32%, contact accuracy is 72.10%, object micro-F1 is 30.69%, and action/subtask metrics remain weak, so it is still a diagnostic baseline rather than a strong model-quality claim.
Raw Xperience-10M redistribution	Not included	DATA_NOTICE.md, docs/data/publication_audit.json	Raw MP4, HDF5, RRD files, private gated data, and full Qwen weights are intentionally excluded.

Fast Research Route

1. Read this status file to establish the current project scope.
2. Open the visual dashboard for the fastest overview of data, tasks, directions, and scale-up status.
3. Inspect RESEARCH_TAKEAWAYS.md and docs/data/research_takeaways.json for the generated result interpretation.
4. Inspect RESEARCH_ROADMAP.md and docs/data/research_roadmap.json for the path from public-sample task work to multi-episode modeling.
5. Inspect FOUNDATION_MODEL_PLAN.md and docs/data/foundation_model_plan.json before choosing a backbone branch.
6. Inspect OMNI_MODEL_EXTENSION_CONTRACT.md and run python scripts/omni/backbone_registry.py --validate --json before adding a new Qwen, Cosmos-style, or VLA/policy branch.
7. Inspect XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md for the long-term full-corpus pretraining goal.
8. Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.
9. Inspect results/audio_ablation/AUDIO_ABLATION_SUMMARY.md before judging whether audio helps the current task suite.
10. Inspect EVALUATION_PROTOCOL.md before judging task metrics or leakage controls.
11. Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md only if you need the detailed upstream dataset-card context.
12. Inspect results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md before comparing simple/NN baselines to the selected 128-episode setup.
13. Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot.

Current Reading Notes

• Cross-episode generalization is a later multi-episode evaluation target; the current results use one public sample episode.
• Public-facing fine-tuning results should come from the verified result package, not from live process logs or setup-only artifacts.
• The final Qwen3-Omni strict-label v3 held-out package verifies the pipeline and meets the strict-JSON target, but not strong action/subtask model quality: JSON validity is 100.00%, action macro-F1 is 0.0022, and subtask accuracy is 0.0022.
• The 128-episode aligned simple/NN baselines use metadata/text features from the derived Qwen JSONL export; they align the split and task ids but do not replace raw-modality baselines for trajectory, retrieval, reconstruction, or misalignment tasks.
• The current reconstruction task reconstructs feature vectors, not pixel depth, meshes, NeRF outputs, or Gaussian splats.
• Audio is part of the current 8,546-dimensional baseline feature vector.
• Audio contribution is evaluated across all 12 task contracts in results/audio_ablation/.
• Foundation-model selection is now explicit: Qwen3-Omni is the immediate trainable pilot, Cosmos 3 is the first world-model branch, and Cosmos3-Super has a camera-pose proxy forward-dynamics contract ready for trainer implementation; policy models such as OpenVLA/openpi/GR00T still wait for robot-compatible action-target conversion.
• Future model branches should be added through the backbone registry and verified package contract, not by creating one-off result folders with incompatible metrics or publication rules.
• The Xperience Embodied Foundation Model is a future native-pretraining goal, not a completed model or current benchmark.