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 remains data-gated, and which artifacts support the next development step.

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.
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 staged path connects public-sample task development to multi-episode data staging, the 32-episode Qwen3-Omni LoRA pilot, robustness runs, and larger omni-model extensions.
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.
Official dataset wording	Verified	XPERIENCE10M_DATASET_CARD_ALIGNMENT.md, docs/data/xperience10m_dataset_card_alignment.json	Public wording is aligned to the official gated Xperience-10M dataset card, public sample card, and HF API metadata, including modalities, scale, access path, sample license/tooling, and current project coverage.
Source alignment	Verified	SOURCE_ALIGNMENT_AUDIT.md, docs/data/source_alignment_audit.json, scripts/validate_source_alignment.py	Source facts, sample details, API-listing notes, and project coverage are checked across repo docs, website, and HF cards.
Website and HF mirrors	Verified	docs/data/website_integrity.json, docs/data/rendered_site_check.json, docs/data/mirror_parity.json, docs/data/live_publication_status.json	Local website links/assets pass, the rendered walkthrough flow has a browser-level check, prepared mirrors match, and public GitHub/HF URLs have been verified after upload.
Public bundle contents	Verified	docs/data/publication_audit.json, QUALITY_GATES.md, docs/data/quality_gates.json	Public bundles exclude raw data, caches, heavy archives, token strings, and stale public-card copy.
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.
Qwen3-Omni fine-tuning	Data-gated; full metrics pending	results/omni_finetune/DATA_ACCESS_STATUS.md, results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md	The 32-episode LoRA pilot is prepared; final held-out metrics require gated data access, manifest construction, training, and evaluation.
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 and EVIDENCE_CONTRACT.md to establish the current project scope.
2. Open docs/data/project_packet.json for the machine-readable project path.
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 staged path from public-sample task work to multi-episode modeling.
5. Inspect docs/data/summary_metrics.json and results/episode_task_suite/neural_mlp/ to check the 12-task outputs.
6. Inspect EVALUATION_PROTOCOL.md before judging task metrics or leakage controls.
7. Inspect SOURCE_ALIGNMENT_AUDIT.md and XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.
8. Inspect results/omni_finetune/DATA_ACCESS_STATUS.md before judging Qwen3-Omni scale-up status.

Current Reading Notes

• Cross-episode generalization is a later multi-episode evaluation target; the current results use one public sample episode.
• Historical 32ep path names refer to setup files, not completed 32-episode training results.
• The current reconstruction task reconstructs feature vectors, not pixel depth, meshes, NeRF outputs, or Gaussian splats.
• AAC audio is decoded from fisheye_cam0.mp4 and included in the current 8,546-dimensional baseline feature vector.