# 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 selected-128 Qwen3-Omni and Cosmos3
diagnostic rows show, 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 selected-128 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.
The current no-new-episode enhancement plan records how to push the selected
128-episode setup harder before asking for more raw storage.

## Two Evidence Lines

| Evidence line | Data unit | Public result status | Correct reading |
| --- | --- | --- | --- |
| Line 1: 1 sample episode | One public sample episode: 5,821 frames and 1,161 aligned 20-frame windows | 2 methods x 20 tasks = 40/40 direct scores | Task construction, raw-sample inspection, reproducibility, and controlled single-episode baselines |
| Line 2: 128 selected episodes | Selected 96/16/16 split over 128 official episode paths, with public-safe processed features | 7 methods x 20 tasks = 140/140 scores: 134 direct + 6 compact-proxy | Same-split metadata/raw baselines, Qwen3-Omni v6 diagnostics, Cosmos3 diagnostics, and scale-up planning |

Qwen3-Omni v1-v6 are run versions inside Line 2, not six project evidence
lines. v1-v4 are pipeline-hardening and ablation evidence, v5 is the pinned
prior multiscale release, and v6 is the current public 20-task Qwen3-Omni row.

| 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. |
| Unified 20-task suite | Verified | `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`, `results/episode_task_suite/`, `results/episode_task_suite/tier2_task_suite/` | All 20 task contracts have committed minimal metrics and share the same 20-frame windows, 5-frame stride, chronological split, and minimal/neural head pattern. The `tier2_task_suite` path is historical provenance inside the unified suite, not a separate public tier. |
| 180-result method matrix | Verified complete | `docs/data/task_method_20_result_matrix.json`, `TASK_METHOD_20_RESULT_MATRIX.md`, `docs/data/task_method_20_gap_audit.json`, `docs/assets/charts/unified_task_model_radar.svg` | The public comparison matrix now has 9 methods x 20 tasks = 180/180 scored method-task records. Six rows are explicitly marked as compact-proxy scores where the public 128-episode export lacks the direct raw target. |
| 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 the walkthrough-backed task contracts; audio improves the primary metric on 6 of those contracts, and a 588-d audio-window representation improves over the baseline audio variant on 6 of those contracts. |
| 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 tracks, and the future Xperience-native pretraining goal. |
| 128-episode task-suite enhancement pack | Current no-new-episode plan | `TASK_SUITE_ENHANCEMENT_128.md`, `docs/data/task_suite_enhancement_128.json`, `results/omni_finetune/task_suite_enhancement_128_v1_20260608/enhancement_plan.json`, `scripts/omni/build_task_suite_enhancement_128.py` | The current 3,808-window selected split can be stressed without more episodes by exporting denser and multiscale windows. The recommended next export is `multiscale_20s10_40s20_80s40`, estimated at 106,095 windows from the observed frame spans; the pack also defines hierarchical action/subtask targets, raw-feature shard priorities for unsupported tasks, and Qwen3-Omni/Cosmos3 follow-up run cards. |
| Foundation-model plan | Current | `FOUNDATION_MODEL_PLAN.md`, `docs/data/foundation_model_plan.json` | Qwen3-Omni remains the first structured JSON LoRA baseline; Cosmos3-Nano is verified as a future-window compatibility diagnostic; Cosmos3-Super is represented by a base-weight Reasoner evaluation and a fine-tuned Forward-Dynamics LoRA adapter. The Super LoRA target is camera-pose-conditioned future vision velocity, not supervised JSON action-token prediction. OpenVLA/openpi/GR00T remain policy candidates after robot-compatible action targets are explicit. |
| Cosmos3-Super action-target contract | Superseded by verified forward-dynamics LoRA | `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 packer and contract audit are now supporting evidence for the trained forward-dynamics adapter; they still do not supervise `preds_action`, so action-token prediction needs a separate policy or inverse-dynamics target export. |
| Cosmos3-Super Forward-Dynamics LoRA | Verified fine-tuned adapter | `configs/omni_backbones/cosmos3_super_forward_dynamics.json`, `scripts/omni/train_cosmos3_super_forward_dynamics_lora.py`, `scripts/omni/eval_cosmos3_super_forward_dynamics_lora.py`, `results/omni_finetune/verified_public/xperience10m_cosmos3_super_forward_dynamics_lora_128ep_train1epoch_256_attn_full8gpu_20260608_eval_test_full_fsdp/verified_result_summary.json`, `results/omni_finetune/verified_public/xperience10m_cosmos3_super_forward_dynamics_lora_128ep_train1epoch_256_attn_full8gpu_20260608_eval_test_full_fsdp/package_audit.json` | The first fine-tuned Cosmos3-Super adapter is locally verified as a public-safe package: 26.2M LoRA parameters, 2,848 train rows, 512 validation rows, 448 held-out test rows, validation MSE 4.0082, and test MSE 3.6853. The package excludes adapter safetensors; weights are published separately at `cy0307/ropedia-cosmos3-super-forward-dynamics-lora-128ep`. |
| 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 rows/adapters 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/a100_128_metadata_task_baselines_20260616_v2/`, `scripts/omni/run_128_task_baselines.py`, `scripts/omni/run_128_raw20_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. Metadata/text simple and neural heads now have 20/20 rows, raw-feature simple and neural heads have 20/20 rows, and compact proxies remain marked for missing direct raw targets. |
| Qwen3-Omni fine-tuning | Latest v6 diagnostic row verified; JSON target met | `QWEN3_OMNI_RUN_LINEAGE.md`, `docs/data/qwen3_omni_run_lineage.json`, `docs/data/omni_finetune_verified_result.json`, `docs/data/qwen3_v5_v6_comparison.json`, `results/omni_finetune/QWEN3_V5_V6_COMPARISON_20260614.md`, `results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_multiscale_cap96_v6_rank64_lr5e5_full8gpu_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` | Qwen3-Omni v1-v6 are one selected-128 run lineage: v1-v4 harden the pipeline and evaluate ablations, v5 is the pinned prior multiscale release, and v6 is the current public 20-task row. The v6 rank64/lr5e-5 package has 34,269 exported windows and 4,032 test predictions. JSON validity is 99.90%, meeting the 98% target; transition accuracy is 98.98%, contact accuracy is 81.77%, object micro-F1 is 30.65%, next-action accuracy is 4.31%, and action/subtask metrics remain weak. v6 improves action macro-F1 and contact accuracy versus v5, but v5 remains stronger on JSON validity, subtask, next-action, transition, and object metrics. |
| 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 `PUBLIC_READER_MAP.md` if you need to choose the right public surface
   before opening detailed artifacts.
2. Read this status file to establish the current project scope.
3. Open the visual dashboard for the fastest overview of data, tasks,
   directions, and scale-up status.
4. Inspect `RESEARCH_TAKEAWAYS.md` and
   `docs/data/research_takeaways.json` for the generated result interpretation.
5. Inspect `RESEARCH_ROADMAP.md` and `docs/data/research_roadmap.json` for
   the path from public-sample task work to multi-episode modeling.
6. Inspect `FOUNDATION_MODEL_PLAN.md` and
   `docs/data/foundation_model_plan.json` before choosing a backbone track.
7. 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 row.
8. Inspect `XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md` for the
   long-term full-corpus pretraining goal.
9. Inspect `TASK_SUITE_20.md`, `docs/data/task_suite_20.json`,
   `docs/data/summary_metrics.json`, and
   `results/episode_task_suite/neural_mlp/` to check the unified 20-task outputs.
10. Inspect `docs/data/tier2_task_suite.json` and
   `results/episode_task_suite/tier2_task_suite/TIER2_TASK_BASELINES.md` only as historical provenance paths inside the unified 20-task suite.
11. Inspect `results/audio_ablation/AUDIO_ABLATION_SUMMARY.md` before judging
   whether audio helps the current task suite.
12. Inspect `EVALUATION_PROTOCOL.md` before judging task metrics or leakage
   controls.
13. Inspect `XPERIENCE10M_DATASET_CARD_ALIGNMENT.md` only if you need the
   detailed upstream dataset-card context.
14. Inspect `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md`
   before comparing simple/NN baselines to the selected 128-episode setup.
15. Inspect `TASK_SUITE_ENHANCEMENT_128.md` and
   `docs/data/task_suite_enhancement_128.json` before deciding whether more
   episodes are needed; the current recommended no-new-episode export is
   `multiscale_20s10_40s20_80s40`.
16. Inspect `docs/data/omni_finetune_verified_result.json` before judging the
   Qwen3-Omni diagnostic pilot.

## Current Reading Notes

- Line 1 uses one public sample episode. Line 2 uses selected-128 public-safe
  processed artifacts linked to official gated episode paths. Do not merge
  those evidence lines when reading scores.
- Public-facing fine-tuning results should come from the verified result
  package, not from live process logs or setup-only artifacts.
- The latest Qwen3-Omni v6 held-out package verifies the current dense
  multiscale run and meets the strict-JSON target, but not strong
  action/subtask model quality: JSON validity is 99.90%, action macro-F1 is
  0.0029, and subtask accuracy is 0.0037. v5 remains the pinned prior release
  row because it is still stronger on several metrics.
- The current 128-episode task suite can be pushed further without more raw
  episodes by using dense/multiscale windows, hierarchical action/subtask
  targets, stronger label-normalized scoring, and compact raw-feature shards
  for the tasks that are currently unsupported by metadata/text baselines.
- 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 the walkthrough-backed 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 track, 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 rows/adapters 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.