Publish Ropedia Xperience-10M derived artifacts

.gitattributes

@@ -58,3 +58,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+results/omni_finetune/adapter_lora/tokenizer.json filter=lfs diff=lfs merge=lfs -text
+results/omni_finetune/hf_upload/tokenizer.json filter=lfs diff=lfs merge=lfs -text

ARTIFACT_GUIDE.md

@@ -8,8 +8,8 @@ The project intentionally separates nine layers:
 
 1. **Reviewer scorecard:** one compact table for first-pass current-state
    decisions.
-2. **Proof boundary:** what is claimed, what is smoke-only, and what remains
-   gated by data access.
+2. **Proof boundary:** what is claimed, what is readiness-only, and what
+   remains gated by data access.
 3. **Official source alignment:** what the upstream Xperience-10M dataset card,
    public sample card, and HF API metadata say, and which parts this repo
    currently covers.
@@ -128,7 +128,7 @@ The project intentionally separates nine layers:
 | Artifact | Current status |
 | --- | --- |
 | [`results/omni_finetune/DATA_BLOCKER_REPORT.md`](results/omni_finetune/DATA_BLOCKER_REPORT.md) | Documents why no real 32-episode Qwen3-Omni result is claimed yet. |
-| [`results/omni_finetune/A100_HF_RELAY_STATUS.md`](results/omni_finetune/A100_HF_RELAY_STATUS.md) | Documents the pending A100-to-H20 relay and selected 32-session pilot plan. |
+| [`results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | Documents the public multi-episode access boundary and selected 32-episode pilot plan without private infrastructure details. |
 | [`scripts/omni/discover_xperience10m_sources.py`](scripts/omni/discover_xperience10m_sources.py) | Discovery gate for valid multi-episode Xperience-10M sources. |
 | [`scripts/omni/train_qwen3_omni_lora.py`](scripts/omni/train_qwen3_omni_lora.py) | Training entrypoint for the Qwen3-Omni LoRA pilot after the data gate passes. |
 

EVALUATION_PROTOCOL.md

@@ -72,7 +72,7 @@ are not foundation models.
 
 - Do not infer cross-episode generalization from this single public sample.
 - Do not treat feature-vector reconstruction as pixel depth, mesh, NeRF, or Gaussian reconstruction.
-- Do not treat Qwen3-Omni smoke artifacts as a real 32-episode fine-tune.
+- Do not treat Qwen3-Omni readiness artifacts as a real 32-episode fine-tune.
 - Do not infer audio-visual learning from the current baseline vector because audio is not featurized.
 
 ## Scale-Up Gate
@@ -87,7 +87,7 @@ being presented as model quality:
 
 Current status: prepared but data-gated. Read
 `results/omni_finetune/DATA_BLOCKER_REPORT.md` and
-`results/omni_finetune/A100_HF_RELAY_STATUS.md` before interpreting any
+`results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md` before interpreting any
 Qwen3-Omni artifact.
 
 ## Machine-Readable Copy

EVIDENCE_CONTRACT.md

@@ -18,8 +18,8 @@ local artifact that a reader can inspect before trusting the dashboard.
 | Minimal and neural heads use the same task contracts. | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/`, `docs/assets/task_architectures.png` | Verified for 12 minimal heads and 12 neural MLP heads | Small heads only; not a foundation model |
 | Four Ropedia research directions are mapped honestly as direct, proxy, or diagnostic evidence. | `results/episode_task_suite/research_directions/research_direction_taxonomy.json`, `docs/data/research_directions.json` | Verified taxonomy | Some directions remain proxy-only |
 | Four extra direction probes are coded and evaluated. | `results/episode_task_suite/research_direction_extensions/research_direction_extension_results.json`, `docs/data/research_direction_extensions.json` | Verified single-episode probes | Not full human modeling, neural rendering, intent modeling, or world modeling solutions |
-| Qwen3-Omni infrastructure has passed technical smoke checks. | `results/omni_finetune/RUN_REPORT.md`, `results/omni_finetune/dataset_manifest.json`, `results/omni_finetune/metrics_eval.json` | Smoke-only evidence | One episode, 128 train windows; not a 32-episode pilot |
-| The real 32-episode LoRA pilot is blocked on gated data access, not on repo presentation. | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/A100_HF_RELAY_STATUS.md`, `results/omni_finetune/source_discovery.json` | Blocker documented | No 32-episode metric should be claimed until the gate passes |
+| Qwen3-Omni infrastructure has passed readiness checks. | `results/omni_finetune/RUN_REPORT.md`, `results/omni_finetune/dataset_manifest.json`, `results/omni_finetune/metrics_eval.json` | Readiness-only evidence | One episode, 128 train windows; not a 32-episode pilot |
+| The real 32-episode LoRA pilot is blocked on gated data access, not on repo presentation. | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`, `results/omni_finetune/source_discovery.json` | Blocker documented | No 32-episode metric should be claimed until the gate passes |
 | Historical `32ep` path strings are not treated as 32-episode results. | `scripts/validate_scope_claims.py`, `docs/data/scope_claims_audit.json` | Verified pass | Classifies old run/path identifiers and fails if public presentation claims real 32-episode metrics |
 | Prepared GitHub/Hugging Face mirrors carry matching critical files. | `scripts/validate_mirror_parity.py`, `docs/data/mirror_parity.json` | Verified pass | Compares prepared data files, visual assets, website HTML, and validator scripts before upload; live URLs are checked after publishing |
 | The public GitHub and Hugging Face bundles are publication-clean. | `scripts/validate_publication_package.py`, `docs/data/publication_audit.json` | Verified pass | Checks public files, HF bundles, and public-card freshness; ignored local scratch outputs are excluded |
@@ -63,11 +63,12 @@ local artifact that a reader can inspect before trusting the dashboard.
 12. Inspect `results/episode_task_suite/neural_mlp/` to compare minimal and
    neural heads under the same splits.
 13. Inspect `docs/data/scope_claims_audit.json` before interpreting historical
-   `32ep` strings in Qwen3-Omni smoke artifacts.
+   `32ep` strings in Qwen3-Omni readiness artifacts.
 14. Inspect `docs/data/mirror_parity.json` before assuming the GitHub and
    Hugging Face mirrors contain the same critical data, visual, HTML, and
    validator files.
-15. Inspect `results/omni_finetune/DATA_BLOCKER_REPORT.md` before interpreting
+15. Inspect `results/omni_finetune/DATA_BLOCKER_REPORT.md` and
+   `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md` before interpreting
    any Qwen3-Omni artifact.
 16. Inspect `QUALITY_GATES.md`, `docs/data/quality_gates.json`,
    `docs/data/publication_audit.json`, and `docs/data/website_integrity.json`

PROJECT_README.md

@@ -14,12 +14,6 @@
 An audit-first embodied-AI learning repo built around one public
 Xperience-10M sample episode released by Ropedia.
 
-The public dashboard and generated figures deliberately follow the visual
-language of [ropedia.com](https://ropedia.com/): near-black 4D-world canvas,
-lime-green identity accents, thin green-tinted cards, point-cloud texture, and
-the Inter Tight / Space Grotesk typography pairing. The layout is original to
-this project, but the style stays aligned with Ropedia's own product site.
-
 The project does one narrow thing carefully: it turns a raw multimodal episode
 into:
 
@@ -50,7 +44,7 @@ This repo is organized around an explicit proof boundary:
 | 12-task suite | `scripts/episode_task_suite.py`, per-task `metrics.json`, predictions | chronological single-episode split |
 | Neural heads | `scripts/neural_task_models.py`, `results/episode_task_suite/neural_mlp/` | compact MLP heads, not a foundation model |
 | Research directions | `research_direction_taxonomy.json`, extension probe results | direct/proxy/diagnostic evidence, not full solutions |
-| Qwen3-Omni | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `A100_HF_RELAY_STATUS.md` | smoke-only until 32 valid episodes are available |
+| Qwen3-Omni | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `MULTI_EPISODE_ACCESS_STATUS.md` | readiness-only until 32 valid episodes are available |
 | Scope claims guard | `scripts/validate_scope_claims.py`, `docs/data/scope_claims_audit.json` | historical `32ep` path strings are provenance, not 32-episode results |
 | Mirror parity | `scripts/validate_mirror_parity.py`, `docs/data/mirror_parity.json` | prepared GitHub/HF mirrors carry matching data, figure, website HTML, and validator files |
 | Publication hygiene | `scripts/validate_publication_package.py`, `docs/data/publication_audit.json` | public repo and HF bundles only; ignored local scratch files are excluded, and public cards must reference the current task-first figure |
@@ -135,7 +129,7 @@ If you are reviewing the project cold, open these in order:
 | 6 | What is one model input? | [`windows.csv`](results/episode_task_suite/windows.csv), [`feature_manifest.json`](results/episode_task_suite/feature_manifest.json), [`available_modalities.json`](results/episode_task_suite/available_modalities.json) | The input is an aligned 8,378-d window vector with explicit feature-block boundaries. |
 | 7 | Are the task results backed by files? | [`summary_report.json`](results/episode_task_suite/summary_report.json), [`neural_mlp/`](results/episode_task_suite/neural_mlp/), [`docs/data/summary_metrics.json`](docs/data/summary_metrics.json) | Each task has minimal and neural-head evidence over the same window contracts. |
 | 8 | Is the website internally coherent? | [`docs/data/website_integrity.json`](docs/data/website_integrity.json), [`scripts/validate_website_integrity.py`](scripts/validate_website_integrity.py) | Local links, anchors, JSON data, and referenced images are checked before publishing. |
-| 9 | What is still pending? | [`DATA_BLOCKER_REPORT.md`](results/omni_finetune/DATA_BLOCKER_REPORT.md), [`A100_HF_RELAY_STATUS.md`](results/omni_finetune/A100_HF_RELAY_STATUS.md), [`scripts/omni/discover_xperience10m_sources.py`](scripts/omni/discover_xperience10m_sources.py) | The 32-episode Qwen3-Omni run is prepared but not yet a real model-quality claim. |
+| 9 | What is still pending? | [`DATA_BLOCKER_REPORT.md`](results/omni_finetune/DATA_BLOCKER_REPORT.md), [`MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md), [`scripts/omni/discover_xperience10m_sources.py`](scripts/omni/discover_xperience10m_sources.py) | The 32-episode Qwen3-Omni run is prepared but not yet a real model-quality claim. |
 
 The machine-readable reviewer packet is
 [`docs/data/reviewer_packet.json`](docs/data/reviewer_packet.json).
@@ -333,13 +327,13 @@ scripts/
   build_artifact_index.py           # builds the source-of-truth reviewer index
   build_quality_gates.py            # builds reviewer-facing publication gates
   validate_mirror_parity.py         # checks prepared GitHub/HF mirror file parity
-  validate_scope_claims.py          # checks Qwen3-Omni smoke/result claim boundaries
+  validate_scope_claims.py          # checks Qwen3-Omni readiness/result claim boundaries
   validate_website_integrity.py     # checks local site links, anchors, JSON, images
   validate_publication_package.py   # checks public repo + HF bundle hygiene
   omni/
-    download_sample_modelscope.py   # mainland-China friendly sample download
+    download_sample_modelscope.py   # ModelScope sample download helper
     build_episode_manifest.py       # metadata-only multi-episode scanner
-    plan_finetune_sample_budget.py  # H20 storage/sample-count planner
+    plan_finetune_sample_budget.py  # storage/sample-count planner
     qwen3_omni_adapter_smoke.py     # real-data Qwen3-Omni adapter smoke test
 
 results/
@@ -352,7 +346,7 @@ results/
     research_directions/            # four-track taxonomy, CSV, and summary
     research_direction_extensions/  # four extra direction probes + predictions
     task_walkthroughs/              # case-study walkthroughs for all 12 tasks
-  omni_exploration/                 # H20/ModelScope smoke-test artifacts
+  omni_exploration/                 # ModelScope readiness-check artifacts
 
 docs/
   index.html                        # GitHub Pages dashboard
@@ -434,7 +428,7 @@ hf download ropedia-ai/xperience-10m-sample \
   --local-dir data/sample/xperience-10m-sample
 ```
 
-On mainland-China servers, use ModelScope instead:
+If Hugging Face access is unavailable in your environment, use ModelScope:
 
 ```bash
 python scripts/omni/download_sample_modelscope.py \
@@ -470,111 +464,52 @@ python scripts/train_min_action_model.py --workspace /path/to/workspace
 python scripts/train_all_modalities_model.py --workspace /path/to/workspace
 ```
 
-## Xperience-10M Fine-Tuning Exploration On H20
+## Xperience-10M Fine-Tuning Exploration
 
-This repo now includes a concrete first step toward a Qwen3-Omni fine-tuning
-pipeline over Xperience-10M. The important separation is:
+This repo includes a first Qwen3-Omni fine-tuning path over Xperience-10M, but
+the current evidence is still readiness evidence rather than model quality.
+The useful distinction is:
 
 - direct Qwen3-Omni inputs: RGB/fisheye video, embedded MP4 audio, and language
   prompts,
 - adapter-required Xperience-10M sensor inputs: depth, pose/SLAM, hand/body
   mocap, contacts, and IMU.
 
-The H20 work now has two separate evidence levels:
-
-- an adapter-side smoke test over one Xperience-10M sample episode, useful for
-  checking sensor feature extraction and label plumbing,
-- a technical Qwen3-Omni LoRA smoke run that loaded the local
-  `Qwen/Qwen3-Omni-30B-A3B-Instruct` weights and trained LoRA parameters on
-  128 windows from the single locally available episode.
-
-Neither is a 32-episode result. The full pilot is still gated on raw
-Xperience-10M access and a held-out episode split.
-
-```bash
-python scripts/omni/build_episode_manifest.py \
-  --data-root /home/cy/Ropedia/modelscope_data \
-  --output outputs/omni_exploration/modelscope_manifest.json
-
-python scripts/omni/qwen3_omni_adapter_smoke.py \
-  --workspace /home/cy/Ropedia/ropedia-xperience-10m-task-suite \
-  --episode-root /home/cy/Ropedia/modelscope_data/xperience-10m-sample \
-  --target action \
-  --window-frames 20 \
-  --stride-frames 100 \
-  --max-windows-per-episode 64 \
-  --epochs 2 \
-  --skip-video-features
-```
-
-Verified H20 run:
-
-| Item | Value |
-| --- | ---: |
-| Server | 8 x NVIDIA H20, 96GB each |
-| Free storage checked | about 1.5TB under `/home/cy` |
-| Data source | ModelScope `ropedia-ai/xperience-10m-sample` |
-| Downloaded minimal data | 1.93GB `annotation.hdf5` + 85.7MB `fisheye_cam0.mp4` |
-| Smoke windows | 59 |
-| Split | single-episode chronological |
-| Feature dim | 4,262 |
-| Adapter soft-token blocks | 11 |
-| Qwen3-Omni weights loaded | adapter smoke: no; LoRA smoke: yes |
-| Result | 0.0000 macro-F1, expected for this single-episode chronological smoke split |
-
-The zero score is not treated as a model claim. It is a useful signal that this
-split is not leaking labels across time: the train segment does not cover every
-action that appears in the held-out segment. The next real step is to add more
-episodes and split by held-out episode.
+The current scale-up artifacts prove that the export, manifest, sensor-feature,
+LoRA, and evaluation scripts can run on the available sample episode. They do
+not prove a real 32-episode result. A real pilot requires at least 32 valid
+episodes, held-out episode splits, training metadata, predictions, metrics, and
+a run report.
 
 ### Sample Count Decision
 
-The local Mac sample is only one episode. For H20 fine-tuning, decide sample
-count by storage and evaluation design, not by the local folder. The current H20
-has about 1.5TB free under `/home/cy`; after reserving space for model weights,
-checkpoints, caches, and logs, a realistic first budget is:
+Do not treat "10M" as a reason to start with the entire dataset. The engineering
+unit that matters first is diverse held-out episodes, not adjacent windows from
+one session.
 
 | Phase | Episodes/samples | Approx windows at stride 5 | Purpose |
 | --- | ---: | ---: | --- |
-| Smoke | 1-3 | 1k-3k | Verify loaders, token alignment, and task heads |
+| Readiness | 1-3 | 1k-3k | Verify loaders, token alignment, and task heads |
 | Pilot | 16-32 | 18k-37k | First held-out-episode evaluation |
 | Useful LoRA run | 64-128 | 74k-149k | Train sensor adapters plus selected Qwen3-Omni LoRA |
 | Storage-heavy run | 256+ | 297k+ | Only after download layout and checkpoint size are stable |
 
-For the next run, use a **32-episode stratified pilot** through the A100 relay,
-then scale to **128 episodes** and later **512 episodes** only after the
-download, transfer, manifest, train, and held-out evaluation path is stable. Do
-not treat "10M" as a reason to start with the entire dataset; the engineering
-unit that matters first is diverse held-out episodes, not adjacent windows from
-one session.
-
 Use the budget helper before downloading:
 
 ```bash
 python scripts/omni/plan_finetune_sample_budget.py \
-  --storage-root /home/cy \
+  --storage-root /path/to/storage \
   --target-free-after-download-gb 800 \
   --all-training-per-episode-gb 2.4 \
   --full-preview-per-episode-gb 5.1
 ```
 
-Refresh charts and the website data bundle:
-
-```bash
-python scripts/research_direction_taxonomy.py
-python scripts/research_direction_extension_tasks.py
-python scripts/task_walkthroughs.py
-python scripts/generate_visualizations.py
-python scripts/render_overview_figures.py
-python scripts/render_task_suite_infographic.py
-```
-
 ### 32-Episode Readiness Gate
 
 ```bash
 python scripts/omni/discover_xperience10m_sources.py \
-  --workspace /home/cy/Ropedia/ropedia-xperience-10m-task-suite \
-  --data-root /home/cy/Ropedia/modelscope_data \
+  --workspace /path/to/ropedia-xperience-10m-task-suite \
+  --data-root /path/to/xperience10m_data \
   --output results/omni_finetune/source_discovery.json \
   --report-output results/omni_finetune/DATA_BLOCKER_REPORT.md
 ```
@@ -584,33 +519,17 @@ Current status in this repo:
 - local_valid_episodes: 1 (degraded-valid: annotation + fisheye_cam0.mp4)
 - local_complete_episodes: 0
 - ready_for_32_episode_pilot: false
-- A100 Hugging Face relay: active watcher, polling gated access every 15 minutes
 - planned 32-episode pilot: stratified across 32 top-level session UUIDs
-- HF full dataset blocker: `ropedia-ai/xperience-10m` returns 403 pending review
+- full-dataset blocker: gated Xperience-10M access is still pending
 - source_discovery: `results/omni_finetune/source_discovery.json`
 - blocker_report: `results/omni_finetune/DATA_BLOCKER_REPORT.md`
-- relay_status: `results/omni_finetune/A100_HF_RELAY_STATUS.md`
-
-Current H20-sourced evidence files in this repo:
-
-- `results/omni_finetune/episode_manifest.json`
-- `results/omni_finetune/dataset_manifest.json`
-- `results/omni_finetune/training_metadata.json`
-- `results/omni_finetune/metrics.json`
-- `results/omni_finetune/progress.jsonl`
-- `results/omni_finetune/RUN_REPORT.md`
-- `results/omni_finetune/DATA_BLOCKER_REPORT.md`
-- `results/omni_finetune/A100_HF_RELAY_STATUS.md`
-
-Use this gate before scheduling any 32-episode full fine-tune run.
+- access_status: `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`
 
-For the A100 Hugging Face relay, the 32-episode pilot should use stratified
-selection, not the first 32 paths in repository order. The current relay script
-scans 64 top-level session UUIDs, filters for complete leaf episodes, excludes
-`visualization.rrd`, applies a `0.25 GB` minimum episode size, and selects 32
-episodes from 32 different session UUIDs. This is still a pilot subset, but it
-is materially better for generalization checks than adjacent episodes from the
-same recording session.
+Use this gate before scheduling any 32-episode full fine-tune run. The pilot
+should use stratified selection, not the first 32 paths in repository order.
+The current selection plan scans 64 top-level session UUIDs, filters for
+complete leaf episodes, excludes `visualization.rrd`, applies a `0.25 GB`
+minimum episode size, and selects 32 episodes from 32 different session UUIDs.
 
 ### Uploading the pilot Qwen3-Omni LoRA
 
@@ -618,7 +537,7 @@ A prepared upload package is available at `results/omni_finetune/hf_upload`.
 
 ```bash
 python3 scripts/omni/upload_qwen3_omni_lora_to_hf.py \
-  --repo-id cy0307/ropedia-qwen3-omni-lora-smoke \
+  --repo-id cy0307/ropedia-qwen3-omni-lora-readiness \
   --source-dir results/omni_finetune/hf_upload \
   --message "Upload Xperience-10M Qwen3-Omni LoRA pilot"
 ```

QUALITY_GATES.md

@@ -12,7 +12,7 @@ These gates validate public packaging, claim boundaries, mirror parity, and webs
 
 | Gate | Command | Report | Current report status | Blocks publication if |
 | --- | --- | --- | --- | --- |
-| Scope claims guard | `python scripts/validate_scope_claims.py` | `docs/data/scope_claims_audit.json` | `pass` | Historical 32ep smoke/provenance strings are presented as real 32-episode metrics. |
+| Scope claims guard | `python scripts/validate_scope_claims.py` | `docs/data/scope_claims_audit.json` | `pass` | Historical 32ep readiness/provenance strings are presented as real 32-episode metrics. |
 | Source alignment audit | `python scripts/validate_source_alignment.py` | `docs/data/source_alignment_audit.json` | `pass` | Official full-dataset facts, sample-card facts, API-listing caveats, or public-card boundary markers are missing or inconsistent. |
 | Website integrity | `python scripts/validate_website_integrity.py` | `docs/data/website_integrity.json` | `pass` | Local links, anchors, JSON bundles, or referenced image assets are missing or invalid. |
 | Evaluation protocol | `python scripts/build_evaluation_protocol.py` | `docs/data/evaluation_protocol.json` | `pass` | Windowing, split policy, leakage controls, task metrics, or unsupported interpretations are not explicit. |
@@ -29,7 +29,7 @@ These gates validate public packaging, claim boundaries, mirror parity, and webs
 | --- | --- | --- |
 | Live publication verifier | `python scripts/verify_live_publication.py` | live GitHub Pages, GitHub raw, HF Space, artifact dataset, and model mirrors match the current release assets |
 | GitHub Pages deployment | `gh run list --repo ChaoYue0307/ropedia-xperience-10m-task-suite --limit 5` | latest pages-build-deployment run succeeds |
-| Rendered browser smoke | `Browser/Playwright page identity, nonblank render, console health, and one local interaction` | no relevant console warnings/errors and target links work |
+| Rendered browser check | `Browser/Playwright page identity, nonblank render, console health, and one local interaction` | no relevant console warnings/errors and target links work |
 
 ## Rerun Order
 

README.md

@@ -30,13 +30,10 @@ This dataset repo contains the derived evidence layer for the public Xperience-1
 
 ![12-task infographic](assets/task_suite_infographic.png?v=xperience10m-taskfirst-v12-modality-xl)
 
-The dashboard assets follow a Ropedia-inspired visual system: dark 4D-world
-canvas, lime-green accents, point-cloud texture, thin green cards, and
-research-grade typography, while all labels and metrics are script-generated
-from committed result files.
-The project logo is a ChatGPT-image-generated X-shaped multimodal camera mark,
-then deterministically packaged into favicon, header, README, Hugging Face
-card, and social-preview assets by `scripts/build_brand_assets.py`.
+The logo, figures, cards, and website assets are packaged together so the
+artifact repo reads as a coherent Xperience-10M multimodal task suite. Labels,
+dimensions, and metrics are generated from committed result files rather than
+hand-edited presentation copy.
 
 The Space starts with a task-first 12-task map, then includes a native
 responsive modality atlas backed by
@@ -79,11 +76,10 @@ the favicon, header, README/HF cards, app icon, and social preview.
 It does **not** contain raw Xperience-10M videos or raw `annotation.hdf5`. Download raw data only from the official Ropedia / Hugging Face sources and follow their terms.
 
 Current scale-up status: the full `ropedia-ai/xperience-10m` Hugging Face
-dataset is still gated for this account. The A100 relay has been configured to
-poll access, download a 32-episode stratified pilot subset after approval,
-validate it, transfer it to H20, and run the readiness gate. Until that
-completes, the committed Qwen3-Omni artifacts remain smoke/debug evidence, not
-real 32-episode held-out metrics.
+dataset is still gated for this account. The multi-episode workflow is prepared
+to select, download, validate, and stage a 32-episode held-out pilot after
+access approval. Until that completes, the committed Qwen3-Omni artifacts
+remain readiness evidence, not real 32-episode held-out metrics.
 
 ## Why This Repo Exists
 
@@ -98,7 +94,7 @@ This is the reviewable half of the project. You can inspect the task outputs, co
 | 3 | How do I reproduce it? | `REPRODUCIBILITY.md`, `docs/data/reproducibility_matrix.json`, `notes/reproducibility_audit.md` |
 | 4 | What is one model input? | `results/episode_task_suite/windows.csv`, `results/episode_task_suite/feature_manifest.json`, `results/episode_task_suite/available_modalities.json` |
 | 5 | Are the task results backed by files? | `results/episode_task_suite/summary_report.json`, `results/episode_task_suite/neural_mlp/`, `docs/data/summary_metrics.json` |
-| 6 | What is still pending? | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/A100_HF_RELAY_STATUS.md`, `scripts/omni/discover_xperience10m_sources.py` |
+| 6 | What is still pending? | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`, `scripts/omni/discover_xperience10m_sources.py` |
 
 Human-readable artifact guide: `ARTIFACT_GUIDE.md`.
 Reviewer scorecard: `REVIEWER_SCORECARD.md` and `docs/data/reviewer_scorecard.json`.
@@ -122,7 +118,7 @@ Source-of-truth brand asset index: `docs/data/brand_assets.json`.
 | 12-task suite | per-task `metrics.json`, predictions, confusion matrices | chronological single-episode split |
 | Neural heads | `results/episode_task_suite/neural_mlp/` | compact MLP heads, not a foundation model |
 | Research directions | `research_direction_taxonomy.json`, extension probe results | direct/proxy/diagnostic evidence, not full solutions |
-| Qwen3-Omni | `DATA_BLOCKER_REPORT.md`, `A100_HF_RELAY_STATUS.md` | smoke-only until 32 valid episodes are available |
+| Qwen3-Omni | `DATA_BLOCKER_REPORT.md`, `MULTI_EPISODE_ACCESS_STATUS.md` | readiness-only until 32 valid episodes are available |
 | Scope claims guard | `docs/data/scope_claims_audit.json`, `scripts/validate_scope_claims.py` | historical `32ep` path strings are provenance, not 32-episode results |
 | Mirror parity | `docs/data/mirror_parity.json`, `scripts/validate_mirror_parity.py` | prepared repo/HF mirrors carry matching critical data, figures, website HTML, and validator files |
 | Publication hygiene | `docs/data/publication_audit.json`, `scripts/validate_publication_package.py` | public files/HF bundles only, with public-card freshness checks |
@@ -156,7 +152,7 @@ Source-of-truth brand asset index: `docs/data/brand_assets.json`.
 - `FIGURE_INDEX.md` and `docs/data/figure_index.json`: visual evidence index for public figures, charts, thumbnails, dimensions, hashes, and source scripts
 - `docs/data/artifact_index.json`: source-of-truth proof-artifact catalog with stable-file hashes
 - `docs/data/mirror_parity.json`: prepared Space/artifact/model mirror parity check, including critical website HTML
-- `docs/data/scope_claims_audit.json`: machine-readable guard against overclaiming historical `32ep` smoke-run identifiers
+- `docs/data/scope_claims_audit.json`: machine-readable guard against overclaiming historical `32ep` readiness/provenance identifiers
 - `docs/data/publication_audit.json`: machine-readable publication hygiene and public-card freshness check
 - `docs/data/website_integrity.json`: machine-readable website local-reference integrity check
 - `QUALITY_GATES.md` and `docs/data/quality_gates.json`: reviewer-facing and machine-readable release gates
@@ -173,7 +169,7 @@ Source-of-truth brand asset index: `docs/data/brand_assets.json`.
 - `scripts/export_modality_atlas_assets.py`: regenerates the responsive modality-card thumbnails and manifest from the local public sample
 - `scripts/build_artifact_index.py`: source-of-truth artifact-index builder
 - `scripts/validate_mirror_parity.py`: prepared mirror parity validator
-- `scripts/validate_scope_claims.py`: validates the Qwen3-Omni smoke/result claim boundary
+- `scripts/validate_scope_claims.py`: validates the Qwen3-Omni readiness/result claim boundary
 - `scripts/validate_publication_package.py`: public bundle validator
 - `scripts/validate_website_integrity.py`: website local-reference validator
 - `notes/*.md`: interpretation and reproducibility notes

REPRODUCIBILITY.md

@@ -40,7 +40,8 @@ hf download ropedia-ai/xperience-10m-sample \
   --local-dir data/sample/xperience-10m-sample
 ```
 
-On mainland-China servers, use the included ModelScope helper:
+If Hugging Face access is unavailable in your environment, use the included
+ModelScope helper:
 
 ```bash
 python scripts/omni/download_sample_modelscope.py \
@@ -136,4 +137,4 @@ Before interpreting any Qwen3-Omni result, read
 [`docs/data/scope_claims_audit.json`](docs/data/scope_claims_audit.json),
 [`results/omni_finetune/DATA_BLOCKER_REPORT.md`](results/omni_finetune/DATA_BLOCKER_REPORT.md)
 and
-[`results/omni_finetune/A100_HF_RELAY_STATUS.md`](results/omni_finetune/A100_HF_RELAY_STATUS.md).
+[`results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md).

REVIEWER_SCORECARD.md

@@ -15,7 +15,7 @@ verified only when committed artifacts and validation reports support it.
 | Website and HF mirrors | Verified | `docs/data/website_integrity.json`, `docs/data/mirror_parity.json`, `docs/data/live_publication_status.json` | Local website links/assets pass, prepared mirrors match, and public GitHub/HF URLs have been checked after upload. |
 | Publication hygiene | Verified | `docs/data/publication_audit.json`, `QUALITY_GATES.md`, `docs/data/quality_gates.json` | Public bundles are checked for raw-data exclusion, cache exclusion, heavy-archive exclusion, token-string hygiene, and stale presentation 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 audit evidence. |
-| Qwen3-Omni fine-tuning | Data-gated, not a model-quality claim | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/A100_HF_RELAY_STATUS.md` | The 32-episode LoRA pilot is prepared, but no real held-out 32-episode result is claimed until gated data access, manifest construction, training, and held-out evaluation pass. |
+| Qwen3-Omni fine-tuning | Data-gated, not a model-quality claim | `results/omni_finetune/DATA_BLOCKER_REPORT.md`, `results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md` | The 32-episode LoRA pilot is prepared, but no real held-out 32-episode result is claimed until gated data access, manifest construction, training, and held-out evaluation pass. |
 | 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 Reviewer Route

docs/data/artifact_index.json

@@ -1,6 +1,6 @@
 {
   "title": "Ropedia Xperience-10M Task Suite Artifact Index",
-  "generated_at_utc": "2026-06-01T13:19:42+00:00",
+  "generated_at_utc": "2026-06-01T14:16:54+00:00",
   "status": "pass",
   "artifact_count": 49,
   "missing": [],
@@ -39,8 +39,8 @@
       "surface": "repo_hf",
       "proves": "Gives a compact verified/data-gated/not-redistributed decision table for first-pass reviewers.",
       "exists": true,
-      "bytes": 4360,
-      "sha256": "2fe1a0c808eb5906e0942cf1bb573c086ba82523cbda8f778cfa8ac0e582fea4"
+      "bytes": 4367,
+      "sha256": "01c3fcd654db595d94b50bc4b68ff1ea9fb13789261dea04dbaba1caa44d5027"
     },
     {
       "id": "reviewer_scorecard_json",
@@ -50,8 +50,8 @@
       "surface": "website_hf",
       "proves": "Machine-readable copy of the current reviewer scorecard for website and HF mirrors.",
       "exists": true,
-      "bytes": 6107,
-      "sha256": "0c368033fa330df11afc1e85010163ba949e108c3e1b3808382d79465a068443"
+      "bytes": 6114,
+      "sha256": "6288bddda015e07c0144bffa827c0849858feae5600086287037c005b87a4197"
     },
     {
       "id": "evidence_contract",
@@ -59,10 +59,10 @@
       "path": "EVIDENCE_CONTRACT.md",
       "kind": "claim_boundary",
       "surface": "repo",
-      "proves": "Defines what is verified, what is smoke-only, and what must not be inferred.",
+      "proves": "Defines what is verified, what is readiness-only, and what must not be inferred.",
       "exists": true,
-      "bytes": 9701,
-      "sha256": "7c78f162e48512ae525c02030f20e667fff60d417ba0d4faee82e733ae211f36"
+      "bytes": 9772,
+      "sha256": "89f79ed4089e3797338be4711c20e4f31a1dc03e8371fc92faf6ce2fe0c1f355"
     },
     {
       "id": "reviewer_packet",
@@ -72,8 +72,8 @@
       "surface": "website_hf",
       "proves": "Gives a short audit path with scope status and public surfaces.",
       "exists": true,
-      "bytes": 7386,
-      "sha256": "212e98170c90d14beed49c4a6374c8ca9ce07093281a85375e33b60beb2d70fb"
+      "bytes": 7401,
+      "sha256": "fefadd40a7ea989dd6d3ff8e4122fc88efc2299f6802a262c24ff1efae8078b9"
     },
     {
       "id": "artifact_guide",
@@ -83,8 +83,8 @@
       "surface": "repo_hf",
       "proves": "Gives the human-readable map from proof boundary to data, tasks, platform mirrors, and scale-up status.",
       "exists": true,
-      "bytes": 12376,
-      "sha256": "6b5e645fdd125dd83b5783f0afce7ec9c353c16fb51bc844d1db701bdbb2662b"
+      "bytes": 12444,
+      "sha256": "209eb82a47e66525585b882382412ec01ce38e8561a843430f8c530e7dbf6acd"
     },
     {
       "id": "official_dataset_card_alignment",
@@ -149,8 +149,8 @@
       "surface": "repo_hf",
       "proves": "Defines the window unit, chronological split, task metrics, leakage controls, and unsupported interpretations.",
       "exists": true,
-      "bytes": 5844,
-      "sha256": "6c82f0d6806d2e929f568fec32fcd4820218c59ec6b30bbefe1a4ec1e1aa1b54"
+      "bytes": 5855,
+      "sha256": "8fd0836dbe0f99306db0214d53ed56de603734b9ea1b7e6d44b25dbf2a37d168"
     },
     {
       "id": "evaluation_protocol_json",
@@ -160,8 +160,8 @@
       "surface": "website_hf",
       "proves": "Machine-readable protocol generated from committed task metrics for website and HF mirrors.",
       "exists": true,
-      "bytes": 13575,
-      "sha256": "d1cd2724820e8b1f13e0eee70f9d3d805f105d8d8e2ccc638aca1486a6a6e21a"
+      "bytes": 13586,
+      "sha256": "01749a94f31059c52adc74cfa36e2a256c43a316a2f318fa4177df3fc2f5a5b4"
     },
     {
       "id": "evaluation_protocol_builder",
@@ -171,8 +171,8 @@
       "surface": "repo_hf",
       "proves": "Regenerates the protocol from committed summary metrics and task artifacts.",
       "exists": true,
-      "bytes": 16084,
-      "sha256": "a859e43fc95b2bce4b85a758050968e504f1dc7870d4b04e230302aec2b74724"
+      "bytes": 16102,
+      "sha256": "0781265b37af226432d93b25c18b6278484ba7d6d78e3c56991aaaf78bb0ba76"
     },
     {
       "id": "figure_index",
@@ -215,8 +215,8 @@
       "surface": "website_hf",
       "proves": "Machine-readable manifest for the ChatGPT-image-generated logo, favicon, social card, dimensions, hashes, and usage roles.",
       "exists": true,
-      "bytes": 3921,
-      "sha256": "b284b2dcfa5073d95c1e9eacc97b9bbb21ac0293f93b8e4e3c0cedb5e994ee6f"
+      "bytes": 3904,
+      "sha256": "b822ab3af24f6d3f2e041bacf6a1caa59fbd9dbff013c8b21df8f3ef2414c865"
     },
     {
       "id": "brand_logo_social_card",
@@ -237,8 +237,8 @@
       "surface": "repo_hf",
       "proves": "Regenerates logo derivatives, favicon variants, app icons, and the Open Graph social card from the generated logo mark.",
       "exists": true,
-      "bytes": 9395,
-      "sha256": "b9976b12d95ca720b92ee2f02b1faecdc8ef9eea7eb1dea343ee0188fec41ba7"
+      "bytes": 9378,
+      "sha256": "90836fea66545f6f90b4a860655efb7263796a6fc86cb243793dd63d459b3a79"
     },
     {
       "id": "quality_gates",
@@ -248,8 +248,8 @@
       "surface": "repo_hf",
       "proves": "Lists the automated and post-publish gates required before presenting a release as current.",
       "exists": true,
-      "bytes": 3936,
-      "sha256": "7e095dc7dd0d98de2913dcad95e23726ee205c81b4a8a277c81e0151df8f9839"
+      "bytes": 3940,
+      "sha256": "8979dae77e24bfbea691070637d73540611e16742f5e1f99e834b577ecd72b63"
     },
     {
       "id": "quality_gate_manifest",
@@ -293,8 +293,8 @@
       "surface": "repo_hf",
       "proves": "Defines public reproduction commands, expected outputs, and non-reproducible scale-up boundaries.",
       "exists": true,
-      "bytes": 6151,
-      "sha256": "f1f340ea58a61aeea1ae0cdaaeee250f0458ea616c59c1b39d018c941c18a08b"
+      "bytes": 6197,
+      "sha256": "f65169e927c17ad2cb8991c05bdec57b3878c9ed0008aa6e978b675876c46ffc"
     },
     {
       "id": "reproducibility_matrix",
@@ -315,8 +315,8 @@
       "surface": "repo_hf",
       "proves": "Generates the selective proof-artifact catalog from local files.",
       "exists": true,
-      "bytes": 18273,
-      "sha256": "a94e0202c08b4d7d9d5ca57e772b5d15d7a7c698ce0db8f8909bd669e2b0c905"
+      "bytes": 18358,
+      "sha256": "180023c3636c67f1a5ca1a1a9afed349011e190c673722a7adf0eb1e7fe1992f"
     },
     {
       "id": "publication_audit",
@@ -327,7 +327,7 @@
       "volatile": true,
       "proves": "Confirms public bundles pass raw-data, cache, archive, and token-string checks.",
       "exists": true,
-      "bytes": 6726,
+      "bytes": 6733,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -339,7 +339,7 @@
       "volatile": true,
       "proves": "Confirms historical 32ep path strings are not presented as real 32-episode results.",
       "exists": true,
-      "bytes": 19964,
+      "bytes": 20089,
       "hash_policy": "existence_and_size_only"
     },
     {
@@ -396,8 +396,8 @@
       "surface": "website_hf",
       "proves": "Mirrors task metrics for the static dashboard.",
       "exists": true,
-      "bytes": 25075,
-      "sha256": "4daac229d7cad0180009041369d5eb8d6ea97b4889a7f8cbad43c2657b131145"
+      "bytes": 25088,
+      "sha256": "6923e492c8b2814b04aea4bdcacef98da94d98dc471ba424a7e977c736dd623f"
     },
     {
       "id": "feature_manifest",
@@ -539,19 +539,19 @@
       "surface": "repo_hf",
       "proves": "Documents why no 32-episode Qwen3-Omni result is claimed yet.",
       "exists": true,
-      "bytes": 803,
-      "sha256": "510d92ba0b1a72bcbe66e6b2e1c1ec325f5e904ed29254a07b1e78a0e5ce3cd3"
+      "bytes": 813,
+      "sha256": "561255372fa151fda72b8791746be68c825a320a75264b6131acb743ae381192"
     },
     {
-      "id": "a100_relay_status",
-      "title": "A100 relay status",
-      "path": "results/omni_finetune/A100_HF_RELAY_STATUS.md",
+      "id": "multi_episode_access_status",
+      "title": "Multi-episode access status",
+      "path": "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
       "kind": "scaleup_status",
       "surface": "repo_hf",
-      "proves": "Documents the pending A100-to-H20 data relay and 32-session pilot selection.",
+      "proves": "Documents the public multi-episode access boundary and 32-episode pilot selection without exposing private infrastructure details.",
       "exists": true,
-      "bytes": 2076,
-      "sha256": "4d82faff5eb050434a917bc36c0325b972f629952b2e01fe0e33f2647ceede53"
+      "bytes": 1362,
+      "sha256": "fd4488d1f21d084b9a2bdf7e769264fe306e48643ff3ef17578f329cfccd5d3c"
     },
     {
       "id": "citation",

docs/data/brand_assets.json

@@ -1,11 +1,11 @@
 {
   "title": "Ropedia Xperience-10M Brand Assets",
   "status": "pass",
-  "generated_at_utc": "2026-06-01T13:12:30+00:00",
+  "generated_at_utc": "2026-06-01T14:15:58+00:00",
   "source": {
     "path": "docs/assets/brand/xperience10m-logo-mark.png",
     "kind": "ChatGPT-image-generated logo mark with chroma-key background removed locally",
-    "prompt_summary": "X-shaped multimodal camera mark with Ropedia-inspired near-black, lime, cyan, trajectory, and point-cloud styling."
+    "prompt_summary": "X-shaped multimodal camera mark with near-black, lime, cyan, trajectory, and point-cloud styling."
   },
   "assets": [
     {

docs/data/evaluation_protocol.json

@@ -2,7 +2,7 @@
   "title": "Ropedia Xperience-10M Task Suite Evaluation Protocol",
   "status": "pass",
   "version": "2026-06-01",
-  "generated_at_utc": "2026-06-01T13:12:55+00:00",
+  "generated_at_utc": "2026-06-01T14:16:26+00:00",
   "source_files": [
     "docs/data/summary_metrics.json",
     "results/episode_task_suite/summary_report.json",
@@ -305,7 +305,7 @@
   "unsupported_interpretations": [
     "Do not infer cross-episode generalization from this single public sample.",
     "Do not treat feature-vector reconstruction as pixel depth, mesh, NeRF, or Gaussian reconstruction.",
-    "Do not treat Qwen3-Omni smoke artifacts as a real 32-episode fine-tune.",
+    "Do not treat Qwen3-Omni readiness artifacts as a real 32-episode fine-tune.",
     "Do not infer audio-visual learning from the current baseline vector because audio is not featurized."
   ],
   "scale_up_gate": {
@@ -318,7 +318,7 @@
     "current_status": "prepared but data-gated",
     "evidence": [
       "results/omni_finetune/DATA_BLOCKER_REPORT.md",
-      "results/omni_finetune/A100_HF_RELAY_STATUS.md"
+      "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md"
     ]
   }
 }

docs/data/evidence_contract.json

@@ -142,9 +142,9 @@
       "boundary": "single-episode probes, not full research-direction solutions"
     },
     {
-      "id": "qwen3_omni_smoke",
-      "claim": "Qwen3-Omni infrastructure has passed technical smoke checks.",
-      "status": "smoke_only",
+      "id": "qwen3_omni_readiness",
+      "claim": "Qwen3-Omni infrastructure has passed technical readiness checks.",
+      "status": "readiness_only",
       "evidence": [
         "results/omni_finetune/RUN_REPORT.md",
         "results/omni_finetune/dataset_manifest.json",
@@ -158,7 +158,7 @@
       "status": "blocked_by_data_access",
       "evidence": [
         "results/omni_finetune/DATA_BLOCKER_REPORT.md",
-        "results/omni_finetune/A100_HF_RELAY_STATUS.md",
+        "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
         "results/omni_finetune/source_discovery.json"
       ],
       "boundary": "no 32-episode metric should be claimed until the gate passes"
@@ -171,7 +171,7 @@
         "scripts/validate_scope_claims.py",
         "docs/data/scope_claims_audit.json"
       ],
-      "boundary": "old run/path identifiers are classified as smoke-artifact provenance and fail validation if public presentation claims real 32-episode metrics"
+      "boundary": "old run/path identifiers are classified as readiness-artifact provenance and fail validation if public presentation claims real 32-episode metrics"
     },
     {
       "id": "mirror_parity",

docs/data/mirror_parity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T13:32:15+00:00",
+  "generated_at_utc": "2026-06-01T14:32:44+00:00",
   "hf_root": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/hf_publish",
   "summary": {
     "group_count": 59,
@@ -36,27 +36,27 @@
       "local": {
         "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/working_repo_copy/docs/data/artifact_index.json",
         "exists": true,
-        "bytes": 21969,
-        "sha256": "489cf9ec3b09c3f876927945a947f4f093db5e80b5b2df4c5d728586457e742d"
+        "bytes": 22054,
+        "sha256": "1a37c2526c6a63344175ac4be212276c53cc0f654b99df7a875bf6f8d433a413"
       },
       "mirrors": {
         "hf_space": {
           "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/hf_publish/space/data/artifact_index.json",
           "exists": true,
-          "bytes": 21969,
-          "sha256": "489cf9ec3b09c3f876927945a947f4f093db5e80b5b2df4c5d728586457e742d"
+          "bytes": 22054,
+          "sha256": "1a37c2526c6a63344175ac4be212276c53cc0f654b99df7a875bf6f8d433a413"
         },
         "hf_artifacts": {
           "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/hf_publish/artifacts/docs/data/artifact_index.json",
           "exists": true,
-          "bytes": 21969,
-          "sha256": "489cf9ec3b09c3f876927945a947f4f093db5e80b5b2df4c5d728586457e742d"
+          "bytes": 22054,
+          "sha256": "1a37c2526c6a63344175ac4be212276c53cc0f654b99df7a875bf6f8d433a413"
         },
         "hf_model": {
           "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/hf_publish/model/metrics/artifact_index.json",
           "exists": true,
-          "bytes": 21969,
-          "sha256": "489cf9ec3b09c3f876927945a947f4f093db5e80b5b2df4c5d728586457e742d"
+          "bytes": 22054,
+          "sha256": "1a37c2526c6a63344175ac4be212276c53cc0f654b99df7a875bf6f8d433a413"
         }
       },
       "failures": []
@@ -67,27 +67,27 @@
       "local": {
         "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/working_repo_copy/docs/data/brand_assets.json",
         "exists": true,
-        "bytes": 3921,
-        "sha256": "b284b2dcfa5073d95c1e9eacc97b9bbb21ac0293f93b8e4e3c0cedb5e994ee6f"
+        "bytes": 3904,
+        "sha256": "b822ab3af24f6d3f2e041bacf6a1caa59fbd9dbff013c8b21df8f3ef2414c865"
       },
       "mirrors": {
         "hf_space": {
           "path": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/hf_publish/space/data/brand_assets.json",
           "exists": true,
-          "bytes": 3921,
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       "required_result": "no relevant console warnings/errors and target links work"
     }

docs/data/reviewer_packet.json

@@ -12,7 +12,7 @@
     "raw_xperience10m_data_in_repo": false,
     "audio_feature_status": "Audio is present in the sample MP4 streams and shown in the figures, but the current baseline feature vector does not include an extracted audio block.",
     "qwen3_omni_32_episode_claim": false,
-    "qwen3_omni_status": "Smoke-only until at least 32 valid episodes are available and held-out episode evaluation finishes."
+    "qwen3_omni_status": "Readiness-only until at least 32 valid episodes are available and held-out episode evaluation finishes."
   },
   "review_path": [
     {
@@ -108,7 +108,7 @@
             "question": "How should this scale beyond one episode?",
       "primary_artifacts": [
         "results/omni_finetune/DATA_BLOCKER_REPORT.md",
-        "results/omni_finetune/A100_HF_RELAY_STATUS.md",
+        "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md",
         "scripts/omni/discover_xperience10m_sources.py"
       ],
       "readout": "The next milestone is a 32-episode held-out-episode Qwen3-Omni LoRA pilot after gated Xperience-10M access is available."
@@ -136,7 +136,7 @@
   },
   "do_not_infer": [
     "Do not infer cross-environment generalization from the single public sample episode.",
-    "Do not treat the Qwen3-Omni smoke run as a 32-episode fine-tune.",
+    "Do not treat the Qwen3-Omni readiness run as a 32-episode fine-tune.",
     "Do not treat feature-vector reconstruction as pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Do not assume raw Xperience-10M data is redistributed in this repo."
   ]

docs/data/reviewer_scorecard.json

@@ -108,7 +108,7 @@
       "status": "data_gated_not_model_quality_claim",
       "evidence": [
         "results/omni_finetune/DATA_BLOCKER_REPORT.md",
-        "results/omni_finetune/A100_HF_RELAY_STATUS.md"
+        "results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md"
       ],
       "readout": "The 32-episode LoRA pilot is prepared, but no real held-out 32-episode result is claimed until gated data access, manifest construction, training, and held-out evaluation pass."
     },

docs/data/scope_claims_audit.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T13:13:35+00:00",
+  "generated_at_utc": "2026-06-01T14:16:38+00:00",
   "summary": {
     "qwen3_omni_32_episode_claim": false,
     "dataset_manifest_num_episodes": 1,
@@ -31,7 +31,7 @@
     {
       "name": "reviewer_packet_forbids_32_episode_inference",
       "status": "pass",
-      "detail": "reviewer packet explicitly warns not to treat the smoke run as a 32-episode fine-tune",
+      "detail": "reviewer packet explicitly warns not to treat the readiness run as a 32-episode fine-tune",
       "evidence": [
         "docs/data/reviewer_packet.json"
       ]
@@ -39,13 +39,13 @@
     {
       "name": "summary_metrics_preserves_omni_claim_boundary",
       "status": "pass",
-      "detail": "No real 32-episode fine-tune is claimed until the watcher downloads data, transfers it to H20, and the held-out evaluation runs.",
+      "detail": "No real 32-episode fine-tune is claimed until gated data is available locally and held-out evaluation runs.",
       "evidence": [
         "docs/data/summary_metrics.json"
       ]
     },
     {
-      "name": "omni_dataset_manifest_is_smoke_only",
+      "name": "omni_dataset_manifest_is_readiness_only",
       "status": "pass",
       "detail": "episodes=1, samples=128, split_counts={'train': 128}",
       "evidence": [
@@ -53,7 +53,7 @@
       ]
     },
     {
-      "name": "omni_training_metadata_is_smoke_only",
+      "name": "omni_training_metadata_is_readiness_only",
       "status": "pass",
       "detail": "train=128, val=0, processes=8",
       "evidence": [
@@ -88,7 +88,7 @@
       ]
     },
     {
-      "name": "historical_32ep_identifiers_are_confined_to_smoke_artifacts",
+      "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
       "detail": "historical identifiers found in result provenance files=157",
       "evidence": [
@@ -140,7 +140,7 @@
   ],
   "historical_identifiers": [
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/HF_UPLOAD.md",
       "line": 5,
       "patterns": [
@@ -150,7 +150,7 @@
       "example": "- `results/omni_finetune/adapter_lora/` (`xperience10m_qwen3_omni_32ep_lora`)"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/RUN_REPORT.md",
       "line": 4,
       "patterns": [
@@ -160,7 +160,7 @@
       "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/RUN_REPORT.md",
       "line": 5,
       "patterns": [
@@ -170,7 +170,7 @@
       "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/RUN_REPORT_eval.md",
       "line": 4,
       "patterns": [
@@ -180,7 +180,7 @@
       "example": "- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/RUN_REPORT_eval.md",
       "line": 5,
       "patterns": [
@@ -190,7 +190,7 @@
       "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/RUN_REPORT_lora.md",
       "line": 4,
       "patterns": [
@@ -200,7 +200,7 @@
       "example": "- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/config.yaml",
       "line": 1,
       "patterns": [
@@ -210,7 +210,7 @@
       "example": "run_id: xperience10m_qwen3_omni_32ep_lora"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/config.yaml",
       "line": 4,
       "patterns": [
@@ -220,7 +220,7 @@
       "example": "dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/config.yaml",
       "line": 5,
       "patterns": [
@@ -228,10 +228,10 @@
         "xperience10m_qwen3_omni_32ep",
         "ropedia-episode-task-suite"
       ],
-      "example": "checkpoint_dir: /home/cy/Ropedia/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora"
+      "example": "checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 1,
       "patterns": [
@@ -242,7 +242,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:0\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 0, \"end_frame\": 19, \"num_frames\": 20}, \"media\": {\"video_paths\": [{"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 2,
       "patterns": [
@@ -253,7 +253,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:1\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 20, \"end_frame\": 39, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 3,
       "patterns": [
@@ -264,7 +264,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:2\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 40, \"end_frame\": 59, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 4,
       "patterns": [
@@ -275,7 +275,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:3\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 60, \"end_frame\": 79, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 5,
       "patterns": [
@@ -286,7 +286,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:4\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 80, \"end_frame\": 99, \"num_frames\": 20}, \"media\": {\"video_paths\": ["
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 6,
       "patterns": [
@@ -297,7 +297,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:5\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 100, \"end_frame\": 119, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 7,
       "patterns": [
@@ -308,7 +308,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:6\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 120, \"end_frame\": 139, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 8,
       "patterns": [
@@ -319,7 +319,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:7\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 140, \"end_frame\": 159, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 9,
       "patterns": [
@@ -330,7 +330,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:8\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 160, \"end_frame\": 179, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 10,
       "patterns": [
@@ -341,7 +341,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:9\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 180, \"end_frame\": 199, \"num_frames\": 20}, \"media\": {\"video_paths\":"
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 11,
       "patterns": [
@@ -352,7 +352,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:10\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 200, \"end_frame\": 219, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 12,
       "patterns": [
@@ -363,7 +363,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:11\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 220, \"end_frame\": 239, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 13,
       "patterns": [
@@ -374,7 +374,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:12\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 240, \"end_frame\": 259, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 14,
       "patterns": [
@@ -385,7 +385,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:13\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 260, \"end_frame\": 279, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 15,
       "patterns": [
@@ -396,7 +396,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:14\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 280, \"end_frame\": 299, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 16,
       "patterns": [
@@ -407,7 +407,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:15\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 300, \"end_frame\": 319, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 17,
       "patterns": [
@@ -418,7 +418,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:40\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 800, \"end_frame\": 819, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 18,
       "patterns": [
@@ -429,7 +429,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:41\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 820, \"end_frame\": 839, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 19,
       "patterns": [
@@ -440,7 +440,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:42\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 840, \"end_frame\": 859, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 20,
       "patterns": [
@@ -451,7 +451,7 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:43\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 860, \"end_frame\": 879, \"num_frames\": 20}, \"media\": {\"video_paths\""
     },
     {
-      "classification": "historical_identifier_in_smoke_artifact",
+      "classification": "historical_identifier_in_readiness_artifact",
       "path": "results/omni_finetune/dataset.jsonl",
       "line": 21,
       "patterns": [

docs/data/source_alignment_audit.json

@@ -1,7 +1,7 @@
 {
   "title": "Ropedia Xperience-10M Source Alignment Audit",
   "status": "pass",
-  "generated_at_utc": "2026-06-01T13:12:55+00:00",
+  "generated_at_utc": "2026-06-01T14:26:41+00:00",
   "alignment_json": "docs/data/xperience10m_dataset_card_alignment.json",
   "alignment_summary": {
     "full_dataset_repo": "ropedia-ai/xperience-10m",

docs/data/summary_metrics.json

@@ -2,8 +2,8 @@
   "omni_relay": {
     "status": "pending_huggingface_gated_access",
     "dataset": "ropedia-ai/xperience-10m",
-    "relay_server": "ANGEL-A100-80Gx4",
-    "training_server": "ANGEL-H20-96GX8",
+    "staging": "prepared_generic_host_to_host_transfer",
+    "training_target": "external_multi_gpu_training_host",
     "selection_strategy": "stratified_round_robin_by_top_level_session",
     "target_episodes": 32,
     "selected_sessions": 32,
@@ -14,7 +14,7 @@
       "visualization.rrd"
     ],
     "blocker": "Hugging Face returns 403 pending review for the full Xperience-10M gated dataset.",
-    "claim_boundary": "No real 32-episode fine-tune is claimed until the watcher downloads data, transfers it to H20, and the held-out evaluation runs."
+    "claim_boundary": "No real 32-episode fine-tune is claimed until gated data is available locally and held-out evaluation runs."
   },
   "models": {
     "motion_action": {

docs/data/website_integrity.json

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-01T13:12:56+00:00",
+  "generated_at_utc": "2026-06-01T14:22:00+00:00",
   "docs_root": "/Users/chaoyue/Documents/Codex/2026-05-29/i-am-learning-this-dataset-https/working_repo_copy/docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
@@ -25,7 +25,7 @@
       "status": "pass",
       "reason": "The reviewer scorecard should appear before the deeper evidence ledger.",
       "scorecard_index": 37042,
-      "evidence_index": 43678
+      "evidence_index": 43688
     },
     {
       "name": "reviewer_scorecard_links_json",
@@ -38,8 +38,8 @@
       "status": "pass",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "scorecard_index": 37042,
-      "protocol_index": 41271,
-      "evidence_index": 43678
+      "protocol_index": 41281,
+      "evidence_index": 43688
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -102,27 +102,27 @@
   "json_files": [
     {
       "path": "data/artifact_index.json",
-      "bytes": 20587,
+      "bytes": 22054,
       "top_level_type": "dict"
     },
     {
       "path": "data/brand_assets.json",
-      "bytes": 3921,
+      "bytes": 3904,
       "top_level_type": "dict"
     },
     {
       "path": "data/evaluation_protocol.json",
-      "bytes": 13575,
+      "bytes": 13586,
       "top_level_type": "dict"
     },
     {
       "path": "data/evidence_contract.json",
-      "bytes": 10906,
+      "bytes": 10929,
       "top_level_type": "dict"
     },
     {
       "path": "data/figure_index.json",
-      "bytes": 11493,
+      "bytes": 13449,
       "top_level_type": "dict"
     },
     {
@@ -132,7 +132,7 @@
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 66049,
+      "bytes": 84322,
       "top_level_type": "dict"
     },
     {
@@ -147,12 +147,12 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 6098,
+      "bytes": 6733,
       "top_level_type": "dict"
     },
     {
       "path": "data/quality_gates.json",
-      "bytes": 5819,
+      "bytes": 6351,
       "top_level_type": "dict"
     },
     {
@@ -172,17 +172,17 @@
     },
     {
       "path": "data/reviewer_packet.json",
-      "bytes": 7386,
+      "bytes": 7401,
       "top_level_type": "dict"
     },
     {
       "path": "data/reviewer_scorecard.json",
-      "bytes": 6107,
+      "bytes": 6114,
       "top_level_type": "dict"
     },
     {
       "path": "data/scope_claims_audit.json",
-      "bytes": 19964,
+      "bytes": 20089,
       "top_level_type": "dict"
     },
     {
@@ -192,7 +192,7 @@
     },
     {
       "path": "data/summary_metrics.json",
-      "bytes": 25075,
+      "bytes": 25088,
       "top_level_type": "dict"
     },
     {
@@ -202,7 +202,7 @@
     },
     {
       "path": "data/website_integrity.json",
-      "bytes": 8528,
+      "bytes": 8813,
       "top_level_type": "dict"
     },
     {

docs/index.html

@@ -1161,9 +1161,9 @@
           <article class="scorecard-card gated">
             <span class="status-pill">data-gated</span>
             <h3>Qwen3-Omni pilot</h3>
-            <p>The 32-episode LoRA path is prepared, but no model-quality claim is made until gated data access, held-out splits, training, and eval pass.</p>
+            <p>The 32-episode LoRA path is prepared, but no model-quality claim is made until gated data access, held-out splits, training, and evaluation pass.</p>
             <div class="scorecard-meta">
-              <span>current claim <strong>smoke only</strong></span>
+              <span>current claim <strong>readiness only</strong></span>
               <span>target gate <strong>32 episodes</strong></span>
               <span>held-out eval <strong>pending</strong></span>
             </div>
@@ -1209,7 +1209,7 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Evidence first, claims second.</h2>
-          <p>A top-level project should make its proof boundary visible. This ledger separates verified single-episode artifacts from smoke-only Qwen3-Omni work and the pending 32-episode gate.</p>
+          <p>A top-level project should make its proof boundary visible. This ledger separates verified single-episode artifacts from readiness-only Qwen3-Omni work and the pending 32-episode gate.</p>
         </div>
         <div class="evidence-grid">
           <article class="evidence-card">
@@ -1240,9 +1240,9 @@
             </div>
           </article>
           <article class="evidence-card">
-            <span class="status-pill">blocked by data access</span>
-            <h3>Qwen3-Omni is smoke-only until 32 episodes land</h3>
-            <p>The H20/A100 pipeline exists, but current Qwen3-Omni artifacts use one episode and 128 train windows. No 32-episode metric is claimed.</p>
+            <span class="status-pill">data-gated</span>
+            <h3>Qwen3-Omni remains readiness-only</h3>
+            <p>The current Qwen3-Omni artifacts use one episode and 128 train windows. No 32-episode metric is claimed.</p>
             <div class="evidence-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">evidence contract</a>
               <a href="data/evidence_contract.json">machine JSON</a>
@@ -1251,7 +1251,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified</span>
             <h3>Scope claims are machine-checked</h3>
-            <p>The audit confirms historical <code>32ep</code> run/path strings stay confined to smoke-artifact provenance and are not presented as real 32-episode results.</p>
+            <p>The audit confirms historical <code>32ep</code> run/path strings stay confined to readiness-artifact provenance and are not presented as real 32-episode results.</p>
             <div class="evidence-links">
               <a href="data/scope_claims_audit.json">scope audit</a>
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/validate_scope_claims.py">validator script</a>
@@ -1340,7 +1340,7 @@
           <article class="review-card">
             <span class="step-index">01</span>
             <h3>Check the claim boundary</h3>
-            <p>Start with the evidence contract, artifact index, scope audit, publication audit, and website integrity report. They separate verified single-episode artifacts from smoke-only Qwen3-Omni work.</p>
+            <p>Start with the evidence contract, artifact index, scope audit, publication audit, and website integrity report. They separate verified single-episode artifacts from readiness-only Qwen3-Omni work.</p>
             <div class="review-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">contract</a>
               <a href="data/evidence_contract.json">JSON</a>
@@ -1378,7 +1378,7 @@
             <p>The multi-episode Qwen3-Omni path is prepared, but no real 32-episode result is claimed until the data gate and held-out evaluation pass.</p>
             <div class="review-links">
               <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">blocker</a>
-              <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/A100_HF_RELAY_STATUS.md">relay</a>
+              <a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">access status</a>
               <a href="data/reviewer_packet.json">review packet</a>
             </div>
           </article>
@@ -1729,7 +1729,7 @@
             </div>
             <div class="artifact-grid">
               <article class="artifact primary-artifact"><div><h3>Artifact guide</h3><p>Human-readable map from proof boundary to data contract, task evidence, platform mirrors, and scale-up status.</p></div><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/ARTIFACT_GUIDE.md">ARTIFACT_GUIDE.md</a></article>
-              <article class="artifact"><h3>Evidence contract</h3><p>Defines verified, smoke-only, blocked, and out-of-scope claims.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">EVIDENCE_CONTRACT.md</a></article>
+              <article class="artifact"><h3>Evidence contract</h3><p>Defines verified, readiness-only, blocked, and out-of-scope claims.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/EVIDENCE_CONTRACT.md">EVIDENCE_CONTRACT.md</a></article>
               <article class="artifact"><h3>Quality gates</h3><p>One release checklist for automated validators and live post-publish checks.</p><a href="data/quality_gates.json">quality_gates.json</a></article>
               <article class="artifact"><h3>Live publication</h3><p>Last public GitHub/HF URL verification after upload.</p><a href="data/live_publication_status.json">live_publication_status.json</a></article>
               <article class="artifact"><h3>Artifact index</h3><p>Selective source-of-truth catalog with existence checks, sizes, and stable-file hashes.</p><a href="data/artifact_index.json">artifact_index.json</a></article>
@@ -1779,8 +1779,8 @@
               <p>The multi-episode Qwen3-Omni path is documented and scripted, but no full-pilot metric is claimed until the data gate and held-out evaluation pass.</p>
             </div>
             <div class="artifact-grid">
-              <article class="artifact"><h3>A100 HF relay status</h3><p>HF full-dataset access is pending; an A100 watcher is ready to download and transfer a 32-session pilot.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/A100_HF_RELAY_STATUS.md">A100_HF_RELAY_STATUS.md</a></article>
-              <article class="artifact"><h3>Qwen3-Omni readiness artifacts</h3><p>Manifests, metadata, metrics, and progress logs from the current smoke/evidence run.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/episode_manifest.json">episode_manifest.json</a></article>
+              <article class="artifact"><h3>Multi-episode access status</h3><p>Public data-access boundary and selected 32-episode pilot plan, without private infrastructure details.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">MULTI_EPISODE_ACCESS_STATUS.md</a></article>
+              <article class="artifact"><h3>Qwen3-Omni readiness artifacts</h3><p>Manifests, metadata, metrics, and progress logs from the current readiness run.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/episode_manifest.json">episode_manifest.json</a></article>
               <article class="artifact"><h3>32-episode data gate</h3><p>The readiness gate remains the source of truth before any full pilot training claim.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/DATA_BLOCKER_REPORT.md">DATA_BLOCKER_REPORT.md</a></article>
             </div>
           </section>
@@ -1792,12 +1792,12 @@
       <div class="wrap">
         <div class="section-head">
           <h2>Qwen3-Omni pilot is approval-ready.</h2>
-          <p>The full Xperience-10M Hugging Face dataset is gated. While access is pending, the A100 relay has already selected a 32-episode pilot across 32 different session UUIDs and will continue automatically after approval.</p>
+          <p>The full Xperience-10M Hugging Face dataset is gated. While access is pending, the public plan has selected a 32-episode pilot across 32 different session UUIDs.</p>
         </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>Stratified round-robin over 64 top-level sessions; 680 complete candidates scanned; 32 sessions selected.</p></article>
-          <article class="artifact"><h3>Transfer</h3><p>A100 downloads from Hugging Face, excludes visualization.rrd, validates files, then rsyncs to H20.</p></article>
-          <article class="artifact"><h3>Boundary</h3><p>The current LoRA artifact is a smoke/pilot checkpoint. A real 32-episode result requires the watcher to finish and held-out evaluation to run.</p></article>
+          <article class="artifact"><h3>Transfer</h3><p>Download raw episodes only from official gated sources, exclude visualization.rrd, validate files, then stage them for training.</p></article>
+          <article class="artifact"><h3>Boundary</h3><p>The current LoRA artifact is a readiness checkpoint. A real 32-episode result requires local gated data and held-out evaluation.</p></article>
         </div>
       </div>
     </section>

results/omni_exploration/modelscope_manifest.json

@@ -11,8 +11,8 @@
   "episodes": [
     {
       "episode_id": "xperience-10m-sample",
-      "path": "/home/cy/Ropedia/modelscope_data/xperience-10m-sample",
-      "annotation": "/home/cy/Ropedia/modelscope_data/xperience-10m-sample/annotation.hdf5",
+      "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample",
+      "annotation": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/annotation.hdf5",
       "files": [
         {
           "name": "annotation.hdf5",

results/omni_finetune/DATA_BLOCKER_REPORT.md

@@ -0,0 +1,21 @@
+# Xperience-10M Fine-Tune Readiness
+
+Target episodes: 32
+Ready for 32-episode pilot: False
+Selected source: none
+
+## Source counts
+- local (degraded-valid): 1 / 1
+- modelscope (degraded-valid): 0 / 0
+- huggingface (degraded-valid): 0 / 0
+
+## Blockers
+- Not enough degraded-valid episodes for a 32-episode pilot. Need 32, local has 1.
+- Current training host path remains one-episode proof-of-stack only.
+- ModelScope probe unavailable or reported no matching episode files.
+- Hugging Face probe unavailable or reported no matching episode files.
+
+## Interpretation
+- Degraded-valid means: annotation.hdf5 and fisheye_cam0.mp4 both exist.
+- Complete means all six MP4 views are present with annotation.
+- A 32-episode pilot must not be claimed unless this script selects a source with 32+ degraded-valid episodes.

results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md

@@ -0,0 +1,39 @@
+# Multi-Episode Access Status
+
+Current blocker: access to the gated full `ropedia-ai/xperience-10m` dataset is
+still pending approval from the dataset authors.
+
+This file records only public-facing readiness facts. It intentionally excludes
+machine aliases, private paths, SSH hosts, token locations, and local server
+details.
+
+## Selection Plan
+
+| Item | Value |
+| --- | ---: |
+| Dataset | `ropedia-ai/xperience-10m` |
+| Target | 32 complete leaf episodes |
+| Strategy | stratified round-robin across top-level session UUIDs |
+| Candidate scan | first 64 top-level session UUIDs |
+| Valid candidates | 680 |
+| Selected sessions | 32 |
+| Minimum episode size | 0.25 GB |
+| Estimated bytes | 72,031,620,552 |
+| Excluded file | `visualization.rrd` |
+
+## Boundary
+
+The current Qwen3-Omni artifacts are readiness artifacts from the locally
+available sample data. They are not 32-episode held-out model-quality results.
+
+A real 32-episode pilot can be claimed only after:
+
+- at least 32 valid episodes are available locally,
+- the manifest builder confirms complete held-out episode splits,
+- training finishes with recorded metadata and progress logs,
+- evaluation runs on held-out test episodes,
+- predictions, metrics, confusion matrices, and a run report are committed.
+
+The source-of-truth blocker report remains:
+
+`results/omni_finetune/DATA_BLOCKER_REPORT.md`

results/omni_finetune/RUN_REPORT.md

@@ -0,0 +1,13 @@
+# Qwen3-Omni LoRA Evaluation
+
+- Base model: `/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct`
+- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`
+- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`
+- Eval split: `train`
+- Samples: `128`
+- Episodes: `1`
+- Accuracy: `0.0000`
+- Macro-F1: `0.0000`
+- Unseen eval labels: `0`
+
+Artifacts include `metrics.json`, `predictions.csv`, `per_class_metrics.csv`, and `confusion_matrix.csv`.

results/omni_finetune/RUN_REPORT_eval.md

@@ -0,0 +1,13 @@
+# Qwen3-Omni LoRA Evaluation
+
+- Base model: `/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct`
+- Adapter: `checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora`
+- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`
+- Eval split: `train`
+- Samples: `128`
+- Episodes: `1`
+- Accuracy: `0.0000`
+- Macro-F1: `0.0000`
+- Unseen eval labels: `0`
+
+Artifacts include `metrics.json`, `predictions.csv`, `per_class_metrics.csv`, and `confusion_matrix.csv`.

results/omni_finetune/RUN_REPORT_lora.md

@@ -0,0 +1,11 @@
+# Qwen3-Omni LoRA Training
+
+- Base model: `/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct`
+- Dataset: `results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl`
+- Train samples: `128`
+- Validation samples: `0`
+- Processes: `8`
+- Epochs: `1`
+- Final train loss: `10.936364`
+
+Only LoRA parameters are trained; the base Qwen3-Omni weights remain frozen.

results/omni_finetune/adapter_lora/README.md

@@ -0,0 +1,206 @@
+---
+base_model: ''
+library_name: peft
+tags:
+- 'base_model:adapter:'
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.18.1

results/omni_finetune/adapter_lora/adapter_config.json

@@ -0,0 +1,49 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen3OmniMoeThinkerForConditionalGeneration",
+    "parent_library": "transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe"
+  },
+  "base_model_name_or_path": "",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "up_proj",
+    "q_proj",
+    "v_proj",
+    "k_proj",
+    "down_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

results/omni_finetune/adapter_lora/chat_template.jinja

@@ -0,0 +1,122 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {{- "<|vision_start|><|image_pad|><|vision_end|>" }}
+                {%- elif content.type == 'audio' or 'audio' in content or 'audio_url' in content %}
+                    {{- "<|audio_start|><|audio_pad|><|audio_end|>" }}
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {{- "<|vision_start|><|video_pad|><|vision_end|>" }}
+                {%- elif content.type == 'text' %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+    {{- '\n\n' }}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {{- '<|im_start|>system\n' +"<|vision_start|><|image_pad|><|vision_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'audio' or 'audio' in content or 'audio_url' in content %}
+                    {{- '<|im_start|>system\n' +"<|audio_start|><|audio_pad|><|audio_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {{- '<|im_start|>system\n' +"<|vision_start|><|video_pad|><|vision_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'text' %}
+                    {{- '<|im_start|>system\n' +content.text+ '<|im_end|>\n' }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = namespace(text="") %}
+        {%- for mcontent in message.content %}
+            {%- if mcontent.type == 'image' or 'image' in mcontent or 'image_url' in mcontent %}
+                {%- set content.text = content.text~"<|vision_start|><|image_pad|><|vision_end|>" %}
+            {%- elif mcontent.type == 'audio' or 'audio' in mcontent or 'audio_url' in mcontent %}
+                {%- set content.text = content.text~"<|audio_start|><|audio_pad|><|audio_end|>" %}
+            {%- elif mcontent.type == 'video' or 'video' in mcontent %}
+                {%- set content.text = content.text~"<|vision_start|><|video_pad|><|vision_end|>" %}
+            {%- elif mcontent.type == 'text' %}
+                {%- set content.text = content.text~mcontent.text %}
+            {%- endif %}
+        {%- endfor %}
+        {%- set content = content.text %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = "" %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+        {%- endif %}
+    {%- endif %}
+    {%- if loop.index0 > ns.last_query_index %}
+        {%- if loop.last or (not loop.last and reasoning_content) %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip("\n") + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+    {%- endif %}
+    {%- if message.tool_calls %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if (loop.first and content) or (not loop.first) %}{{- '\n' }}{%- endif %}
+            {%- if tool_call.function %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {%- if tool_call.arguments is string %}
+                {{- tool_call.arguments }}
+            {%- else %}
+                {{- tool_call.arguments | tojson }}
+            {%- endif %}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}{{- '<|im_start|>user' }}{%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}{{- '<|im_end|>\n' }}{%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}{{- '<think>\n\n</think>\n\n' }}{%- endif %}
+{%- endif %}

results/omni_finetune/adapter_lora/processor_config.json

@@ -0,0 +1,114 @@
+{
+  "feature_extractor": {
+    "chunk_length": 30,
+    "dither": 0.0,
+    "feature_extractor_type": "WhisperFeatureExtractor",
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_pixels": 12845056,
+    "merge_size": 2,
+    "min_pixels": 3136,
+    "n_fft": 400,
+    "n_samples": 480000,
+    "nb_max_frames": 3000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "return_attention_mask": true,
+    "sampling_rate": 16000,
+    "temporal_patch_size": 2
+  },
+  "image_processor": {
+    "data_format": "channels_first",
+    "dither": 0.0,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessorFast",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "n_fft": 400,
+    "n_samples": 4800000,
+    "nb_max_frames": 30000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_attention_mask": true,
+    "sampling_rate": 16000,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3OmniMoeProcessor",
+  "video_processor": {
+    "data_format": "channels_first",
+    "default_to_square": true,
+    "dither": 0.0,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": false,
+    "feature_extractor_type": "WhisperFeatureExtractor",
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "n_fft": 400,
+    "n_samples": 4800000,
+    "nb_max_frames": 30000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_attention_mask": true,
+    "return_metadata": false,
+    "sampling_rate": 16000,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen2VLVideoProcessor"
+  }
+}

results/omni_finetune/adapter_lora/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7d41145c408f3062e824f965be1c29854cd809e111cdf8170aa8b0bcd5d1fab
+size 11424262

results/omni_finetune/adapter_lora/tokenizer_config.json

@@ -0,0 +1,52 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<|audio_start|>",
+    "<|audio_end|>",
+    "<tts_pad>",
+    "<tts_text_bos>",
+    "<tts_text_bos_single>",
+    "<|audio_pad|>"
+  ],
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "model_max_length": 131072,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen3OmniMoeProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

results/omni_finetune/adapter_lora/training_metadata.json

@@ -0,0 +1,32 @@
+{
+  "run_id": "xperience10m_qwen3_omni_32ep_lora",
+  "model_id": "/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl",
+  "checkpoint_dir": "/path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora",
+  "num_processes": 8,
+  "num_train_samples": 128,
+  "num_val_samples": 0,
+  "history": [
+    {
+      "epoch": 1,
+      "train_loss": 10.936363816261292,
+      "val_loss": null,
+      "global_step": 16
+    }
+  ],
+  "lora": {
+    "r": 16,
+    "alpha": 32,
+    "dropout": 0.05,
+    "target_modules": [
+      "q_proj",
+      "k_proj",
+      "v_proj",
+      "o_proj",
+      "gate_proj",
+      "up_proj",
+      "down_proj"
+    ]
+  },
+  "use_audio_in_video": false
+}

results/omni_finetune/config.yaml

@@ -0,0 +1,10 @@
+run_id: xperience10m_qwen3_omni_32ep_lora
+stage: qwen_lora_text_video_audio
+model_id: /path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct
+dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl
+checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora
+num_processes: 8
+epochs: 1
+learning_rate: 0.0001
+lora_r: 16
+lora_alpha: 32

results/omni_finetune/confusion_matrix_eval.csv

@@ -0,0 +1,20 @@
+true\pred,Close bottle cap,Grasp coffee scoop,Grasp gooseneck kettle,Hold coffee carafe,Hold gooseneck kettle,Lift gooseneck kettle,Move kettle,Move kettle away,Pick up kettle,Pick up white bottle,Place item on table,Place kettle on table,Position kettle to pour,Pour coffee,Pour liquid from white bottle,Pour milk into coffee,Transfer coffee to dripper,Wait/Prepare for pouring,unknown
+Close bottle cap,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,3,5
+Grasp coffee scoop,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6
+Grasp gooseneck kettle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,5
+Hold coffee carafe,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7
+Hold gooseneck kettle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8
+Lift gooseneck kettle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,6
+Move kettle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7
+Move kettle away,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8
+Pick up kettle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,2
+Pick up white bottle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2
+Place item on table,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,4
+Place kettle on table,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,3
+Position kettle to pour,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,7,1
+Pour coffee,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,7
+Pour liquid from white bottle,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,4,2
+Pour milk into coffee,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1
+Transfer coffee to dripper,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,3,5
+Wait/Prepare for pouring,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,8
+unknown,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0

results/omni_finetune/dataset_manifest.json

@@ -0,0 +1,175 @@
+{
+  "run_id": "xperience10m_qwen3_omni_32ep_dataset",
+  "dataset_path": "/path/to/ropedia_workspace/ropedia-episode-task-suite/results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl",
+  "num_samples": 128,
+  "num_episodes": 1,
+  "split_counts": {
+    "train": 128
+  },
+  "label_counts": {
+    "Close bottle cap": 9,
+    "Pick up kettle": 8,
+    "Position kettle to pour": 8,
+    "Move kettle": 8,
+    "Hold coffee carafe": 8,
+    "Grasp coffee scoop": 8,
+    "Transfer coffee to dripper": 8,
+    "Hold gooseneck kettle": 8,
+    "Lift gooseneck kettle": 8,
+    "Move kettle away": 8,
+    "Wait/Prepare for pouring": 8,
+    "Pour coffee": 8,
+    "Grasp gooseneck kettle": 6,
+    "Place kettle on table": 6,
+    "Pick up white bottle": 6,
+    "Pour liquid from white bottle": 6,
+    "Place item on table": 6,
+    "Pour milk into coffee": 1
+  },
+  "action_options": [
+    "Close bottle cap",
+    "Grasp coffee scoop",
+    "Grasp gooseneck kettle",
+    "Hold coffee carafe",
+    "Hold gooseneck kettle",
+    "Lift gooseneck kettle",
+    "Move kettle",
+    "Move kettle away",
+    "Pick up kettle",
+    "Pick up white bottle",
+    "Place item on table",
+    "Place kettle on table",
+    "Position kettle to pour",
+    "Pour coffee",
+    "Pour liquid from white bottle",
+    "Pour milk into coffee",
+    "Transfer coffee to dripper",
+    "Wait/Prepare for pouring"
+  ],
+  "subtask_options": [
+    "Handle gooseneck kettle",
+    "Lift gooseneck kettle",
+    "Move kettle",
+    "Pick up and position kettle",
+    "Pour and close white bottle",
+    "Pour coffee",
+    "Pour milk into coffee",
+    "Prepare coffee equipment and scoop grounds",
+    "Prepare for pouring",
+    "Set down kettle and retrieve white bottle",
+    "Transfer coffee grounds to dripper"
+  ],
+  "clip_policy": {
+    "label_window_frames": 20,
+    "qwen_context_frames": 120,
+    "max_video_frames": 16,
+    "audio_span": "same_as_video_context",
+    "mosaic": "2x3 multi-camera grid"
+  },
+  "feature_manifest": [
+    {
+      "name": "hand_left_joints",
+      "start": 0,
+      "end": 441,
+      "dim": 441
+    },
+    {
+      "name": "hand_right_joints",
+      "start": 441,
+      "end": 882,
+      "dim": 441
+    },
+    {
+      "name": "body_joints",
+      "start": 882,
+      "end": 1974,
+      "dim": 1092
+    },
+    {
+      "name": "body_contacts",
+      "start": 1974,
+      "end": 2121,
+      "dim": 147
+    },
+    {
+      "name": "camera_translation",
+      "start": 2121,
+      "end": 2142,
+      "dim": 21
+    },
+    {
+      "name": "camera_rotation_matrix",
+      "start": 2142,
+      "end": 2205,
+      "dim": 63
+    },
+    {
+      "name": "imu_accel_gyro",
+      "start": 2205,
+      "end": 2247,
+      "dim": 42
+    },
+    {
+      "name": "depth_confidence",
+      "start": 2247,
+      "end": 3227,
+      "dim": 980
+    },
+    {
+      "name": "caption_objects_interaction_text",
+      "start": 3227,
+      "end": 4123,
+      "dim": 896
+    },
+    {
+      "name": "slam_point_cloud",
+      "start": 4123,
+      "end": 4145,
+      "dim": 22
+    },
+    {
+      "name": "calibration",
+      "start": 4145,
+      "end": 4262,
+      "dim": 117
+    }
+  ],
+  "available_modalities": [
+    {
+      "episode_id": "xperience-10m-sample",
+      "modalities": [
+        {
+          "modality": "depth_confidence",
+          "shape": [
+            5821,
+            140
+          ]
+        },
+        {
+          "modality": "caption_text",
+          "shape": [
+            5821,
+            128
+          ],
+          "fields": "objects,interaction"
+        },
+        {
+          "modality": "slam_point_cloud_static",
+          "shape": [
+            22
+          ]
+        },
+        {
+          "modality": "calibration_static",
+          "shape": [
+            117
+          ]
+        }
+      ]
+    }
+  ],
+  "notes": [
+    "Assistant answers are strict JSON for episode understanding, not robot-control policies.",
+    "Sensor features are stored as NPZ pointers; raw annotation.hdf5 is not copied into the dataset records."
+  ]
+}

results/omni_finetune/episode_manifest.json

@@ -0,0 +1,219 @@
+{
+  "summary": {
+    "num_episodes": 1,
+    "total_bytes": 2021338279,
+    "train_minimal_bytes": 2021338279,
+    "split_counts": {
+      "train": 1
+    },
+    "split_fractions": {
+      "train": 0.8,
+      "val": 0.0,
+      "test": 0.2,
+      "seed": 7
+    },
+    "windowing": {
+      "window_frames": 20,
+      "stride_frames": 20,
+      "min_label_fraction": 0.6
+    },
+    "notes": [
+      "train_minimal_bytes excludes visualization.rrd because model training does not need it.",
+      "This file is metadata-only; it does not copy or download raw data.",
+      "Splits are assigned by whole episode to avoid window leakage."
+    ]
+  },
+  "episodes": [
+    {
+      "episode_id": "xperience-10m-sample",
+      "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample",
+      "annotation": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/annotation.hdf5",
+      "frame_count": 5821,
+      "main_task": "Making pour-over coffee",
+      "files": [
+        {
+          "name": "annotation.hdf5",
+          "bytes": 1931496028,
+          "exists": true
+        },
+        {
+          "name": "fisheye_cam0.mp4",
+          "bytes": 89842251,
+          "exists": true
+        },
+        {
+          "name": "fisheye_cam1.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "fisheye_cam2.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "fisheye_cam3.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "stereo_left.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "stereo_right.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "visualization.rrd",
+          "bytes": 0,
+          "exists": false
+        }
+      ],
+      "videos": [
+        {
+          "name": "fisheye_cam0.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/fisheye_cam0.mp4",
+          "bytes": 89842251,
+          "exists": true
+        },
+        {
+          "name": "fisheye_cam1.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/fisheye_cam1.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "fisheye_cam2.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/fisheye_cam2.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "fisheye_cam3.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/fisheye_cam3.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "stereo_left.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/stereo_left.mp4",
+          "bytes": 0,
+          "exists": false
+        },
+        {
+          "name": "stereo_right.mp4",
+          "path": "/path/to/ropedia_workspace/modelscope_data/xperience-10m-sample/stereo_right.mp4",
+          "bytes": 0,
+          "exists": false
+        }
+      ],
+      "hdf5_modalities": {
+        "calibration": true,
+        "slam_pose": true,
+        "slam_point_cloud": true,
+        "depth": true,
+        "depth_confidence": true,
+        "hand_mocap": true,
+        "body_mocap": true,
+        "contacts": true,
+        "imu": true,
+        "caption": true,
+        "captions": false
+      },
+      "label_stats": {
+        "main_task": "Making pour-over coffee",
+        "segments": 18,
+        "frame_labels": {
+          "action": {
+            "Hold gooseneck kettle": 800,
+            "Pour coffee": 800,
+            "Position kettle to pour": 640,
+            "Lift gooseneck kettle": 480,
+            "Close bottle cap": 480,
+            "Wait/Prepare for pouring": 480,
+            "Transfer coffee to dripper": 439,
+            "Grasp coffee scoop": 321,
+            "Hold coffee carafe": 240,
+            "Move kettle": 200,
+            "Pick up kettle": 160,
+            "Move kettle away": 160,
+            "Grasp gooseneck kettle": 120,
+            "Place kettle on table": 120,
+            "Pick up white bottle": 120,
+            "Pour liquid from white bottle": 120,
+            "Place item on table": 120,
+            "Pour milk into coffee": 21
+          },
+          "subtask": {
+            "Handle gooseneck kettle": 839,
+            "Pick up and position kettle": 761,
+            "Pour coffee": 761,
+            "Prepare for pouring": 600,
+            "Prepare coffee equipment and scoop grounds": 561,
+            "Lift gooseneck kettle": 561,
+            "Pour and close white bottle": 561,
+            "Transfer coffee grounds to dripper": 400,
+            "Set down kettle and retrieve white bottle": 400,
+            "Move kettle": 200,
+            "Pour milk into coffee": 60,
+            "Position kettle to pour": 39,
+            "Secure coffee container": 39,
+            "Move bottle to coffee equipment": 39
+          }
+        },
+        "window_labels": {
+          "action": {
+            "Hold gooseneck kettle": 40,
+            "Pour coffee": 40,
+            "Position kettle to pour": 32,
+            "Lift gooseneck kettle": 24,
+            "Close bottle cap": 24,
+            "Wait/Prepare for pouring": 24,
+            "Transfer coffee to dripper": 22,
+            "Grasp coffee scoop": 16,
+            "Hold coffee carafe": 12,
+            "Move kettle": 10,
+            "Pick up kettle": 8,
+            "Move kettle away": 8,
+            "Grasp gooseneck kettle": 6,
+            "Place kettle on table": 6,
+            "Pick up white bottle": 6,
+            "Pour liquid from white bottle": 6,
+            "Place item on table": 6,
+            "Pour milk into coffee": 1
+          },
+          "subtask": {
+            "Handle gooseneck kettle": 42,
+            "Pick up and position kettle": 38,
+            "Pour coffee": 38,
+            "Prepare for pouring": 30,
+            "Prepare coffee equipment and scoop grounds": 28,
+            "Lift gooseneck kettle": 28,
+            "Pour and close white bottle": 28,
+            "Transfer coffee grounds to dripper": 20,
+            "Set down kettle and retrieve white bottle": 20,
+            "Move kettle": 10,
+            "Pour milk into coffee": 3,
+            "Position kettle to pour": 2,
+            "Secure coffee container": 2,
+            "Move bottle to coffee equipment": 2
+          }
+        },
+        "num_labeled_windows": {
+          "action": 291,
+          "subtask": 291
+        }
+      },
+      "total_bytes": 2021338279,
+      "train_minimal_bytes": 2021338279,
+      "has_annotation": true,
+      "has_any_video": true,
+      "has_all_videos": false,
+      "has_rrd": false,
+      "split": "train"
+    }
+  ]
+}

results/omni_finetune/hf_upload/README.md

@@ -0,0 +1,61 @@
+---
+license: other
+base_model: Qwen/Qwen3-Omni-30B-A3B-Instruct
+library_name: peft
+tags:
+  - robotics
+  - embodied-ai
+  - multimodal
+  - xperience-10m
+  - qwen3-omni
+  - lora
+  - readiness-check
+datasets:
+  - ropedia-ai/xperience-10m
+pipeline_tag: image-text-to-text
+---
+
+# Xperience-10M Qwen3-Omni LoRA (pilot artifact)
+
+## What this is
+This repository contains a Qwen3-Omni LoRA adapter produced from an initial end-to-end
+technical readiness run on Xperience-10M data.
+
+It is a **readiness artifact** from one validated training run with:
+
+- Backbone: `Qwen/Qwen3-Omni-30B-A3B-Instruct`
+- Adapter: LoRA, rank=16, alpha=32, dropout=0.05
+- Data source: `/path/to/ropedia_workspace/modelscope_data` (single available episode set at run time)
+- Windows used: `128` train windows
+- Processes: `8`
+- Train split episode-level leakage control: **not a full 32-episode run yet**
+
+## Current Scale-Up Status
+
+The real 32-episode pilot is prepared but not complete:
+
+- staging workflow: configured
+- Selection: 32 complete episodes from 32 different session UUIDs
+- Estimated raw subset: about 72 GB, excluding `visualization.rrd`
+- Blocker: full `ropedia-ai/xperience-10m` access is still pending Hugging Face gated approval
+- Claim boundary: no 32-episode held-out metrics are claimed until multi-episode data access, manifest building, training, and evaluation finish
+
+## Files
+- `README.md` — this file
+- `adapter_config.json` — LoRA configuration metadata
+- `adapter_model.safetensors` — LoRA checkpoint weights
+- `training_metadata.json` — run metadata and hyperparameters
+- `processor_config.json`, `tokenizer.json`, `tokenizer_config.json`, `chat_template.jinja`
+
+## Reproducibility note
+This artifact is intentionally labeled as a pilot artifact. It should be treated as:
+
+1. A proof of successful Qwen3-Omni pipeline wiring
+2. Not a final benchmark model for Xperience-10M full 32-episode fine-tuning
+3. A starting point for the next run once full pilot data are available
+
+## Source
+Project and task definitions are documented in:
+- `https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite`
+- `https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/`
+- https://cy0307-ropedia-xperience-10m-task-suite.static.hf.space/

results/omni_finetune/hf_upload/adapter_config.json

@@ -0,0 +1,49 @@
+{
+  "alora_invocation_tokens": null,
+  "alpha_pattern": {},
+  "arrow_config": null,
+  "auto_mapping": {
+    "base_model_class": "Qwen3OmniMoeThinkerForConditionalGeneration",
+    "parent_library": "transformers.models.qwen3_omni_moe.modeling_qwen3_omni_moe"
+  },
+  "base_model_name_or_path": "",
+  "bias": "none",
+  "corda_config": null,
+  "ensure_weight_tying": false,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "peft_version": "0.18.1",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "gate_proj",
+    "up_proj",
+    "q_proj",
+    "v_proj",
+    "k_proj",
+    "down_proj",
+    "o_proj"
+  ],
+  "target_parameters": null,
+  "task_type": null,
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

results/omni_finetune/hf_upload/chat_template.jinja

@@ -0,0 +1,122 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- messages[0].content }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {{- "<|vision_start|><|image_pad|><|vision_end|>" }}
+                {%- elif content.type == 'audio' or 'audio' in content or 'audio_url' in content %}
+                    {{- "<|audio_start|><|audio_pad|><|audio_end|>" }}
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {{- "<|vision_start|><|video_pad|><|vision_end|>" }}
+                {%- elif content.type == 'text' %}
+                    {{- content.text }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+    {{- '\n\n' }}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {%- if messages[0].content is string %}
+            {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+        {%- else %}
+            {%- for content in messages[0].content %}
+                {%- if content.type == 'image' or 'image' in content or 'image_url' in content %}
+                    {{- '<|im_start|>system\n' +"<|vision_start|><|image_pad|><|vision_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'audio' or 'audio' in content or 'audio_url' in content %}
+                    {{- '<|im_start|>system\n' +"<|audio_start|><|audio_pad|><|audio_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'video' or 'video' in content %}
+                    {{- '<|im_start|>system\n' +"<|vision_start|><|video_pad|><|vision_end|>"+ '<|im_end|>\n' }}
+                {%- elif content.type == 'text' %}
+                    {{- '<|im_start|>system\n' +content.text+ '<|im_end|>\n' }}
+                {%- endif %}
+            {%- endfor %}
+        {%- endif %}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = namespace(text="") %}
+        {%- for mcontent in message.content %}
+            {%- if mcontent.type == 'image' or 'image' in mcontent or 'image_url' in mcontent %}
+                {%- set content.text = content.text~"<|vision_start|><|image_pad|><|vision_end|>" %}
+            {%- elif mcontent.type == 'audio' or 'audio' in mcontent or 'audio_url' in mcontent %}
+                {%- set content.text = content.text~"<|audio_start|><|audio_pad|><|audio_end|>" %}
+            {%- elif mcontent.type == 'video' or 'video' in mcontent %}
+                {%- set content.text = content.text~"<|vision_start|><|video_pad|><|vision_end|>" %}
+            {%- elif mcontent.type == 'text' %}
+                {%- set content.text = content.text~mcontent.text %}
+            {%- endif %}
+        {%- endfor %}
+        {%- set content = content.text %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set reasoning_content = "" %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+            {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+            {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+        {%- endif %}
+    {%- endif %}
+    {%- if loop.index0 > ns.last_query_index %}
+        {%- if loop.last or (not loop.last and reasoning_content) %}
+            {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip("\n") + '\n</think>\n\n' + content.lstrip('\n') }}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+    {%- endif %}
+    {%- if message.tool_calls %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if (loop.first and content) or (not loop.first) %}{{- '\n' }}{%- endif %}
+            {%- if tool_call.function %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {%- if tool_call.arguments is string %}
+                {{- tool_call.arguments }}
+            {%- else %}
+                {{- tool_call.arguments | tojson }}
+            {%- endif %}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+    {%- endif %}
+    {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}{{- '<|im_start|>user' }}{%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}{{- '<|im_end|>\n' }}{%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}{{- '<think>\n\n</think>\n\n' }}{%- endif %}
+{%- endif %}

results/omni_finetune/hf_upload/processor_config.json

@@ -0,0 +1,114 @@
+{
+  "feature_extractor": {
+    "chunk_length": 30,
+    "dither": 0.0,
+    "feature_extractor_type": "WhisperFeatureExtractor",
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_pixels": 12845056,
+    "merge_size": 2,
+    "min_pixels": 3136,
+    "n_fft": 400,
+    "n_samples": 480000,
+    "nb_max_frames": 3000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "return_attention_mask": true,
+    "sampling_rate": 16000,
+    "temporal_patch_size": 2
+  },
+  "image_processor": {
+    "data_format": "channels_first",
+    "dither": 0.0,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessorFast",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "merge_size": 2,
+    "n_fft": 400,
+    "n_samples": 4800000,
+    "nb_max_frames": 30000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_attention_mask": true,
+    "sampling_rate": 16000,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2
+  },
+  "processor_class": "Qwen3OmniMoeProcessor",
+  "video_processor": {
+    "data_format": "channels_first",
+    "default_to_square": true,
+    "dither": 0.0,
+    "do_convert_rgb": true,
+    "do_normalize": true,
+    "do_rescale": true,
+    "do_resize": true,
+    "do_sample_frames": false,
+    "feature_extractor_type": "WhisperFeatureExtractor",
+    "feature_size": 128,
+    "hop_length": 160,
+    "image_mean": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "image_processor_type": "Qwen2VLImageProcessor",
+    "image_std": [
+      0.5,
+      0.5,
+      0.5
+    ],
+    "max_frames": 768,
+    "merge_size": 2,
+    "min_frames": 4,
+    "n_fft": 400,
+    "n_samples": 4800000,
+    "nb_max_frames": 30000,
+    "padding_side": "right",
+    "padding_value": 0.0,
+    "patch_size": 16,
+    "resample": 3,
+    "rescale_factor": 0.00392156862745098,
+    "return_attention_mask": true,
+    "return_metadata": false,
+    "sampling_rate": 16000,
+    "size": {
+      "longest_edge": 12845056,
+      "shortest_edge": 3136
+    },
+    "temporal_patch_size": 2,
+    "video_processor_type": "Qwen2VLVideoProcessor"
+  }
+}

results/omni_finetune/hf_upload/tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a7d41145c408f3062e824f965be1c29854cd809e111cdf8170aa8b0bcd5d1fab
+size 11424262

results/omni_finetune/hf_upload/tokenizer_config.json

@@ -0,0 +1,52 @@
+{
+  "add_prefix_space": false,
+  "audio_bos_token": "<|audio_start|>",
+  "audio_eos_token": "<|audio_end|>",
+  "audio_token": "<|audio_pad|>",
+  "backend": "tokenizers",
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>",
+    "<|audio_start|>",
+    "<|audio_end|>",
+    "<tts_pad>",
+    "<tts_text_bos>",
+    "<tts_text_bos_single>",
+    "<|audio_pad|>"
+  ],
+  "image_token": "<|image_pad|>",
+  "is_local": true,
+  "model_max_length": 131072,
+  "model_specific_special_tokens": {
+    "audio_bos_token": "<|audio_start|>",
+    "audio_eos_token": "<|audio_end|>",
+    "audio_token": "<|audio_pad|>",
+    "image_token": "<|image_pad|>",
+    "video_token": "<|video_pad|>",
+    "vision_bos_token": "<|vision_start|>",
+    "vision_eos_token": "<|vision_end|>"
+  },
+  "pad_token": "<|endoftext|>",
+  "processor_class": "Qwen3OmniMoeProcessor",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null,
+  "video_token": "<|video_pad|>",
+  "vision_bos_token": "<|vision_start|>",
+  "vision_eos_token": "<|vision_end|>"
+}

results/omni_finetune/hf_upload/training_metadata.json

@@ -0,0 +1,32 @@
+{
+  "run_id": "xperience10m_qwen3_omni_32ep_lora",
+  "model_id": "/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl",
+  "checkpoint_dir": "/path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora",
+  "num_processes": 8,
+  "num_train_samples": 128,
+  "num_val_samples": 0,
+  "history": [
+    {
+      "epoch": 1,
+      "train_loss": 10.936363816261292,
+      "val_loss": null,
+      "global_step": 16
+    }
+  ],
+  "lora": {
+    "r": 16,
+    "alpha": 32,
+    "dropout": 0.05,
+    "target_modules": [
+      "q_proj",
+      "k_proj",
+      "v_proj",
+      "o_proj",
+      "gate_proj",
+      "up_proj",
+      "down_proj"
+    ]
+  },
+  "use_audio_in_video": false
+}

results/omni_finetune/lora_config.yaml

@@ -0,0 +1,10 @@
+run_id: xperience10m_qwen3_omni_32ep_lora
+stage: qwen_lora_text_video_audio
+model_id: /path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct
+dataset_jsonl: results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl
+checkpoint_dir: /path/to/ropedia_workspace/ropedia-episode-task-suite/checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora
+num_processes: 8
+epochs: 1
+learning_rate: 0.0001
+lora_r: 16
+lora_alpha: 32

results/omni_finetune/metrics.json

@@ -0,0 +1,68 @@
+{
+  "num_samples": 128,
+  "accuracy": 0.0,
+  "macro_f1": 0.0,
+  "labels": [
+    "Close bottle cap",
+    "Grasp coffee scoop",
+    "Grasp gooseneck kettle",
+    "Hold coffee carafe",
+    "Hold gooseneck kettle",
+    "Lift gooseneck kettle",
+    "Move kettle",
+    "Move kettle away",
+    "Pick up kettle",
+    "Pick up white bottle",
+    "Place item on table",
+    "Place kettle on table",
+    "Position kettle to pour",
+    "Pour coffee",
+    "Pour liquid from white bottle",
+    "Pour milk into coffee",
+    "Transfer coffee to dripper",
+    "Wait/Prepare for pouring",
+    "unknown"
+  ],
+  "model_id": "/path/to/ropedia_workspace/modelscope_models/Qwen__Qwen3-Omni-30B-A3B-Instruct",
+  "adapter_dir": "checkpoints/xperience10m_qwen3_omni_32ep_lora/adapter_lora",
+  "dataset_jsonl": "results/omni_finetune/xperience10m_qwen3_omni_32ep_dataset/dataset.jsonl",
+  "eval_split": "train",
+  "train_split": "train",
+  "num_eval_episodes": 1,
+  "unseen_eval_labels": [],
+  "num_unseen_label_samples": 0,
+  "seen_label_accuracy": 0.0,
+  "unseen_label_accuracy": null,
+  "eval_label_counts": {
+    "Close bottle cap": 9,
+    "Pick up kettle": 8,
+    "Position kettle to pour": 8,
+    "Move kettle": 8,
+    "Hold coffee carafe": 8,
+    "Grasp coffee scoop": 8,
+    "Transfer coffee to dripper": 8,
+    "Hold gooseneck kettle": 8,
+    "Lift gooseneck kettle": 8,
+    "Move kettle away": 8,
+    "Wait/Prepare for pouring": 8,
+    "Pour coffee": 8,
+    "Grasp gooseneck kettle": 6,
+    "Place kettle on table": 6,
+    "Pick up white bottle": 6,
+    "Pour liquid from white bottle": 6,
+    "Place item on table": 6,
+    "Pour milk into coffee": 1
+  },
+  "json_validity_rate": 1.0,
+  "action_macro_f1": 0.0,
+  "subtask_accuracy": 0.015625,
+  "transition_accuracy": 0.0,
+  "next_action_accuracy": 0.0078125,
+  "contact_accuracy": 0.0,
+  "object_micro_f1": 0.031496062992125984,
+  "caption_window_grounding": {
+    "mrr": null,
+    "recall_at_5": null,
+    "note": "Grounding ranking requires a retrieval candidate set; JSON evidence_window is stored for later scoring."
+  }
+}