Datasets:

cy0307
/

ropedia-xperience-10m-task-suite-artifacts

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+  <title>Redirecting | Ropedia Xperience-10M Task Suite</title>
+  <link rel="canonical" href="https://chaoyue0307.github.io/ropedia-xperience-10m-task-suite/">
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+    <h1>Redirecting</h1>
+    <p>Go to <a href="/ropedia-xperience-10m-task-suite/">Ropedia Xperience-10M Task Suite</a>.</p>
+  </main>
+</body>
+</html>

ADDITIONAL_DEVELOPMENT_DIRECTIONS.md CHANGED Viewed

@@ -23,7 +23,7 @@ not completed benchmark results.
 3. Add representation-learning and skill-graph objectives once enough episodes
    are staged.
 4. Add affordance, 3D/4D memory, and policy-retargeting branches after the
-   labels and action targets are auditable.
 The current public sample is useful for prototyping the contracts and visual
 explanations. Strong claims for these directions require multi-episode training,

 3. Add representation-learning and skill-graph objectives once enough episodes
    are staged.
 4. Add affordance, 3D/4D memory, and policy-retargeting branches after the
+   labels and action targets are measurable.
 The current public sample is useful for prototyping the contracts and visual
 explanations. Strong claims for these directions require multi-episode training,

PROJECT_README.md CHANGED Viewed

@@ -9,7 +9,7 @@
 [![License](https://img.shields.io/badge/license-code%20MIT%20%2B%20data%20terms-ccffa0)](LICENSE)
 <p align="center">
-  <img src="assets/brand/xperience10m-logo-social-card.png" alt="Ropedia Xperience-10M Task Suite logo card" width="760">
 </p>
 A research-development project built on the public Xperience-10M sample episode
@@ -74,7 +74,7 @@ before the multi-episode omni-model stage becomes a real held-out evaluation.
 | Task suite | 12 human-readable embodied-AI task contracts with input, process, output, metrics, predictions, and case-study walkthroughs |
 | Baselines | Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split |
 | Research directions | Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling |
-| Scale-up path | The gated Xperience-10M dataset is available for a selected 128-episode pilot before Qwen3-Omni LoRA, followed by Cosmos 3/world-model and VLA/policy branches; the long-term goal is an Xperience-native embodied foundation model if full-corpus data, storage, and compute are available |
 | Public surfaces | GitHub repo, GitHub Pages dashboard, GHCR static-site package, HF Space, HF artifact dataset, HF baseline-model repo, and HF collection |
 For the fastest interpretation of the current metrics, start with
@@ -111,7 +111,7 @@ This project is best read as a staged embodied-AI research study:
 | Task suite | Twelve human-readable tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, and synchronization questions. | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json) |
 | Baselines | Minimal heads and compact PyTorch MLP heads provide a first controlled comparison on the same chronological split. | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/) |
 | Diagnostics | Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard. | [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md), [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) |
-| Scale-up | A selected 128-episode Qwen3-Omni LoRA pilot is being prepared from the gated dataset; held-out model metrics will be added only after training and evaluation finish. The long-term native-pretraining plan is documented separately as a future research goal. | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md), [`results/omni_finetune/DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md) |
 Detailed dataset notes, reproduction checks, and generated JSON reports are
 included for readers who want to inspect the implementation, but they are
@@ -133,7 +133,7 @@ They give the current research state in one compact table:
 | Dataset context | Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented |
 | Evaluation protocol | Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics |
 | Website and Hub pages | Public dashboard, Hugging Face Space, artifact dataset, baseline model repo, and collection use the same project framing and links |
-| Qwen3-Omni multi-episode pilot | The gated Xperience-10M dataset is available for selected 128-episode preparation, with full metrics pending completed preprocessing, training, and held-out evaluation |
 | Raw Xperience-10M data / full Qwen weights | Not redistributed |
 ## 90-Second Research Project Path
@@ -152,7 +152,7 @@ If you are reading the project cold, open these in order:
 | 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json) | The tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
 | 9 | Which foundation model comes next? | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 is the first world-model branch; policy models wait for explicit action targets; Xperience-native pretraining is the full-corpus future goal. |
 | 10 | How do I reproduce it? | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md), [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) | Public commands and expected outputs are documented for the sample-episode task suite. |
-| 11 | What is still pending? | [`DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md), [`MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | Multi-episode Qwen3-Omni model quality will be reported after preprocessing, training, and held-out evaluation complete. |
 A compact reader-path summary is available at
 [`docs/data/project_packet.json`](docs/data/project_packet.json).
@@ -208,8 +208,9 @@ The current verified public-sample subset is:
 Detailed dataset notes are available in
 [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
 for readers who need the full upstream-card and access-term context. The
-practical boundary is simple: current results come from the public sample, and
-multi-episode model quality is pending the selected held-out pilot.
 Start with the visual dashboard:
@@ -479,8 +480,9 @@ python scripts/train_all_modalities_model.py --workspace /path/to/workspace
 ## Xperience-10M Fine-Tuning Exploration
 This repo includes a first Qwen3-Omni fine-tuning path over Xperience-10M. The
-current artifacts are setup-stage evidence, with held-out multi-episode metrics
-pending completed staging, preprocessing, training, and evaluation.
 The useful distinction is:
 - direct Qwen3-Omni inputs: RGB/fisheye video, embedded MP4 audio, and language
@@ -497,11 +499,12 @@ adds depth/pose/mocap/IMU features, LoRA adapters are trained on prepared
 train/val episodes, and sealed held-out test evaluation produces predictions,
 metrics, run reports, and upload-ready adapter artifacts.
-The current scale-up artifacts show that the export, manifest, sensor-feature,
-LoRA, and evaluation scripts can run on the available sample episode. They do
-not show a real multi-episode result. A real pilot requires valid prepared
-episodes, held-out episode splits, training metadata, predictions, metrics, and
-a run report; the current selected pilot target is 128 episodes.
 ### Sample Count Decision
@@ -539,9 +542,15 @@ Current status in this repo:
 - public_sample_valid_episodes: 1 (degraded-valid: annotation + fisheye_cam0.mp4)
 - gated_metadata_audit: 12,102 complete visible episodes across 802 complete sessions
-- selected_episode_plan: 128 metadata-balanced episodes, 96/16/16 train/val/test
 - selected_download_size: 277.71 GiB excluding `visualization.rrd`
-- ready_for_held_out_pilot: false until the selected episodes are fully prepared and checked
 - gated dataset: available for selected multi-episode data preparation
 - source_discovery: `results/omni_finetune/source_discovery.json`
 - data_status: `results/omni_finetune/DATA_ACCESS_STATUS.md`
@@ -590,8 +599,8 @@ Once all selected episodes are complete, use the fixed selected-episode split:
 - 16 held-out test episodes.
 The clean full-run launcher validates the selected split, exports all splits in
-parallel, trains Qwen3-Omni LoRA on train/val only, then evaluates on the held-
-out test split:
 ```bash
 RUN_ID=xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
@@ -599,9 +608,17 @@ DATA_ROOT=/path/to/xperience10m_128 \
 SELECTION_JSON=results/omni_finetune/xperience10m_128_episode_selection.json \
 MODEL_DIR=/path/to/Qwen__Qwen3-Omni-30B-A3B-Instruct \
 NUM_PROCESSES=8 \
 scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh
 ```
 Monitor the run with:
 ```bash
@@ -609,6 +626,10 @@ python scripts/omni/monitor_omni_progress.py \
   --run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu
 ```
 Validate the run artifacts stage by stage:
 ```bash
@@ -622,6 +643,62 @@ python scripts/omni/validate_omni_finetune_run.py \
   --min-json-validity 0.98
 ```
 After dataset export, a model-neutral window index can be created for future
 backbones:
@@ -634,14 +711,18 @@ This produces `window_index.jsonl` and `window_index_manifest.json` so Cosmos-
 style world models and VLA/policy branches can reuse the same split-checked
 windows without depending on Qwen chat-message records.
-### Uploading the pilot Qwen3-Omni LoRA
-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-readiness \
-  --source-dir results/omni_finetune/hf_upload \
   --message "Upload Xperience-10M Qwen3-Omni LoRA pilot"
 ```
@@ -678,6 +759,41 @@ registry can be checked with:
 python scripts/omni/backbone_registry.py --validate --json
 ```
 ## Additional Development Directions
 Beyond backbone selection and fine-tuning, Xperience-10M supports several

 [![License](https://img.shields.io/badge/license-code%20MIT%20%2B%20data%20terms-ccffa0)](LICENSE)
 <p align="center">
+  <img src="docs/assets/brand/xperience10m-logo-social-card.png" alt="Ropedia Xperience-10M Task Suite logo card" width="760">
 </p>
 A research-development project built on the public Xperience-10M sample episode
 | Task suite | 12 human-readable embodied-AI task contracts with input, process, output, metrics, predictions, and case-study walkthroughs |
 | Baselines | Minimal linear/ridge/logistic heads plus compact PyTorch MLP task heads over the same chronological split |
 | Research directions | Task mapping and extension probes for human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling |
+| Scale-up path | A first selected-episode Qwen3-Omni LoRA diagnostic pilot has completed on the 96/16/16 split; it proves the multi-episode export/train/eval/package loop, but the weak held-out metrics make it a baseline for error analysis rather than a strong model. Cosmos 3/world-model and VLA/policy branches reuse the same split and package contract after their targets are implemented. |
 | Public surfaces | GitHub repo, GitHub Pages dashboard, GHCR static-site package, HF Space, HF artifact dataset, HF baseline-model repo, and HF collection |
 For the fastest interpretation of the current metrics, start with
 | Task suite | Twelve human-readable tasks cover action, procedure, contact, object, language, retrieval, reconstruction, order, and synchronization questions. | [`RESEARCH_TAKEAWAYS.md`](RESEARCH_TAKEAWAYS.md), [`results/episode_task_suite/summary_report.json`](results/episode_task_suite/summary_report.json) |
 | Baselines | Minimal heads and compact PyTorch MLP heads provide a first controlled comparison on the same chronological split. | [`results/episode_task_suite/neural_mlp/`](results/episode_task_suite/neural_mlp/) |
 | Diagnostics | Audio contribution, modality ablations, timeline overlays, object labels, and alignment stress tests show which signals are useful and which tasks remain hard. | [`results/audio_ablation/AUDIO_ABLATION_SUMMARY.md`](results/audio_ablation/AUDIO_ABLATION_SUMMARY.md), [`docs/single_episode_explorer.html`](docs/single_episode_explorer.html) |
+| Scale-up | The selected 128-episode Qwen3-Omni LoRA diagnostic pilot has a verified validation-aware held-out package: 96/16/16 selected episodes, 3,808 exported windows, 512 validation windows, 448 held-out test windows, and public-safe metrics/predictions. JSON validity is 87.50%, below the 98% target, so the next pass focuses on structured-output reliability and task-quality error analysis. | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`results/omni_finetune/verified_public/`](results/omni_finetune/verified_public/) |
 Detailed dataset notes, reproduction checks, and generated JSON reports are
 included for readers who want to inspect the implementation, but they are
 | Dataset context | Official Xperience-10M links, sample-vs-gated-data boundary, modality coverage, and redistribution policy are documented |
 | Evaluation protocol | Verified generated protocol for windowing, split policy, leakage controls, and per-task metrics |
 | Website and Hub pages | Public dashboard, Hugging Face Space, artifact dataset, baseline model repo, and collection use the same project framing and links |
+| Qwen3-Omni multi-episode pilot | Verified diagnostic result package exists for the selected 96/16/16 episode split; current held-out metrics are weak and below the JSON-validity quality target |
 | Raw Xperience-10M data / full Qwen weights | Not redistributed |
 ## 90-Second Research Project Path
 | 8 | What research directions does this support? | [`RESEARCH_ROADMAP.md`](RESEARCH_ROADMAP.md), [`docs/data/research_directions.json`](docs/data/research_directions.json), [`docs/data/research_direction_extensions.json`](docs/data/research_direction_extensions.json) | The tasks are mapped to human modeling, 3D/4D reconstruction, egocentric interaction, and world modeling. |
 | 9 | Which foundation model comes next? | [`FOUNDATION_MODEL_PLAN.md`](FOUNDATION_MODEL_PLAN.md), [`docs/data/foundation_model_plan.json`](docs/data/foundation_model_plan.json), [`XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md`](XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md) | Qwen3-Omni is the first held-out LoRA baseline; Cosmos 3 is the first world-model branch; policy models wait for explicit action targets; Xperience-native pretraining is the full-corpus future goal. |
 | 10 | How do I reproduce it? | [`REPRODUCIBILITY.md`](REPRODUCIBILITY.md), [`notes/reproducibility_audit.md`](notes/reproducibility_audit.md) | Public commands and expected outputs are documented for the sample-episode task suite. |
+| 11 | What is still pending? | [`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json), [`DATA_ACCESS_STATUS.md`](results/omni_finetune/DATA_ACCESS_STATUS.md), [`MULTI_EPISODE_ACCESS_STATUS.md`](results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md) | The first held-out diagnostic pilot is verified; strong model quality remains pending because JSON validity is 87.50% and action/subtask metrics remain weak. |
 A compact reader-path summary is available at
 [`docs/data/project_packet.json`](docs/data/project_packet.json).
 Detailed dataset notes are available in
 [`XPERIENCE10M_DATASET_CARD_ALIGNMENT.md`](XPERIENCE10M_DATASET_CARD_ALIGNMENT.md)
 for readers who need the full upstream-card and access-term context. The
+practical boundary is simple: current task-suite results come from the public
+sample, and the first multi-episode Qwen3-Omni diagnostic pilot is verified but
+not yet strong model quality.
 Start with the visual dashboard:
 ## Xperience-10M Fine-Tuning Exploration
 This repo includes a first Qwen3-Omni fine-tuning path over Xperience-10M. The
+repository separates public-sample evidence from multi-episode fine-tuning
+artifacts. The validation-aware selected-episode held-out package is now verified as a
+diagnostic pilot, not a strong final model.
 The useful distinction is:
 - direct Qwen3-Omni inputs: RGB/fisheye video, embedded MP4 audio, and language
 train/val episodes, and sealed held-out test evaluation produces predictions,
 metrics, run reports, and upload-ready adapter artifacts.
+The scale-up path requires valid prepared episodes, held-out episode splits,
+training metadata, predictions, metrics, and a run report. A result is ready
+for public README, website, or Hugging Face updates only after the validator
+passes and `scripts/omni/package_verified_omni_result.py` creates a
+public-safe derived-artifact package. The current verified package is listed in
+[`docs/data/omni_finetune_verified_result.json`](docs/data/omni_finetune_verified_result.json).
 ### Sample Count Decision
 - public_sample_valid_episodes: 1 (degraded-valid: annotation + fisheye_cam0.mp4)
 - gated_metadata_audit: 12,102 complete visible episodes across 802 complete sessions
+- selected_episode_plan: 128 source-balanced episodes, 96/16/16 train/val/test
 - selected_download_size: 277.71 GiB excluding `visualization.rrd`
+- verified_validation_aware_diagnostic_package: true
+- selected_split: 96 train / 16 validation / 16 held-out test episodes
+- exported_windows: 2,848 train / 512 validation / 448 test
+- validation_samples_used: 512
+- held_out_eval: 448 test windows from 14 exported test episodes
+- train_loss / val_loss: 0.4130 / 0.0331
+- current_quality_target: JSON validity 87.50%, below the 98% target
 - gated dataset: available for selected multi-episode data preparation
 - source_discovery: `results/omni_finetune/source_discovery.json`
 - data_status: `results/omni_finetune/DATA_ACCESS_STATUS.md`
 - 16 held-out test episodes.
 The clean full-run launcher validates the selected split, exports all splits in
+parallel, trains Qwen3-Omni LoRA on train episodes while optionally monitoring
+validation loss, then evaluates on the held-out test split:
 ```bash
 RUN_ID=xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
 SELECTION_JSON=results/omni_finetune/xperience10m_128_episode_selection.json \
 MODEL_DIR=/path/to/Qwen__Qwen3-Omni-30B-A3B-Instruct \
 NUM_PROCESSES=8 \
+TRAIN_VAL_SPLIT=val \
+MAX_VAL_SAMPLES=512 \
 scripts/omni/run_128_fullsplit_parallel_export_8gpu.sh
 ```
+The current verified diagnostic package uses the same selected split and 8-GPU
+training path, records validation loss over 512 validation windows, and keeps
+the held-out test split sealed for final evaluation. The next pass should keep
+this package contract while tightening JSON decoding, target formatting, and
+action/subtask error analysis.
 Monitor the run with:
 ```bash
   --run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu
 ```
+The monitor reads training `progress.jsonl`, new evaluator partial-prediction
+progress, and legacy generation logs, so long held-out evals can still expose
+sample-level progress even before final metrics are written.
 Validate the run artifacts stage by stage:
 ```bash
   --min-json-validity 0.98
 ```
+After the eval validator passes, create the public-safe result package:
+```bash
+python scripts/omni/package_verified_omni_result.py \
+  --dataset-run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+  --train-run-id <train_run_id> \
+  --eval-run-id <eval_run_id>
+```
+For long-running remote jobs, the packaging step can be watched automatically:
+```bash
+python scripts/omni/watch_verified_omni_package.py \
+  --dataset-run-id xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu \
+  --train-run-id <train_run_id> \
+  --eval-run-id <eval_run_id>
+```
+While waiting, the watcher can append `eval_progress_observed` events from
+partial prediction files or legacy generation logs. This keeps the package
+status file useful during long held-out evaluations.
+The package copies only small derived artifacts such as metrics, predictions,
+confusion matrices, run reports, manifests, validation summaries, and training
+metadata. The exact required eval files and primary metrics come from the
+selected backbone contract in `configs/omni_backbones`, so Qwen3-Omni,
+Cosmos-style world models, and VLA/policy branches can share the same verified
+publication gate once their model-specific evaluators exist. The package
+excludes raw Xperience-10M files, base-model weights, adapter or checkpoint
+weights, full checkpoints, and large archives.
+For hardware setups that can run multiple eval workers, the Qwen evaluator also
+supports deterministic sample shards:
+```bash
+python scripts/omni/eval_qwen3_omni_lora.py \
+  --dataset-jsonl results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl \
+  --adapter-dir checkpoints/<train_run_id>/adapter_lora \
+  --run-id <eval_shard_0> \
+  --eval-split test \
+  --sample-offset 0 \
+  --sample-stride 4
+python scripts/omni/merge_qwen3_omni_eval_shards.py \
+  --dataset-jsonl results/omni_finetune/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_dataset/dataset.jsonl \
+  --output-dir results/omni_finetune/<merged_eval_run_id> \
+  --shard-dir results/omni_finetune/<eval_shard_0> \
+  --shard-dir results/omni_finetune/<eval_shard_1> \
+  --shard-dir results/omni_finetune/<eval_shard_2> \
+  --shard-dir results/omni_finetune/<eval_shard_3>
+```
+Only the merged eval directory should be validated and reported publicly,
+because the merger checks coverage and recomputes the metrics from all
+held-out predictions.
 After dataset export, a model-neutral window index can be created for future
 backbones:
 style world models and VLA/policy branches can reuse the same split-checked
 windows without depending on Qwen chat-message records.
+### Uploading Qwen3-Omni LoRA artifacts
+The public-safe verified package intentionally excludes raw data, base Qwen
+weights, LoRA weights, and full checkpoints. Adapter upload is a separate step:
+use it only when the intended adapter directory is present and the model card
+clearly distinguishes older smoke weights from the selected-episode diagnostic
+or validation-aware run.
 ```bash
 python3 scripts/omni/upload_qwen3_omni_lora_to_hf.py \
+  --repo-id cy0307/ropedia-qwen3-omni-lora-smoke \
+  --source-dir /path/to/adapter_upload_package \
   --message "Upload Xperience-10M Qwen3-Omni LoRA pilot"
 ```
 python scripts/omni/backbone_registry.py --validate --json
 ```
+Verify that every configured backbone can pass the public-safe packaging
+contract on synthetic derived artifacts:
+```bash
+python scripts/omni/smoke_test_backbone_packaging.py
+```
+After a real held-out package is created, audit it before updating README,
+website, or Hugging Face pages:
+```bash
+python scripts/omni/audit_verified_omni_package.py \
+  --package-dir results/omni_finetune/verified_public/<eval_run_id>
+```
+Create a new planned backbone branch from an existing contract template with:
+```bash
+python scripts/omni/scaffold_omni_backbone.py \
+  --template-backbone policy_vla_branch \
+  --id new_policy_branch \
+  --display-name "New Policy Branch" \
+  --model-family "Model family name" \
+  --dataset-contract xperience10m_observation_action_v1 \
+  --training-objective observation_to_action_policy \
+  --checkpoint-gate policy_checkpoint_action_space_and_normalizer \
+  --dry-run
+```
+Each backbone config declares the checkpoint gate, required train/eval files,
+allowed public artifacts, and forbidden private or heavyweight artifacts. This
+keeps Qwen3-Omni, Cosmos-style world models, and policy/VLA branches on the same
+split, validation, and publication discipline even though their training targets
+are different.
 ## Additional Development Directions
 Beyond backbone selection and fine-tuning, Xperience-10M supports several

README.md CHANGED Viewed

@@ -34,7 +34,7 @@ Cosmos3-Nano future-window compatibility package.
 | Inspect single-episode task results | `results/episode_task_suite/summary_report.json` |
 | Inspect 128-episode same-split baselines | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md` |
 | Inspect final Qwen3-Omni held-out diagnostic result | `docs/data/omni_finetune_verified_result.json` |
-| Compare the current three result versions | `docs/data/omni_model_comparison.json` |
 ## Dataset Boundary
@@ -48,7 +48,10 @@ The implemented public-sample task suite uses one public Xperience-10M sample
 episode. The selected 128-episode Qwen3-Omni final diagnostic result uses a
 gated local dataset copy and publishes only public-safe metrics, predictions,
 manifests, reports, and audits. The public LoRA adapter weights are published
-separately at `cy0307/ropedia-qwen3-omni-lora-128ep`.
 ## Derived Artifacts
@@ -65,6 +68,8 @@ This bundle includes derived artifacts such as:
   selected split family.
 - 128-episode same-split simple/NN metadata baselines for the same 12 task ids,
   with unsupported markers where raw 128 sensor feature blocks are still needed.
 ## Related Hub Repositories

 | Inspect single-episode task results | `results/episode_task_suite/summary_report.json` |
 | Inspect 128-episode same-split baselines | `results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md` |
 | Inspect final Qwen3-Omni held-out diagnostic result | `docs/data/omni_finetune_verified_result.json` |
+| Compare current versions and model groups | `docs/data/omni_model_comparison.json` |
 ## Dataset Boundary
 episode. The selected 128-episode Qwen3-Omni final diagnostic result uses a
 gated local dataset copy and publishes only public-safe metrics, predictions,
 manifests, reports, and audits. The public LoRA adapter weights are published
+separately at `cy0307/ropedia-qwen3-omni-lora-128ep`. Cosmos3-Nano is currently
+published here as an artifacts-only future-window compatibility result; create a
+separate Cosmos model repository only after real Cosmos adapter or fine-tuned
+weights exist.
 ## Derived Artifacts
   selected split family.
 - 128-episode same-split simple/NN metadata baselines for the same 12 task ids,
   with unsupported markers where raw 128 sensor feature blocks are still needed.
+- A model-family grouped comparison that pairs 1-episode and 128-episode entries
+  for task heads, Qwen3-Omni LoRA, and Cosmos3-Nano without mixing target types.
 ## Related Hub Repositories

REPRODUCIBILITY.md CHANGED Viewed

@@ -14,7 +14,7 @@ outside the current public data scope.
 | Neural MLP heads | Yes, when `torch` is installed | Compact task heads only, not a foundation model. |
 | Website figures and charts | Yes | Generated from committed metrics and sample thumbnails. |
 | Public bundle contents | Yes | Covers public repo and prepared HF bundles. |
-| Multi-episode Qwen3-Omni LoRA pilot | Not yet | The gated full dataset is available for the selected pilot; held-out metrics require completed preprocessing, training, and evaluation. |
 ## Environment
@@ -127,10 +127,9 @@ Evidence:
 ## Non-Reproducible From This Public Repo Alone
 The following require gated data, large model weights, or private compute
-state, so this repo does not yet provide public reproduction for:
-- a real held-out multi-episode Qwen3-Omni LoRA run,
-- held-out episode metrics for Qwen3-Omni,
 - full Xperience-10M-scale pretraining,
 - raw Xperience-10M video or annotation redistribution,
 - full Qwen weights or large full checkpoints.

 | Neural MLP heads | Yes, when `torch` is installed | Compact task heads only, not a foundation model. |
 | Website figures and charts | Yes | Generated from committed metrics and sample thumbnails. |
 | Public bundle contents | Yes | Covers public repo and prepared HF bundles. |
+| Multi-episode Qwen3-Omni LoRA pilot | Yes, as a public-safe verified result package | The selected 96/16/16 episode split produced a validation-monitored diagnostic held-out result package with 3,808 exported windows, 512 validation windows, 448 test predictions, and weak model-quality metrics that motivate the next structured-output improvement pass. |
 ## Environment
 ## Non-Reproducible From This Public Repo Alone
 The following require gated data, large model weights, or private compute
+state, so this repo does not provide public reproduction for:
+- rerunning the multi-episode Qwen3-Omni LoRA pilot from raw gated data,
 - full Xperience-10M-scale pretraining,
 - raw Xperience-10M video or annotation redistribution,
 - full Qwen weights or large full checkpoints.

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Git LFS Details

SHA256: 3b1a1b7be85b816f3ba2333224b8776de255843ae5f6a761cb56d468ae2273cb
Pointer size: 130 Bytes
Size of remote file: 40.5 kB

configs/omni_backbones/cosmos3_super_reasoner.json ADDED Viewed

	@@ -0,0 +1,94 @@

+{
+  "id": "cosmos3_super_reasoner",
+  "display_name": "Cosmos3-Super Reasoner",
+  "status": "implemented",
+  "model_family": "Cosmos3 / physical-world foundation models",
+  "default_model_id": "nv-community/Cosmos3-Super",
+  "local_model_env": "COSMOS3_SUPER_MODEL_DIR",
+  "dataset_contract": "xperience10m_episode_json_qa_v1",
+  "training_objective": "zero_shot_structured_episode_understanding_json_qa_via_vllm_reasoner",
+  "split_policy": {
+    "unit": "episode",
+    "default_counts": {
+      "train": 96,
+      "val": 16,
+      "test": 16
+    },
+    "leakage_guard": "uses the same 96/16/16 selected episode split as the Qwen3-Omni LoRA branch; no Super weights are updated"
+  },
+  "modalities": {
+    "direct_inputs": [
+      "multi-camera rendered mosaic video",
+      "language prompt and label options"
+    ],
+    "conditioning_inputs": [
+      "prompt-side task schema and episode/window metadata"
+    ],
+    "targets": [
+      "structured action/subtask/contact/transition/object JSON"
+    ],
+    "excluded_inputs": [
+      "visualization.rrd",
+      "raw annotation HDF5",
+      "audio in the current vLLM Reasoner path"
+    ]
+  },
+  "entrypoints": {
+    "selection_manifest": "scripts/omni/build_selection_episode_manifest.py",
+    "export": "scripts/omni/parallel_export_qwen3_omni_action_dataset.py",
+    "neutral_index": "scripts/omni/export_model_neutral_window_index.py",
+    "train": "",
+    "eval": "scripts/omni/eval_cosmos3_super_reasoner.py",
+    "launcher": "scripts/omni/run_cosmos3_super_reasoner_eval.sh",
+    "validate": "scripts/omni/validate_omni_finetune_run.py"
+  },
+  "primary_metrics": [
+    "json_validity_rate",
+    "action_macro_f1",
+    "subtask_accuracy",
+    "transition_accuracy",
+    "next_action_accuracy",
+    "contact_accuracy",
+    "object_micro_f1",
+    "held_out_episode_count"
+  ],
+  "artifact_contract": {
+    "checkpoint_gate": "base_weight_vllm_reasoner_setup_metadata",
+    "required_eval_files": [
+      "metrics.json",
+      "predictions.jsonl",
+      "predictions.csv",
+      "per_class_metrics.csv",
+      "confusion_matrix.csv",
+      "server_info.json",
+      "RUN_REPORT.md"
+    ],
+    "required_training_files": [
+      "training_metadata.json",
+      "progress.jsonl"
+    ],
+    "public_package_allowed": [
+      "metrics",
+      "predictions",
+      "confusion matrices",
+      "run reports",
+      "server/model setup metadata",
+      "episode and dataset manifests",
+      "validation summaries"
+    ],
+    "public_package_forbidden": [
+      "raw MP4",
+      "annotation HDF5",
+      "Rerun RRD",
+      "base-model weights",
+      "fine-tuned weights",
+      "checkpoints",
+      "large archives"
+    ]
+  },
+  "extension_requirements": [
+    "This branch evaluates staged Cosmos3-Super Reasoner base weights through vLLM on the 128-episode held-out JSON task; it does not fine-tune or release new Cosmos weights.",
+    "Create a separate Cosmos3-Super adapter/model repository only after a real fine-tuning run produces new adapter or checkpoint weights.",
+    "Keep it separate from the Cosmos3-Nano future-window compatibility branch, which answers a different world-model retrieval target."
+  ]
+}

configs/omni_backbones/cosmos_world_model.json CHANGED Viewed

@@ -1,9 +1,9 @@
 {
   "id": "cosmos_world_model",
-  "display_name": "Cosmos-Style World Model",
-  "status": "planned_adapter",
   "model_family": "Cosmos / physical-world foundation models",
-  "default_model_id": null,
   "local_model_env": "COSMOS_MODEL_DIR",
   "dataset_contract": "xperience10m_future_window_world_model_v0",
   "training_objective": "future_window_and_action_conditioned_world_modeling",
@@ -41,10 +41,10 @@
   "entrypoints": {
     "selection_manifest": "scripts/omni/build_selection_episode_manifest.py",
     "neutral_index": "scripts/omni/export_model_neutral_window_index.py",
-    "export": null,
-    "train": null,
-    "eval": null,
-    "launcher": null,
     "validate": "scripts/omni/validate_omni_finetune_run.py"
   },
   "primary_metrics": [
@@ -92,9 +92,9 @@
     ]
   },
   "extension_requirements": [
-    "Add a future-window exporter that creates context/target pairs instead of JSON QA records.",
-    "Define whether the target is pixels, latent video state, sensor features, or retrieval among candidate futures.",
-    "Add an evaluator that scores held-out future prediction without training on held-out test episodes.",
     "Record generated or retrieved qualitative examples separately from task-classification metrics."
   ]
 }

 {
   "id": "cosmos_world_model",
+  "display_name": "Cosmos3-Nano Future-Window World Model",
+  "status": "implemented",
   "model_family": "Cosmos / physical-world foundation models",
+  "default_model_id": "nvidia/Cosmos3-Nano",
   "local_model_env": "COSMOS_MODEL_DIR",
   "dataset_contract": "xperience10m_future_window_world_model_v0",
   "training_objective": "future_window_and_action_conditioned_world_modeling",
   "entrypoints": {
     "selection_manifest": "scripts/omni/build_selection_episode_manifest.py",
     "neutral_index": "scripts/omni/export_model_neutral_window_index.py",
+    "export": "scripts/omni/export_cosmos3_future_window_dataset.py",
+    "train": "scripts/omni/eval_cosmos3_future_window_retrieval.py",
+    "eval": "scripts/omni/eval_cosmos3_future_window_retrieval.py",
+    "launcher": "scripts/omni/run_cosmos3_nano_future_window_compat.sh",
     "validate": "scripts/omni/validate_omni_finetune_run.py"
   },
   "primary_metrics": [
     ]
   },
   "extension_requirements": [
+    "Current implementation starts with Cosmos3-Nano compatibility over same-split future sensor-feature retrieval; it does not fine-tune Cosmos diffusion weights yet.",
+    "Install a Cosmos3 Diffusers training stack before replacing the compatibility adapter with LoRA or diffusion post-training.",
+    "Keep target windows inside the same episode and never train on held-out test episodes.",
     "Record generated or retrieved qualitative examples separately from task-classification metrics."
   ]
 }

data/live_publication_status.json CHANGED Viewed

@@ -1,8 +1,8 @@
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   "status": "pass",
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data/omni_model_comparison.json CHANGED Viewed

@@ -1,8 +1,9 @@
 {
-  "title": "Ropedia Xperience-10M Current Result Versions",
-  "generated_at_utc": "2026-06-06T23:26:13+00:00",
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@@ -362,7 +363,9 @@
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@@ -406,7 +409,9 @@
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@@ -450,7 +455,9 @@
               "val_loss": null,
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@@ -500,12 +507,322 @@
               "val_loss": 0.027823254466056824,
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     "Promote Cosmos3 from compatibility adapter to full Cosmos3 fine-tuning only after a separate environment with matching Diffusers/Cosmos dependencies is prepared."

 {
+  "title": "Ropedia Xperience-10M Current Result Versions and Model Groups",
+  "generated_at_utc": "2026-06-07T07:49:32+00:00",
   "status": "pass",
   "version_count": 3,
+  "model_group_count": 3,
   "comparison_rule": "Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and Cosmos3 future-window metrics answer different questions.",
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               "val_loss": null,
               "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run"
             }
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+          "weights_repository": "planned separate Cosmos3 model repo after a real Cosmos diffusion/LoRA fine-tune exists; current result remains artifacts-only"
         },
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         },
         {
           "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
               "val_loss": null,
               "global_step": 356
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+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
               "val_loss": 0.027823254466056824,
               "global_step": 712
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+          "is_current": true,
+          "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
         }
       ],
       "interpretation": "This layer contains the held-out foundation-model packages. Qwen3-Omni packages evaluate structured JSON task prediction; Cosmos3-Nano currently evaluates a future-window world-model compatibility adapter, not a full diffusion-weight fine-tune."
     }
   ],
+  "model_groups": [
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+      "model_family": "Minimal and Neural Task Heads",
+      "model_type": "lightweight supervised/self-supervised task heads",
+      "weight_repository": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines",
+      "one_episode_runs": [
+        {
+          "id": "task_heads_single_episode_public_sample",
+          "title": "Single-Episode Public-Sample Task Suite",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "verified",
+          "source": "results/episode_task_suite/summary_report.json",
+          "split": "chronological 70/30 within one episode",
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+            "frames": 5821,
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+          "weights": "baseline model files in the baseline model repo; no foundation-model weights",
+          "interpretation": "Raw multimodal feature task harness on the public sample."
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+      "multi_episode_128_runs": [
+        {
+          "id": "task_heads_128_episode_metadata_baselines",
+          "title": "128-Episode Aligned Simple/NN Baselines",
+          "scope": "selected 128-episode 96/16/16 split",
+          "status": "pass",
+          "source": "results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md",
+          "split": "train/val/test by selected episode/session",
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+          "weights": "metadata/text baseline artifacts; raw 128 sensor-feature model weights not yet complete",
+          "interpretation": "Same selected 96/16/16 split and task ids as the model branches, but metadata/text features only."
+        }
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+      "comparison_note": "This is the cleanest 1-episode versus 128-episode grouping for the same simple/NN task-head family, but the feature surface changes from raw public-sample features to public-safe 128-episode metadata/text features."
+    },
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+      "model_family": "Qwen3-Omni LoRA",
+      "model_type": "PEFT LoRA adapter over Qwen/Qwen3-Omni-30B-A3B-Instruct",
+      "weight_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep",
+      "one_episode_runs": [
+        {
+          "id": "qwen3_omni_sensor_adapter_smoke_1ep",
+          "title": "Qwen3-Omni Sensor-Adapter Smoke",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "verified_smoke",
+          "source": "results/omni_exploration/qwen3_adapter_smoke/metrics.json",
+          "split": "single_episode_chronological",
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+            "windows": 59,
+            "train_windows": 41,
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+          "qwen3_loaded": false,
+          "weights": "no Qwen3 base weights or LoRA adapter weights; adapter-token readiness smoke only",
+          "interpretation": "This validates the sensor-adapter token path on one real episode before loading or LoRA-tuning Qwen3-Omni. It is not comparable to the 128-episode held-out LoRA result."
+        }
+      ],
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+        {
+          "id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
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+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_lora",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
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+          "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
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+          "status": "verified",
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+          "dataset_contract": "xperience10m_episode_json_qa_v1",
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+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
+        },
+        {
+          "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
+          "backbone": "qwen3_omni_lora",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "structured_episode_understanding_json_qa",
+          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
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+            "val_samples": 512,
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+          "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
+        }
+      ],
+      "comparison_note": "The one-episode Qwen entry is only a sensor-adapter smoke test with Qwen3 weights unloaded. The 128-episode entries are real held-out LoRA diagnostics; the current final adapter belongs in the separate Qwen model repo."
+    },
+    {
+      "id": "cosmos3_nano_world_model",
+      "model_family": "Cosmos3-Nano Future-Window World Model",
+      "model_type": "world-model/future-window branch",
+      "weight_repository": "planned: cy0307/ropedia-cosmos3-nano-future-window-lora-128ep after real adapter weights exist",
+      "one_episode_runs": [
+        {
+          "id": "cosmos3_nano_one_episode",
+          "title": "Cosmos3-Nano One-Episode Fine-Tune",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "not_run",
+          "source": null,
+          "weights": "none",
+          "interpretation": "No Cosmos3 one-episode adapter or diffusion-weight fine-tune is currently published. Use the public-sample task suite only as model-agnostic evidence."
+        }
+      ],
+      "multi_episode_128_runs": [
+        {
+          "id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full",
+          "title": "Cosmos3-Nano Future-Window World Model",
+          "status": "verified",
+          "backbone": "cosmos_world_model",
+          "dataset_contract": "xperience10m_future_window_world_model_v0",
+          "training_objective": "future_window_and_action_conditioned_world_modeling",
+          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat",
+          "train_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter",
+          "eval_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full",
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+            "val_samples": 432,
+            "eval_samples": 378,
+            "held_out_episode_count": 14,
+            "num_processes": 1
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+          "primary_metrics": {
+            "future_retrieval_mrr": 0.022138720585222767,
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+            "temporal_consistency": 0.09523809523809523,
+            "feature_reconstruction_error": 3479.218317102503,
+            "transition_accuracy": 0.9682539682539683,
+            "contact_accuracy": 0.7433862433862434,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 0,
+              "train_loss": null,
+              "val_loss": null,
+              "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run"
+            }
+          ],
+          "is_current": true,
+          "weights_repository": "planned separate Cosmos3 model repo after a real Cosmos diffusion/LoRA fine-tune exists; current result remains artifacts-only"
+        }
+      ],
+      "comparison_note": "The current 128-episode Cosmos result is a public-safe future-window compatibility adapter. It is not yet a full Cosmos diffusion/LoRA weight release."
+    }
+  ],
+  "model_group_reading_notes": [
+    "Use model_groups when comparing one-episode and 128-episode artifacts within the same model family.",
+    "Task-head baselines have both a one-episode public-sample run and a 128-episode same-split metadata/text run.",
+    "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; only the final 128-episode adapter belongs in the Qwen LoRA model repo.",
+    "Cosmos3-Nano currently has only a 128-episode future-window compatibility package; create a separate Cosmos model repo only after real Cosmos adapter/fine-tuned weights exist."
+  ],
   "pending": [
     "Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.",
     "Promote Cosmos3 from compatibility adapter to full Cosmos3 fine-tuning only after a separate environment with matching Diffusers/Cosmos dependencies is prepared."

data/project_status.json CHANGED Viewed

@@ -204,7 +204,7 @@
         "results/omni_finetune/OMNI_MODEL_COMPARISON.md",
         "scripts/omni/build_omni_model_comparison.py"
       ],
-      "readout": "The public comparison separates three layers: the single-episode raw-feature task suite, the selected 128-episode simple/NN metadata baselines, and verified foundation-model branch packages for Qwen3-Omni and Cosmos3-Nano future-window compatibility."
     },
     {
       "area": "Qwen3-Omni fine-tuning",
@@ -254,15 +254,15 @@
     "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
     "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
     "Inspect results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md before comparing simple/NN baselines to the selected 128-episode setup.",
-    "Inspect docs/data/omni_model_comparison.json before comparing the current three result versions.",
     "Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot."
   ],
   "current_reading_notes": [
     "The final Qwen3-Omni diagnostic result is verified and meets the strict-JSON target, but action/subtask held-out quality is still weak.",
-    "Use docs/data/omni_model_comparison.json to compare the single-episode task suite, 128-episode aligned baselines, and verified Qwen3/Cosmos branch packages without mixing incompatible metric targets.",
     "Use docs/data/omni_finetune_verified_result.json and the latest verified_public final Qwen package for current held-out results.",
     "The 128-episode aligned simple/NN baselines use metadata/text features from the derived Qwen JSONL export; they align the split and task ids but do not replace raw-modality baselines for trajectory, retrieval, reconstruction, or misalignment tasks.",
-    "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result; full Cosmos diffusion-weight fine-tuning remains pending.",
     "The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Audio is one of the synchronized source modalities in the current task representation.",
     "The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",

         "results/omni_finetune/OMNI_MODEL_COMPARISON.md",
         "scripts/omni/build_omni_model_comparison.py"
       ],
+      "readout": "The public comparison now has two views: the three result layers and a model-family grouping. The model grouping pairs 1-episode and 128-episode entries for task-head baselines, separates Qwen3-Omni sensor-adapter smoke from 128-episode LoRA diagnostics, and marks Cosmos3-Nano as 128-episode compatibility-only until real Cosmos weights exist."
     },
     {
       "area": "Qwen3-Omni fine-tuning",
     "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
     "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
     "Inspect results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md before comparing simple/NN baselines to the selected 128-episode setup.",
+    "Inspect docs/data/omni_model_comparison.json before comparing the current three result versions or the model-family 1-episode versus 128-episode groupings.",
     "Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot."
   ],
   "current_reading_notes": [
     "The final Qwen3-Omni diagnostic result is verified and meets the strict-JSON target, but action/subtask held-out quality is still weak.",
+    "Use docs/data/omni_model_comparison.json to compare both views: the single-episode/128-baseline/model-branch result layers and the model-family grouping for task heads, Qwen3-Omni LoRA, and Cosmos3-Nano.",
     "Use docs/data/omni_finetune_verified_result.json and the latest verified_public final Qwen package for current held-out results.",
     "The 128-episode aligned simple/NN baselines use metadata/text features from the derived Qwen JSONL export; they align the split and task ids but do not replace raw-modality baselines for trajectory, retrieval, reconstruction, or misalignment tasks.",
+    "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result; one-episode Cosmos fine-tuning and full Cosmos diffusion-weight fine-tuning remain pending, so no Cosmos weight repo should be published yet.",
     "The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Audio is one of the synchronized source modalities in the current task representation.",
     "The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",

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     {
@@ -312,13 +313,16 @@
       "source": "results/omni_finetune/verified_public/",
       "split": "episode/session held-out split; exact task target depends on backbone contract",
       "counts": {
-        "verified_branch_count": 4,
         "qwen3_verified_package_count": 3,
-        "cosmos3_verified_package_count": 1
       },
       "models": [
         "Qwen3-Omni LoRA",
-        "Cosmos3-Nano future-window compatibility branch"
       ],
       "branches": [
         {
@@ -362,8 +366,286 @@
               "val_loss": null,
               "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run"
             }
-          ]
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
           "title": "Qwen3-Omni LoRA",
@@ -406,7 +688,9 @@
               "val_loss": 0.0330660454928875,
               "global_step": 356
             }
-          ]
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
@@ -450,7 +734,9 @@
               "val_loss": null,
               "global_step": 356
             }
-          ]
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
@@ -500,14 +786,147 @@
               "val_loss": 0.027823254466056824,
               "global_step": 712
             }
-          ]
         }
       ],
-      "interpretation": "This layer contains the held-out foundation-model packages. Qwen3-Omni packages evaluate structured JSON task prediction; Cosmos3-Nano currently evaluates a future-window world-model compatibility adapter, not a full diffusion-weight fine-tune."
     }
   ],
   "pending": [
     "Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.",
-    "Promote Cosmos3 from compatibility adapter to full Cosmos3 fine-tuning only after a separate environment with matching Diffusers/Cosmos dependencies is prepared."
   ]
 }

 {
+  "title": "Ropedia Xperience-10M Current Result Versions and Model Groups",
+  "generated_at_utc": "2026-06-07T09:05:41+00:00",
   "status": "pass",
   "version_count": 3,
+  "model_group_count": 4,
+  "comparison_rule": "Compare only rows with the same scope and target. Single-episode raw-feature metrics, 128-episode metadata baselines, Qwen3 structured JSON metrics, and the two Cosmos3 targets answer different questions: Nano future-window retrieval versus Super structured JSON Reasoner evaluation.",
   "version_reading_notes": [
     "Version 1 is the public-sample 12-task harness with minimal and neural heads.",
     "Version 2 is the selected 128-episode same-split simple/NN baseline alignment.",
+    "Version 3 is the verified model-branch layer: the current final Qwen3-Omni LoRA package is the JSON-task diagnostic result, Cosmos3-Nano is a future-window compatibility result, and Cosmos3-Super Reasoner is a base-weight JSON-task evaluation rather than a new fine-tuned weight release."
   ],
   "versions": [
     {
       "source": "results/omni_finetune/verified_public/",
       "split": "episode/session held-out split; exact task target depends on backbone contract",
       "counts": {
+        "verified_branch_count": 5,
         "qwen3_verified_package_count": 3,
+        "cosmos3_verified_package_count": 2,
+        "cosmos3_nano_verified_package_count": 1,
+        "cosmos3_super_verified_package_count": 1
       },
       "models": [
         "Qwen3-Omni LoRA",
+        "Cosmos3-Nano future-window compatibility branch",
+        "Cosmos3-Super Reasoner base-weight evaluation"
       ],
       "branches": [
         {
               "val_loss": null,
               "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run"
             }
+          ],
+          "is_current": true,
+          "weights_repository": "planned separate Cosmos3 model repo after a real Cosmos diffusion/LoRA fine-tune exists; current result remains artifacts-only"
         },
+        {
+          "id": "xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607",
+          "title": "Cosmos3-Super Reasoner",
+          "status": "verified",
+          "backbone": "cosmos3_super_reasoner",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "zero_shot_structured_episode_understanding_json_qa_via_vllm_reasoner",
+          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_cosmos3_super_reasoner_base_vllm_8gpu_20260607",
+          "eval_run_id": "xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
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+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 512,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 0.5111607142857143,
+            "action_macro_f1": 0.0008284021201089245,
+            "subtask_accuracy": 0.0,
+            "transition_accuracy": 0.36830357142857145,
+            "next_action_accuracy": 0.013392857142857142,
+            "contact_accuracy": 0.32142857142857145,
+            "object_micro_f1": 0.13704276146316333,
+            "held_out_episode_count": 14
+          },
+          "history": [],
+          "is_current": true,
+          "weights_repository": "none for this run: staged base nv-community/Cosmos3-Super weights were evaluated through vLLM; create a separate repo only after new adapter or fine-tuned weights exist"
+        },
+        {
+          "id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
+          "backbone": "qwen3_omni_lora",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "structured_episode_understanding_json_qa",
+          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_lora",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 512,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 0.875,
+            "action_macro_f1": 0.0026621494447581404,
+            "subtask_accuracy": 0.006696428571428571,
+            "transition_accuracy": 0.8504464285714286,
+            "next_action_accuracy": 0.024553571428571428,
+            "contact_accuracy": 0.6450892857142857,
+            "object_micro_f1": 0.22299431459254582,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 1,
+              "train_loss": 0.41304643672440994,
+              "val_loss": 0.0330660454928875,
+              "global_step": 356
+            }
+          ],
+          "is_current": false,
+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
+        },
+        {
+          "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
+          "backbone": "qwen3_omni_lora",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "structured_episode_understanding_json_qa",
+          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 0,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 0.8526785714285714,
+            "action_macro_f1": 0.00213753459655099,
+            "subtask_accuracy": 0.004464285714285714,
+            "transition_accuracy": 0.828125,
+            "next_action_accuracy": 0.022321428571428572,
+            "contact_accuracy": 0.6517857142857143,
+            "object_micro_f1": 0.23062730627306272,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 1,
+              "train_loss": 0.4121775626560694,
+              "val_loss": null,
+              "global_step": 356
+            }
+          ],
+          "is_current": false,
+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
+        },
+        {
+          "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
+          "title": "Qwen3-Omni LoRA",
+          "status": "verified",
+          "backbone": "qwen3_omni_lora",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "structured_episode_understanding_json_qa",
+          "source": "results/omni_finetune/verified_public/xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora",
+          "eval_run_id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 512,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 0.9977678571428571,
+            "action_macro_f1": 0.0024331644885523347,
+            "subtask_accuracy": 0.002232142857142857,
+            "transition_accuracy": 0.9709821428571429,
+            "next_action_accuracy": 0.029017857142857144,
+            "contact_accuracy": 0.71875,
+            "object_micro_f1": 0.30160427807486634,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 1,
+              "train_loss": 0.41282760031950355,
+              "val_loss": 0.03288277983665466,
+              "global_step": 356
+            },
+            {
+              "epoch": 2,
+              "train_loss": 0.027745448225544075,
+              "val_loss": 0.027823254466056824,
+              "global_step": 712
+            }
+          ],
+          "is_current": true,
+          "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
+        }
+      ],
+      "interpretation": "This layer contains the held-out foundation-model packages. Qwen3-Omni packages evaluate structured JSON task prediction; Cosmos3-Nano evaluates a future-window world-model compatibility adapter; Cosmos3-Super Reasoner evaluates staged base weights through vLLM on the JSON task. Neither Cosmos branch is a new fine-tuned weight release yet."
+    }
+  ],
+  "model_groups": [
+    {
+      "id": "task_head_baselines",
+      "model_family": "Minimal and Neural Task Heads",
+      "model_type": "lightweight supervised/self-supervised task heads",
+      "weight_repository": "https://huggingface.co/cy0307/ropedia-xperience-10m-task-baselines",
+      "one_episode_runs": [
+        {
+          "id": "task_heads_single_episode_public_sample",
+          "title": "Single-Episode Public-Sample Task Suite",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "verified",
+          "source": "results/episode_task_suite/summary_report.json",
+          "split": "chronological 70/30 within one episode",
+          "counts": {
+            "episodes": 1,
+            "windows": 1161,
+            "frames": 5821,
+            "feature_dim": 8546,
+            "task_count": 12,
+            "neural_task_count": 12
+          },
+          "weights": "baseline model files in the baseline model repo; no foundation-model weights",
+          "interpretation": "Raw multimodal feature task harness on the public sample."
+        }
+      ],
+      "multi_episode_128_runs": [
+        {
+          "id": "task_heads_128_episode_metadata_baselines",
+          "title": "128-Episode Aligned Simple/NN Baselines",
+          "scope": "selected 128-episode 96/16/16 split",
+          "status": "pass",
+          "source": "results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md",
+          "split": "train/val/test by selected episode/session",
+          "counts": {
+            "rows": 3808,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "episode_counts": {
+              "test": 16,
+              "train": 96,
+              "val": 16
+            },
+            "task_count": 12,
+            "simple_supported_task_count": 8,
+            "neural_supported_task_count": 6
+          },
+          "weights": "metadata/text baseline artifacts; raw 128 sensor-feature model weights not yet complete",
+          "interpretation": "Same selected 96/16/16 split and task ids as the model branches, but metadata/text features only."
+        }
+      ],
+      "comparison_note": "This is the cleanest 1-episode versus 128-episode grouping for the same simple/NN task-head family, but the feature surface changes from raw public-sample features to public-safe 128-episode metadata/text features."
+    },
+    {
+      "id": "qwen3_omni_lora",
+      "model_family": "Qwen3-Omni LoRA",
+      "model_type": "PEFT LoRA adapter over Qwen/Qwen3-Omni-30B-A3B-Instruct",
+      "weight_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep",
+      "one_episode_runs": [
+        {
+          "id": "qwen3_omni_sensor_adapter_smoke_1ep",
+          "title": "Qwen3-Omni Sensor-Adapter Smoke",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "verified_smoke",
+          "source": "results/omni_exploration/qwen3_adapter_smoke/metrics.json",
+          "split": "single_episode_chronological",
+          "counts": {
+            "episodes": 1,
+            "windows": 59,
+            "train_windows": 41,
+            "test_windows": 18,
+            "feature_dim": 4262,
+            "adapter_tokens": 11
+          },
+          "primary_metrics": {
+            "accuracy": 0.0,
+            "macro_f1": 0.0,
+            "train_final_loss": 1.4479121318677577
+          },
+          "base_model_target": "Qwen/Qwen3-Omni-30B-A3B-Thinking",
+          "qwen3_loaded": false,
+          "weights": "no Qwen3 base weights or LoRA adapter weights; adapter-token readiness smoke only",
+          "interpretation": "This validates the sensor-adapter token path on one real episode before loading or LoRA-tuning Qwen3-Omni. It is not comparable to the 128-episode held-out LoRA result."
+        }
+      ],
+      "multi_episode_128_runs": [
         {
           "id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605_eval",
           "title": "Qwen3-Omni LoRA",
               "val_loss": 0.0330660454928875,
               "global_step": 356
             }
+          ],
+          "is_current": false,
+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_fullsplit_fast8gpu_lora_fsdp_full_train_noval_tail_logits_fullstatesave_v6_eval_test_full",
               "val_loss": null,
               "global_step": 356
             }
+          ],
+          "is_current": false,
+          "weights_repository": "historical diagnostic package; keep separate from the final 128-episode adapter repo"
         },
         {
           "id": "xperience10m_qwen3_omni_128ep_structured_json_v2_reuse_full8gpu_lora_eval_test_full",
               "val_loss": 0.027823254466056824,
               "global_step": 712
             }
+          ],
+          "is_current": true,
+          "weights_repository": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep"
+        }
+      ],
+      "comparison_note": "The one-episode Qwen entry is only a sensor-adapter smoke test with Qwen3 weights unloaded. The 128-episode entries are real held-out LoRA diagnostics; the current final adapter belongs in the separate Qwen model repo."
+    },
+    {
+      "id": "cosmos3_nano_world_model",
+      "model_family": "Cosmos3-Nano Future-Window World Model",
+      "model_type": "world-model/future-window branch",
+      "weight_repository": "planned: cy0307/ropedia-cosmos3-nano-future-window-lora-128ep after real adapter weights exist",
+      "one_episode_runs": [
+        {
+          "id": "cosmos3_nano_one_episode",
+          "title": "Cosmos3-Nano One-Episode Fine-Tune",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "not_run",
+          "source": null,
+          "weights": "none",
+          "interpretation": "No Cosmos3 one-episode adapter or diffusion-weight fine-tune is currently published. Use the public-sample task suite only as model-agnostic evidence."
+        }
+      ],
+      "multi_episode_128_runs": [
+        {
+          "id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full",
+          "title": "Cosmos3-Nano Future-Window World Model",
+          "status": "verified",
+          "backbone": "cosmos_world_model",
+          "dataset_contract": "xperience10m_future_window_world_model_v0",
+          "training_objective": "future_window_and_action_conditioned_world_modeling",
+          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat",
+          "train_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter",
+          "eval_run_id": "xperience10m_cosmos3_nano_128ep_future_window_h5_compat_adapter_eval_test_full",
+          "counts": {
+            "dataset_samples": 3213,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2403,
+              "test": 378,
+              "val": 432
+            },
+            "train_samples": 2403,
+            "val_samples": 432,
+            "eval_samples": 378,
+            "held_out_episode_count": 14,
+            "num_processes": 1
+          },
+          "primary_metrics": {
+            "future_retrieval_mrr": 0.022138720585222767,
+            "future_retrieval_recall_at_5": 0.015873015873015872,
+            "temporal_consistency": 0.09523809523809523,
+            "feature_reconstruction_error": 3479.218317102503,
+            "transition_accuracy": 0.9682539682539683,
+            "contact_accuracy": 0.7433862433862434,
+            "held_out_episode_count": 14
+          },
+          "history": [
+            {
+              "epoch": 0,
+              "train_loss": null,
+              "val_loss": null,
+              "note": "closed-form mean-delta adapter; no Cosmos diffusion weights fine-tuned in this compatibility run"
+            }
+          ],
+          "is_current": true,
+          "weights_repository": "planned separate Cosmos3 model repo after a real Cosmos diffusion/LoRA fine-tune exists; current result remains artifacts-only"
+        }
+      ],
+      "comparison_note": "The current 128-episode Cosmos result is a public-safe future-window compatibility adapter. It is not yet a full Cosmos diffusion/LoRA weight release."
+    },
+    {
+      "id": "cosmos3_super_reasoner",
+      "model_family": "Cosmos3-Super Reasoner",
+      "model_type": "base-weight vLLM Reasoner evaluation over nv-community/Cosmos3-Super",
+      "weight_repository": "none for this run; staged base weights only, no new fine-tuned weights",
+      "one_episode_runs": [
+        {
+          "id": "cosmos3_super_one_episode",
+          "title": "Cosmos3-Super One-Episode Fine-Tune",
+          "scope": "one public Xperience-10M sample episode",
+          "status": "not_run",
+          "source": null,
+          "weights": "none",
+          "interpretation": "No one-episode Cosmos3-Super adapter or fine-tuned weight run is published. The available Super result is the 128-episode held-out base-weight evaluation."
         }
       ],
+      "multi_episode_128_runs": [
+        {
+          "id": "xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607",
+          "title": "Cosmos3-Super Reasoner",
+          "status": "verified",
+          "backbone": "cosmos3_super_reasoner",
+          "dataset_contract": "xperience10m_episode_json_qa_v1",
+          "training_objective": "zero_shot_structured_episode_understanding_json_qa_via_vllm_reasoner",
+          "source": "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/verified_result_summary.json",
+          "dataset_run_id": "xperience10m_qwen3_omni_128ep_96train_16val_16test_valmon_20260605",
+          "train_run_id": "xperience10m_cosmos3_super_reasoner_base_vllm_8gpu_20260607",
+          "eval_run_id": "xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607",
+          "counts": {
+            "dataset_samples": 3808,
+            "dataset_episodes": 119,
+            "split_counts": {
+              "train": 2848,
+              "val": 512,
+              "test": 448
+            },
+            "train_samples": 2848,
+            "val_samples": 512,
+            "eval_samples": 448,
+            "held_out_episode_count": 14,
+            "num_processes": 8
+          },
+          "primary_metrics": {
+            "json_validity_rate": 0.5111607142857143,
+            "action_macro_f1": 0.0008284021201089245,
+            "subtask_accuracy": 0.0,
+            "transition_accuracy": 0.36830357142857145,
+            "next_action_accuracy": 0.013392857142857142,
+            "contact_accuracy": 0.32142857142857145,
+            "object_micro_f1": 0.13704276146316333,
+            "held_out_episode_count": 14
+          },
+          "history": [],
+          "is_current": true,
+          "weights_repository": "none for this run: staged base nv-community/Cosmos3-Super weights were evaluated through vLLM; create a separate repo only after new adapter or fine-tuned weights exist"
+        }
+      ],
+      "comparison_note": "Cosmos3-Super is now represented by a verified 448-window held-out Reasoner evaluation on the same JSON task as Qwen3. It uses staged base weights through vLLM, so it is a model-branch diagnostic, not a weight release."
     }
   ],
+  "model_group_reading_notes": [
+    "Use model_groups when comparing one-episode and 128-episode artifacts within the same model family.",
+    "Task-head baselines have both a one-episode public-sample run and a 128-episode same-split metadata/text run.",
+    "Qwen3-Omni has a one-episode sensor-adapter smoke test and separate 128-episode LoRA diagnostic packages; only the final 128-episode adapter belongs in the Qwen LoRA model repo.",
+    "Cosmos3-Nano has a 128-episode future-window compatibility package.",
+    "Cosmos3-Super has a 128-episode base-weight Reasoner evaluation on the JSON task; create a separate Cosmos model repo only after real Cosmos adapter/fine-tuned weights exist."
+  ],
   "pending": [
     "Use the final Qwen3 full-eval package as the current Qwen result; older Qwen package rows remain historical diagnostics for comparison.",
+    "Promote Cosmos3 from Nano compatibility and Super base-weight evaluation to true fine-tuning only after a dedicated Cosmos adapter/diffusion training path produces new weights."
   ]
 }

docs/data/project_packet.json CHANGED Viewed

@@ -41,7 +41,7 @@
         "docs/data/scope_claims_audit.json",
         "docs/data/website_integrity.json"
       ],
-      "readout": "The project status table and roadmap give the compact current-state summary. Single-episode task engineering, metrics, visualizations, public website integrity, mirror parity, same-split 128-episode baselines, the final selected-episode Qwen3-Omni diagnostic result, and the Cosmos3-Nano compatibility package are implemented; stronger action/subtask and full Cosmos model quality remain follow-ups."
     },
     {
       "step": 2,

         "docs/data/scope_claims_audit.json",
         "docs/data/website_integrity.json"
       ],
+      "readout": "The project status table and roadmap give the compact current-state summary. Single-episode task engineering, metrics, visualizations, public website integrity, mirror parity, same-split 128-episode baselines, the final selected-episode Qwen3-Omni diagnostic result, the Cosmos3-Nano compatibility package, and the Cosmos3-Super base-weight Reasoner evaluation are implemented; stronger action/subtask and real Cosmos fine-tuned model quality remain follow-ups."
     },
     {
       "step": 2,

docs/data/project_status.json CHANGED Viewed

@@ -31,6 +31,9 @@
     "qwen3_omni_lora_adapter_repo": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep",
     "cosmos3_nano_future_window_compatibility_verified": true,
     "cosmos3_nano_future_window_test_predictions": 378,
     "omni_model_comparison_available": true,
     "multi_episode_128_aligned_baselines": true,
     "multi_episode_128_baseline_window_counts": {
@@ -116,7 +119,7 @@
         "FOUNDATION_MODEL_PLAN.md",
         "docs/data/foundation_model_plan.json"
       ],
-      "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is now represented by a verified Cosmos3-Nano future-window compatibility package and remains the first world-model/action-generation branch; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
     },
     {
       "area": "Omni model extension contract",
@@ -204,7 +207,7 @@
         "results/omni_finetune/OMNI_MODEL_COMPARISON.md",
         "scripts/omni/build_omni_model_comparison.py"
       ],
-      "readout": "The public comparison separates three layers: the single-episode raw-feature task suite, the selected 128-episode simple/NN metadata baselines, and verified foundation-model branch packages for Qwen3-Omni and Cosmos3-Nano future-window compatibility."
     },
     {
       "area": "Qwen3-Omni fine-tuning",
@@ -230,6 +233,17 @@
       ],
       "readout": "The Cosmos3-Nano branch now has a public-safe verified future-window compatibility package with 3,213 future-window samples, 378 held-out test predictions, future retrieval MRR 0.0221, temporal consistency 0.0952, transition accuracy 0.9683, and contact accuracy 0.7434. It is a compatibility adapter result, not a full Cosmos diffusion-weight fine-tune."
     },
     {
       "area": "Raw Xperience-10M redistribution",
       "status": "not_included",
@@ -254,15 +268,15 @@
     "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
     "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
     "Inspect results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md before comparing simple/NN baselines to the selected 128-episode setup.",
-    "Inspect docs/data/omni_model_comparison.json before comparing the current three result versions.",
     "Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot."
   ],
   "current_reading_notes": [
     "The final Qwen3-Omni diagnostic result is verified and meets the strict-JSON target, but action/subtask held-out quality is still weak.",
-    "Use docs/data/omni_model_comparison.json to compare the single-episode task suite, 128-episode aligned baselines, and verified Qwen3/Cosmos branch packages without mixing incompatible metric targets.",
     "Use docs/data/omni_finetune_verified_result.json and the latest verified_public final Qwen package for current held-out results.",
     "The 128-episode aligned simple/NN baselines use metadata/text features from the derived Qwen JSONL export; they align the split and task ids but do not replace raw-modality baselines for trajectory, retrieval, reconstruction, or misalignment tasks.",
-    "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result; full Cosmos diffusion-weight fine-tuning remains pending.",
     "The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Audio is one of the synchronized source modalities in the current task representation.",
     "The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",

     "qwen3_omni_lora_adapter_repo": "https://huggingface.co/cy0307/ropedia-qwen3-omni-lora-128ep",
     "cosmos3_nano_future_window_compatibility_verified": true,
     "cosmos3_nano_future_window_test_predictions": 378,
+    "cosmos3_super_reasoner_verified": true,
+    "cosmos3_super_reasoner_test_predictions": 448,
+    "cosmos3_super_reasoner_json_validity_rate": 0.5111607142857143,
     "omni_model_comparison_available": true,
     "multi_episode_128_aligned_baselines": true,
     "multi_episode_128_baseline_window_counts": {
         "FOUNDATION_MODEL_PLAN.md",
         "docs/data/foundation_model_plan.json"
       ],
+      "readout": "Qwen3-Omni remains the first trainable held-out LoRA baseline; Cosmos 3 is now represented by a verified Cosmos3-Nano future-window compatibility package plus a verified Cosmos3-Super base-weight Reasoner evaluation; OpenVLA/openpi/GR00T are policy candidates after action targets are explicit."
     },
     {
       "area": "Omni model extension contract",
         "results/omni_finetune/OMNI_MODEL_COMPARISON.md",
         "scripts/omni/build_omni_model_comparison.py"
       ],
+      "readout": "The public comparison now has two views: the three result layers and a model-family grouping. The model grouping pairs 1-episode and 128-episode entries for task-head baselines, separates Qwen3-Omni sensor-adapter smoke from 128-episode LoRA diagnostics, and separates Cosmos3-Nano future-window compatibility from Cosmos3-Super base-weight Reasoner evaluation."
     },
     {
       "area": "Qwen3-Omni fine-tuning",
       ],
       "readout": "The Cosmos3-Nano branch now has a public-safe verified future-window compatibility package with 3,213 future-window samples, 378 held-out test predictions, future retrieval MRR 0.0221, temporal consistency 0.0952, transition accuracy 0.9683, and contact accuracy 0.7434. It is a compatibility adapter result, not a full Cosmos diffusion-weight fine-tune."
     },
+    {
+      "area": "Cosmos3-Super Reasoner branch",
+      "status": "verified_base_weight_result",
+      "evidence": [
+        "configs/omni_backbones/cosmos3_super_reasoner.json",
+        "scripts/omni/eval_cosmos3_super_reasoner.py",
+        "scripts/omni/run_cosmos3_super_reasoner_eval.sh",
+        "results/omni_finetune/verified_public/xperience10m_cosmos3_super_reasoner_128ep_test_full_20260607/verified_result_summary.json"
+      ],
+      "readout": "Cosmos3-Super Reasoner now has a public-safe verified 448-window held-out evaluation on the same structured JSON task as Qwen3. It uses staged nv-community/Cosmos3-Super base weights through an 8-GPU vLLM server, not fine-tuned weights: JSON validity 0.5112, action macro-F1 0.0008, transition accuracy 0.3683, contact accuracy 0.3214, and object micro-F1 0.1370."
+    },
     {
       "area": "Raw Xperience-10M redistribution",
       "status": "not_included",
     "Inspect SOURCE_ALIGNMENT_AUDIT.md before judging source-card consistency across public surfaces.",
     "Inspect XPERIENCE10M_DATASET_CARD_ALIGNMENT.md before judging dataset wording.",
     "Inspect results/omni_finetune/multi_episode_128_task_baselines/BASELINE_ALIGNMENT_REPORT.md before comparing simple/NN baselines to the selected 128-episode setup.",
+    "Inspect docs/data/omni_model_comparison.json before comparing the current three result versions or the model-family 1-episode versus 128-episode groupings.",
     "Inspect docs/data/omni_finetune_verified_result.json before judging the Qwen3-Omni diagnostic pilot."
   ],
   "current_reading_notes": [
     "The final Qwen3-Omni diagnostic result is verified and meets the strict-JSON target, but action/subtask held-out quality is still weak.",
+    "Use docs/data/omni_model_comparison.json to compare both views: the single-episode/128-baseline/model-branch result layers and the model-family grouping for task heads, Qwen3-Omni LoRA, Cosmos3-Nano, and Cosmos3-Super.",
     "Use docs/data/omni_finetune_verified_result.json and the latest verified_public final Qwen package for current held-out results.",
     "The 128-episode aligned simple/NN baselines use metadata/text features from the derived Qwen JSONL export; they align the split and task ids but do not replace raw-modality baselines for trajectory, retrieval, reconstruction, or misalignment tasks.",
+    "The Cosmos3-Nano future-window branch is verified as a compatibility adapter result, and Cosmos3-Super Reasoner is verified as a base-weight evaluation; one-episode Cosmos fine-tuning and full Cosmos adapter/diffusion-weight fine-tuning remain pending, so no Cosmos weight repo should be published yet.",
     "The current reconstruction task reconstructs feature vectors, not pixel-depth, mesh, NeRF, or Gaussian reconstruction.",
     "Audio is one of the synchronized source modalities in the current task representation.",
     "The audio ablation report compares audio/no-audio variants across all 12 task contracts in results/audio_ablation/.",

docs/data/publication_audit.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-06T23:31:25+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
@@ -182,8 +182,8 @@
     "github_repo": {
       "root": "repo",
       "exists": true,
-      "file_count": 586,
-      "text_file_count": 497,
       "largest_file": {
         "path": "tmp/omni_128_dataset_fetch/dataset.jsonl",
         "bytes": 582271586
@@ -193,8 +193,8 @@
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
-      "file_count": 460,
-      "text_file_count": 380,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -204,8 +204,8 @@
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
-      "file_count": 631,
-      "text_file_count": 527,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
@@ -215,8 +215,8 @@
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
-      "file_count": 819,
-      "text_file_count": 680,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-07T09:16:36+00:00",
   "checks": [
     {
       "name": "required_publication_assets_present",
     "github_repo": {
       "root": "repo",
       "exists": true,
+      "file_count": 618,
+      "text_file_count": 523,
       "largest_file": {
         "path": "tmp/omni_128_dataset_fetch/dataset.jsonl",
         "bytes": 582271586
     "hf_space_bundle": {
       "root": "hf_publish/space",
       "exists": true,
+      "file_count": 519,
+      "text_file_count": 425,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
     "hf_artifact_bundle": {
       "root": "hf_publish/artifacts",
       "exists": true,
+      "file_count": 695,
+      "text_file_count": 577,
       "largest_file": {
         "path": "results/episode_task_suite/modality_reconstruction/predictions.npz",
         "bytes": 55702978
     "hf_model_bundle": {
       "root": "hf_publish/model",
       "exists": true,
+      "file_count": 883,
+      "text_file_count": 730,
       "largest_file": {
         "path": "pytorch_model.bin",
         "bytes": 93495480

docs/data/scope_claims_audit.json CHANGED Viewed

@@ -1,6 +1,6 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-06T23:27:07+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,
@@ -9,7 +9,7 @@
     "eval_num_samples": 448,
     "eval_json_validity_rate": 0.9977678571428571,
     "quality_target_met": true,
-    "historical_identifier_count": 131,
     "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
@@ -84,7 +84,7 @@
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
-      "detail": "historical identifiers found in result provenance files=131",
       "evidence": [
         "results/omni_finetune/"
       ]
@@ -424,6 +424,6 @@
       "example": "{\"id\": \"xperience-10m-sample:qa:53\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1060, \"end_frame\": 1079, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
-  "historical_identifier_total_count": 131,
   "failures": []
 }

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-07T00:00:35+00:00",
   "summary": {
     "qwen3_omni_verified_diagnostic_pilot": true,
     "dataset_manifest_num_episodes": 119,
     "eval_num_samples": 448,
     "eval_json_validity_rate": 0.9977678571428571,
     "quality_target_met": true,
+    "historical_identifier_count": 189,
     "public_32_episode_status_file_count": 1,
     "failure_count": 0
   },
     {
       "name": "historical_32ep_identifiers_are_confined_to_readiness_artifacts",
       "status": "pass",
+      "detail": "historical identifiers found in result provenance files=189",
       "evidence": [
         "results/omni_finetune/"
       ]
       "example": "{\"id\": \"xperience-10m-sample:qa:53\", \"episode_id\": \"xperience-10m-sample\", \"split\": \"train\", \"target\": \"episode_qa\", \"prompt_type\": \"json_episode_understanding\", \"center_window\": {\"start_frame\": 1060, \"end_frame\": 1079, \"num_frames\": 20}, \"media\": {\"video_path"
     }
   ],
+  "historical_identifier_total_count": 189,
   "failures": []
 }

docs/data/website_integrity.json CHANGED Viewed

@@ -1,11 +1,11 @@
 {
   "status": "pass",
-  "generated_at_utc": "2026-06-06T23:27:27+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
-    "local_references": 136,
     "external_reference_count": 106,
     "json_files": 35,
     "image_assets_referenced": 22,
@@ -75,7 +75,7 @@
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 67412,
-      "evidence_index": 90421
     },
     {
       "name": "project_status_links_json",
@@ -154,7 +154,7 @@
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 67412,
       "protocol_index": 87159,
-      "evidence_index": 90421
     },
     {
       "name": "evaluation_protocol_links_json",
@@ -228,7 +228,7 @@
     {
       "path": "index.html",
       "id_count": 77,
-      "reference_count": 113,
       "image_count": 24
     },
     {
@@ -287,12 +287,12 @@
     },
     {
       "path": "data/live_publication_status.json",
-      "bytes": 68749,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
-      "bytes": 235815,
       "top_level_type": "dict"
     },
     {
@@ -307,7 +307,7 @@
     },
     {
       "path": "data/omni_model_comparison.json",
-      "bytes": 21433,
       "top_level_type": "dict"
     },
     {
@@ -322,12 +322,12 @@
     },
     {
       "path": "data/project_packet.json",
-      "bytes": 7943,
       "top_level_type": "dict"
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     {
       "path": "data/project_status.json",
-      "bytes": 15049,
       "top_level_type": "dict"
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     {
@@ -337,7 +337,7 @@
     },
     {
       "path": "data/publication_audit.json",
-      "bytes": 7212,
       "top_level_type": "dict"
     },
     {
@@ -412,7 +412,7 @@
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     {
       "path": "data/website_integrity.json",
-      "bytes": 15777,
       "top_level_type": "dict"
     },
     {

 {
   "status": "pass",
+  "generated_at_utc": "2026-06-07T09:09:07+00:00",
   "docs_root": "docs",
   "site_base": "/ropedia-xperience-10m-task-suite/",
   "summary": {
     "html_pages": 4,
+    "local_references": 137,
     "external_reference_count": 106,
     "json_files": 35,
     "image_assets_referenced": 22,
       "status": "pass",
       "reason": "The project overview should appear before the deeper progress ledger.",
       "overview_index": 67412,
+      "evidence_index": 90476
     },
     {
       "name": "project_status_links_json",
       "reason": "The evaluation protocol should appear before the deeper evidence ledger.",
       "overview_index": 67412,
       "protocol_index": 87159,
+      "evidence_index": 90476
     },
     {
       "name": "evaluation_protocol_links_json",
     {
       "path": "index.html",
       "id_count": 77,
+      "reference_count": 114,
       "image_count": 24
     },
     {
     },
     {
       "path": "data/live_publication_status.json",
+      "bytes": 75168,
       "top_level_type": "dict"
     },
     {
       "path": "data/mirror_parity.json",
+      "bytes": 291137,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/omni_model_comparison.json",
+      "bytes": 42015,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/project_packet.json",
+      "bytes": 8005,
       "top_level_type": "dict"
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+      "bytes": 16400,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/publication_audit.json",
+      "bytes": 9859,
       "top_level_type": "dict"
     },
     {
     },
     {
       "path": "data/website_integrity.json",
+      "bytes": 15375,
       "top_level_type": "dict"
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     {

docs/index.html CHANGED Viewed

@@ -2489,7 +2489,7 @@
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch, policy models wait for explicit action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
-          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni pilot is now a verified validation-monitored diagnostic result, and Cosmos3-Nano has a separate verified future-window compatibility package; the next stage is structured-output reliability, error analysis, full Cosmos fine-tuning, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Scale-up requirement</h3><p>Future Omni, Cosmos, and policy branches use the same episode split discipline, training metadata, held-out predictions, metrics, run report, and public-safe package gate.</p><a href="data/foundation_model_plan.json">scale-up status</a></article>
         </div>
       </div>
@@ -2552,7 +2552,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified diagnostic</span>
             <h3>Qwen3-Omni and Cosmos3 branches</h3>
-            <p>The selected 96/16/16 episode split produced verified Qwen3-Omni packages with 448 held-out test predictions. Cosmos3-Nano now has a separate verified future-window compatibility package with 378 held-out predictions.</p>
             <div class="evidence-links">
               <a href="data/omni_model_comparison.json">result comparison</a>
               <a href="data/omni_finetune_verified_result.json">pilot result</a>
@@ -3156,11 +3156,11 @@
               <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored diagnostic held-out pilot. Stronger model-quality metrics require structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
-              <article class="artifact primary-artifact"><div><h3>Project scope</h3><p>Connects implemented single-episode artifacts, 128-episode aligned simple/NN baselines, verified Qwen3-Omni packages, the Cosmos3 future-window branch, and later model-extension milestones.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Backbone selection matrix covering Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style policy candidates, and the future Xperience-native pretraining goal.</p><a href="data/foundation_model_plan.json">foundation model plan</a></article>
               <article class="artifact"><h3>Multi-episode data access</h3><p>Public data-access path, selected 128-episode pilot plan, and preparation requirements.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a></article>
-              <article class="artifact"><h3>Qwen3-Omni diagnostic result</h3><p>Verified selected-episode package with split counts, held-out metrics, validation summaries, and public-safe artifact policy.</p><a href="data/omni_finetune_verified_result.json">result summary</a></article>
-              <article class="artifact"><h3>Cosmos3-Nano compatibility result</h3><p>Verified future-window world-model compatibility package with held-out retrieval, temporal consistency, transition, and contact metrics.</p><a href="data/omni_model_comparison.json">comparison summary</a></article>
               <article class="artifact"><h3>Scale-up requirement</h3><p>Future runs need validation tracking, held-out predictions, quality-target reporting, and the same public-safe package gate.</p><a href="data/foundation_model_plan.json">training requirements</a></article>
               <article class="artifact"><h3>Xperience-native pretraining</h3><p>Future plan for a domain-specific embodied foundation model trained from scratch over full-corpus video, audio, geometry, motion, inertial, and language streams.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
             </div>
@@ -3197,8 +3197,8 @@
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</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>Current LoRA artifact</h3><p>The current LoRA artifact is the selected 128-episode diagnostic pilot: 2,848 train examples, 448 held-out test predictions, and a verified public-safe result package.</p></article>
-          <article class="artifact"><h3>Backbone branches</h3><p>Qwen3-Omni is the immediate LoRA path; Cosmos 3 is the first world-model branch; GR00T/OpenVLA/openpi become policy branches after action targets are well-defined.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
         </div>
       </div>
@@ -3215,7 +3215,7 @@
           <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
-          <article class="artifact"><h3>Multi-episode pilot status</h3><p>The Qwen3-Omni diagnostic pilot is verified with held-out predictions and metrics, and Cosmos3-Nano has a separate future-window compatibility package. Stronger Qwen quality remains a follow-up because JSON validity is below target.</p><a href="data/omni_model_comparison.json">comparison</a></article>
         </div>
         <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git

           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch, policy models wait for explicit action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
+          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight Reasoner JSON-task evaluation. The next stage is action/subtask error analysis, true Cosmos fine-tuning, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Scale-up requirement</h3><p>Future Omni, Cosmos, and policy branches use the same episode split discipline, training metadata, held-out predictions, metrics, run report, and public-safe package gate.</p><a href="data/foundation_model_plan.json">scale-up status</a></article>
         </div>
       </div>
           <article class="evidence-card">
             <span class="status-pill">verified diagnostic</span>
             <h3>Qwen3-Omni and Cosmos3 branches</h3>
+            <p>The selected 96/16/16 episode split produced verified Qwen3-Omni packages with 448 held-out test predictions. Cosmos3-Nano has 378 held-out future-window predictions, and Cosmos3-Super Reasoner has 448 held-out base-weight JSON-task predictions.</p>
             <div class="evidence-links">
               <a href="data/omni_model_comparison.json">result comparison</a>
               <a href="data/omni_finetune_verified_result.json">pilot result</a>
               <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored diagnostic held-out pilot. Stronger model-quality metrics require structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
+              <article class="artifact primary-artifact"><div><h3>Model-family comparison</h3><p>Compares the three result layers and also groups 1-episode and 128-episode entries by model family: task heads, Qwen3-Omni LoRA, Cosmos3-Nano, and Cosmos3-Super.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Backbone selection matrix covering Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style policy candidates, and the future Xperience-native pretraining goal.</p><a href="data/foundation_model_plan.json">foundation model plan</a></article>
               <article class="artifact"><h3>Multi-episode data access</h3><p>Public data-access path, selected 128-episode pilot plan, and preparation requirements.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a></article>
+              <article class="artifact"><h3>Qwen3-Omni LoRA group</h3><p>Separates the 1-episode sensor-adapter smoke test from the current 128-episode LoRA adapter package and older diagnostics.</p><a href="data/omni_model_comparison.json">Qwen group</a></article>
+              <article class="artifact"><h3>Cosmos3 groups</h3><p>Shows the verified Nano future-window compatibility package and the Super base-weight Reasoner JSON-task evaluation; neither is a new fine-tuned Cosmos weight release.</p><a href="data/omni_model_comparison.json">Cosmos groups</a></article>
               <article class="artifact"><h3>Scale-up requirement</h3><p>Future runs need validation tracking, held-out predictions, quality-target reporting, and the same public-safe package gate.</p><a href="data/foundation_model_plan.json">training requirements</a></article>
               <article class="artifact"><h3>Xperience-native pretraining</h3><p>Future plan for a domain-specific embodied foundation model trained from scratch over full-corpus video, audio, geometry, motion, inertial, and language streams.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
             </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</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>Current LoRA artifact</h3><p>The current Qwen3-Omni LoRA artifact is the selected 128-episode diagnostic adapter. The 1-episode Qwen entry is only a sensor-adapter smoke test.</p><a href="data/omni_model_comparison.json">model groups</a></article>
+          <article class="artifact"><h3>Backbone branches</h3><p>Qwen3-Omni uses a separate LoRA model repo; Cosmos3-Nano and Cosmos3-Super remain artifacts-only diagnostics until real Cosmos adapter or fine-tuned weights exist.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
         </div>
       </div>
           <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
+          <article class="artifact"><h3>Multi-episode pilot status</h3><p>The comparison JSON now supports both the three-version reading and model-family grouping, so 1-episode and 128-episode entries can be compared within the same model family.</p><a href="data/omni_model_comparison.json">comparison</a></article>
         </div>
         <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git

episode_task_suite.md ADDED Viewed

	@@ -0,0 +1,231 @@

+# Episode Task Suite
+Script:
+```text
+scripts/episode_task_suite.py
+```
+This script turns the single public Xperience-10M sample episode into many end-to-end tasks. It is designed for learning, debugging, and task design. It is **not** a generalization benchmark because the data is still one episode.
+Run:
+```bash
+cd /path/to/Ropedia
+source .venv/bin/activate
+python scripts/episode_task_suite.py
+```
+Output:
+```text
+outputs/episode_task_suite/
+```
+Shared setup:
+```text
+sample episode: 5821 frames
+windows:        1161
+window size:    20 frames
+stride:         5 frames
+feature dim:    8546
+split:          chronological, first 70% train and last 30% test
+```
+## Implemented Tasks
+| Task | Input | Output | Main artifact |
+| --- | --- | --- | --- |
+| Action Recognition | all modality window | current action label | `timeline_action/metrics.json` |
+| Procedure Step Recognition | all modality window | current subtask label | `timeline_subtask/metrics.json` |
+| Action Boundary Detection | all modality window | steady vs action boundary | `transition_detection/metrics.json` |
+| Next-Action Prediction | current all modality window | action 20 frames later | `next_action/metrics.json` |
+| Hand Trajectory Forecasting | current all modality window | future 10-frame left/right hand joints | `hand_trajectory_forecast/predictions.npz` |
+| Contact State Prediction | non-contact modalities | any body contact in window | `contact_prediction/metrics.json` |
+| Object Relevance Prediction | non-caption modalities | relevant object set | `object_relevance/predictions.csv` |
+| Language Grounding | caption objects/interaction query + sensor candidates | matching time window | `caption_grounding/metrics.json` |
+| Cross-Modal Retrieval | motion/IMU/camera/audio query | matching depth/video window | `cross_modal_retrieval/metrics.json` |
+| Cross-Modal Reconstruction | motion/IMU/camera/audio | depth/video feature vector | `modality_reconstruction/predictions.npz` |
+| Temporal Order Verification | two adjacent windows | whether order is correct | `temporal_order/metrics.json` |
+| Multimodal Synchronization Detection | motion+visual/audio pair | aligned vs shifted | `misalignment_detection/metrics.json` |
+## Minimal Model Architectures
+All tasks share the same window builder unless a task explicitly removes a
+feature block to avoid label leakage.
+```text
+raw sample episode
+  -> 20-frame sliding windows, stride 5
+  -> all-modality feature vector X_all, 8,546 dimensions
+  -> chronological split, first 70% train and last 30% test
+  -> train-only z-score scaler
+  -> task-specific minimal head
+```
+The task suite intentionally uses simple heads:
+| Family | Formula | Tasks |
+| --- | --- | --- |
+| Linear softmax | `softmax(z(X)W + b)`, cross-entropy, L2 | Action Recognition; Procedure Step Recognition; Action Boundary Detection; Next-Action Prediction; Contact State Prediction; Temporal Order Verification; Multimodal Synchronization Detection |
+| Ridge regression/projection | dual ridge regression with L2=10 on z-scored X/Y | Hand Trajectory Forecasting; Language Grounding; Cross-Modal Retrieval; Cross-Modal Reconstruction |
+| Multi-label logistic | `sigmoid(z(X)W + b)`, weighted object heads | Object Relevance Prediction |
+Task-specific architecture details:
+| Task | Input tensor/vector | Minimal head | Output target |
+| --- | --- | --- | --- |
+| Action Recognition | `X_all`, 8,546d | class-weighted linear softmax | current action label |
+| Procedure Step Recognition | `X_all`, 8,546d | class-weighted linear softmax | current subtask label |
+| Action Boundary Detection | `X_all`, 8,546d | class-weighted linear softmax | steady vs transition near action boundary |
+| Next-Action Prediction | `X_all(t)`, 8,546d | class-weighted linear softmax | action at `t+20` frames |
+| Hand Trajectory Forecasting | `X_all(t)`, 8,546d | ridge regression | future 10 frames of left/right hand joints, 1,260d |
+| Contact State Prediction | all features except `body_contacts` and caption text, 7,503d | linear softmax on observed labels | any body contact in window |
+| Object Relevance Prediction | all features except caption text, 7,650d | multi-label logistic regression | 34-object multi-hot vector |
+| Language Grounding | sensor features, 7,650d, projected into 896d text space | ridge projection plus cosine ranking | matching time window for a text query |
+| Cross-Modal Retrieval | motion/IMU/camera/audio, 2,415d, projected into 5,096d visual space | ridge projection plus cosine ranking | matching depth/video window |
+| Cross-Modal Reconstruction | motion/IMU/camera/audio, 2,415d | ridge regression | depth/video feature vector, 5,096d |
+| Temporal Order Verification | `[x_t, x_t+1, x_t+1-x_t]`, 25,638d | binary linear softmax | correct vs reversed order |
+| Multimodal Synchronization Detection | motion plus visual/audio pair, 7,511d | binary linear softmax | aligned vs shifted by 8 windows |
+Diagram:
+```text
+docs/assets/task_architectures.png
+```
+## Neural Baseline
+The suite can also run a lightweight PyTorch MLP baseline for every selected
+task while preserving the NumPy baseline artifacts:
+```bash
+python scripts/episode_task_suite.py \
+  --output-dir results/episode_task_suite \
+  --include-neural
+```
+This requires `torch`; use `requirements-omni.txt` when the base environment
+does not already include PyTorch.
+The neural path reuses the same windows, features, chronological split, leakage
+filters, and metrics as the minimal heads. It writes parallel artifacts under:
+```text
+results/episode_task_suite/neural_mlp/<task>/
+```
+Each neural task directory contains `metrics.json`, `history.json`, a
+`model.pt` checkpoint, and the same prediction artifact shape used by the
+corresponding minimal task (`predictions.csv` or `predictions.npz`). The suite
+rollup adds a `neural_tasks` section to `summary_report.json`; visualization
+generation adds neural-only and minimal-vs-neural score charts when those
+metrics are present.
+Useful knobs:
+```bash
+python scripts/episode_task_suite.py \
+  --include-neural \
+  --neural-epochs 80 \
+  --neural-hidden-dim 128 \
+  --neural-batch-size 128 \
+  --neural-device auto
+```
+This neural baseline is intentionally small. It tests whether a nonlinear head
+over the current handcrafted feature vector improves per-task behavior before
+moving to heavier sequence or vision-language models.
+## Qwen/Omni Neural Track
+The Qwen3-Omni scripts remain a separate neural/VLM track under
+`scripts/omni/`. They are better suited for action/subtask adapter checks, sensor-adapter
+experiments, and LoRA fine-tuning than for the full 12-task matrix. A useful
+comparison order is:
+- current NumPy task suite
+- lightweight `neural_mlp` task suite
+- adapter-only setup checks from `scripts/omni/qwen3_omni_adapter_smoke.py`
+- Qwen3-Omni zero-shot or LoRA runs where GPU/model access is available
+## Current Results
+```text
+Action Recognition:
+  accuracy: 0.0292
+  macro_f1: 0.0500
+  note: future test region contains unseen action classes
+Procedure Step Recognition:
+  accuracy: 0.0581
+  macro_f1: 0.0506
+  note: future test region contains unseen subtask classes
+Action Boundary Detection:
+  accuracy: 0.9080
+  macro_f1: 0.6118
+  boundary_f1: 0.1250
+Next-Action Prediction:
+  accuracy: 0.0345
+  macro_f1: 0.0593
+  note: same unseen-future-class problem as Action Recognition
+Hand Trajectory Forecasting:
+  MPJPE: 0.8647
+  final-frame MPJPE: 1.0331
+Contact State Prediction:
+  accuracy: 1.0000
+  note: degenerate on this sample because the binary contact label has only one class
+Object Relevance Prediction:
+  micro_f1: 0.1803
+  macro_f1: 0.0633
+Language Grounding:
+  top1: 0.0029
+  top5: 0.0115
+  MRR: 0.0160
+Cross-Modal Retrieval:
+  top1: 0.1638
+  top5: 0.3678
+  top10: 0.4713
+  MRR: 0.2693
+Cross-Modal Reconstruction:
+  R2: -0.0153
+Temporal Order Verification:
+  accuracy: 0.4540
+  f1: 0.5400
+Multimodal Synchronization Detection:
+  accuracy: 0.5159
+  f1: 0.5052
+```
+## How To Read These Results
+Low scores are useful here. They show which tasks are not learnable from this one chronological sample with this minimal model.
+The strongest signal is Cross-Modal Retrieval: motion/IMU/camera/audio features can retrieve the matching depth/video window better than random. That means the modalities are synchronized and contain shared temporal structure.
+The weakest supervised timeline tasks are weak mainly because of the split. The last 30% of a single ordered episode contains actions/subtasks not present in the first 70%, so a classifier trained on the first part cannot predict labels it never saw.
+For serious research, keep the same task code but change the dataset unit:
+```text
+many episodes -> train episodes -> test unseen episodes
+```
+For single-episode learning, these tasks are best used as:
+- data pipeline tests
+- modality ablations
+- label-alignment checks
+- self-supervised retrieval experiments
+- debugging templates before scaling to many episodes

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@@ -2489,7 +2489,7 @@
           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch, policy models wait for explicit action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
-          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target, and Cosmos3-Nano has a separate verified future-window compatibility package; the next stage is action/subtask error analysis, full Cosmos fine-tuning, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Scale-up requirement</h3><p>Future Omni, Cosmos, and policy branches use the same episode split discipline, training metadata, held-out predictions, metrics, run report, and public-safe package gate.</p><a href="data/foundation_model_plan.json">scale-up status</a></article>
         </div>
       </div>
@@ -2552,7 +2552,7 @@
           <article class="evidence-card">
             <span class="status-pill">verified diagnostic</span>
             <h3>Qwen3-Omni and Cosmos3 branches</h3>
-            <p>The selected 96/16/16 episode split produced verified Qwen3-Omni packages with 448 held-out test predictions. Cosmos3-Nano now has a separate verified future-window compatibility package with 378 held-out predictions.</p>
             <div class="evidence-links">
               <a href="data/omni_model_comparison.json">result comparison</a>
               <a href="data/omni_finetune_verified_result.json">pilot result</a>
@@ -3156,11 +3156,11 @@
               <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored diagnostic held-out pilot. Stronger model-quality metrics require structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
-              <article class="artifact primary-artifact"><div><h3>Project scope</h3><p>Connects implemented single-episode artifacts, 128-episode aligned simple/NN baselines, verified Qwen3-Omni packages, the Cosmos3 future-window branch, and later model-extension milestones.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Backbone selection matrix covering Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style policy candidates, and the future Xperience-native pretraining goal.</p><a href="data/foundation_model_plan.json">foundation model plan</a></article>
               <article class="artifact"><h3>Multi-episode data access</h3><p>Public data-access path, selected 128-episode pilot plan, and preparation requirements.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a></article>
-              <article class="artifact"><h3>Qwen3-Omni diagnostic result</h3><p>Verified selected-episode package with split counts, held-out metrics, validation summaries, and public-safe artifact policy.</p><a href="data/omni_finetune_verified_result.json">result summary</a></article>
-              <article class="artifact"><h3>Cosmos3-Nano compatibility result</h3><p>Verified future-window world-model compatibility package with held-out retrieval, temporal consistency, transition, and contact metrics.</p><a href="data/omni_model_comparison.json">comparison summary</a></article>
               <article class="artifact"><h3>Scale-up requirement</h3><p>Future runs need validation tracking, held-out predictions, quality-target reporting, and the same public-safe package gate.</p><a href="data/foundation_model_plan.json">training requirements</a></article>
               <article class="artifact"><h3>Xperience-native pretraining</h3><p>Future plan for a domain-specific embodied foundation model trained from scratch over full-corpus video, audio, geometry, motion, inertial, and language streams.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
             </div>
@@ -3197,8 +3197,8 @@
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</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>Current LoRA artifact</h3><p>The current LoRA artifact is the selected 128-episode diagnostic pilot: 2,848 train examples, 448 held-out test predictions, and a verified public-safe result package.</p></article>
-          <article class="artifact"><h3>Backbone branches</h3><p>Qwen3-Omni is the immediate LoRA path; Cosmos 3 is the first world-model branch; GR00T/OpenVLA/openpi become policy branches after action targets are well-defined.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
         </div>
       </div>
@@ -3215,7 +3215,7 @@
           <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
-          <article class="artifact"><h3>Multi-episode pilot status</h3><p>The Qwen3-Omni final diagnostic result is verified with held-out predictions and metrics, and Cosmos3-Nano has a separate future-window compatibility package. Stronger Qwen action/subtask quality remains a follow-up even though JSON validity now meets target.</p><a href="data/omni_model_comparison.json">comparison</a></article>
         </div>
         <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git

           <article class="artifact"><h3>Leakage controls</h3><p>Scalers fit on train windows only; future labels, target-side signals, caption/object labels, and contact labels stay on the target side unless explicitly queried.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/scripts/build_evaluation_protocol.py">builder script</a></article>
           <article class="artifact"><h3>Audio ablation</h3><p>Audio and no-audio variants are evaluated across all 12 task contracts under the same chronological split.</p><a href="data/audio_ablation_summary.json">audio summary</a></article>
           <article class="artifact"><h3>Foundation branch selection</h3><p>Qwen3-Omni is the first trainable baseline, Cosmos 3 becomes the world-model branch, policy models wait for explicit action targets, and Xperience-native pretraining remains a later full-corpus goal.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
+          <article class="artifact"><h3>Next evaluation stage</h3><p>This public-sample run covers single-episode task development. The selected multi-episode Qwen3-Omni final diagnostic result is verified and meets the JSON-validity target; Cosmos3-Nano has a verified future-window compatibility package; and Cosmos3-Super has a verified base-weight Reasoner JSON-task evaluation. The next stage is action/subtask error analysis, true Cosmos fine-tuning, and policy-target conversion.</p><a href="data/omni_model_comparison.json">result comparison</a></article>
           <article class="artifact"><h3>Scale-up requirement</h3><p>Future Omni, Cosmos, and policy branches use the same episode split discipline, training metadata, held-out predictions, metrics, run report, and public-safe package gate.</p><a href="data/foundation_model_plan.json">scale-up status</a></article>
         </div>
       </div>
           <article class="evidence-card">
             <span class="status-pill">verified diagnostic</span>
             <h3>Qwen3-Omni and Cosmos3 branches</h3>
+            <p>The selected 96/16/16 episode split produced verified Qwen3-Omni packages with 448 held-out test predictions. Cosmos3-Nano has 378 held-out future-window predictions, and Cosmos3-Super Reasoner has 448 held-out base-weight JSON-task predictions.</p>
             <div class="evidence-links">
               <a href="data/omni_model_comparison.json">result comparison</a>
               <a href="data/omni_finetune_verified_result.json">pilot result</a>
               <p>The multi-episode Qwen3-Omni path is documented, scripted, and verified as a validation-monitored diagnostic held-out pilot. Stronger model-quality metrics require structured-output and error-analysis improvements.</p>
             </div>
             <div class="artifact-grid">
+              <article class="artifact primary-artifact"><div><h3>Model-family comparison</h3><p>Compares the three result layers and also groups 1-episode and 128-episode entries by model family: task heads, Qwen3-Omni LoRA, Cosmos3-Nano, and Cosmos3-Super.</p></div><a href="data/omni_model_comparison.json">result comparison</a></article>
               <article class="artifact"><h3>Foundation-model plan</h3><p>Backbone selection matrix covering Qwen3-Omni, Cosmos 3, GR00T, OpenVLA/openpi, Gemini Robotics, Octo, SmolVLA-style policy candidates, and the future Xperience-native pretraining goal.</p><a href="data/foundation_model_plan.json">foundation model plan</a></article>
               <article class="artifact"><h3>Multi-episode data access</h3><p>Public data-access path, selected 128-episode pilot plan, and preparation requirements.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/results/omni_finetune/MULTI_EPISODE_ACCESS_STATUS.md">data access</a></article>
+              <article class="artifact"><h3>Qwen3-Omni LoRA group</h3><p>Separates the 1-episode sensor-adapter smoke test from the current 128-episode LoRA adapter package and older diagnostics.</p><a href="data/omni_model_comparison.json">Qwen group</a></article>
+              <article class="artifact"><h3>Cosmos3 groups</h3><p>Shows the verified Nano future-window compatibility package and the Super base-weight Reasoner JSON-task evaluation; neither is a new fine-tuned Cosmos weight release.</p><a href="data/omni_model_comparison.json">Cosmos groups</a></article>
               <article class="artifact"><h3>Scale-up requirement</h3><p>Future runs need validation tracking, held-out predictions, quality-target reporting, and the same public-safe package gate.</p><a href="data/foundation_model_plan.json">training requirements</a></article>
               <article class="artifact"><h3>Xperience-native pretraining</h3><p>Future plan for a domain-specific embodied foundation model trained from scratch over full-corpus video, audio, geometry, motion, inertial, and language streams.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
             </div>
         <div class="artifact-grid">
           <article class="artifact"><h3>Selection</h3><p>128 complete episodes selected from 128 unique top-level sessions, balanced across episode-size bands and split 96/16/16 for train/val/test.</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>Current LoRA artifact</h3><p>The current Qwen3-Omni LoRA artifact is the selected 128-episode diagnostic adapter. The 1-episode Qwen entry is only a sensor-adapter smoke test.</p><a href="data/omni_model_comparison.json">model groups</a></article>
+          <article class="artifact"><h3>Backbone branches</h3><p>Qwen3-Omni uses a separate LoRA model repo; Cosmos3-Nano and Cosmos3-Super remain artifacts-only diagnostics until real Cosmos adapter or fine-tuned weights exist.</p><a href="data/foundation_model_plan.json">backbone plan</a></article>
           <article class="artifact"><h3>Native foundation model</h3><p>The long-term goal is a full-corpus Xperience Embodied Foundation Model trained on synchronized perception, geometry, motion, inertial, audio, and language streams after smaller scaling stages validate the approach.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/XPERIENCE_EMBODIED_FOUNDATION_MODEL_PRETRAINING.md">pretraining plan</a></article>
         </div>
       </div>
           <article class="artifact"><h3>Reproducibility matrix</h3><p>Machine-readable command matrix covering sample download, baselines, 12 tasks, figures, and validation.</p><a href="data/reproducibility_matrix.json">reproducibility matrix</a></article>
           <article class="artifact"><h3>Exact-match reproduction record</h3><p>The last metric rebuild reproduced the public-sample outputs from a fresh cache and matched the committed metrics.</p><a href="https://github.com/ChaoYue0307/ropedia-xperience-10m-task-suite/blob/main/notes/reproducibility_audit.md">reproduction audit</a></article>
           <article class="artifact"><h3>Project dashboard</h3><p>The website organizes the dataset sample, tasks, methods, results, directions, and scale-up path in one tabbed reader flow.</p><a href="#artifacts">project materials</a></article>
+          <article class="artifact"><h3>Multi-episode pilot status</h3><p>The comparison JSON now supports both the three-version reading and model-family grouping, so 1-episode and 128-episode entries can be compared within the same model family.</p><a href="data/omni_model_comparison.json">comparison</a></article>
         </div>
         <p class="repro-note">Minimal path: install the toolkit dependencies, download the official sample, run the 12-task suite with neural heads, regenerate visualizations, then rebuild the supporting project reports.</p>
         <pre class="code-panel"><button type="button" data-copy="setup">Copy</button><code id="setup">git clone https://github.com/Ropedia/HOMIE-toolkit.git

research_roadmap.html CHANGED Viewed

@@ -626,7 +626,7 @@
           </div>
           <div class="route-step">
             <strong>02</strong>
-            <div><b>Data staging</b><span>episode-level split and missing-view manifest</span></div>
             <em id="routeData">32 target</em>
           </div>
           <div class="route-step">

           </div>
           <div class="route-step">
             <strong>02</strong>
+            <div><b>Data preparation</b><span>episode-level split and missing-view manifest</span></div>
             <em id="routeData">32 target</em>
           </div>
           <div class="route-step">

results/episode_task_suite/caption_grounding/metrics.json CHANGED Viewed

@@ -11,5 +11,6 @@
   "output": "matching time window",
   "split": "chronological",
   "num_train_windows": 813,
-  "num_test_windows": 348
-}

   "output": "matching time window",
   "split": "chronological",
   "num_train_windows": 813,
+  "num_test_windows": 348,
+  "task_display_name": "Language Grounding"
+}

results/episode_task_suite/contact_prediction/metrics.json CHANGED Viewed

@@ -15,5 +15,6 @@
   "majority_baseline_accuracy": 1.0,
   "train_final_accuracy": 1.0,
   "train_final_loss": 0.0006056802230887115,
-  "unseen_test_classes": []
-}

   "majority_baseline_accuracy": 1.0,
   "train_final_accuracy": 1.0,
   "train_final_loss": 0.0006056802230887115,
+  "unseen_test_classes": [],
+  "task_display_name": "Contact State Prediction"
+}

results/episode_task_suite/cross_modal_retrieval/metrics.json CHANGED Viewed

@@ -11,5 +11,6 @@
   "output": "matching depth/video window",
   "split": "chronological",
   "num_train_windows": 813,
-  "num_test_windows": 348
-}

   "output": "matching depth/video window",
   "split": "chronological",
   "num_train_windows": 813,
+  "num_test_windows": 348,
+  "task_display_name": "Cross-Modal Retrieval"
+}

results/episode_task_suite/hand_trajectory_forecast/metrics.json CHANGED Viewed

@@ -11,5 +11,6 @@
   "forecast_frames": 10,
   "mpjpe": 0.8646570444107056,
   "final_frame_mpjpe": 1.0330793857574463,
-  "target_dim": 1260
-}

   "forecast_frames": 10,
   "mpjpe": 0.8646570444107056,
   "final_frame_mpjpe": 1.0330793857574463,
+  "target_dim": 1260,
+  "task_display_name": "Hand Trajectory Forecasting"
+}

results/episode_task_suite/misalignment_detection/metrics.json CHANGED Viewed

@@ -15,5 +15,6 @@
   "num_samples": 2306,
   "num_train_samples": 1614,
   "num_test_samples": 692,
-  "train_final_accuracy": 0.49380421313506817
-}

   "num_samples": 2306,
   "num_train_samples": 1614,
   "num_test_samples": 692,
+  "train_final_accuracy": 0.49380421313506817,
+  "task_display_name": "Multimodal Synchronization Detection"
+}

results/episode_task_suite/modality_reconstruction/metrics.json CHANGED Viewed

@@ -8,5 +8,6 @@
   "split": "chronological",
   "num_train_windows": 813,
   "num_test_windows": 348,
-  "target_dim": 5096
-}

   "split": "chronological",
   "num_train_windows": 813,
   "num_test_windows": 348,
+  "target_dim": 5096,
+  "task_display_name": "Cross-Modal Reconstruction"
+}

results/episode_task_suite/neural_mlp/caption_grounding/metrics.json CHANGED Viewed

@@ -22,5 +22,6 @@
   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
-  "train_final_loss": 0.06317874967483723
-}

   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
+  "train_final_loss": 0.06317874967483723,
+  "task_display_name": "Language Grounding"
+}

results/episode_task_suite/neural_mlp/contact_prediction/metrics.json CHANGED Viewed

@@ -24,5 +24,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.0,
-  "train_final_accuracy": 1.0
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.0,
+  "train_final_accuracy": 1.0,
+  "task_display_name": "Contact State Prediction"
+}

results/episode_task_suite/neural_mlp/cross_modal_retrieval/metrics.json CHANGED Viewed

@@ -22,5 +22,6 @@
   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
-  "train_final_loss": 0.21891545446596464
-}

   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
+  "train_final_loss": 0.21891545446596464,
+  "task_display_name": "Cross-Modal Retrieval"
+}

results/episode_task_suite/neural_mlp/hand_trajectory_forecast/metrics.json CHANGED Viewed

@@ -21,5 +21,6 @@
   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
-  "train_final_loss": 0.055699273420247435
-}

   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
+  "train_final_loss": 0.055699273420247435,
+  "task_display_name": "Hand Trajectory Forecasting"
+}

results/episode_task_suite/neural_mlp/misalignment_detection/metrics.json CHANGED Viewed

@@ -26,5 +26,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.010604870708167664,
-  "train_final_accuracy": 0.9956629491945477
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.010604870708167664,
+  "train_final_accuracy": 0.9956629491945477,
+  "task_display_name": "Multimodal Synchronization Detection"
+}

results/episode_task_suite/neural_mlp/modality_reconstruction/metrics.json CHANGED Viewed

@@ -18,5 +18,6 @@
   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
-  "train_final_loss": 0.21891545446596464
-}

   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
+  "train_final_loss": 0.21891545446596464,
+  "task_display_name": "Cross-Modal Reconstruction"
+}

results/episode_task_suite/neural_mlp/next_action/metrics.json CHANGED Viewed

@@ -29,5 +29,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.000416612956025105,
-  "train_final_accuracy": 1.0
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.000416612956025105,
+  "train_final_accuracy": 1.0,
+  "task_display_name": "Next-Action Prediction"
+}

results/episode_task_suite/neural_mlp/object_relevance/metrics.json CHANGED Viewed

@@ -21,5 +21,6 @@
   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
-  "train_final_loss": 0.003651880362182214
-}

   "neural_weight_decay": 0.0001,
   "neural_dropout": 0.1,
   "neural_device": "cpu",
+  "train_final_loss": 0.003651880362182214,
+  "task_display_name": "Object Relevance Prediction"
+}

results/episode_task_suite/neural_mlp/temporal_order/metrics.json CHANGED Viewed

@@ -26,5 +26,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.0005108328477586757,
-  "train_final_accuracy": 1.0
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.0005108328477586757,
+  "train_final_accuracy": 1.0,
+  "task_display_name": "Temporal Order Verification"
+}

results/episode_task_suite/neural_mlp/timeline_action/metrics.json CHANGED Viewed

@@ -29,5 +29,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.04246756529782,
-  "train_final_accuracy": 0.9875156054931336
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 0.04246756529782,
+  "train_final_accuracy": 0.9875156054931336,
+  "task_display_name": "Action Recognition"
+}

results/episode_task_suite/neural_mlp/timeline_subtask/metrics.json CHANGED Viewed

@@ -29,5 +29,6 @@
   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 5.4104819144748596e-05,
-  "train_final_accuracy": 1.0
-}

   "neural_dropout": 0.1,
   "neural_device": "cpu",
   "train_final_loss": 5.4104819144748596e-05,
+  "train_final_accuracy": 1.0,
+  "task_display_name": "Procedure Step Recognition"
+}

results/episode_task_suite/neural_mlp/transition_detection/metrics.json CHANGED Viewed

@@ -31,5 +31,6 @@
   "matched_boundaries": 3,
   "true_boundaries": 4,
   "predicted_boundaries": 42,
-  "mean_abs_timing_error_frames": 2.6666666666666665
-}

   "matched_boundaries": 3,
   "true_boundaries": 4,
   "predicted_boundaries": 42,
+  "mean_abs_timing_error_frames": 2.6666666666666665,
+  "task_display_name": "Action Boundary Detection"
+}

results/episode_task_suite/next_action/metrics.json CHANGED Viewed

@@ -20,5 +20,6 @@
     "Pour coffee",
     "Pour milk into coffee",
     "Wait/Prepare for pouring"
-  ]
-}

     "Pour coffee",
     "Pour milk into coffee",
     "Wait/Prepare for pouring"
+  ],
+  "task_display_name": "Next-Action Prediction"
+}

results/episode_task_suite/object_relevance/metrics.json CHANGED Viewed

@@ -10,5 +10,6 @@
   "num_windows": 1161,
   "num_train_windows": 813,
   "num_test_windows": 348,
-  "num_objects": 34
-}

   "num_windows": 1161,
   "num_train_windows": 813,
   "num_test_windows": 348,
+  "num_objects": 34,
+  "task_display_name": "Object Relevance Prediction"
+}